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Weenkus/Machine-Learning-University-of-Washington | Regression/assignments/Multiple Linear Regression Programming Assignment 1.ipynb | 1 | 52868 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initialise the libs"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pa\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import linear_model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# load the data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dtype_dict = {'bathrooms':float, 'waterfront':int, 'sqft_above':int, 'sqft_living15':float, 'grade':int,\n",
" 'yr_renovated':int, 'price':float, 'bedrooms':float, 'zipcode':str, 'long':float, 'sqft_lot15':float,\n",
" 'sqft_living':float, 'floors':str, 'condition':int, 'lat':float, 'date':str, 'sqft_basement':int,\n",
" 'yr_built':int, 'id':str, 'sqft_lot':int, 'view':int}\n",
"\n",
"regressionDir = '/home/weenkus/workspace/Machine Learning - University of Washington/Regression'\n",
"\n",
"house = pa.read_csv(regressionDir + '/datasets/kc_house_data.csv', dtype = dtype_dict)\n",
"house_test = pa.read_csv(regressionDir + '/datasets/kc_house_test_data.csv', dtype = dtype_dict)\n",
"house_train = pa.read_csv(regressionDir + '/datasets/kc_house_train_data.csv', dtype = dtype_dict)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore the data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>date</th>\n",
" <th>price</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>sqft_living</th>\n",
" <th>sqft_lot</th>\n",
" <th>floors</th>\n",
" <th>waterfront</th>\n",
" <th>view</th>\n",
" <th>...</th>\n",
" <th>grade</th>\n",
" <th>sqft_above</th>\n",
" <th>sqft_basement</th>\n",
" <th>yr_built</th>\n",
" <th>yr_renovated</th>\n",
" <th>zipcode</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>sqft_living15</th>\n",
" <th>sqft_lot15</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7129300520</td>\n",
" <td>20141013T000000</td>\n",
" <td>221900</td>\n",
" <td>3</td>\n",
" <td>1.00</td>\n",
" <td>1180</td>\n",
" <td>5650</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>1180</td>\n",
" <td>0</td>\n",
" <td>1955</td>\n",
" <td>0</td>\n",
" <td>98178</td>\n",
" <td>47.5112</td>\n",
" <td>-122.257</td>\n",
" <td>1340</td>\n",
" <td>5650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6414100192</td>\n",
" <td>20141209T000000</td>\n",
" <td>538000</td>\n",
" <td>3</td>\n",
" <td>2.25</td>\n",
" <td>2570</td>\n",
" <td>7242</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>2170</td>\n",
" <td>400</td>\n",
" <td>1951</td>\n",
" <td>1991</td>\n",
" <td>98125</td>\n",
" <td>47.7210</td>\n",
" <td>-122.319</td>\n",
" <td>1690</td>\n",
" <td>7639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5631500400</td>\n",
" <td>20150225T000000</td>\n",
" <td>180000</td>\n",
" <td>2</td>\n",
" <td>1.00</td>\n",
" <td>770</td>\n",
" <td>10000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>770</td>\n",
" <td>0</td>\n",
" <td>1933</td>\n",
" <td>0</td>\n",
" <td>98028</td>\n",
" <td>47.7379</td>\n",
" <td>-122.233</td>\n",
" <td>2720</td>\n",
" <td>8062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2487200875</td>\n",
" <td>20141209T000000</td>\n",
" <td>604000</td>\n",
" <td>4</td>\n",
" <td>3.00</td>\n",
" <td>1960</td>\n",
" <td>5000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>1050</td>\n",
" <td>910</td>\n",
" <td>1965</td>\n",
" <td>0</td>\n",
" <td>98136</td>\n",
" <td>47.5208</td>\n",
" <td>-122.393</td>\n",
" <td>1360</td>\n",
" <td>5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1954400510</td>\n",
" <td>20150218T000000</td>\n",
" <td>510000</td>\n",
" <td>3</td>\n",
" <td>2.00</td>\n",
" <td>1680</td>\n",
" <td>8080</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>1680</td>\n",
" <td>0</td>\n",
" <td>1987</td>\n",
" <td>0</td>\n",
" <td>98074</td>\n",
" <td>47.6168</td>\n",
" <td>-122.045</td>\n",
" <td>1800</td>\n",
" <td>7503</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" id date price bedrooms bathrooms sqft_living \\\n",
"0 7129300520 20141013T000000 221900 3 1.00 1180 \n",
"1 6414100192 20141209T000000 538000 3 2.25 2570 \n",
"2 5631500400 20150225T000000 180000 2 1.00 770 \n",
"3 2487200875 20141209T000000 604000 4 3.00 1960 \n",
"4 1954400510 20150218T000000 510000 3 2.00 1680 \n",
"\n",
" sqft_lot floors waterfront view ... grade sqft_above \\\n",
"0 5650 1 0 0 ... 7 1180 \n",
"1 7242 2 0 0 ... 7 2170 \n",
"2 10000 1 0 0 ... 6 770 \n",
"3 5000 1 0 0 ... 7 1050 \n",
"4 8080 1 0 0 ... 8 1680 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"0 0 1955 0 98178 47.5112 -122.257 \n",
"1 400 1951 1991 98125 47.7210 -122.319 \n",
"2 0 1933 0 98028 47.7379 -122.233 \n",
"3 910 1965 0 98136 47.5208 -122.393 \n",
"4 0 1987 0 98074 47.6168 -122.045 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"0 1340 5650 \n",
"1 1690 7639 \n",
"2 2720 8062 \n",
"3 1360 5000 \n",
"4 1800 7503 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"house.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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m5kk++9ky/vznO4jFinE4qrjwwivZvbsepWbS2LiVUCjJ++8/gFJ5rF69Gqfz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\nwzCMBOb/AejemCR4kHfVAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4d7bc854a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show plots in jupyter\n",
"%matplotlib inline\n",
"\n",
"plt.scatter(house.sqft_living, house.price, alpha=0.5)\n",
"plt.ylabel('')\n",
"plt.xlabel('price')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add aditional features"
]
},
{
"cell_type": "code",
"execution_count": 63,
"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>date</th>\n",
" <th>price</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>sqft_living</th>\n",
" <th>sqft_lot</th>\n",
" <th>floors</th>\n",
" <th>waterfront</th>\n",
" <th>view</th>\n",
" <th>...</th>\n",
" <th>yr_renovated</th>\n",
" <th>zipcode</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>sqft_living15</th>\n",
" <th>sqft_lot15</th>\n",
" <th>bedrooms_squared</th>\n",
" <th>bed_bath_rooms</th>\n",
" <th>log_sqft_living</th>\n",
" <th>lat_plus_long</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7129300520</td>\n",
" <td>20141013T000000</td>\n",
" <td>221900</td>\n",
" <td>3</td>\n",
" <td>1.00</td>\n",
" <td>1180</td>\n",
" <td>5650</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>98178</td>\n",
" <td>47.5112</td>\n",
" <td>-122.257</td>\n",
" <td>1340</td>\n",
" <td>5650</td>\n",
" <td>9</td>\n",
" <td>3.00</td>\n",
" <td>7.073270</td>\n",
" <td>-74.7458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6414100192</td>\n",
" <td>20141209T000000</td>\n",
" <td>538000</td>\n",
" <td>3</td>\n",
" <td>2.25</td>\n",
" <td>2570</td>\n",
" <td>7242</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1991</td>\n",
" <td>98125</td>\n",
" <td>47.7210</td>\n",
" <td>-122.319</td>\n",
" <td>1690</td>\n",
" <td>7639</td>\n",
" <td>9</td>\n",
" <td>6.75</td>\n",
" <td>7.851661</td>\n",
" <td>-74.5980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5631500400</td>\n",
" <td>20150225T000000</td>\n",
" <td>180000</td>\n",
" <td>2</td>\n",
" <td>1.00</td>\n",
" <td>770</td>\n",
" <td>10000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>98028</td>\n",
" <td>47.7379</td>\n",
" <td>-122.233</td>\n",
" <td>2720</td>\n",
" <td>8062</td>\n",
" <td>4</td>\n",
" <td>2.00</td>\n",
" <td>6.646391</td>\n",
" <td>-74.4951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2487200875</td>\n",
" <td>20141209T000000</td>\n",
" <td>604000</td>\n",
" <td>4</td>\n",
" <td>3.00</td>\n",
" <td>1960</td>\n",
" <td>5000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>98136</td>\n",
" <td>47.5208</td>\n",
" <td>-122.393</td>\n",
" <td>1360</td>\n",
" <td>5000</td>\n",
" <td>16</td>\n",
" <td>12.00</td>\n",
" <td>7.580700</td>\n",
" <td>-74.8722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1954400510</td>\n",
" <td>20150218T000000</td>\n",
" <td>510000</td>\n",
" <td>3</td>\n",
" <td>2.00</td>\n",
" <td>1680</td>\n",
" <td>8080</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>98074</td>\n",
" <td>47.6168</td>\n",
" <td>-122.045</td>\n",
" <td>1800</td>\n",
" <td>7503</td>\n",
" <td>9</td>\n",
" <td>6.00</td>\n",
" <td>7.426549</td>\n",
" <td>-74.4282</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" id date price bedrooms bathrooms sqft_living \\\n",
"0 7129300520 20141013T000000 221900 3 1.00 1180 \n",
"1 6414100192 20141209T000000 538000 3 2.25 2570 \n",
"2 5631500400 20150225T000000 180000 2 1.00 770 \n",
"3 2487200875 20141209T000000 604000 4 3.00 1960 \n",
"4 1954400510 20150218T000000 510000 3 2.00 1680 \n",
"\n",
" sqft_lot floors waterfront view ... yr_renovated zipcode \\\n",
"0 5650 1 0 0 ... 0 98178 \n",
"1 7242 2 0 0 ... 1991 98125 \n",
"2 10000 1 0 0 ... 0 98028 \n",
"3 5000 1 0 0 ... 0 98136 \n",
"4 8080 1 0 0 ... 0 98074 \n",
"\n",
" lat long sqft_living15 sqft_lot15 bedrooms_squared \\\n",
"0 47.5112 -122.257 1340 5650 9 \n",
"1 47.7210 -122.319 1690 7639 9 \n",
"2 47.7379 -122.233 2720 8062 4 \n",
"3 47.5208 -122.393 1360 5000 16 \n",
"4 47.6168 -122.045 1800 7503 9 \n",
"\n",
" bed_bath_rooms log_sqft_living lat_plus_long \n",
"0 3.00 7.073270 -74.7458 \n",
"1 6.75 7.851661 -74.5980 \n",
"2 2.00 6.646391 -74.4951 \n",
"3 12.00 7.580700 -74.8722 \n",
"4 6.00 7.426549 -74.4282 \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"house['bedrooms_squared'] = house['bedrooms'].apply(lambda x : x*x)\n",
"house_test['bedrooms_squared'] = house_test['bedrooms'].apply(lambda x : x*x)\n",
"house_train['bedrooms_squared'] = house_train['bedrooms'].apply(lambda x : x*x)\n",
"\n",
"house['bed_bath_rooms'] = house.apply(lambda x : x['bedrooms'] * x['bathrooms'], axis=1)\n",
"house_test['bed_bath_rooms'] = house_test.apply(lambda x : x['bedrooms'] * x['bathrooms'], axis=1)\n",
"house_train['bed_bath_rooms'] = house_train.apply(lambda x : x['bedrooms'] * x['bathrooms'], axis=1)\n",
"\n",
"house['log_sqft_living'] = house['sqft_living'].apply(lambda x : np.log(x))\n",
"house_test['log_sqft_living'] = house_test['sqft_living'].apply(lambda x : np.log(x))\n",
"house_train['log_sqft_living'] = house_train['sqft_living'].apply(lambda x : np.log(x))\n",
"\n",
"house['lat_plus_long'] = house.apply(lambda x : x['lat'] + x['long'], axis=1)\n",
"house_test['lat_plus_long'] = house_test.apply(lambda x : x['lat'] + x['long'], axis=1)\n",
"house_train['lat_plus_long'] = house_train.apply(lambda x : x['lat'] + x['long'], axis=1)\n",
"\n",
"house.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculating the mean of new features"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bedrooms_squared mean: 12.45\n",
"Bed_sqft_living mean: 7.5\n",
"Log_sqft_living mean: 7.55\n",
"Lat_plus_long mean: -74.65\n"
]
}
],
"source": [
"print ('Bedrooms_squared mean: ', np.round(np.mean(house_test['bedrooms_squared']),2))\n",
"print ('Bed_sqft_living mean: ', np.round(np.mean(house_test['bed_bath_rooms']),2))\n",
"print ('Log_sqft_living mean: ', np.round(np.mean(house_test['log_sqft_living']),2))\n",
"print ('Lat_plus_long mean: ', np.round(np.mean(house_test['lat_plus_long']),2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creater linear Regression models"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model1 = linear_model.LinearRegression()\n",
"model1_features = house_train[['sqft_living', 'bedrooms', 'bathrooms', 'lat', 'long']]\n",
"model1_features_test = house_test[['sqft_living', 'bedrooms', 'bathrooms', 'lat', 'long']]\n",
"model1.fit(model1_features, house_train['price'])\n",
"\n",
"model2 = linear_model.LinearRegression()\n",
"model2_features = house_train[['sqft_living', 'bedrooms', 'bathrooms', 'lat', 'long', 'bed_bath_rooms']]\n",
"model2_features_test = house_test[['sqft_living', 'bedrooms', 'bathrooms', 'lat', 'long', 'bed_bath_rooms']]\n",
"model2.fit(model2_features, house_train['price'])\n",
"\n",
"model3 = linear_model.LinearRegression()\n",
"model3_features = house_train[['sqft_living', 'bedrooms', 'bathrooms', 'lat', 'long', 'bed_bath_rooms',\n",
" 'bedrooms_squared', 'log_sqft_living', 'lat_plus_long']]\n",
"model3_features_test = house_test[['sqft_living', 'bedrooms', 'bathrooms', 'lat', 'long', 'bed_bath_rooms',\n",
" 'bedrooms_squared', 'log_sqft_living', 'lat_plus_long']]\n",
"model3.fit(model3_features, house_train['price'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the Regression models"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model1: [ 3.12258646e+02 -5.95865332e+04 1.57067421e+04 6.58619264e+05\n",
" -3.09374351e+05]\n"
]
}
],
"source": [
"print ('Model1: ', model1.coef_)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model2: [ 3.06610053e+02 -1.13446368e+05 -7.14613083e+04 6.54844630e+05\n",
" -2.94298969e+05 2.55796520e+04]\n"
]
}
],
"source": [
"print ('Model2: ', model2.coef_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Computing the RSS of all models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RSS for the training data"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model1 RSS: 967879963049551.38\n",
"Model2 RSS: 958419635074070.62\n",
"Model3 RSS: 903436455050479.75\n"
]
}
],
"source": [
"print(\"Model1 RSS: %.2f\" % ((model1.predict(model1_features) - house_train['price']) ** 2).sum())\n",
"print(\"Model2 RSS: %.2f\" % ((model2.predict(model2_features) - house_train['price']) ** 2).sum())\n",
"print(\"Model3 RSS: %.2f\" % ((model3.predict(model3_features) - house_train['price']) ** 2).sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RSS for the test data"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model1 RSS: 225500469795490.25\n",
"Model2 RSS: 223377462976466.56\n",
"Model3 RSS: 259236319207176.81\n"
]
}
],
"source": [
"print(\"Model1 RSS: %.2f\" % ((model1.predict(model1_features_test) - house_test['price']) ** 2).sum())\n",
"print(\"Model2 RSS: %.2f\" % ((model2.predict(model2_features_test) - house_test['price']) ** 2).sum())\n",
"print(\"Model3 RSS: %.2f\" % ((model3.predict(model3_features_test) - house_test['price']) ** 2).sum())"
]
},
{
"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 |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb | 1 | 188623 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "knOigRU1UJ9Y"
},
"source": [
"# Estimating Mandatory Tour Frequency\n",
"\n",
"This notebook illustrates how to re-estimate a single model component for ActivitySim. This process \n",
"includes running ActivitySim in estimation mode to read household travel survey files and write out\n",
"the estimation data bundles used in this notebook. To review how to do so, please visit the other\n",
"notebooks in this directory."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:14.518184Z",
"start_time": "2021-03-29T21:00:13.596748Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "s53VwlPwtNnr",
"outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0"
},
"outputs": [],
"source": [
"import os\n",
"import larch # !conda install larch -c conda-forge # for estimation\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:14.522291Z",
"start_time": "2021-03-29T21:00:14.520102Z"
}
},
"outputs": [],
"source": [
"os.chdir('test')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load data and prep model for estimation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:15.006614Z",
"start_time": "2021-03-29T21:00:14.525309Z"
}
},
"outputs": [],
"source": [
"modelname = \"mandatory_tour_frequency\"\n",
"\n",
"from activitysim.estimation.larch import component_model\n",
"model, data = component_model(modelname, return_data=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Review data loaded from the EDB\n",
"\n",
"The next step is to read the EDB, including the coefficients, model settings, utilities specification, and chooser and alternative data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Coefficients"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:15.022825Z",
"start_time": "2021-03-29T21:00:15.008626Z"
}
},
"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>value</th>\n",
" <th>constrain</th>\n",
" </tr>\n",
" <tr>\n",
" <th>coefficient_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>coef_unavailable</th>\n",
" <td>-999.0000</td>\n",
" <td>T</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_ft_worker_work2_asc</th>\n",
" <td>-3.3781</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pt_worker_work2_asc</th>\n",
" <td>-3.0476</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work1_asc</th>\n",
" <td>2.1660</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work2_asc</th>\n",
" <td>-1.3965</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_school2_asc</th>\n",
" <td>-3.7429</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work_and_school_asc</th>\n",
" <td>0.1073</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_driving_age_child_school2_asc</th>\n",
" <td>-3.1360</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_driving_age_child_work_and_school_asc</th>\n",
" <td>-4.4362</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pre_driving_age_child_school2_asc</th>\n",
" <td>-3.9703</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_work1</th>\n",
" <td>0.1737</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_work2</th>\n",
" <td>-0.2255</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_school1</th>\n",
" <td>0.1592</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_school2</th>\n",
" <td>0.1140</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_work_and_school</th>\n",
" <td>-0.3442</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_univ_work1</th>\n",
" <td>0.1737</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work1</th>\n",
" <td>-0.4629</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work2</th>\n",
" <td>-0.1375</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school1</th>\n",
" <td>0.7218</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school2</th>\n",
" <td>1.2750</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work_and_school</th>\n",
" <td>0.9761</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_can_walk_to_work_work2</th>\n",
" <td>0.5268</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_can_walk_to_work_school2</th>\n",
" <td>0.7114</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_can_walk_to_work_and_school</th>\n",
" <td>0.1391</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work2</th>\n",
" <td>-0.0035</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_school2</th>\n",
" <td>-0.0034</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work_and_school</th>\n",
" <td>-0.0031</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_student_employed</th>\n",
" <td>3.0140</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_student_goes_to_school</th>\n",
" <td>3.8830</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_no_cars_in_hh_work2</th>\n",
" <td>-1.3060</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_no_cars_in_hh_school2</th>\n",
" <td>-1.4130</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_no_cars_in_hh_work_and_school</th>\n",
" <td>-1.3020</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_few_cars_than_drivers_school2</th>\n",
" <td>-0.5759</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work1</th>\n",
" <td>0.2191</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work2</th>\n",
" <td>-0.1478</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school1</th>\n",
" <td>-0.1335</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school2</th>\n",
" <td>-0.5577</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work_and_school</th>\n",
" <td>-0.1251</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_non_workers_in_hh_school1</th>\n",
" <td>0.2574</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_work</th>\n",
" <td>-0.0528</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_school1</th>\n",
" <td>0.0347</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_worker_work_and_school</th>\n",
" <td>0.0347</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_student_work_and_school</th>\n",
" <td>-0.0528</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_family_hh_category1</th>\n",
" <td>-0.2500</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_family_hh_category2</th>\n",
" <td>-0.1792</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_work2</th>\n",
" <td>0.1804</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_school2</th>\n",
" <td>0.0866</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_work_and_school</th>\n",
" <td>-0.1955</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_work1</th>\n",
" <td>-0.2831</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_work2</th>\n",
" <td>0.2308</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_school1</th>\n",
" <td>-0.1361</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_school2</th>\n",
" <td>0.3170</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_work_and_school</th>\n",
" <td>-0.3509</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value constrain\n",
"coefficient_name \n",
"coef_unavailable -999.0000 T\n",
"coef_ft_worker_work2_asc -3.3781 F\n",
"coef_pt_worker_work2_asc -3.0476 F\n",
"coef_univ_work1_asc 2.1660 F\n",
"coef_univ_work2_asc -1.3965 F\n",
"coef_univ_school2_asc -3.7429 F\n",
"coef_univ_work_and_school_asc 0.1073 F\n",
"coef_driving_age_child_school2_asc -3.1360 F\n",
"coef_driving_age_child_work_and_school_asc -4.4362 F\n",
"coef_pre_driving_age_child_school2_asc -3.9703 F\n",
"coef_female_work1 0.1737 F\n",
"coef_female_work2 -0.2255 F\n",
"coef_female_school1 0.1592 F\n",
"coef_female_school2 0.1140 F\n",
"coef_female_work_and_school -0.3442 F\n",
"coef_female_univ_work1 0.1737 F\n",
"coef_under_35_work1 -0.4629 F\n",
"coef_under_35_work2 -0.1375 F\n",
"coef_under_35_school1 0.7218 F\n",
"coef_under_35_school2 1.2750 F\n",
"coef_under_35_work_and_school 0.9761 F\n",
"coef_can_walk_to_work_work2 0.5268 F\n",
"coef_can_walk_to_work_school2 0.7114 F\n",
"coef_can_walk_to_work_and_school 0.1391 F\n",
"coef_round_trip_auto_time_to_work_work2 -0.0035 F\n",
"coef_round_trip_auto_time_to_work_school2 -0.0034 F\n",
"coef_round_trip_auto_time_to_work_work_and_school -0.0031 F\n",
"coef_student_employed 3.0140 F\n",
"coef_non_student_goes_to_school 3.8830 F\n",
"coef_no_cars_in_hh_work2 -1.3060 F\n",
"coef_no_cars_in_hh_school2 -1.4130 F\n",
"coef_no_cars_in_hh_work_and_school -1.3020 F\n",
"coef_few_cars_than_drivers_school2 -0.5759 F\n",
"coef_num_preschool_in_hh_work1 0.2191 F\n",
"coef_num_preschool_in_hh_work2 -0.1478 F\n",
"coef_num_preschool_in_hh_school1 -0.1335 F\n",
"coef_num_preschool_in_hh_school2 -0.5577 F\n",
"coef_num_preschool_in_hh_work_and_school -0.1251 F\n",
"coef_num_non_workers_in_hh_school1 0.2574 F\n",
"coef_hh_income_gt_50k_work -0.0528 F\n",
"coef_hh_income_gt_50k_school1 0.0347 F\n",
"coef_hh_income_gt_50k_worker_work_and_school 0.0347 F\n",
"coef_hh_income_gt_50k_student_work_and_school -0.0528 F\n",
"coef_non_family_hh_category1 -0.2500 F\n",
"coef_non_family_hh_category2 -0.1792 F\n",
"coef_num_under_16_not_at_school_work2 0.1804 NaN\n",
"coef_num_under_16_not_at_school_school2 0.0866 NaN\n",
"coef_num_under_16_not_at_school_work_and_school -0.1955 NaN\n",
"coef_home_urban_work1 -0.2831 NaN\n",
"coef_home_urban_work2 0.2308 NaN\n",
"coef_home_urban_school1 -0.1361 NaN\n",
"coef_home_urban_school2 0.3170 NaN\n",
"coef_home_urban_work_and_school -0.3509 NaN"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.coefficients"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utility specification"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:15.036116Z",
"start_time": "2021-03-29T21:00:15.024990Z"
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"outputs": [
{
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" <th>Description</th>\n",
" <th>Expression</th>\n",
" <th>work1</th>\n",
" <th>work2</th>\n",
" <th>school1</th>\n",
" <th>school2</th>\n",
" <th>work_and_school</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>util_ft_worker</td>\n",
" <td>Full-time worker alternative-specific constants</td>\n",
" <td>ptype == 1</td>\n",
" <td>0</td>\n",
" <td>coef_ft_worker_work2_asc</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>util_pt_worker</td>\n",
" <td>Part-time worker alternative-specific constants</td>\n",
" <td>ptype == 2</td>\n",
" <td>0</td>\n",
" <td>coef_pt_worker_work2_asc</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>util_univ</td>\n",
" <td>University student alternative-specific constants</td>\n",
" <td>ptype == 3</td>\n",
" <td>coef_univ_work1_asc</td>\n",
" <td>coef_univ_work2_asc</td>\n",
" <td>0</td>\n",
" <td>coef_univ_school2_asc</td>\n",
" <td>coef_univ_work_and_school_asc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>util_non_working_adult</td>\n",
" <td>Non-working adult alternative-specific constants</td>\n",
" <td>ptype == 4</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>4</th>\n",
" <td>util_retired</td>\n",
" <td>Retired alternative-specific constants</td>\n",
" <td>ptype == 5</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>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>util_availability_driving_age_child</td>\n",
" <td>Unavailable: Driving-age child</td>\n",
" <td>ptype == 6</td>\n",
" <td>coef_unavailable</td>\n",
" <td>coef_unavailable</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>util_availability_pre_driving_age_student</td>\n",
" <td>Unavailable: Pre-driving age child who is in s...</td>\n",
" <td>ptype == 7</td>\n",
" <td>NaN</td>\n",
" <td>coef_unavailable</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>coef_unavailable</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>util_availability_pre_driving_age_not_in_school</td>\n",
" <td>Unavailable: Pre-driving age child who is not ...</td>\n",
" <td>ptype == 8</td>\n",
" <td>coef_unavailable</td>\n",
" <td>coef_unavailable</td>\n",
" <td>NaN</td>\n",
" <td>coef_unavailable</td>\n",
" <td>coef_unavailable</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>util_availability_work_tours_no_usual_work_loc...</td>\n",
" <td>Unavailable: Work tours for those with no usua...</td>\n",
" <td>~(workplace_zone_id > -1)</td>\n",
" <td>coef_unavailable</td>\n",
" <td>coef_unavailable</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>coef_unavailable</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>util_availability_school_tours_no_usual_school...</td>\n",
" <td>Unavailable: School tours for those with no us...</td>\n",
" <td>~(school_zone_id > -1)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>coef_unavailable</td>\n",
" <td>coef_unavailable</td>\n",
" <td>coef_unavailable</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Label \\\n",
"0 util_ft_worker \n",
"1 util_pt_worker \n",
"2 util_univ \n",
"3 util_non_working_adult \n",
"4 util_retired \n",
".. ... \n",
"95 util_availability_driving_age_child \n",
"96 util_availability_pre_driving_age_student \n",
"97 util_availability_pre_driving_age_not_in_school \n",
"98 util_availability_work_tours_no_usual_work_loc... \n",
"99 util_availability_school_tours_no_usual_school... \n",
"\n",
" Description \\\n",
"0 Full-time worker alternative-specific constants \n",
"1 Part-time worker alternative-specific constants \n",
"2 University student alternative-specific constants \n",
"3 Non-working adult alternative-specific constants \n",
"4 Retired alternative-specific constants \n",
".. ... \n",
"95 Unavailable: Driving-age child \n",
"96 Unavailable: Pre-driving age child who is in s... \n",
"97 Unavailable: Pre-driving age child who is not ... \n",
"98 Unavailable: Work tours for those with no usua... \n",
"99 Unavailable: School tours for those with no us... \n",
"\n",
" Expression work1 work2 \\\n",
"0 ptype == 1 0 coef_ft_worker_work2_asc \n",
"1 ptype == 2 0 coef_pt_worker_work2_asc \n",
"2 ptype == 3 coef_univ_work1_asc coef_univ_work2_asc \n",
"3 ptype == 4 NaN NaN \n",
"4 ptype == 5 NaN NaN \n",
".. ... ... ... \n",
"95 ptype == 6 coef_unavailable coef_unavailable \n",
"96 ptype == 7 NaN coef_unavailable \n",
"97 ptype == 8 coef_unavailable coef_unavailable \n",
"98 ~(workplace_zone_id > -1) coef_unavailable coef_unavailable \n",
"99 ~(school_zone_id > -1) NaN NaN \n",
"\n",
" school1 school2 work_and_school \n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0 coef_univ_school2_asc coef_univ_work_and_school_asc \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
".. ... ... ... \n",
"95 NaN NaN NaN \n",
"96 NaN NaN coef_unavailable \n",
"97 NaN coef_unavailable coef_unavailable \n",
"98 NaN NaN coef_unavailable \n",
"99 coef_unavailable coef_unavailable coef_unavailable \n",
"\n",
"[100 rows x 8 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.spec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chooser data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:15.066277Z",
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},
"outputs": [
{
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>person_id</th>\n",
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" <th>util_pre_driving_age_child</th>\n",
" <th>...</th>\n",
" <th>HSENROLL</th>\n",
" <th>COLLFTE</th>\n",
" <th>COLLPTE</th>\n",
" <th>TOPOLOGY</th>\n",
" <th>TERMINAL</th>\n",
" <th>household_density</th>\n",
" <th>employment_density</th>\n",
" <th>density_index</th>\n",
" <th>is_cbd</th>\n",
" <th>override_choice_code</th>\n",
" </tr>\n",
" <tr>\n",
" <th>household_id</th>\n",
" <th></th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>629</th>\n",
" <td>629</td>\n",
" <td>school1</td>\n",
" <td>school1</td>\n",
" <td>0.0</td>\n",
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" <td>0.00000</td>\n",
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" <td>1.64217</td>\n",
" <td>13.280000</td>\n",
" <td>4.535000</td>\n",
" <td>3.380567</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1274</th>\n",
" <td>1274</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
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" <td>2.44180</td>\n",
" <td>19.776119</td>\n",
" <td>7.179104</td>\n",
" <td>5.267062</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" </tr>\n",
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" <th>27266</th>\n",
" <td>27266</td>\n",
" <td>school1</td>\n",
" <td>school1</td>\n",
" <td>0.0</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",
" <td>...</td>\n",
" <td>26.92893</td>\n",
" <td>2035.58118</td>\n",
" <td>20.60887</td>\n",
" <td>2</td>\n",
" <td>5.22542</td>\n",
" <td>97.634722</td>\n",
" <td>550.205552</td>\n",
" <td>82.920387</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28012</th>\n",
" <td>28012</td>\n",
" <td>school1</td>\n",
" <td>school1</td>\n",
" <td>0.0</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",
" <td>...</td>\n",
" <td>0.00000</td>\n",
" <td>690.54974</td>\n",
" <td>0.00000</td>\n",
" <td>3</td>\n",
" <td>4.73802</td>\n",
" <td>117.769796</td>\n",
" <td>246.205869</td>\n",
" <td>79.663609</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>29476</td>\n",
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" <td>0.0</td>\n",
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" <td>...</td>\n",
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" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>2</td>\n",
" <td>4.75017</td>\n",
" <td>71.898080</td>\n",
" <td>273.023745</td>\n",
" <td>56.911108</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>1</td>\n",
" <td>3.95752</td>\n",
" <td>65.596535</td>\n",
" <td>32.655666</td>\n",
" <td>21.802041</td>\n",
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" </tr>\n",
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" <th>2823442</th>\n",
" <td>7514777</td>\n",
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" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>2</td>\n",
" <td>4.51942</td>\n",
" <td>56.706023</td>\n",
" <td>144.861886</td>\n",
" <td>40.753220</td>\n",
" <td>True</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2823850</th>\n",
" <td>7515185</td>\n",
" <td>work_and_school</td>\n",
" <td>work_and_school</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>3</td>\n",
" <td>2.46800</td>\n",
" <td>0.296858</td>\n",
" <td>36.296472</td>\n",
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" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2836262</th>\n",
" <td>7527597</td>\n",
" <td>school1</td>\n",
" <td>school1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>3961.04761</td>\n",
" <td>17397.79102</td>\n",
" <td>11152.93652</td>\n",
" <td>1</td>\n",
" <td>2.28992</td>\n",
" <td>3.984127</td>\n",
" <td>23.820106</td>\n",
" <td>3.413233</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2849737</th>\n",
" <td>7541072</td>\n",
" <td>school2</td>\n",
" <td>school2</td>\n",
" <td>0.0</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",
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" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
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" <td>3</td>\n",
" <td>3.35329</td>\n",
" <td>34.477273</td>\n",
" <td>15.454545</td>\n",
" <td>10.671163</td>\n",
" <td>True</td>\n",
" <td>4</td>\n",
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"</table>\n",
"<p>2620 rows × 220 columns</p>\n",
"</div>"
],
"text/plain": [
" person_id model_choice override_choice util_ft_worker \\\n",
"household_id \n",
"629 629 school1 school1 0.0 \n",
"1274 1274 school1 school1 0.0 \n",
"27266 27266 school1 school1 0.0 \n",
"28012 28012 school1 school1 0.0 \n",
"29476 29476 school1 school1 0.0 \n",
"... ... ... ... ... \n",
"2823069 7514404 work1 work1 1.0 \n",
"2823442 7514777 work1 work1 0.0 \n",
"2823850 7515185 work_and_school work_and_school 0.0 \n",
"2836262 7527597 school1 school1 0.0 \n",
"2849737 7541072 school2 school2 0.0 \n",
"\n",
" util_pt_worker util_univ util_non_working_adult util_retired \\\n",
"household_id \n",
"629 0.0 1.0 0.0 0.0 \n",
"1274 0.0 1.0 0.0 0.0 \n",
"27266 0.0 1.0 0.0 0.0 \n",
"28012 0.0 1.0 0.0 0.0 \n",
"29476 0.0 1.0 0.0 0.0 \n",
"... ... ... ... ... \n",
"2823069 0.0 0.0 0.0 0.0 \n",
"2823442 0.0 1.0 0.0 0.0 \n",
"2823850 0.0 1.0 0.0 0.0 \n",
"2836262 0.0 1.0 0.0 0.0 \n",
"2849737 0.0 1.0 0.0 0.0 \n",
"\n",
" util_driving_age_child util_pre_driving_age_child ... \\\n",
"household_id ... \n",
"629 0.0 0.0 ... \n",
"1274 0.0 0.0 ... \n",
"27266 0.0 0.0 ... \n",
"28012 0.0 0.0 ... \n",
"29476 0.0 0.0 ... \n",
"... ... ... ... \n",
"2823069 0.0 0.0 ... \n",
"2823442 0.0 0.0 ... \n",
"2823850 0.0 0.0 ... \n",
"2836262 0.0 0.0 ... \n",
"2849737 0.0 0.0 ... \n",
"\n",
" HSENROLL COLLFTE COLLPTE TOPOLOGY TERMINAL \\\n",
"household_id \n",
"629 0.00000 0.00000 0.00000 1 1.64217 \n",
"1274 506.23721 0.00000 0.00000 1 2.44180 \n",
"27266 26.92893 2035.58118 20.60887 2 5.22542 \n",
"28012 0.00000 690.54974 0.00000 3 4.73802 \n",
"29476 0.00000 0.00000 0.00000 2 4.75017 \n",
"... ... ... ... ... ... \n",
"2823069 0.00000 0.00000 0.00000 1 3.95752 \n",
"2823442 0.00000 0.00000 0.00000 2 4.51942 \n",
"2823850 0.00000 0.00000 0.00000 3 2.46800 \n",
"2836262 3961.04761 17397.79102 11152.93652 1 2.28992 \n",
"2849737 0.00000 0.00000 0.00000 3 3.35329 \n",
"\n",
" household_density employment_density density_index is_cbd \\\n",
"household_id \n",
"629 13.280000 4.535000 3.380567 False \n",
"1274 19.776119 7.179104 5.267062 False \n",
"27266 97.634722 550.205552 82.920387 False \n",
"28012 117.769796 246.205869 79.663609 False \n",
"29476 71.898080 273.023745 56.911108 False \n",
"... ... ... ... ... \n",
"2823069 65.596535 32.655666 21.802041 True \n",
"2823442 56.706023 144.861886 40.753220 True \n",
"2823850 0.296858 36.296472 0.294449 True \n",
"2836262 3.984127 23.820106 3.413233 False \n",
"2849737 34.477273 15.454545 10.671163 True \n",
"\n",
" override_choice_code \n",
"household_id \n",
"629 3 \n",
"1274 3 \n",
"27266 3 \n",
"28012 3 \n",
"29476 3 \n",
"... ... \n",
"2823069 1 \n",
"2823442 1 \n",
"2823850 5 \n",
"2836262 3 \n",
"2849737 4 \n",
"\n",
"[2620 rows x 220 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.chooser_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Estimate\n",
"\n",
"With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has a built-in estimation methods including BHHH, and also offers access to more advanced general purpose non-linear optimizers in the `scipy` package, including SLSQP, which allows for bounds and constraints on parameters. BHHH is the default and typically runs faster, but does not follow constraints on parameters."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:21.363279Z",
"start_time": "2021-03-29T21:00:15.067647Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"req_data does not request avail_ca or avail_co but it is set and being provided\n"
]
},
{
"data": {
"text/html": [
"<h3>Iteration 050 [Optimization terminated successfully] </h3>"
],
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"<IPython.core.display.HTML object>"
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{
"data": {
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"<p>Best LL = -410.2201502048449</p>"
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" <th>coef_driving_age_child_school2_asc</th>\n",
" <td>-13.067731</td>\n",
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" <th>coef_driving_age_child_work_and_school_asc</th>\n",
" <td>-12.605997</td>\n",
" <td>-4.4362</td>\n",
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" <tr>\n",
" <th>coef_female_school1</th>\n",
" <td>0.247086</td>\n",
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" <th>coef_female_work1</th>\n",
" <td>-2.242748</td>\n",
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" <td>NaN</td>\n",
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" <th>coef_female_work2</th>\n",
" <td>-0.387411</td>\n",
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" <th>coef_female_work_and_school</th>\n",
" <td>-3.218280</td>\n",
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" <td>NaN</td>\n",
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" <td>-3.218280</td>\n",
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" <tr>\n",
" <th>coef_few_cars_than_drivers_school2</th>\n",
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" <th>coef_ft_worker_work2_asc</th>\n",
" <td>-5.517933</td>\n",
" <td>-3.3781</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>coef_hh_income_gt_50k_school1</th>\n",
" <td>0.034700</td>\n",
" <td>0.0347</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>coef_hh_income_gt_50k_student_work_and_school</th>\n",
" <td>-0.921175</td>\n",
" <td>-0.0528</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>4.026796</td>\n",
" <td>-0.2831</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>coef_home_urban_work2</th>\n",
" <td>2.624854</td>\n",
" <td>0.2308</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>2.624854</td>\n",
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" <th>coef_home_urban_work_and_school</th>\n",
" <td>-0.447070</td>\n",
" <td>-0.3509</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>coef_no_cars_in_hh_school2</th>\n",
" <td>-0.620465</td>\n",
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" <th>coef_no_cars_in_hh_work2</th>\n",
" <td>-0.725967</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>coef_no_cars_in_hh_work_and_school</th>\n",
" <td>-0.775934</td>\n",
" <td>-1.3020</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td></td>\n",
" <td>-0.775934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_family_hh_category1</th>\n",
" <td>-0.250000</td>\n",
" <td>-0.2500</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>5.000771</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>5.000771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_student_goes_to_school</th>\n",
" <td>3.883000</td>\n",
" <td>3.8830</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>3.883000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_non_workers_in_hh_school1</th>\n",
" <td>0.257400</td>\n",
" <td>0.2574</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>0.257400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school1</th>\n",
" <td>-0.133500</td>\n",
" <td>-0.1335</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-0.133500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school2</th>\n",
" <td>0.356660</td>\n",
" <td>-0.5577</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>0.356660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work1</th>\n",
" <td>-2.930501</td>\n",
" <td>0.2191</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-2.930501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work2</th>\n",
" <td>0.225173</td>\n",
" <td>-0.1478</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>0.225173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work_and_school</th>\n",
" <td>-29.197569</td>\n",
" <td>-0.1251</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-29.197569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_school2</th>\n",
" <td>-0.638293</td>\n",
" <td>0.0866</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-0.638293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_work2</th>\n",
" <td>-0.118991</td>\n",
" <td>0.1804</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-0.118991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_work_and_school</th>\n",
" <td>-15.357907</td>\n",
" <td>-0.1955</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-15.357907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pre_driving_age_child_school2_asc</th>\n",
" <td>3.679726</td>\n",
" <td>-3.9703</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>3.679726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pt_worker_work2_asc</th>\n",
" <td>-4.743242</td>\n",
" <td>-3.0476</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-4.743242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_school2</th>\n",
" <td>0.064411</td>\n",
" <td>-0.0034</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>0.064411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work2</th>\n",
" <td>-0.025905</td>\n",
" <td>-0.0035</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-0.025905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work_and_school</th>\n",
" <td>-0.021804</td>\n",
" <td>-0.0031</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-0.021804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_student_employed</th>\n",
" <td>13.457255</td>\n",
" <td>3.0140</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>13.457255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_unavailable</th>\n",
" <td>-999.000000</td>\n",
" <td>-999.0000</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td></td>\n",
" <td>-999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school1</th>\n",
" <td>0.721800</td>\n",
" <td>0.7218</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>0.721800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school2</th>\n",
" <td>15.011164</td>\n",
" <td>1.2750</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>15.011164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work1</th>\n",
" <td>1.038242</td>\n",
" <td>-0.4629</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>1.038242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work2</th>\n",
" <td>-0.253875</td>\n",
" <td>-0.1375</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-0.253875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work_and_school</th>\n",
" <td>2.257035</td>\n",
" <td>0.9761</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>2.257035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_school2_asc</th>\n",
" <td>-10.233810</td>\n",
" <td>-3.7429</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>-10.233810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work1_asc</th>\n",
" <td>6.475896</td>\n",
" <td>2.1660</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>6.475896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work2_asc</th>\n",
" <td>4.833029</td>\n",
" <td>-1.3965</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>4.833029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work_and_school_asc</th>\n",
" <td>8.180927</td>\n",
" <td>0.1073</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>8.180927</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value initvalue \\\n",
"0 0.000000 0.0000 \n",
"coef_can_walk_to_work_and_school 0.071630 0.1391 \n",
"coef_can_walk_to_work_school2 1.368092 0.7114 \n",
"coef_can_walk_to_work_work2 0.651120 0.5268 \n",
"coef_driving_age_child_school2_asc -13.067731 -3.1360 \n",
"coef_driving_age_child_work_and_school_asc -12.605997 -4.4362 \n",
"coef_female_school1 0.247086 0.1592 \n",
"coef_female_school2 -0.301339 0.1140 \n",
"coef_female_work1 -2.242748 0.1737 \n",
"coef_female_work2 -0.387411 -0.2255 \n",
"coef_female_work_and_school -3.218280 -0.3442 \n",
"coef_few_cars_than_drivers_school2 -0.862451 -0.5759 \n",
"coef_ft_worker_work2_asc -5.517933 -3.3781 \n",
"coef_hh_income_gt_50k_school1 0.034700 0.0347 \n",
"coef_hh_income_gt_50k_student_work_and_school -0.921175 -0.0528 \n",
"coef_hh_income_gt_50k_work -1.418551 -0.0528 \n",
"coef_hh_income_gt_50k_worker_work_and_school 0.034700 0.0347 \n",
"coef_home_urban_school1 -0.136100 -0.1361 \n",
"coef_home_urban_school2 -8.455615 0.3170 \n",
"coef_home_urban_work1 4.026796 -0.2831 \n",
"coef_home_urban_work2 2.624854 0.2308 \n",
"coef_home_urban_work_and_school -0.447070 -0.3509 \n",
"coef_no_cars_in_hh_school2 -0.620465 -1.4130 \n",
"coef_no_cars_in_hh_work2 -0.725967 -1.3060 \n",
"coef_no_cars_in_hh_work_and_school -0.775934 -1.3020 \n",
"coef_non_family_hh_category1 -0.250000 -0.2500 \n",
"coef_non_family_hh_category2 5.000771 -0.1792 \n",
"coef_non_student_goes_to_school 3.883000 3.8830 \n",
"coef_num_non_workers_in_hh_school1 0.257400 0.2574 \n",
"coef_num_preschool_in_hh_school1 -0.133500 -0.1335 \n",
"coef_num_preschool_in_hh_school2 0.356660 -0.5577 \n",
"coef_num_preschool_in_hh_work1 -2.930501 0.2191 \n",
"coef_num_preschool_in_hh_work2 0.225173 -0.1478 \n",
"coef_num_preschool_in_hh_work_and_school -29.197569 -0.1251 \n",
"coef_num_under_16_not_at_school_school2 -0.638293 0.0866 \n",
"coef_num_under_16_not_at_school_work2 -0.118991 0.1804 \n",
"coef_num_under_16_not_at_school_work_and_school -15.357907 -0.1955 \n",
"coef_pre_driving_age_child_school2_asc 3.679726 -3.9703 \n",
"coef_pt_worker_work2_asc -4.743242 -3.0476 \n",
"coef_round_trip_auto_time_to_work_school2 0.064411 -0.0034 \n",
"coef_round_trip_auto_time_to_work_work2 -0.025905 -0.0035 \n",
"coef_round_trip_auto_time_to_work_work_and_school -0.021804 -0.0031 \n",
"coef_student_employed 13.457255 3.0140 \n",
"coef_unavailable -999.000000 -999.0000 \n",
"coef_under_35_school1 0.721800 0.7218 \n",
"coef_under_35_school2 15.011164 1.2750 \n",
"coef_under_35_work1 1.038242 -0.4629 \n",
"coef_under_35_work2 -0.253875 -0.1375 \n",
"coef_under_35_work_and_school 2.257035 0.9761 \n",
"coef_univ_school2_asc -10.233810 -3.7429 \n",
"coef_univ_work1_asc 6.475896 2.1660 \n",
"coef_univ_work2_asc 4.833029 -1.3965 \n",
"coef_univ_work_and_school_asc 8.180927 0.1073 \n",
"\n",
" nullvalue minimum \\\n",
"0 0.0 0.0 \n",
"coef_can_walk_to_work_and_school 0.0 NaN \n",
"coef_can_walk_to_work_school2 0.0 NaN \n",
"coef_can_walk_to_work_work2 0.0 NaN \n",
"coef_driving_age_child_school2_asc 0.0 NaN \n",
"coef_driving_age_child_work_and_school_asc 0.0 NaN \n",
"coef_female_school1 0.0 NaN \n",
"coef_female_school2 0.0 NaN \n",
"coef_female_work1 0.0 NaN \n",
"coef_female_work2 0.0 NaN \n",
"coef_female_work_and_school 0.0 NaN \n",
"coef_few_cars_than_drivers_school2 0.0 NaN \n",
"coef_ft_worker_work2_asc 0.0 NaN \n",
"coef_hh_income_gt_50k_school1 0.0 NaN \n",
"coef_hh_income_gt_50k_student_work_and_school 0.0 NaN \n",
"coef_hh_income_gt_50k_work 0.0 NaN \n",
"coef_hh_income_gt_50k_worker_work_and_school 0.0 NaN \n",
"coef_home_urban_school1 0.0 NaN \n",
"coef_home_urban_school2 0.0 NaN \n",
"coef_home_urban_work1 0.0 NaN \n",
"coef_home_urban_work2 0.0 NaN \n",
"coef_home_urban_work_and_school 0.0 NaN \n",
"coef_no_cars_in_hh_school2 0.0 NaN \n",
"coef_no_cars_in_hh_work2 0.0 NaN \n",
"coef_no_cars_in_hh_work_and_school 0.0 NaN \n",
"coef_non_family_hh_category1 0.0 NaN \n",
"coef_non_family_hh_category2 0.0 NaN \n",
"coef_non_student_goes_to_school 0.0 NaN \n",
"coef_num_non_workers_in_hh_school1 0.0 NaN \n",
"coef_num_preschool_in_hh_school1 0.0 NaN \n",
"coef_num_preschool_in_hh_school2 0.0 NaN \n",
"coef_num_preschool_in_hh_work1 0.0 NaN \n",
"coef_num_preschool_in_hh_work2 0.0 NaN \n",
"coef_num_preschool_in_hh_work_and_school 0.0 NaN \n",
"coef_num_under_16_not_at_school_school2 0.0 NaN \n",
"coef_num_under_16_not_at_school_work2 0.0 NaN \n",
"coef_num_under_16_not_at_school_work_and_school 0.0 NaN \n",
"coef_pre_driving_age_child_school2_asc 0.0 NaN \n",
"coef_pt_worker_work2_asc 0.0 NaN \n",
"coef_round_trip_auto_time_to_work_school2 0.0 NaN \n",
"coef_round_trip_auto_time_to_work_work2 0.0 NaN \n",
"coef_round_trip_auto_time_to_work_work_and_school 0.0 NaN \n",
"coef_student_employed 0.0 NaN \n",
"coef_unavailable 0.0 NaN \n",
"coef_under_35_school1 0.0 NaN \n",
"coef_under_35_school2 0.0 NaN \n",
"coef_under_35_work1 0.0 NaN \n",
"coef_under_35_work2 0.0 NaN \n",
"coef_under_35_work_and_school 0.0 NaN \n",
"coef_univ_school2_asc 0.0 NaN \n",
"coef_univ_work1_asc 0.0 NaN \n",
"coef_univ_work2_asc 0.0 NaN \n",
"coef_univ_work_and_school_asc 0.0 NaN \n",
"\n",
" maximum holdfast note \\\n",
"0 0.0 1 \n",
"coef_can_walk_to_work_and_school NaN 0 \n",
"coef_can_walk_to_work_school2 NaN 0 \n",
"coef_can_walk_to_work_work2 NaN 0 \n",
"coef_driving_age_child_school2_asc NaN 0 \n",
"coef_driving_age_child_work_and_school_asc NaN 0 \n",
"coef_female_school1 NaN 0 \n",
"coef_female_school2 NaN 0 \n",
"coef_female_work1 NaN 0 \n",
"coef_female_work2 NaN 0 \n",
"coef_female_work_and_school NaN 0 \n",
"coef_few_cars_than_drivers_school2 NaN 0 \n",
"coef_ft_worker_work2_asc NaN 0 \n",
"coef_hh_income_gt_50k_school1 NaN 0 \n",
"coef_hh_income_gt_50k_student_work_and_school NaN 0 \n",
"coef_hh_income_gt_50k_work NaN 0 \n",
"coef_hh_income_gt_50k_worker_work_and_school NaN 0 \n",
"coef_home_urban_school1 NaN 0 \n",
"coef_home_urban_school2 NaN 0 \n",
"coef_home_urban_work1 NaN 0 \n",
"coef_home_urban_work2 NaN 0 \n",
"coef_home_urban_work_and_school NaN 0 \n",
"coef_no_cars_in_hh_school2 NaN 0 \n",
"coef_no_cars_in_hh_work2 NaN 0 \n",
"coef_no_cars_in_hh_work_and_school NaN 0 \n",
"coef_non_family_hh_category1 NaN 0 \n",
"coef_non_family_hh_category2 NaN 0 \n",
"coef_non_student_goes_to_school NaN 0 \n",
"coef_num_non_workers_in_hh_school1 NaN 0 \n",
"coef_num_preschool_in_hh_school1 NaN 0 \n",
"coef_num_preschool_in_hh_school2 NaN 0 \n",
"coef_num_preschool_in_hh_work1 NaN 0 \n",
"coef_num_preschool_in_hh_work2 NaN 0 \n",
"coef_num_preschool_in_hh_work_and_school NaN 0 \n",
"coef_num_under_16_not_at_school_school2 NaN 0 \n",
"coef_num_under_16_not_at_school_work2 NaN 0 \n",
"coef_num_under_16_not_at_school_work_and_school NaN 0 \n",
"coef_pre_driving_age_child_school2_asc NaN 0 \n",
"coef_pt_worker_work2_asc NaN 0 \n",
"coef_round_trip_auto_time_to_work_school2 NaN 0 \n",
"coef_round_trip_auto_time_to_work_work2 NaN 0 \n",
"coef_round_trip_auto_time_to_work_work_and_school NaN 0 \n",
"coef_student_employed NaN 0 \n",
"coef_unavailable NaN 1 \n",
"coef_under_35_school1 NaN 0 \n",
"coef_under_35_school2 NaN 0 \n",
"coef_under_35_work1 NaN 0 \n",
"coef_under_35_work2 NaN 0 \n",
"coef_under_35_work_and_school NaN 0 \n",
"coef_univ_school2_asc NaN 0 \n",
"coef_univ_work1_asc NaN 0 \n",
"coef_univ_work2_asc NaN 0 \n",
"coef_univ_work_and_school_asc NaN 0 \n",
"\n",
" best \n",
"0 0.000000 \n",
"coef_can_walk_to_work_and_school 0.071630 \n",
"coef_can_walk_to_work_school2 1.368092 \n",
"coef_can_walk_to_work_work2 0.651120 \n",
"coef_driving_age_child_school2_asc -13.067731 \n",
"coef_driving_age_child_work_and_school_asc -12.605997 \n",
"coef_female_school1 0.247086 \n",
"coef_female_school2 -0.301339 \n",
"coef_female_work1 -2.242748 \n",
"coef_female_work2 -0.387411 \n",
"coef_female_work_and_school -3.218280 \n",
"coef_few_cars_than_drivers_school2 -0.862451 \n",
"coef_ft_worker_work2_asc -5.517933 \n",
"coef_hh_income_gt_50k_school1 0.034700 \n",
"coef_hh_income_gt_50k_student_work_and_school -0.921175 \n",
"coef_hh_income_gt_50k_work -1.418551 \n",
"coef_hh_income_gt_50k_worker_work_and_school 0.034700 \n",
"coef_home_urban_school1 -0.136100 \n",
"coef_home_urban_school2 -8.455615 \n",
"coef_home_urban_work1 4.026796 \n",
"coef_home_urban_work2 2.624854 \n",
"coef_home_urban_work_and_school -0.447070 \n",
"coef_no_cars_in_hh_school2 -0.620465 \n",
"coef_no_cars_in_hh_work2 -0.725967 \n",
"coef_no_cars_in_hh_work_and_school -0.775934 \n",
"coef_non_family_hh_category1 -0.250000 \n",
"coef_non_family_hh_category2 5.000771 \n",
"coef_non_student_goes_to_school 3.883000 \n",
"coef_num_non_workers_in_hh_school1 0.257400 \n",
"coef_num_preschool_in_hh_school1 -0.133500 \n",
"coef_num_preschool_in_hh_school2 0.356660 \n",
"coef_num_preschool_in_hh_work1 -2.930501 \n",
"coef_num_preschool_in_hh_work2 0.225173 \n",
"coef_num_preschool_in_hh_work_and_school -29.197569 \n",
"coef_num_under_16_not_at_school_school2 -0.638293 \n",
"coef_num_under_16_not_at_school_work2 -0.118991 \n",
"coef_num_under_16_not_at_school_work_and_school -15.357907 \n",
"coef_pre_driving_age_child_school2_asc 3.679726 \n",
"coef_pt_worker_work2_asc -4.743242 \n",
"coef_round_trip_auto_time_to_work_school2 0.064411 \n",
"coef_round_trip_auto_time_to_work_work2 -0.025905 \n",
"coef_round_trip_auto_time_to_work_work_and_school -0.021804 \n",
"coef_student_employed 13.457255 \n",
"coef_unavailable -999.000000 \n",
"coef_under_35_school1 0.721800 \n",
"coef_under_35_school2 15.011164 \n",
"coef_under_35_work1 1.038242 \n",
"coef_under_35_work2 -0.253875 \n",
"coef_under_35_work_and_school 2.257035 \n",
"coef_univ_school2_asc -10.233810 \n",
"coef_univ_work1_asc 6.475896 \n",
"coef_univ_work2_asc 4.833029 \n",
"coef_univ_work_and_school_asc 8.180927 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-7-5091d419e17b>:1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n",
" model.estimate()\n",
"/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.771611294971759e-29 in general_inverse\n",
" warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n",
"<ipython-input-7-5091d419e17b>:1: RuntimeWarning: invalid value encountered in sqrt\n",
" model.estimate()\n"
]
},
{
"data": {
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"<div><table style=\"margin-top:1px;\"><tr><th>key</th><th style=\"text-align:left;\">value</th></tr><tr><td>x</td><td style=\"text-align:left;\"><table border=\"1\" class=\"dataframe\">\n",
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" <tr>\n",
" <th>0</th>\n",
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" <tr>\n",
" <th>coef_driving_age_child_school2_asc</th>\n",
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" <tr>\n",
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" <th>coef_female_school1</th>\n",
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" <tr>\n",
" <th>coef_few_cars_than_drivers_school2</th>\n",
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" <th>coef_hh_income_gt_50k_student_work_and_school</th>\n",
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" <tr>\n",
" <th>coef_hh_income_gt_50k_worker_work_and_school</th>\n",
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" <th>coef_home_urban_school1</th>\n",
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" <td>-8.455615</td>\n",
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" <th>coef_home_urban_work1</th>\n",
" <td>4.026796</td>\n",
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" <td>2.624854</td>\n",
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" <tr>\n",
" <th>coef_home_urban_work_and_school</th>\n",
" <td>-0.447070</td>\n",
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" <tr>\n",
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" <td>-0.620465</td>\n",
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" <tr>\n",
" <th>coef_no_cars_in_hh_work2</th>\n",
" <td>-0.725967</td>\n",
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" <tr>\n",
" <th>coef_no_cars_in_hh_work_and_school</th>\n",
" <td>-0.775934</td>\n",
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" <tr>\n",
" <th>coef_non_family_hh_category1</th>\n",
" <td>-0.250000</td>\n",
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" <tr>\n",
" <th>coef_non_family_hh_category2</th>\n",
" <td>5.000771</td>\n",
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" <tr>\n",
" <th>coef_non_student_goes_to_school</th>\n",
" <td>3.883000</td>\n",
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" <tr>\n",
" <th>coef_num_non_workers_in_hh_school1</th>\n",
" <td>0.257400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school1</th>\n",
" <td>-0.133500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school2</th>\n",
" <td>0.356660</td>\n",
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" <tr>\n",
" <th>coef_num_preschool_in_hh_work1</th>\n",
" <td>-2.930501</td>\n",
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" <tr>\n",
" <th>coef_num_preschool_in_hh_work2</th>\n",
" <td>0.225173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work_and_school</th>\n",
" <td>-29.197569</td>\n",
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" <tr>\n",
" <th>coef_num_under_16_not_at_school_school2</th>\n",
" <td>-0.638293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_work2</th>\n",
" <td>-0.118991</td>\n",
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" <tr>\n",
" <th>coef_num_under_16_not_at_school_work_and_school</th>\n",
" <td>-15.357907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pre_driving_age_child_school2_asc</th>\n",
" <td>3.679726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pt_worker_work2_asc</th>\n",
" <td>-4.743242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_school2</th>\n",
" <td>0.064411</td>\n",
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" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work2</th>\n",
" <td>-0.025905</td>\n",
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" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work_and_school</th>\n",
" <td>-0.021804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_student_employed</th>\n",
" <td>13.457255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_unavailable</th>\n",
" <td>-999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school1</th>\n",
" <td>0.721800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school2</th>\n",
" <td>15.011164</td>\n",
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" <tr>\n",
" <th>coef_under_35_work1</th>\n",
" <td>1.038242</td>\n",
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" <th>coef_under_35_work2</th>\n",
" <td>-0.253875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work_and_school</th>\n",
" <td>2.257035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_school2_asc</th>\n",
" <td>-10.233810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work1_asc</th>\n",
" <td>6.475896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work2_asc</th>\n",
" <td>4.833029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work_and_school_asc</th>\n",
" <td>8.180927</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></td></tr><tr><td>loglike</td><td style=\"text-align:left;\">-410.2201502048449</td></tr><tr><td>d_loglike</td><td style=\"text-align:left;\"><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_can_walk_to_work_and_school</th>\n",
" <td>-2.159761e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_can_walk_to_work_school2</th>\n",
" <td>-7.694778e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_can_walk_to_work_work2</th>\n",
" <td>4.202731e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_driving_age_child_school2_asc</th>\n",
" <td>-4.255256e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_driving_age_child_work_and_school_asc</th>\n",
" <td>2.167724e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_school1</th>\n",
" <td>-6.957308e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_school2</th>\n",
" <td>-2.554309e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_work1</th>\n",
" <td>-7.357671e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_work2</th>\n",
" <td>8.686757e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_female_work_and_school</th>\n",
" <td>1.188196e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_few_cars_than_drivers_school2</th>\n",
" <td>-2.286678e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_ft_worker_work2_asc</th>\n",
" <td>5.501466e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_school1</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_student_work_and_school</th>\n",
" <td>6.367109e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_work</th>\n",
" <td>-1.223654e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_hh_income_gt_50k_worker_work_and_school</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_school1</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_school2</th>\n",
" <td>-1.028755e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_work1</th>\n",
" <td>-4.465713e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_work2</th>\n",
" <td>1.557017e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_home_urban_work_and_school</th>\n",
" <td>1.077213e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_no_cars_in_hh_school2</th>\n",
" <td>8.945645e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_no_cars_in_hh_work2</th>\n",
" <td>3.899664e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_no_cars_in_hh_work_and_school</th>\n",
" <td>-3.695621e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_family_hh_category1</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_family_hh_category2</th>\n",
" <td>1.251878e-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_non_student_goes_to_school</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_non_workers_in_hh_school1</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school1</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_school2</th>\n",
" <td>3.508996e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work1</th>\n",
" <td>-2.217197e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work2</th>\n",
" <td>8.498540e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_preschool_in_hh_work_and_school</th>\n",
" <td>-3.475886e-12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_school2</th>\n",
" <td>-8.486532e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_work2</th>\n",
" <td>-3.959448e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_num_under_16_not_at_school_work_and_school</th>\n",
" <td>-2.351049e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pre_driving_age_child_school2_asc</th>\n",
" <td>6.171829e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_pt_worker_work2_asc</th>\n",
" <td>1.420463e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_school2</th>\n",
" <td>-4.638296e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work2</th>\n",
" <td>3.215960e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_round_trip_auto_time_to_work_work_and_school</th>\n",
" <td>1.679772e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_student_employed</th>\n",
" <td>2.170492e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_unavailable</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school1</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_school2</th>\n",
" <td>-1.082319e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work1</th>\n",
" <td>-9.399930e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work2</th>\n",
" <td>7.788570e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_under_35_work_and_school</th>\n",
" <td>-1.237585e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_school2_asc</th>\n",
" <td>-1.090048e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work1_asc</th>\n",
" <td>-4.465713e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work2_asc</th>\n",
" <td>-4.135926e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coef_univ_work_and_school_asc</th>\n",
" <td>8.604407e-05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></td></tr><tr><td>nit</td><td style=\"text-align:left;\">50</td></tr><tr><td>nfev</td><td style=\"text-align:left;\">84</td></tr><tr><td>njev</td><td style=\"text-align:left;\">50</td></tr><tr><td>status</td><td style=\"text-align:left;\">0</td></tr><tr><td>message</td><td style=\"text-align:left;\">'Optimization terminated successfully'</td></tr><tr><td>success</td><td style=\"text-align:left;\">True</td></tr><tr><td>elapsed_time</td><td style=\"text-align:left;\">0:00:03.886165</td></tr><tr><td>method</td><td style=\"text-align:left;\">'slsqp'</td></tr><tr><td>n_cases</td><td style=\"text-align:left;\">2620</td></tr><tr><td>iteration_number</td><td style=\"text-align:left;\">50</td></tr><tr><td>logloss</td><td style=\"text-align:left;\">0.15657257641406294</td></tr></table></div>"
],
"text/plain": [
"┣ x: 0 0.000000\n",
"┃ coef_can_walk_to_work_and_school 0.071630\n",
"┃ coef_can_walk_to_work_school2 1.368092\n",
"┃ coef_can_walk_to_work_work2 0.651120\n",
"┃ coef_driving_age_child_school2_asc -13.067731\n",
"┃ coef_driving_age_child_work_and_school_asc -12.605997\n",
"┃ coef_female_school1 0.247086\n",
"┃ coef_female_school2 -0.301339\n",
"┃ coef_female_work1 -2.242748\n",
"┃ coef_female_work2 -0.387411\n",
"┃ coef_female_work_and_school -3.218280\n",
"┃ coef_few_cars_than_drivers_school2 -0.862451\n",
"┃ coef_ft_worker_work2_asc -5.517933\n",
"┃ coef_hh_income_gt_50k_school1 0.034700\n",
"┃ coef_hh_income_gt_50k_student_work_and_school -0.921175\n",
"┃ coef_hh_income_gt_50k_work -1.418551\n",
"┃ coef_hh_income_gt_50k_worker_work_and_school 0.034700\n",
"┃ coef_home_urban_school1 -0.136100\n",
"┃ coef_home_urban_school2 -8.455615\n",
"┃ coef_home_urban_work1 4.026796\n",
"┃ coef_home_urban_work2 2.624854\n",
"┃ coef_home_urban_work_and_school -0.447070\n",
"┃ coef_no_cars_in_hh_school2 -0.620465\n",
"┃ coef_no_cars_in_hh_work2 -0.725967\n",
"┃ coef_no_cars_in_hh_work_and_school -0.775934\n",
"┃ coef_non_family_hh_category1 -0.250000\n",
"┃ coef_non_family_hh_category2 5.000771\n",
"┃ coef_non_student_goes_to_school 3.883000\n",
"┃ coef_num_non_workers_in_hh_school1 0.257400\n",
"┃ coef_num_preschool_in_hh_school1 -0.133500\n",
"┃ coef_num_preschool_in_hh_school2 0.356660\n",
"┃ coef_num_preschool_in_hh_work1 -2.930501\n",
"┃ coef_num_preschool_in_hh_work2 0.225173\n",
"┃ coef_num_preschool_in_hh_work_and_school -29.197569\n",
"┃ coef_num_under_16_not_at_school_school2 -0.638293\n",
"┃ coef_num_under_16_not_at_school_work2 -0.118991\n",
"┃ coef_num_under_16_not_at_school_work_and_school -15.357907\n",
"┃ coef_pre_driving_age_child_school2_asc 3.679726\n",
"┃ coef_pt_worker_work2_asc -4.743242\n",
"┃ coef_round_trip_auto_time_to_work_school2 0.064411\n",
"┃ coef_round_trip_auto_time_to_work_work2 -0.025905\n",
"┃ coef_round_trip_auto_time_to_work_work_and_school -0.021804\n",
"┃ coef_student_employed 13.457255\n",
"┃ coef_unavailable -999.000000\n",
"┃ coef_under_35_school1 0.721800\n",
"┃ coef_under_35_school2 15.011164\n",
"┃ coef_under_35_work1 1.038242\n",
"┃ coef_under_35_work2 -0.253875\n",
"┃ coef_under_35_work_and_school 2.257035\n",
"┃ coef_univ_school2_asc -10.233810\n",
"┃ coef_univ_work1_asc 6.475896\n",
"┃ coef_univ_work2_asc 4.833029\n",
"┃ coef_univ_work_and_school_asc 8.180927\n",
"┃ dtype: float64\n",
"┣ loglike: -410.2201502048449\n",
"┣ d_loglike: 0 0.000000e+00\n",
"┃ coef_can_walk_to_work_and_school -2.159761e-05\n",
"┃ coef_can_walk_to_work_school2 -7.694778e-05\n",
"┃ coef_can_walk_to_work_work2 4.202731e-05\n",
"┃ coef_driving_age_child_school2_asc -4.255256e-08\n",
"┃ coef_driving_age_child_work_and_school_asc 2.167724e-05\n",
"┃ coef_female_school1 -6.957308e-05\n",
"┃ coef_female_school2 -2.554309e-04\n",
"┃ coef_female_work1 -7.357671e-05\n",
"┃ coef_female_work2 8.686757e-05\n",
"┃ coef_female_work_and_school 1.188196e-05\n",
"┃ coef_few_cars_than_drivers_school2 -2.286678e-04\n",
"┃ coef_ft_worker_work2_asc 5.501466e-05\n",
"┃ coef_hh_income_gt_50k_school1 0.000000e+00\n",
"┃ coef_hh_income_gt_50k_student_work_and_school 6.367109e-06\n",
"┃ coef_hh_income_gt_50k_work -1.223654e-05\n",
"┃ coef_hh_income_gt_50k_worker_work_and_school 0.000000e+00\n",
"┃ coef_home_urban_school1 0.000000e+00\n",
"┃ coef_home_urban_school2 -1.028755e-04\n",
"┃ coef_home_urban_work1 -4.465713e-05\n",
"┃ coef_home_urban_work2 1.557017e-04\n",
"┃ coef_home_urban_work_and_school 1.077213e-04\n",
"┃ coef_no_cars_in_hh_school2 8.945645e-05\n",
"┃ coef_no_cars_in_hh_work2 3.899664e-05\n",
"┃ coef_no_cars_in_hh_work_and_school -3.695621e-05\n",
"┃ coef_non_family_hh_category1 0.000000e+00\n",
"┃ coef_non_family_hh_category2 1.251878e-10\n",
"┃ coef_non_student_goes_to_school 0.000000e+00\n",
"┃ coef_num_non_workers_in_hh_school1 0.000000e+00\n",
"┃ coef_num_preschool_in_hh_school1 0.000000e+00\n",
"┃ coef_num_preschool_in_hh_school2 3.508996e-05\n",
"┃ coef_num_preschool_in_hh_work1 -2.217197e-05\n",
"┃ coef_num_preschool_in_hh_work2 8.498540e-05\n",
"┃ coef_num_preschool_in_hh_work_and_school -3.475886e-12\n",
"┃ coef_num_under_16_not_at_school_school2 -8.486532e-05\n",
"┃ coef_num_under_16_not_at_school_work2 -3.959448e-05\n",
"┃ coef_num_under_16_not_at_school_work_and_school -2.351049e-07\n",
"┃ coef_pre_driving_age_child_school2_asc 6.171829e-06\n",
"┃ coef_pt_worker_work2_asc 1.420463e-04\n",
"┃ coef_round_trip_auto_time_to_work_school2 -4.638296e-03\n",
"┃ coef_round_trip_auto_time_to_work_work2 3.215960e-03\n",
"┃ coef_round_trip_auto_time_to_work_work_and_school 1.679772e-03\n",
"┃ coef_student_employed 2.170492e-05\n",
"┃ coef_unavailable 0.000000e+00\n",
"┃ coef_under_35_school1 0.000000e+00\n",
"┃ coef_under_35_school2 -1.082319e-04\n",
"┃ coef_under_35_work1 -9.399930e-05\n",
"┃ coef_under_35_work2 7.788570e-05\n",
"┃ coef_under_35_work_and_school -1.237585e-04\n",
"┃ coef_univ_school2_asc -1.090048e-04\n",
"┃ coef_univ_work1_asc -4.465713e-05\n",
"┃ coef_univ_work2_asc -4.135926e-05\n",
"┃ coef_univ_work_and_school_asc 8.604407e-05\n",
"┃ dtype: float64\n",
"┣ nit: 50\n",
"┣ nfev: 84\n",
"┣ njev: 50\n",
"┣ status: 0\n",
"┣ message: 'Optimization terminated successfully'\n",
"┣ success: True\n",
"┣ elapsed_time: datetime.timedelta(seconds=3, microseconds=886165)\n",
"┣ method: 'slsqp'\n",
"┣ n_cases: 2620\n",
"┣ iteration_number: 50\n",
"┣ logloss: 0.15657257641406294"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.estimate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimated coefficients"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:21.417205Z",
"start_time": "2021-03-29T21:00:21.367001Z"
}
},
"outputs": [
{
"data": {
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" font-family: monospace;\n",
" }</style><table id=\"T_f940c_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Value</th> <th class=\"col_heading level0 col1\" >Std Err</th> <th class=\"col_heading level0 col2\" >t Stat</th> <th class=\"col_heading level0 col3\" >Signif</th> <th class=\"col_heading level0 col4\" >Like Ratio</th> <th class=\"col_heading level0 col5\" >Null Value</th> <th class=\"col_heading level0 col6\" >Constrained</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_f940c_row0_col0\" class=\"data row0 col0\" > 0.00</td>\n",
" <td id=\"T_f940c_row0_col1\" class=\"data row0 col1\" > NA</td>\n",
" <td id=\"T_f940c_row0_col2\" class=\"data row0 col2\" > NA</td>\n",
" <td id=\"T_f940c_row0_col3\" class=\"data row0 col3\" ></td>\n",
" <td id=\"T_f940c_row0_col4\" class=\"data row0 col4\" > NA</td>\n",
" <td id=\"T_f940c_row0_col5\" class=\"data row0 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row0_col6\" class=\"data row0 col6\" >fixed value</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row1\" class=\"row_heading level0 row1\" >coef_can_walk_to_work_and_school</th>\n",
" <td id=\"T_f940c_row1_col0\" class=\"data row1 col0\" > 0.0716</td>\n",
" <td id=\"T_f940c_row1_col1\" class=\"data row1 col1\" > 0.710</td>\n",
" <td id=\"T_f940c_row1_col2\" class=\"data row1 col2\" > 0.10</td>\n",
" <td id=\"T_f940c_row1_col3\" class=\"data row1 col3\" ></td>\n",
" <td id=\"T_f940c_row1_col4\" class=\"data row1 col4\" > NA</td>\n",
" <td id=\"T_f940c_row1_col5\" class=\"data row1 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row1_col6\" class=\"data row1 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row2\" class=\"row_heading level0 row2\" >coef_can_walk_to_work_school2</th>\n",
" <td id=\"T_f940c_row2_col0\" class=\"data row2 col0\" > 1.37</td>\n",
" <td id=\"T_f940c_row2_col1\" class=\"data row2 col1\" > 0.878</td>\n",
" <td id=\"T_f940c_row2_col2\" class=\"data row2 col2\" > 1.56</td>\n",
" <td id=\"T_f940c_row2_col3\" class=\"data row2 col3\" ></td>\n",
" <td id=\"T_f940c_row2_col4\" class=\"data row2 col4\" > NA</td>\n",
" <td id=\"T_f940c_row2_col5\" class=\"data row2 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row2_col6\" class=\"data row2 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row3\" class=\"row_heading level0 row3\" >coef_can_walk_to_work_work2</th>\n",
" <td id=\"T_f940c_row3_col0\" class=\"data row3 col0\" > 0.651</td>\n",
" <td id=\"T_f940c_row3_col1\" class=\"data row3 col1\" > 0.397</td>\n",
" <td id=\"T_f940c_row3_col2\" class=\"data row3 col2\" > 1.64</td>\n",
" <td id=\"T_f940c_row3_col3\" class=\"data row3 col3\" ></td>\n",
" <td id=\"T_f940c_row3_col4\" class=\"data row3 col4\" > NA</td>\n",
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" <td id=\"T_f940c_row3_col6\" class=\"data row3 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row4\" class=\"row_heading level0 row4\" >coef_driving_age_child_school2_asc</th>\n",
" <td id=\"T_f940c_row4_col0\" class=\"data row4 col0\" >-13.1</td>\n",
" <td id=\"T_f940c_row4_col1\" class=\"data row4 col1\" > 4.56e+03</td>\n",
" <td id=\"T_f940c_row4_col2\" class=\"data row4 col2\" >-0.00</td>\n",
" <td id=\"T_f940c_row4_col3\" class=\"data row4 col3\" ></td>\n",
" <td id=\"T_f940c_row4_col4\" class=\"data row4 col4\" > NA</td>\n",
" <td id=\"T_f940c_row4_col5\" class=\"data row4 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row4_col6\" class=\"data row4 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row5\" class=\"row_heading level0 row5\" >coef_driving_age_child_work_and_school_asc</th>\n",
" <td id=\"T_f940c_row5_col0\" class=\"data row5 col0\" >-12.6</td>\n",
" <td id=\"T_f940c_row5_col1\" class=\"data row5 col1\" > NA</td>\n",
" <td id=\"T_f940c_row5_col2\" class=\"data row5 col2\" > NA</td>\n",
" <td id=\"T_f940c_row5_col3\" class=\"data row5 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row5_col4\" class=\"data row5 col4\" > 68.07</td>\n",
" <td id=\"T_f940c_row5_col5\" class=\"data row5 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row5_col6\" class=\"data row5 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row6\" class=\"row_heading level0 row6\" >coef_female_school1</th>\n",
" <td id=\"T_f940c_row6_col0\" class=\"data row6 col0\" > 0.247</td>\n",
" <td id=\"T_f940c_row6_col1\" class=\"data row6 col1\" > 1.35</td>\n",
" <td id=\"T_f940c_row6_col2\" class=\"data row6 col2\" > 0.18</td>\n",
" <td id=\"T_f940c_row6_col3\" class=\"data row6 col3\" ></td>\n",
" <td id=\"T_f940c_row6_col4\" class=\"data row6 col4\" > NA</td>\n",
" <td id=\"T_f940c_row6_col5\" class=\"data row6 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row6_col6\" class=\"data row6 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row7\" class=\"row_heading level0 row7\" >coef_female_school2</th>\n",
" <td id=\"T_f940c_row7_col0\" class=\"data row7 col0\" >-0.301</td>\n",
" <td id=\"T_f940c_row7_col1\" class=\"data row7 col1\" > 0.785</td>\n",
" <td id=\"T_f940c_row7_col2\" class=\"data row7 col2\" >-0.38</td>\n",
" <td id=\"T_f940c_row7_col3\" class=\"data row7 col3\" ></td>\n",
" <td id=\"T_f940c_row7_col4\" class=\"data row7 col4\" > NA</td>\n",
" <td id=\"T_f940c_row7_col5\" class=\"data row7 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row7_col6\" class=\"data row7 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row8\" class=\"row_heading level0 row8\" >coef_female_work1</th>\n",
" <td id=\"T_f940c_row8_col0\" class=\"data row8 col0\" >-2.24</td>\n",
" <td id=\"T_f940c_row8_col1\" class=\"data row8 col1\" > 1.34</td>\n",
" <td id=\"T_f940c_row8_col2\" class=\"data row8 col2\" >-1.68</td>\n",
" <td id=\"T_f940c_row8_col3\" class=\"data row8 col3\" ></td>\n",
" <td id=\"T_f940c_row8_col4\" class=\"data row8 col4\" > NA</td>\n",
" <td id=\"T_f940c_row8_col5\" class=\"data row8 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row8_col6\" class=\"data row8 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row9\" class=\"row_heading level0 row9\" >coef_female_work2</th>\n",
" <td id=\"T_f940c_row9_col0\" class=\"data row9 col0\" >-0.387</td>\n",
" <td id=\"T_f940c_row9_col1\" class=\"data row9 col1\" > 0.246</td>\n",
" <td id=\"T_f940c_row9_col2\" class=\"data row9 col2\" >-1.57</td>\n",
" <td id=\"T_f940c_row9_col3\" class=\"data row9 col3\" ></td>\n",
" <td id=\"T_f940c_row9_col4\" class=\"data row9 col4\" > NA</td>\n",
" <td id=\"T_f940c_row9_col5\" class=\"data row9 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row9_col6\" class=\"data row9 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row10\" class=\"row_heading level0 row10\" >coef_female_work_and_school</th>\n",
" <td id=\"T_f940c_row10_col0\" class=\"data row10 col0\" >-3.22</td>\n",
" <td id=\"T_f940c_row10_col1\" class=\"data row10 col1\" > 1.40</td>\n",
" <td id=\"T_f940c_row10_col2\" class=\"data row10 col2\" >-2.30</td>\n",
" <td id=\"T_f940c_row10_col3\" class=\"data row10 col3\" >*</td>\n",
" <td id=\"T_f940c_row10_col4\" class=\"data row10 col4\" > NA</td>\n",
" <td id=\"T_f940c_row10_col5\" class=\"data row10 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row10_col6\" class=\"data row10 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row11\" class=\"row_heading level0 row11\" >coef_few_cars_than_drivers_school2</th>\n",
" <td id=\"T_f940c_row11_col0\" class=\"data row11 col0\" >-0.862</td>\n",
" <td id=\"T_f940c_row11_col1\" class=\"data row11 col1\" > 0.715</td>\n",
" <td id=\"T_f940c_row11_col2\" class=\"data row11 col2\" >-1.21</td>\n",
" <td id=\"T_f940c_row11_col3\" class=\"data row11 col3\" ></td>\n",
" <td id=\"T_f940c_row11_col4\" class=\"data row11 col4\" > NA</td>\n",
" <td id=\"T_f940c_row11_col5\" class=\"data row11 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row11_col6\" class=\"data row11 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row12\" class=\"row_heading level0 row12\" >coef_ft_worker_work2_asc</th>\n",
" <td id=\"T_f940c_row12_col0\" class=\"data row12 col0\" >-5.52</td>\n",
" <td id=\"T_f940c_row12_col1\" class=\"data row12 col1\" > NA</td>\n",
" <td id=\"T_f940c_row12_col2\" class=\"data row12 col2\" > NA</td>\n",
" <td id=\"T_f940c_row12_col3\" class=\"data row12 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row12_col4\" class=\"data row12 col4\" > BIG</td>\n",
" <td id=\"T_f940c_row12_col5\" class=\"data row12 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row12_col6\" class=\"data row12 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row13\" class=\"row_heading level0 row13\" >coef_hh_income_gt_50k_school1</th>\n",
" <td id=\"T_f940c_row13_col0\" class=\"data row13 col0\" > 0.0347</td>\n",
" <td id=\"T_f940c_row13_col1\" class=\"data row13 col1\" > NA</td>\n",
" <td id=\"T_f940c_row13_col2\" class=\"data row13 col2\" > NA</td>\n",
" <td id=\"T_f940c_row13_col3\" class=\"data row13 col3\" >[]</td>\n",
" <td id=\"T_f940c_row13_col4\" class=\"data row13 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row13_col5\" class=\"data row13 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row13_col6\" class=\"data row13 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row14\" class=\"row_heading level0 row14\" >coef_hh_income_gt_50k_student_work_and_school</th>\n",
" <td id=\"T_f940c_row14_col0\" class=\"data row14 col0\" >-0.921</td>\n",
" <td id=\"T_f940c_row14_col1\" class=\"data row14 col1\" > 1.85</td>\n",
" <td id=\"T_f940c_row14_col2\" class=\"data row14 col2\" >-0.50</td>\n",
" <td id=\"T_f940c_row14_col3\" class=\"data row14 col3\" ></td>\n",
" <td id=\"T_f940c_row14_col4\" class=\"data row14 col4\" > NA</td>\n",
" <td id=\"T_f940c_row14_col5\" class=\"data row14 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row14_col6\" class=\"data row14 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row15\" class=\"row_heading level0 row15\" >coef_hh_income_gt_50k_work</th>\n",
" <td id=\"T_f940c_row15_col0\" class=\"data row15 col0\" >-1.42</td>\n",
" <td id=\"T_f940c_row15_col1\" class=\"data row15 col1\" > 1.92</td>\n",
" <td id=\"T_f940c_row15_col2\" class=\"data row15 col2\" >-0.74</td>\n",
" <td id=\"T_f940c_row15_col3\" class=\"data row15 col3\" ></td>\n",
" <td id=\"T_f940c_row15_col4\" class=\"data row15 col4\" > NA</td>\n",
" <td id=\"T_f940c_row15_col5\" class=\"data row15 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row15_col6\" class=\"data row15 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row16\" class=\"row_heading level0 row16\" >coef_hh_income_gt_50k_worker_work_and_school</th>\n",
" <td id=\"T_f940c_row16_col0\" class=\"data row16 col0\" > 0.0347</td>\n",
" <td id=\"T_f940c_row16_col1\" class=\"data row16 col1\" > NA</td>\n",
" <td id=\"T_f940c_row16_col2\" class=\"data row16 col2\" > NA</td>\n",
" <td id=\"T_f940c_row16_col3\" class=\"data row16 col3\" >[]</td>\n",
" <td id=\"T_f940c_row16_col4\" class=\"data row16 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row16_col5\" class=\"data row16 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row16_col6\" class=\"data row16 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row17\" class=\"row_heading level0 row17\" >coef_home_urban_school1</th>\n",
" <td id=\"T_f940c_row17_col0\" class=\"data row17 col0\" >-0.136</td>\n",
" <td id=\"T_f940c_row17_col1\" class=\"data row17 col1\" > NA</td>\n",
" <td id=\"T_f940c_row17_col2\" class=\"data row17 col2\" > NA</td>\n",
" <td id=\"T_f940c_row17_col3\" class=\"data row17 col3\" >[]</td>\n",
" <td id=\"T_f940c_row17_col4\" class=\"data row17 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row17_col5\" class=\"data row17 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row17_col6\" class=\"data row17 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row18\" class=\"row_heading level0 row18\" >coef_home_urban_school2</th>\n",
" <td id=\"T_f940c_row18_col0\" class=\"data row18 col0\" >-8.46</td>\n",
" <td id=\"T_f940c_row18_col1\" class=\"data row18 col1\" > NA</td>\n",
" <td id=\"T_f940c_row18_col2\" class=\"data row18 col2\" > NA</td>\n",
" <td id=\"T_f940c_row18_col3\" class=\"data row18 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row18_col4\" class=\"data row18 col4\" > BIG</td>\n",
" <td id=\"T_f940c_row18_col5\" class=\"data row18 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row18_col6\" class=\"data row18 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row19\" class=\"row_heading level0 row19\" >coef_home_urban_work1</th>\n",
" <td id=\"T_f940c_row19_col0\" class=\"data row19 col0\" > 4.03</td>\n",
" <td id=\"T_f940c_row19_col1\" class=\"data row19 col1\" > NA</td>\n",
" <td id=\"T_f940c_row19_col2\" class=\"data row19 col2\" > NA</td>\n",
" <td id=\"T_f940c_row19_col3\" class=\"data row19 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row19_col4\" class=\"data row19 col4\" > 165.42</td>\n",
" <td id=\"T_f940c_row19_col5\" class=\"data row19 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row19_col6\" class=\"data row19 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row20\" class=\"row_heading level0 row20\" >coef_home_urban_work2</th>\n",
" <td id=\"T_f940c_row20_col0\" class=\"data row20 col0\" > 2.62</td>\n",
" <td id=\"T_f940c_row20_col1\" class=\"data row20 col1\" > NA</td>\n",
" <td id=\"T_f940c_row20_col2\" class=\"data row20 col2\" > NA</td>\n",
" <td id=\"T_f940c_row20_col3\" class=\"data row20 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row20_col4\" class=\"data row20 col4\" > 134.83</td>\n",
" <td id=\"T_f940c_row20_col5\" class=\"data row20 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row20_col6\" class=\"data row20 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row21\" class=\"row_heading level0 row21\" >coef_home_urban_work_and_school</th>\n",
" <td id=\"T_f940c_row21_col0\" class=\"data row21 col0\" >-0.447</td>\n",
" <td id=\"T_f940c_row21_col1\" class=\"data row21 col1\" > NA</td>\n",
" <td id=\"T_f940c_row21_col2\" class=\"data row21 col2\" > NA</td>\n",
" <td id=\"T_f940c_row21_col3\" class=\"data row21 col3\" >[]</td>\n",
" <td id=\"T_f940c_row21_col4\" class=\"data row21 col4\" > 1.59</td>\n",
" <td id=\"T_f940c_row21_col5\" class=\"data row21 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row21_col6\" class=\"data row21 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row22\" class=\"row_heading level0 row22\" >coef_no_cars_in_hh_school2</th>\n",
" <td id=\"T_f940c_row22_col0\" class=\"data row22 col0\" >-0.620</td>\n",
" <td id=\"T_f940c_row22_col1\" class=\"data row22 col1\" > 1.18</td>\n",
" <td id=\"T_f940c_row22_col2\" class=\"data row22 col2\" >-0.52</td>\n",
" <td id=\"T_f940c_row22_col3\" class=\"data row22 col3\" ></td>\n",
" <td id=\"T_f940c_row22_col4\" class=\"data row22 col4\" > NA</td>\n",
" <td id=\"T_f940c_row22_col5\" class=\"data row22 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row22_col6\" class=\"data row22 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row23\" class=\"row_heading level0 row23\" >coef_no_cars_in_hh_work2</th>\n",
" <td id=\"T_f940c_row23_col0\" class=\"data row23 col0\" >-0.726</td>\n",
" <td id=\"T_f940c_row23_col1\" class=\"data row23 col1\" > 0.336</td>\n",
" <td id=\"T_f940c_row23_col2\" class=\"data row23 col2\" >-2.16</td>\n",
" <td id=\"T_f940c_row23_col3\" class=\"data row23 col3\" >*</td>\n",
" <td id=\"T_f940c_row23_col4\" class=\"data row23 col4\" > NA</td>\n",
" <td id=\"T_f940c_row23_col5\" class=\"data row23 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row23_col6\" class=\"data row23 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row24\" class=\"row_heading level0 row24\" >coef_no_cars_in_hh_work_and_school</th>\n",
" <td id=\"T_f940c_row24_col0\" class=\"data row24 col0\" >-0.776</td>\n",
" <td id=\"T_f940c_row24_col1\" class=\"data row24 col1\" > 0.703</td>\n",
" <td id=\"T_f940c_row24_col2\" class=\"data row24 col2\" >-1.10</td>\n",
" <td id=\"T_f940c_row24_col3\" class=\"data row24 col3\" ></td>\n",
" <td id=\"T_f940c_row24_col4\" class=\"data row24 col4\" > NA</td>\n",
" <td id=\"T_f940c_row24_col5\" class=\"data row24 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row24_col6\" class=\"data row24 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row25\" class=\"row_heading level0 row25\" >coef_non_family_hh_category1</th>\n",
" <td id=\"T_f940c_row25_col0\" class=\"data row25 col0\" >-0.250</td>\n",
" <td id=\"T_f940c_row25_col1\" class=\"data row25 col1\" > NA</td>\n",
" <td id=\"T_f940c_row25_col2\" class=\"data row25 col2\" > NA</td>\n",
" <td id=\"T_f940c_row25_col3\" class=\"data row25 col3\" >[]</td>\n",
" <td id=\"T_f940c_row25_col4\" class=\"data row25 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row25_col5\" class=\"data row25 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row25_col6\" class=\"data row25 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row26\" class=\"row_heading level0 row26\" >coef_non_family_hh_category2</th>\n",
" <td id=\"T_f940c_row26_col0\" class=\"data row26 col0\" > 5.00</td>\n",
" <td id=\"T_f940c_row26_col1\" class=\"data row26 col1\" > NA</td>\n",
" <td id=\"T_f940c_row26_col2\" class=\"data row26 col2\" > NA</td>\n",
" <td id=\"T_f940c_row26_col3\" class=\"data row26 col3\" >[]</td>\n",
" <td id=\"T_f940c_row26_col4\" class=\"data row26 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row26_col5\" class=\"data row26 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row26_col6\" class=\"data row26 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row27\" class=\"row_heading level0 row27\" >coef_non_student_goes_to_school</th>\n",
" <td id=\"T_f940c_row27_col0\" class=\"data row27 col0\" > 3.88</td>\n",
" <td id=\"T_f940c_row27_col1\" class=\"data row27 col1\" > NA</td>\n",
" <td id=\"T_f940c_row27_col2\" class=\"data row27 col2\" > NA</td>\n",
" <td id=\"T_f940c_row27_col3\" class=\"data row27 col3\" >[]</td>\n",
" <td id=\"T_f940c_row27_col4\" class=\"data row27 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row27_col5\" class=\"data row27 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row27_col6\" class=\"data row27 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row28\" class=\"row_heading level0 row28\" >coef_num_non_workers_in_hh_school1</th>\n",
" <td id=\"T_f940c_row28_col0\" class=\"data row28 col0\" > 0.257</td>\n",
" <td id=\"T_f940c_row28_col1\" class=\"data row28 col1\" > NA</td>\n",
" <td id=\"T_f940c_row28_col2\" class=\"data row28 col2\" > NA</td>\n",
" <td id=\"T_f940c_row28_col3\" class=\"data row28 col3\" >[]</td>\n",
" <td id=\"T_f940c_row28_col4\" class=\"data row28 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row28_col5\" class=\"data row28 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row28_col6\" class=\"data row28 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row29\" class=\"row_heading level0 row29\" >coef_num_preschool_in_hh_school1</th>\n",
" <td id=\"T_f940c_row29_col0\" class=\"data row29 col0\" >-0.134</td>\n",
" <td id=\"T_f940c_row29_col1\" class=\"data row29 col1\" > NA</td>\n",
" <td id=\"T_f940c_row29_col2\" class=\"data row29 col2\" > NA</td>\n",
" <td id=\"T_f940c_row29_col3\" class=\"data row29 col3\" >[]</td>\n",
" <td id=\"T_f940c_row29_col4\" class=\"data row29 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row29_col5\" class=\"data row29 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row29_col6\" class=\"data row29 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row30\" class=\"row_heading level0 row30\" >coef_num_preschool_in_hh_school2</th>\n",
" <td id=\"T_f940c_row30_col0\" class=\"data row30 col0\" > 0.357</td>\n",
" <td id=\"T_f940c_row30_col1\" class=\"data row30 col1\" > 0.576</td>\n",
" <td id=\"T_f940c_row30_col2\" class=\"data row30 col2\" > 0.62</td>\n",
" <td id=\"T_f940c_row30_col3\" class=\"data row30 col3\" ></td>\n",
" <td id=\"T_f940c_row30_col4\" class=\"data row30 col4\" > NA</td>\n",
" <td id=\"T_f940c_row30_col5\" class=\"data row30 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row30_col6\" class=\"data row30 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row31\" class=\"row_heading level0 row31\" >coef_num_preschool_in_hh_work1</th>\n",
" <td id=\"T_f940c_row31_col0\" class=\"data row31 col0\" >-2.93</td>\n",
" <td id=\"T_f940c_row31_col1\" class=\"data row31 col1\" > 1.55</td>\n",
" <td id=\"T_f940c_row31_col2\" class=\"data row31 col2\" >-1.89</td>\n",
" <td id=\"T_f940c_row31_col3\" class=\"data row31 col3\" ></td>\n",
" <td id=\"T_f940c_row31_col4\" class=\"data row31 col4\" > NA</td>\n",
" <td id=\"T_f940c_row31_col5\" class=\"data row31 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row31_col6\" class=\"data row31 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row32\" class=\"row_heading level0 row32\" >coef_num_preschool_in_hh_work2</th>\n",
" <td id=\"T_f940c_row32_col0\" class=\"data row32 col0\" > 0.225</td>\n",
" <td id=\"T_f940c_row32_col1\" class=\"data row32 col1\" > 0.192</td>\n",
" <td id=\"T_f940c_row32_col2\" class=\"data row32 col2\" > 1.17</td>\n",
" <td id=\"T_f940c_row32_col3\" class=\"data row32 col3\" ></td>\n",
" <td id=\"T_f940c_row32_col4\" class=\"data row32 col4\" > NA</td>\n",
" <td id=\"T_f940c_row32_col5\" class=\"data row32 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row32_col6\" class=\"data row32 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row33\" class=\"row_heading level0 row33\" >coef_num_preschool_in_hh_work_and_school</th>\n",
" <td id=\"T_f940c_row33_col0\" class=\"data row33 col0\" >-29.2</td>\n",
" <td id=\"T_f940c_row33_col1\" class=\"data row33 col1\" > NA</td>\n",
" <td id=\"T_f940c_row33_col2\" class=\"data row33 col2\" > NA</td>\n",
" <td id=\"T_f940c_row33_col3\" class=\"data row33 col3\" >[**]</td>\n",
" <td id=\"T_f940c_row33_col4\" class=\"data row33 col4\" > 4.26</td>\n",
" <td id=\"T_f940c_row33_col5\" class=\"data row33 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row33_col6\" class=\"data row33 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row34\" class=\"row_heading level0 row34\" >coef_num_under_16_not_at_school_school2</th>\n",
" <td id=\"T_f940c_row34_col0\" class=\"data row34 col0\" >-0.638</td>\n",
" <td id=\"T_f940c_row34_col1\" class=\"data row34 col1\" > 1.02</td>\n",
" <td id=\"T_f940c_row34_col2\" class=\"data row34 col2\" >-0.63</td>\n",
" <td id=\"T_f940c_row34_col3\" class=\"data row34 col3\" ></td>\n",
" <td id=\"T_f940c_row34_col4\" class=\"data row34 col4\" > NA</td>\n",
" <td id=\"T_f940c_row34_col5\" class=\"data row34 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row34_col6\" class=\"data row34 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row35\" class=\"row_heading level0 row35\" >coef_num_under_16_not_at_school_work2</th>\n",
" <td id=\"T_f940c_row35_col0\" class=\"data row35 col0\" >-0.119</td>\n",
" <td id=\"T_f940c_row35_col1\" class=\"data row35 col1\" > 0.306</td>\n",
" <td id=\"T_f940c_row35_col2\" class=\"data row35 col2\" >-0.39</td>\n",
" <td id=\"T_f940c_row35_col3\" class=\"data row35 col3\" ></td>\n",
" <td id=\"T_f940c_row35_col4\" class=\"data row35 col4\" > NA</td>\n",
" <td id=\"T_f940c_row35_col5\" class=\"data row35 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row35_col6\" class=\"data row35 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row36\" class=\"row_heading level0 row36\" >coef_num_under_16_not_at_school_work_and_school</th>\n",
" <td id=\"T_f940c_row36_col0\" class=\"data row36 col0\" >-15.4</td>\n",
" <td id=\"T_f940c_row36_col1\" class=\"data row36 col1\" > 2.03e+03</td>\n",
" <td id=\"T_f940c_row36_col2\" class=\"data row36 col2\" >-0.01</td>\n",
" <td id=\"T_f940c_row36_col3\" class=\"data row36 col3\" ></td>\n",
" <td id=\"T_f940c_row36_col4\" class=\"data row36 col4\" > NA</td>\n",
" <td id=\"T_f940c_row36_col5\" class=\"data row36 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row36_col6\" class=\"data row36 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row37\" class=\"row_heading level0 row37\" >coef_pre_driving_age_child_school2_asc</th>\n",
" <td id=\"T_f940c_row37_col0\" class=\"data row37 col0\" > 3.68</td>\n",
" <td id=\"T_f940c_row37_col1\" class=\"data row37 col1\" > NA</td>\n",
" <td id=\"T_f940c_row37_col2\" class=\"data row37 col2\" > NA</td>\n",
" <td id=\"T_f940c_row37_col3\" class=\"data row37 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row37_col4\" class=\"data row37 col4\" > 18.81</td>\n",
" <td id=\"T_f940c_row37_col5\" class=\"data row37 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row37_col6\" class=\"data row37 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row38\" class=\"row_heading level0 row38\" >coef_pt_worker_work2_asc</th>\n",
" <td id=\"T_f940c_row38_col0\" class=\"data row38 col0\" >-4.74</td>\n",
" <td id=\"T_f940c_row38_col1\" class=\"data row38 col1\" > NA</td>\n",
" <td id=\"T_f940c_row38_col2\" class=\"data row38 col2\" > NA</td>\n",
" <td id=\"T_f940c_row38_col3\" class=\"data row38 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row38_col4\" class=\"data row38 col4\" > 562.79</td>\n",
" <td id=\"T_f940c_row38_col5\" class=\"data row38 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row38_col6\" class=\"data row38 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row39\" class=\"row_heading level0 row39\" >coef_round_trip_auto_time_to_work_school2</th>\n",
" <td id=\"T_f940c_row39_col0\" class=\"data row39 col0\" > 0.0644</td>\n",
" <td id=\"T_f940c_row39_col1\" class=\"data row39 col1\" > 0.0331</td>\n",
" <td id=\"T_f940c_row39_col2\" class=\"data row39 col2\" > 1.95</td>\n",
" <td id=\"T_f940c_row39_col3\" class=\"data row39 col3\" ></td>\n",
" <td id=\"T_f940c_row39_col4\" class=\"data row39 col4\" > NA</td>\n",
" <td id=\"T_f940c_row39_col5\" class=\"data row39 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row39_col6\" class=\"data row39 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row40\" class=\"row_heading level0 row40\" >coef_round_trip_auto_time_to_work_work2</th>\n",
" <td id=\"T_f940c_row40_col0\" class=\"data row40 col0\" >-0.0259</td>\n",
" <td id=\"T_f940c_row40_col1\" class=\"data row40 col1\" > 0.0249</td>\n",
" <td id=\"T_f940c_row40_col2\" class=\"data row40 col2\" >-1.04</td>\n",
" <td id=\"T_f940c_row40_col3\" class=\"data row40 col3\" ></td>\n",
" <td id=\"T_f940c_row40_col4\" class=\"data row40 col4\" > NA</td>\n",
" <td id=\"T_f940c_row40_col5\" class=\"data row40 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row40_col6\" class=\"data row40 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row41\" class=\"row_heading level0 row41\" >coef_round_trip_auto_time_to_work_work_and_school</th>\n",
" <td id=\"T_f940c_row41_col0\" class=\"data row41 col0\" >-0.0218</td>\n",
" <td id=\"T_f940c_row41_col1\" class=\"data row41 col1\" > 0.0402</td>\n",
" <td id=\"T_f940c_row41_col2\" class=\"data row41 col2\" >-0.54</td>\n",
" <td id=\"T_f940c_row41_col3\" class=\"data row41 col3\" ></td>\n",
" <td id=\"T_f940c_row41_col4\" class=\"data row41 col4\" > NA</td>\n",
" <td id=\"T_f940c_row41_col5\" class=\"data row41 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row41_col6\" class=\"data row41 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row42\" class=\"row_heading level0 row42\" >coef_student_employed</th>\n",
" <td id=\"T_f940c_row42_col0\" class=\"data row42 col0\" > 13.5</td>\n",
" <td id=\"T_f940c_row42_col1\" class=\"data row42 col1\" > 1.31e+03</td>\n",
" <td id=\"T_f940c_row42_col2\" class=\"data row42 col2\" > 0.01</td>\n",
" <td id=\"T_f940c_row42_col3\" class=\"data row42 col3\" ></td>\n",
" <td id=\"T_f940c_row42_col4\" class=\"data row42 col4\" > NA</td>\n",
" <td id=\"T_f940c_row42_col5\" class=\"data row42 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row42_col6\" class=\"data row42 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row43\" class=\"row_heading level0 row43\" >coef_unavailable</th>\n",
" <td id=\"T_f940c_row43_col0\" class=\"data row43 col0\" >-999.</td>\n",
" <td id=\"T_f940c_row43_col1\" class=\"data row43 col1\" > NA</td>\n",
" <td id=\"T_f940c_row43_col2\" class=\"data row43 col2\" > NA</td>\n",
" <td id=\"T_f940c_row43_col3\" class=\"data row43 col3\" ></td>\n",
" <td id=\"T_f940c_row43_col4\" class=\"data row43 col4\" > NA</td>\n",
" <td id=\"T_f940c_row43_col5\" class=\"data row43 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row43_col6\" class=\"data row43 col6\" >fixed value</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row44\" class=\"row_heading level0 row44\" >coef_under_35_school1</th>\n",
" <td id=\"T_f940c_row44_col0\" class=\"data row44 col0\" > 0.722</td>\n",
" <td id=\"T_f940c_row44_col1\" class=\"data row44 col1\" > 0.00</td>\n",
" <td id=\"T_f940c_row44_col2\" class=\"data row44 col2\" > NA</td>\n",
" <td id=\"T_f940c_row44_col3\" class=\"data row44 col3\" >[]</td>\n",
" <td id=\"T_f940c_row44_col4\" class=\"data row44 col4\" > 0.00</td>\n",
" <td id=\"T_f940c_row44_col5\" class=\"data row44 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row44_col6\" class=\"data row44 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row45\" class=\"row_heading level0 row45\" >coef_under_35_school2</th>\n",
" <td id=\"T_f940c_row45_col0\" class=\"data row45 col0\" > 15.0</td>\n",
" <td id=\"T_f940c_row45_col1\" class=\"data row45 col1\" > 1.14e+03</td>\n",
" <td id=\"T_f940c_row45_col2\" class=\"data row45 col2\" > 0.01</td>\n",
" <td id=\"T_f940c_row45_col3\" class=\"data row45 col3\" ></td>\n",
" <td id=\"T_f940c_row45_col4\" class=\"data row45 col4\" > NA</td>\n",
" <td id=\"T_f940c_row45_col5\" class=\"data row45 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row45_col6\" class=\"data row45 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row46\" class=\"row_heading level0 row46\" >coef_under_35_work1</th>\n",
" <td id=\"T_f940c_row46_col0\" class=\"data row46 col0\" > 1.04</td>\n",
" <td id=\"T_f940c_row46_col1\" class=\"data row46 col1\" > 0.886</td>\n",
" <td id=\"T_f940c_row46_col2\" class=\"data row46 col2\" > 1.17</td>\n",
" <td id=\"T_f940c_row46_col3\" class=\"data row46 col3\" ></td>\n",
" <td id=\"T_f940c_row46_col4\" class=\"data row46 col4\" > NA</td>\n",
" <td id=\"T_f940c_row46_col5\" class=\"data row46 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row46_col6\" class=\"data row46 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row47\" class=\"row_heading level0 row47\" >coef_under_35_work2</th>\n",
" <td id=\"T_f940c_row47_col0\" class=\"data row47 col0\" >-0.254</td>\n",
" <td id=\"T_f940c_row47_col1\" class=\"data row47 col1\" > 0.245</td>\n",
" <td id=\"T_f940c_row47_col2\" class=\"data row47 col2\" >-1.03</td>\n",
" <td id=\"T_f940c_row47_col3\" class=\"data row47 col3\" ></td>\n",
" <td id=\"T_f940c_row47_col4\" class=\"data row47 col4\" > NA</td>\n",
" <td id=\"T_f940c_row47_col5\" class=\"data row47 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row47_col6\" class=\"data row47 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row48\" class=\"row_heading level0 row48\" >coef_under_35_work_and_school</th>\n",
" <td id=\"T_f940c_row48_col0\" class=\"data row48 col0\" > 2.26</td>\n",
" <td id=\"T_f940c_row48_col1\" class=\"data row48 col1\" > 1.21</td>\n",
" <td id=\"T_f940c_row48_col2\" class=\"data row48 col2\" > 1.87</td>\n",
" <td id=\"T_f940c_row48_col3\" class=\"data row48 col3\" ></td>\n",
" <td id=\"T_f940c_row48_col4\" class=\"data row48 col4\" > NA</td>\n",
" <td id=\"T_f940c_row48_col5\" class=\"data row48 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row48_col6\" class=\"data row48 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row49\" class=\"row_heading level0 row49\" >coef_univ_school2_asc</th>\n",
" <td id=\"T_f940c_row49_col0\" class=\"data row49 col0\" >-10.2</td>\n",
" <td id=\"T_f940c_row49_col1\" class=\"data row49 col1\" > NA</td>\n",
" <td id=\"T_f940c_row49_col2\" class=\"data row49 col2\" > NA</td>\n",
" <td id=\"T_f940c_row49_col3\" class=\"data row49 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row49_col4\" class=\"data row49 col4\" > 556.91</td>\n",
" <td id=\"T_f940c_row49_col5\" class=\"data row49 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row49_col6\" class=\"data row49 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row50\" class=\"row_heading level0 row50\" >coef_univ_work1_asc</th>\n",
" <td id=\"T_f940c_row50_col0\" class=\"data row50 col0\" > 6.48</td>\n",
" <td id=\"T_f940c_row50_col1\" class=\"data row50 col1\" > NA</td>\n",
" <td id=\"T_f940c_row50_col2\" class=\"data row50 col2\" > NA</td>\n",
" <td id=\"T_f940c_row50_col3\" class=\"data row50 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row50_col4\" class=\"data row50 col4\" > 349.23</td>\n",
" <td id=\"T_f940c_row50_col5\" class=\"data row50 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row50_col6\" class=\"data row50 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row51\" class=\"row_heading level0 row51\" >coef_univ_work2_asc</th>\n",
" <td id=\"T_f940c_row51_col0\" class=\"data row51 col0\" > 4.83</td>\n",
" <td id=\"T_f940c_row51_col1\" class=\"data row51 col1\" > NA</td>\n",
" <td id=\"T_f940c_row51_col2\" class=\"data row51 col2\" > NA</td>\n",
" <td id=\"T_f940c_row51_col3\" class=\"data row51 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row51_col4\" class=\"data row51 col4\" > 26.10</td>\n",
" <td id=\"T_f940c_row51_col5\" class=\"data row51 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row51_col6\" class=\"data row51 col6\" ></td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f940c_level0_row52\" class=\"row_heading level0 row52\" >coef_univ_work_and_school_asc</th>\n",
" <td id=\"T_f940c_row52_col0\" class=\"data row52 col0\" > 8.18</td>\n",
" <td id=\"T_f940c_row52_col1\" class=\"data row52 col1\" > NA</td>\n",
" <td id=\"T_f940c_row52_col2\" class=\"data row52 col2\" > NA</td>\n",
" <td id=\"T_f940c_row52_col3\" class=\"data row52 col3\" >[***]</td>\n",
" <td id=\"T_f940c_row52_col4\" class=\"data row52 col4\" > 132.81</td>\n",
" <td id=\"T_f940c_row52_col5\" class=\"data row52 col5\" > 0.00</td>\n",
" <td id=\"T_f940c_row52_col6\" class=\"data row52 col6\" ></td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fd9c965f4f0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.parameter_summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "TojXWivZsx7M"
},
"source": [
"# Output Estimation Results"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:21.427663Z",
"start_time": "2021-03-29T21:00:21.419556Z"
}
},
"outputs": [],
"source": [
"from activitysim.estimation.larch import update_coefficients\n",
"result_dir = data.edb_directory/\"estimated\"\n",
"update_coefficients(\n",
" model, data, result_dir,\n",
" output_file=f\"{modelname}_coefficients_revised.csv\",\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Write the model estimation report, including coefficient t-statistic and log likelihood"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:21.905947Z",
"start_time": "2021-03-29T21:00:21.429565Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<larch.util.excel.ExcelWriter at 0x7fd940025910>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.to_xlsx(\n",
" result_dir/f\"{modelname}_model_estimation.xlsx\", \n",
" data_statistics=False,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Next Steps\n",
"\n",
"The final step is to either manually or automatically copy the `*_coefficients_revised.csv` file to the configs folder, rename it to `*_coefficients.csv`, and run ActivitySim in simulation mode."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-29T21:00:21.919319Z",
"start_time": "2021-03-29T21:00:21.907251Z"
}
},
"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>coefficient_name</th>\n",
" <th>value</th>\n",
" <th>constrain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>coef_unavailable</td>\n",
" <td>-999.000000</td>\n",
" <td>T</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>coef_ft_worker_work2_asc</td>\n",
" <td>-5.517933</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>coef_pt_worker_work2_asc</td>\n",
" <td>-4.743242</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>coef_univ_work1_asc</td>\n",
" <td>6.475896</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>coef_univ_work2_asc</td>\n",
" <td>4.833029</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>coef_univ_school2_asc</td>\n",
" <td>-10.233810</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>coef_univ_work_and_school_asc</td>\n",
" <td>8.180927</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>coef_driving_age_child_school2_asc</td>\n",
" <td>-13.067731</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>coef_driving_age_child_work_and_school_asc</td>\n",
" <td>-12.605997</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>coef_pre_driving_age_child_school2_asc</td>\n",
" <td>3.679726</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>coef_female_work1</td>\n",
" <td>-2.242748</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>coef_female_work2</td>\n",
" <td>-0.387411</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>coef_female_school1</td>\n",
" <td>0.247086</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>coef_female_school2</td>\n",
" <td>-0.301339</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>coef_female_work_and_school</td>\n",
" <td>-3.218280</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>coef_female_univ_work1</td>\n",
" <td>0.173700</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>coef_under_35_work1</td>\n",
" <td>1.038242</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>coef_under_35_work2</td>\n",
" <td>-0.253875</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>coef_under_35_school1</td>\n",
" <td>0.721800</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>coef_under_35_school2</td>\n",
" <td>15.011164</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>coef_under_35_work_and_school</td>\n",
" <td>2.257035</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>coef_can_walk_to_work_work2</td>\n",
" <td>0.651120</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>coef_can_walk_to_work_school2</td>\n",
" <td>1.368092</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>coef_can_walk_to_work_and_school</td>\n",
" <td>0.071630</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>coef_round_trip_auto_time_to_work_work2</td>\n",
" <td>-0.025905</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>coef_round_trip_auto_time_to_work_school2</td>\n",
" <td>0.064411</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>coef_round_trip_auto_time_to_work_work_and_school</td>\n",
" <td>-0.021804</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>coef_student_employed</td>\n",
" <td>13.457255</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>coef_non_student_goes_to_school</td>\n",
" <td>3.883000</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>coef_no_cars_in_hh_work2</td>\n",
" <td>-0.725967</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>coef_no_cars_in_hh_school2</td>\n",
" <td>-0.620465</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>coef_no_cars_in_hh_work_and_school</td>\n",
" <td>-0.775934</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>coef_few_cars_than_drivers_school2</td>\n",
" <td>-0.862451</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>coef_num_preschool_in_hh_work1</td>\n",
" <td>-2.930501</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>coef_num_preschool_in_hh_work2</td>\n",
" <td>0.225173</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>coef_num_preschool_in_hh_school1</td>\n",
" <td>-0.133500</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>coef_num_preschool_in_hh_school2</td>\n",
" <td>0.356660</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>coef_num_preschool_in_hh_work_and_school</td>\n",
" <td>-29.197569</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>coef_num_non_workers_in_hh_school1</td>\n",
" <td>0.257400</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>coef_hh_income_gt_50k_work</td>\n",
" <td>-1.418551</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>coef_hh_income_gt_50k_school1</td>\n",
" <td>0.034700</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>coef_hh_income_gt_50k_worker_work_and_school</td>\n",
" <td>0.034700</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>coef_hh_income_gt_50k_student_work_and_school</td>\n",
" <td>-0.921175</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>coef_non_family_hh_category1</td>\n",
" <td>-0.250000</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>coef_non_family_hh_category2</td>\n",
" <td>5.000771</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>coef_num_under_16_not_at_school_work2</td>\n",
" <td>-0.118991</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>coef_num_under_16_not_at_school_school2</td>\n",
" <td>-0.638293</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>coef_num_under_16_not_at_school_work_and_school</td>\n",
" <td>-15.357907</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>coef_home_urban_work1</td>\n",
" <td>4.026796</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>coef_home_urban_work2</td>\n",
" <td>2.624854</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>coef_home_urban_school1</td>\n",
" <td>-0.136100</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>coef_home_urban_school2</td>\n",
" <td>-8.455615</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>coef_home_urban_work_and_school</td>\n",
" <td>-0.447070</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" coefficient_name value constrain\n",
"0 coef_unavailable -999.000000 T\n",
"1 coef_ft_worker_work2_asc -5.517933 F\n",
"2 coef_pt_worker_work2_asc -4.743242 F\n",
"3 coef_univ_work1_asc 6.475896 F\n",
"4 coef_univ_work2_asc 4.833029 F\n",
"5 coef_univ_school2_asc -10.233810 F\n",
"6 coef_univ_work_and_school_asc 8.180927 F\n",
"7 coef_driving_age_child_school2_asc -13.067731 F\n",
"8 coef_driving_age_child_work_and_school_asc -12.605997 F\n",
"9 coef_pre_driving_age_child_school2_asc 3.679726 F\n",
"10 coef_female_work1 -2.242748 F\n",
"11 coef_female_work2 -0.387411 F\n",
"12 coef_female_school1 0.247086 F\n",
"13 coef_female_school2 -0.301339 F\n",
"14 coef_female_work_and_school -3.218280 F\n",
"15 coef_female_univ_work1 0.173700 F\n",
"16 coef_under_35_work1 1.038242 F\n",
"17 coef_under_35_work2 -0.253875 F\n",
"18 coef_under_35_school1 0.721800 F\n",
"19 coef_under_35_school2 15.011164 F\n",
"20 coef_under_35_work_and_school 2.257035 F\n",
"21 coef_can_walk_to_work_work2 0.651120 F\n",
"22 coef_can_walk_to_work_school2 1.368092 F\n",
"23 coef_can_walk_to_work_and_school 0.071630 F\n",
"24 coef_round_trip_auto_time_to_work_work2 -0.025905 F\n",
"25 coef_round_trip_auto_time_to_work_school2 0.064411 F\n",
"26 coef_round_trip_auto_time_to_work_work_and_school -0.021804 F\n",
"27 coef_student_employed 13.457255 F\n",
"28 coef_non_student_goes_to_school 3.883000 F\n",
"29 coef_no_cars_in_hh_work2 -0.725967 F\n",
"30 coef_no_cars_in_hh_school2 -0.620465 F\n",
"31 coef_no_cars_in_hh_work_and_school -0.775934 F\n",
"32 coef_few_cars_than_drivers_school2 -0.862451 F\n",
"33 coef_num_preschool_in_hh_work1 -2.930501 F\n",
"34 coef_num_preschool_in_hh_work2 0.225173 F\n",
"35 coef_num_preschool_in_hh_school1 -0.133500 F\n",
"36 coef_num_preschool_in_hh_school2 0.356660 F\n",
"37 coef_num_preschool_in_hh_work_and_school -29.197569 F\n",
"38 coef_num_non_workers_in_hh_school1 0.257400 F\n",
"39 coef_hh_income_gt_50k_work -1.418551 F\n",
"40 coef_hh_income_gt_50k_school1 0.034700 F\n",
"41 coef_hh_income_gt_50k_worker_work_and_school 0.034700 F\n",
"42 coef_hh_income_gt_50k_student_work_and_school -0.921175 F\n",
"43 coef_non_family_hh_category1 -0.250000 F\n",
"44 coef_non_family_hh_category2 5.000771 F\n",
"45 coef_num_under_16_not_at_school_work2 -0.118991 NaN\n",
"46 coef_num_under_16_not_at_school_school2 -0.638293 NaN\n",
"47 coef_num_under_16_not_at_school_work_and_school -15.357907 NaN\n",
"48 coef_home_urban_work1 4.026796 NaN\n",
"49 coef_home_urban_work2 2.624854 NaN\n",
"50 coef_home_urban_school1 -0.136100 NaN\n",
"51 coef_home_urban_school2 -8.455615 NaN\n",
"52 coef_home_urban_work_and_school -0.447070 NaN"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv(result_dir/f\"{modelname}_coefficients_revised.csv\")"
]
}
],
"metadata": {
"colab": {
"name": "asim_tutorial.ipynb",
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"toc_visible": true
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"name": "python3"
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"name": "ipython",
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| bsd-3-clause |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/managed_notebooks/pricing_optimization/pricing-optimization.ipynb | 1 | 27386 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "d1cc1c1fa076"
},
"source": [
"# Pricing Optimization \n",
"## Table of contents\n",
"* [Overview](#section-1)\n",
"* [Dataset](#section-2)\n",
"* [Objective](#section-3)\n",
"* [Costs](#section-4)\n",
"* [Create a BigQuery dataset](#section-5)\n",
"* [Load the dataset from Cloud Storage](#section-6)\n",
"* [Data analysis](#section-7)\n",
"* [Preprocess the data for training](#section-8)\n",
"* [Train the model using BigQuery ML](#section-9)\n",
"* [Generate forecasts from the model](#section-10)\n",
"* [Interpret the results to choose the best price](#section-11)\n",
"* [Clean up](#section-12)\n",
"\n",
"## Overview\n",
"<a name=\"section-1\"></a>\n",
"\n",
"This notebook demonstrates analysis of pricing optimization on [CDM Pricing Data](https://github.com/trifacta/trifacta-google-cloud/tree/main/design-pattern-pricing-optimization) and automating the workflow using Vertex AI Workbench managed notebooks.\n",
"\n",
"*Note: This notebook file was developed to run in a [Vertex AI Workbench managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/list/managed) instance using the Python (Local) kernel. Some components of this notebook may not work in other notebook environments.*\n",
"\n",
"## Dataset\n",
"<a name=\"section-2\"></a>\n",
"\n",
"The dataset used in this notebook is a part of the [CDM Pricing dataset](https://github.com/trifacta/trifacta-google-cloud/blob/main/design-pattern-pricing-optimization/CDM_Pricing_large_table.csv), which consists of product sales information on specified dates.\n",
"\n",
"## Objective\n",
"<a name=\"section-3\"></a>\n",
"\n",
"The objective of this notebook is to build a pricing optimization model using Vertex AI. The following steps have been followed: \n",
"\n",
"- Load the required dataset from a Cloud Storage bucket.\n",
"- Analyze the fields present in the dataset.\n",
"- Process the data to build a model.\n",
"- Build a BigQuery ML forecast model on the processed data.\n",
"- Get forecasted values from the BigQuery ML model.\n",
"- Interpret the forecasts to identify the best prices.\n",
"- Clean up.\n",
"\n",
"## Costs\n",
"<a name=\"section-4\"></a>\n",
"\n",
"This tutorial uses the following billable components of Google Cloud:\n",
"\n",
"- Vertex AI\n",
"- BigQuery\n",
"- Cloud Storage\n",
"\n",
"\n",
"Learn about [Vertex AI\n",
"pricing](https://cloud.google.com/vertex-ai/pricing), [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and [Cloud Storage\n",
"pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
"Calculator](https://cloud.google.com/products/calculator/)\n",
"to generate a cost estimate based on your projected usage.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5ed1f5e85640"
},
"source": [
"## Before you begin\n",
"\n",
"### Set your project ID\n",
"\n",
"**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "c3f30148b66d"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"PROJECT_ID = \"\"\n",
"\n",
"# Get your Google Cloud project ID from gcloud\n",
"if not os.getenv(\"IS_TESTING\"):\n",
" shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT_ID = shell_output[0]\n",
" print(\"Project ID: \", PROJECT_ID)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "750bf2883c2d"
},
"source": [
"Otherwise, set your project ID here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3c6db1ca88b9"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
" PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2a1c270c7d34"
},
"source": [
"### Import the required libraries and define constants\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "acc6fac1fa55"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"from google.cloud import bigquery\n",
"from google.cloud.bigquery import Client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "a06006dff8f9"
},
"outputs": [],
"source": [
"DATASET = \"[your-bigquery-dataset-id]\" # set the BigQuery dataset-id\n",
"TRAINING_DATA_TABLE = \"[your-bigquery-table-id-to-store-the-training-data]\" # set the BigQuery table-id to store the training data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "016c3d47cc69"
},
"source": [
"## Create a BigQuery dataset\n",
"<a name=\"section-5\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "12ccd8d7956e"
},
"source": [
"#@bigquery\n",
"-- create a dataset in BigQuery\n",
"\n",
"CREATE SCHEMA pricing_optimization\n",
"OPTIONS(\n",
" location=\"us\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c106b978a79b"
},
"source": [
"## Load the dataset from Cloud Storage\n",
"<a name=\"section-6\"></a>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8aeae9da9796"
},
"outputs": [],
"source": [
"DATA_LOCATION = \"gs://cloud-samples-data/ai-platform-unified/datasets/tabular/cdm_pricing_large_table.csv\"\n",
"df = pd.read_csv(DATA_LOCATION)\n",
"print(df.shape)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7b98d5f09842"
},
"source": [
"You will build a forecast model on this data and thus determine the best price for a product. For this type of model, you will not be using many fields: only the sales and price related ones. For the current execrcise, focus on the following fields:\n",
"\n",
"- `Product_ID`\n",
"- `Customer_Hierarchy`\n",
"- `Fiscal_Date`\n",
"- `List_Price_Converged`\n",
"- `Invoiced_quantity_in_Pieces`\n",
"- `Net_Sales`\n",
"\n",
"## Data Analysis\n",
"<a name=\"section-7\"></a>\n",
"\n",
"First, explore the data and distributions.\n",
"\n",
"Select the required columns from the dataframe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "af4b41c5eb1f"
},
"outputs": [],
"source": [
"id_col = \"Product_ID\"\n",
"date_col = \"Fiscal_Date\"\n",
"categ_cols = [\"Customer_Hierarchy\"]\n",
"num_cols = [\"List_Price_Converged\", \"Invoiced_quantity_in_Pieces\", \"Net_Sales\"]\n",
"\n",
"df = df[[id_col, date_col] + categ_cols + num_cols].copy()\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3d780043ee5b"
},
"source": [
"Check the column types and null values in the dataframe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f54c445a1288"
},
"outputs": [],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cd817b414c4d"
},
"source": [
"This data description reveals that there are no null values in the data. Also, the field `Fiscal_Date` which is a date field is loaded as an object type. \n",
"\n",
"Change the type of the date field to datetime."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b160fac085c8"
},
"outputs": [],
"source": [
"df[\"Fiscal_Date\"] = pd.to_datetime(df[\"Fiscal_Date\"], infer_datetime_format=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fb4778578064"
},
"source": [
"Plot the distributions for the categorical fields."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dd0467cd57c3"
},
"outputs": [],
"source": [
"for i in categ_cols:\n",
" df[i].value_counts(normalize=True).plot(kind=\"bar\")\n",
" plt.title(i)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "145deed255e0"
},
"source": [
"Plot the distributions for the numerical fields."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f934137c6d82"
},
"outputs": [],
"source": [
"for i in num_cols:\n",
" _, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
" df[i].plot(kind=\"box\", ax=ax[0])\n",
" df[i].plot(kind=\"hist\", ax=ax[1])\n",
" ax[0].set_title(i + \"-Boxplot\")\n",
" ax[1].set_title(i + \"-Histogram\")\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f9b9c2e58380"
},
"source": [
"Check the maximum date and minimum date in Fiscal_Date column."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2a10aa689f9d"
},
"outputs": [],
"source": [
"print(df[\"Fiscal_Date\"].max())\n",
"print(df[\"Fiscal_Date\"].min())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4834f63e2e59"
},
"source": [
"Check the product distribution across each category."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4664877f5304"
},
"outputs": [],
"source": [
"grp_cols = [\"Customer_Hierarchy\", \"Product_ID\"]\n",
"grp_df = df[grp_cols].groupby(by=grp_cols).count().reset_index()\n",
"grp_df.groupby(\"Customer_Hierarchy\").nunique()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "01ed02b9c8fd"
},
"source": [
"Check the percentage changes in the orders based on the percentage changes in the price."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0b2c428cb135"
},
"outputs": [],
"source": [
"# aggregate the data\n",
"df_aggr = (\n",
" df.groupby([\"Product_ID\", \"List_Price_Converged\"])\n",
" .agg({\"Fiscal_Date\": min, \"Invoiced_quantity_in_Pieces\": sum, \"Net_Sales\": sum})\n",
" .reset_index()\n",
")\n",
"# rename the aggregated columns\n",
"df_aggr.rename(\n",
" columns={\n",
" \"Fiscal_Date\": \"First_price_date\",\n",
" \"Invoiced_quantity_in_Pieces\": \"Total_ordered_pieces\",\n",
" \"Net_Sales\": \"Total_net_sales\",\n",
" },\n",
" inplace=True,\n",
")\n",
"\n",
"# sort values chronologically\n",
"df_aggr.sort_values(by=[\"Product_ID\", \"First_price_date\"], inplace=True)\n",
"df_aggr.reset_index(drop=True, inplace=True)\n",
"\n",
"# add columns for previous values\n",
"df_aggr[\"Previous_List\"] = df_aggr.groupby([\"Product_ID\"])[\n",
" \"List_Price_Converged\"\n",
"].shift()\n",
"df_aggr[\"Previous_Total_ordered_pieces\"] = df_aggr.groupby([\"Product_ID\"])[\n",
" \"Total_ordered_pieces\"\n",
"].shift()\n",
"\n",
"# average price change across sku's\n",
"df_aggr[\"price_change_perc\"] = (\n",
" (df_aggr[\"List_Price_Converged\"] - df_aggr[\"Previous_List\"])\n",
" / df_aggr[\"Previous_List\"].fillna(0)\n",
" * 100\n",
")\n",
"df_aggr[\"order_change_perc\"] = (\n",
" (df_aggr[\"Total_ordered_pieces\"] - df_aggr[\"Previous_Total_ordered_pieces\"])\n",
" / df_aggr[\"Previous_Total_ordered_pieces\"].fillna(0)\n",
" * 100\n",
")\n",
"\n",
"# plot a scatterplot to visualize the changes\n",
"sns.scatterplot(\n",
" x=\"price_change_perc\",\n",
" y=\"order_change_perc\",\n",
" data=df_aggr,\n",
" hue=\"Product_ID\",\n",
" legend=False,\n",
")\n",
"plt.title(\"Percentage of change in price vs order\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8259e916fe25"
},
"source": [
"For most of the products, the percentage change in orders are high where the percentage changes in the prices are low. This suggests that too much change in the prices can affect the number of orders. \n",
"\n",
"**Note**: There seem to be some outliers in the data as percentage changes greater than 800 are found. In the current exercise, do not take any manual measures to deal with outliers as you will create a BigQuery ML timeseries model that already deals with outliers.\n",
"\n",
"## Preprocess the data for training\n",
"<a name=\"section-8\"></a>\n",
"\n",
"Check which `Product_ID`'s have the maximum orders."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f5cbc7709c6a"
},
"outputs": [],
"source": [
"df_orders = df.groupby([\"Product_ID\", \"Customer_Hierarchy\"], as_index=False)[\n",
" \"Invoiced_quantity_in_Pieces\"\n",
"].sum()\n",
"df_orders.loc[\n",
" df_orders.groupby(\"Customer_Hierarchy\")[\"Invoiced_quantity_in_Pieces\"].idxmax()\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fd6d227e513e"
},
"source": [
"From the above result, you can infer the following:\n",
"\n",
"- Under the **Food** category, **SKU 62** has the maximum orders.\n",
"- Under the **Manufacturing** category, **SKU 17** has the maximum orders.\n",
"- Under the **Paper** category, **SKU 107** has the maximum orders.\n",
"- Under the **Publishing** category, **SKU 8** has the maximum orders.\n",
"- Under the **Utilities** category, **SKU 140** has the maximum orders.\n",
"\n",
"Given that there are too many ids and only a few records for most of them, consider only the above `Product_ID`s for which there are a maximum number of orders. \n",
"\n",
"**Note**: The `Invoiced_quantity_in_Pieces` field seems to be a *float* type rather than an *int* type as it should be. This could be because the data itself might be averaged in the first place."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2dbc0d64d157"
},
"source": [
"Check the various prices available for these `Product_ID`s."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "acc1dbd2d838"
},
"outputs": [],
"source": [
"df_type_food = df[(df[\"Product_ID\"] == \"SKU 62\") & (df[\"Customer_Hierarchy\"] == \"Food\")]\n",
"print(\"Food :\")\n",
"print(df_type_food[\"List_Price_Converged\"].value_counts())\n",
"df_type_manuf = df[\n",
" (df[\"Product_ID\"] == \"SKU 17\") & (df[\"Customer_Hierarchy\"] == \"Manufacturing\")\n",
"]\n",
"print(\"Manufacturing :\")\n",
"print(df_type_manuf[\"List_Price_Converged\"].value_counts())\n",
"df_type_paper = df[\n",
" (df[\"Product_ID\"] == \"SKU 107\") & (df[\"Customer_Hierarchy\"] == \"Paper\")\n",
"]\n",
"print(\"Paper :\")\n",
"print(df_type_paper[\"List_Price_Converged\"].value_counts())\n",
"df_type_pub = df[\n",
" (df[\"Product_ID\"] == \"SKU 8\") & (df[\"Customer_Hierarchy\"] == \"Publishing\")\n",
"]\n",
"print(\"Publishing :\")\n",
"print(df_type_pub[\"List_Price_Converged\"].value_counts())\n",
"df_type_util = df[\n",
" (df[\"Product_ID\"] == \"SKU 140\") & (df[\"Customer_Hierarchy\"] == \"Utilities\")\n",
"]\n",
"print(\"Utilities :\")\n",
"print(df_type_util[\"List_Price_Converged\"].value_counts())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f023af578c0f"
},
"source": [
"In the publishing category, `Product_ID` `SKU 8` and `SKU 17` are less than or equal to two different prices in the entire data and so you will exclude them and consider the rest for building the forecast model. The idea here is to train a forecast model on the timeseries data for products with different prices.\n",
"\n",
"Join the data for all the `Product_ID`s into one dataframe and remove duplicate records."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "a44771cc4c20"
},
"outputs": [],
"source": [
"df_final = pd.concat([df_type_food, df_type_paper, df_type_util])\n",
"df_final = (\n",
" df_final[\n",
" [\n",
" \"Product_ID\",\n",
" \"Fiscal_Date\",\n",
" \"Customer_Hierarchy\",\n",
" \"List_Price_Converged\",\n",
" \"Invoiced_quantity_in_Pieces\",\n",
" ]\n",
" ]\n",
" .drop_duplicates()\n",
" .reset_index(drop=True)\n",
")\n",
"df_final.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "add5063df368"
},
"source": [
"Save the data to a BigQuery table."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fd82ba56571f"
},
"outputs": [],
"source": [
"bq_client = bigquery.Client(project=PROJECT_ID)\n",
"\n",
"job_config = bigquery.LoadJobConfig(\n",
" # Specify a (partial) schema. All columns are always written to the\n",
" # table. The schema is used to assist in data type definitions.\n",
" schema=[\n",
" bigquery.SchemaField(\"Product_ID\", bigquery.enums.SqlTypeNames.STRING),\n",
" bigquery.SchemaField(\"Fiscal_Date\", bigquery.enums.SqlTypeNames.DATE),\n",
" bigquery.SchemaField(\"List_Price_Converged\", bigquery.enums.SqlTypeNames.FLOAT),\n",
" bigquery.SchemaField(\n",
" \"Invoiced_quantity_in_Pieces\", bigquery.enums.SqlTypeNames.FLOAT\n",
" ),\n",
" ],\n",
" # Optionally, set the write disposition. BigQuery appends loaded rows\n",
" # to an existing table by default, but with WRITE_TRUNCATE write\n",
" # disposition it replaces the table with the loaded data.\n",
" write_disposition=\"WRITE_TRUNCATE\",\n",
")\n",
"\n",
"# save the dataframe to a table in the created dataset\n",
"job = bq_client.load_table_from_dataframe(\n",
" df_final,\n",
" \"{}.{}.{}\".format(PROJECT_ID, DATASET, TRAINING_DATA_TABLE),\n",
" job_config=job_config,\n",
") # Make an API request.\n",
"job.result() # Wait for the job to complete."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fca77641b03b"
},
"source": [
"# Train the model using BigQuery ML\n",
"<a name=\"section-9\"></a>\n",
"\n",
"Train an [Arima-Plus](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series) model on the data using BigQuery ML."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cded27507891"
},
"source": [
"#@bigquery\n",
"create or replace model pricing_optimization.bqml_arima\n",
"options\n",
" (model_type = 'ARIMA_PLUS',\n",
" time_series_timestamp_col = 'Fiscal_Date',\n",
" time_series_data_col = 'Invoiced_quantity_in_Pieces',\n",
" time_series_id_col = 'ID'\n",
" ) as\n",
"select\n",
" Fiscal_Date,\n",
" Concat(Product_ID,\"_\" ,Cast(List_Price_Converged as string)) as ID,\n",
" Invoiced_quantity_in_Pieces\n",
"from\n",
" pricing_optimization.TRAINING_DATA\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "332fd11ff32b"
},
"source": [
"## Generate forecasts from the model\n",
"<a name=\"section-10\"></a>\n",
"\n",
"Predict the sales for the next 30 days for each id and save to a dataframe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ef926cdbf28e"
},
"outputs": [],
"source": [
"client = Client()\n",
"\n",
"query = '''\n",
"DECLARE HORIZON STRING DEFAULT \"30\"; #number of values to forecast\n",
"DECLARE CONFIDENCE_LEVEL STRING DEFAULT \"0.90\"; ## required confidence level\n",
"\n",
"EXECUTE IMMEDIATE format(\"\"\"\n",
" SELECT\n",
" *\n",
" FROM \n",
" ML.FORECAST(MODEL pricing_optimization.bqml_arima, \n",
" STRUCT(%s AS horizon, \n",
" %s AS confidence_level)\n",
" )\n",
" \"\"\",HORIZON,CONFIDENCE_LEVEL)'''\n",
"job = client.query(query)\n",
"dfforecast = job.to_dataframe()\n",
"dfforecast.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "608c7de72dae"
},
"source": [
"## Interpret the results to choose the best price\n",
"<a name=\"section-11\"></a>\n",
"\n",
"Calculate average forecast values for the forecast duration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "e1e193680400"
},
"outputs": [],
"source": [
"dfforecast_avg = (\n",
" dfforecast[[\"ID\", \"forecast_value\"]].groupby(\"ID\", as_index=False).mean()\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5ce395d652a3"
},
"source": [
"Extract the ID and Price fields from the ID field."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "452c56fa58ed"
},
"outputs": [],
"source": [
"dfforecast_avg[\"Product_ID\"] = dfforecast_avg[\"ID\"].apply(lambda x: x.split(\"_\")[0])\n",
"dfforecast_avg[\"Price\"] = dfforecast_avg[\"ID\"].apply(lambda x: x.split(\"_\")[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3cee67f4028f"
},
"source": [
"Plot the average forecasted sales vs. the price of the product."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fb351c8f383d"
},
"outputs": [],
"source": [
"for i in dfforecast_avg[\"Product_ID\"].unique():\n",
" dfforecast_avg[dfforecast_avg[\"Product_ID\"] == i].set_index(\"Price\").sort_values(\n",
" \"forecast_value\"\n",
" ).plot(kind=\"bar\")\n",
" plt.title(\"Price vs. Average Sales for \" + i)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "67ff3acc74a5"
},
"source": [
"Based on the plots for price vs. the average forecasted orders, it can be said that to use the maximum orders, each of the considered `Product_ID`s can follow the below prices:\n",
"\n",
"- SKU 107's price range can be from 4.44 - 4.73 units\n",
"- SKU 140's price can be 1.95 units\n",
"- SKU 62's price can be 4.23 units\n",
"\n",
"\n",
"## Clean Up\n",
"<a name=\"section-12\"></a>\n",
"\n",
"To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial. The following code deletes the entire dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "d78908b8134d"
},
"outputs": [],
"source": [
"# Construct a BigQuery client object.\n",
"client = bigquery.Client()\n",
"\n",
"# TODO(developer): Set model_id to the ID of the model to fetch.\n",
"dataset_id = \"{PROJECT}.{DATASET}\".format(PROJECT=PROJECT_ID, DATASET=DATASET)\n",
"\n",
"# Use the delete_contents parameter to delete a dataset and its contents.\n",
"# Use the not_found_ok parameter to not receive an error if the dataset has already been deleted.\n",
"client.delete_dataset(\n",
" dataset_id, delete_contents=True, not_found_ok=True\n",
") # Make an API request.\n",
"\n",
"print(\"Deleted dataset '{}'.\".format(dataset_id))"
]
}
],
"metadata": {
"colab": {
"name": "pricing-optimization.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
tschinz/iPython_Workspace | 01_Mine/MachineLearning/tensorflow-examples_nb/2_BasicModels/word2vec.ipynb | 3 | 34258 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Word2Vec (Word Embedding)\n",
"\n",
"Implement Word2Vec algorithm to compute vector representations of words.\n",
"This example is using a small chunk of Wikipedia articles to train from.\n",
"\n",
"More info: [Mikolov, Tomas et al. \"Efficient Estimation of Word Representations in Vector Space.\", 2013](https://arxiv.org/pdf/1301.3781.pdf)\n",
"\n",
"\n",
"- Author: Aymeric Damien\n",
"- Project: https://github.com/aymericdamien/TensorFlow-Examples/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import division, print_function, absolute_import\n",
"\n",
"import collections\n",
"import os\n",
"import random\n",
"import urllib\n",
"import zipfile\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Training Parameters\n",
"learning_rate = 0.1\n",
"batch_size = 128\n",
"num_steps = 3000000\n",
"display_step = 10000\n",
"eval_step = 200000\n",
"\n",
"# Evaluation Parameters\n",
"eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain']\n",
"\n",
"# Word2Vec Parameters\n",
"embedding_size = 200 # Dimension of the embedding vector\n",
"max_vocabulary_size = 50000 # Total number of different words in the vocabulary\n",
"min_occurrence = 10 # Remove all words that does not appears at least n times\n",
"skip_window = 3 # How many words to consider left and right\n",
"num_skips = 2 # How many times to reuse an input to generate a label\n",
"num_sampled = 64 # Number of negative examples to sample"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading the dataset... (It may take some time)\n",
"Done!\n"
]
}
],
"source": [
"# Download a small chunk of Wikipedia articles collection\n",
"url = 'http://mattmahoney.net/dc/text8.zip'\n",
"data_path = 'text8.zip'\n",
"if not os.path.exists(data_path):\n",
" print(\"Downloading the dataset... (It may take some time)\")\n",
" filename, _ = urllib.urlretrieve(url, data_path)\n",
" print(\"Done!\")\n",
"# Unzip the dataset file. Text has already been processed\n",
"with zipfile.ZipFile(data_path) as f:\n",
" text_words = f.read(f.namelist()[0]).lower().split()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Words count: 17005207\n",
"Unique words: 253854\n",
"Vocabulary size: 50000\n",
"Most common words: [('UNK', 418391), ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]\n"
]
}
],
"source": [
"# Build the dictionary and replace rare words with UNK token\n",
"count = [('UNK', -1)]\n",
"# Retrieve the most common words\n",
"count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1))\n",
"# Remove samples with less than 'min_occurrence' occurrences\n",
"for i in range(len(count) - 1, -1, -1):\n",
" if count[i][1] < min_occurrence:\n",
" count.pop(i)\n",
" else:\n",
" # The collection is ordered, so stop when 'min_occurrence' is reached\n",
" break\n",
"# Compute the vocabulary size\n",
"vocabulary_size = len(count)\n",
"# Assign an id to each word\n",
"word2id = dict()\n",
"for i, (word, _)in enumerate(count):\n",
" word2id[word] = i\n",
"\n",
"data = list()\n",
"unk_count = 0\n",
"for word in text_words:\n",
" # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary\n",
" index = word2id.get(word, 0)\n",
" if index == 0:\n",
" unk_count += 1\n",
" data.append(index)\n",
"count[0] = ('UNK', unk_count)\n",
"id2word = dict(zip(word2id.values(), word2id.keys()))\n",
"\n",
"print(\"Words count:\", len(text_words))\n",
"print(\"Unique words:\", len(set(text_words)))\n",
"print(\"Vocabulary size:\", vocabulary_size)\n",
"print(\"Most common words:\", count[:10])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_index = 0\n",
"# Generate training batch for the skip-gram model\n",
"def next_batch(batch_size, num_skips, skip_window):\n",
" global data_index\n",
" assert batch_size % num_skips == 0\n",
" assert num_skips <= 2 * skip_window\n",
" batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
" labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
" # get window size (words left and right + current one)\n",
" span = 2 * skip_window + 1\n",
" buffer = collections.deque(maxlen=span)\n",
" if data_index + span > len(data):\n",
" data_index = 0\n",
" buffer.extend(data[data_index:data_index + span])\n",
" data_index += span\n",
" for i in range(batch_size // num_skips):\n",
" context_words = [w for w in range(span) if w != skip_window]\n",
" words_to_use = random.sample(context_words, num_skips)\n",
" for j, context_word in enumerate(words_to_use):\n",
" batch[i * num_skips + j] = buffer[skip_window]\n",
" labels[i * num_skips + j, 0] = buffer[context_word]\n",
" if data_index == len(data):\n",
" buffer.extend(data[0:span])\n",
" data_index = span\n",
" else:\n",
" buffer.append(data[data_index])\n",
" data_index += 1\n",
" # Backtrack a little bit to avoid skipping words in the end of a batch\n",
" data_index = (data_index + len(data) - span) % len(data)\n",
" return batch, labels"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Input data\n",
"X = tf.placeholder(tf.int32, shape=[None])\n",
"# Input label\n",
"Y = tf.placeholder(tf.int32, shape=[None, 1])\n",
"\n",
"# Ensure the following ops & var are assigned on CPU\n",
"# (some ops are not compatible on GPU)\n",
"with tf.device('/cpu:0'):\n",
" # Create the embedding variable (each row represent a word embedding vector)\n",
" embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n",
" # Lookup the corresponding embedding vectors for each sample in X\n",
" X_embed = tf.nn.embedding_lookup(embedding, X)\n",
"\n",
" # Construct the variables for the NCE loss\n",
" nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n",
" nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
"\n",
"# Compute the average NCE loss for the batch\n",
"loss_op = tf.reduce_mean(\n",
" tf.nn.nce_loss(weights=nce_weights,\n",
" biases=nce_biases,\n",
" labels=Y,\n",
" inputs=X_embed,\n",
" num_sampled=num_sampled,\n",
" num_classes=vocabulary_size))\n",
"\n",
"# Define the optimizer\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
"train_op = optimizer.minimize(loss_op)\n",
"\n",
"# Evaluation\n",
"# Compute the cosine similarity between input data embedding and every embedding vectors\n",
"X_embed_norm = X_embed / tf.sqrt(tf.reduce_sum(tf.square(X_embed)))\n",
"embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True))\n",
"cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 1, Average Loss= 520.3188\n",
"Evaluation...\n",
"\"five\" nearest neighbors: brothers, swinging, dissemination, fruitful, trichloride, dll, timur, torre,\n",
"\"of\" nearest neighbors: malting, vaginal, cecil, xiaoping, arrangers, hydras, exhibits, splits,\n",
"\"going\" nearest neighbors: besht, xps, sdtv, mississippi, frequencies, tora, reciprocating, tursiops,\n",
"\"hardware\" nearest neighbors: burgh, residences, mares, attested, whirlwind, isomerism, admiration, ties,\n",
"\"american\" nearest neighbors: tensile, months, baffling, cricket, kodak, risky, nicomedia, jura,\n",
"\"britain\" nearest neighbors: superstring, interpretations, genealogical, munition, boer, occasional, psychologists, turbofan,\n",
"Step 10000, Average Loss= 202.2640\n",
"Step 20000, Average Loss= 96.5149\n",
"Step 30000, Average Loss= 67.2858\n",
"Step 40000, Average Loss= 52.5055\n",
"Step 50000, Average Loss= 42.6301\n",
"Step 60000, Average Loss= 37.3644\n",
"Step 70000, Average Loss= 33.1220\n",
"Step 80000, Average Loss= 30.5835\n",
"Step 90000, Average Loss= 28.2243\n",
"Step 100000, Average Loss= 25.5532\n",
"Step 110000, Average Loss= 24.0891\n",
"Step 120000, Average Loss= 21.8576\n",
"Step 130000, Average Loss= 21.2192\n",
"Step 140000, Average Loss= 19.8834\n",
"Step 150000, Average Loss= 19.3362\n",
"Step 160000, Average Loss= 18.3129\n",
"Step 170000, Average Loss= 17.4952\n",
"Step 180000, Average Loss= 16.8531\n",
"Step 190000, Average Loss= 15.9615\n",
"Step 200000, Average Loss= 15.0718\n",
"Evaluation...\n",
"\"five\" nearest neighbors: three, four, eight, six, seven, two, nine, one,\n",
"\"of\" nearest neighbors: the, is, a, was, with, in, and, on,\n",
"\"going\" nearest neighbors: time, military, called, with, used, state, most, new,\n",
"\"hardware\" nearest neighbors: deaths, system, three, at, zero, two, s, UNK,\n",
"\"american\" nearest neighbors: UNK, and, s, about, in, when, from, after,\n",
"\"britain\" nearest neighbors: years, were, from, both, of, these, is, many,\n",
"Step 210000, Average Loss= 14.9267\n",
"Step 220000, Average Loss= 15.4700\n",
"Step 230000, Average Loss= 14.0867\n",
"Step 240000, Average Loss= 14.5337\n",
"Step 250000, Average Loss= 13.2458\n",
"Step 260000, Average Loss= 13.2944\n",
"Step 270000, Average Loss= 13.0396\n",
"Step 280000, Average Loss= 12.1902\n",
"Step 290000, Average Loss= 11.7444\n",
"Step 300000, Average Loss= 11.8473\n",
"Step 310000, Average Loss= 11.1306\n",
"Step 320000, Average Loss= 11.1699\n",
"Step 330000, Average Loss= 10.8638\n",
"Step 340000, Average Loss= 10.7910\n",
"Step 350000, Average Loss= 11.0721\n",
"Step 360000, Average Loss= 10.6309\n",
"Step 370000, Average Loss= 10.4836\n",
"Step 380000, Average Loss= 10.3482\n",
"Step 390000, Average Loss= 10.0679\n",
"Step 400000, Average Loss= 10.0070\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, three, six, seven, eight, two, one, zero,\n",
"\"of\" nearest neighbors: and, in, the, a, for, by, is, while,\n",
"\"going\" nearest neighbors: name, called, made, military, music, people, city, was,\n",
"\"hardware\" nearest neighbors: power, a, john, the, has, see, and, system,\n",
"\"american\" nearest neighbors: s, british, UNK, john, in, during, and, from,\n",
"\"britain\" nearest neighbors: from, general, are, before, first, after, history, was,\n",
"Step 410000, Average Loss= 10.1151\n",
"Step 420000, Average Loss= 9.5719\n",
"Step 430000, Average Loss= 9.8267\n",
"Step 440000, Average Loss= 9.4704\n",
"Step 450000, Average Loss= 9.5561\n",
"Step 460000, Average Loss= 9.1479\n",
"Step 470000, Average Loss= 8.8914\n",
"Step 480000, Average Loss= 9.0281\n",
"Step 490000, Average Loss= 9.3139\n",
"Step 500000, Average Loss= 9.1559\n",
"Step 510000, Average Loss= 8.8257\n",
"Step 520000, Average Loss= 8.9081\n",
"Step 530000, Average Loss= 8.8572\n",
"Step 540000, Average Loss= 8.5835\n",
"Step 550000, Average Loss= 8.4495\n",
"Step 560000, Average Loss= 8.4193\n",
"Step 570000, Average Loss= 8.3399\n",
"Step 580000, Average Loss= 8.1633\n",
"Step 590000, Average Loss= 8.2914\n",
"Step 600000, Average Loss= 8.0268\n",
"Evaluation...\n",
"\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n",
"\"of\" nearest neighbors: and, the, in, including, with, for, on, or,\n",
"\"going\" nearest neighbors: popular, king, his, music, and, time, name, being,\n",
"\"hardware\" nearest neighbors: power, over, then, than, became, at, less, for,\n",
"\"american\" nearest neighbors: english, s, german, in, french, since, john, between,\n",
"\"britain\" nearest neighbors: however, were, state, first, group, general, from, second,\n",
"Step 610000, Average Loss= 8.1733\n",
"Step 620000, Average Loss= 8.2522\n",
"Step 630000, Average Loss= 8.0434\n",
"Step 640000, Average Loss= 8.0930\n",
"Step 650000, Average Loss= 7.8770\n",
"Step 660000, Average Loss= 7.9221\n",
"Step 670000, Average Loss= 7.7645\n",
"Step 680000, Average Loss= 7.9534\n",
"Step 690000, Average Loss= 7.7507\n",
"Step 700000, Average Loss= 7.7499\n",
"Step 710000, Average Loss= 7.6629\n",
"Step 720000, Average Loss= 7.6055\n",
"Step 730000, Average Loss= 7.4779\n",
"Step 740000, Average Loss= 7.3182\n",
"Step 750000, Average Loss= 7.6399\n",
"Step 760000, Average Loss= 7.4364\n",
"Step 770000, Average Loss= 7.6509\n",
"Step 780000, Average Loss= 7.3204\n",
"Step 790000, Average Loss= 7.4101\n",
"Step 800000, Average Loss= 7.4354\n",
"Evaluation...\n",
"\"five\" nearest neighbors: three, four, six, seven, eight, two, one, nine,\n",
"\"of\" nearest neighbors: and, the, its, a, with, at, in, for,\n",
"\"going\" nearest neighbors: were, man, music, now, great, support, popular, her,\n",
"\"hardware\" nearest neighbors: power, system, then, military, high, against, since, international,\n",
"\"american\" nearest neighbors: english, british, born, b, john, french, d, german,\n",
"\"britain\" nearest neighbors: government, second, before, from, state, several, the, at,\n",
"Step 810000, Average Loss= 7.2603\n",
"Step 820000, Average Loss= 7.1646\n",
"Step 830000, Average Loss= 7.3155\n",
"Step 840000, Average Loss= 7.1274\n",
"Step 850000, Average Loss= 7.1237\n",
"Step 860000, Average Loss= 7.1528\n",
"Step 870000, Average Loss= 7.0673\n",
"Step 880000, Average Loss= 7.2167\n",
"Step 890000, Average Loss= 7.1359\n",
"Step 900000, Average Loss= 7.0940\n",
"Step 910000, Average Loss= 7.1114\n",
"Step 920000, Average Loss= 6.9328\n",
"Step 930000, Average Loss= 7.0108\n",
"Step 940000, Average Loss= 7.0630\n",
"Step 950000, Average Loss= 6.8371\n",
"Step 960000, Average Loss= 7.0466\n",
"Step 970000, Average Loss= 6.8331\n",
"Step 980000, Average Loss= 6.9670\n",
"Step 990000, Average Loss= 6.7357\n",
"Step 1000000, Average Loss= 6.6453\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, three, six, eight, seven, two, nine, zero,\n",
"\"of\" nearest neighbors: the, became, including, first, second, from, following, and,\n",
"\"going\" nearest neighbors: near, music, popular, made, while, his, works, most,\n",
"\"hardware\" nearest neighbors: power, system, before, its, using, for, thus, an,\n",
"\"american\" nearest neighbors: b, born, d, UNK, nine, john, english, seven,\n",
"\"britain\" nearest neighbors: of, following, government, home, from, state, end, several,\n",
"Step 1010000, Average Loss= 6.7193\n",
"Step 1020000, Average Loss= 6.9297\n",
"Step 1030000, Average Loss= 6.7905\n",
"Step 1040000, Average Loss= 6.7709\n",
"Step 1050000, Average Loss= 6.7337\n",
"Step 1060000, Average Loss= 6.7617\n",
"Step 1070000, Average Loss= 6.7489\n",
"Step 1080000, Average Loss= 6.6259\n",
"Step 1090000, Average Loss= 6.6415\n",
"Step 1100000, Average Loss= 6.7209\n",
"Step 1110000, Average Loss= 6.5471\n",
"Step 1120000, Average Loss= 6.6508\n",
"Step 1130000, Average Loss= 6.5184\n",
"Step 1140000, Average Loss= 6.6202\n",
"Step 1150000, Average Loss= 6.7205\n",
"Step 1160000, Average Loss= 6.5821\n",
"Step 1170000, Average Loss= 6.6200\n",
"Step 1180000, Average Loss= 6.5089\n",
"Step 1190000, Average Loss= 6.5587\n",
"Step 1200000, Average Loss= 6.4930\n",
"Evaluation...\n",
"\"five\" nearest neighbors: three, four, six, seven, eight, two, nine, zero,\n",
"\"of\" nearest neighbors: the, and, including, in, first, with, following, from,\n",
"\"going\" nearest neighbors: near, popular, works, today, large, now, when, both,\n",
"\"hardware\" nearest neighbors: power, system, computer, its, both, for, using, which,\n",
"\"american\" nearest neighbors: born, d, john, german, b, UNK, english, s,\n",
"\"britain\" nearest neighbors: state, following, government, home, became, people, were, the,\n",
"Step 1210000, Average Loss= 6.5985\n",
"Step 1220000, Average Loss= 6.4534\n",
"Step 1230000, Average Loss= 6.5083\n",
"Step 1240000, Average Loss= 6.4913\n",
"Step 1250000, Average Loss= 6.4326\n",
"Step 1260000, Average Loss= 6.3891\n",
"Step 1270000, Average Loss= 6.1601\n",
"Step 1280000, Average Loss= 6.4479\n",
"Step 1290000, Average Loss= 6.3813\n",
"Step 1300000, Average Loss= 6.5335\n",
"Step 1310000, Average Loss= 6.2971\n",
"Step 1320000, Average Loss= 6.3723\n",
"Step 1330000, Average Loss= 6.4234\n",
"Step 1340000, Average Loss= 6.3130\n",
"Step 1350000, Average Loss= 6.2867\n",
"Step 1360000, Average Loss= 6.3505\n",
"Step 1370000, Average Loss= 6.2990\n",
"Step 1380000, Average Loss= 6.3012\n",
"Step 1390000, Average Loss= 6.3112\n",
"Step 1400000, Average Loss= 6.2680\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, three, six, two, seven, eight, one, zero,\n",
"\"of\" nearest neighbors: the, its, and, including, in, with, see, for,\n",
"\"going\" nearest neighbors: near, great, like, today, began, called, an, another,\n",
"\"hardware\" nearest neighbors: power, computer, system, for, program, high, control, small,\n",
"\"american\" nearest neighbors: english, german, french, born, john, british, s, references,\n",
"\"britain\" nearest neighbors: state, great, government, people, following, became, along, home,\n",
"Step 1410000, Average Loss= 6.3157\n",
"Step 1420000, Average Loss= 6.3466\n",
"Step 1430000, Average Loss= 6.3090\n",
"Step 1440000, Average Loss= 6.3330\n",
"Step 1450000, Average Loss= 6.2072\n",
"Step 1460000, Average Loss= 6.2363\n",
"Step 1470000, Average Loss= 6.2736\n",
"Step 1480000, Average Loss= 6.1793\n",
"Step 1490000, Average Loss= 6.2977\n",
"Step 1500000, Average Loss= 6.1899\n",
"Step 1510000, Average Loss= 6.2381\n",
"Step 1520000, Average Loss= 6.1027\n",
"Step 1530000, Average Loss= 6.0046\n",
"Step 1540000, Average Loss= 6.0747\n",
"Step 1550000, Average Loss= 6.2524\n",
"Step 1560000, Average Loss= 6.1247\n",
"Step 1570000, Average Loss= 6.1937\n",
"Step 1580000, Average Loss= 6.0450\n",
"Step 1590000, Average Loss= 6.1556\n",
"Step 1600000, Average Loss= 6.1765\n",
"Evaluation...\n",
"\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n",
"\"of\" nearest neighbors: the, and, its, for, from, modern, in, part,\n",
"\"going\" nearest neighbors: great, today, once, now, while, her, like, by,\n",
"\"hardware\" nearest neighbors: power, system, high, program, control, computer, typically, making,\n",
"\"american\" nearest neighbors: born, english, british, german, john, french, b, d,\n",
"\"britain\" nearest neighbors: country, state, home, government, first, following, during, from,\n",
"Step 1610000, Average Loss= 6.1029\n",
"Step 1620000, Average Loss= 6.0501\n",
"Step 1630000, Average Loss= 6.1536\n",
"Step 1640000, Average Loss= 6.0483\n",
"Step 1650000, Average Loss= 6.1197\n",
"Step 1660000, Average Loss= 6.0261\n",
"Step 1670000, Average Loss= 6.1012\n",
"Step 1680000, Average Loss= 6.1795\n",
"Step 1690000, Average Loss= 6.1224\n",
"Step 1700000, Average Loss= 6.0896\n",
"Step 1710000, Average Loss= 6.0418\n",
"Step 1720000, Average Loss= 6.0626\n",
"Step 1730000, Average Loss= 6.0214\n",
"Step 1740000, Average Loss= 6.1206\n",
"Step 1750000, Average Loss= 5.9721\n",
"Step 1760000, Average Loss= 6.0782\n",
"Step 1770000, Average Loss= 6.0291\n",
"Step 1780000, Average Loss= 6.0187\n",
"Step 1790000, Average Loss= 5.9761\n",
"Step 1800000, Average Loss= 5.7518\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n",
"\"of\" nearest neighbors: the, from, in, became, and, second, first, including,\n",
"\"going\" nearest neighbors: today, which, once, little, made, before, now, etc,\n",
"\"hardware\" nearest neighbors: computer, power, program, system, high, typically, current, eventually,\n",
"\"american\" nearest neighbors: b, d, born, actor, UNK, robert, william, english,\n",
"\"britain\" nearest neighbors: government, state, country, from, world, great, of, in,\n",
"Step 1810000, Average Loss= 5.9839\n",
"Step 1820000, Average Loss= 5.9931\n",
"Step 1830000, Average Loss= 6.0794\n",
"Step 1840000, Average Loss= 5.9072\n",
"Step 1850000, Average Loss= 5.9831\n",
"Step 1860000, Average Loss= 6.0023\n",
"Step 1870000, Average Loss= 5.9375\n",
"Step 1880000, Average Loss= 5.9250\n",
"Step 1890000, Average Loss= 5.9422\n",
"Step 1900000, Average Loss= 5.9339\n",
"Step 1910000, Average Loss= 5.9235\n",
"Step 1920000, Average Loss= 5.9692\n",
"Step 1930000, Average Loss= 5.9022\n",
"Step 1940000, Average Loss= 5.9599\n",
"Step 1950000, Average Loss= 6.0174\n",
"Step 1960000, Average Loss= 5.9530\n",
"Step 1970000, Average Loss= 5.9479\n",
"Step 1980000, Average Loss= 5.8870\n",
"Step 1990000, Average Loss= 5.9271\n",
"Step 2000000, Average Loss= 5.8774\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, three, six, seven, eight, two, nine, zero,\n",
"\"of\" nearest neighbors: and, the, from, in, within, first, including, with,\n",
"\"going\" nearest neighbors: today, before, another, little, work, etc, now, him,\n",
"\"hardware\" nearest neighbors: computer, program, system, both, making, designed, power, simple,\n",
"\"american\" nearest neighbors: actor, born, d, robert, john, b, german, writer,\n",
"\"britain\" nearest neighbors: government, state, following, great, england, became, country, from,\n",
"Step 2010000, Average Loss= 5.9373\n",
"Step 2020000, Average Loss= 5.9113\n",
"Step 2030000, Average Loss= 5.9158\n",
"Step 2040000, Average Loss= 5.9020\n",
"Step 2050000, Average Loss= 5.8608\n",
"Step 2060000, Average Loss= 5.7379\n",
"Step 2070000, Average Loss= 5.7143\n",
"Step 2080000, Average Loss= 5.9379\n",
"Step 2090000, Average Loss= 5.8201\n",
"Step 2100000, Average Loss= 5.9390\n",
"Step 2110000, Average Loss= 5.7295\n",
"Step 2120000, Average Loss= 5.8290\n",
"Step 2130000, Average Loss= 5.9042\n",
"Step 2140000, Average Loss= 5.8367\n",
"Step 2150000, Average Loss= 5.7760\n",
"Step 2160000, Average Loss= 5.8664\n",
"Step 2170000, Average Loss= 5.7974\n",
"Step 2180000, Average Loss= 5.8523\n",
"Step 2190000, Average Loss= 5.8047\n",
"Step 2200000, Average Loss= 5.8172\n",
"Evaluation...\n",
"\"five\" nearest neighbors: three, four, six, eight, two, seven, one, zero,\n",
"\"of\" nearest neighbors: the, with, group, in, its, and, from, including,\n",
"\"going\" nearest neighbors: produced, when, today, while, little, before, had, like,\n",
"\"hardware\" nearest neighbors: computer, system, power, technology, program, simple, for, designed,\n",
"\"american\" nearest neighbors: english, canadian, german, french, author, british, film, born,\n",
"\"britain\" nearest neighbors: government, great, state, established, british, england, country, army,\n",
"Step 2210000, Average Loss= 5.8847\n",
"Step 2220000, Average Loss= 5.8622\n",
"Step 2230000, Average Loss= 5.8295\n",
"Step 2240000, Average Loss= 5.8484\n",
"Step 2250000, Average Loss= 5.7917\n",
"Step 2260000, Average Loss= 5.7846\n",
"Step 2270000, Average Loss= 5.8307\n",
"Step 2280000, Average Loss= 5.7341\n",
"Step 2290000, Average Loss= 5.8519\n",
"Step 2300000, Average Loss= 5.7792\n",
"Step 2310000, Average Loss= 5.8277\n",
"Step 2320000, Average Loss= 5.7196\n",
"Step 2330000, Average Loss= 5.5469\n",
"Step 2340000, Average Loss= 5.7177\n",
"Step 2350000, Average Loss= 5.8139\n",
"Step 2360000, Average Loss= 5.7849\n",
"Step 2370000, Average Loss= 5.7022\n",
"Step 2380000, Average Loss= 5.7447\n",
"Step 2390000, Average Loss= 5.7667\n",
"Step 2400000, Average Loss= 5.7625\n",
"Evaluation...\n",
"\"five\" nearest neighbors: three, four, six, seven, two, eight, zero, nine,\n",
"\"of\" nearest neighbors: the, and, from, part, in, following, within, including,\n",
"\"going\" nearest neighbors: where, once, little, now, again, while, off, produced,\n",
"\"hardware\" nearest neighbors: system, computer, high, power, using, designed, systems, simple,\n",
"\"american\" nearest neighbors: author, actor, english, born, writer, british, b, d,\n",
"\"britain\" nearest neighbors: great, established, government, england, country, state, army, former,\n",
"Step 2410000, Average Loss= 5.6953\n",
"Step 2420000, Average Loss= 5.7413\n",
"Step 2430000, Average Loss= 5.7242\n",
"Step 2440000, Average Loss= 5.7397\n",
"Step 2450000, Average Loss= 5.7755\n",
"Step 2460000, Average Loss= 5.6881\n",
"Step 2470000, Average Loss= 5.7471\n",
"Step 2480000, Average Loss= 5.8159\n",
"Step 2490000, Average Loss= 5.7452\n",
"Step 2500000, Average Loss= 5.7547\n",
"Step 2510000, Average Loss= 5.6945\n",
"Step 2520000, Average Loss= 5.7318\n",
"Step 2530000, Average Loss= 5.6682\n",
"Step 2540000, Average Loss= 5.7660\n",
"Step 2550000, Average Loss= 5.6956\n",
"Step 2560000, Average Loss= 5.7307\n",
"Step 2570000, Average Loss= 5.7015\n",
"Step 2580000, Average Loss= 5.6932\n",
"Step 2590000, Average Loss= 5.6386\n",
"Step 2600000, Average Loss= 5.4734\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n",
"\"of\" nearest neighbors: the, and, in, from, became, including, for, with,\n",
"\"going\" nearest neighbors: little, again, just, a, now, where, to, for,\n",
"\"hardware\" nearest neighbors: computer, program, system, software, designed, systems, technology, current,\n",
"\"american\" nearest neighbors: actor, d, writer, b, born, singer, author, robert,\n",
"\"britain\" nearest neighbors: great, established, government, england, country, in, from, state,\n",
"Step 2610000, Average Loss= 5.7291\n",
"Step 2620000, Average Loss= 5.6412\n",
"Step 2630000, Average Loss= 5.7485\n",
"Step 2640000, Average Loss= 5.5833\n",
"Step 2650000, Average Loss= 5.6548\n",
"Step 2660000, Average Loss= 5.7159\n",
"Step 2670000, Average Loss= 5.6569\n",
"Step 2680000, Average Loss= 5.6080\n",
"Step 2690000, Average Loss= 5.7037\n",
"Step 2700000, Average Loss= 5.6360\n",
"Step 2710000, Average Loss= 5.6707\n",
"Step 2720000, Average Loss= 5.6811\n",
"Step 2730000, Average Loss= 5.6237\n",
"Step 2740000, Average Loss= 5.7050\n",
"Step 2750000, Average Loss= 5.6991\n",
"Step 2760000, Average Loss= 5.6691\n",
"Step 2770000, Average Loss= 5.7057\n",
"Step 2780000, Average Loss= 5.6162\n",
"Step 2790000, Average Loss= 5.6484\n",
"Step 2800000, Average Loss= 5.6627\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, six, three, seven, eight, nine, two, one,\n",
"\"of\" nearest neighbors: the, in, following, including, part, and, from, under,\n",
"\"going\" nearest neighbors: again, before, little, away, once, when, eventually, then,\n",
"\"hardware\" nearest neighbors: computer, system, software, program, systems, designed, for, design,\n",
"\"american\" nearest neighbors: actor, writer, singer, author, born, robert, d, john,\n",
"\"britain\" nearest neighbors: established, england, great, government, france, army, the, throughout,\n",
"Step 2810000, Average Loss= 5.5900\n",
"Step 2820000, Average Loss= 5.7053\n",
"Step 2830000, Average Loss= 5.6064\n",
"Step 2840000, Average Loss= 5.6891\n",
"Step 2850000, Average Loss= 5.5571\n",
"Step 2860000, Average Loss= 5.4490\n",
"Step 2870000, Average Loss= 5.5428\n",
"Step 2880000, Average Loss= 5.6832\n",
"Step 2890000, Average Loss= 5.5973\n",
"Step 2900000, Average Loss= 5.5816\n",
"Step 2910000, Average Loss= 5.5647\n",
"Step 2920000, Average Loss= 5.6001\n",
"Step 2930000, Average Loss= 5.6459\n",
"Step 2940000, Average Loss= 5.5622\n",
"Step 2950000, Average Loss= 5.5707\n",
"Step 2960000, Average Loss= 5.6492\n",
"Step 2970000, Average Loss= 5.5633\n",
"Step 2980000, Average Loss= 5.6323\n",
"Step 2990000, Average Loss= 5.5440\n",
"Step 3000000, Average Loss= 5.6209\n",
"Evaluation...\n",
"\"five\" nearest neighbors: four, three, six, eight, seven, two, zero, one,\n",
"\"of\" nearest neighbors: the, in, and, including, group, includes, part, from,\n",
"\"going\" nearest neighbors: once, again, when, quickly, before, eventually, little, had,\n",
"\"hardware\" nearest neighbors: computer, system, software, designed, program, simple, systems, sound,\n",
"\"american\" nearest neighbors: canadian, english, author, german, french, british, irish, australian,\n",
"\"britain\" nearest neighbors: established, england, great, government, throughout, france, british, northern,\n"
]
}
],
"source": [
"# Initialize the variables (i.e. assign their default value)\n",
"init = tf.global_variables_initializer()\n",
"\n",
"with tf.Session() as sess:\n",
"\n",
" # Run the initializer\n",
" sess.run(init)\n",
"\n",
" # Testing data\n",
" x_test = np.array([word2id[w] for w in eval_words])\n",
"\n",
" average_loss = 0\n",
" for step in xrange(1, num_steps + 1):\n",
" # Get a new batch of data\n",
" batch_x, batch_y = next_batch(batch_size, num_skips, skip_window)\n",
" # Run training op\n",
" _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})\n",
" average_loss += loss\n",
"\n",
" if step % display_step == 0 or step == 1:\n",
" if step > 1:\n",
" average_loss /= display_step\n",
" print(\"Step \" + str(step) + \", Average Loss= \" + \\\n",
" \"{:.4f}\".format(average_loss))\n",
" average_loss = 0\n",
"\n",
" # Evaluation\n",
" if step % eval_step == 0 or step == 1:\n",
" print(\"Evaluation...\")\n",
" sim = sess.run(cosine_sim_op, feed_dict={X: x_test})\n",
" for i in xrange(len(eval_words)):\n",
" top_k = 8 # number of nearest neighbors\n",
" nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
" log_str = '\"%s\" nearest neighbors:' % eval_words[i]\n",
" for k in xrange(top_k):\n",
" log_str = '%s %s,' % (log_str, id2word[nearest[k]])\n",
" print(log_str)\n"
]
}
],
"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
}
| gpl-2.0 |
TeamLab/lab_study_group | 2017/coursera/code/0120/RNN_Basic.ipynb | 1 | 9703 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"char_rdic = ['h', 'e', 'l', 'o'] #voca\n",
"char_dic = {w:i for i,w in enumerate(char_rdic)}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'e': 1, 'h': 0, 'l': 2, 'o': 3}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"char_dic"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ground_truth = [char_dic[c] for c in 'hello'] #y_data\n",
"#ground_truth = [char_dic[c] for c in char_rdic]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 2, 3]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ground_truth"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# word = 'hell'\n",
"word = char_rdic[:]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'one_hot:0' shape=(4, 4) dtype=float32>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# x_data = np.zeros((len(word), len(char_dic)), dtype='f')\n",
"\n",
"# for i,c in enumerate(word):\n",
"# x_data[i][char_dic[c]] = 1\n",
"\n",
"# x_data\n",
"\n",
"x_data = tf.one_hot(ground_truth[:-1], len(char_dic), \n",
" 1.0, 0.0, -1)\n",
"x_data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Configuration\n",
"\n",
"char_vocab_size = len(char_dic)\n",
"rnn_size = char_vocab_size\n",
"time_step_size = len(word)\n",
"batch_size = 1"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# RNN Model\n",
"\n",
"rnn_cell = tf.nn.rnn_cell.BasicRNNCell(rnn_size)\n",
"state = tf.zeros([batch_size, rnn_cell.state_size])\n",
"X_split = tf.split(0, time_step_size, x_data)\n",
"outputs, state = tf.nn.rnn(rnn_cell, X_split, state)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[<tf.Tensor 'RNN/BasicRNNCell/Tanh:0' shape=(1, 4) dtype=float32>, <tf.Tensor 'RNN/BasicRNNCell_1/Tanh:0' shape=(1, 4) dtype=float32>, <tf.Tensor 'RNN/BasicRNNCell_2/Tanh:0' shape=(1, 4) dtype=float32>, <tf.Tensor 'RNN/BasicRNNCell_3/Tanh:0' shape=(1, 4) dtype=float32>]\n",
"Tensor(\"RNN/BasicRNNCell_3/Tanh:0\", shape=(1, 4), dtype=float32)\n"
]
}
],
"source": [
"print(outputs)\n",
"print(state)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"logits = tf.reshape(tf.concat(1, outputs), [-1, rnn_size])\n",
"targets = tf.reshape(ground_truth[1:], [-1])\n",
"weights = tf.ones(time_step_size*batch_size)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"loss = tf.nn.seq2seq.sequence_loss_by_example(\n",
" [logits], [targets], [weights])\n",
"cost = tf.reduce_sum(loss) / batch_size\n",
"train_op = tf.train.AdamOptimizer(0.01, 0.9).minimize(cost)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 1] ['o', 'h', 'l', 'e']\n",
"[3 0 2 3] ['o', 'h', 'l', 'o']\n",
"[3 2 2 3] ['o', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n",
"[1 2 2 3] ['e', 'l', 'l', 'o']\n"
]
}
],
"source": [
"#Launch the graph in session\n",
"\n",
"with tf.Session() as sess:\n",
" init = tf.global_variables_initializer()\n",
" sess.run(init)\n",
" for i in range(100):\n",
" sess.run(train_op)\n",
" result = sess.run(tf.argmax(logits, 1))\n",
" print(result, [char_rdic[t] for t in result])"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
slundberg/shap | notebooks/image_examples/image_classification/Multi-input Gradient Explainer MNIST Example.ipynb | 1 | 262687 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-input Gradient Explainer MNIST Example\n",
"\n",
"Here we demonstrate how to use GradientExplainer when you have multiple inputs to your Keras/TensorFlow model. To keep things simple but also mildly interesting we feed two copies of MNIST into our model, where one copy goes into a conv-net layer and the other copy goes directly into a feedforward network."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import Input\n",
"from tensorflow.keras.layers import Flatten, Dense, Dropout, Conv2D\n",
"\n",
"# load the MNIST data\n",
"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
"x_train = x_train.astype('float32')\n",
"x_test = x_test.astype('float32')\n",
"x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
"x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
"\n",
"# define our model\n",
"input1 = Input(shape=(28,28,1))\n",
"input2 = Input(shape=(28,28,1))\n",
"input2c = Conv2D(32, kernel_size=(3, 3), activation='relu')(input2)\n",
"joint = tf.keras.layers.concatenate([Flatten()(input1), Flatten()(input2c)])\n",
"out = Dense(10, activation='softmax')(Dropout(0.2)(Dense(128, activation='relu')(joint)))\n",
"model = tf.keras.models.Model(inputs = [input1, input2], outputs=out)\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 60000 samples\n",
"Epoch 1/3\n",
"60000/60000 [==============================] - 32s 535us/sample - loss: 0.1623 - accuracy: 0.9507\n",
"Epoch 2/3\n",
"60000/60000 [==============================] - 31s 525us/sample - loss: 0.0635 - accuracy: 0.9801\n",
"Epoch 3/3\n",
"60000/60000 [==============================] - 31s 517us/sample - loss: 0.0442 - accuracy: 0.9852\n"
]
},
{
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x636e08da0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fit the model\n",
"model.fit([x_train, x_train], y_train, epochs=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explain the predictions made by the model using GradientExplainer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import shap\n",
"\n",
"# since we have two inputs we pass a list of inputs to the explainer\n",
"explainer = shap.GradientExplainer(model, [x_train, x_train])\n",
"\n",
"# we explain the model's predictions on the first three samples of the test set\n",
"shap_values = explainer.shap_values([x_test[:3], x_test[:3]])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"# since the model has 10 outputs we get a list of 10 explanations (one for each output)\n",
"print(len(shap_values))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n"
]
}
],
"source": [
"# since the model has 2 inputs we get a list of 2 explanations (one for each input) for each output\n",
"print(len(shap_values[0]))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x436.364 with 34 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# here we plot the explanations for all classes for the first input (this is the feed forward input)\n",
"shap.image_plot([shap_values[i][0] for i in range(10)], x_test[:3])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x436.364 with 34 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# here we plot the explanations for all classes for the second input (this is the conv-net input)\n",
"shap.image_plot([shap_values[i][1] for i in range(10)], x_test[:3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimating the sampling error\n",
"\n",
"By setting `return_variances=True` we get an estimate of how accurate our explanations are. We can see that the default number of samples (200) that were used provide fairly low variance estimates (compared to the magnitude of the shap_values above). Note that you can always use the `nsamples` parameter to control how many samples are used."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# get the variance of our estimates\n",
"shap_values, shap_values_var = explainer.shap_values([x_test[:3], x_test[:3]], return_variances=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x436.364 with 34 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# here we plot the explanations for all classes for the first input (this is the feed forward input)\n",
"shap.image_plot([shap_values_var[i][0] for i in range(10)], x_test[:3])"
]
}
],
"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 |
albi3ro/M4 | Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb | 1 | 347860 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Monte Carlo Markov Chain\n",
"\n",
"## Christina Lee\n",
"\n",
"## Category: Numerics\n",
"\n",
"### Monte Carlo Physics Series\n",
"* [Monte Carlo: Calculation of Pi](../Numerics_Prog/Monte-Carlo-Pi.ipynb)\n",
"* [Monte Carlo Markov Chain](../Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb)\n",
"* [Monte Carlo Ferromagnet](../Prerequisites/Monte-Carlo-Ferromagnet.ipynb)\n",
"* [Phase Transitions](../Prerequisites/Phase-Transitions.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Intro\n",
"\n",
"If you didn't check it out already, take a look at the post that introduces using random numbers in calculations. Any such simulation is a <i>Monte Carlo</i> simulation. The most used kind of Monte Carlo simulation is a <i>Markov Chain</i>, also known as a random walk, or drunkard's walk. A Markov Chain is a series of steps where\n",
"* each new state is chosen probabilistically\n",
"* the probabilities only depend on the current state (no memory)\n",
"\n",
"Imagine a drunkard trying to walk. At any one point, they could progress either left or right rather randomly. Also, just because they had been traveling in a straight line so far does not guarantee they will continue to do. They've just had extremely good luck. \n",
"\n",
"We use Markov Chains to <b>approximate probability distributions</b>. \n",
"\n",
"To create a good Markov Chain, we need\n",
"* <b> Ergodicity</b>: All states can be reached\n",
"* <b> Global Balance</b>: A condition that ensures the proper equilibrium distribution\n",
"\n",
"### The Balances\n",
"\n",
"Let $\\pi_i$ be the probability that a particle is at site $i$, and $p_{ij}$ be the probability that a particle moves from $i$ to $j$. Then Global Balance can be written as,\n",
"\n",
"\\begin{equation}\n",
"\\sum\\limits_j \\pi_i p_{i j} = \\sum\\limits_j \\pi_j p_{j i} \\;\\;\\;\\;\\; \\forall i.\n",
"\\end{equation}\n",
"\n",
"In non-equation terms, this says the amount of \"chain\" leaving site $i$ is the same as the amount of \"chain\" entering site $i$ for every site in equilibrium. There is no flow. \n",
"\n",
"Usually though, we actually want to work with a stricter rule than Global Balance, <b> Detailed Balance </b>, written as\n",
"\n",
"\\begin{equation}\n",
"\\pi_i p_{i j} = \\pi_j p_{j i}.\n",
"\\end{equation}\n",
"\n",
"Detailed Balance further constricts the transition probabilities we can assign and makes it easier to design an algorithm. Almost all MCMC algorithms out there use detailed balance, and only lately have certain applied mathematicians begun looking at breaking detailed balance to increase efficiency in certain classes of problems. \n",
"\n",
"### Today's Test Problem\n",
"\n",
"I will let you know now; this might be one of the most powerful numerical methods you could ever learn. I was going to put down a list of applications, but the only limit to such a list is your imagination. \n",
"\n",
"Today though, we will not be trying to predict stock market crashes, calculate the PageRank of a webpage, or calculate the entropy a quantum spin liquid at zero temperature. We just want to calculate an uniform probability distribution, and look at how Monte Carlo Markov Chains behave.\n",
"\n",
"* We will start with an $l\\times l$ grid\n",
"\n",
"* Our chain starts somewhere in that grid\n",
"\n",
"* We can then move up, down, left or right equally \n",
"\n",
"* If we hit an edge, we come out the opposite side <i>(toroidal boundary conditions)</i>\n",
"\n",
"<b>First question!</b> Is this ergodic?\n",
"\n",
"Yes! Nothing stops us from reaching any location.\n",
"\n",
"<b>Second question!</b> Does this obey detailed balance?\n",
"\n",
"Yes! In equilibrium, each block has a probability of $\\pi_i = \\frac{1}{l^2}$, and can travel to any of its 4 neighbors with probability of $p_{ij} = \\frac{1}{4}$. For any two neighbors\n",
"\\begin{equation}\n",
"\\frac{1}{l^2}\\frac{1}{4} = \\frac{1}{l^2}\\frac{1}{4},\n",
"\\end{equation}\n",
"and if they are not neighbors,\n",
"\\begin{equation}\n",
"0 = 0.\n",
"\\end{equation}"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"init_cell": true
},
"outputs": [
{
"data": {
"text/plain": [
"Plots.GRBackend()"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Statistics\n",
"using Plots\n",
"gr()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"armod (generic function with 1 method)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is just the equivalent of `mod`\n",
"# for using in an array that indexes from 1. \n",
"function armod(i,j)\n",
" return (mod(i-1+j,j)+1) \n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# input the size of the grid\n",
"l=5;\n",
"n=l^2;"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Transition (generic function with 1 method)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function Transition(i)\n",
" #randomly chose up, down, left or right\n",
" d=rand(1:4);\n",
" if d==1 #if down\n",
" return armod(i-l,n);\n",
" \n",
" elseif d==2 #if left\n",
" row=convert(Int,floor((i-1)/l));\n",
" return armod(i-1,l)+l*row;\n",
" \n",
" elseif d==3 #if right\n",
" row=convert(Int,floor((i-1)/l));\n",
" return armod(i+1,l)+l*row;\n",
" \n",
" else # otherwise up\n",
" return armod(i+l,n);\n",
" end \n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# The centers of blocks.\n",
"# Will be using for pictoral purposes\n",
"\n",
"pos=zeros(Float64,2,n);\n",
"pos[1,:]=[floor((i-1)/l) for i in 1:n].+0.5;\n",
"pos[2,:]=[mod(i-1,l) for i in 1:n].+0.5;"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# How many timesteps\n",
"tn=2000;\n",
"\n",
"# Array of timesteps\n",
"ti=Array{Int64,1}()\n",
"# Array of errors\n",
"err=Array{Float64,1}()\n",
"\n",
"# Stores current location, initialized randomly\n",
"current=rand(1:n);\n",
"# Stores last location, used for pictoral purposes\n",
"last=current;\n",
"\n",
"#Keeps track of where chain went\n",
"Naccumulated=zeros(Int64,l,l);\n",
"\n",
"# put in our first point\n",
"# can index 2d array as 1d\n",
"Naccumulated[current]+=1;"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"for ii in 1:tn\n",
" \n",
" last=current;\n",
" # Determine the new point\n",
" current=Transition(current);\n",
" Naccumulated[current]+=1;\n",
" \n",
" # add new time steps and error points\n",
" push!(ti,ii)\n",
" push!(err,std(Naccumulated/ii))\n",
" \n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When I was using an old version and pyplot I created this video of the state at each time point. https://www.youtube.com/watch?v=gxX3Fu1uuCs "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
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"execution_count": 8,
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],
"source": [
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]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
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"scatter(ti,log10.(err), markerstrokewidth=0)\n",
"\n",
"plot!(xlabel=\"Step\"\n",
" ,ylabel=\"Log10 std\"\n",
" ,title=\"Error for a $l x$l\")"
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},
{
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"So, after running the above code and trying to figure out how it works (mostly plotting stuff), go back and study some properties of the system.\n",
"\n",
"* How long does it take to forget it's initial position?\n",
"\n",
"* How does the behaviour change with system size?\n",
"\n",
"* How long would you have to go to get a certain accuracy? (especially if you didn't know what distribution you where looking for)\n",
"\n",
"So hopefully you enjoyed this tiny introduction to an incredibly rich subject. Feel free to explore all the nooks and crannies to really understand the basics of this kind of simulation, so you can gain more control over the more complex simulations. \n",
"\n",
"Monte Carlo simulations are as much of an art as a science. You need to live them, love them, and breathe them till you find out exactly why they are behaving like little kittens that can finally jump on top of your countertops, or open your bedroom door at 1am. \n",
"\n",
"For all their misbehaving, you love the kittens anyway.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"@ARTICLE{1970Bimka..57...97H,\n",
" title = \"{Monte Carlo Sampling Methods using Markov Chains and their Applications}\",\n",
" journal = {Biometrika, Vol.~57, No.~1, p.~97-109, 1970},\n",
" year = 1970,\n",
" month = apr,\n",
" volume = 57,\n",
" pages = {97-109},\n",
" doi = {10.1093/biomet/57.1.97},\n",
" adsurl = {http://adsabs.harvard.edu/abs/1970Bimka..57...97H},\n",
" adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n",
"}"
]
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| mit |
odelab/odes-plot | Apps/mpl2JSON/mpl2JSON/test2.ipynb | 1 | 2283 | {
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"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import numpy as np\n",
"from itertools import product\n",
"\n",
"def squiggle_xy(a, b, c, d, i=np.arange(0.0, 2*np.pi, 0.05)):\n",
" return np.sin(i*a)*np.cos(i*b), np.sin(i*c)*np.cos(i*d)\n",
"\n",
"fig = plt.figure(figsize=(8, 8))\n",
"\n",
"# gridspec inside gridspec\n",
"outer_grid = gridspec.GridSpec(4, 4, wspace=0.0, hspace=0.0)\n",
"\n",
"for i in range(16):\n",
" inner_grid = gridspec.GridSpecFromSubplotSpec(3, 3,\n",
" subplot_spec=outer_grid[i], wspace=0.0, hspace=0.0)\n",
" a, b = int(i/4)+1,i%4+1\n",
" for j, (c, d) in enumerate(product(range(1, 4), repeat=2)):\n",
" ax = plt.Subplot(fig, inner_grid[j])\n",
" ax.plot(*squiggle_xy(a, b, c, d))\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" fig.add_subplot(ax)\n",
"\n",
"all_axes = fig.get_axes()\n",
"\n",
"#show only the outside spines\n",
"for ax in all_axes:\n",
" for sp in ax.spines.values():\n",
" sp.set_visible(False)\n",
" if ax.is_first_row():\n",
" ax.spines['top'].set_visible(True)\n",
" if ax.is_last_row():\n",
" ax.spines['bottom'].set_visible(True)\n",
" if ax.is_first_col():\n",
" ax.spines['left'].set_visible(True)\n",
" if ax.is_last_col():\n",
" ax.spines['right'].set_visible(True)\n",
"\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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
austinjalexander/sandbox | python/tensorflow/TensorflowCore.ipynb | 2 | 7624 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'tensorflow' from '/usr/local/lib/python3.6/site-packages/tensorflow/__init__.py'>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Const:0\", shape=(), dtype=float32) Tensor(\"Const_1:0\", shape=(), dtype=float32)\n"
]
}
],
"source": [
"input1 = tf.constant(2.0)\n",
"input2 = tf.constant(5.0)\n",
"\n",
"print(input1, input2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.Session()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2.0, 5.0]\n"
]
}
],
"source": [
"print(sess.run([input1, input2]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Geez, maybe should have been called 'sum' instead of 'add'?\n",
"add_node = tf.add(input1, input2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Add_2:0\", shape=(), dtype=float32)\n",
"7.0\n"
]
}
],
"source": [
"print(add_node)\n",
"print(sess.run(add_node))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p1 = tf.placeholder(tf.float32)\n",
"p2 = tf.placeholder(tf.float32)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Placeholder:0\", dtype=float32) Tensor(\"Placeholder_1:0\", dtype=float32)\n"
]
}
],
"source": [
"print(p1, p2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"add_2:0\", dtype=float32)\n"
]
}
],
"source": [
"add_ph = p1 + p2 # Apparently equivalent to tf.add(p1, p2).\n",
"print(add_ph)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7.0\n",
"4.7\n",
"[ 6. 10.]\n"
]
}
],
"source": [
"print(sess.run(add_ph, feed_dict={p1: 2, p2: 5}))\n",
"print(sess.run(add_ph, {p1: 1.2, p2: 3.5}))\n",
"print(sess.run(add_ph, {p1: [1, 2], p2: [5, 8]}))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32)\n",
"y = tf.placeholder(tf.float32)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = tf.Variable([1], dtype=tf.float32)\n",
"b = tf.Variable([-2], dtype=tf.float32)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<tf.Variable 'Variable:0' shape=(1,) dtype=float32_ref> <tf.Variable 'Variable_1:0' shape=(1,) dtype=float32_ref>\n"
]
}
],
"source": [
"print(a, b)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"add_4:0\", dtype=float32)\n"
]
}
],
"source": [
"linear_model = a*x + b\n",
"print(linear_model)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name: \"init\"\n",
"op: \"NoOp\"\n",
"input: \"^Variable/Assign\"\n",
"input: \"^Variable_1/Assign\"\n",
"\n",
"None\n"
]
}
],
"source": [
"init = tf.global_variables_initializer()\n",
"print(init)\n",
"print(sess.run(init))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-2. -1. 0. 1. 2. 3.]\n"
]
}
],
"source": [
"print(sess.run(linear_model, {x: [0,1,2,3,4,5]}))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Square:0\", dtype=float32)\n",
"Tensor(\"Sum:0\", dtype=float32)\n"
]
}
],
"source": [
"squared_deltas = tf.square(linear_model - y)\n",
"print(squared_deltas)\n",
"loss = tf.reduce_sum(squared_deltas)\n",
"print(loss)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.75\n"
]
}
],
"source": [
"feed_dict = {\n",
" x: [0,1,2,3,4,5],\n",
" y: [-1,-0.5,0,0.5,1,1.5]\n",
"}\n",
"print(sess.run(loss, feed_dict))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Assign_5:0\", shape=(1,), dtype=float32_ref)\n",
"Tensor(\"Assign_6:0\", shape=(1,), dtype=float32_ref)\n",
"[array([0.25], dtype=float32), array([0.], dtype=float32)]\n"
]
}
],
"source": [
"assignA = tf.assign(a, [0.25])\n",
"print(assignA)\n",
"assignB = tf.assign(b, [0])\n",
"print(assignB)\n",
"print(sess.run([assignA, assignB]))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Assign_7:0\", shape=(1,), dtype=float32_ref)\n",
"Tensor(\"Assign_8:0\", shape=(1,), dtype=float32_ref)\n",
"[array([0.5], dtype=float32), array([-1.], dtype=float32)]\n",
"0.0\n"
]
}
],
"source": [
"assignA = tf.assign(a, [0.5])\n",
"print(assignA)\n",
"assignB = tf.assign(b, [-1])\n",
"print(assignB)\n",
"print(sess.run([assignA, assignB]))\n",
"print(sess.run(loss, feed_dict))"
]
}
],
"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
}
| mit |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/canesm5/atmoschem.ipynb | 1 | 102063 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Atmoschem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: CCCMA \n",
"**Source ID**: CANESM5 \n",
"**Topic**: Atmoschem \n",
"**Sub-Topics**: Transport, Emissions Concentrations, Gas Phase Chemistry, Stratospheric Heterogeneous Chemistry, Tropospheric Heterogeneous Chemistry, Photo Chemistry. \n",
"**Properties**: 84 (39 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmoschem?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 16:53:46"
],
"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', 'cccma', 'canesm5', 'atmoschem')"
],
"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 --> Timestep Framework --> Split Operator Order](#4.-Key-Properties--->-Timestep-Framework--->-Split-Operator-Order) \n",
"[5. Key Properties --> Tuning Applied](#5.-Key-Properties--->-Tuning-Applied) \n",
"[6. Grid](#6.-Grid) \n",
"[7. Grid --> Resolution](#7.-Grid--->-Resolution) \n",
"[8. Transport](#8.-Transport) \n",
"[9. Emissions Concentrations](#9.-Emissions-Concentrations) \n",
"[10. Emissions Concentrations --> Surface Emissions](#10.-Emissions-Concentrations--->-Surface-Emissions) \n",
"[11. Emissions Concentrations --> Atmospheric Emissions](#11.-Emissions-Concentrations--->-Atmospheric-Emissions) \n",
"[12. Emissions Concentrations --> Concentrations](#12.-Emissions-Concentrations--->-Concentrations) \n",
"[13. Gas Phase Chemistry](#13.-Gas-Phase-Chemistry) \n",
"[14. Stratospheric Heterogeneous Chemistry](#14.-Stratospheric-Heterogeneous-Chemistry) \n",
"[15. Tropospheric Heterogeneous Chemistry](#15.-Tropospheric-Heterogeneous-Chemistry) \n",
"[16. Photo Chemistry](#16.-Photo-Chemistry) \n",
"[17. Photo Chemistry --> Photolysis](#17.-Photo-Chemistry--->-Photolysis) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Key properties of the atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmospheric chemistry model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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 atmospheric chemistry model code.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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. Chemistry Scheme Scope\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Atmospheric domains covered by the atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.chemistry_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 atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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",
"### *Form of prognostic variables in the atmospheric chemistry component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.prognostic_variables_form') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"3D mass/mixing ratio for gas\" \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 advected tracers in the atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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",
"### *Atmospheric chemistry calculations (not advection) 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.atmoschem.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": [
"### 1.8. Coupling With Chemical Reactivity\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Atmospheric chemistry transport scheme turbulence is couple with chemical reactivity?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.coupling_with_chemical_reactivity') \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.atmoschem.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.atmoschem.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.atmoschem.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",
"*Timestepping in the atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Mathematical method deployed to solve the evolution of a given variable*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Operator splitting\" \n",
"# \"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 chemical species advection (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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 physics (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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. Split Operator Chemistry Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for chemistry (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_chemistry_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. Split Operator Alternate Order\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_alternate_order') \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": [
"### 3.6. Integrated Timestep\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Timestep for the atmospheric chemistry model (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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": [
"### 3.7. 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.atmoschem.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 --> Timestep Framework --> Split Operator Order \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Turbulence\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for turbulence scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.turbulence') \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. Convection\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for convection scheme This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.convection') \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. Precipitation\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for precipitation scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.precipitation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.4. Emissions\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for emissions scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.emissions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.5. Deposition\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for deposition scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.deposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.6. Gas Phase Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for gas phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.gas_phase_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.7. Tropospheric Heterogeneous Phase Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for tropospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.tropospheric_heterogeneous_phase_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.8. Stratospheric Heterogeneous Phase Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for stratospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.stratospheric_heterogeneous_phase_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.9. Photo Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for photo chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.photo_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.10. Aerosols\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for aerosols scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.aerosols') \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 --> Tuning Applied \n",
"*Tuning methodology for atmospheric chemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. 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.atmoschem.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": [
"### 5.2. 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.atmoschem.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": [
"### 5.3. 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.atmoschem.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": [
"### 5.4. 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.atmoschem.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": [
"# 6. Grid \n",
"*Atmospheric chemistry grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general structure of the atmopsheric chemistry grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.grid.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": [
"### 6.2. Matches Atmosphere Grid\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### * Does the atmospheric chemistry grid match the atmosphere grid?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.grid.matches_atmosphere_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": [
"# 7. Grid --> Resolution \n",
"*Resolution in the atmospheric chemistry grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. 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.atmoschem.grid.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": [
"### 7.2. 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.atmoschem.grid.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": [
"### 7.3. 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.atmoschem.grid.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": [
"### 7.4. 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.atmoschem.grid.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": [
"### 7.5. 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.atmoschem.grid.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": [
"# 8. Transport \n",
"*Atmospheric chemistry transport*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General overview of transport implementation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.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": [
"### 8.2. Use Atmospheric Transport\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is transport handled by the atmosphere, rather than within atmospheric cehmistry?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.transport.use_atmospheric_transport') \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": [
"### 8.3. Transport Details\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If transport is handled within the atmospheric chemistry scheme, describe it.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.transport.transport_details') \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. Emissions Concentrations \n",
"*Atmospheric chemistry emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview atmospheric chemistry emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_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": [
"# 10. Emissions Concentrations --> Surface Emissions \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Sources\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Sources of the chemical species emitted at the surface that 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.atmoschem.emissions_concentrations.surface_emissions.sources') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Vegetation\" \n",
"# \"Soil\" \n",
"# \"Sea surface\" \n",
"# \"Anthropogenic\" \n",
"# \"Biomass burning\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Method\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Methods used to define chemical species emitted directly into model layers above the surface (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.atmoschem.emissions_concentrations.surface_emissions.method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Climatology\" \n",
"# \"Spatially uniform mixing ratio\" \n",
"# \"Spatially uniform concentration\" \n",
"# \"Interactive\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Prescribed Climatology Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface and prescribed via a climatology, and the nature of the climatology (E.g. CO (monthly), C2H6 (constant))*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_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": [
"### 10.4. Prescribed Spatially Uniform Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface 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.atmoschem.emissions_concentrations.surface_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": [
"### 10.5. Interactive Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface 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.atmoschem.emissions_concentrations.surface_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": [
"### 10.6. Other Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface and specified via any other method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_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": [
"# 11. Emissions Concentrations --> Atmospheric Emissions \n",
"*TO DO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Sources\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Sources of chemical species emitted in the atmosphere that 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.atmoschem.emissions_concentrations.atmospheric_emissions.sources') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Aircraft\" \n",
"# \"Biomass burning\" \n",
"# \"Lightning\" \n",
"# \"Volcanos\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Method\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Methods used to define the chemical species emitted in the atmosphere (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.atmoschem.emissions_concentrations.atmospheric_emissions.method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Climatology\" \n",
"# \"Spatially uniform mixing ratio\" \n",
"# \"Spatially uniform concentration\" \n",
"# \"Interactive\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.3. Prescribed Climatology Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere and prescribed via a climatology (E.g. CO (monthly), C2H6 (constant))*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_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": [
"### 11.4. Prescribed Spatially Uniform Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere 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.atmoschem.emissions_concentrations.atmospheric_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": [
"### 11.5. Interactive Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere 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.atmoschem.emissions_concentrations.atmospheric_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": [
"### 11.6. Other Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere 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.atmoschem.emissions_concentrations.atmospheric_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": [
"# 12. Emissions Concentrations --> Concentrations \n",
"*TO DO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. 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.atmoschem.emissions_concentrations.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": [
"### 12.2. 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.atmoschem.emissions_concentrations.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": [
"# 13. Gas Phase Chemistry \n",
"*Atmospheric chemistry transport*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview gas phase atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.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": [
"### 13.2. Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Species included in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HOx\" \n",
"# \"NOy\" \n",
"# \"Ox\" \n",
"# \"Cly\" \n",
"# \"HSOx\" \n",
"# \"Bry\" \n",
"# \"VOCs\" \n",
"# \"isoprene\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Number Of Bimolecular Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of bi-molecular reactions in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_bimolecular_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Number Of Termolecular Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of ter-molecular reactions in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_termolecular_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Number Of Tropospheric Heterogenous Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of reactions in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_tropospheric_heterogenous_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.6. Number Of Stratospheric Heterogenous Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of reactions in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_stratospheric_heterogenous_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.7. Number Of Advected Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of advected species in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_advected_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": [
"### 13.8. Number Of Steady State Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of gas phase species for which the concentration is updated in the chemical solver assuming photochemical steady state*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_steady_state_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": [
"### 13.9. Interactive Dry Deposition\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.interactive_dry_deposition') \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": [
"### 13.10. Wet Deposition\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is wet deposition included? Wet deposition describes the moist processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_deposition') \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": [
"### 13.11. Wet Oxidation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is wet oxidation included? Oxidation describes the loss of electrons or an increase in oxidation state by a molecule*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_oxidation') \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. Stratospheric Heterogeneous Chemistry \n",
"*Atmospheric chemistry startospheric heterogeneous chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview stratospheric heterogenous atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.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. Gas Phase Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Gas phase species included in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.gas_phase_species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Cly\" \n",
"# \"Bry\" \n",
"# \"NOy\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Aerosol Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Aerosol species included in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.aerosol_species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sulphate\" \n",
"# \"Polar stratospheric ice\" \n",
"# \"NAT (Nitric acid trihydrate)\" \n",
"# \"NAD (Nitric acid dihydrate)\" \n",
"# \"STS (supercooled ternary solution aerosol particule))\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.4. Number Of Steady State Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of steady state species in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.number_of_steady_state_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": [
"### 14.5. Sedimentation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is sedimentation is included in the stratospheric heterogeneous chemistry scheme or not?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.sedimentation') \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": [
"### 14.6. Coagulation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is coagulation is included in the stratospheric heterogeneous chemistry scheme or not?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.coagulation') \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. Tropospheric Heterogeneous Chemistry \n",
"*Atmospheric chemistry tropospheric heterogeneous chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview tropospheric heterogenous atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.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. Gas Phase Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of gas phase species included in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.gas_phase_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": [
"### 15.3. Aerosol Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Aerosol species included in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.aerosol_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",
"# \"Polar stratospheric ice\" \n",
"# \"Secondary organic aerosols\" \n",
"# \"Particulate organic matter\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Number Of Steady State Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of steady state species in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.number_of_steady_state_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": [
"### 15.5. Interactive Dry Deposition\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.interactive_dry_deposition') \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. Coagulation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is coagulation is included in the tropospheric heterogeneous chemistry scheme or not?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.coagulation') \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": [
"# 16. Photo Chemistry \n",
"*Atmospheric chemistry photo chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview atmospheric photo chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.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. Number Of Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of reactions in the photo-chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.number_of_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Photo Chemistry --> Photolysis \n",
"*Photolysis scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Photolysis scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Offline (clear sky)\" \n",
"# \"Offline (with clouds)\" \n",
"# \"Online\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.2. Environmental Conditions\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any environmental conditions taken into account by the photolysis scheme (e.g. whether pressure- and temperature-sensitive cross-sections and quantum yields in the photolysis calculations are modified to reflect the modelled conditions.)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.environmental_conditions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \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 |
kubeflow/pipelines | components/gcp/dataflow/launch_template/sample.ipynb | 1 | 10020 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Name\n",
"Data preparation by using a template to submit a job to Cloud Dataflow\n",
"\n",
"# Labels\n",
"GCP, Cloud Dataflow, Kubeflow, Pipeline\n",
"\n",
"# Summary\n",
"A Kubeflow Pipeline component to prepare data by using a template to submit a job to Cloud Dataflow.\n",
"\n",
"# Details\n",
"\n",
"## Intended use\n",
"Use this component when you have a pre-built Cloud Dataflow template and want to launch it as a step in a Kubeflow Pipeline.\n",
"\n",
"## Runtime arguments\n",
"Argument | Description | Optional | Data type | Accepted values | Default |\n",
":--- | :---------- | :----------| :----------| :---------- | :----------|\n",
"project_id | The ID of the Google Cloud Platform (GCP) project to which the job belongs. | No | GCPProjectID | | |\n",
"gcs_path | The path to a Cloud Storage bucket containing the job creation template. It must be a valid Cloud Storage URL beginning with 'gs://'. | No | GCSPath | | |\n",
"launch_parameters | The parameters that are required to launch the template. The schema is defined in [LaunchTemplateParameters](https://cloud.google.com/dataflow/docs/reference/rest/v1b3/LaunchTemplateParameters). The parameter `jobName` is replaced by a generated name. | Yes | Dict | A JSON object which has the same structure as [LaunchTemplateParameters](https://cloud.google.com/dataflow/docs/reference/rest/v1b3/LaunchTemplateParameters) | None |\n",
"location | The regional endpoint to which the job request is directed.| Yes | GCPRegion | | None |\n",
"staging_dir | The path to the Cloud Storage directory where the staging files are stored. A random subdirectory will be created under the staging directory to keep the job information. This is done so that you can resume the job in case of failure.| Yes | GCSPath | | None |\n",
"validate_only | If True, the request is validated but not executed. | Yes | Boolean | | False |\n",
"wait_interval | The number of seconds to wait between calls to get the status of the job. | Yes | Integer | | 30 |\n",
"\n",
"## Input data schema\n",
"\n",
"The input `gcs_path` must contain a valid Cloud Dataflow template. The template can be created by following the instructions in [Creating Templates](https://cloud.google.com/dataflow/docs/guides/templates/creating-templates). You can also use [Google-provided templates](https://cloud.google.com/dataflow/docs/guides/templates/provided-templates).\n",
"\n",
"## Output\n",
"Name | Description\n",
":--- | :----------\n",
"job_id | The id of the Cloud Dataflow job that is created.\n",
"\n",
"## Caution & requirements\n",
"\n",
"To use the component, the following requirements must be met:\n",
"- Cloud Dataflow API is enabled.\n",
"- The component can authenticate to GCP. Refer to [Authenticating Pipelines to GCP](https://www.kubeflow.org/docs/gke/authentication-pipelines/) for details.\n",
"- The Kubeflow user service account is a member of:\n",
" - `roles/dataflow.developer` role of the project.\n",
" - `roles/storage.objectViewer` role of the Cloud Storage Object `gcs_path.`\n",
" - `roles/storage.objectCreator` role of the Cloud Storage Object `staging_dir.` \n",
"\n",
"## Detailed description\n",
"You can execute the template locally by following the instructions in [Executing Templates](https://cloud.google.com/dataflow/docs/guides/templates/executing-templates). See the sample code below to learn how to execute the template.\n",
"Follow these steps to use the component in a pipeline:\n",
"1. Install the Kubeflow Pipeline SDK:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"\n",
"!pip3 install kfp --upgrade"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Load the component using KFP SDK"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import kfp.components as comp\n",
"\n",
"dataflow_template_op = comp.load_component_from_url(\n",
" 'https://raw.githubusercontent.com/kubeflow/pipelines/1.7.0-rc.3/components/gcp/dataflow/launch_template/component.yaml')\n",
"help(dataflow_template_op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample\n",
"\n",
"Note: The following sample code works in an IPython notebook or directly in Python code.\n",
"In this sample, we run a Google-provided word count template from `gs://dataflow-templates/latest/Word_Count`. The template takes a text file as input and outputs word counts to a Cloud Storage bucket. Here is the sample input:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil cat gs://dataflow-samples/shakespeare/kinglear.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Set sample parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"# Required Parameters\n",
"PROJECT_ID = '<Please put your project ID here>'\n",
"GCS_WORKING_DIR = 'gs://<Please put your GCS path here>' # No ending slash"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Optional Parameters\n",
"EXPERIMENT_NAME = 'Dataflow - Launch Template'\n",
"OUTPUT_PATH = '{}/out/wc'.format(GCS_WORKING_DIR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Example pipeline that uses the component"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import kfp.dsl as dsl\n",
"import json\n",
"@dsl.pipeline(\n",
" name='Dataflow launch template pipeline',\n",
" description='Dataflow launch template pipeline'\n",
")\n",
"def pipeline(\n",
" project_id = PROJECT_ID, \n",
" gcs_path = 'gs://dataflow-templates/latest/Word_Count', \n",
" launch_parameters = json.dumps({\n",
" 'parameters': {\n",
" 'inputFile': 'gs://dataflow-samples/shakespeare/kinglear.txt',\n",
" 'output': OUTPUT_PATH\n",
" }\n",
" }), \n",
" location = '',\n",
" validate_only = 'False', \n",
" staging_dir = GCS_WORKING_DIR,\n",
" wait_interval = 30):\n",
" dataflow_template_op(\n",
" project_id = project_id, \n",
" gcs_path = gcs_path, \n",
" launch_parameters = launch_parameters, \n",
" location = location, \n",
" validate_only = validate_only,\n",
" staging_dir = staging_dir,\n",
" wait_interval = wait_interval)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Compile the pipeline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pipeline_func = pipeline\n",
"pipeline_filename = pipeline_func.__name__ + '.zip'\n",
"import kfp.compiler as compiler\n",
"compiler.Compiler().compile(pipeline_func, pipeline_filename)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Submit the pipeline for execution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Specify pipeline argument values\n",
"arguments = {}\n",
"\n",
"#Get or create an experiment and submit a pipeline run\n",
"import kfp\n",
"client = kfp.Client()\n",
"experiment = client.create_experiment(EXPERIMENT_NAME)\n",
"\n",
"#Submit a pipeline run\n",
"run_name = pipeline_func.__name__ + ' run'\n",
"run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Inspect the output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil cat $OUTPUT_PATH*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"* [Component python code](https://github.com/kubeflow/pipelines/blob/master/components/gcp/container/component_sdk/python/kfp_component/google/dataflow/_launch_template.py)\n",
"* [Component docker file](https://github.com/kubeflow/pipelines/blob/master/components/gcp/container/Dockerfile)\n",
"* [Sample notebook](https://github.com/kubeflow/pipelines/blob/master/components/gcp/dataflow/launch_template/sample.ipynb)\n",
"* [Cloud Dataflow Templates overview](https://cloud.google.com/dataflow/docs/guides/templates/overview)\n",
"\n",
"## License\n",
"By deploying or using this software you agree to comply with the [AI Hub Terms of Service](https://aihub.cloud.google.com/u/0/aihub-tos) and the [Google APIs Terms of Service](https://developers.google.com/terms/). To the extent of a direct conflict of terms, the AI Hub Terms of Service will control.\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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
} | apache-2.0 |
chichilalescu/pyNT | examples/project2.ipynb | 1 | 863202 | {
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"cells": [
{
"cell_type": "code",
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"input": [
"import numpy as np\n",
"import sympy as sp\n",
"\n",
"%matplotlib nbagg\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from pyNT import Wiener, SDE, ODE"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"C1 = sp.Symbol('C_1')\n",
"C2 = sp.Symbol('C_2')\n",
"A = sp.Symbol('A')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lambda11 = 40\n",
"lambda21 = -2\n",
"lambda12 = -5\n",
"lambda22 = 10\n",
"delta1 = .5\n",
"delta2 = .5\n",
"delta0 = 1.\n",
"mu1 = 20\n",
"mu2 = 20\n",
"\n",
"coeff_system = ODE(x = [C1, C2, A],\n",
" f = [-lambda11*C1 - lambda21*C2 - C1**2/2 + delta1,\n",
" -lambda12*C1 - lambda22*C2 - C2**2/2 + delta2,\n",
" mu1*C1 + mu2*C2 + delta0])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"for solver in [['Heun', 'cRK'],\n",
" ['cRK', 'Taylor4']]:\n",
" evdt = coeff_system.get_evdt(\n",
" X0 = np.zeros(3),\n",
" solver = solver,\n",
" h0 = .5,\n",
" exp_range = range(10))\n",
" # no errorbars since there's only one trajectory\n",
" ax.plot(evdt[:, 0],\n",
" evdt[:, 2],\n",
" label = '{0} vs {1}'.format(solver[0], solver[1]),\n",
" marker = '.')\n",
"ax.set_xscale('log')\n",
"ax.set_yscale('log')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
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"\n",
"mpl.get_websocket_type = function() {\n",
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
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"\n",
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" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
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" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
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" }\n",
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"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
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"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
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"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
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"\n",
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" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
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"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
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" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb90c3958d0>"
]
},
{
"html": [
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb90c3959e8>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.legend.Legend at 0x7fb90c3959b0>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"coeff_time = np.linspace(.0, 1., 1025)\n",
"coeff_traj = coeff_system.cRK(2.**(-10), 2**10, np.zeros(3))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(coeff_time, coeff_traj[:, 0], label = '$C_1$')\n",
"ax.plot(coeff_time, coeff_traj[:, 1], label = '$C_2$')\n",
"ax.plot(coeff_time, coeff_traj[:, 2], label = '$A$')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb909619780>"
]
},
{
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb90c384358>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.legend.Legend at 0x7fb90956e470>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# reverse coefficients\n",
"traj_C1 = coeff_traj[::-1, 0]\n",
"traj_C2 = coeff_traj[::-1, 1]\n",
"traj_A = coeff_traj[::-1, 2]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = sp.Symbol('x')\n",
"y = sp.Symbol('y')\n",
"xy_sys = SDE(\n",
" x = [x, y],\n",
" a = [mu1 - lambda11*x - lambda12*y,\n",
" mu2 - lambda21*x - lambda22*y],\n",
" b = [[x**.5, 0],\n",
" [0, y**.5]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dt0, evdt0, deltae0 = xy_sys.get_evdt_vs_M(\n",
" ntraj = 32,\n",
" X0 = np.ones(2),\n",
" h0 = .125,\n",
" solver = ['Milstein', 'explicit_1p0'],\n",
" exp_range = range(7))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb90957eac8>"
]
},
{
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uLLvXNKfl3rF5nJJT3v/75dn+GfPGuXuezg47X38ZZ3I0M4v5dalDnU9qHhdyXUz0+v8z5EeAlq8eUEj3fufq7c3NqcHnga5UpyE3P0ckjtD/6m6nl1vVmkCTym2x96uW1JugWdhwDypd+NSDJG76piQu4F5L7nfE/+34/0s79mi/NeTQ1Mez3T15HLlH3i+rZ63k6+VfR1aR8l7jzKd2fU1e8+bn+U5fgBtSXu8VfkHnt785vriqqzAMz/Lc4KWaT4lbb/NoR2pdldyuaeecPqHgveZg732+CcKEnL3As1Q78JPXMULPMcEzGtV5v1/i4K3w18rGU4ybWsd3hS0jgeplzvR2a7kclskt5/MdAInruzLppB94HxvXnOG9/ZPBneo+/3RQMVoVe47W78cJqiMwH+fTBv8WnJ1ygvkDvT9jSf98rHU807Jv5E16F6bnl6frZ3jVPzA8ze5g6lsUy5js6S3MZ7lOb9R94MY/6D08hzak7qjG67cD/j9G8kP9P79upJnJid78/yrZ3sruJTDX9af5dDO1Hu4/B/7u3J4Xfp9Ri3KvA0MV3EDj+R2Nn4Y3klumfww7TnNKk3g8RVze+ztuiX1bnv0xZV/4PmJ95pM92Rle88W5b+elDrwdMiVPmVb5tkO7p9WcnvL1rtjs3o+OPc75bgs7fFWEXiCNKt3y9S/LzKXTgvuzaEd6fzf0mO9f+8pd7C/XpbXDJe7avp7JQ/gv1ZuvcSliqt4gSfot69VmdfFFmUPPHPU8+9b0L1M2awqd1Xo10pezUvIXaVJH5ssrtwCT2/2gfOVPfDsreQyDyqlBypCq3Lb2fq9bSUknZLlfXI5w7GukuUWCbmzrKl62in5ZyqmZphf7MDjH1zkU17h77DT71sp1DjvfbOVtPlXEFJL5fwfw8vz/HvDlfsB/0Xec++R63FmhZJhLqLkPUvbyoXVhwPew1/Wy+SuAuXK3yndk8drfC3q28BTr2QZ2Edy5X4rpz2nWaUNPKkK3R57u25JfR94/J4mg8pdenrPFuW/nuyS8nim0jO/R6t8As/olPfN1k17NsfLrXt+T3nPK3MHKs3eczKVSDUoecCWXtbTqvwDT29+c31xVVbgyXeZHuA99pUy/0b63VKfnmF+Nv69IH6HBw/IlRHnaqSSPQ+uUPd7urKJK3yBxz95GXSit5gGyZ348k9AHZc2P67sy6aQfeB877WZStqOVHK/TEkb+sTJcvesvCh3ObsYP9qt6nln2yC3sSfkrvJk6wwg/YfkOknjM7zvc95rpqU9HrRTGqRkd8f+ZeCgwLN+htf31E4p8wHWGnI7nkxdAEvubPnTSh64+r1IdSjzmbFxcmfpgrpYzsRvY6YDncFKXl5PrdP1e6Cbm+W9b5D74Uv9focqeOeyhrr/8G2m5FnZy9W9S1K/PvxKucv3hwa0YQ0le5DKdGl/E7nldkTKY34vWf/M8BrJrWd/ULIrXl+L+jbwSMkz/wlJ5wbMb1ZpAk8ptsferltS3wce/yp1UODxDzAzbectyn89aVKyvj9T5wJ+l9X5BJ4GubLehLpuB6lWk1vfg3rHGylXfnqA3Pf4tvdeF2Z4r2ZvfrtcD2np/Pu0gu5ValX+gac3v7m+uPILPKnrcqFalH/gyXeZ+gOPJuS2kXQbKnmFpTcDjw5S1178lsp1MBOkVd3He5JccPZPvH0rj7/9jrp/d6uq6xWJ4erbwLO/9/jnyhy+D5frLTHXTgsuUPD+T0r2hpt+H1VPy6aQfeB8ZQ9LPd0PCBTVFLnL2/4l0/PkxlNIv+yZr1Zl39muLOmX3nM+UObLmf77pP+QxJX5Jjo/RKXfx/GJ3I/ZFimPfSbXS5yUHGAsqJb+XBU/8EjJLlIfC5g3WO7m5yfSHvdrrYM6SGiU+/z/UX7d6vptTKh7N8+S+1wJdS93WE8uiPhlVOm2ltuxXZ/2+EAll6e/U/Zvfgy60uef7W9X97OLB6XMa1fmgwx/vJNMZxV/IXeFK7UUcICSBzpBPZCtJ3c2+5GAeS3KvA0MU2kCT53cju1BBXfO0KzcAk+mA8L5Ct7px1X87bG365ZUeODJdkAcxL+XLOiA9BAlv+t8A0+29cTv3fI36n4Fxe85Kd/AI7lxNhJyBzBBZ/pnKDluSqpGuXs7Urun3c177jJ1HfTQ15zSzjMD5t+tzNtsqzJ/x9mWW29+c6Xs60VP63KhWpR/4OnNMvVveg/qbdS/yT5o2IJc+Tf3fyy3PDNd+Uuoa+c4vjolu6wO6qo+E78sOjVgvSr3O+nr6Td5voobeKTkWDRBvaqtIneP0x8ztCdTGzOFB3/Zn5P2eE/LppB94Hwl983p3XUPkrvXr0OZq2qAonpJXevrB8idwdm3l++3utzZnwflVvQH5Fb0teXOoE+Q22H6vWv9St03BMkdEKe+z7Xev/2z/wuVPOgfLRdCdpQ7e5GQuwqQHtoe8uZdLTc2hv/j7nezPEbJM1DXy50J20KuhvgzJXciOyp5ULxShnb6YWNtJQ/I3/M+a2o9bkSud6aE3I/m9nLhb3+5cPGuuo8LtKqSHQjcIvfDP8z7HM/Jlebl0juL//dT2/ih3I+dP/DoSHUdeHSLgPcYL3eW6iO5e2s2krsqc5LcwcPzCr5c7f/YnyH3/V0rt+5tHvDcK7znrlD35RFV8upc0EGMr1HJ7kMfluvedKjcDbUPeZ8x6Id3G7kA+ZXczmIzuXKKw+XONr6hriOX95dbpn6PRv424C+DIXIH8P7OdS3lfr/V2t70byXXtUz3cKTy11O/rMRfF1PP1A5Jee+E3LJYW2478rdr/z6uM9R1PS/F9ijlv25l+92oV/D2uGraa4M+f082VPJG/Xu8ZbCJpO/KHQz7nTn47ykVvp70V7Lr6YflfjuGypVtvaNk0PTfN59R2/3vbZ5cKeJQ7zP5B72p91D4n2OG3O/Vhkous9WVPLP8D7nvOfX3r9mb9ztvOkvuO95cySt8b6vrIJm5fMfZllu+v7nZtgtfLutyvlaVG0hyKyXLn16R6y59K2W+ybtZ+S9TX53c/Tn+stlKbj/gnyx6VsFdKOfKL8VMqPv9hakSctvMpUqu17soWSaZ7z1Efk+w9yq5jXTIlfT1tM7461v6b1+9kut+0P7f37emb9+pJ+Q2kNt/LJcrFd9abl2cIhdE/qvMvTAG8ccm+4VcJcMwuf1Xi9y+82/qfp9NtmXjy3cf6Juv5Lr0B7l7doaKgUdRBhG5H5X00oW/K3iMhVzMV/IHLbWHlYTc2fMPvOfMUPZxLlozvI9/dmUTuTKJF+UONJZ57/283E2RQWcm15K7qvR/csHmdXW/mrCd3I2Z/kHKB3I/HqmlZAklzw63BLQz9Sxg6jx/fvpZujq5UeTb5M5ot3ttu1aZD4QjcmdifyN3QPiV95qblN/Nf+PS2viO3JmXn8gdxC6VC0H3KPvNx0O9v/0v7zWLvLZ9V5kv128id8PmF177/6TuVwF823ptzDTg3S+99mcqw/HVec+ZJ7ceLJa7VP8zZe9KeQ25beLv3ms+kdshXaDuB5LNyr7uxtV9nXhHuQl636Azh+laFLwupp7NDGpXh9z3Pj/DPH89L9X2KOW3brUq87IfpuBlcHfaa9M/49CANgUZIVdeuUju838kd0JnVyXPSqcu8+YsbZVyW0/6S7pYLkx8Lbfsn5I7OPQHLvSnTJ2xZLKv914fyy33d+S+r/Txa/zPEbTM5gd8hnkBr50ntx3N9D73UrmAcZMyj82Wz3ecvtzy+c2NB7xf+nqR67qcj2Z1/f5S/37QfiT9dfks03RHyW3L/r7lD3IH+oVWfkiugmSZggOXbzu5KzyvyJ1s/FruBMUcZd5HZLOy3FXYRd57va3k1Zjhyr7OtAbM838XmzPM20Pd963+//t/17eS3EnFP8kt68/lltEMBY8Tls36cgH3OSV/hxbJXXk9U8EljpmWTfrVt3z2gb75Sm6bu8iVivrbyJ+Ue3fbQMHWlVsZJ6c9/oKCy7oAACiWZmU/eEf+msUyRTjMV9eTOEDZZAo8L4rAAwAorWZxcF5szWKZIhzmi8CDXtpFyd6AejJK0pNy5UiL5H78xqU9p5+CS9peV+bRzQEAKIZmcXBebM1imSIc5ovAgzwNlHSzXE38F+q5Z6cxcjWWd8nVeDfK1cOukLsZLtVLcjet+VZSYZ0WAACQjX+zt9+Jxkvq2oEE8scyRVj01MkNkNGDcjeVraZk15iZ1Mv1xPFfde9N5S9yV3tSxxeYKtf7hn8T4/lyg+gV4+ZEAADSNav3HXBUkqFyN6J/meN0WAF/q1m1sUwRfvOVvZMbIKPUASbjyh54xilzjzwXevOOTXv8FLneNl5S8QYeBQCgljXI9d6X65Q+2CkA1Ky4sgeey+VCzakB8/ZR7/qwBwAAAIA+EVf2wOMPgHZgwLxR3rxXit8sAAAAAChcXNkDzzNyoSZohOeR3ry3itSWhCSTG7SNiYmJiYmJiYmJqbeTKbeeiFED4gpP4LGepkgkYgMGDKj6Kcyfs6/bVoq/V6z37O379OZ1+bymVM+t5inMy4FtrvD3CMs2F+b1rK+nMC+Lvmxbqf5WrWxzkUikx2PHIh2josLFFZ6StnZJBrNoNFruJmTU120rxd8r1nv29n1687p8XlOq51azMC8HtrnC3yMs21yY17O+FuZl0ZdtK9XfYpszGzBgAIEHneLKHnguU+ZOCyaruJ0WEHg87AhK+/cIPIW1pRqFeTmwzRX+HmHZ5sK8nvW1MC8LAk/h7xGGbY7Ag1Rx5dYt9R0B8y7y5jUXqS0EHg87gtL+PQJPYW2pRmFeDmxzhb9HWLa5MK9nfS3My4LAU/h7hGGbI/AgVVzZA0+d3MCjH0pqSnv8Ve/xlYrUFgKPJxaLlbsJGfV120rx94r1nr19n968Lp/X5PPcMB909CW2udL+vWK8ZyHvEZZtju0tiW2utH+LbY7Ag67iyh54JGm0pMVyIyxHJTVKmi5puaQpRWwLgQfoYxyAAX2H7Q3oOwQeTJa0xJs6vMn/d9C9OpLroOAJSYu8aZ6ksUVuF4EH6GMcgAF9h+0N6DsEHoQVgQfoYxyAAX2H7Q3oOwQehFV7JBIp9/YB1JQw19ED1YbtDeg7XuBZVu6DWyBdeyQSsWg02jmxcwAAAEAuYrFY5zGk3NUdAg9Cp33AgAHl3lYAAABQ4bwrPO3lPrgF0hF4AAAAUDACD8KKwAMAAICCEXgQVgQeAAAAFIzAg7Ai8AAAAKBgBB6EFYEHAAAABSPwIKwIPAAAACgYgQdhReABAABAwQg8CCsCDwAAAApG4EFYEXgAAABQMAIPworAAwAAgIIReBBW7ZFIxKLRaOcUi8XKvb0AAACgAsRisc5jSEkmaVm5D26BdFzhAQAAQMG4woOwIvAAAACgYAQehBWBBwAAAAUj8CCsCDwAAAAoGIEHYUXgAQAAQMEIPAgrAg8AAAAKRuBBWBF4AAAAUDACD8KKwAMAAICCEXgQVgQeAAAAFIzAg7Ai8AAAAKBgBB6EFYEHAAAABSPwIKwIPAAAACgYgQdhReABAABAwQg8CCsCDwAAAApG4EFYEXgAAABQMAIPwqo9EolYNBrtnGKxWLm3FwAAAFSAWCzWeQwpySQtK/fBLZCOKzwAAAAoGFd4EFYEHgAAABSMwIOwIvAAAACgYAQehBWBBwAAAAUj8CCsCDwAAAAoGIEHYUXgAQAAQMEIPAgrAg8AAAAKRuBBWBF4AAAAUDACD8KKwAMAAICCEXgQVgQeAAAAFIzAg7Ai8AAAAKBgBB6EFYEHAAAABSPwIKwIPAAAACgYgQdhReABAABAwQg8CCsCDwAAAApG4EFYEXgAAABQMAIPworAAwAAgIIReBBWBB4AAAAUjMCDsCLwAAAAoGAEHoQVgQcAAAAFI/AgrAg8AAAAKBiBB2FF4AEAAEDBCDwIKwIPAAAACkbgQVgReAAAAFAwAg/CisADAACAgo0l0jkAACAASURBVBF4EFYEHgAAABSMwIOwIvAAAACgYAQehBWBBwAAAAUj8CCs2iORiEWj0c4pFouVe3sBAABABYjFYp3HkJJM0rJyH9wC6bjCAwAAgIJxhQdhReABAABAwQg8CCsCDwAACI1EImGLly0udzPQCwQehBWBBwAAlM3H7R/bU/98yi6Zd4lNuneSDbpqkJ0/9/xyNwu9QOBBWBF4AABAn1iyfIm9/O+X7Ucv/8gO++VhNuLHI0wt6jLVX1Zvx//q+HI3Fb1A4EFYEXgAAEDRJRIJ+8f//mH3/OUeO/2p022n23eyyOWRbgFn/RvWt4MfPNiuefEaWxBfYF99/VW5m45eIvAgrAg8AACgYEGlaenhZuUZK9uerXvaBXMvsMfeeMw++OKDcjcbRUTgQVgReAAAQF5yLU0bdesoO/HxE+3OP95pf130V1vRsaLcTUcJEXgQVgQeAACQEaVpyBWBB2FF4AEAAJ0oTUNvEXgQVgQeAABqFKVpKCYCD8KKwAMAQA1ILU077anTbMfbd6Q0DUVF4EFYEXgAAKhClKahrxF4EFYEHgAAKlyupWnb3LoNpWkoGQIPworAAwBABaE0DWFF4EFfWEPSvZISebyGwAMAQIh93P6xPfmPJ3MuTXv09UcpTUNZEHhQajtJmi/pZhF4AACoSJSmoZIReFBq60tqlNQsAg8AAKFHaRqqDYEHfaVZBB4AAELHL027eN7FlKahKhF40FeaReABAKCsKE1DLSLwoK80i8ADAECfoTQNcAg8tW0XSX9XbkFklKQnJX0oaZGkeZLGpT1nkqQ2b5qXNq85x7/jI/AAAJCH1NK0ve/dm9I0wEPgqU0D5XpN+0DSF5I6enj+GEmLJd0lqb9cJwTTJa2QNCXHv9ksAg8AAEVBaRqQOwJPbXpQ0p2SVpMUV/bAUy/pb5L+K6lf2ry/yF3tWTmHv9ksAg8AAHmjNA0oDIGnNq2X8v9xZQ884+SCyu0B8y705h2b5fWDJD0s6RXv7zws6fwc2kjgAQDUJErTgOIi8CCu7IHncrlQc2rAvH28efcWv1kEHgBA9VuyfIn95r3fUJoGlBCBB3FlDzwPyYWaAwPmjfLmvVL8ZhF4AADVhdI0oDwIPIgre+B5Ri7UTAyYN9Kb91bxm6X2SCRi0Wg04xSLxcq9/QAAkBGlaUDficViGY8ZJZmkZSU4XkWFiCukgYcrPACASkFpGhBeXOFBXJS0AQCQs45EB6VpQAUh8CCu7IHnMmXutGCy6LQAAFDl0kvTVrtqNUrTgApC4EFcuXVLfUfAvIu8ec3FbpQIPACAMqA0Dag+BB7ElT3w1MkNPPqhpKa0x1/1Hl+pBO0i8AAASope04DaQOBBXNkDjySNlrRY0t2SopIaJU2XtFzSlBK1i8ADACiqfHpNO3/u+ZSmAVWCwFObJkta4k0d3uT/O+heHcl1UPCEpEXeNE/S2BK2kcADAOi1JcuX2Mv/fpnSNAAEHoQWgQcAkBNK0wBkQ+BBWBF4AACBUkvTJt07qccBPR974zFK04AaRuBBWLVHIpEuo+TGYrFyby8AgD6Wa2naqFtHUZoGoFMsFus8hpRkkpaV++AWSMcVHgCoMZSmASgFrvAgrAg8AFDl/NK0S+ZdQmkagJIh8CCsCDwAUEUoTQNQLgQehBWBBwAqVHpp2k6370RpGoCyIfAgrAg8AFAhPm7/2J7651OUpgEIJQIPworAAwAhRGkagEpD4EFYEXgAoMxSS9NOf+p0StMAVCQCD8KKwAMAfYzSNADViMCDsCLwAEAJ5VOadtLjJ1GaBqBiEXgQVgQeACgSStMA1DICD8KqPRKJWDQa7ZxisVi5txcAqAiUpgGodbFYrPMYUpJJWlbug1sgHVd4ACAHlKYBQHZc4UFYEXgAIA2laQCQPwIPworAA6DmUZoGAIUj8CCsCDwAakqupWnb3LoNA3oCQB4IPAgrAg+AqkVpGgD0HQIPworAA6Bq5Fua9ujrj1KaBgBFQuBBWBF4AFQkStMAIFwIPAgrAg+A0KM0DQDCj8CDsCLwAAidXEvTxrWOs/Pnnk9pGgCEAIEHYUXgAVBWlKYBQHUg8CCsCDwA+kxvStPmL5xvX379ZbmbDgDoAYEHYUXgAVAylKYBQO0g8CCsCDwAioLSNACobQQehBWBB0De8i1Nu/rFqylNA4AqR+BBWBF4APSI0jQAQE8IPAgrAg+ALihNAwD0BoEHYdUeiUQsGo12TrFYrNzbC4A+klqadtpTp1GaBgDISywW6zyGlGSSlpX74BZIxxUeoIZQmgYAKBWu8CCsCDxAlaI0DQDQlwg8CCsCD1AF0kvTdrx9R0rTAAB9isCDsCLwABWI0jQAQNgQeBBWBB4g5ChNAwBUAgIPworAA4QIpWkAgEpF4EFYEXiAMvq4/WN78h9PUpoGAKh4BB6EFYEH6COUpgFAbhKJcrcAvUHgQVgReIASYEBPAMjPihVmjz5qNm6c2R13lLs16A0CD8KKwAMUAaVpANA7n3xidvXVZsOGmUlumjix3K1CbxB4EFYEHiBPvSlNe+3D1yhNA4AUr71mduKJZtFoMujssovZAw+YLVtW7tahNwg8CCsCD5BFvr2mXfPiNbYgvoDSNAAIsHx5smzNDzn9+pkdc4zZH/5Q7tahUAQehBWBB0jBgJ4AUHxBZWvrrGN2xRVmixaVu3UoFgIPworAg5pFr2kAUFqUrdUWAg/CisCDmkBpGgD0DcrWaheBB2FF4EFVyrU0bc/WPSlNA4AioGwNBB6EFYEHFY/SNAAoH8rW4CPwIKwIPKgo+Q7o6ZemffX1V+VuOgBUDcrWEITAg7Ai8CDU8ilNu2DuBfbYG49RmgYAJULZGrIh8CCsCDwIjVxL00bdOorSNADoQ5StIRcEHoQVgQdlkVqadvpTp1OaBgAhQ9ka8kXgQVgReNAnKE0DgMoQVLa27rqUraFnBB6EFYEHJfFx+8c5laad9PhJlKYBQAhQtoZCEXgQVgQelET80zilaQAQcpStoZgIPAgrAg9KIpFI2GXzL6M0DQBCiLI1lAKBB2FF4AEAoEZQtoZSIvAgrAg8AABUMcrW0FcIPAgrAg8AAFWIsjX0NQIPwqo9EolYNBrtnGKxWLm3FwAA0EuUraEvxWKxzmNISSZpWbkPboF0XOEBAKDCUbaGMOAKD8KKwAMAQIWibA1hQuBBWBF4AACoMJStIYwIPAgrAg8AABWAsjWEHYEHYUXgAQAgxChbQ6Ug8CCsCDwAAIQQZWuoNAQehBWBBwCAkKBsDZWMwIOwIvAAAFBmlK2hGhB4EFYEHgAAyoSyNVQTAg/CisADAEAfomwN1YrAg7Ai8AAA0Af8srWhQylbQ3Ui8CCsCDwAAJTQq6+anXACZWuofgQehBWBBwCAIqNsDbWIwIOwIvAAAFAklK2hlhF4UEqHSpoj6VlJv5f0c0nr5vhaAg8AAAWibA0g8KC0Fkva0fv/BkmPSHolx9cSeAAA6AXK1oCuCDwopR+m/Xt/SQlJQ3J4LYEHAIA8ULYGBCPwoC99W25l65/Dcwk8AADkgLI1IDsCD/rSI5KuzvG5BB4AADLIVLZ29NGUrQHpCDzoK9+S9IKkSI7PJ/AAAJCGsjUgfwSe2rWLpL/L3VPTk1GSnpT0oaRFkuZJGpf2nEmS2rxpXtq88ZJelLRqHu0j8AAA4KFsDeg9Ak/tGSjpZkkfSPpCUkcPzx8j19vaXXL33jRKmi5phaQpOfy9vSU9r2TY2cBrQ08IPACAmkZva0BxEHhqz4OS7pS0mqS4sgeeekl/k/RfSf3S5v1F7mrPylleP1muG+r1vOetLGmGpLE5tJPAAwCoSZStAcVF4Kk966X8f1zZA884uZK32wPmXejNOzbDa1eStMR7/0TK1CFpjxzaSeABANQUytaA0iDw1La4sgeey+VCyqkB8/bx5t1b/GZJIvAAAGoAZWtA6RF4altc2QPPQ3Kh5sCAeaO8ea8Uv1mSCDwAgCpG2RrQdwg8tS2u7IHnGblQMzFg3khv3lvFb5YkAg8AoApRtgb0PQJPbYsrxIEnEolYNBrNOMVisXJvPwAA9IiyNaD0YrFYxmNGSSZpWYmOWRFycVHSBgBASVC2BoQDV3hqW1zZA89lytxpwWTRaQEAAN1QtgaEC4GntsWVW7fUdwTMu8ib11zsRnkIPACAikHZGhBeBJ7aFlf2wFMnN/Doh5Ka0h5/1Xt8pRK1jcADAAg9ytaA8CPw1La4sgceSRotabGkuyVFJTVKmi5puaQpJWwbgQcAEFqUrQGVg8BTeyZLWuJNHd7k/zvoXh3JdVDwhKRF3jRP0tgSt5PAAwAIFcrWgMpE4EFYEXgAAKFA2RpQ2Qg8CCsCDwCgrChbA6oDgQdh1W3gUQYaBQCUGmVrQHVIHYhUDDyKkOIKDwCgz1C2BlQvrvAgrAg8AICSo2wNqH4EHoQVgQcAUBKUrQG1hcCDsCLwAACKirI1oDYReBBWBB4AQFFQtgbUNgIPworAAwDoNcrWAPgIPAgrAg8AIG+UrQFIR+BBWBF4AAA5o2wNQCYEHoQVgQcAkBVlawByQeBBWBF4AACB/LK1YcMoWwPQMwIPworAAwDo4rXXzE48kbI1APkh8CCs2iORiEWj0c4pFouVe3sBAPQxytYA9EYsFus8hpRkkpaV++AWSMcVHgCoYZStASgWrvAgrAg8AFCDKFtDKH32mdkpp5i98kq5W4JeIPAgrAg8AFAjKFtDqM2a5S4vSma77Vbu1qAXCDwIKwIPAFQ5ytYQav/9r9m3vpVcOQ84wOz998vdKvQCgQdhReABgCpF2RpCLZEwu/tus0GD3Mq55ppmDz3kHkdFIvAgrAg8AFBFKFtDRXj7bbPx45Mr6bHHukuRqGgEHoQVgQcAqgBla6gIy5ebXXdd8rLjhhuazZ1b7lahSAg8CCsCDwBUMMrWUDH+/GezHXZwK2l9vdk555h99VW5W4UiIvAgrAg8AFBhKFtDRVmyxOwHPzBraHAr6zbbmP3+9+VuFUqAwIOwIvAAQIWgbA0VZ8ECs002cStrU5PZjBlceqxiBB6EFYEHAEKOsjVUnM8+Mzv55OQKu/vuZm++We5WocQIPAgrAg8AhBBla6hYqQOIDhxodtttZh0d5W4V+gCBB2FF4AGAEKFsrfrFYu5qnT9tssltNnhwgzU0yAYPbrBNNrmty/xYrNwtzhEDiNY8Ag/CisADACFA2VrtaDthgrVHZO0R2RMjZdGoTEpO0ah73H9O2wkTyt3k7BhAFB4CD8KKwAMAZULZWm06/IAx1tQka2rqGnTSJ/85hx8wptxNzuxf/2IAUXQi8CCsCDwA0Mc++cTsmmsoW6tln3/+ucUuPzVj2GloULmbmN3y5WbXXssAouiCwIOwao9EIhaNRjunWMUUCwNAZaFsDQsX/tN22n5k1is76ixtC+l+OX0A0WnTGEC0hsVisc511Vt3l5XpmBbIiCs8AFBCmcrWjj6asRdryYsvPm377LWF1de7MFNfLxs3Tvb9s+oC7+GZeUODtc1usrbZTfaX1mnlbr7DAKLoAVd4EFYEHgAoAXpbw4oVK+wX995o220xpDPMDBggO3iPteycY8+1YT+dY6NueMau3+QsW3NwvTU0yNYcXG/Xb3KWbXvDHIvOnm3R2bMt1tpa7o/CAKLICYEHYUXgAYAiomwNX375hc245CRbf+3+nUFnzSGy4yZuZqd+92rb67SzbKULBlvDJVGLtERt/8MOsIcHP2tzG+bZw4Oftf0PO8CaLo9aNOam2IIylrQxgCjyQOBBWBF4AKBA9LYGM7P33nvLTjlqvK2ycn1n0NlsZIOdNmWsHfW9H9mYqdfY6kMW2/nnmy1cmHzdSy+Z1dW59WaDDdy/Q4EBRJEnAg/CisADAL1E2RrMzF5+cbbtt+em1tjoQk5dnWy37Vay0w4/wg4+8yrbYuef2bbbJezuu80WLw5+jwED3BQKDCCKXiLwIKwIPACQJ8rW0NHRYb+461rbYcvBnVdz+veX7b/rOnbSydNsn+9eYkM3fN4OO8xdselpDM5QBB4GEEWBCDwIKwIPAOSAsjWYmX311Rd25fnH2tB1mzqDzhqDZYdN2NKOPHu6TTziIhs+/F1raTH7z3+C3yMWc2E516lPeqVmAFEUAYEHYUXgAYAsKFuDmdl7C9+0735nrK06sK4z6Iwc3mhHTx1v35o23faYeKXttssSu/9+s6+/Lndr88AAoigiAg/CisADAAEoW4OZ2cvP/soOGDvSIo3JcXLGbL2KHXnMkTblrItsx23vseOOTdgf/1julvYCA4iiyAg8CCsCDwB4KFuDmXd/zswrbPRWq3eGnH79ZHt/Y1079Hvn2JQTzrGdtn7Rrr7a7H//K3dre4EBRFEiBB6EFYEHQM2jbA1mZl998aldecYRtuH6/TqDzqDV6uzAidvYQedeYvtPPdcm7/lvmzXLbMWKcre2l+bPZwBRlAyBB2FF4AFQsyhbg5nZu6//1b570C42aLXk/TnD14/Y1IMn2EHnXmj77BGz009Zaq+/Xu6WFuCzz8xOOokBRFFSBB6EFYEHQE2hbA2+lx550A7cY0Pr1y95f852Wwy0KccfaYecepbt+417bOaPE/b55+VuaYEYQBR9hMCDsCLwAKgJftna0KGUrdWyFcuW289nXGQ7b7NaZ8iJRGR7jFnf9j37DDvs8NPsyMm/sTlzqiATpA8gOmUKA4iipAg8CCsCD4Cq9uqrZiecQNlarfv8P4tsxkmH2Mjhkc6gs+rAOpu497Z24Lnn2JHfPNN+cNr79vbb5W5pETCAKMqEwIOwIvAAqDqUrcH39ou/s9Om7GSDV0/en7P+OhGb+O2JduiZp9sx4y6zO29Zau3t5W5pkTCAKMqIwIOwIvAAqBqUrcHMLNGRsOfv/KkdtMdQ698/eX/OVpuuauNPPtyajz3Rztj3Z/b8gkT1XPRgAFGEAIEHYUXgAVDxKFuDmdnXny62+34wzXbfbqDV1bmQ09goGzNmqO191sl2ytTjbPqpv6m+21gYQBQhQeBBWBF4AFQkytbg++Sv79qMY/a3TTdq7Lyas8rKdbbrpG1t6pkn21mTvmv33/KBLV1a7pYWGQOIImQIPAgrAg+AikLZGsxc2dobj7TZ6ftta2sNSd6fs/aa/WyX70y0Y0841i7e71L7/YtLyt3U0mAAUYQQgQdhReABUBEoW4OZ2bLPl9m8a26yQ8auZwMGJO/P2WTjVW3Xk75tZ3z7CJt5wj320aJquTknDQOIIsQIPAgrAg+A0Fq+3OyRRyhbg9lXb31q955xuo3dcWWrr3chp75ets3oYTbp1GPsov0Ot1k3vGzLl5e7pSXEAKIIOQIPworAAyB0KFuDmVkikbD/PP26zThskm25afL+nAHROtvum9vZ4SceZVfve4L9dd4H5W5qaTGAKCoEgQdhReABEBqUrcHMbMVXK+y1m5+w7+27pa2zdrJsbY01+tn2h423Mw871O484hL79L9Ven+OL5Ewu+sus9VWYwBRVAQCD8KKwAOgrChbq13vvzDH2n65prXNbbC2X65p78x63Ob+4Br7zl5r20orJYPO8I1Ws52PO9AuPWCqzb70HutYUQMH/P/6l9leezGAKCoKgQdhReABUBaUrdWe1glxm60FNlsLbM6WN9nMa/vZkCGyhgbZoEGyHXZw/y/v/pzttxhm+x53iP1on4PszVm/LXfz+0b6AKIjRjCAKCoGgQdhReAB0KcoW6tdN/5iT5s9r8lmz2uymTNl0WjyKo4/9esnm3pQnd19X8Suu21n+/yfNXSvStAAou3t5W4VkDMCD8KqPRKJWDQa7ZxisVi5txcAVYayNZiZxWIxi/aPWlNjpFvQSZ2aGhtra3/EAKKoYLFYrPMY0tuGl5XtqBbIgCs8AEqGsjX4Pv/z/+zRM86xA8etav37Zw47DQ0qd1P7VvoAotOnc6kTFYsrPAgrAg+AoqNsDWZmiY6Evf+r1+yGw/ex0ds1Zr2qkzo1NbnptIN2K/dHKB0GEEUVIvAgrAg8AIqCsjX4ln+53P78k4ftzP03tw3WTwaZgQMbbfihe1rj/b+whhtvtIampq5Xd5qarOHHMy26YIFFFyywWDxe7o9SGgwgiipF4EFYEXgAFISyNfgWx7+0J39wsR2852AbMCAZZNYbNtA2O+lbduWU/ezTaJO1N8raI7KZIza0hsGDTQ0N1jB4sM0csaG1R9Q5tZ0wodwfqbgYQBRVjsCDsCLwAOgVytbg+/jFt+0nx021nbfvZ3V1yW6lNx4zwnY4+xS7fvIke+7cq216yzKLRi3nqWr6LEgkzO6+22zQIAYQRVUj8CCsCDwAcrZ8udmjj1K2BrOO5R32xr2zbdqB29qwDeo6r+asvHKDbTx1V5v8/TPtJ+Mn2O9mPljbB/b/+pfZ+PEMIIqaQOBBWBF4APSIsjX4vv5kic2dfrUdOn4tW3nlZNnauuuvbMNOO9xOOO1Uu3XsJPvrY78pd1PLiwFEUYMIPAgrAg+AjChbg+/z1/9jt516uO22Y3+rr0+WrW26w1Bbv+Vcu+CYI+22vQ62ha9UaUcD+WAAUdQoAg/CisADoAvK1uBLJBL29pMv2A8OGW0jhifL1lYaUG9b7DvGNr72Kpt+0BT7f/ueYosWflHu5pYfA4iixhF4EFYEHgBmlixbGzaMsrVat3zxcnv+JzfZ4RPXs4GrpJStrTvARpzyHdvtqivtur0n2P87Yrp98emKcjc3HBYs6DqA6IwZXAZFzSHwIKwIPECNe+01sxNPpGwNZu0ffGI/nXa8jd1pQGfZWl2dbPNt1rM1YhfaIT/8oV2/+zi76+xf2JLFNdwRQarPPjM7+WQGEAWMwIPwIvAANYiyNaR6/zd/skuO2N02HlHfeTVnQLTORk3cwQbeeYud9r3T7OpvTLCfX/OSLV9e7taGCAOIAl0QeBBWBB6ghgSVra2zDmVrtahjRYf97p677ZjJw2211ZJla+us1d82P/YgW+uen9lFRx9uV+w0xWb97J2a7lm6GwYQBQIReBBWBB6gBlC2Bt/Sz76w+1vOsL3GrGINDcmgs8WWa9u6l55nW9x5p82Yuq/9cOfjre3pzwk6qRhAFMiKwIOwIvAAVYqyNaT66PU37IrjJtpmGzd0hpxo/zrbfuw2NuCu2238ddfZ1XvvaeeNb7E/vEJHBN28/TYDiAI9IPCglKZIekrSHEm/kzRf0o45vpbAA1QZytaQ6s+PP2zH77exDRqUvJqz1hpNtu139rOGWY/Z4RdeaFfuupudM7WVe+2DLF9udt11DCAK5IDAg1KaLWlqyr+vkrQox9cSeIAqQdkafMu/XmqPXPsD23uX1ayxMRl0Nt90iG10/lm28q9+Zd879RRr2X6sTTtuvr33XrlbHFIMIArkhcCDUtpeUl3Kv/eTlJC0ag6vJfAAFYyyNaT67IN37apT97MtN23sDDn9m+psp122sEG33GzrP/igXXzkoTZtm33th9Peso8/LneLQ4oBRIFeIfCgr6wi6TFJrTk+n8ADVCDK1pDqjRdm2ylTt7Q1Biev5qw5OGJjpu5tDY89ZtvefrtdOWWSnbjl0Xbl9P+zzz8vd4tDjAFEgV4j8KAvPCjpC++/q+T4GgIPUEEoW4Ovo2O5PfX/YrbP7mtYJJJStjZykG39vVNNzz5r37zqKrt6713t21tfaDffvNyWLCl3q0OMAUSBghF40FcaJV0j6S1JA3N4PoEHCLkVKyhbQ9JXn35oN047xLbZvF9nyOnXT/aN0ZvYetfdaJFnnrGjzz/Ppu82xibveIfdd1+CwUJ7wgCiQFEQeGrXLpL+LndPTU9GSXpS0odynQ7MkzQu7TmTJLV507wM71MvabGk7+bwNwk8QEhRtoZUC199wU4/dDtbc0hdZ9BZY1Cj7b7PnhZ94Je26hNP2NmnnGAX7bCLTdj9GXv8cY7Ze8QAokBREXhqz0BJN0v6QK7MrKOH54+RCyl3Seovd6VmuqQVct1OZ3NxwGP/J2laDu0k8AAhk6ls7f77KVurNYlEh829/0Y7YM+1rakppWxtw1Vt9HHHm+Y+a8Puv98uPfxgO23rifbNSX+3+fMZB7NHDCAKlASBp/Y8KOlOSatJiit74KmX9DdJ/5XUL23eX+Su9qyc5fWLJW2d8u9mSUslbZVDOwk8QAhkKls7+mg6h6pFS5d8ajddfLRtt2X/zpATaZTtst0I2/jyq01tbbbjrbfalVPG26GbfcemfusjyhtzxQCiQMkQeGrPein/H1f2wDNOruTt9oB5F3rzjs3y+jMkvSRX4vaipOclTcyxnQQeoIw++cTsmmsoW4Pz77f/YGcf/Q1be61k2drg1Rpsr/G72qDWB01tbbZ/LGZXTRxte21yjh3dvMTeeKPcra4Q6QOIbrghA4gCRUbgqW1xZQ88l8uFmlMD5u3jzbu3+M2SROAByoKytdo165YbbMjgemtokA0ZXG+zbr7Bnv/1HXbQhPWtf/9k2dpmw1axsYcdZQ1PzbGmOXPsuGnn2OW7b2ejNv6JnXFmB4OF5uMvfzHbcceuA4h+9VW5WwVUHQJPbYsre+B5SC7UHBgwb5Q375XiN0sSgQfoM5St1abWCXGbrQU2Wwvshg2/b9FoMtRIsvr65P83NsrGbj7UtjvvCtO8Nlt91iybduKxdsHoHWzEyF/ZD39o9tFH5f5EFYQBRIE+ReCpbXFlDzzPyIWaoDK0kd68t4rfLEkEHqDkKFurbQcdv5E1NalLpwNBU0OD61561PE72Eb33WeXfWeKnbT9HrbBxn+0q682BgvNFwOIVqREImG3vP++ffz11+VuCnqBwFPb4gpx4IlEIhaNRjNOsVis3NsPUJEoW4OZWay11aKzgfhxJgAAIABJREFUf23fuOFsq6vLHHbU1mZ1zz1nB1x6kR209UE2bPN/2y23GIOF5osBRCvWso4OO/6NN0xtbTbhL3+xBL3mhVIsFst4zOj9pi0r0TErQi4uStqAmuCXre25Z9eytaOOopKmFi1d+qkdMHH7jEEndYrU11l9XaMNWfuHdt99xmChvcEAohXr02XLbPyf/2xqa7PBL7xgL3z6abmbhF7gCk9tiyt74LlMmTstmCw6LQBCj7I1pHr37ZftrGNGd+ltbdAg2TcnqUvHBJIsGpXdOLPB5syJ2rx5UVu4kKvqefvwQ7NDDmEA0Qr1zuLFtvnvfmdqa7ONf/tbe6u9vdxNQi8ReGpbXLl1S31HwLyLvHnNxW6Uh8ADFICyNfgSiQ579tEb7IC90gYJ3WAV+8HwI+zxyByb3dRmN4w8u0svbTeMPNue7jfPFkQX2ILoAovH4uX+KJWDAUQr3suffWZDXnzR1NZme/zpT/Y/fjgrGoGntsWVPfDUyQ08+qGkprTHX/UeX6lEbSPwAHnKVLZGb2u1qb19kf3okkNt1BZNXQYJHbfNcBt9dszqnmszzZtn9c8+a43PPGOaN8/U1pac5s2zfnOetuiCBRZdsMBicQJPThhAtOI9tGiR9V+wwNTWZke9/rotpfyw4hF4altc2QOPJI2WtFjS3ZKikholTZe0XNKUEraNwAPkiLI1pPrX356z0w4fZUPWSF7NWWO1Rttn7G623i1ukNDBs2bZ908+yf6xztq2uK7RlihiL265pW3wwAPWOHeubfDAA/billvaEkXcZcJo1IyOYrJjANGKl0gk7Mp4vDP0X7ZwIR0UVAkCT+2ZLGmJN3V4k//voHt1JNdBwROSFnnTPEljS9xOAg/QA8rW4OvoWGZP3HeZTR67hkUiyaCz1fDVbMK3j7N+Tzxjamuz0bfcYtMP3ttOGL2jrbzPUVZ/SX9rujxqTZdHbedjGu3dgbJl9bJ3B8p2PqbRmi6PWjTmptgCAk9GDCBa8VJ7Yus3f779/MMPy90kFBGBB2FF4AECULaGVF989p5dfd7+tsWmjZ0hp19ENm7bjW2bC683zWuz/k8/bcecd57FJm5lu293lK2z6bt2xRVmH3zQ9b1eesmsrs6tUxts4P6NHixZYnbhhWaNjQwgWsHoia36EXgQVgQeIAVla0j199/PshMP2cxWXz15NWet1SM2aa+9bMhdj5ja2mzEfffZxUcdYufstpmts/lNts8BS+3JJ11ozmTAADchBwwgWhXoia02EHgQVt0GHmWgUdQiytbgW758sT1yx3k2YddVrbExGXS2GTHYdj/yNGuY/azVPfec7Td9us3Y7xs2dae9bO2tfmuXXGL23nu5/Q0CTw4YQLRq0BNbdUsdiFQMPIqQ4goPahZla0j1yUdv2hVnTbRNRjZ0hpz+TXU2bvstbNOWmzo7ITjzuyfZpXuOtM23nWbj9/vEZs3Kf5BQAk8PGEC0atATW23hCg/CisCDmkPZGnyJRML+/Py91jx1hK26avJqzrprNNn4vfexgffNMrW12Zibb3adEOy8ja2//UN2wYUrbOHC3P5GLJbsgC2XqaYvsjOAaNWgJ7baROBBWBF4UDMoW4Nv2bLP7RczT7NxO69k9fXJoLPdyLVszHHnWN0zz3V2QjBj7y1s9x2/bbvs+0/75S9ZV0qCAUSrCj2x1S4CD8KKwIOqRtkaUi3695/sklN3txEb1neGnAH962zsTtva8KtuN7W12UZeJwTTdh9hG4251s4+r93eeqvcLa9iDCBaVeiJrbYReBBWBB5UJcrW4EskOuy3c261w/df31ZZJXk1Z4M1ozZ2n4Ms+tCTnZ0QXLnfaJu68262037z7f77E7Z0ablbX8UYQLTq0BMbCDwIKwIPqgpla/AtXfI/u/vqZtt1p/5WV+dCTl2dbPtN1rNtv3uh6dl5NnjWLDvrlBPt0j03tO12O81OOuc/dATWF9IHED3nHAYQrXD0xAYzAg/Ci8CDikfZGlK9//aLdv4JO9mwoXWdV3NWHlBvu39jJ1vnxtbOTghmHDTRTthtc9t+/59Z673LbMmScre8BjCAaFWiJzb4CDwIKwIPKhZla/B1dCyzBbOutUMmr2UrrZQsWxu21kq265TDrN9jT1n/p5+25vPOtRl7b2Z77X6AHXHWa/a3v5W75TWEAUSrDj2xIR2BB2FF4EHFoWwNvvYvP7DbLjvExuzQrzPk1NfJdtxsmG1x1uWm5+YlOyEYO8x2POByu/muz4xbC/rQZ5+ZnXIKA4hWGXpiQxACD8KKwIOKQNkaUr3z19n2/eatbf31kldzBq7UYLvuupsNvuXnVvfcc7Z/LGZX7reTHbLHaDvw9F/bn/5MmU2fYwDRqkRPbMiEwIOwIvAg1Chbg2/FiiU29/5Lbeqk1a1//2TQGbHuQBv9rWOt4ck5nZ0QtOw11PbY7wS76ta4fflluVtegxhAtGrRExuyIfAgrNojkYhFo9HOKVbTw3wjLChbg++LT9+2H1+4r20/qrEz5DQ0yHbccqSNPP9q07x5nZ0QnDRupH3zlNvspd/RA0FZMIBoVaMnNgSJxWKdx5Deb/Sy8h7aAt1xhQehQdkafIlEwt78/cN2+hGb2NprJ6/mDFolYruM3csG3v1AshOCSZvaPvtMsvN//Fv7/PNyt7yGMYBoVXuYntiQA67wIKwIPCg7ytbgW778C3virnNsvwkDrakpGXQ2Xn+QbX/YKVY3+xnb6L777JIjD7Fz91rPJh3/A5v9/EdcQCin9AFER4xgANEqQk9syAeBB2FF4EHZULZW3WIx992mTv73nP74Zhv+1c49Zrxts1V9Z8iJNMp23GZz2+DSH3V2QnDVfjvZ4RNH2SnX/NL+98mKcn9EpA8gOm2a0QVe9aAnNuSLwIOwIvCgT1G2Vrvef2GOPfvgmjZvboO1/XJNe//52fbX539qJ39nuA0ZkryaM3hgPxuz12SL3veIrfHYY3b2ySdYy/j1bd+jjrBfzHmTqzlhwACiVY+e2NAbBB6EFYEHfYKytdr2/gtz7CfX9rMhQ1yHA4MGyUaPlkUiyaCz6dAhtlXzWaZn5to3brrJZhw00U6ZMNSOvOxGi/+HrtZCgwFEqx49saG3CDwIKwIPSoqytdr16I0H2bzZTTZvdpPNnCmLRpPhxp8aG2X7fLPObr+p0a6/vtmazzvXrpy0iR186Fj70WNt1tHB5ZzQSB9AdI89GEC0CtETGwpB4EFYEXhQEm++SdlarZtw0EGmpiarizR2CzpdpqYmU1OTjdplBzv0vLPt1XcYryV0GEC0JtATGwpF4EFYEXhQEv/7n1n//pSt1aoVKxbb4z8717655ypdytbSp4YG2TEHbGYX33uvLVn2dbmbjXQMIFoT6IkNxULgQVgReFAyv/sdZWu15ssv3rXrL9rPtt6iIftVnZSpqclN06ZNKHfz4WMA0ZpBT2woJgIPworAA6Bg7/zzWTv9qK1szZTe1oas2s+OX38fezDymF0fmWnR+qYuQSda32Q3NM60BdEFtiC6wOKxeLk/Bsy6DyB63HEMIFql6IkNxUbgQVgReAD0SiLRYfOeut4OmLCm9euXDDJbDF3ddj30dGuY/azVP/ecNT4zx+rnPm2aOdO05pqmhgb335kzrW7uHIsuWGDRBQssFifwlFXQAKLPPlvuVqFE6IkNpUDgQVgReADkZenSz+zW65ptu62TV2wijbLdttrYtv3+9Vb37HM29bIWu3v3LW1pY72t6N/fljU12bytt7QNHnjAGufOtQ0eeMDmbb2lLWtqSo4+GouV+6PVLgYQrSn0xIZSIfAgrAg8AHLywQev2rTv7mprr1WXHCR01QabvOtYG/bjh2zAr39tx33/e3b2lI3tnJsvsEVfftTl9bc99ZLp++uaLm60hv/P3t3HN1Xf//9/vU8vUyqj2JZI2yEIXkCpE/wUEL7Ax4sPMC8/KHSDrYB8KBezoEUQmAPUwGRjMhkgReYYQ7lwAoLFgpRQRxiogE7n2JgS+QlltAiyVaDFPn9/tISmSdO05CTnJM/77XZuN5JzQt5l6PrwpK/3kxlYXuQI0VdCLtxANOJwEhvpicFDRsXgISKf9u19FY/cez3i4698bO2WjGtw/72j0GbddrR74w08Nn4kxudkYnnJ73HxUu20NVupDRabxe2Qn9YeDZ+32CywlfIOT1BxA9GI0nAS27OcxEY6YPCQUTF4iMhDdfVFrP7tNGTfdo3bJqEDu2Zg0OhnEfe2HV1/9zs8NnoIJjx2N/70+V6/ft+EhNqDQsjbBqJ//3uoV0U64iQ2ChYGDxlVZUxMDCwWi+uw8XP0RBGrouIL/Gz6fUhrf2WsdFJrDf/7Xz1xx8zVUCV2DHxhISb9qC9m/Hoivvy6eXuyMHhCjBuIRhxOYiO92Ww21/eQdf+/URXab22JPPEODxHhL3/ZiZGPZCIh4crH1rqkx+NHdz6EG3+zDdHvvIOH5sxEXm4vvPT2i/im6psmf0+b7co8An8O/rcWHXED0YjESWwUbLzDQ0bF4CGKUN9++y3++Pov0K9XCpSqjRxNEwzomorcoU8iZe0utN66FT94cjwm/uQuvPP3HfzMv9k03EC0XTvg9de5gWgE4CQ2CgUGDxkVg4cownz99WnMt+WiQ8aVsdKtr1EYfvstGDbuJbTaasd3163FiMnDMWPBj/HP0/8M9ZKpJT77DLj7bm4gGoE4iY1ChcFDRsXgIYoQhw9/gLGj+iCx1ZWx0p0yYjDh/92Fu2ZtQtQ7dnyv8CWM/Ml9+M3GZ/H1ha9DvWRqCW4gGrE4iY1CjcFDRsXgIQpjNTU1KCr6Hf67/3fdPrbWL/M7eHzQ/yFr0U7ILjsG/tKGR5+4D1sP/RHf1vC/BpsWNxCNWJzERkbA4CGjYvAQhaH//Oc/WLRoCjp3THTdzUlMFAzP7ojJw55H+mo74rZvx+BnpuLxeT/Cxyf/Euol09VouIHorbdyA9EIwklsZBQMHmNoH+oFGBCDhyiMHD36D0yaOAitr9FcodMhQ0N+v14Y/X+vovVmO67dtAn3/3QcFqyZgYrKilAvma4WNxCNaK5JbN/5DlR8PCwJCUjgwUOnIzk52effx4QEBo8RFNX7da8G5/5HRO4I4lqMgsFDZHI1NTUoKdmMwf9zM6K02shRSnDHrQmYfecjuG9aMWK229Fpze/xyE9z8dqffouqS/yG2PS4gWjEqz+JTYuPh4iE/BtiHuF7XP775UvddQyeENtW79ebGpyLEpEVQVyLUTB4iEzq/PnzWL78WdxyU5Lrbk6rVoJH+loxb8g0ZD+/C7LLju+99GuMmf9/2HfUEeolU6BwA9GI13AS2+VvSon04s/fMQaPMbwjIkl1v24YPCIiC4O4FqNg8BCZzJdf/n8oKBiOpDYxrtDJSFPI/+9M/GzoMtzwsh3azp24Y+EcTF/5JI6fOx7qJVOgcAPRiNfYJDYGD+mNwWMeP5Da6LlOPIPHIiIbgr6i0GPwEJnEn/5Uggcf7ImoKHGFTvb3YmC76x5MGrURyRvsaFVUhLuefxKLixfjfPX5UC+ZAoUbiBJ8T2Jj8JDeGDzmskREvhGRYyIyTURGichsEfm7iIwL4bpChcFDZGAXL17EK6+8iKzuVlfkxMcLHhrwHSy9+//wSP4OxG+zI/WNDXhw4RMo+vht7rsRbriBKKHpSWwMHtIbg8d8RonIpyJSU3ccE5HxIV1R6DB4iAzo5MmTmDVrHJKvjXeFznXXCSYN6oTl98xD/7m7oO20o9PqlRhTOBOHTx0J9ZIp0LiBKNVxTWKz29Fl3z4c8bK3EoOH9MbgMa82IpIiIirUCwkhBg+Rgbz33j4MH94fMTHKFTo9btXw3L298fy9q9B1Se3n9m9dvhAz3/gVzl04F+olkx64gSjVqT+Jrf/Bg6hoZOQ4g4f0xuAhM2PwEIVYVVUVXnvtd7j99utdkRMXJ7jvLgtWDB6OJ3K2oP0aO2J27EDfZc+hcO8GfFvDiVxhydsGoh98EOpVUYg0nMR2wcckPgZP40aOHIkZM2aEehmmx+AhM2PwEIVIeXk5nnlmKqzWRFfotGsnyHugHdbeORUj8nYicYsdrbe+iXtXPIfdzgOhXjLpiRuIUp3GJrH5wuDx7tNPP4VSCq1atUJ5eXmT1xcXF6Nbt25BWJn5MHjIzBg8RDpyOBxIT09HdHQ00tPT4XA48OGHH+LHP74XcXGaK3SysgTPPJKJV/svwv/MsCN6hx3tXn8NuWt/hS/O/SvUXwbpiRuIUj2+JrH5wuDxbvTo0cjPz4dSyq+7POPGjUPfvn2DsDLzYfCQmTF4iALIZqv9GXOLBYiNdSBas7iiRkSg1JWfzYmJEQy5OxovDx2Mxf/9GnostEN22dF5zQrM2vkHXLjE/7of9hpuIFpYyA1EI1hTk9h8YfB4OnLkCMaPH4/KykokJyejdevWONPEn2nXrl3x1FNPBWmF5sLgITNj8BAF0E+G9kNcXO3P4Ig0fkRFCWJjBT/o+z1c/4odqqQEPX+/GL/7eHeovwQKBm4gSg34M4nNFwaPp7y8PHz22WcAgPnz50MphTlz5jR6/ZkzZ6BpGrZs2RKkFZoLg4fMjMFDFEC2UhssNgviHouDUo3HzpgflyDpDTviit/G3WuXYO+/joZ66RQM3ECUvPB3EpsvDB53TqcTY8aMcT0+d+4ckpKSkJSUhHPn3Kdbbty4EYMHD0b37t2haRoGDx6MwYMH4/333w/2sg2NwUNmxuAhCpDq6mpk9eoFqfextUaPuDhIXBz6Pzw01MumYGm4geiYMdxAlJo1ic0XBo+7/Px8HD582O25uXPnQimFefPmeX3NzJkzkZmZGYzlmRKDh8yMwUN0lU6fPo3582cjLa2NK2iuvVZw7/cF8fHuoWOxCBYvFpSWWlBaaoHTaQv18klv3ECUvGjJJDZfWho8yclAQkJwjuTkFn95zXL8+HHk5uZ6PH/mzBm0bt0aycnJqPTykcF+/fphwoQJwViiKTF4yMwYPEQt9NFHH2HUqKGIj49yBU23boKfjc7Ajv4/xTu3LsLiX8YiNbX2Y2ypqYLFv4zF9lsXo9RSilJLKZw2Z6i/DNITNxAlL1o6ic0XBs8VBQUF+Pjjj72emzVrFpRSWLhwodvzFy9ehMViwZo1a4KxRFNi8JCZMXiImqG6uhqvv74Bd9zRzW3a2qB7FJbn9sGrA5bi+0/aEfd27SCC+1b+BjtfT8Wud6Kw8/VU3LfyN0hfv+7KKDcb7/CEJW4gSo24mklsvvAjbbVOnTqFESNGNHq+oqICiYmJsFqtOH/+vOt5h8MBpRScTv5HqMYweMjMGDxEfqioqMC8ebPR/rrvuEInOVkw9gcJeP2RH2Lu/X9E1yW1Y6WlpARSsh2ysxiya5frIytit9c+3lkM2fk2ZOfbuOF3q0L9pVGgcQNRakT9SWw3tmASmy8MnlozZ87EgQO+N2l+8sknoZTC4sWLXc8tWLAAGRkZrseHDx9GdXW1bus0IwYPmRmDh8iHDz/8ELm5/4u4uCsfW8vMFMwek46N987AyHHvoM1GOyxvF+HO15dj18mjbq9f/o8/IWr7HyEl7yBq+x+x/B9/Cs0XQvrjBqLkQ8NJbKcDHMEMHuCrr75CTk5Ok9eVlZXBYrEgPT3dFTVDhw7F8OHDXddMmjRJt3WaFYOHzIzBQ9RAdXU1NmxYjz59urp9bG3wPQqFo3tj2YPL0Pc5O7SddmRs+D1+smsdTl/4JtTLplDiBqLkQ6AmsfnC4AFmz56NAQMGYOzYsU0eaWlpUEqhsLAQAPDwww9j+vTpAIAVK1bAbreH8CsxJgYPmRmDh6hOeXk5nnvuZ54fW/uhBRt++AM88eONSF9tR+z2YvRe8yLWHHnvqqYqURjgBqLkQ6AnsfkS6cFz/vx5tG3bFpqmQSnV5KFpGjRNww033AAAOHDgAO68804UFBRg3bp1If5qjInBQ2ZWGRMTA4vF4jps/CFqijAHDx7Ej370EGJjr3xsLStLMOfRdKwZNgP3P7kD8dvsaPf6GozYshxH/10R6iVTqHEDUWqCHpPYfIn04CH9NfZ3zGazub6HrPv/0KrQfmtL5Il3eCgiVVVVYf36deiVfYvbx9aGDFIoHNsbC370Err9xo6oHe/ge6tewC8PvIVL314K9bLJCD77DLjrrit3dR59lBuIkhu9JrH5wuAhvfEOD5kZg4ciyqlTp/Dss0/DmtraFTopKYJxIy14NfcH+L+fvIGkN+y49o11uG/tL7Hv5D9CvWQyCm4gSn7QcxKbLwwe0huDh8yMwUMR4cCBAxg58kHExmhuH1ubOzYdK8bMwoDndiBqRwlueeUFTN31O/z7IjeHpHq4gSj5Qe9JbL4weEhvDB4yMwYPha2qqiqsXfsasm+/2RU5sbGC7w9WWD6uF342cRkyfm/Hd958AwN/+yzeOPInDiEgd9xAlPwUjElsvjB4SG8MHjIzBg+FnX/961+YO/enaJdy5WNr7doJ8n5kwaq8HOTMeAPx2+zo/MqvMXrTQhz7uizUSyYj4gai5IdgTmLzhcFDemPwkJkxeChsfPDBBxjxwwcQE33lY2vf+55g7rg0/PqxGbj1NzuQuHULspc9jRc/WI+qS/zmlbw4exYYP54biFKTgj2JzRcGD+mNwUNmxuAhU6uqqsJrr72K22+7yRU5cXGCe4coLJvYCwXTliLpDTuu//0yPPD7p/Fe2V9CvWQysoYbiC5fzg1EyatQTGLzhcFDemPwkJkxeMiUTp48iTlzZiL12mvcPrY2/scWrJjyAwz5+R/R6q1tuHXxT/HUO4tx5nxovxkhgysr4wai5LdQTWLzhcFDemPwkJkxeMhU3nvvPfzgkfvcPrZ2222CuePb4+fTZ6DTqu3IePW3GPCbJ7Dx78X4tob/dZ584Aai1EyhnMTmC4OH9MbgITNj8JDhXbx4Ea++ugY9ut/o9rG1++5V+M3kXsh7Zim+s3k7ui/+GX689mf47PRnoV4ymQE3EKVmCvUkNl8YPKQ3Bg+ZGYOHDOvkyZOY/fQMJCclun9sLdeCxdNz0G/J67hu3RpkL5iIRXt/i2+qvgn1kskMuIEoNZNRJrH5wuAhvTF4yMwYPGQ4+/fvx/CH7kV0VIOPrf2kPebMfQrt121D5pJncc+Sx7DnC4fhvvEgA+MGotRMRprE5guDh/TG4CEzY/CQIVy8eBGrV6/Gbbd0dkVOfLzg/nsVFj2ZjREvLIF1/Xr0nJ+H/I02nPy3Mb/pIIM6fx6YOROIirqygej774d6VWRwRpvE5guDxxj27NkT6iXohsFDZsbgoZAqKyvD0zOeQnKbKx9bu+46wYTR8Vg4ezi+9/vX0e2lBeg9PxerD77OvXOo+biBKLWAESex+cLgMYbJkyeHegm6YfCQUawXkaPNfA2Dh0Ji3759eOT7Q9w+ttazp2DO5Osw/RfT0eG19bh9/gTctzQffzn5caiXS2bEDUSphYw6ic0XBo++Tpw4gaFDh/q85pNPPsHLL7/selxUVIRFixZh6tSpGDFiBCoqKvRepq4YPGQEd4nIVyLyeTNfx+ChgLDZan8GvP5x+fvMy4/j4y8gPmYVUhNvcPvY2gP3K/xqVjYeLPwNbilchB6zH8HMt37NvXOo5biBKLWQkSex+cLg0c/SpUsxefJkXH/99T6vmzNnDs6ePQsAOHr0KF544QXXuSlTpuD+++/XdZ16Y/BQqMWIyH4RmSm8w0MGsXnZC0i5VkNUlODaJA2P3D0Q17Zu5Qqd9u0FE8bEY/78HNz6h1XIfj4fvZ77ITZ/vJ1751DLlZUBjzzCDUSp2cwwic0XBo++nE6nz+CpqanB448/7nr85ptvIj4+HhcuXAAAbNmyxfT/+zB4KNSmi8hUERklDB4ygM3LXoDFIq64qX/cfrtg9hPXIX/JNGSuXILbn/5f/O+yafhnxeehXjaZGTcQpatglklsvjB4Gjdy5EjMmDHjqn6Po0eP+gye3bt3Y+PGja7H1dXVOHjwoOvx0qVLcdttt13VGkKNwUOhlCa1d3eiRGS0MHgoRIbf1RVxcbUbgoqX0Ll8XL7mof++Gc+99VtUVhn7B4HJBLiBKF0FM01i84XB492nn34KpRRatWqF8vLyJq8vLi5Gt27dPJ5vKnimTZuGqkZ+1uvixYvIysqCw+Hwf+EGxOChUForIv9d9+vRwuChELHZbLBYLLBYLD6DR4uOQlx8HJ577rlQL5nMjhuI0lUy2yQ2Xxg83o0ePRr5+flQSvl1l2fcuHHo27evx/O+gufChQuYNm1ao7/nE088gaKiIv8XbVAMHmrMHSLyVxGp8ePaW0XkLRE5KSL/EpFdIjKwwTWDRMRe77hLaiezXTZaGDwUIk6nDaWlFpSWWpCS4j12UlLEdY3TaQv1ksnMDh0CevbkBqLUYmacxOYLg8fTkSNHMH78eFRWViI5ORmtW7fGmSbu4HXt2hVPPfWUx/O+gueNN95AaWmp13OLFy923dk5cuRIM78CY2HwUEOtRWSpiBwXkXMi8m0T1/cSkW9E5LciEi8i0SIyT0QuiciDPl73C6n9ONvlAPqbiJyv+/ULfq6VwUMB4bQ5UWopRamlFItueNzjZ3gsFsGiGx53XeO0OUO9ZDKjhhuIZmVxA1FqNrNOYvOFweMpLy8Pn332GQBg/vz5UEphzpw5jV5/5swZaJqGLVu2eJzzFTz1hxXUt2bNGmzduhVlZWUoKyvDggULmv9FGAiDhxpaLyIrRaSNiDjFd/BoIvKJiJSJSGyDcx9K7d2eRD/fd7TwDg+FiG3VKljefhuWt99GbHEx7pjxuGtKW8q1Gu6Y8Thii4td19hWrQr1kslsuIEoXSWzT2LzhcHjzul0YswXTUETAAAgAElEQVSYMa7H586dQ1JSEpKSknDu3Dm3azdu3IjBgweje/fu0DQNgwcPxuDBg/HBBx8AAF555RWMGDECFosFU6dOxa5du1yvPXPmjNeIcjgciI6OhlLKdXAsNYWbtHq/dorv4BkotR95W+Hl3Ky6c2P8eM/fi8h7IvJvEXldRO7z4zUiDB4KEFupDRabBRabBbHPxSLzqW5ov2Edot95B+03rEPmU90Q+1ys6xpbKT/SRn7ytoHo4cOhXhWZTDhMYvOFweMuPz8fhxv8e2Lu3LlQSmHevHleXzNz5kxkZmY2631efvllfPLJJy1ep5kweMgXp/gOnmelNmomeTn3/bpzfwj8slwYPKQLxzEHVEEG5GfRyHghA45j5p5OQyHCDUQpAMJlEpsvDJ4rjh8/jtzcXI/nz5w5g9atWyM5ORmVXn7mr1+/fpgwYUKz3mvy5MktXqfZMHjIF6f4Dp4NUhs1D3k5d2vdufcCvywXBg/pJiGh9iBqNm4gSgESTpPYfGlp8CT/IhkJ8xKCciT/IlmHr9xTQUEBPv74Y6/nZs2aBaUUFi5c6Pb8xYsXYbFYsGbNGr/fp7y8HC+++OJVrdVMGDzki1N8B88OqY2ae7yc61x37kjgl+XC4KGAsNlqpwP7e9j4iTbypuEGoqmpwIYN3ECUWiTcJrH5wuCpderUKYwYMaLR8xUVFUhMTITVasX58+ddzzscDiil4HRyoE5jGDzki1MMHjwxMTGu/VO8HTZ+Z0pEwfDPf7pvIDpmDDcQpRYLx0lsvvAjbbVmzpyJAwcO+LzmySefhFIKixcvdj23YMECZGRkuB4fPnwY1dXVuq3TjC7/Hau/717DQ2ons1bp+H0rGZRT+JE2IqLGVVcDv/yl+wai77wT6lWRSYXzJDZfGDzAV199hZycnCavKysrg8ViQXp6uitqhg4diuHDh7uumTRpkm7rNCve4SFfnOI7eJ6RxocWDBEOLSCicMYNRCmAwn0Smy8MHmD27NkYMGAAxo4d2+SRlpYGpRQKCwsBAA8//DCmT58OAFixYgXsdnsIvxJjYvCQL07xbyz1y17O/bTu3OhAL6oeBg8RBR83EKUAi4RJbL5EevCcP38ebdu2haZpbnvfNHZomgZN03DDDTcAAA4cOIA777wTBQUFWLduXYi/GmNi8JAvTvEdPEpqNx49KSJxDZ7/qO75VnotThg8RBRsu3dzA1EKqEiZxOZLpAcP6Y/BQ744xXfwiIhki8g3IvKKiFhEJFpE5olItYg8qOfihMFDRMHScAPR//f/uIEoXbVImsTmC4OH9MbgoYaGiMj5uuPbuuPyY28/qyNSO6Bgq4j8q+7YJSIDdF8pg4eIgoEbiJIOIm0Smy8MHtIbg4fMjMFDRPppuIHoAw9wA1G6apE6ic0XBg/pjcFDZsbgIaLA4waipJNInsTmC4OH9MbgITPz2HiUG40S0VXhBqKkk0ifxOYLg4f01tjfsfobkQo3HiWD4h0eIgqMhhuIduzIDUQpYDiJzTcGD+mNd3jIzBg8RHT1vG0g+p//hHpVFCY4ia1pDB7SG4OHzIzBQ0Qtxw1ESWecxOYfBg/pjcFDZsbgIaKWKS3lBqKkG05iax4GD+mNwUNmxuAhoubhBqKkM05iaz4GD+mNwUNmxuAhIv9xA1HSGSextQyDh/TG4CEzY/AQUdMabiD64IPcQJQCjpPYWo7BQ3pj8JCZMXiIqHHcQJSChJPYrg6Dh/TG4CEzY/AQkXeffcYNRCkoOInt6jF4jGHPnj2hXoJuGDxkZgweInJXXQ0sXMgNREl3NTU1eP6LLziJLQAYPMYwefLkUC9BNwweMjMGDxFdwQ1EKUg4iS2wGDz62bZtG/7whz/gpZdewujRo1FeXu71uk8++QQvv/yy63FRUREWLVqEqVOnYsSIEaioqAjWknXB4CEzq4yJiYHFYnEdNpstSP/oEJFhcANRCiJOYgs8Bo8+vv76a0RHR+Pzzz8HADz22GN49NFHvV47Z84cnD17FgBw9OhRvPDCC65zU6ZMwf3336//gnXU2N8xm83m+h5SRCAiVaH+5paoId7hIYp03ECUgoiT2PTB4NHPhx9+6Pr1T37yE+Tk5HhcU1NTg8cff9z1+M0330R8fDwuXLgAANiyZYvp//fhHR4yMwYPUaTiBqIUZJzEph8GT+NGjhyJGTNmXPXvU1VVhW7duuGjjz7yOLd7925s3LjR9bi6uhoHDx50PV66dCluu+22q15DKDF4yMwYPESRiBuIUpBt4CQ2XTF4vPv000+hlEKrVq0a/dmb+oqLi9GtWzeP57dv3478/Hy89NJLXl83bdo0VDUS8BcvXkRWVhYcDkfzFm8wDB4yMwYPUSQpKwOGDeMGohQ0NTU1+LnTyUlsOmPweDd69Gjk5+dDKeXXXZ5x48ahb9++jZ4fMWIEnn76abfnLly4gGnTpjX6mieeeAJFRUX+L9qgGDxkZgweokjADUQpBDiJLXgYPJ6OHDmC8ePHo7KyEsnJyWjdujXONDEgo2vXrnjqqadcjzds2IBOnTq5Hr/00kto1aqV22veeOMNlJaWev39Fi9e7Lqzc+TIkZZ+KYbA4CEzY/AQhTtuIEohwElswcXg8ZSXl4fPPvsMADB//nwopTBnzpxGrz9z5gw0TcOWLVtcz23YsAETJkxwPX7qqacwcOBAt9fVH1ZQ35o1a7B161aUlZWhrKwMCxYsuIqvJvQYPGRmDB6icMUNRClEOIkt+Bg87pxOJ8aMGeN6fO7cOSQlJSEpKQnnzp1zu3bjxo0YPHgwunfvDk3TMHjwYAwePBgffPABAODFF1/EokWL8Itf/AIjR45EWVmZ67VnzpzxGlEOhwPR0dFQSrmOcB1L3fAaYfCQATF4iMLRhx8Ct9/ODUQp6DiJLTQYPO7y8/NxuMHUyblz50IphXnz5nl9zcyZM5GZmdms93n55ZfxySeftHidZsLgITNj8BCFk/PngVmzgOhobiBKQcdJbKHD4Lni+PHjyM3N9Xj+zJkzaN26NZKTk1Hp5a5jv3793D6+5o/Jkye3eJ1mw+AhM2PwEIULbiBKIcJJbKHX4uBJTgYSEoJzJCcH/gv3oqCgAB9//LHXc7NmzYJSCgsXLnR7/uLFi7BYLFizZo3f71NeXo4XX3zxqtZqJgweMjMGD5HZcQNRCiFOYjMGBk+tU6dOYcSIEY2er6ioQGJiIqxWK86fP+963uFwQCkFp9Op+xrNisFDZsbgITIzbiBKIcRJbMbBj7TVmjlzJg4cOODzmieffBJKKSxevNj13IIFC5CRkeF6fPjwYVRXV+u2TjNi8JCZMXiIzOjkSfcNRB94gBuIUlBxEpuxMHiAr776Cjk5OU1eV1ZWBovFgvT0dFfUDB06FMOHD3ddM2nSJN3WaVYMHjIzBg+RmXADUTIATmIzHgYPMHv2bAwYMABjx45t8khLS4NSCoWFhQCAhx9+GNOnTwcArFixAna7PYRfiTExeMjMGDxEZsENRMkAOInNmCI9eM6fP4+2bdtC0zS3vW8aOzRNg6ZpuOGGGwAABw4cwJ133omCggKsW7cuxF+NMTF4yMwYPERGxw1EyQA4ic3YIj14SH8MHjKzypiYGFgsFtdhs9mC9I8OETWJG4iSAXASm/ExeEhvjf0ds9lsru8hRQQiUhXqb26JGuIdHiIj4gaiZBCcxGYODB7SG+/wkJkxeIiMhhuIkkFwEpt5MHhIbwweMjMGD5FRcANRMhBOYjMXBg/pjcFDZsbgITICbiBKBsJJbObD4CG9MXjIzBg8RKHEDUTJQDiJzbwYPKQ3Bg+ZGYOHKBS4gSgZDCexmRuDh/TG4CEzY/AQBRs3ECWD4SQ282PwkN4YPGRmDB6iYOEGomRAnMQWHhg8pDcGD5kZg4coGLiBKBkQJ7GFDwYP6Y3BQ2bG4CHSEzcQJYPiJLbwwuAhvTF4yMwYPER6abiB6Lx53ECUQo6T2MITg4f0xuAhM2PwEAXa2bPAhAncQJQMh5PYwheDh/TG4CEzY/AQBRI3ECWD4iS28MbgIb0xeMjMGDxEgcANRMnAOIkt/DF49FNUVIRFixZh6tSpGDFiBCoqKkK9pJBg8JCZMXiIrgY3ECWD4yS2yMDg0cfRo0fxwgsvuB5PmTIF999/fwhXFDoMHjIzBg9RS3EDUTI4TmKLHAwefbz55puIj4/HhQsXAABbtmyJ2D9nBg+ZGYOHqLm4gSgZHCexRR4GT+NGjhyJGTNmtOi11dXVOHjwoOvx0qVLcdtttwVqaabC4CEzY/AQNQc3ECWD4yS2yMTg8e7TTz+FUgqtWrVCeXl5k9cXFxejW7duXs9dvHgRWVlZcDgcgV6mKTB4yMwYPET+4AaiZAKcxBa5GDzejR49Gvn5+VBK+XWXZ9y4cejbt6/Xc0888QSKiooCvUTTYPCQmTF4iJrScAPR+fO5gSgZDiexRTYGj6cjR45g/PjxqKysRHJyMlq3bo0zTfxHgK5du+Kpp57yeH7x4sWuOztHjhzRZb1Gx+AhM2PwEDWGG4iSSXASGzF4POXl5eGzzz4DAMyfPx9KKcyZM6fR68+cOQNN07Blyxa359esWYOtW7eirKwMZWVlWLBggZ7LNiwGD5kZg4fIG24gSibBSWwEMHgacjqdGDNmjOvxuXPnkJSUhKSkJJw7d87t2o0bN2Lw4MHo3r07NE3D4MGDMXjwYLz//vtwOByIjo6GUsp1cCy172uEwUMGxOAhqq/hBqIPPsgNRMmQOImN6mPwuMvPz8fhBnfk586dC6UU5s2b5/U1M2fORGZmZjCWZ0oMHjIzBg8RwA1EyVQ4iY0aamnwJO/Zg4TS0qAcyXv26PCVezp+/Dhyc3M9nj9z5gxat26N5ORkVHr5Gbd+/fphwoQJwViiKTF4yMwYPETcQJRMhJPYyBsGzxUFBQX4+OOPvZ6bNWsWlFJYuHCh2/MXL16ExWLBmjVrgrFEU2LwkJkxeChycQNRMhlOYqPG8CNttU6dOoURI0Y0er6iogKJiYmwWq04f/6863mHwwGlFJxOZzCWaUoMHjIzBg9FJm4gSibDSWzkC4On1syZM3HgwAGf1zz55JNQSmHx4sWu5xYsWICMjAzX48OHD6O6ulq3dZoRg4fMrDImJgYWi8V12Gy2IP2jQxQC3ECUTIiT2KgpDB7gq6++Qk5OTpPXlZWVwWKxID093RU1Q4cOxfDhw13XTJo0Sbd1mlVjf8dsNpvre0gRgYhUhfqbW6KGeIeHIgc3ECWT4SQ28heDB5g9ezYGDBiAsWPHNnmkpaVBKYXCwkIAwMMPP4zp06cDAFasWAG73R7Cr8SYeIeHzIzBQ+Hv7Flg/HhuIEqmwkls1ByRHjznz59H27ZtoWma2545jR2apkHTNNxwww0AgAMHDuDOO+9EQUEB1q1bF+KvxpgYPGRmDB4Kb9xAlEyIk9iouSI9eEh/DB4yMwYPhSduIEomxUls1BIMHtIbg4fMjMFD4YUbiJKJcRIbtRSDh/TG4CEzY/BQ+OAGomRinMRGV4PBQ3pj8JCZMXjI/BpuINqpEzcQJdPgJDYKBAYP6Y3BQ2bG4CFz87aBKH/mgUyCk9goUBg8pDcGD5kZg4fMiRuIkslxEhsFEoOH9MbgITNj8JD5cANRMjlOYqNAY/CQ3hg8ZGYMHjIPbiBKYYCT2EgPDB7SG4OHzIzBQ+bADUQpDHASG+mFwUN6Y/CQmTF4yNi4gSiFAU5iI70xeEhvDB4yMwYPGRM3EKUwwUlsFAwMHtIbg4fMjMFDxsMNRClMcBIbBYs/34zabLXbldU/Lv9rtuHzFkvt9S0RrPeh4GLwUKjNFZFDImKvdyz087UMHjIObiBKYYST2CiYWnqHJyGh9tBbsN6H9MPgoVCbIyL9W/haBg8ZAzcQpTDCSWwUbJEePO3atUNKSgqUUlBKoaioqMnXbN68GUopREVFwWq1IicnR78FNoPD4UDXrl2hlGry2vLycuTl5SEtLQ2pqanIyspCYWGhLuti8FCozRGRAS18LYOHQosbiFKY4SQ2CoVIDx4AcDqdruDp1atXk9f36NEDSil07NhR34X56euvv8akSZPQvn17XHPNNdA0zef1FRUV6Ny5M3r27IkTJ04AADZt2oTY2FgUFBQEfH0MHgq1OSKyWmo/yrZHRH4jItf6+VoGD4UONxClMMJJbBRKDB7g6NGjUEq5Qqa4uLjRa7du3Wq44Bk+fDjGjh2LM2fOoEOHDk0Gz4QJE6CUwqFDh9yenzx5MpRS2LdvX0DXx+ChUBslIj8RESUi0SLyWxH5q4jE+PFaBg8FX8MNRPv35waiZGqcxEahxuC5EjwbN26EUgp9+vRp9Nrs7GzXdUYJni/rbbnQVPBUVlYiISEBXbp08TjncDiglEJubm5A18fgIaNJEpEaERnqx7UMHgoubiBKYYaT2MgIGDxXggcAMjMzoZTCjh07PK7btm0bsrKyAMBQwVNfU8FTUlICpRSGDRvmca6yshJKKaSnpwd0TQwe8uUOqb3bUuPHtbeKyFsiclJE/iUiu0RkYINrBsmVSWy7fPxeZ0Rkqh/vyeCh4OAGohSGOImNjKIlweNwXPlXcnp67WM9BOt96gfP2rVroZRCv379PK7r3bs31q9fD6D5wXPs2DFYrVa/j+zs7BZ9LU0Fz9KlS6GUwpQpU7yeb9OmDTRNQ2UA/53E4CFvWovIUhE5LiLnROTbJq7vJSLfSO3H0eKl9qNp80Tkkog82MRrf93gcau69xvuxzoZPKQvbiBKYYqT2MhImhs8DgeQmHglRERqHwc6RoL1PoB78NTU1OCmm26CUgolJSWua7Zv346bb77Z9disd3jmzZsHpRRmzZrl9XxaWho0TXP7mNzVYvCQN+tFZKWItBERp/gOHk1EPhGRMhGJbXDuQ6m925Po4/WfS+2dn8tsIvKF1IZPUxg8pB9uIEphipPYyGiaGzzp6e4RcvlQ6srHzwJxeHsPESAjI/B/BvWDBwBWrVoFpRT69+/veq5v375YvXq16zGDx38MHvImrd6vneI7eAZK7UfeVng5N6vu3Bgfrx8hVz7m9mcR2SwiXfxcJ4OH9PHnP3MDUQo7nMRGRtXc4Lm8E4C3IxjBEx0d+D+DhsFz6dIldOrUCUop2O127Ny5E506dcK39f4DhVmDhx9pIyNyiu/geVZqo2aSl3Pfrzv3h8AvS0QYPKSXixeBW2/lBqIUNjiJjYwsUHd4An3nJVjvA3gGDwCsWLECSikMHDgQAwYMwMqVK93Om/VneHbt2sWhBWQ4TvEdPBukNmoe8nLu1rpz7wV+WSLC4CE9XbwY6hUQBQQnsZHR8Wd4vAdPVVUVMjIyoJRChw4dUF1d7XberHd4fI2l3rNnD5RSGDVqVEDXxOChpjjFd/DskNqoucfLuc51544EflkiwuAhIvKJk9jIDFo6pU2pK3dc9JzSFoz38RY8ALBkyRJomoZly5Z5nDNr8ADAxIkToZTCwYMH3Z7Pz8+HUgr79+8P6JoYPNQUpxg4eGJiYmCxWBo9bDZbQP+BISIyC05iI7PgPjyNBw8At5/bqc/MwXP69Gl06dIFPXr0wPHjxwEAmzZtQlxcHKZOnRrwNV3+O2az2Rr9nlFEICJVOn3PSgbnFH6kjYjIVDiJjcwk0oOne/fuSElJgaZpsFqtGDJkiM/rBw0aBKvVCk3TEB0dDavViokTJ+q3QD9s27YN8fHxiI+Ph6Zp0DTN9Xjp0qVeX1NeXo68vDy0b98eqampyMrKQmFhoS7r4x0eaopTfAfPM9L40IIhwqEFRERBw0lsZEaRHjykPwYPNcUp/o2lftnLuZ/WnRsd6EXVYfAQEdXhJDYyKwYP6Y3BQ01xiu/gUVK78ehJEYlr8PxHdc/7s4loSzB4iIjASWxkbgwe0huDh5riFN/BIyKSLSLfiMgrImIRkWgRmSci1SLyoI5rY/AQUcTjJDYyO3++GbXZaveD9vdo6cyiYL0PBReDh7wZIiLn645v647Lj739rI5I7YCCrSLyr7pjl4gM0HmdDB4iimicxEbhoKV3eIj8xeAhM2PwEFHE4iQ2ChcMHtIbg4fMjMFDRBGHk9go3DB4SG8MHjIzj41HudEoEYUzTmKjcMTgIb019nes/kakwo1HyaB4h4eIIgYnsVG4YvCQ3niHh8yMwUNEEaH+JLYunMRGYYbBQ3pj8JCZMXiIKOw1nMRWwUlsFGYYPKQ3Bg+ZGYOHiMIaJ7FRJGDwkN4YPGRmDB4iCkucxEaRxK+NR0ttsNgsbofMFchc8XjeYrPAVtqyIUbBeh8KLgYPmRmDh4jCDiexUaRp6R2ehHkJSJin//cBwXof0g+Dh8yMwUNEYYWT2CgSRXrwtGvXDikpKVBKQSmFoqKiJl+zefNmKKUQFRUFq9WKnJwc3dbXlAsXLmDt2rUYMmQI2rVrh7Zt28JqteKRRx7B+++/3+jrysvLkZeXh7S0NKSmpiIrKwuFhYW6rJHBQ2bG4CGisMFJbBSpIj14AMDpdLqCp1evXk1e36NHDyil0LFjR13X5Y/8/HwopfDMM8/g/PnzAIC//e1v6NGjB2JiYrB161aP11RUVKBz587o2bMnTpw4AQDYtGkTYmNjUVBQEPA1MnjIzBg8RBQWOImNIhmDBzh69CiUUq6QKS4ubvTarVu3Gip4HnvsMdx9990ez3/66adQSqFDhw4e5yZMmAClFA4dOuT2/OTJk6GUwr59+wK6RgYPmRmDh4hMj5PYKNIxeK4Ez8aNG6GUQp8+fRq9Njs723WdEYLnrbfeQklJiddzSUlJ0DQNZWVlrucqKyuRkJCALl26eFzvcDiglEJubm5A18jgITNj8BCRaXESG1GtlgSP45jDNT0t/YV0OI45dFlbsN7ncvAAQGZmJpRS2LFjh8d127ZtQ1ZWFgAYJnh8SUxMRHR0tOujbgBQUlICpRSGDRvmcX1lZSWUUkhPTw/oOhg8ZGaVMTExsFgsrsNm43hIIjI+TmIjuqK5weM45kDi/ERXiMhcQeL8xIDHSLDeB3APnrVr10IphX79+nlc17t3b6xfvx5A84Pn2LFjsFqtfh/Z2dlX9TV9/PHHUErhoYcecnt+6dKlUEphypQpXl/Xpk0baJqGygD+HGNjf8dsNpvre0gRgYhUhfqbW6KGeIeHiEyHk9iI3DU3eNJfSHeLkMuHmqtcHz8LxOHtPWSuIOOFjID/GdQPnpqaGtx0001QSrl9VGz79u24+eabXY+Nfofn0UcfRWJiIv7+97+7PT9v3jwopTBr1iyvr0tLS4Omafjyyy8Dthbe4SEzY/AQkalwEhuRp+YGT/Sz0Y3GSDCCJ/rZ6ID/GdQPHgBYtWoVlFLo37+/67m+ffti9erVrsdGDp633noLUVFRePXVVz3OMXiImofBQ0SmwUlsRN4F6g5PoO+8BOt9AM/guXTpEjp16gSlFOx2O3bu3IlOnTrh23pDTYwaPPv27UNiYiIWL17s9byRPtLW8Bph8JABMXiIyBQ4iY2ocfwZHs/gAYAVK1ZAKYWBAwdiwIABWLlypdt5I/4Mz/79+5GUlIRFixY1es2uXbs4tICoGRg8RGRonMRG1LSWTmlTc5XrjoueU9qC8T7egqeqqgoZGRmuvWyqq6vdzhvtDs+f//xntGnTBkuWLHF7/i9/+Qv+/e9/ux77Gku9Z88eKKUwatSogK6NwUNmxuAhIsPiJDYi/3AfHu/BAwBLliyBpmlYtmyZxzkjBY/D4UCbNm2wfPlyj3MdOnRAaWmp23MTJ06EUgoHDx50ez4/Px9KKezfvz+g62PwkJkxeIjIkDiJjch/DJ7GgweA28/t1GeU4NmzZw+uueYaZGVlYe7cuZgzZ47b0aZNG4/gOX36NLp06YIePXrg+PHjAIBNmzYhLi4OU6dODfgaGTxkZgweIjIcTmIjap5ID57u3bsjJSUFmqbBarViyJAhPq8fNGgQrFYrNE1DdHQ0rFYrJk6cqNv6mvLQQw9B0zRomgallMehaZpH8ABAeXk58vLy0L59e6SmpiIrKwuFhYW6rJHBQ2bG4CEiQ+EkNqLmi/TgIf0xeMjMGDxEZBicxEbUMgwe0huDh8yMwUNEIddwEtsznMRG1CwMHtIbg4fMjMFDRCHFSWxEV8+fb0ZtpTZYbBa/D1uprUVrCdb7UHAxeMjMGDxEFDKcxEYUGC29w0PkLwYPmRmDh4hCgpPYiAKHwUN6Y/CQmTF4iCjoOImNKLAYPKQ3Bg+ZGYOHiIKKk9iIAo/BQ3pj8JCZVcbExMBisbgOm40/PEhEgcdJbET6YfCQ3hr7O2az2VzfQ4oIRKQq1N/cEjXEOzxEpDtOYiPSF4OH9MY7PGRmDB4i0lXDSWzvchIbUcAxeEhvDB4yMwYPEemGk9iIgsOv4LHZAIvF/RCpPRo+b7HUXt8SwXofCioGD5kZg4eIdMFJbETB0+I7PAkJtYfegvU+pBsGD5kZg4eIAo6T2IiCi8FDemPwkJkxeIgoYDiJjSg0Ij142rVrh5SUFCiloJRCUVFRk6/ZvHkzlFKIioqC1WpFTk6Obutryrfffov169cjNzcXXbt2Rfv27ZGamor/+q//wvLly1FdXe31deXl5cjLy0NaWhpSU1ORlZWFwsJCXdbI4CEzY/AQUUBwEhtR6ER68ACA0+l0BU+vXr2avL5Hjx5QSqFjx466rssf5eXlUEohOzsb//jHPwAAVVVVeP7556GUwrBhwzxeU1FRgc6dO6Nnz544ceIEAGDTpk2IjY1FQUFBwNfI4CEzY/AQ0VVrOIntT5zERhRUDB7g6NGjUEq5Qqa4uLjRa7du3WrI4Pnoo488zmVnZ0Mphc8//9zt+QkTJkAphUOHDrk9P3nyZCilsG/fvoCukcFDZsbgIS7r7u4AACAASURBVKKrwklsRKHH4LkSPBs3boRSCn369Gn02uzsbNd1RgieS5cuYe/evV7PPfzwwx5hU1lZiYSEBHTp0sXjeofDAaUUcnNzA7pGBg+ZGYOHiFqMk9iIjKFFweNwXBkXnZ5e+1gPQXqfy8EDAJmZmVBKYceOHR7Xbdu2DVlZWQBgmOBpTHV1NW644QZYrVZcuHDB9XxJSUmjH3WrrKyEUgrp6ekBXQuDh8yMwUNELcJJbETG0ezgcTiAxMQrISJS+zjQMRKs94F78KxduxZKKfTr18/jut69e2P9+vUAmh88x44dg9Vq9fvIzs5u0ddy6dIlHD58GDk5OUhNTYXdbnc7v3TpUiilMGXKFK+vb9OmDTRNQ2UA77gzeMjMGDxE1CycxEZkPM0OnvR09wi5fCh15eNngTi8vYcIkJER8D+D+sFTU1ODm266CUoplJSUuK7Zvn07br75ZtdjI97hWbduHRITE6GUwm233YZ3333X45p58+ZBKYVZs2Z5/T3S0tKgaRq+/PLLgK2LwUNmxuAhIr9xEhuRMTU7eKKjG4+RYARPdHTA/wzqBw8ArFq1Ckop9O/f3/Vc3759sXr1atdjIwbPZadOncLPf/5zxMTEYOzYsfi23l10Bg9R8zB4iMgvnMRGZFwBu8MT6DsvwXofeAbPpUuX0KlTJyilYLfbsXPnTnTq1MktHIwcPJdNnz4dSin86le/cj3Hj7QRNQ+Dh4iaxElsRMbGn+HxDB4AWLFiBZRSGDhwIAYMGICVK1e6nTfqz/DU9+6773rcqdq1axeHFhA1A4OHiHziJDYi42vxlDalrtxx0XNKWxDex1vwVFVVISMjA0opdOjQAdXV1W7njXKHp6SkBOvWrfN67v3334dSCt26dXM952ss9Z49e6CUwqhRowK6RgYPmRmDh4gaxUlsRObAfXi8Bw8ALFmyBJqmYdmyZR7njBI8c+bMQdeuXb0OgHn++eehlMKYMWPcnp84cSKUUjh48KDb8/n5+VBKYf/+/QFdI4OHzIzBQ0QeOImNyFwYPI0HDwC3n9upzyjBM3fuXCilkJeXh5N1w2AuXLiAVatWISEhAe3bt8cXX3zh9prTp0+jS5cu6NGjB44fPw4A2LRpE+Li4jB16tSAr5HBQ2bG4CEiN5zERmQ+kR483bt3R0pKCjRNg9VqxZAhQ3xeP2jQIFitVmiahujoaFitVkycOFG39TWloqICy5Ytwz333IOOHTsiNTUVrVu3RmZmJqZNm4by8nKvrysvL0deXh7at2+P1NRUZGVlobCwUJc1MnjIzBg8ROTCSWxE5hTpwUP6Y/CQmTF4iAgAJ7ERmRmDh/TG4CEzY/AQESexEZmcX8FjswEWi/+HzdayxQTrfSioGDxkZgweogjHSWxE5tfiOzxEfmLwkJkxeIgiFCexEYUPBg/pjcFDZsbgIYpAnMRGFF4YPKQ3Bg+ZGYOHKMJwEhtR+GHwkN4YPGRmDB6iCMJJbEThicFDemPwkJlVxsTEwGKxuA4bp6UQhSVOYiMKXwwe0ltjf8dsNpvre0gRgYhUhfqbW6KGeIeHKAK8zklsRGGNwUN64x0eMjMGD1EY4yQ2osjA4CG9MXjIzBg8RGGKk9iIIoc/34zanE5YSkvdjsv/MaTh85bSUticzhatJVjvQ8HF4CEzY/AQhSFOYiOKLC29w5NQWoqE0lIdVhSa9yH9MHjIzBg8RGGGk9iIIk+kB0+7du2QkpICpRSUUigqKmryNZs3b4ZSClFRUbBarcjJydFtfS3x2GOPQSmF0aNHN3pNeXk58vLykJaWhtTUVGRlZaGwsFCX9TB4yMwYPERhhJPYiCJTpAcPADidTlfw9OrVq8nre/ToAaUUOnbsqOu6WmLnzp3QNA1KKYwZM8brNRUVFejcuTN69uyJEydOAAA2bdqE2NhYFBQUBHxNDB4yMwYPUZjgJDaiyMXgAY4ePQqllCtkiouLG71269athg2er7/+Gt/97nfx4x//2GfwTJgwAUopHDp0yO35yZMnQymFffv2BXRdDB4yMwYPkclxEhsRMXiuBM/GjRuhlEKfPn0avTY7O9t1ndGCZ8yYMbjvvvuwe/fuRoOnsrISCQkJ6NKli8c5h8MBpRRyc3MDui4GD5kZg4fIxDiJjYiAlgWP4+xZ138oSd+7F46zZ3VZW7De53LwAEBmZiaUUtixY4fHddu2bUNWVhYAGC54tm7dirZt26KsrAx2u73R4CkpKYFSCsOGDfM4V1lZCaUU0tPTA7o2Bg+ZGYOHyKQ4iY2ILmtu8DjOnkXiu++6QkTsdiS++27AYyRY7wO4B8/atWuhlEK/fv08ruvduzfWr18PoPnBc+zYMVitVr+P7Oxsv3/v06dPw2q14rXXXgMAn8GzdOlSKKUwZcoUr79XmzZtoGkaKgM4tIbBQ2bG4CEyIU5iI6L6mhs86Xv3ukXI5UPZ7a6PnwXi8PYeYrcjY+/egP8Z1A+empoa3HTTTVBKoaSkxHXN9u3bcfPNN7seG+kOT05ODh555BHXY1/BM2/ePCilMGvWLK+/V1paGjRNw5dffhmw9TF4yMwYPEQmw0lsRNRQc4MnevfuRmMkGMETvXt3wP8M6gcPAKxatQpKKfTv39/1XN++fbF69WrXY6MEz4YNG9CuXTtUVFS4nmPwEAUOg4fIRDiJjYi8CdQdnkDfeQnW+wCewXPp0iV06tQJSinY7Xbs3LkTnTp1wrf1/r1phOA5efIkrr32WmzatMnt+cvB420fHn6kjah5GDxEJsBJbETkC3+GxzN4AGDFihVQSmHgwIEYMGAAVq5c6XbeCD/Ds2bNGlxzzTUer23bti2UUrBYLK7nLn88b9euXRxaQNQMDB4ig+MkNiJqSkuntKl6d1z0nNIWjPfxFjxVVVXIyMiAUgodOnRAdXW123kj3OFpTEvHUu/ZswdKKYwaNSqg62HwkBHcLiLFIrJHRA7W/fpaP17H4CEyME5iIyJ/cB8e78EDAEuWLIGmaVi2bJnHOSMHj6+f4QGAiRMnQimFgwcPuj2fn58PpRT2798f0PUweCjUbhGRz0XkprrHSkQ2iEi6H69l8BAZFCexEZG/GDyNBw8At5/bqc8MwePtZ3iA2jHWXbp0QY8ePXD8+HEAwKZNmxAXF4epU6cGfD0MHgq1dSJia+FrGTxEBsRJbETUHJEePN27d0dKSgo0TYPVasWQIUN8Xj9o0CBYrVZomobo6GhYrVZMnDhRt/U1x3PPPYf4+HjExsZC0zRERUUhPj7ebZz2ZeXl5cjLy0P79u2RmpqKrKwsFBYW6rIuBg+FkhKRsyIyQ2rDZ4+IbBaR7/n5egYPkcFwEhsRNVekBw/pj8FDoZQsIjUi8i+58pG2R0XkP8KPtBGZSk1NDZ7/4gtOYiOiZmPwkN4YPBRK6VIbPCsaPH9MRH7mx+sZPEQGwElsRHQ1GDykNwYP+XKHiPxVaqOkKbeKyFsiclJq79jsEpGBDa4ZJCL2umOXiCSIyLciMrfBdXtF5GU/3pPBQxRinMRGRFfLn29GbU4nLKWlfh82p7NFawnW+1BwMXjIm9YislREjovIOamNEl96icg3IvJbEYkXkWgRmScil0TkwSZe+7F43uH5p4jM9mOdDB6iEOIkNiIKhJbe4SHyF4OHvFkvIitFpI2IOMV38Ggi8omIlIlIbINzH0rt3Z5EH69/VETKRaR93eP/EZF/i8h3/Vgng4coRPZ9/TVSOYmNiAKAwUN6Y/CQN2n1fu0U38EzULz/HI6IyKy6c2OaeL+pUvvRuXdFpERq7xj5g8FDFAKcxEZEgcTgIb0xeKgpTvEdPM9KbdRM8nLu+3Xn/hD4ZYkIg4coqDiJjYj0wOAhvTF4qClO8R08G6Q2ah7ycu7WunPvBX5ZIsLgIQoaTmIjIr0weEhvDB5qilN8B88OqY2ae7yc61x37kjglyUiDB6ioOAkNiLSE4OH9MbgoaY4xcDBExMTA4vF0uhhs9mC9I8SUXg6yklsRKQzBg/p7fLfMZvN1uj3jCICEanS6XtWMjin8CNtRBGJk9iIKBj8CR6nzYlSS6nbYRc77GL3eL7UUgqnrWX74wTrfSi4eIeHmuIU38HzjDQ+tGCIcGgBkSlxEhsRBUtL7/CUJpSiNKFUhxWF5n1IPwweaopT/BtL/bKXcz+tOzc60Iuqw+AhCrCGk9ie5SQ2ItIZg4f0xuChpjjFd/Aoqd149KSIxDV4/qO651vptDYGD1EAcRIbEYVCpAdPu3btkJKSAqUUlFIoKipq8jWbN2+GUgpRUVGwWq3IycnRbX3+qq6uxosvvojs7Gykp6cjKSkJN954I3Jzc3Hs2DGP68vLy5GXl4e0tDSkpqYiKysLhYWFuqyNwUNNcYrv4BERyRaRb0TkFRGxiEi0iMwTkWoReVDHtTF4iAKEk9iIKFQiPXgAwOl0uoKnV69eTV7fo0cPKKXQsWNHXdflr8rKSgwcOBC9e/fGX//6VwBAVVUVnn76aSilUFrq/udXUVGBzp07o2fPnjhx4gQAYNOmTYiNjUVBQUHA18fgIW+GiMj5uuPbuuPyY28/qyNSO6Bgq4j8q+7YJSIDdF4ng4coAD7nJDYiCiEGD3D06FEopVwhU1xc3Oi1W7duNVzwPPbYY2jTpg1Onz7tce7uu+/GwYMH3Z6bMGEClFI4dOiQ2/OTJ0+GUgr79u0L6PoYPGRmDB6iq8RJbEQUagyeK8GzceNGKKXQp0+fRq/Nzs52XWeE4Dl58iSio6MxduxYv66vrKxEQkICunTp4nHO4XBAKYXc3NyArpHBQ2bG4CG6CpzERkRG0JLgOes46xoXvTd9L846zuqytmC9z+XgAYDMzEwopbBjxw6P67Zt24asrCwAMEzwLF++HEoprFixwq/rS0pKoJTCsGHDPM5VVlZCKYX09PSArpHBQ2bmsfEoNxolahonsRGRkTQ3eM46zuLdxHddIWIXO95NfDfgMRKs9wHcg2ft2rVQSqFfv34e1/Xu3Rvr168H0PzgOXbsGKxWq99Hdna2X7/vuHHjoJTChg0b8MwzzyAzMxMpKSm48cYbMXnyZJw6dcrt+qVLl0IphSlTpnj9/dq0aQNN01AZwI9XN/Z3rP5GpMKNR8mgeIeHqJk4iY2IjKa5wbM3fa9bhLgOZXd9/CwQh9f3EDv2ZuwN+J9B/eCpqanBTTfdBKUUSkpKXNds374dN998s+uxUe7w3HfffVBKITU1FT/84Q9x4sQJVFdX46233sK1116L7373uzhZ7/9r5s2bB6UUZs2a5fX3S0tLg6Zp+PLLLwO2Rt7hITNj8BA1AyexEZERNTd4dkfvbjRGghE8u6N3B/zPoH7wAMCqVauglEL//v1dz/Xt2xerV692PTZK8Nx1111QSuG6667DxYsX3c4tWbIESimMHz/e9RyDh6h5GDxEfuIkNiIyqkDd4Qn0nZdgvQ/gGTyXLl1Cp06doJSC3W7Hzp070alTJ3xb72ctjRI8DzzwAJRSGD58uMe5zz//HEopZGRkuJ4z0kfaGl4jDB4yIAYPkR84iY2IjIw/w+MZPACwYsUKKKUwcOBADBgwACtXrnQ7b5Sf4ZkyZQqUUpg0aZLHuf/85z9QSiEuLs713K5duzi0gKgZGDxETeAkNiIyuhZPaVNX7rjoOqUtCO/jLXiqqqqQkZEBpRQ6dOiA6upqt/NGucPzxz/+EUopPPzwwx7nvN3h8TWWes+ePVBKYdSoUQFdI4OHzIzBQ9QITmIjIrPgPjzegweo/RkYTdOwbNkyj3NGCZ4LFy4gLS0N1157rcfH0C6PrM7Pz3d7fuLEiVBKeWxImp+fD6UU9u/fH9A1MnjIzBg8RF5wEhsRmQmDp/HgAeD2czv1GSV4AKC4uBixsbEYPnw4Tp8+jZqaGuzevRupqam45ZZb8NVXX7ldf/r0aXTp0gU9evTA8ePHAQCbNm1CXFwcpk6dGvD1MXjIzBg8RA1wEhsRmU2kB0/37t2RkpICTdNgtVoxZMgQn9cPGjQIVqsVmqYhOjoaVqsVEydO1G19/tq/fz/uvfdetG3bFklJSbjxxhsxc+ZMnDt3zuv15eXlyMvLQ/v27ZGamoqsrCwUFhbqsjYGD5kZg4eoHk5iIyIzivTgIf0xeMjMGDxEdTiJjYjMisFDemPwkJkxeIjASWxEZG4MHtIbg4fMrDImJgYWi8V12Gy2IP2jQxR6nMRGROHAn29GnTYnSi2lfh9Om7NFawnW+1BwNfZ3zGazub6HFBGISFWov7klaoh3eChicRIbEYWLlt7hIfIX7/CQmTF4KCJxEhsRhRMGD+mNwUNmxuChiPP5N9+gKyexEVEYYfCQ3hg8ZGYMHooonMRGROGIwUN6Y/CQmTF4KGJwEhsRhSsGD+mNwUNmxuChsMdJbEQU7hg8pDcGD5kZg4fCGiexEVEkYPCQ3hg8ZGYMHgpbnMRGRJHCr314nDaUllrcDrtdYLeLx/OlpRY4nS3bly9Y70PBxeAhM2PwUFjiJDYiiiQtvcNTWpqA0lL9vw8I1vuQfhg8ZGYMHgo7nMRGRJEm0oOnXbt2SElJgVIKSikUFRU1+ZrNmzdDKYWoqChYrVbk5OTotr7mcDgc6Nq1K5RSTV5bXl6OvLw8pKWlITU1FVlZWSgsLGz0+urqasyfPx833ngjUlNTcf3112P69Omo9OM/CjJ4yMwYPBRWOImNiCJRpAcPADidTlfw9OrVq8nre/ToAaUUOnbsqOu6/PX1119j0qRJaN++Pa655hpomubz+oqKCnTu3Bk9e/bEiRMnAACbNm1CbGwsCgoKPK6vqanB0KFDkZSUhL179wIA/va3v6FDhw644447cPHiRZ/vx+AhM2PwUFjgJDYiimQMHuDo0aNQSrlCpri4uNFrt27darjgGT58OMaOHYszZ86gQ4cOTQbPhAkToJTCoUOH3J6fPHkylFLYt2+f2/Pr1q2DUgqLFi1ye37jxo1QSmHBggU+34/BQ2bG4CHT4yQ2Iop0DJ4rwXP5G/g+ffo0em12drbrOqMEz5dffun6dVPBU1lZiYSEBHTp0sXjnMPhgFIKubm5bs8PGDAAmqbh+PHjbs9XV1cjISEB119/vc/1MXjIzBg8ZGqcxEZExOABrgQPAGRmZkIphR07dnhct23bNmRlZQGAoYKnvqaCp6SkBEopDBs2zONcZWUllFJI///bu//YKM47j+OfZ1gCpqZn97CzAhOnFg45KRjJLU7qRJhT2kOuropbkssfSIVWOlRXJZHKiVPpnZJUhmt6OipFkAsc7dGUiLg9QiVIWiJ+JcVuaKS0TQq5HugYcUnTitLmmoNQG/jeH7NevPZ6vWvvjGe875c0qnfmmX2+bOeJ5+OZfaahIbvuypUrlkqlrK6uLu/7LVu2zJxzdubMmTH7JPAgyQg8SKzhM7HdxkxsACrYRALPu+/2ZaeL7u9vsHff7Qultqj6GR549u7da845u+eee0a1u+uuu6y3t9fMSg8858+ft3Q6XfTS1tY2oX/LeIFn+/bt5pyzhx9+OO/2mpoa8zwvOxnBqVOnzDlnS5cuzdu+q6tr3MkeCDxIskszZ860qqqq7NLTw3z4iL+RM7FdZCY2ABWs1MDz7rt99vLL1dkgcuyY7OWXq8seRqLqxyw38Fy/ft0WL15szjk7cuRIts2hQ4fs9ttvz75O6hWezZs3m3PONm3alHf7ggULzPO87G1yQ7e5tbe3522/evVqc87Znj17xuxzrGOsp6cnew4pySQNTPXJLTASV3iQOMzEBgC5Sg08/f0NOSHkxuKyt5+VY8nfh6y/f2HZP4PhgcfMbPfu3eacs+XLl2fX3X333fb0009nXxN4ApMJPCPbiCs8iCECDxKDmdgAIL9SA8/x46kxw0gUgef48VTZP4ORgefq1avW1NRkzjk7duyYHT582JqamuzasD+SJTXwcEsbUBoCDxKBmdgAYGzlusJT7isvUfVjNjrwmJnt3LnTnHO2YsUK6+josF27duVsT+p3eI4ePVrWSQva2trM8zw7e/bsmH0SeJBkBB7EHjOxAUBhfIcnf+AZGBiwhQsXmnPOGhsbbXBwMGd7Uq/wFJqW+sSJE+acszVr1uSs7+joMOdczvTXZsFnxLTUmO4IPIg1ZmIDgPFNfJY2l73iEu4sbeH3ky/wmJlt27bNPM+zJ598ctS2pAYeM7Pu7m5zztlrr72Ws379+vXmnLOTJ0/mrO/t7TXnnG3dujVn/b59+3jwKKY9Ag9ii5nYAKA4PIdn7MBjZjnf2xkuyYHn4sWL1tzcbK2trdmHie7fv99mzZplGzZsyLvPqlWrrLa21vr7+83M7PTp03bLLbdYe3u7DYzzO5bAgyQj8CCWmIkNAIpX6YFnyZIlVldXZ57nWTqdts7OzoLtV65cael02jzPs1QqZel02rq7u0OrrxgvvPCCzZ4922bPnm2e55nnednX27dvz7vPhQsXbN26dTZ//nyrr6+3lpYW27Fjx5h9DA4O2pYtW6y5udnq6+utsbHRNm7caJcvXx63PgIPkozAg1hhJjYAKF2lBx6Ej8CDJCPwIDaYiQ0AJobAg7AReJBkBB7EAjOxAcDEEXgQNgIPkozAgynHTGwAMDnFnIz6fo+99FJV0Yvv90yolqj6QbQIPEgyAg+mFDOxAcDkTfQKD1AsAg+SjMCDKcNMbABQHgQehI3AgyQj8CByzMQGAOVF4EHYCDxIMgIPIsVMbABQfgQehI3AgyQj8CAyzMQGAOEg8CBsBB4kGYEHkWAmNgAID4EHYSPwIMkIPAgdM7EBQLgIPAgbgQdJRuBBqJiJDQDCR+BB2Ag8SDICD0LBTGwAEJ1iTkZ7enqsqqoqZ5Fkkkatr6qqsp6eiT0QNKp+EC0CD5KMwINQHP/DH5iJDQAiMtErPFFdGeIKVPIReJBkBB6E5mvnzjETGwBEoNIDz80332x1dXXmnDPnnD3//PPj7vODH/zAnHM2Y8YMS6fT9uCDD4ZW33iuXLlie/futc7OTrv55pvtQx/6kKXTabv//vvt1VdfHXO/Cxcu2Lp162zBggVWX19vLS0ttmPHjjHbDw4O2pYtW+y2226z+vp6u/XWW23jxo12qYiJhAg8SDICDwAACVfpgcfMzPf9bOC58847x23f2tpqzjn78Ic/HGpdxVi/fr055+yxxx6z999/38zM3nzzTWttbbWZM2fagQMHRu3zu9/9zhYtWmQf+chH7Ne//rWZme3fv99uuukm+/KXvzyq/fXr1+0zn/mM1dbWWn9/f7aPxsZGa29vtz/96U8FayTwIMkIPAAAJByBx+zcuXPmnMsGmR/96Edjtj1w4ECsAs+XvvQl+/jHPz5q/enTp805Z42NjaO2feELXzDnnP3sZz/LWf/QQw+Zc85eeeWVnPXPPvusOefsm9/8Zs765557zpxz9vjjjxeskcCDJCPwAACQcASeG4Fn6AT+Yx/72Jht29rasu3iEHgOHjxoR44cybuttrbWPM+zd955J7vu0qVLNmfOHGtubh7Vvq+vz5xz9tnPfjZnfUdHh3meZ2+//XbO+sHBQZszZ47deuutBWsk8CDJCDwAACTcRAJFX19fdva0hoYG6+vrC6W2qPoZCjxmZnfccYc55+zFF18c1e6FF16wlpYWM7PYBJ5CqqurLZVKZW91MzM7cuSIOefsgQceGNX+0qVL5pyzhoaG7LorV65YKpWyurq6vH0sW7bMnHN25syZMesg8CDJCDwAACRcqYGnr6/Pqqurs0FEklVXV5c9jETVj1lu4Nm7d6855+yee+4Z1e6uu+6y3t5eMys98Jw/f97S6XTRS1tb26T+TW+88YY556yrqytn/fbt2805Zw8//HDe/WpqaszzvOxkBKdOnTLnnC1dujRv+66urnEneyDwIMkIPAAAJFypgaehoSEnhAwtzrnse5VjydeHJFu4cGHZP4Phgef69eu2ePFic87l3Cp26NAhu/3227Ov436F5/Of/7xVV1fbr371q5z1mzdvNuecbdq0Ke9+CxYsMM/z7K233jKzG7e5tbe3522/evVqc87Znj17xqyFwIMkuzRz5kweAAYAQIKVGnhSqdSYYSSKwJNKpcr+GQwPPGZmu3fvNuecLV++PLvu7rvvtqeffjr7Os6B5+DBgzZjxgx75plnRm2LU+AZ/qDZzP+/A1NxQgsUwhUeAAASrlxXeMp95SWqfsxGB56rV69aU1OTOefs2LFjdvjwYWtqarJr165l28Q18LzyyitWXV1tTzzxRN7t3NIGlIbAAwBAwvEdntGBx8xs586d5pyzFStWWEdHh+3atStnexy/w3Py5Emrra0dNX30cEePHi3rpAVtbW3meZ6dPXt2zD4JPEgyAg8AAAk30VnanHPZKy5hztIWRT/5As/AwIAtXLgw+yybwcHBnO1xu8Lzk5/8xGpqamzbtm05619//XV77733sq8LTUt94sQJc87ZmjVrctZ3dHSYcy57m9uQgYEBpqXGtEfgAQAg4XgOT/7AY2a2bds28zzPnnzyyVHb4hR4+vr6rKamxp566qlR2xobG+2ll17KWdfd3W3OOXvttddy1q9fv96cc3by5Mmc9b29veacs61bt+as37dvHw8exbRH4AEAIOEIPGMHHjPL+d7OcHEJPCdOnLC5c+daS0uLPfroo/bII4/kLDU1NaMCz8WLF625udlaW1uzDxPdv3+/zZo1yzZs2JC3n1WrVlltba319/ebmdnp06ftlltusfb2dhsYGChYI4EHSUbgAQAg4So98CxZssTq6urM8zxLp9PW2dlZsP3KlSstnU6b53mWSqUsnU5bd3d3aPWNp6uryzzPM8/zzDk3avE8b1TgMTO7cOGCrVu3zubPn2/19fXW0tJiO3bsGLOfjjrmqwAACgdJREFUwcFB27JlizU3N1t9fb01Njbaxo0b7fLly+PWSOBBkhF4AABIuEoPPAgfgQdJRuABACDhCDwIG4EHSUbgAQAg4Qg8CBuBB0lG4AEAIOGKORnt6emxqqqqopeenp4J1RJVP4gWgQdJRuABACDhuIKCsBF4kGQEHgAAEo7Ag7AReJBkBB4AABKOwIOwEXiQZAQeAAASjsCDsBF4MNX+U9KxEctvJD1axL4EHgAAEo7Ag7AReDDV9o947SSdk7SoiH0JPAAAJByBB2Ej8CBuOiUdLrItgQcAgIQj8CBsBB7EzXOSHiiyLYEHAICEy5xoZk9KWVjKvQwdX0UchwQehC4t6W1JqSLbE3gAAEi4efPmTfkJMcv0X+bNm1fwOJwzh8CDaHxF0tdLaH9p5syZEf3nGICZ8VRxIEKMNyA6mcAzENI5LmKsXdIpSdeLaLtU0kEFM6z9VtJRSStGtFmpGzOxHR2xzUn6L0lNJdR3SdJUjw+golRVVU11CUDFYLwB0ckEHivhPBQJ90FJ2xXcXvZHSdfGaX+npMuSviVptoJb0jZLuirpviL7vFfSiyXWSeABIsYJGBAdxhsQHQJP5emVtEtSjSRfhQOPJ+mXkt6RdNOIbT9XcLWnuog+n5W0qsQ6CTxAxDgBA6LDeAOiQ+CpPAuG/eyrcOBZoeCWt515tm3KbPvcOP3Ny/Qzo9gCMwg8GXG+zzvq2sLor1zvOdH3mch+pexTSltOwAKMuXD7K8d7TuY94jLmGG83MObC7YsxR+CpdL4KB56vKQg1X8yz7ZOZbd8tf1mSCDxZcf6lGHVtYfRXrvec6PtMZL9S9gmr7XQW58+BMTf594jLmIvzcRa1OH8WUdYWVl+MOQJPpfNVOPB8T0Go6cqzbWlm20/LX5YkAk8WvwjC7Y/AM7lapqM4fw6Mucm/R1zGXJyPs6jF+bMg8Ez+PeIw5gg8lc1X4cDzooJQ84k82xZltp0pf1mSgoNyyud1j8MS588h6trC6K9c7znR95nIfqXsE1bb6bzE+XNgzE3+PeIy5uJ8nEW9xPmziLK2sPpizGXDDoGnQvmKb+C5rhsH51jLgIIrQdN9ifO/M+rawuivXO850feZyH6l7BNW2+m8xPlzYMxN/j3iMubifJxFvcT5s4iytrD6qpQxN6DC543FPIoF05Cv+N7SBgAAAACT4qtw4HlMY09a0KlwJy0AAAAAgEnxVdy01P+WZ9tXM9vWlrsoAAAAACgHX4UDj1Pw4NHfSJo1Yv0vMus/EFZxAAAAADAZvgoHHklqk3RZ0rclVUlKSdosaVDSfWEWBwAAAACl6pT0fma5llmGXuf7ro4UTFBwQNJvM8tRSR2hVwoAAAAAyNEr6dxUFwFMc/dJel7SIUknJR2X9NGpLAiY5h5UMN4OS3pV0jOS5k9pRcD0N0/B5FxMZ41YuVfS7yX991QXAkxzP5T06WGvv67g6i+AcFzWjT8qzJC0TzwWAgjTMgV/zNsuAg9iZKaCvzR/RVzhAcLWqmDykiF/reAXwp9NTTnAtPcPI15/SsGYq5uCWoBK0KDge+trReBBjGyUtEHSGhF4gCjNlbRf0u4prgOoJH+j4Mnys6e6EGCaWysCD2JigYKrOzMUHJgEHiAavZL+mPnfuVNcC1BJ9kl6fKqLACrAWhF4EBN7Jf1l5ue1IvAAUUpJ+oakM5I+OMW1AJXgfkk/VnArN4BwrRWBB0Vol3RKxR0sSyUdVPBA06Hpr1eMaLNS0rFhy70K/ro8ZK0IPKhsYY65o2O8j6fgS9XdpZcLJF6UY+5eSSfE9+VQ2aIcc2uL7AcV6oMKZrZ4W8EtL+M94PROBSdM31JwT/LQA06vqvADTr+h4Ha2oQP1TQXPFDomaevEywcSJ6oxJ0n/mGfd7yX9XQn1AkkX5ZiTpL+S9LJuhJ2F4qoqKkvUY04i8GAcvZJ2SaqR5KvwQelJ+qWkdyTdNGLbzxUk8uoi+10rrvCgMkU55i5LWjLs9VpJVyTdUUrBQMJFOeY6FUxDvSDTrlrSFvEQcFSWqTi3XCsCDwpYMOxnX4UPyhUKDqadebZtymz7XBF9fkfBL4T3JH1fwVS5QKWIcsw9JKlPwaX/Ewr+6vyJ4ksFpoWoxtwHFNy5cC3Tbmi5Jml5KQUDCRfl77laBeeSP830831Jf198qahEvgoflF9TcOB9Mc+2T2a2fbf8ZQHTli/GHBAlX4w5IEq+GHOIGV+FD8rvKTjwuvJsW5rZxhOlgeL5YswBUfLFmAOi5Isxh5jxVfigfFHBgZfvlphFmW1nyl8WMG35YswBUfLFmAOi5Isxh5jxxUEJRMkXYw6Iki/GHBAlX4w5xIwvLjsCUfLFmAOi5IsxB0TJF2MOMeOr8EH5mMb+Ylmn+GIZUCpfjDkgSr4Yc0CUfDHmEDO+ips68N/ybPtqZtvachcFTGO+GHNAlHwx5oAo+WLMIWZ8FT4onYKHQ/1G0qwR63+RWf+BsIoDpiFfjDkgSr4Yc0CUfDHmEDO+Ch+UktSm4Ont35ZUJSklabOkQUn3hVkcMA35YswBUfLFmAOi5IsxhxjoVPBk6KGnQ18b9jrf/ZRS8CWyA5J+m1mOSuoIvVJgemDMAdFizAHRYswBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAibJH0j9NdREAAAAAUG5/Iem6pP+TNK+I9isl/TLUigAAAACgTP5d0hMKQk8xV3l2SjoRakUAAAAAUAaLJD0laY6kC5L+V1LNOPuckvT1kOsCAAAAgEnbIakp8/NXFFzlebRA+xpJ1yR9KtyyAAAAAGByGiV9e9jruZJ+n1nmjmj7aUk/lPS6gsDzw8zy0fDLBAAAAIDSPSFp8Yh1jyi4yrNpjH22SHojzKIAAAAAYLLmS/pOnvU1Cr7Hc0HB93pG+rGkfw2xLgAAAACYtH+RdMcY2zYruMqzYcT6myRdlrQ6xLoAAAAAYFLqJD1TYPufS3pP0juSZg9b364gCDWGVxoAAAAATM4WSa3jtPlnBeFm/bB1GyWdH/Z6saRUeUsDAAAAgImrlfRsEe3SCm5f+x/dCDX7JPUOa7O9vKUBAAAAwOQ8Jum4pF1FLG8puMqzLrPvf0h6PPPz30paEVHNAAAAADCu2ZIuKniOzvUilmuZ5Wxm/1ZJRxRMePBglIUDAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKLz/ygiU7OOcwgHAAAAAElFTkSuQmCC\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb90957ea90>"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python3.4/dist-packages/numpy/__init__.py:1: RuntimeWarning: invalid value encountered in sqrt\n",
" \"\"\"\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(dt0)\n",
"print(evdt0)\n",
"print(deltae0)\n",
"print(2.**(-8))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0.00048828125, 0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125]\n",
"[0.00012339943196514481, 0.0002465097038549323, 0.00049515002858411909, 0.00098551557812916863, 0.0019784141351204976, 0.0040736005562633845, nan]\n",
"[2.4313381434823996e-06, 5.5098180464226142e-06, 1.1076887260120123e-05, 2.1570155895018871e-05, 4.7824231650751466e-05, 9.8734530666718379e-05, nan]\n",
"0.00390625\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get solution\n",
"nbatches = 20\n",
"ntraj = 64\n",
"tstep_index = 1\n",
"# with dt = 2^(-8) = 0.0039..., the error is 0.0034 after integrating 1.\n",
"w = Wiener(nsteps = int(1 / dt0[tstep_index]),\n",
" dt = dt0[tstep_index],\n",
" noise_dimension = xy_sys.get_noise_dimension(),\n",
" solution_shape = [nbatches, ntraj])\n",
"w.initialize()\n",
"xy_traj = xy_sys.explicit_1p0(w, np.ones(2))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# check that components of the Wiener process are different\n",
"print(w.W.shape)\n",
"print(w.dW.shape)\n",
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(w.get_time(), w.W[:, 0, 0, 0], label = '$X$')\n",
"ax.plot(w.get_time(), w.W[:, 1, 0, 0], label = '$Y$')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1025, 2, 20, 64)\n",
"(1024, 2, 20, 64)\n"
]
},
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb9094e0828>"
]
},
{
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb9094e0860>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.legend.Legend at 0x7fb909426358>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(xy_traj.shape)\n",
"traj_C1 = traj_C1\n",
"traj_C2 = traj_C2\n",
"traj_A = traj_A\n",
"print(traj_C1.shape)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1025, 2, 20, 64)\n",
"(1025,)\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = xy_traj[:, 0]\n",
"Y = xy_traj[:, 1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(w.get_time(), X[:, 0, 0], label = '$X$')\n",
"ax.plot(w.get_time(), Y[:, 0, 0], label = '$Y$')\n",
"ax.legend(loc = 'best')\n",
"# the curves look very similar to the Wiener components,\n",
"#but they are different"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb90942df60>"
]
},
{
"html": [
"<img 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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb90942dfd0>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<matplotlib.legend.Legend at 0x7fb9093eeeb8>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# naive B\n",
"batch_pick = np.random.randint(0, nbatches)\n",
"traj_pick = np.random.randint(0, ntraj)\n",
"B = np.exp(-X*traj_C1[:, None, None]\n",
" -Y*traj_C2[:, None, None]\n",
" -traj_A[:, None, None])\n",
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(w.get_time(), B[:, batch_pick, traj_pick], label = '$B$')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb90940ba90>"
]
},
{
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb90940bba8>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<matplotlib.legend.Legend at 0x7fb9093cd710>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# compute error\n",
"final_error = evdt0[tstep_index]*8 # the 8 comes from using h0=0.125\n",
"rel_error_plus = 1 + np.linspace(0, final_error, X.shape[0])\n",
"rel_error_minus = 1 + np.linspace(0, -final_error, X.shape[0])\n",
"# note: I ignored the errors in C_1, C_2 and A since they're at least ten\n",
"#orders of magnitude less than the SDE errors...\n",
"\n",
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(w.get_time(), rel_error_plus)\n",
"ax.plot(w.get_time(), rel_error_minus)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb9093d8710>"
]
},
{
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb9093d8828>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"[<matplotlib.lines.Line2D at 0x7fb909396f60>]"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"batch_pick = np.random.randint(0, nbatches)\n",
"traj_pick = np.random.randint(0, ntraj)\n",
"B = np.exp(-X*traj_C1[:, None, None]\n",
" -Y*traj_C2[:, None, None]\n",
" -traj_A[:, None, None])\n",
"Bpp = np.exp(\n",
" -X*traj_C1[:, None, None]*rel_error_plus[:, None, None]\n",
" -Y*traj_C2[:, None, None]*rel_error_plus[:, None, None]\n",
" -traj_A[:, None, None])\n",
"Bpm = np.exp(\n",
" -X*traj_C1[:, None, None]*rel_error_plus[:, None, None]\n",
" -Y*traj_C2[:, None, None]*rel_error_minus[:, None, None]\n",
" -traj_A[:, None, None])\n",
"Bmp = np.exp(\n",
" -X*traj_C1[:, None, None]*rel_error_minus[:, None, None]\n",
" -Y*traj_C2[:, None, None]*rel_error_plus[:, None, None]\n",
" -traj_A[:, None, None])\n",
"Bmm = np.exp(\n",
" -X*traj_C1[:, None, None]*rel_error_minus[:, None, None]\n",
" -Y*traj_C2[:, None, None]*rel_error_minus[:, None, None]\n",
" -traj_A[:, None, None])\n",
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(w.get_time(), B[:, batch_pick, traj_pick], label = '$B$')\n",
"ax.plot(w.get_time(), Bpp[:, batch_pick, traj_pick], label = '$B_{++}$')\n",
"ax.plot(w.get_time(), Bpm[:, batch_pick, traj_pick], label = '$B_{+-}$')\n",
"ax.plot(w.get_time(), Bmp[:, batch_pick, traj_pick], label = '$B_{-+}$')\n",
"ax.plot(w.get_time(), Bmm[:, batch_pick, traj_pick], label = '$B_{--}$')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb909340048>"
]
},
{
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb9093402b0>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"<matplotlib.legend.Legend at 0x7fb9093062b0>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Comment:**\n",
"\n",
"The result at $t=1$ is always 1 because we impose that $C_1$, $C_2$ and $A$ are 0 there (so we impose that $B(1) = 1$, it's independent of what methods we're using).\n",
"The difference between different estimates of $B$ must therefore be 0 at $t=1$.\n",
"It is also 0 at $t=0$ since the errors are 0 there as well."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# statistics on fluctuations in B\n",
"fullB = np.array([Bpp, Bpm, Bmp, Bmm])\n",
"minB = np.min(fullB, axis = 0)\n",
"maxB = np.max(fullB, axis = 0)\n",
"deltaB = maxB - minB\n",
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(w.get_time(), deltaB[:, batch_pick, traj_pick], label = '$\\delta B$')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb9093a1ac8>"
]
},
{
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb90939cb38>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"<matplotlib.legend.Legend at 0x7fb906abfb70>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(6,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(w.get_time(),\n",
" np.average(deltaB, axis = (1, 2)),\n",
" label = '$\\delta B$')\n",
"ax.plot(w.get_time(),\n",
" np.nanmax(deltaB, axis = (1, 2)),\n",
" label = '$\\delta B$')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb906ac8b70>"
]
},
{
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FnYhSU7ZMCVgPpIB4/xz3jP0HsIAF+xYQBhgjU9qFNahiTgKPOImCjwRSsddRESiTUKPhIDV0kI5ceYES3QqwUs/vDTXKmg+ny9XaHl28rMkyQ0HNxAGePfQuwO+s+bAmpBhBwb48MiHSZIjV43MtUBBznH+KznVP65OGLCf+cn5EtRYo8AjbqLAE6F03EVEJJqkZ6YHhIitaVtDjvM/P9NwTMOQ/etT17PRmEaEAcZ1juPsbbPt+ct3K08Y4EdLP7LHf7XuK8IAm3zZJFfguezDyzh85XAmLUwiDNCYb9jfS96XzH9/9297bM6rU+mZ6fQYHv6u++8K8QhFFgUecRMFngil4y4iItFkS9qWgLAxZcOUkOO6LepGGGCnBZ3ynC8zO5PpmekBbW2/bWvPv/f4XpK038+Tc0npq3tfzcu7X54rAPVZ0idgvmxvdq7b4Up3KU2SrNCtAuM6x53v4Yh4CjziJgo8EUrHXUREoon/uRf/5+3Zb4cc13pKa8IAv17/9Tlv49CpQ/b8Hed35A39b7Cv5BzJOMIan9Sw+x8Y/kCuwDNs5bBccwaPqdijIkny6t5XEwZ4Ouv0OdcZDRR4xE0UeCKUjruIiESTL9Z8QRhg3SF1CQO8f/j9Icf5V2hbtX/VeW2n1+JeuUJKozGNSJK/7P+FFbpV4PCVw/mPyf/INW7irxNzzRc8puqAqiTJmwfeTBhg6snU86oz0inwiJso8EQoHXcREYkm3X/sbt+qBgO8oucVAf0nzpxgjx970GN4ApaMPlcLdy7MFVJenPai3e9fCKHj/I65xvmfB8qp5+KevKjrRfaYOwfdSfLsinPbDm87rzojnQKPuIkCT4TScRcRkWjynxn/IQxw5KqRLN+tPOM6x9Hn89Hn8/GrdV8xvmt8QPg4X8dPH88VZHr82CPXuJzP9Pxp9J9Yuktp7j62O+ScOZ8/qj+yPkny4ZEPX9CVqEinwCNuosAToXTcRUQkmrSa1IowwBlbZrBy38qEAZ44cyLky0TrDKlzQdsKnu+z5Z/lGjN5w2S73+fz8WjG0bDz5QxRj3/5OEnaq761ntKau47uuqB6I5ECj7iJAk+E0nEXEZFIku3NzrO/wegGhAEu37uct392O2GAu47u4ohfRthh4o+f/JEfL/uYy/Ysu6Ba/O/L8X+W/LYk1xifz8fuP3bn0j1L850v5/t//jvzvyTJZ75+JmDJ6lijwCNuosAToXTcRUQkUqzYu4LxXeM5cOnAsGNqfVaLMMCdR3ay/sj69u1gH/zwgb1q2/HTxwulnv0n9nNo8lDuOrqLs7bOKpQ5O87vyGcnP8tDpw6RJF/67qVCuQUvUinwiJso8EQoHXcREYkU1/e7Pt8T/2v7XEsY4KnMU2z2VTPCAOdtn8dXpr9CGOCoVaOKseIL13tJb3ufL/7gYqfLKXQKPOImCjznYPTo0axfvz4rVarEevXq8dtvv7X7MjMz2ahRI9544430eDysUqUKGzZsaH9q1arFxMREzp07t0Db0nEXEZFIkJ6Zbp/4V+5bOeQYn8/HskllGd/V/Hfr+anP20tUN5/QPOwKaZEs25vNedvnEQboMTzMzM50uqRCpcATHW4DMA3AAQApAOYBSDzHORIADAKwx5pjNYB/5TG+FIB3AGyyxu8A0B1AfB7faQZgqTV+P4CxAK4LMa4EgOYARgL4FcBe6zvLALS1th3MAHDCmjf4syqPmnJS4CmgY8eO8bXXXiNJ7tixgy1atOCUKbnfIj1mzBh6PB4uWRJ4P7HP52Pz5s1ZunRpLlq0KN/t6biLiEgkGLd2nB14gpea9jtx5kRAIHrj+zfs7yT0SCAMcG3K2uIsu9D4b9Xbfni706UUKgWeyFcXQDqAYQDKwgwDXQFkA2hcwDkuB7AFwAoAV1ltTQCcAdA7xHgPgEkADgO4x2q7GcBOAIsBlA7xnVcA+GAGFgC4DMBMAKkArg8am2CNXQqgmtUWB+Btq318iPk7Ang/9O4VmAJPAaWlpfH++0O/RC2nF198keXLl2d2du6HO6dNm0aPx8OXXnop33l03EVEJBK8OO3FgGdZMrIyco3ZcWRHwDts/j7x77lWUvM/GxNt/Ku1LdixwOlSCpUCT2QrAWAdzKsYwSFjFcyrIuULMM+nMIPE7UHt/a32ukHtLaz2/wS1P261vxXUXhnAaQBTgtqvhBmqpge1+wNPzRC1LrX6gkNSR+tzIRR4zkGNGjU4alTe9yDXrFmTDRo0CNk3aNAgejweduvWLd9t6biLiEgkaDGhRUBw2ZK2hePWjuPNA2/m5kObSZqLGsAAG4w2//0LXor6ip5X2C8EjTbtprUjDHDsmrFOl1KoFHgiWyLMk//BIfo6WH2t85kjHuYPvDlE373WHCOD2hcA8AK4Oqi9lDXXjqB2w5qnZYhtzELuAFMSZ68cBZuI0OFMgaeYDRkyhBUqVODGjRtD9h89epQlS5Zk165dc/WdOnWK1atX5y233MKjR8O/C8BPx11ERCLBo6MeJQzw1o9vJQyw84LOdpDp/mN3krSfdXly/JP293K++HPlvpVOlX/BuizsQhhgnyV9nC6lUCnwRLbOME/+24Xoe8zqG53PHPUR/jaxeKtvd462MgCyYN6KFsoy6ztVc7Qtstqqhxjfw+prk0+dgBmotsK8olUmqE+BpxgdO3aMPXr0YHx8PO+4446Q/5+qGTNm0OPx8Mcffwxo37RpE++//342btyY+/btK9D2dNxFRCQS3DnoTnuVteDb1F7//nWSZ1/y+dw3z9nfm7Jhij0uy5vlVPkXbEjyEMIA35z1ptOlFCoFnsg2HmZYaBKi7zarb1k+c7SzxvUL038E5tUc/2IE1a3x4RYCmGz1P5ajLdWa49IQ41+1xvfIo8aSAP4A4EuYt+klhhhjAJhqfbZa21wG80pXXgsp5FRkgSehRwLju8YXyyehR8L5/vdeICtWrOB9993H5ORk9u/fnx6Ph1OnTs017t1332Xp0qX5+uuvs3379mzfvj2ff/55VqlShX379j2nbSrwiIhIJLih/w2EAR5OP8xSnUsFBJ4nxj9BkvbLRV+b+Zr9vZX7VsbEO2ymbZpGGOAzXz/jdCmFSoEnsvlvB3s0RF9Vq29LPnP4b33rGqZ/D8yw8nvrz/7b3BaHGT/G6m+Voy3TmiMuxPg21vghYeZrAXP1NR+AlQAeCDOuI4D1AB60/lwe5u18J2CuOBcqbAVT4MnHzp07WalSJa5Zs4akeaWndOnSfOONN3KNTUxMDLmwwaFDh3jjjTeyZcuWBd6uAo+IiESCSz+8lCU7laTP57PfteP/1B5cm+TZ29c6LegU8N2v1n3FdSnrnCi70PifT3pk1CNOl1KoFHgimxsCj19FAO2tuYbCXLAhpwowV6kL5l/ZLdwVrJx0S1s+mjZtyjZt2gS0Va9enS+88EJAW2ZmJuPj4/nWW2+FnOe9994Le2UoFLcfdxERcV62N9teWpok6w6pGxB4KvaoSJL2cz39furnZLlFYu/xvfYzTLFEgSeyueWWtpy6W+NfK+B4f/DbWYCxCjx5SEtLY6lSpXK9LLROnTq5VlpbunQpPR5PyHfzkOTAgQPp8XjYs2fPAm3bzcddREQiw6FThwgDrDagGkny8S8fz/UcT5Y3y37vzvCVwx2uuPBlebNYslNJXvrhpU6XUqgUeCJbJ4RftKARCrZoQT0U7qIFS2GGmxtztOW1aIE/wDyXT51+D1jjFxZwfHlr/OkCjD0VFxfHcuXKhf3ExcW59sTbH2JyrqqWnZ3NChUqMDk5OWBs79696fF4mJqaGnKuZ555hh6Ph9OnTy/QthV4RETEaZsPbSYMsO6QuiQDA0+1AdUIAzx46iCfn/o8YYCT1k9yuOKiUblvZcIAj50+5nQp5yQpKSns+R0AwryLSCJQIsLfDvau1ffPfObIa1nq+6w5RgS1L7Dafx/UHoe8l6VuhdxmwwxIVXK01Yf57E4ota251uVou8TaRqkQ46tZ438LM19OusKThx07dtDj8TAj4+xL1mbNmsWaNWvmGtu0aVNWrVo15Dzr1q1jmTJlWLt27QJv283HXUREIsP0zdMJA2w8rjFJstWkVoQBXt37at499G7CAJfvXW6HoDnb5jhccdG4f/j9hAGuTVnrdCmFRld4IpsH5on/AQQu0+yB+aD+AQAX5WgvA6BSiHk+gRkKagW1D7Da6wS1N7fa/xvU3hThXzyaAeCboPZKMNN08ItHDQC/WvsRzP9MzvAcbVWsttohxr9h9X0Soi+YAk8+Hn74YU6cOJEkmZWVxQcffJAzZswIGOP1ennFFVfw6aefzvX9H374gddccw1r1KjB/fv3F3i7bj/uIiLivFemv0IYYP+f+5Mk9xzbw2e+fobbD29nwzENCQNMHJFoB57le5c7XHHR8Ae9aZumOV1KoVHgiXx1AKTDDADlYF7l6ArztrPGQWPXwrya0jSo/Xcwr/Ak4+zLRJvAvA2sV5jtTgRwGGdfEHoLgF0wFzMItTjByzCDR1uYQeYyADNhLjN9fdDYjtbYQQCutNrKAHgW5l/GvTBDlF8Va/wvMJ9d8o9/CsAxa98SwuxHTgo8+UhLS2Pr1q3Ztm1bPvXUU5wwYYLdd+DAAdarV4+33norS5QowauuuoqJiYn25/bbb2etWrXYp08fZmWd2zsI3H7cRUTEWV6fl1X6VSEMcNOhTbn6n5r4lL1wgT/wbDwY+sXc0a7DnA6EAb4641WnSyk0CjzR4TYA38IMDykA5gF4KMS4bwAcQuilnRNgBoy91hyrAfwrj22WAvAOzDCRAnNRgO4wQ1c4zWA+45MK8+WhYwFcF2Lc5QBehLkK3XZr/mMwA1sP5A4vJWA+s/Q5gA0wr2wdh3n1KwnAxXnUlJMCT4TScRcRESfN3zGfMMBan9UK2d9uWruAxQtKdynNzOzMYq6yeIxcNdLez73H9zpdTqFQ4BE3UeCJUDruIiLipEZjGhEG+NHSj0L2vzv33YDAs+3wtmKusPhkZmfyDx/9IaZua1PgETdR4IlQOu4iIuKUlJMp9u1q6ZnpIcf0WtzLDjsew8PTWaeLucri1WVhF8IAu//Y3elSCoUCj7iJAk+E0nEXERGn/LDrB8IA//zFn8OOGZo81A48V/W6qhirc8ak9ZMIA3x28rNOl1IoFHjETRR4IpSOu4iIOGXELyPyfUh/6sapduB5d+67xVidMzYe3EgYYO3BBX/FRCRT4BE3UeCJUDruIiLihNNZp/nWrLfyfH7HP67DnA78aOlHzPKe20qk0SjLm8XSXUrzoq4X0evzOl3OBVPgETdR4IlQOu4iIuKEG/rfYF+5mb55utPlRJQan9QgDHDHkR1Ol3LBFHjETRR4IpSOu4iIFLeMrIyAldc2H9rsdEkR5e8T/04Y4Hebv3O6lAumwCNuosAToXTcRUSkuO06uisg8JzJPuN0SRGl4/yOhAH2+6mf06VcMAUecRMFngil4y4iIsVt6Z6lAYFHAg1JHkIY4Juz3nS6lAumwCNuosAToXTcRUSkuOVceU2BJ7fpm6cTBthyUkunS7lgCjziJgUKPADsE3B9iufjP+YiIiLFIT0zPSDsPPbFY06XFHFWH1hNGOBDnz/kdCkXTIFH3CTfwJOQkOD4yb9bPwkJCcX0f/ZERMTtJm+YbIedR0Y9wuOnjztdUsQ5dOoQYYBVB1R1upQLpsAjbpJv4BEREZHY12txLzvwDFs5zOlyIpLP52OZLmVYLqkcfT6f0+VcEAUecRMFHhEREeH/Tfk/wgBrfVZLq7Plwf+eosPph50u5YIo8IibKPCIiIi43PK9y+2rO7+m/up0ORHtgeEPEAa4LmWd06VcEAUecRMFHhEREZd7f977hAGW6FSCWd4sp8uJaP6Xj36/9XunS7kgCjziJgo8IiIiLvfitBcJA/xy7ZdOlxLxXv/+dcIAh68c7nQpF0SBR9xEgUdERMTlnhj/BGGAcw0rLmsAACAASURBVLfPdbqUiNdnSR/CALss7OJ0KRdEgUfcRIFHRETE5R76/CHCANccWON0KRHvq3VfEQb4wrcvOF3KBVHgETdR4BEREXG56h9XJwxw/4n9TpcS8fwLPET7y0cVeMRNFHhERERcrmKPioQBZmZnOl1KxDuddZqlu5TmRV0vYrY32+lyzpsCj7iJAo+IiIiLeX1eluhUgpd9eJnTpUSNOkPqRP0tgAo84iYKPCIiIi6WcjKFMMCbPrrJ6VKihv8lrV+v/9rpUs6bAo+4iQKPiIiIi83eNpswwL+N+5vTpUSNDnM6EAb42fLPnC7lvCnwiJso8IiIiLjUiTMnGNc5jjDATgs6OV1O1Oj3U7+oP2YKPOImCjwiIiIukO3N5vwd83kq85TdNuKXEYQBwgCnbZrmYHXRZdzacYQBtpvWzulSzpsCj7iJAo+IiEiMyvJmcc2BNfT5fLx76N2EARrzDXp9Xn74w4f80+g/2YHn5JmTTpcbNeZun0sY4BPjn3C6lPOmwCNuosAjIiISo/r+1JcwwEdGPWIHm4s/uNhu93/mbZ/ndKlRZV3KOsIAHxj+AHcd3cVXpr/CtPQ0p8s6Jwo84iYKPCIiIjHq+n7XBwSbcJ99x/c5XWpUST2ZmusYvvTdS06XdU4UeMRNFHhERERi1F/G/sU+Ib/0w0tDhp0rel5Bn8/ndKlRxevz8to+1wYcxyZfNnG6rHOiwCNuosAjIiISoxqMbmCfkCfvS84Vdl6d8WpUvzzTSav2r+Jdg++yj+W9w+51uqRzosAjbqLAIyIiEqPuGHQHYYA7j+zk6azTuQLPnG1znC4x6p04c4IlOpXgZR9eFlVXyhR4xE0UeERERGKMz+fjqv2rWKlXpYAV2IIDz4aDGxyuNDbcPPBmwgDXpaxzupQCU+ARN1HgERERiTEjV420Q025pHJ2e3DgyflOHjl/HeZ0IAzwvXnvOV1KgSnwiJso8IiIiMSYeiPq2aHm2j7X2u05w06LCS0crDC2LPltCWGAj4561OlSCkyBR9xEgUdERCTGPDrqUTvY3DnoTrvd3/bitBeZ5c1ysMLYkpaeZh/bagOqcdqmaU6XlC8FHnETBR4REZEY02hMI/sEvNWkVnb7/B3z+c6cd+j1eR2sLjYFL/sd6RR4xE0UeERERGLMg58/aJ94Jy1McrocV6g9uLYCj0iEUuARERGJMdUGVLNPvD9b/pnT5bjC3yf+3T7ml3xwidPl5EuBR9xEgUdERCSGHMk4Yp94l+9WniknU5wuyRUGLh1oH/cq/ao4XU6+FHjETRR4REREYkjbb9sSBthgdAM9q1OMtqRtsQPPzQNvdrqcfCnwiJso8IiIiMSIbG82K/aoSI/h4a6ju5wux3Venv4yYYCVelVyupR8KfBEh9sATANwAEAKgHkAEs9xjgQAgwDsseZYDeBfeYwvBeAdAJus8TsAdAcQn8d3mgFYao3fD2AsgOtCjCsBoDmAkQB+BbDX+s4yAG2tbRfGPgRT4BEREYkRP+3+iTDAukPqOl2Ka5VLKseySWWdLiNfCjyRry6AdADDAJSFGQa6AsgG0LiAc1wOYAuAFQCustqaADgDoHeI8R4AkwAcBnCP1XYzgJ0AFgMoHeI7rwDwwQwsAHAZgJkAUgFcHzQ2wRq7FEA1qy0OwNtW+/hC2IdQFHhERERixIc/fEgY4Pvz3ne6FNe6uvfVhAFmZGU4XUqeFHgiWwkA62BeLQkOGatgXuUoX4B5PoUZJG4Pau9vtdcNam9htf8nqP1xq/2toPbKAE4DmBLUfiXMQDI9qN0feGqGqHWp1Rccks51H0JR4BEREYkR/vfvzNk2x+lSXKv6x9UJA9x/Yr/TpeRJgSeyJcI8mR8coq+D1dc6nzniYf7Am0P03WvNMTKofQEAL4Crg9pLWXPtCGo3rHlahtjGLOQOMCVx9spRsInIHWzOZx9CUeARERGJAdnebFboVoGlOpfiyTMnnS7Hte4bdh9hgOtT1ztdSp4UeCJbZ5gn8+1C9D1m9Y3OZ476CH+bWLzVtztHWxkAWTBvRQtlmfWdqjnaFllt1UOM72H1tcmnTsAMVFthXtEqk6P9XPchHAUeERGRKOX1eXnX4LsIA/aJ9t1D73a6LFf7y9i/EAa45LclTpeSJwWeyDYe5sl8kxB9t1l9y/KZo501rl+Y/iMwr+b4FyOobo1fFWb8ZKv/sRxtqdYcl4YY/6o1vkceNZYE8AcAX8K8TS/xAvchHAUeERGRKLI+dT2nbpxKktx3fJ+9FLL/89astxyu0N2e/vppwgC/2/yd06XkSYEnsvlvB3s0RF9Vq29LPnP4b33rGqZ/D8yw8Hvrz/5bxBaHGT/G6m+Voy3TmiMuxPg21vghYeZrAeCENWYlgAcKYR/CUeARERGJEutS1tnBZkvaFntVtpyfib9OdLpMV/MvTf3Fmi+cLiVPCjyRzQ2Bx68igPbWXENhLthwvvsQjgKPiIhIlOj+Y3c72IxdM5ZV+lXJFXg2HtzodJmu9t689wgDHLh0oNOl5EmBJ7K55Za2nLpb41/L0aZb2kRERFym3bR2drC56aObcoUdGGCWN8vpMl2tz5I+hAEmLUxyupQ8KfBEtk4Iv2hBIxRs0YJ6KNxFC5bCDBc35mjLa9ECf4B5Lp86/R6wxi/M0Xau+xDOqbi4OJYrVy7sJykpsv+DFRERcQv/A/F5fcRZw1cOJwzw9e9fd7oUJiUlhT2/A0CYdxFJBEpE+NvB3rX6/pnPHHkt6XyfNceIoPYFVnvwLWJxyHtZ6lbIbTbMgFQlR1t9mM/uhFLbmmtdjrbz2YdQdIVHREQkStT4pEbIkPPg5w/y/Xnv85f9vzhdoutN3jCZMMDnvnnO6VLypCs8kc0D88T/AAKXafYAWG21X5SjvQyASiHm+QRmKKgV1D7Aaq8T1N7cav9vUHtThH/xaAaAb4LaK8FM08EvHjUA/GrtR7C3rW0Mv8B9CEWBR0REJEpc8sElLNmpJC/+4GI77MzYMsPpsiSH+TvmEwbY9KumTpeSJwWeyFcHQDrMAFAO5rtqusK87axx0Ni1MK+mNA1q/x3MqyPJOPsy0SYATgPoFWa7EwEcxtkXhN4CYBfMxQxCLU7wMszg0RZmkLkMwEyYy0xfHzS2ozV2EIArrbYyAJ6F+ZdxL8wQdaH7EEyBR0REJAqcPHOSMMBr+lzDB4Y/YAeeX1N/dbo0yWHV/lWEAdYbUc/pUvKkwBMdbgPwLczwkAJgHoCHQoz7BsAhhF7aOQFmwNhrzbEawL/y2GYpAO/ADBkpAHbCfB6nXB7faQbzGZ9UmC8PHQvguhDjLgfwIsxV6LZb8x+DGdh6WLWGcq77EEyBR0REJArsPLKTMMBan9Xii9NetAPP8dPHnS5NcvC/G+maPtfwTPYZ7ju+z+mSQlLgETdR4BEREYkCyfuSCQN8ZNQjXJuylk2/asqPl33sdFkSwp2D7iQM8I5BdxAGuHDnQqdLykWBR9xEgUdERCQKfL/1e8IA/z7x706XIvno+1PfgEUlrulzDdMz050uK4ACj7iJAo+IiEgUGLtmLGGAL333ktOlSD4OnDjAkp1KBoSeSPvdFHjETRR4REREzsHBUwe55sCasP1en7dItjvg5wGEAXac37FI5pfCVXdI3Yh+R5ICj7iJAo+IiEgBbUnbwks/vJQlO5Xkqv2rcvW3ntKaMMCSnUpy5paZhbrtjvM7EgY44OcBhTqvFI2nv346IOyU7FTS6ZICKPCImyjwiIiIFFCHOR3sE9i3Zr2Vqz/nCW6ZLmX48+6fL2h7vZf05l/H/pUZWRl86buXCAMcu2bsBc0pxePVGa/musKTlp7mdFk2BR5xEwUeERGRAvKvugUDrPlpzYC+jKwMu++eofcQBtj227bntR2vz8vEEYn2fNM3T+eT458kDHDW1lmFsStSxOZun0sY4L3D7uWfv/hzxK3WpsAjbqLAIyIiUgCns07TY3h4yQeXMK5zHC/qehF9Pp/dvzVtK2GAdw66k3O2zSEM8MnxT57XtjYd2pTr6kBCjwTCALcf3l5YuyRFbOmepTycfphvz36bMBBRy4gr8IibKPCIiIgUwOZDmwkDvGvwXaw2oBphgKknU1l/ZH02HNOQ83fMJwywyZdNuHzvcsIAG4xucM7b8fq8fGTUI7kCDwywbFJZZnuzi2DvpCiNXj2aMMAXp73odCk2BR5xEwUeERGRApi1dZZ91abB6AZ2+PGHkU+Xf0oY4MvTX+aWtC2EAdYdUpekGWK2pm0NmG/f8X08nH6Y78x5hyv2rrDbJ2+YbM/Ze0lv/mfGf8LeRifR4Zf9vxAG+IeP/sDM7EynyyGpwCPuosAjIiJSAINXDCYM8M1Zb/KxLx7LdfWlyZdNCAPsubgnU0+m2ie4JPnCty/Yz+KQ5I4jO3J9f/+J/STJ9rPb220/7/6ZJ86csP/89NdPO7b/cv6yvFm8sf+NhAFe2fNKnsk+43RJCjziKgo8IiIiBeBfoe3jZR9z2MphIW85gwHO3T6XZ7LPEAZYqVclJu9Ltvv8t7iNXDUy1/cm/DqBJO0w9f689+1tt5/dnn/+4s/ccWSHE7suheDHXT/av/Wvqb86XY4Cj7iKAo+IiEgBNJ/Q3L5Kk+3N5hvfv5ErtHgMD4+fPk6SLJdUjmWTyvLxLx8PWKra6/Oy95Leub7bZWEXZnuzWbFHRcIAj2QccXiPpbD9c8o/7d/7t6O/OVqLAo+4iQKPiIhIHr5c+yVX7F3BWz++lTDAXUd3kSR/O/obK3SrEBBa/vjJH+3vXdXrKru9Yo+KrNSrkr3Qgf8Wt5yfVpNa2Qsf+J/9kdjSbVE3+/c25huO1qLAI26iwCMiIhLG9sPbA0JJhW4VApaiPpN9hruO7rL723zTxu67eeDNdvvgFYPt9+qsPrCa9UfWt/su+/Ay+3/3Xw3qs6SPE7srRSznrZDdFnVztBYFHnETBR4REZEwvt30bUDguXvo3SHH+ft7Lu5pt93Q/wa7/dCpQ3xq4lMBc13U9SIezTjKbG82//DRHwL6ftj1Q3HtohSjAycO2L/xK9NfcbQWBR5xEwUeERGRMHot7mWfoF7Z80p+uvzTkOP8z/dsPrTZbvMvPnDX4LtIkq9//3pAqHlz1pv22LT0NJbqXMruSz2ZWrQ7Jo6ZvW22vby5kxR4xE0UeERERMJ4furzhAGOWzsuz3Hpmencd3xfQNuKvSuYtDCJJ8+cJEm+P+99O9B8te4rns46HTC+8bjGdn/O2+YktqxLWUcY4P3D73e0DgUecRMFHhERkTDqjahHGGDyvuQLnqv7j93tQBPK6NWj8+yX2JCWnkYY4I39b3S0DgUecRMFHhERkTCqf1w94KWgFyI9M50vT3+Zaw6sCdnv9XnZc3FPLv5t8QVvSyKXz+djmS5lGN813tEreQo84iYKPCIiImEk9EggDDDLm+V0KRJDrut7HWGAx04fc6wGBR5xEwUeERGRELK8WfQYHl7e/XKnS5EYc/fQuwkD3Hhwo2M1KPCImyjwiIiIhOBfQrj6x9WdLkVijP99S/N3zHesBgUecRMFHhERkRDWHFhDGGDiiESnS5EY025aO8IAx64Z61gNCjziJgo8IiIiIczZNocwwOYTmjtdisSYLgu7EAbYe0lvx2pQ4BE3UeAREREJ4Ys1XxAG+NJ3LzldisSYoclDCQN8beZrjtWgwCNuosAjIiISQucFnQkD7LW4l9OlSIxZvnc5YYB3Db7LsRoUeMRNFHhERERCaDmpJWGA32761ulSJMZ4fV4m9Eigx/Dw4KmDjtSgwCNuosAjIiJimbpxKsslleOS35bwjkF3EAa4JW2L02VJDPrL2L8QBvjjrh8d2b4Cj7iJAo+IiIgFBgI+cZ3j9NJRKRKtp7QmDPCbjd84sn0FHnETBR4RERFLcOBp9lUzp0uSGPXG928QBjh85XBHtq/AI26iwCMiImIJDjy7j+12uiSJUR/88AFhgD1+7OHI9hV4xE0UeEREREieyjwVEHamb57udEkSwwavGEwY4Nuz33Zk+wo84iYKPCIiIiSX7VkWEHh+O/qb0yVJDJu0fhJhgG2+aePI9hV4xE0UeERExJXWpqzlk+Of5N7je0mSA5cOJAzwip5XsOuirg5XJ7Fu4c6FhAE2+bKJI9tX4BE3UeARERFXuqbPNYQBNp/QnCT5zNfPEAY44dcJDlcmbrAuZR1hgA8Mf8CR7SvwiJso8IiIiOtkZGXYt65VG1CNJHn7Z7cTBrjt8DaHqxM3OHjqIGGA1/a5lj6fr9i3r8AjbqLAIyIirrNy38qA53UW7lzI8t3KM65zHLO92U6XJy5x00c3EQa4NW1rsW9bgUfcRIFHRERiXmZ2Jr0+r/3nqRunBgSeuwbfRRjgTR/d5GCV4jbtprUjDHBI8pBi37YCj7iJAo+IiMS0lJMpTOiRwH9N/Zfd9vGyjwkD/M+M/zCuc5wdfBqNaeRgpeI2Ti5NrcAjbqLAIyIiMa3/z/3tQON/VqL97PaEAX66/FM++PmDdv+/v/u3w9WKm0zfPJ0wwFaTWhX7thV4osNtAKYBOAAgBcA8AInnOEcCgEEA9lhzrAbwrzzGlwLwDoBN1vgdALoDiM/jO80ALLXG7wcwFsB1IcaVAfB3ANNh7lOaNX4CgNph5jYAnLDGBX9W5VFTTgo8IiIS05p+1dQONJsPbSZJtprUijDAaZumsfOCznb/yFUjHa5W3GTNgTWEAT74+YPFvm0FnshXF0A6gGEAysIMIl0BZANoXMA5LgewBcAKAFdZbU0AnAHQO8R4D4BJAA4DuMdquxnATgCLAZQO8Z1XAPgAtLX+fBmAmQBSAVwfNHaANfZ9a5/88ycDyATwlxDzd7TGXwgFHhERiWmV+1a2A83o1aOZcjKFl3xwCWGAaw6s4eLfFutlo+KIw+mHCQO8of8Nxb5tBZ7IVgLAOphXMYJDxiqYV1LKF2CeT2EGjNuD2vtb7XWD2ltY7f8Jan/can8rqL0ygNMApgS1XwkzVE0Pav8IwOwQdd5izb8zRF9H63MhFHhERCRmnThzImBxgnbT2vF/c/9HGOC9w+6l1+dlZnYmqw6oynuH3et0ueIyPp+PF3W9iKW7lA5YVKM4KPBEtkSYAWBwiL4OVl/rfOaIh/kDbw7Rd681x8ig9gUAvACuDmovZc21I6jdsOZpGWIbs6y+nFd5/gygfph6D1vbrhTUrsAjIiKSh+V7lxMGeGXPKwOCDwxw0vpJ9riMrAyeyT7jYKXiVjcPvJkwwD3H9hTrdhV4IltnmGGhXYi+x6y+0fnMUd8aNz5EX7zVtztHWxkAWTBvRQtlmfWdqjnaFllt1UOM72H1tcmnTr8T1vbLBrUr8IiIiORhSPIQwgBfnPYiL/7g4oDAsyVti9PlifDJ8U/az5MVJwWeyDYeZlhoEqLvNqtvWT5ztLPG9QvTfwTmFRX/YgTVrfHhFgKYbPU/lqMt1Zrj0hDjX7XG98inTgD4ozV2cog+A8BU67PV2uYymFe68lpIIScFHhERiVn+Fdi+Xv81v9v8HesMqWMHnuK+hUgklG6LuhEG2GVhl2LdrgJPZPPfDvZoiL6qVt+WfObw3/rWNUz/Hphh5ffWn/23uS0OM36M1d8qR1umNUdciPFtrPFD8qkTMBdmOAHgphB9HQGsB/Cg9efyMG/nOwFzxblQYSuYAo+IiMSkvcf3EgZYqVclZmZnkjSfmfjf3P/x42UfO1ydiGnmlpmEATb7qlmxbleBJ7K5KfD8GebKc6GeAwKACsh9mxsAvI28r2DlpMAjIiIxZ9/xffaVnH9M/ofT5YiEte3wNsIA7xx0Z7FuV4Ensrnllra6MK/UvJzHmHD8wW9nAcYq8IiISMwZtnKYHXg+/+Vzp8sRCet01ml6DA8r9qhYrNtV4IlsnRB+0YJGKNiiBfVQuIsWLIUZbm7M0ZbXogXdrb7nwsxXB+bKbMFLYBdUeWv+0wUYeyouLo7lypUL+0lKSirW/wBFREQuVPvZ7e3Ac+z0MafLEclTpV6VCANMz0wv1HmTkpLCnt8BIMw7kiQCJSL87WDvWn3/zGeOvJalvs+aY0RQ+wKr/fdB7XHIe1nqVshtNsyAVCVE390wrzC9FNReA4HvF7rE2kapEHNUs7b9W4i+YLrCIyIiMefxLx8nDHDOtjlOlyKSr7sG30UY4OZDm4ttm7rCE9k8MF88egDmlZec7aut9otytJdB7vfXAMAnMENBraD2AVZ7naD25lb7f4PamyL8i0czAHwT1F4JZpoOfvEoYD4rdARA2xB9O3F2cQLADEs+ALVDjH3D6vskRF8wBR4REYk5twy8xZF3m4icj2ZfNSv2gK7AE/nqAEgHMBxAOZhXObrCvO2scdDYtTCvpjQNav8dzCs8yTj7MtEmMG8D6xVmuxNh3mp2j/XnWwDsgrmYQajFCV6GGTzawgxklwGYCSAFgS8dBcwrS8dhhraOMK/e5PwcQejA8wvMZ5cAM9w9BeCYtW8JYfYjJwUeERGJKVneLMZ1jmP5buXp8/mcLkckX69Mf4UwwNGrRxfbNhV4osNtAL6FGR5SAMwD8FCIcd8AOATggRB9CQAGAdhrzbEawL/y2GYpAO/ADBMpMK+6dIcZusJpBvMZn1QA+wGMBXBdiHGTYQYzL8wgE/zxIjDwlID5zNLnADbAvLJ1HObVryQAF+dRU04KPCIiElO2pG0hDPCOQXc4XYoU0Pr15Icfkl27kklJ5Lvvko0bk7ffTjZoQFarRl51Ffnww+TUqeTKleTmzeT+/WQsZNpOCzoRBtjvp37Ftk0FHnETBR4REYkp0zZNIwzwqYlPOV2KhOHzkUuWkK1akTffTALn/7n6avLpp8nhw8nDh53es/MzcOlAwgDfm/desW1TgUfcRIFHRERiSq/FvQgD7LSgk9OlSJDTp8mxY8k6dQJDy3XXkW+/TXbubF7l6dePnDOH3L6d/Plncvdu8tgx8r33yMcfN6/63HsvedNNgfOUKEHedRc5dCh5/LjTe1tw49aOIwyw3bR2xbZNBR5xEwUeERGJKc9OfpYwwPHrxjtdilj27SP79ycTEs6GkxtuMIPN1q0XdlvagQPkuHFk8+ZkqVJn5y9Xzmzbvbvw9qOozNo6izDAFhNaFNs2FXjETRR4REQkpvzxkz8SBrjt8DanS3G9I0fIZ58NvArToAE5eTKZnV342/N6yRkzyGbNyNKlze2VLEm+/z6ZlVX42yssK/etJAzwkVGPFNs2FXjETRR4REQkZpw8c5IlOpXgZR9ephXaHHTwINmxo7nQAECWL28GnUWLireGZ589G3weeYQ8erT4tn8udh3dRRhgrc9qFds2FXjETRR4REQkZgxbOYwwwEZjGjldimuNGEFefPHZKzp/+5t525lT1qwxV3nz30Y3ZUrkrex28sxJwgAr961cbNtU4BE3UeAREZGYUXtwbcIAZ22d5XQprrN6NdmwoRksPB7yH/8g1651uirToUPmFZ6ct9WlpTld1Vk+n49lk8qyXFK5YrsyqcAjbqLAIyIiMSHLm8XSXUozvms8s71F8ICIhHTiBPn66+azMv5loqdMcbqq3Hw+cvx48vrrzTqrViU3bHC6qrNu6H8DYYBHMo4Uy/YUeMRNFHhERCQm/Jr6K2GAdw2+y+lSXOObb8hrrzUDRNmy5pLSGRlOV5W3tDTy0UfNmn/3O3LSJKcrMj0w/AHCANemFM9lMQUecRMFHhERiQlfrv2SMMDnvnnO6VJiWnY2OXMmWb/+2VvEGjYkt0XRonhZWWTbtmfrf+op8swZZ2t6auJThAHO3DKzWLanwCNuosAjIiIxocOcDoQB9vupn9OlxKw5c8jq1c8GhauuMm8Ti7RFAArq66/JK64w9+XBB8n9+52r5c1ZbxIGODR5aLFsT4FH3ESBR0REYsJfx/6VMMB52+c5XUrMycoiP/rIXIwAIGvWJIcNi/zb1wpi27azt+XdfTd5+rQzdfT7qR9hgMZ8o1i2p8AjbqLAIyIiUS/1ZCphgDDAg6cOOl1OTPn5Z/KWW86uvjZwoPmCz1hy7JgZ4gBzdTknrlhN3jCZMMBnvn6mWLanwCNuosAjIiJR70+j/0QY4DV9rnG6lJjh85Fvv332qk7t2uT8+U5XVXS2bycrVjT39b33HNj+4e12aF+4c2GRb0+BR9xEgUdERKLa2pS19olicT3wHet8PvOkHyDLlyf79TMXK4h1y5aR8fHmfn/xRfFu2+fz2X+PYaDIt6fAI26iwCMiIlGtzTdtCAPsvKCz06XEhMOHyWbNzJP+cuXIxYudrqh4TZli7ntCgvnC0uJUd0hdO/AU9bukFHjETRR4REQkql364aUs2akkU0+mOl1K1Nu6lbzhhrMvEF1Y9HdWRaSWLc1j8EzxPE5j23Z4mx14dh3dVaTbUuARN1HgERGRqHU04yhhgFX6VXG6lKi3aBFZqZJ5ot+gAXnQxWs/HDhAXn65eSw++KB4t/23cX8rlud4FHjETRR4REQkavmf33lg+ANOlxK1vF6ySxeyRAnzBL9lSzIz0+mqnDd7NlmmjHlMZswovu2+Mv0VwgBH/DKiSLejwCNuosAjIiJR67vN3xEG2HJSS6dLiUqHD5NNmpgn9aVLm+/aidaXiBaFkSPNY1OpUvFd8er7U99ieR+PAo+4iQKPiIhErUdGPUIY4Nuz33a6lKizYQNZubJ5Qn/tteSKFU5XFHl8PvLJJ81j1LRp8YTBKRumEAb47ORni3Q7CjziJgo8IiISlX7Z/4v9gPfApQOdLieqzJ1LXnqpeSL/5z+TKSlOVxS50tLMBRwAcsyYot/eqv2rCAN86POHinQ7CjziJgo8IiISlT5Z9okdeLakbXG6nKgxdSpZtqx5GdXw0wAAIABJREFUAt+uHZmV5XRFkW/6dPN4Va5MpqcX7bb8C3FU7lu5SLejwCNuosAjIiJRqfWU1oQBjls7zulSokJWFtm+vXniDpgLFUjBPfKIedzaty/6bV324WUs0akEM7OLbvUIBR5xEwUeERGJSrd9epuu7hRQevrZE/b4eHL0aKcrij5btpgLO5QpQ+7ZU7Tbunvo3YQBrthbdA9WKfCImyjwiIhI1MnyZrFMlzKM7xpPr8/rdDkRLTWVfOghM+xUq0auW+d0RdHr3/82j+OTTxbtdt6d+y5hgB/8UHQvAVLgETdR4BERkaiz+dBmwgDvHHSn06VEtHXryOuuM0/Sb76Z3L3b6YqiW1oaeeWV5vFcvLjotjN/x3zCAP80+k9Ftg0FHnETBR4REYk6/qV7n/76aadLiVjJyeTll59die3YMacrig0ff2we0/r1i24bh04dIgzwhv43FNk2FHjETRR4REQk6jw7+VnCAHsu7ul0KRFpzhyyQgXzxLxNGzI72+mKYsfp02ffXzRvXtFsw+fz8ZIPLmHJTiWLbOECBR5xEwUeERGJKkcyjrBEpxIs3608D5065HQ5EWfcODIuzjwhf+010qtHnArd0KHm8b3vvqJ7Gemdg+4s0kU5FHjETRR4REQkqvyw6wfCABuOaeh0KRGnX7+zy0736uV0NbErM5O88UbzOM+YUTTbaD6hOWGAM7fMLJL5FXjETRR4REQkqgxaMYgwwNe/f93pUiKGz0e+9ZZ5Al6qFDlmjNMVxb5Ro8zjXbt20Vzl6TCnA2GAA5cOLPzJqcAj7qLAIyIiUeFIxhGOXTOW//7u34QBDl853OmSIkJmJvmPf5gn3xddRH7/vdMVuUN2trnyHVA0x3zYymGEAf535n8Lf3Iq8Ii7KPCIiEhUuGXgLYQB+/Pz7p+dLslxx4+TDRuaJ90VK5LLlztdkbsMG2Ye+8ceK/y5F+5cSBjg38b9rfAnpwKPuIsCj4iIRLwNBzcEhB0Y4PHTx50uy1G7dpE1apgn3DfcQG4pmmfbJQ8ZGWRCgvkbbNxYuHPvObaHMMDqH1cv3IktCjziJgo8IiISsU6cOcHNhzbzm43fBISdyn0rO12ao9LSyCpVzBPte+8lU1Odrsi9/vc/83do165w5/X6vCyXVI5lk8rS6yv8pfYUeMRNFHhERCRi/d+U/yMM8K9j/xoQeB76/CGnS3PMmTNnb2P705/MqwzinL17zWXA4+PJw4cLd+5bP76VMMDdx3YX7sRU4BF3UeAREZGIM3PLTCaOSMx1G9s9Q+9hfNf4Ilu5KtIdOkQ+9JAZdq6/vvBPsOX8PP20+Zv06FG48zYe15gwwAU7FhTuxFTgEXdR4BERkYhzXd/rcoUdGOB3m79jtjfb6fIcsXHj2Xe/3HILuW2b0xWJ34oV5u9y7bVkVlbhzfvazNcIAxyaPLTwJrUo8IibKPCIiEhECbVAgf+z5sAap8tzxKZNZKVKZ29jO3rU6Yok2P33m7/PhAmFN+cnyz4hDLD97PaFN6lFgUfcRIFHREQiyj1D7wl5K1vFHhV5KvOU0+UVu23byGuuMU+mW7Ys3CsIUngmTjR/o/vuK7w5f9j1A2GAD37+YOFNalHgiQ63AZgG4ACAFADzACSe4xwJAAYB2GPNsRrAv/IYXwrAOwA2WeN3AOgOID6P7zQDsNQavx/AWADXhRhXBsDfAUyHuU9p1vgJAGoX4j4EU+AREZGIkZGVwZKdSrJMlzJsN60dYYA3fXQTM7MzmZ6Z7nR5xW7LlrNhp1kzhZ1IlpVFXned+VstW1Y4c2ZmZ/KSDy5hyU4leSTjSOFMalHgiXx1AaQDGAagLMwg0hVANoDGBZzjcgBbAKwAcJXV1gTAGQC9Q4z3AJgE4DCAe6y2mwHsBLAYQOkQ33kFgA9AW+vPlwGYCSAVwPVBYwdYY9+39sk/fzKATAB/KYR9CEWBR0REIsbyvcvtVdgysjL4v7n/4+ZDm50uyxEbN5JXX22eQDdtaq7OJpGtVy/z92rVqvDmfHL8k4QBjl83vvAmpQJPpCsBYB3Mqx/BIWMVzKsc5Qswz6cwA8btQe39rfa6Qe0trPb/BLU/brW/FdReGcBpAFOC2q+EGUimB7V/BGB2iDpvsebfWQj7EIoCj4iIRIwhyUMIA3x1xqtOl+KoX389+8xO8+ZkZqbTFUlBHDlCXnQRWaqUuVx1Yfj8l88JA/znlH8WzoQWBZ7IlgjzZH5wiL4OVl/rfOaIh/kDbw7Rd681x8ig9gUAvACuDmovZc21I6jdsOZpGWIbs6y+nFd5/gygfph6D1vbrpSj7Xz2IRQFHhERiRj/nfnfIluVKlokJ5NXXKFndqLVSy+Zv9277xbOfHuP7yUMsNqAaoUzoUWBJ7J1hnky3y5E32NW3+h85qhvjRsfoi/e6tudo60MgCyYt6KFssz6TtUcbYustuohxvew+trkU6ffCWv7ZXO0nes+hKPAIyIiEWHX0V32IgU/7PrB6XIcMXeueYUAIJ99lsx25wrcUW3TJvP3u/xyMr0QHjvz+rws2akkL/3w0v9n777Do6j2N4C/CQkQBERARMCLXLGADXtDwfKzYAGxoIDdi14bdq8FHSBIB6VKEaRIr4KINJHekY6AEDoJPQRSd9/fH2ezySabvslsMu/nefI87jlnZr4ru5N5MzNnCr6ydBR4gtsEmIP5Zn76rvf0rcphHW95xn2XRf9JmDMqqZMR1PeM/yuL8VM9/U3StcV41lHJz/i2nvHdcqgTAK7xjJ2aoT2v7yErCjwiIhIUGvzQwBt4YuJi7C6nyC1aRFasaA6W//c/0uWyuyLJr0cfNf+Ow4YFZn0XdruQsMBkV+BO9ynwBLfUy8H+z09fXU/fzhzWkXrpW6cs+g/AhIWantepl4gtzWL8aE9/q3RtSZ51hPsZ/7pn/JAc6gTMxAxnAFyRoT2v7yErCjwiIhIU0k9D7Xa77S6nSPXrR5YpYw6SP/zQ7mqkoGbONP+WjRsHZn1X9buKsMDouOjArJAKPMHOSYHnUZiZ5/zdB6TAIyIiJUbHPzt6w07rKa3tLqdIffedOTgOCSG//ZZ0WNYrkZKSyAsvNP+ue/cWfH0NhzUkLHBrzNaCr8xDgSe4OeWStttgzuy8m0W/LmkTEZGglZiSyHdnvZvjvTgut4tbY7Z6w0617tWKqMLgMGWKCTphYeRvv9ldjQTSu++awNO5c8HX1XRs04Df26bAE9zaI+tJCx5B7iYtuBeBnbRgJUy4uCxdW3aTFnT19L2WxfpuhZmZLeMU2Onl9T1k5Wx4eDgjIiKy/ImMjAzYl0tEREqmGX/P4AezP/A+HHHEXyO8ISa7Zcp0LMPQ9qE+l7M5xZo1ZESEOSgeMcLuaiTQVq0y/7b16xf8rN2r014lLHDqtql5Wi4yMjLL4zsAhLkiSYJQY2R9OdiXnr6Xc1hHdlM63+VZx08Z2hd62jNeIhaO7KelboXM5sIEpEv99N0Oc3bm7Qzt18L3+UL5eQ/+6AyPiIgUyMYjG71h5anxT5Ekeyzt4W07ce6Ez/hey3rx7V/f5i2Db/EJOrDAAasG2PEWityhQ2TNmuaA+Kuv7K5GCoPbTV5xhfk3Xru2YOv6ZM4nAZ+uXWd4glsIzINHj8CceUnfvsHTfl669jLwfX5NqgEwoeCGDO19PO23Zmh/1tP+QYb25sj6waPxAKZnaK8Ok6YzPngUMPcKnQTwhp++KAD3FPA9+KPAIyIiBTJg1QCf0DJw9UDePexun9ep0oej1J8Lu13IhXsWMiE5wcZ3UXTi48nbbjMHwk89pdnYSrKOHc2/8wcfFGw93ZZ0Iyzw0zmfBqYwKvAUB7cCOAdgGIAImId/doK57KxphrGbYM6mNM/QXhnm7MhapD1MtBmABAA9stjuJJhLze7wvK4HYC/MZAb+Jid4FyZ4vAETyC4AMBtANHwfOgqYszKxMKHtG5gzROl/TiJz4MnPe8hIgUdERArkrZlvZQox6X9q9qzpnWa67W9tM/UnpiTa/A6KzunT5H33mYPgBg3IuDi7K5LCtHu3+be+6KKCPUB2c/RmhrYPZblO5RibEBuQ2hR4iofrAcyACQ/RABYAaORn3HQAxwDc7aevKoBBAA561rEBQJtsthkG4HOYkBENc9alK0zoyspTMPf4xAA4DGAMgNp+xk2FCWYumJCU8ceFzIEnP+8hIwUeEREpkHuG30NY4I2DbswUZlLP9Hww2/yJO3W2qc/nfU5YYIMfGthcfdE5fNiEHIC88kpy/367K5Ki0LCh+TefPbtg67lvxH2EBa4+uDogdSnwiJMo8IiISK653W5O3DKRh88cptvtZs9lPQkLLNOxDJfuW8pq3avxkdGPMLR9KD+b+xm3Hd1GWGC9fvXodrt5QZcLGNo+lPHJ8Zyzaw63H91u91sqEjt3knXqmAPf224jjx61uyIpKoMGmX/3Vq0Ktp42v7QhLHDcpnEBqUuBR5xEgUdERHJt9IbRhAVeN/A6dljYwXsm55s/vvEZdy7pHEkTkGr1qkVY4LJ9ywgLvLzP5TZUbp+FC9OeydKkiS5jc5oTJ8jSpcly5cgzZ/K/nq5LuhIW2GlRp4DUpcAjTqLAIyIiufbytJd97s2BBf647ke6s5l394PZHxAWeEXfKwgLbD6+eRFWbK/Jk8lSpUzYeekl80BKcZ7mzc1nYOTI/K9j4paJhAW+Ou3VgNSkwCNOosAjIiI5GrBqAF+c+qLP7GupISYnUSejWKp9Ke8y/Vb2K4KK7ffnn+R555kD3V69Cv4sFim+pkwxn4MHH8z/OtYeWktY4H0j7gtITQo84iQKPCIikq2dx3dmOQNbbv/a3HJyS+8yO4/vLOSK7bdggbmECSDbtbO7GrFbQgJZsSIZFmYuccuPQ7GHCAus379+QGpS4BEnUeAREZFsdVncJcvAsyhqUa7WceD0Abb9rS37ruxbyNXab+XKtLDz1Vc6syNGixbmMzF6dP6WT3YlM8QKYZWuVQJSjwKPOIkCj4iIZKv1lNbegBPaPpQLdi/g/+b+j72W9bK7tKCzYwdZtao5sP3f/+yuRoLJ2LHmc/H00/lfx4XdLiQsMCml4DeDKfCIkyjwiIhItm744QbvpWiBONAqqfbtIy+91BzUvvyyzuyIr1OnyPBwc19XfHz+1nHNgGsICzwYe7DA9SjwiJMo8IiISJai46JZqn0pRkRGMMWVYnc5QSs21jxMFCAfe0yzsYl/Dz1kPiMzZ+Zv+ftH3E9Y4NpDawtciwKPOIkCj4iI+OV2u9nk5yaEBT436Tm7ywlaSUnkU0+ZA9lGjfL/13sp+QYONJ+T11/P3/KtJrciLHDWjlkFrkWBR5xEgUdERPxavn85YYG1etXi6YTTdpcTlM6dIx991BzEXnwxeeCA3RVJMDt40HxWLrqIdLnyvvyncz4lLARk8g8FHnESBR4REfFr8JrBhAV+MucTu0sJSrGxZOPG5gD20kvJXbvsrkiKg1tuMZ+ZZcvyvuykLZMIC2w1uVWB61DgESdR4BEREb++mPcFYYH9V/W3u5Sgk5hINmxoDlzr1dOZHcm9yEjzufnss7wvezD2IGGBdb6rU+A6FHjESRR4RETEr9SHhf6641e7Swkqbjf5wgvmoPWaa8iYGLsrkuJk06a0oJwfgZqaWoFHnESBR0RE/LrzxzsJC9wSs8XuUoLKZ5+ZA9Zq1ch//rG7Gilu3G6yTh3zGdqxI+/L1+tXj7DA6LjoAtWhwCNOosAjIiJ+XdzjYsIC4xLj7C4laPTsaQ5Uy5cn16yxuxoprt5/33yOunfP+7J3/XgXYYFbY7YWqAYFHnESBR4REcnkdMJpwgJr9KxhdylBwe0mP/zQHKSGhZFz5thdkRRnf/xhPksNG+Z92SfGPkFY4JK9SwpUgwKPOIkCj4iIZLJi/wrCAu8fcb/dpQSFr74yB6gVK5K//WZ3NVLcJSeTF1xAhobm/R6wV6a9Qljg9O3TC1SDAo84iQKPiIhkMnz9cMIC3531rt2l2K5LFxN2ypUjlxTsj+oiXq1amc/VqFF5W+6j3z8iLHDYumEF2r4CjziJAo+IiGTyxow3CAscsGqA3aXYxu0mv/nGHJSWLk3OnWt3RVKS/Pyz+Wy1bJm35b5d9C1hgT2W9ijQ9hV4xEkUeERExMep+FMs16kcwzqE8cBpZz5gxuUi337bHJBGRJC/amZuCbCjR8mQELJKFTIlJffL/bD6B8ICP5/3eYG2r8AjTqLAIyIiPhbvXUxY4COjH7G7FNu89ZYJO5Uq6TI2KTy33WY+ZytW5H6ZiVsmEhbY5pc2Bdq2Ao84iQKPiIj4GPnXSEffvzNoUFrY2bDB7mqkJLMs81mzrNwvsyhqEWGBTcc2LdC2FXjESRR4RETER4eFHQgL7Lmsp92lFLnRo83MWaGh5OzZdlcjJd3SpXmfnnrX8V2EBd4y+JYCbVuBR5xEgUdERHy8Ou1VwgInb51sdylFatQoE3RCQsgff7S7GnGC5GSyQgXzbKfY2NwtczbpLGGBtXrVKtC2FXjESRR4RETEKz45npd+dylhgWsPrbW7nCKTPuwMK9hsvyJ58sQT5izPzJm5X+b8zuczrEMYXW5XvrerwCNOosAjIiJeXZd0JSywdu/ajE+Ot7ucIjFypAk6ISHk8OF2VyNO06ePCTxt2+Z+mav6XUVYYHRcdL63q8AjTqLAIyIiJEm3282r+19NWODivYvtLqdIDB+eFnZ++snuasSJtm0zgefqq3O/zH0j7iMscN2hdfnergKPOIkCj4iI8FT8Kd497G7CAq/qdxXdbrfdJRW6oUNN0AkNNWd5ROzgdpM1a5rQc+hQ7pZ559d3CAv8fsX3+d6uAo84iQKPiIhw7KaxhAXCAv+M+tPucgqV201+/LE5wAwNNU+8F7HTSy+Zz2Nug/fcf+YSFtj4p8b53qYCjziJAo+IiLDTok6EBX429zO7SylUKSnk22+bg8uKFckpU+yuSMRMhw6QL76Yu/GJKYkMsUJYpWuVfG9TgUecRIFHRET42vTXCAuctGWS3aUUmrg48vHH0x4qunSp3RWJGIcPm89ljRrmDGRuXNzjYsICzyWdy9c2FXjESRR4RETEexN0SZ2KOiHBPNwRIP/1L3LLFrsrEvFVr575fO7albvxtwy+hbDAncd35mt7CjziJAo8IiIOkJSSxAW7F2SaanrI2iFsPaU1wzqEERZ44twJmyosPElJZLNmaTNh5fbGcJGi1KaN+Yzmdmr0ZuOaERa4cM/CfG1PgUecRIFHRMQB+q3sR1hg/f71ue/UPpKky+1i6Y6lvZMV1O9f3+YqA8/lIp9/3hxIXnIJuWeP3RWJ+DdypPmcvvZa7sanztQ2esPofG1PgUecRIFHRMQBmo5t6g02T457kimuFN465FZv23mdzuO8f+bZXWZApaSQb75pDiKrV8/9pUIidtizx3xWr7gid+O7LO5CWGDnxZ3ztT0FHnESBR4RkRIuKSWJlbpU8oabmj1rcvCawd7XX83/iimuFLvLDKj4ePKJJ9ImKNiwwe6KRLLndpO1apnP7JEjOY+fuGUiYYGvTHslX9tT4BEnUeARESnh3v71bcICbx96O//V+1+EBe8MT60mt+LZpLN2lxhQsbHkffelTVCwcaPdFYnkznPPmc/t5Mk5j90Ss4WwwNuG3JavbSnwiJMo8IiIlEBnk86y+fjmvPenewkLrNi5Ince38kmPzfxntm5su+VdOd2Dtxi4vhx8rbbzEHjlVeS+/bZXZFI7vXvbz6777+f89jElESGdQhjhW8r5Ot7rMAjTqLAIyJSAk3bNs0bbGCB1h8WSbLrkq7etr4r+9pcZWDFxJDXX28OGG+4wbwWKU42bDCf35tuyt34q/pdRVjgodi8Tz2owCNOosAjIlICtVvQzifwbIkxD55JSkniWzPf4hsz3ihR9+388w9Zv745WLzzTvLkSbsrEsk7l8vccxYaai7NzEnq87PWH16f520p8BQP1wOYCeAIgGgACwA0zuM6qgIYBOCAZx0bALTJZnwYgM8B/O0ZvwdAVwDlslnmKQArPeMPAxgDoHYOdd0JYAsAdw7jLABnPOvN+PNXDsumUuARESnmYuJieHX/q9lrWS9v2yOjHyEs8IIuF7D5+OYl7tK19NavJy+6yISdRo1yd6AoEqwefdR8lufMyXnssxOfJSxw7j9z87wdBZ7gdxuAcwB+BFAWJoh0ApACoGku11EFwE4AawBc7GlrBiARQE8/40MATAZwAsAdnrarAEQBWAqgtJ9l3oMJLW94Xl8AYDaAGAB1/IyvCKA/gIMAYgG4cngP3wD4OocxOVHgEREp5rov7e49k+N2uzl4zWCGWCGMiIzg6YTTdpdXqBYtIitWNAeIrVuTiYl2VyRSMJ07m89zu3Y5j31r5luEBY7dNDbP21HgCW6hADbDnMXIGDL+gjmTUj4X6xkIE0YaZGj/3tN+W4b2Fp729zO0P+lp/zRD+78AJACYlqH9IphQNctPTeMBDAVQCSZI5SbwfJPDmJwo8IiIFHMtJ7f0Bp70z9YZsnaI3aUVqhkzyLJlzcFh27bmciCR4m7JEvOZvvfenMd+veDrfN+Pp8AT3BrDBIzBfvq+8PS9ksM6ysH8A+/w03enZx0jMrQvhAkgNTK0h3nWtSdDu+VZT0s/25jj6ct4lqdmuv+OggKPiIjkIMWVwmrdq/ncrxPWIYy/bP/F7tIK1YgRZKlS5sCwQwfzDBORkiAhgSxThoyIyPmMZZ8VfQgL/OaPb/K8HQWe4NYBJiy85aeviadvVA7ruM8zboKfvnKevv3p2soASIa5FM2fVZ5l6qZrW+Rpq+9nfDdP3+vZ1BgFBR4REcnBoqhF3oeJpgae/NzAXFy43eS335qgExJipvEVKWnuvtt8xpcvz37cmI1jCAt8+9e387wNBZ7gNgEmLDTz03e9p29VDut4yzPuuyz6T8KEjdTJCOp7xmc1EcBUT3+TdG0xnnVU8jO+rWd8t2xqjELOgccC8IvnZ5dnm6tgznRlN5FCego8IiLF2BfzviAssMviLly6bymX78/hCKkYS0kh337bHAiWKUNOmmR3RSKF44svzOe8e/fsx83ZNYewwBYTW+R5Gwo8wS31crD/89NX19O3M4d1pF761imL/gMwYSP1ErPUy9yWZjF+tKe/Vbq2JM86wv2Mf90zfkg2NUYhd2d4tgK4x/O6PMzlfGdgZpzzF7YyUuARESnGUqelXbpvqd2lFKqzZ8nmzc1BYKVKZrICkZJq1izzWX/iiezHrTu0jrDA+0bcl+dtKPAENwWeNBVgZqnL6DNkfwYrPQUeEZFiqt/KfoQFhlghPJd0zu5yCs3eveZBogBZqxa5ebPdFYkUrlOnzCWblStnPxnHkTNHCAu8su+Ved6GAk9w0yVtOUsNflG5GKvAIyJSDHX8s6P3np27h91tdzmFZvFislo1E3ZuuYU8cMDuikSKRoMG5nO/ZUvWY1xuF0t3LM3zOp2X52dtKfAEt/bIetKCR5C7SQvuRWAnLVgJE04uS9eW3aQFXT19r2VTYxTyH3jKe9afkIuxZ8PDwxkREZHlT2RkZAG/siIiEkibozf7zMpWUp+1M2ECGR5uDvpeeIGMj7e7IpGi88475rM/JIfZ5et8V4ewwJPxJzP1RUZGZnl8B4AwVyRJEGqMrC8H+9LT93IO68huWuq7POv4KUP7Qk97zQzt4ch+WupWyGwuTJi5NJsao5B94Dnfs40wP32Xe7a9L5vlU+kMj4hIMfPJnE+8Yaf38t52l1MoRo0iQ0PNAV+3bpp2Wpxn5Ejz+W/TJvtx9wy/h7DAjUc25mn9OsMT3EJgHjx6BObMS/r2DZ7289K1lwFQ3c96BsCEghsytPfxtN+aof1ZT/sHGdqbI+sHj8YDmJ6hvTpMmvb34NH0opB94LnUs92b/fR97OkbkMM2AAUeEZGg5na7M92f03BYQ8ICF+5ZmOfLWIqDH3809y+EhJCDB9tdjYg9tm41geeGG7Ifl/rg4Vk7ZuVp/Qo8we9WAOcADAMQAXOWoxPMZWdNM4zdBBMcmmdorwxzhmct0h4m2gzmMrAeWWx3EoATAO7wvK4HYC/MZAb+Jid4FyZ4vAETyC4AMBtANDI/dDSjKOQu8KyHuXcJMOHueQCnYd5b1Ry2ASjwiIgEFZfbxV93/Mp3fn2H4zaNY7sF7RhihfD+Effz0Z8fZd+VfVn+2/IM6xDGhOQEu8sNuEGDzEFeaKh5uKiIU7lcZIUKZFhY9pdzfjrnU8ICh6zN4dq3DBR4iofrAcyACQ/RABYAaORn3HQAxwDc7aevKoBBAA561rEBQJtsthkG4HOYMBENE0q6woSurDwFc49PDIDDAMYAqJ3F2EdgzgrFw4QdV7rXGe9ZCvWMHw5gG8yZrViYs1+RACpmU1N6CjwiIkHkx3U/+tyfk9XP9QOvt7vUgOvb14SdUqXIsWPtrkbEfo0bm+/EihVZj+m+tDthgZ0Xd87TuhV4xEkUeEREgshNg27KVeB5a+ZbdpcaMC4X+dlnaWFnwgS7KxIJDp98Yr4XfftmPWb4+uGEBX70+0d5WrcCjziJAo+ISJDYGrOVIVaIN9C0/a0tOy3qxHNJ5zhl6xR+veBrb+BZfXC13eUGREIC2bKlOag77zxy9my7KxIJHhMmmO/Giy9mPWbG3zMIC3xp6kt5WrcCjziJAo+ISBDYcGSDN8y0ntLa75iklCR2X9qdvZb1KhGTFZw8Sd57rzmgu+gicu1auysSCS579pjvR716WY9Zvn85YYGP/vxontatwCNOosAjIhIEvpr/lTfwzPx7pt3lFLroaPLaa83B3JVXmgM7EfHldpNVqpgZC2Nj/Y/ZdXwXYYG3Drk1T+tBOgDdAAAgAElEQVRW4BEnUeAREbHZ/N3zvWGn458dS8TZm+wcPZoWdu68kzx+3O6KRILXww+b78rChf77T8WfIizw39//O0/rVeARJ1HgERGxSWJKIoesHcImPzfxHrC43C67yypUMTHk9debA7i77iLPnLG7IpHg1q6d+b507+6/3+12M6xDGCt2rpin9SrwiJMo8IiI2KTDwg4+M69Fx0XbXVKhOnyYvPpqc/B2xx1ZX6IjIml++cV8Z559Nusx1XtUJywwMSUx1+tV4BEnUeAREbFJ3T51vWGnfv/6dpdTqHbvJi+/3By43X23wo5Ibh0+bL43/87mirVrBlxDWODB2IO5Xq8CjziJAo+IiA1OnDvhDTv3jbiPK/Zn82TBYm7ZMrJ6dXPQ9sADZFyc3RWJFC8XX2y+PydP+u+/96d7CQvccGRDrtepwCNOosAjImKDef/MIyzw8TGP211KoZowgQwPNwdrLVuSibm/4kZEPB591HyH/vjDf/8zE54hLHD+7vm5XqcCjziJAo+ISBE5nXDa+9/dlnQjLPCbP76xr6BC5HaTXbqY6XQBsnNn0yYieZc6cUHPnv77/zvzv4QFjt88PtfrVOARJ1HgEREpArN2zCIssP+q/iTJpmObEhY44+8ZNlcWeImJ5MsvmwO00qXJkSPtrkikeJs61XyfWrXy399uQTuf/UtuKPCIkyjwiIgUgYbDGnrv2XlhygssG1mWZSPL8kxiyZqX+cQJ8t57zcFZ1arkkiV2VyRS/EVFme9U/SzmNvl+xfeEBVp/WLlepwKPOIkCj4hIIUt2JbPCtxV8pqCGBTYf39zu0gLqn3/Iq64yB2ZXXknu2mV3RSIlg9tNVq5Mhob6n/RjzMYxhAW+8+s7uV6nAo84iQKPiEgh23dqH2GB1w64lm1/a0tYYM2eNfnPiX/sLi1gpk0jL7jAhJ3Gjc2ZHhEJnAceMN+vZcsy983fPZ+wwCfHPZnr9SnwiJMo8IiIFLJl+5YRFvjI6EeYkJzAWTtmMTah5DyIpk+ftMkJ2rTRTGwiheGTT8x3rF+/zH17Tu4hLLDBDw1yvT4FHnESBR4RkUI2bN0wwgL/88t/7C4loJKTyf/+1xyEhYWRw4fbXZFIyTV2rPmuvfpq5r5kVzLDOoSxwrcV6M7ldIgKPOIkCjwiIoVo/Obx3nt22i9sb3c5AXPmDNmkiTkAq1SJnDvX7opESra//zbftxtu8N9ft09dwgKPnj2aq/Up8IiTKPCIiBSShOQEn0kKBq8ZbHdJAREdTd50kzn4qlOH3L7d7opESj6Xiyxf3jzINyEhc/+Dox4kLHDlgZW5Wp8CjziJAo+ISCGZvn26T+CZum2q3SUV2O7d5OWXm7Bz883kkSN2VyTiHA0bmu/e2rWZ+96c8SZhgWM2jsnVuhR4xEkUeEREAuhc0jnuOr6LSSlJvOGHGwgL/Pj3j/ntom/pcrvsLq9A1q4lq1UzB1z/93/msjYRKTrvvmu+f0OHZu7rvrQ7YYGRf0bmal0KPOIkCjwiIgHUekprn7M6N/xwA5NSkuwuq8AWLiQrVEh72rtmYhMpekOHmu9g27aZ+yZvnUxY4KvT/Mxq4IcCjziJAo+ISIDEJ8dnerjo+sPr7S6rwGbMIMuWNQdaH35o7iUQkaK3cqX5Ht57b+a+9YfXExbYaHijXK1LgUecRIFHRCRAhq4d6hN2NkdvtrukAhs9mixVyhxkRUaaJ76LiD3i4swzr6pUyfxdPJ1wmrDAS3pdkqt1KfCIkyjwiIgEQHxyPKt0rUJYYP3+9dljaQ+7SyqwXr3MwVVICNm/v93ViAiZNmnIoUOZ+6p2q8oQK4QJyX6mcctAgUecRIFHRCSfjp877n3I388bfyYs8OHRD9tcVcElJpJvvmkOqsLDyTG5m/RJRIpA8+bmu/n775n7bh1yK2GB24/mPFe8Ao84iQKPiEgeud1uPjTqIe8Nwm63my0ntyQscNymcXaXVyAHD5K33572QNH58+2uSETSsyzz/ezePXPf85OeJyxw1o5ZOa5HgUecRIFHRCSPVuxf4XOvzpxdc1ijZw3CAg/F+rnOpJhYuJC86CJzMHXtteSuXXZXJCIZTZ5svqMvvpi578v5XxIW2G9lvxzXo8AjTqLAIyKSR69Pf917c3D64FO7d227S8sXt5vs1i1tcoKWLc3N0SISfHbuNN/TBg0y9w1eM5iwwE/nfJrjehR4xEkUeERE8iA6LprndTqP4R3Cue7QOp/A0/HPjnaXl2enTpFPPpl2v06/fpqJTSSYuVxkuXJk6dJkUoZHfE3dNjXXz+JR4BEnUeAREfEjITmB4zePzzTb0SOjHyEssNXkViTJ2Ttn88WpL3Lb0W12lFkgGzaQdeuasFOrFrl8ud0ViUhu3Hqr+d5u2eLbvmTvEsICnxj7RI7rUOARJ1HgERHJwO128+VpLxMW+N6s9/jV/K/4xbwvePzccYa2D2XFzhV5OuG03WUWyPjxZESEOWi6/34yJsbuikQkt157zXx3x471bd9+dDthgXcMvSPHdSjwiJMo8IiIpHMu6Zx3Brasfp6b9JzdZeaby0V++aU5WALIzz8nU1LsrkpE8uL7783394svfNuPnztOWGDdPnVzXIcCjziJAo+IiMe2o9vYZXEXwgKrda/GSl0q+Q0807ZNs7vUfDl8mHzwQXOgVK6cOcsjIsXPH3+Y7/Fjj/m2u9wulmpfipW6VMpxHQo84iQKPCIiJDce2chS7Ut5Q80v23/JNCkBLPD+Efd7HzZanMyfT1arZg6S6tQh16+3uyIRya9jx8x3uXbtzH3VulcjLDApJSlzZzoKPOIkCjwiIqT3zA4ssPy35Xk26SxJclHUInZb0o2JKYlMSE5giqt4Xf/lcpGdO5OhoeYA6fnnydPF+/YjEWHaHzAyTiF/df+rCQs8fOZwtssr8IiTKPCIiJB8YOQDhAV+Me8Lrjyw0u5yAuLQIfKBB9KmnB4wQFNOi5QUjRub7/batb7tjYY3Iixw45GN2S6vwCNOosAjIo63cM9CwgIv6HIBzyWds7ucgJg3j7zwQnNAdNll5KpVdlckIoH03/+a7/eoUb7tT094mrDABbsXZLu8Ao84iQKPiDjeC1NeICyw38p+dpdSYC4X2alT2iVsrVqRsbF2VyUigdanj/+Z2t6c8SZhgeM3Zz8riQKPOIkCj4g4WoorhVW7VWWIFcLouGi7yymQY8fMrE2AeQr7oEG6hE2kpJo3z3zXmzXzbW+3oF2u/oCjwCNOosAjIo51Kv4Ubx58M2GBd/14l93lFMj8+WTNmmkzN61ebXdFIlKYDh403/crr/Rt77OiD2GB1h9Wtssr8BQP1wOYCeAIgGgACwA0zuM6qgIYBOCAZx0bALTJZnwYgM8B/O0ZvwdAVwDlslnmKQArPeMPAxgDoHYOdd0JYAsAd47vIO/vISMFHhFxJJfbxXt/utc7M9vELRPtLilfkpPNw0NDQtL+2nvsmN1ViUhhc7vJ888nS5UiExPT2sdsHENY4Nu/vp3t8go8we82AOcA/AigLEwQ6QQgBUDTXK6jCoCdANYAuNjT1gxAIoCefsaHAJgM4ASAOzxtVwGIArAUQGk/y7wHE1re8Ly+AMBsADEA6vgZXxFAfwAHAcQCcAX4PfijwCMijjTyr5GEBYZ1CGOvZb2K5bN19uxJm6kpIkKXsIk4ze23m+//5s1pbXP/mUtYYIuJLbJdVoEnuIUC2AxztiRjyPgL5ixH+VysZyBMGGmQof17T/ttGdpbeNrfz9D+pKf90wzt/wKQAGBahvaLYALJLD81jQcwFEAlmCCVU+DJ63vwR4FHRBzB5XZ5n6ETlxjHGj1rEBb4+67fba4sf0aPJitUMAc7V1zhe8AjIs7wyitmHzAx3Qnq9YfXExZ470/3ZrusAk9wawxzMD/YT98Xnr5XclhHOZh/4B1++u70rGNEhvaFMAGkRob2MM+69mRotzzraelnG3M8fRnP8tRM999RyD7w5Oc9+KPAIyIlXoorhdcMuIb//v7ffHLck7zzxzsJC3x49MN2l5Znp06RrVubgxyAfPvtzA8eFBFn6N7d7Afat09ri4mLISzwir5XZLusAk9w6wBzMP+Wn74mnr5ROazjPs+4CX76ynn69qdrKwMgGeZSNH9WeZapm65tkaetvp/x3Tx9r2dTYxSyDzx5fQ9ZUeARkRJn6rap/Oj3j/jh7A+5dN9SLt+/3HuvTvqfsZvG2l1qnsyfT15yiTnAqVqVnDnT7opExE4zZ5r9wXPPpbW53W6WjSzLspFls71UV4EnuE2AOZhv5qfvek/fqhzW8ZZn3HdZ9J+ECRupkxHU94z/K4vxUz39TdK1xXjWUcnP+Lae8d2yqTEK2QeevL6HrCjwiEiJcjL+pN9wAwus3qO6d6KCKl2r8HTCabvLzZVz58i2bdPO6jz6KHn4sN1ViYjddu82+4Trr/dtv6LvFYSFbKfaV+AJbqmXg/2fn766nr6dOawj9dK3Tln0H4AJC6mXmKVeIrY0i/GjPf2t0rUledYR7mf8657xQ7KpMQrZB568voesKPCISIkxf/f8LMMOLHDHsR0kyWNnjzEmLsbmanNn9WryqqvMQU358uSQIZqYQEQMl8tMWFKmDJmSktb+fyP/j7DA1Qeznp9egSe4KfAYCjwi4njD1w/nE2Of4KboTWzzSxufcNPk5yY8FX/K+3rJ3iV2l5snSUnmuvywMBN2GjYk//nH7qpEJNg0aGD2Ebt2pbW9Nv01wgInb52c5XIKPMFNl7QZuqRNRBxrc/Rm1u1TN9uzOcv3LydJDl4zmO0WtCtW005v307ecos5iCldmuzWzfevtyIiqVq2NPuKX35Ja2u3oB1hgf1W9styOQWe4NYeWU9a8AhyN2nBvQjspAUrYcLFZenaspu0oKun77VsaoxC9oEnr+8hK2fDw8MZERGR5U9kZGQRfF1FRHLvpkE3ZRl0rh1wLTcc2WB3ifnidpP9+plLVFKvy9+40e6qRCSYdehg9hfdu6e19V7em7DA+1+/P8vjOwCEuSJJglBjZH052JeevpdzWEd2Uzrf5VnHTxnaF3raM14iFo7sp6VuhczmwoSZS7OpMQr5n5Y6q/fgj87wiEixEpsQ6xNw3p31rs/rHkt72F1ivhw7Rj7+uDlwCQ0lP/+cTEiwuyoRCXbjxpn9xn/+k9b20/qfCAv8YPYHWS6nMzzBLQTmwaNHYM68pG/f4Gk/L117GQDV/axnAEwouCFDex9P+60Z2p/1tH+Qob05sn7waDyA6Rnaq8OkaX8PHk0vCjk/eDSv78EfBR4RKTbOJp3lw6MfJiywbp+6XLx3MeMS49htSTfuPrGb07dP9z5ctDiZMYOsVcsctFxyCbl4sd0ViUhxsW6d2Xc0apTW9sv2XwgLfHnay1kup8AT/G4FcA7AMAARMA//7ARz2VnTDGM3wQSH5hnaK8OcHVmLtIeJNgOQAKBHFtudBOAEgDs8r+sB2AszmYG/yQnehQkeb8AEsgsAzAYQjcwPHc0oCjkHnvy8h4wUeESkWIhNiGXjnxoTFliuUznuPL7T7pIKLC4u7UnpANm0KXn8uN1ViUhxcuaM2X/UqJHWtmTvEsICnxj7RJbLKfAUD9cDmAETHqIBLADQyM+46QCOAbjbT19VAIMAHPSsYwOANtlsMwzA5zAhIxomlHSFCV1ZeQrmHp8YAIcBjAFQO4uxj8CcFYqHCTuudK/93bOUn/eQkQKPiBQLLSa2KLYzrvnz119p001XrkyOHavppkUkf2rUMPuSM2fM6y0xWwgLbDisYZbLKPCIkyjwiIitFu5ZyDdmvMFtR7d5204nnPbOqrb/9H7O+HuGN+y8Nv01u0oNCLeb7NvXPDcj9TKUAwfsrkpEirPGjc3+ZN068/rwmcOEBV7d/+osl1HgESdR4BERW/y641fuP73fZ8a1uf/M9Xl46EOjHvKZkCC769GLg+PHzWVrqRMTtG+v6aZFpODatDH7lXHjzOuE5ATCAi/ucXGWyyjwiJMo8IhIkVtzcE22z9DJ+FO9R3XeM/weHjt7zO7S823KFLJ6dXNQUqsWuWiR3RWJSEnRo4fZt3TokNZWrlM5lulYJstlFHjESRR4RKTIfbf8O59Ac37n831e//v7f/OdX9/hwNUDs31SeHFw7Bj5zDNpExM8/bQmJhCRwPrlF7N/ad06re1fvf9FWGBsQqzfZRR4xEkUeESkyLWa3Mon4CzZu4QprhQmu5IZExdjd3kB88cfZpppgLzoInJy8c5uIhKktm83+5lbb01ru33o7YQF/n3sb7/LKPCIkyjwiEihcLvdbLegHfuu7MumY5ty1IZRJMmYuBhW6lKJsMCPf/+YvZb1srnSwDt9mnzzzbSzOk88Yc70iIgUhsREslQpslKltNkenxz3JGGBf+z5w+8yCjziJAo8IlIo/oz6M9O9OJ/N/Ywf//4xYYEvTHnB7hILxW+/pZ3VqViRHDxY002LSOGrW9fsd2I8J8nf/vVtwgJ/3viz3/EKPOIkCjwiUihen/56thMRrD642u4SA+rECfLll9PO6jRpQu7fb3dVIuIUTZqYfc8Sz2PKOi3qRFhgj6U9/I5X4BEnUeARkUJRt09dwgLf+fUdrjqwiu0XtmfpjqUJC6zZs6b3OTslwdSpaTOwVa5MjhqlszoiUrTef9/sg4YNM6+HrRtGWOCHsz/0O16BR5xEgUdEAi71oXc1etbwaZ+0ZRKfGv8U1xxcY1NlgXXkCPnss2lndZ56yrSJiBS1AQPMfuh//zOvZ++cTVjgc5Oe8ztegUecRIFHRALK7XazXKdyhAU+Oe5Ju8spFCkp5uCiUiVzgFGtGjlxot1ViYiTzZtn9kfNm5vXG49sJCyw0fBGfscr8IiTKPCISEDtOLbDe5/O3H/m2l1OwK1da6Z+TT2r88oreq6OiNhv3z6zT7rmGvP66NmjhAVe3udyv+MVeMRJFHhEJKBG/DWCsMD//PIfu0sJqPh48tNPydDQtIOKxYvtrkpExHC5yIgIskwZ899ut5vhHcJZ/tvyfscr8IiTKPCISEC9Ou1VwgKHrRtmdykBs2QJWb++CToREWS3bmRSkt1ViYj4uu46s5+KijKva/euTVhgbEJsprEKPOIkCjwiEhDxyfHeWYFCrBAeij1kd0kFdvy4uWQt9fK1O+8kd+ywuyoREf+eftrsq+bMMa9vH3o7YYHbj27PNFaBR5xEgUdECuz93973ecZOVjfJFhduNzlhAnnRRebg4fzzzSQFKSl2VyYikrUvvjD7rH79zOsnxz1JWOAfe/7INFaBR5xEgUdECuRM4hmfsFO9R3W/f00sLlatIu++O+2szjPPaKppESkehg83+622bc3rN2a8QVjghM0TMo1V4BEnUeARkXzZd2oft8Rs4SvTXvGGnebjm9tdVr4dOEC+8EJa0Pn3v80DRUVEiotFi8z+67HHzOsv539JWODA1QMzjVXgESdR4BGRPHO73byy75XeoHN+5/O57tA6utwuu0vLs7NnyQ4dyHLlzIFCpUpkr15kYqLdlYmI5M2BA2Y/Vr++ed17eW/CAjv+2THTWAUecRIFHhHJsy0xW3wuY4v8M9LukvIsPp787juyenVzgFCqFPnOO+SxY3ZXJiKSPy4XWbas+XG5yJF/jSQssO1vbTONVeARJ1HgEZE8+3rB196w02pyK56MP2l3SbmWnEwOHkzWrJl2+dqjj5JbtthdmYhIwaVOoX/gADlrxyzvfjojBR5xEgUeEcnSxiMbeeOgG/nYmMc4eetkHjlzhM9Pep6wwND2odwcvdnuEnPN5SLHjSMvvzwt6Dz4ILlihd2ViYgEzmOPmf3bokXkqgOrCAt8ePTDmcYp8IiTKPCIiF/7T+/nhd0u9Ll0Lf3PZ3M/s7vEXHG7yd9+I2+4IS3o3H47+ccfdlcmIhJ4bdua/dzw4eTuE7sJC7x58M2ZxinwiJMo8IiIX1/N/4qwwIdGPcQHRj7gDToVO1fk6A2j6Xa77S4xR5s3m7M4qUHnmmvI6dNNCBIRKYn69DH7u6++ImMTYgkLrN27dqZxCjziJAo8IpKJ2+1m/f71CQtcf3g9zyWd4+fzPuf4zeOZ4gr+p2+eOEG+956ZiAAgL7mEHDVKDw4VkZJv5kyz32vZ0uzLK3xbgeEdwjPtuxV4xEkUeEQkk/6r+hMWeFW/q4rFmZxUKSlmQoKqVc0v/IgIM+X0uXN2VyYiUjS2bTP7v9tuM6+vG3gdYYF7T+31GafAI06iwCPiYDFxMdx3ah/jEuMYlxhHkmw9pbX38rXFexfbXGHuLV1K3nhj2uVrzz1H7ttnd1UiIkUrPp4MCTF/+CHJJ8Y+QVjgn1F/+oxT4BEnUeARKcGiTkbx3Vnvcv3h9STJHcd2sP+q/ly6bynbLWjH8t+W94ab6wdez6X7lnpfPzH2CZurz519+8jnn08LOtddR/75Z87LiYiUVLVqmf3h6dNk29/aEhb40/qffMYo8IiTKPCIlDAut4ubojfx0zmfesNLRGQEVx9czbp96mY561r6n6cnPM0ziWfsfivZOn6c/PJLc9kaQFauTPbvb56zIyLiZI0amf3i+vVk7+W9CQu0/rB8xijwiJMo8IiUMN8u+jbHQFO5a2U+NuYxfvPHN3xmwjN8ffrrDLFCCAu89LtLeS4peG96OXWKtCyyYkXzC71UKTNBwfHjdlcmIhIcXn3V7B8nTSLHbx5PWOCbM970GaPAI06iwCNSAmw/up2D1wymy+3itQOuJSzwsTGP8c+oP3ns7DHeOuRW1u9fn89Neo6rDqzi2aSzmdbhcrs4e+dsRp2MsuEd5Cw2luzUibzgAvOLPCSEbN2a3LHD7spERIJLZKTZT3btSi7cs5CwwCfHPekzRoFHnESBR6SYGbh6IO8Yegd/3fGrdwa1Wr1qeS9dgwVe0uuSYjW7Wnbi4swv7SpV0u7TadGC3LrV7spERILT2LFmX/nGG+S2o9sIC7zzxzt9xijwiJMo8IgUI4fPHPa5NK3R8EZsMbFFpkvWui7panepBXbuHNm7N1mtWlrQad6c3LjR7spERILbqlVmn/nAA+Txc8cJC7zs+8t8xijwiJMo8IgUE8v2Lcv2vpzSHUvzpakvsffy3nS5XXaXm28JCWTfvuTFF6cFnccfJ9ets7syEZHi4dgxs++sU8c8fDS8QzgrfFvBZ4wCjziJAo9IMZDsSuaVfa/0hpu9p/ay4bCGjIiMYL+V/bzTThdniYnkDz+kTacKkI88Yv5SKSIiued2k+efbyZ1SUoia/asSVjwmZBGgUecRIFHJMi53W5+OPtDb9hZFLWIJBmXGMfouGibqyu4pCTyxx/JSy9NCzoPPGAeJCoiIvnToIHZn+7ZQ9446EbCAvec3OPtV+ARJ1HgEQkibrebZ5PO+gSZ1GcohLYP5coDK22sLrBSUsiRI8nLLksLOvfcQy5caHdlIiLFX7NmZr+6YAH5wMgHCAtcdyjt2mAFHnESBR4RG22O3swXp77IZuOa8faht/vck1OjZw2uPriaV/e/mrDAH9f9aHe5AeFymRmErrwyLejccQc5b565DENERArugw/M/nXYMPKZCc8QFjh/93xvvwKPOIkCj4hNZv49kxW+rZDjQ0JhgVf0vaLYTzPtcpmH4F19dVrQueUW8rffFHRERALt++/Nfvbrr8n//PIfwgInb53s7VfgESdR4BGxwcYjGxnaPpSwwDIdy/CaAdewz4o+XHdoHT+b+xm7LO7Cf/X+lzfwzPtnnt0l51t8PDloEHnFFWlB54YbyBkzFHRERArL9Olmf/vii+Sncz4lLHDo2qHefgUecRIFHhEbtJ7SmrDAT+Z8kuWZm13Hd/H+Efdz9IbRRVxdYBw/Tnbs6PscnRtvJCdPVtARESlsGzak3Rv57aJvCQvssbSHt1+BR5xEgUekiLncLlbuWpmh7UN5/Nxxu8sJuN27yXffJcuV851eev58BR0RkaJy+rTZ/15yCTlw9UDCAr+c/6W3X4GneLgewEwARwBEA1gAoHEe11EVwCAABzzr2ACgTTbjwwB8DuBvz/g9ALoCKJfNMk8BWOkZfxjAGAC1sxmfl/dlATjjWW/Gn7+y2UZ6CjwiRSgmLoa1e9cmLPCmQTfZXU5ArVlDtmhBhoaaX7Lh4eRLL5GbNtldmYiIM1WubPbJo9aPJSzw7V/f9vYp8AS/2wCcA/AjgLIwQaQTgBQATXO5jioAdgJYA+BiT1szAIkAevoZHwJgMoATAO7wtF0FIArAUgCl/SzzHgA3gDc8ry8AMBtADIA6AXhf3wD42v/byzUFHpFCdibxDB8Z/QhvHXIry0aW9d6X035he7tLKzC3m5w1i7z33rSzORUrkp98Qh44YHd1IiLOduONZr88fPFswgJbTm7p7VPgCW6hADbDnMXIGDL+gjkrUj4X6xkIE0YaZGj/3tN+W4b2Fp729zO0P+lp/zRD+78AJACYlqH9IphQNStDe37e1zeen4JQ4BEpZJ/M+cQbcsp0LMPnJj3H33b+xqSUJLtLy7fERHL4cN8Z12rWJLt3J0+dsrs6EREhyebNzf6539QVhAU2+bmJt0+BJ7g1hgkYg/30feHpeyWHdZSD+Qfe4afvTs86RmRoXwjABaBGhvYwz7r2ZGi3POtp6Wcbczx96c/yNEbe35cCj0iQW7F/BUOsEMICJ22ZxNiEWLtLKpBTp8iuXckaNdKCzrXXkiNGmBAkIiLB48MPzX660w9/ExZ4+9DbvX0KPMGtA8zB/1t++pp4+kblsI77POMm+Okr5+nbn66tDIBkmEvR/FnlWaZuurZFnrb6fsZ38/S9nq4tP+9LgUckyL067VXCArst6WZ3KQUSFUV+9BFZoUJa0CEf1W0AACAASURBVLn/fj1DR0QkmPXta/bXH311grDAun3qevsUeILbBJiD/2Z++q739K3KYR1vecZ9l0X/SZizOamTEdT3jM9qIoCpnv4m6dpiPOuo5Gd8W8/4buna8vO+LAC/eH52eba5CuaMUHYTKaSnwCNSCHYc28Gha4cSFliqfSlGx0XbXVK+rF5NPvts2kQEpUqRzz9Prl1rd2UiIpKTGTPMvrtVazfDOoSxYueK3j4FnuCWejnY//npq+vp25nDOlIvEeuURf8BmLBS0/M69TK3pVmMH+3pb5WuLcmzjnA/41/3jB+Sri0/7+sbAFsB3ON5XR7msrczMDPO+QtbGSnwiATYH3v+8N6zAwtsNq6Z3SXlSVISOW4cedddvhMRfPghuWeP3dWJiEhubdpk9uENG5I1etYgLDA+OZ6kAk+wU+BJUwFmNreMPkP2Z7DSU+ARKSCX28XYhFgOWDWAXy/42ifsnNfpPK4+uNruEnPl2DGyc2cz+UBq0LnkErJHD/M8BxERKV7OnDH78lq1yAY/NCAscN+pfSQVeIKdLmnLWWpAisrFWAUekXxYeWAlbx96O6/qd5VPwEn/s+/UPu9f0oKV200uW0a+/DIZEZEWdO65h5w0iUxOtrtCEREpiKpVyZAQ8oERDxIWuObgGpIKPMGuPbK+uf8R5G7SgnsR2EkLVsKEm8vStWU3aUFXT99r6doC8b5SlfeMT8jF2LPh4eGMiIjI8icyMtLmr6pI8DgZf5KvTX+N53U6L8ugUzayLHce32l3qdk6fpz8/nvfaaVLlzYPCl23zu7qREQkECIjIxkaGkEggqVKlyLCwDJlyzAiIoIACHNFkgShxsh8OViqLz19L+ewjuympb7Ls46fMrQv9LTXzNAejuynpW6FzObCBKRL07U1Rt7e1/mebYT5GX+5Z/w+P30Z6QyPSC653W4+N+k5b7B5esLT/HHdj+yxtAfHbRpH0gSi3Sd221ypf243uWgR2bo1WaZMWtC56iqyVy/y6FG7KxQRkUB7+mmzr39q8AeEBf60/ieSOsMT7EJgHtB5BObMS/r2DZ7289K1lwFQ3c96BsCEghsytPfxtN+aof1ZT/sHGdqbI+sHj8YDmJ6hvTpMms744NG8vq9LPdu9GZl97Okb4KcvIwUekRy43C663W52X9qdsMDKXStzU/Qmu8vKtaNHyZ49TbBJDTlly5IvvEAuXqxppUVESrKPPzb7/Wa9OhEW2HNZT5IKPMXBrQDOARgGIALmLEcnmMvOmmYYuwnmbErzDO2VYc7wrEXaw0SbwVwG1iOL7U4CcALAHZ7X9QDshZnMwN/kBO/CBI83YILLBQBmA4iG70NH8/O+LvWsez3MPT6ACUrPAzjteW9Vs3gf6SnwiPiR7ErmtqPb+MW8L1iqfSnvJWyh7UO5fP9yu8vLkdtNLlhAPvecuVQtNehccw3Zpw954oTdFYqISFHo18/s/x/+aiBhgV/O/5KkAk9xcT2AGTDhIRrAAgCN/IybDuAYgLv99FUFMAjAQc86NgBok802wwB8DhMmomEmBegKE06y8hTMPT4xAA4DGAOgdjbjc/u+QmHu7RkOYBvMGaBYmLNEkQAqZrON9BR4RPz439z/+dyXE9o+lLDArxd8bXdp2YqOJrt2JevWTQs5ERHkK6+Qy5frbI6IiNOkPovn7jfHExb435n/JanAI86iwCOSzqKoRVwUtcgn7Py28zceOH2Afx3+y+7y/EpJIWfNMtdph4enBZ0GDcgBA8hTp+yuUERE7PLXX+Z3wvXN5hEW2GJiC5IKPOIsCjziaO50pzz6r+rvE3Qu73M5F+9dbGN12YuKIr/80ve5OeedR/7nP+Tq1TqbIyIi5hJmgKx58zrCAh8Y+QBJBR5xFgUecZyklCQmu5L588afvZeq1e1TN9P00qnPKggmycnkzJnkY4+Z5yqkBp077ySHDiVjY+2uUEREgonbbf4YFlYlirDAGwfdSFKBR5xFgUccIzElka9MeyXL5+fAApuObcqtMVu5YPcCu8v1sXEj+dFHZPXqaSHnggvIDz8kt22zuzoREQlm9eqRKB1LWOCl311KUoFHnEWBR0o8t9vNdYfWsXbv2j7hpnLXyvxq/leMOhnFObvmsN/Kfjxy5ojd5XpFR5O9e5t7cVJDDkA2bEj+9BN57pzdFYqISHHw0EMk4Gap9mGs2LkiSQUecRYFHimxok5GscfSHjy/8/nekHNF3ys4YfMErjqwyu7y/EpMJKdOJZs2JcPC0kJOnTrkN9+Qu3bZXaGIiBQ3r79ufpec37EaYYFJKUkKPOIoCjxSIq07tI6l2pfyOaNTr189JruS7S4tE5eLXLiQbNPGXKaWGnLKlydffZVctEgTEIiISP516GB+r1zUoR5hgdFx0Qo84igKPFKidF/anZf0usQbckq1L8WmY5ty7j9zefTsUbvL83K7zVShn3xC1qqVFnJCQ8kHHiBHjSLj4uyuUkRESoKffjK/Y2p8dRdhgduOblPgEUdR4JESYcX+FXxhygs+Z3Rq9KzB2ITgmrZs1y4yMpK8+mrf+3JuucXcr3PokN0ViohISTN/vvldU/3DxwkLXLpvqQKPOIoCjxRrSSlJfHHqiz5B57lJz/Hj3z/mruPBccPLgQNkr14m1KQPOZdfTloW+fffdlcoIiIl2c6d5vdOlddeIixwxt8zFHjEURR4pFhyuV18b9Z73pBTtVtVfvz7x9x+dLvdpZE0v1x69iTvucf3eTk1a5qppPVgUBERKSrx8eZ3ULknPyAscMRfIxR4xFEUeKRYeG/We7yk1yUctGYQf93xKx/9+VFv2Kndu7btZ3NcLnLFCvJ///M87yDdmZwqVcg33jATE7hctpYpIiIOVa0aibsjCQvsvby3Ao84igKPBL3Fexf7fUho1W5VOWXrFJ6KP2VLXUlJ5Lx55FtvkTVq+IacOnXI998nFyww40REROx0880kbh5AWOBX879S4BFHUeCRoDT3n7mcvn06v1v+Hc/rdB5hgZW6VGKLiS3YaHgjdvyzI6Pjoou8rnPnyOnTyZdfJitX9g05N91kJiTYtEmXq4mISHB58kkSV48jLPCtmW8p8IijKPBIUHG73RyzcUymszmfz/ucbptSxMmT5OjR5FNPkeed5zuFdKNG5HffkXv32lKaiIhIrrRtS+Lfc7yT+yjwiJMo8EjQOJ1wmjcPvtkn6IR1CGPb39oWeS27d5N9+5IPPkiGh6eFnNKlyUceIYcMIaOL/gSTiIhIvvTsSeLiNYQFPjjqQQUecRQFHrGNy+3imoNr+OX8L3n70NtZr18977053y3/jm63m4kpiUVSS3IyuWgR+emnZP36vpeqlS9PtmhBjh1Lnj5dJOWIiIgE1IQJJCrtJizw5sE3K/CIoyjwSJE6m3SWE7dM5MvTXub5nc/PdOnaFX2vKLJ7c44fJ3/+mWzZkrzgAt+QU7s2+fbb5KxZZEJCkZQjIiJSaFasIFHmNGGBdb6ro8AjjqLAI0Xm540/s2bPmt5wU6p9KTb4oQHbLWjHQWsGccRfI3gy/mShbT8piVy8mPz6a/KOO8w9OOnvx2nYkOzShdy8WZMOiIhIyXLoEAm4ia9L8fzO5yvwiKMo8EihSHGlcO4/czl+83h+9PtHvH3o7d6gc1H3i9h1SVcePXu0UGtwu8lt28g+fcjHHycrVPA9i1OpEvn88+Ysz7FjhVqKiIiIrVwuc09qyKcXEhYUeMRRFHgkYFxuF2ftmMX+q/rzuoHXZbpcrUrXKhy0ZhCTUgrvwTTR0SbAvPIKWauWb8AJDyfvuYfs2NGc2k9OLrQyREREgs6ll5J450oFHnEcBR7Jt4TkBE7ZOoUbj2zkhM0TWL1HdZ+Ac9n3l7H5+Obsuawn1xxcw3NJ5wJew7lz5O+/kx9/TF5/vW/AAcwEBG3bkjNnkmfOBHzzIiIixUbDhiReu4OwwIhyEQo84hgKPJIn+0/vp9vt5soDK3nbkNsyncWBBTYf35wz/55Jl9sV8O27XOTateZem/vvJ8uU8Q04F11Etm5NjhhBHjgQ8M2LiIgUWy1akHj+McICy0aUVeARx1DgkRzFxMXwi3lf8OHRD/sNOLDAq/pdxa5LunL3id0B3/7eveTQoWZHXbWqb8CJiCAfftg8X2DjRk02ICIikpUPPyTR7EXCAstElFHgEcdQ4BGSpNvtZs9lPVmvXz2+NPUlvjj1Rbaa3IqvTHuFZSPLZgo4V/e/mp0WdeLJ+JPcd2pfQM/mHD1KTp1qpoW+4grfgBMSQt58M/n55+SCBZoyWkREJLd69SLx0PuEBZYuW1qBRxxDgceh3G43f9n+Cz+Z8wmfm/QcwzqEZXn2JsQK4YXdLuRr01/jbzt/4/aj2wNaS1QUOWoU2aYNWa9e5vtw6tQxfRMnajY1ERGR/Bo/nsQ9HRR4xHEUeBzkTOIZdlnchXW+q8ObBt3kE2pKtS/FGwfdyO5Lu/PL+V9y9IbR7LWsF9+b9R7/OvxXwGpwu8ktW8iBA80DPy+5JHPAufhi8tlnzZhduwK2aREREUdbupTELf0ICwwvG67AI46hwFPCJaUkMS4xjn8f+5t1+9T1CTnVe1Rn96XdOWTtEMbExRTO9pPIlSvJHj3Ipk3JKlUyB5zLLydffZUcPtwEHN2HIyIiEnhRUSSuGUNYYFiZMAUecQwFnhJsw5ENvGbANT4h584f7+SQtUM4eetknow/GfBtHjlCzphBtmtnZlErVy7zPTgNGpDvvmsuUTt8OOAliIiIiB+JiSQu+91c2VGmlAKPOIYCTzHm9pwKORV/ihO3TOS0bdM48q+RfGvmW7y8z+U+QadMxzL8cPaHAX3o55kz5MKFZPfu5DPPkLVrZz57U7q0mff/88/JWbPIU6cCtnkRERHJo8rXrCYsMLS0Ao84hwJPMXH07FF2XdKVA1cP5O4Tu/npnE9ZrlM5RkRGMMQK8TvZwEXdL+KX879kbEIsU1wpBdp+UhK5bh35ww/m8rNrriFDQzMHnNq1Tfjp3p38808yPj4w719EREQK7uqG/5gJiUqHKvCIYyjwBKn9p/ez/cL2bDisIa8dcC1LdyztN9SUjSzLKl2rsOnYpmw9pTXfnPEmh6wdwgGrBjA6Ljpf23a7yZ07yZ9/Jt9/n7zzTrJs2czhpnJl8wycr782l7EdORLg/wkiIiISUI88edIEnvAQBR5xDAWeILDr+C4OWzeMz0x4hpf3uZx3DL2D4R3CfYJNRGQEHx/zOJuNa8brBl7HZuOacdvRbQHZfvr7bh56yASZjOGmbFnyrrtMABozRpMLiIiIFEdv/tdFfB1KhEOBRxxDgcdGLreLA1YN8PsMnIjICL47610u27eMe0/tLfAlaalS77vp1i3r+25CQ8lrryVfe40cNIhc///t3Xt4VNW5x/FfSEIuiFdUvBy0otZQIVo9oFZsoFovRUVA8QD6xMdbi54Kp6IiVtEGhTxKhUbwUhUsYkXxQqGAHrHijYsaOKn1kqcaTLjEWhQUQUjmPX+sHRkmexJmZk8yw3w/z7MfyVp7VtZ2rZnZb/a6VLohbQAAIL2VlZnppgMIeJBRCHjaQWOo0d5Z+46dP/v87wOcoc8MtTl/n2N1m+rsvXXv2eZtmxP+PRs2mC1caHbPPW5fm2OPdaukRQY4Rx7p8u+912zpUhcUAQCAPc+MGWa6/lgCHmQUAp429M1339ifq/5sxdOLd9kL59VPX02o3G3bzFavdnNubrnF7Nxzzbp2bR7YSGYHHuiGrd1+u9n8+Wb18U3zAQAAaejll8105SkEPMgoBDwBq91Ua/Xf1NtXW7+yJZ8ssXkfzrPSF0qt1/Relve7vO8Dne5TulvZa2Ux7YWzY4fZhx+aPfus2fjxZkOGmBUVmWVn+wc33bubDR7sHl/Pn2+2di3zbgAAyGQffGCmYecR8CCjEPAkaNuObbZy7Uq7e+nduzy58TsKygrsgqcusNn/N7vF/XAaG80+/dQFKRMnmo0Y4TbrzMvzD2z22cctKHDttWZTp7phaex3AwAAIm3ebKaLRhDwIKMQ8OyGUChkb9e+bZPfmmwXz7nYfvzQj63/zP5WMqPE9pu43y5BzeGTD7cDyw+0fSfua+fPPt+GzR1m5W+U2ycbP7HGUOMu5TY2mv3zn26FtEmTzC6/3Ozkk806dfIPbAoKXH5pqZtvs3ChWW0tT20AAMDuy73g1wQ8yCgEPBEaQ4322Vef2YKPF9jjlY/bjYtvtOOnHd/ik5vTHj3Nblx8oy2rXWYhL/rY+V+zf/3LbNkys1mz3FC04cPNTjzRBTB+gU1urtvY89JL3XC0F15wy0A3NrZUcwAAgNZ1GXwnAQ8ySkYHPKFQyJbVLrPpK6fb9Quut76P9bX8snzfoKbHAz1s3Cvj7PkPnrfqf1db5fpKq6qvsrpNdRYKma1b54aSPfaY2bhxZkOHmp10khtu5hfUNO1tc8IJZsOGucDmuefcHJ0dO9r7/wwAANhT/fCyPxDwpIliSfMlbZBUL2mJpJIYy+gi6SFJdV4ZqyVd08L5OZLGSvrIO/9TSZMkFbbwmsGSlnvnr5c0W9IRLZwf63XFeg2RMibgCYVCtqJuhVUsr7D73rrPSl8otaKKomaBTX5ZvvWc1tMGPT3Iblh4gz2w4gFbUbfCGhpCtmaN2SuvuL1pxowxGzTIrFcvs8LC6EFNdrZbPODss82uu85s8mSzefPMqqvNGoLZWgcAAGC3nXHdkwQ8aaCPpG8lPSopXy4QmSCpQdKFu1nGAZKqJb0j6RAvbaCk7yTd53N+lqS5kjZKOtVLO05SjaQ3JXX0ec2vJYUkXev9vJ+kRZI+l/SDAK4r1mvwsyU3N7e933eB2rRtky35ZIkt+HiBzaicYaMXjbb+M/tbl/Iuvk9uiiqKbMxLY2xG5Qx7u+Yde//D72zRIrOKCrNRo8wGDDA77rjoCwZIZh07unMGDHCvqahw82uqq1N/w86ysrL2rgLaEe0P+kBmo/0z06W3LWwKeLbv5v0i2lgHSX+Xe1oSGWSsknvKsddulDNdLhg5ISJ9ipfeJyJ9qJc+KiL9Ii/9poj0bpK2SXohIv1guYDkrxHp8VxXrNfgZ4uk9n7fxaWhscEq11faM+8/YxNfn2i3vXKbnfXEWZZ7V65vYJM1Pst+9IdiK511iw1/cIINLX/YRox514Zc3Gh9+pgdcoj/ppzhCwb07Gl20UXu6c5DD7mnPTU16f2kpqCgoL2rgHZE+4M+kNlo/8x005TlTQGP7ca9ItpBidzN/MM+ebd6eVe0Ukah3I3+xz55p3llzIxI/5ukRkmHRqTneGV9GpE+3itnmM/veMnLC3/KU6LYriuea/CT0gFPQ2ODrflqja2oW2EzV820iuUVNvZ/x9o5s86xve/Z2zewyb+rkxVPGmDF4y+3Hr+53opGPGzdTl1u+Z23RA1mwjflPOkks0suMbv1VjcfZ+lSNz9nT10JjS+7zEb7gz6Q2Wj/zPTgnGoCnhR3l9zN/EifvPO8vD+1UkZ/77w5PnmFXl5tWFqepB1yQ9H8rPBec3RY2lIvrYfP+eVe3lVhabFeV6zXEE27BTyhUMg2frvRVm9YbW9+9qbN/cdcm/TGJLt63tV25hNnWvcp3aM+rdF4WdYdOdZ59CnWefjVltNvgqnP/aYjl5hytvoGM3l5ZsccY/azn5ldcYXZHXeYPfqo23H4o4/Mtmxpl/8N7Y4vu8xG+4M+kNlo/8z0ylsbCXhS3By5m/mBPnnFXt6KVsoY6Z13f5T8L+We5jQtRtDDO39VlPOf9/LPC0v73CtjX5/zb/DOLw9Li/W6Yr2GaJIW8Hz93df28Rcf22s1r9lTVU9Z+Rvldt2C6+y8Wb+woqnHW6eyzi0u9azxMo3tbPplL9Pwc0wDrjX1+62pzxRTt6Wmjl/vstrZUUeZnX66ezozerTZ739vNneu2cqVZvX1e+4TmkTxZZfZaH/QBzIb7Z+ZausaCXhSXNNwsLN88o728qpbKaNpiNiEKPl1csHCYd7PTUPE3oxy/iwvf3hY2navjFyf86/yzn8kLC3W64r1GqKJKeDZ3rDd1m1eZ6vWr7JF1Yvtwbdn2s3zJtqwmaOspGKoFZWfYQf97hjrOH6v1oOZ3+aYbjjSVHqG6b/ON11Yauo7wfSjp02HrjQVfGE5uSE77DCz3r3NBg50q5xNmGD2+ONmixebVVWZbdxIMJMIvuwyG+0P+kBmo/0z044dRsCT4gh44ruGaLZIsrKn/2qj/jjbLpsyzQZMvNtOv/Mm6zXuGjvq5ovtoJtKbK+biyzn1gNaD2K+D2ZyTaO6ma7qYxo60PSLX5lOv9vU80nr3OMN635irfU9o8GGDDEbOdJtxjltmnsi8/rrbojZl18SyLQFvuwyG+0P+kBmo/0zl3KyCHhSGEPa4ruGaFxnz43hyMky5XQwZWebsnMsKzvXsrI7WoecPMvOKbCc3ELr2NEdeXmFlp9faAUF7igs5Ei1Q1K714GD9uegD3DQ/hxt3/Yi4ElZdyr65P5ztXuLFvRTsIsWLJcLLrqHpbW0aMEkL+/KsLRYryvWa4gmpJ0dPtqxXe5JEMeeedC+mX3Q/hz0gcw+aP89+9iulu/xQkJKKlHz4WBNxnl5pa2U0dKSzj/xypgRkf43Lz1yiFiuV1a0ZamHq7mX5QKkI8PSShTbdcVzDQAAAABSXJbcBp0b5J68hKev9tI7haXnSerqU840uaDgxIj0qV5674j0S7z00RHpgxR949Gtkl6MSO8qF21Hbjwa63XFcw0AAAAA0kBvSd9KekxSgdzmnxPkhp1dGHFuldzTlEER6fvLPR15Vzs3Ex0oaZuke6P83mclbZR0qvdzkaQ1cosZ+C1O8N9ygce1coHLfpIWSarXrpuOxnNd8V4DAAAAgDRQLOkvcsFDvaQlkn7qc96Lkr6Q1Ncnr4ukhySt9cpYLemaFn5njqSxckFGvaQaufk4BS28ZrDcHJ/PJa2XNFvSES2cv7vXFe81AAAAAAAAAAAAAAAAAADQhoolzZdb6KBpWFxJjGU0DZWrE0Pl0k2i7f9TSU/IDdn8Qm7e2qtyC3cg9QXx/g83WG4uYuRKlEhdQfWBAXLzTj+T9G+5OasvxlkW2k4Q7d9P0kK5tq+X9IHcvn8HB1ZLJNNpkt5X/EtOcw+IlNdHbuGDRyXla+fCBw3yX/jAzwGSqiW9I+kQL22gpO8k3RdkZRG4RNu/adXB57RzdcP95faACkmaGHB9Eawg3v/hDpKbfxiS9ElAdURyBdUHJsndMJ8Tlnah3KI4twdSUyRDEO1/udx7/hlJ+3hpvSWtk7sB7hJgfRGsvSU9IDeve7Pcol2x4h4QKa+D3NLW6yV1jMhbJRel77Ub5UyX+7A7ISJ9ipfeJ7FqIkmCaP8hkr5U84U4Osr9pa9R0lEJ1xTJENT7P9zzck/7CHjSQ1B9oOmp3pk+eeWSrkqgjkieoNq/Wm512L0j0ptWnh2bWDWRRE9L+qOkfeVGacQT8HAPiJRXItcZH/bJu9XLu6KVMlra0PQ0r4yZ8VcRSVSixNu/h6Qro+Q955VxaZz1Q3KVKPH2D3e5pFq5L04CnvRQomD6QJXcTS/SS4mCaf+tckFTpHO9Mh6Is35IvsPC/l2j2AMe7gGRFu6S64wjffLO8/L+1EoZ/b3z5vjkFXp5tQnUEckTRPu3ZL5Xxs8TKAPJE2T7Hy43d+ts72cCnvQQRB/4oXfek8FWDW0gqM+AZXJD4PaPSP8frwzmcqSHGsUe8HAPiLQwR64zDvTJK/byVrRSxkjvvPuj5H8p9wYqjLOOSJ4g2j+aHLnx/OvUfKgEUkOQ7b9YbsJqEwKe9BBEHxjmnXevpBGS3pB7738mN1ymOKjKInBBfQacLNfm8+Q2Nc+We3q0QW4hg+wA6orkq1HsAQ/3gEgLL8l11LN88o728lobptD02HtClPw6uc5+WJR8tJ8g2j+ay7zXD4/z9Ui+oNr/l3LBTaewNAKe9BBEH/iNd169pEpJvSRlSerp/bxVbgUvpJ4gvwN6ygW7IbmFKrbLTVjPT7yaaCM1ij3g4R4QaYGAJ7MlK+DpJrc89YPxVw1tIIj2P0rSJrmlycMR8KSHIPrAbd55jWr+NOd4L+/DxKqJJAnqO+AqucD2EbllqLPkJqr/wzu6B1FZJF2NCHiwh2JIW2ZLxpC2LnL7L8yR+9JD6kq0/TtIek1uJZ5IBDzpIYjPgNHa+YTHT9Nqjdz0pp4g2v9Yuac5VT55J3llvJZAHdF2asSQNuyh7lT0CYtNq6u0NmGxn5iwlq6CaP9wXeQ2G5sjxmyng0Tbv5ukr+VudNdHHCG5ScxNP98WWK0RpCA+AwZ6570fJX+ll39KnHVE8gTR/r9SyyuxfS33WdApSj5SR41iD3i4B0RaKJHrjI/45I3z8kpbKaOlJQl/4pUxI94KIqlKlHj7NzlQ7i98s+X+8t/kCO3ciAyppUTBtX8knvCkhxIl3gcOkrtJ+iJK/hqxH1eqKlHi7T/GO29qlPxNcu1/cFw1RFuqUbDLUnMPiJSRJbfp2AZJeRHpq7308L/K5Enq6lPONLlOfWJE+lQvvXdA9UWwgmr/g71ynlDzYWwzJN0RTHURsKDa3w8BT3oIqg/MlWvzyA0Gi7z01QHVF8EKov37yrVxpU/5vcRnQTqpUcsBD/eASGu9JX0r6TFJBXLLCU+Q2zX5wohzq+TeDIMi0veXi+7flVuSUnLDHLbJLVWK1JVo+3eVm5S6UW54xPiIo1LS7cmpOgIQxPvfDzc56SOIPvAfcpOTq+T25ZGkH0haLvcX/v9MRsURiCDav2ku0GRJe3lpPSS9F6UcpKYatRzwcA+ItFcs6S9yY/HrJS1R81WXJOlFuWELfX3yY2qRawAAARVJREFUusjtw7HWK2O12GwsXSTS/qO0c4WmkM/RKAKeVBfE+1+SzpBbqWmrXLs3ev/+Vu6GGKkriD5wqNzQqLVyfwBZK/eE9+jgq4uAJdr+HeTmAb0tF+BulJu796yaP/VDajlX/p/bW9V8bhf3gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACQMf4fKQqF1q5fbjcAAAAASUVORK5CYII=\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb906ac8cc0>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"<matplotlib.legend.Legend at 0x7fb906a9aac8>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xy_traj_alt = xy_sys.Milstein(w, np.ones(2))\n",
"from pyNT.sde import get_t1ma_nm1\n",
"deltaB = (\n",
" np.abs(\n",
" np.exp(\n",
" -xy_traj[:, 0]*traj_C1[:, None, None]\n",
" -xy_traj[:, 1]*traj_C2[:, None, None]\n",
" -traj_A[:, None, None]) -\n",
" np.exp(\n",
" -xy_traj_alt[:, 0]*traj_C1[:, None, None]\n",
" -xy_traj_alt[:, 1]*traj_C2[:, None, None]\n",
" -traj_A[:, None, None])) /\n",
" np.exp(\n",
" -xy_traj[:, 0]*traj_C1[:, None, None]\n",
" -xy_traj[:, 1]*traj_C2[:, None, None]\n",
" -traj_A[:, None, None]))\n",
"avdB = np.average(deltaB,\n",
" axis = (1, 2))\n",
"sigma2 = (np.sum((np.average(deltaB, axis = 2) - avdB[:, None])**2, axis = 1) /\n",
" (deltaB.shape[1] - 1))\n",
"errB = (get_t1ma_nm1(0.99, deltaB.shape[1] - 1) *\n",
" (sigma2 / deltaB.shape[1])**.5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax = plt.figure(figsize=(6,4)).add_subplot(111)\n",
"ax.plot(w.get_time(),\n",
" avdB,\n",
" color = 'blue',\n",
" label = '$\\\\langle \\\\delta B \\\\rangle$')\n",
"ax.plot(w.get_time(),\n",
" np.max([np.min(deltaB,\n",
" axis = (1, 2)),\n",
" avdB - errB], axis = 0),\n",
" color = 'blue',\n",
" dashes = (1,1))\n",
"ax.plot(w.get_time(),\n",
" np.min([np.max(deltaB,\n",
" axis = (1, 2)),\n",
" avdB + errB], axis = 0),\n",
" color = 'blue',\n",
" dashes = (1,1))\n",
"ax.plot(w.get_time(),\n",
" np.max(deltaB, axis = (1, 2)),\n",
" color = 'red',\n",
" label = '$\\\\max \\\\delta B$')\n",
"ax.plot(w.get_time(),\n",
" np.min(deltaB, axis = (1, 2)),\n",
" color = 'green',\n",
" label = '$\\\\min \\\\delta B$')\n",
"ax.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fb90932a908>"
]
},
{
"html": [
"<img 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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fb90943ed30>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"<matplotlib.legend.Legend at 0x7fb9069faac8>"
]
}
],
"prompt_number": 22
}
],
"metadata": {}
}
]
} | gpl-3.0 |
eric-svds/flask-with-docker | app/.ipynb_checkpoints/my_notebook-checkpoint.ipynb | 1 | 78477 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sample PCA analysis with Iris dataset\n",
"\n",
"The following are required for this notebook:\n",
"- pip install matplotlib\n",
"- pip install scikit-learn\n",
"\n",
"This notebook plots (and pickles) the Iris data set before and after Principal Component Analysis. Output is intended to be imported by a Flask application and passed to an HTML template. D3.js can be used to create scatterplots similar to the two shown below (only nicer, hopefully).\n",
"\n",
"The following cell imports sckikit-learn and the data set. A PCA is performed with 4 principal components. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn import datasets\n",
"from sklearn.decomposition import PCA\n",
"import numpy as np\n",
"%matplotlib inline\n",
"\n",
"# Import the infamous Iris Dataset\n",
"iris = datasets.load_iris()\n",
"\n",
"# Keep only the first two features (Sepal length, width)\n",
"X = iris.data\n",
"Y = iris.target\n",
"\n",
"# Perform PCA on 4D data, keeping 2 principal components\n",
"X_PCA = PCA(n_components=4).fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot Original Data Set\n",
"\n",
"Plot the Sepal width vs. Sepal length on the original data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/eric/miniconda/envs/py2/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": 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7guV7zsFa0Rz/mj3bBBmzuvbqq68i4ewJfDuiLdY+4o8oTysM7BNr6rQqxMX2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\nB0PqHGn2/YOfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a3ec850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the first two features BEFORE doing the PCA\n",
"plt.figure(2, figsize=(8, 6))\n",
"\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)\n",
"plt.xlabel('Sepal length (cm)')\n",
"plt.ylabel('Sepal width (cm)')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot Data After PCA\n",
"\n",
"After performing a PCA, the first two components are plotted. Note that the two components plotted are linear combinations of the original 4 features of the data set."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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s4uLChVNRRQU++nQUzfy9SyGpKO/c3d3YdymFKnaW6JQiMimb3sPa896ECYSG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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a4f3410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the first two principal components AFTER the PCA\n",
"plt.figure(2, figsize=(8, 6))\n",
"\n",
"plt.scatter(X_PCA[:, 0], X_PCA[:, 1], c=Y, cmap=plt.cm.Paired)\n",
"plt.xlabel('Component 1')\n",
"plt.ylabel('Component 2')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save Output\n",
"\n",
"The Flask application will make use of the following [D3 Scatterplot example](http://bl.ocks.org/weiglemc/6185069). Data has to be in a particular format (see link for example), this cell flips the data sets into that format and pickles the output."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Pickle pre- and post-PCA data\n",
"import pickle\n",
"\n",
"features = []\n",
"for full_label in iris.feature_names:\n",
" name = full_label[:-5].split() # remove trailing ' (cm)'\n",
" features.append(name[0]+name[1].capitalize())\n",
"features.append(\"species\")\n",
"\n",
"# Create full set for Iris data\n",
"data1 = []\n",
"data_PCA = []\n",
"for i, vals in enumerate(X):\n",
" row1 = dict()\n",
" row_PCA = dict()\n",
" for k, val in enumerate(np.append(X[i], iris.target_names[Y[i]])):\n",
" row1[features[k]] = val\n",
" data1.append(row1)\n",
" for k, val in enumerate(np.append(X_PCA[i], iris.target_names[Y[i]])):\n",
" row_PCA[features[k]] = val\n",
" data_PCA.append(row_PCA)\n",
"\n",
"pickle.dump(data1, open(\"pkl/data1.pkl\", \"wb\"))\n",
"pickle.dump(data_PCA, open(\"pkl/data_PCA.pkl\", \"wb\"))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.4\n"
]
},
{
"data": {
"text/plain": [
"numpy.string_"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ttt = data1[0].values()[3]\n",
"print ttt\n",
"type(ttt)"
]
},
{
"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-2.0 |
bollwyvl/watermark | docs/watermark.ipynb | 2 | 8836 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Sebastian Raschka](http://sebastianraschka.com) \n",
"\n",
"<hr>\n",
"I would be happy to hear your comments and suggestions. \n",
"Please feel free to drop me a note via\n",
"[twitter](https://twitter.com/rasbt), [email](mailto:[email protected]), or [google+](https://plus.google.com/+SebastianRaschka).\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IPython magic function documentation - `%watermark`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I wrote this simple `watermark` IPython magic function to conveniently add date- and time-stamps to my IPython notebooks. Also, I often want to document various system information, e.g., for my [Python benchmarks](https://github.com/rasbt/One-Python-benchmark-per-day) series.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `watermark` line magic can be directly installed from my GitHub repository via"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installed watermark.py. To use it, type:\n",
" %load_ext watermark\n"
]
}
],
"source": [
"%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the `%watermark` magic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To load the `date` magic, execute the following line in your IPython notebook or current IPython shell"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext watermark"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Usage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to display the optional `watermark` arguments, type"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%watermark?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<pre> %watermark [-a AUTHOR] [-d] [-e] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME]\n",
" [-v] [-p PACKAGES] [-h] [-m] [-g] [-w]\n",
"\n",
" \n",
"IPython magic function to print date/time stamps \n",
"and various system information.\n",
"\n",
"watermark version 1.2.1\n",
"\n",
"optional arguments:\n",
" -a AUTHOR, --author AUTHOR\n",
" prints author name\n",
" -d, --date prints current date as MM/DD/YYYY\n",
" -e, --eurodate prints current date as DD/MM/YYYY\n",
" -n, --datename prints date with abbrv. day and month names\n",
" -t, --time prints current time\n",
" -z, --timezone appends the local time zone\n",
" -u, --updated appends a string \"Last updated: \"\n",
" -c CUSTOM_TIME, --custom_time CUSTOM_TIME\n",
" prints a valid strftime() string\n",
" -v, --python prints Python and IPython version\n",
" -p PACKAGES, --packages PACKAGES\n",
" prints versions of specified Python modules and\n",
" packages\n",
" -h, --hostname prints the host name\n",
" -m, --machine prints system and machine info\n",
" -g, --githash prints current Git commit hash\n",
" -w, --watermark prints the current version of watermark\n",
"</pre>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"06/17/2015 15:04:35\n",
"\n",
"CPython 3.4.3\n",
"IPython 3.1.0\n",
"\n",
"compiler : GCC 4.2.1 (Apple Inc. build 5577)\n",
"system : Darwin\n",
"release : 14.3.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU cores : 4\n",
"interpreter: 64bit\n"
]
}
],
"source": [
"%watermark"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"06/17/2015 15:04:36 \n"
]
}
],
"source": [
"%watermark -d -t"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last updated: Wed Jun 17 2015 15:04:36 EDT\n"
]
}
],
"source": [
"%watermark -u -n -t -z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPython 3.4.3\n",
"IPython 3.1.0\n"
]
}
],
"source": [
"%watermark -v"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"compiler : GCC 4.2.1 (Apple Inc. build 5577)\n",
"system : Darwin\n",
"release : 14.3.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU cores : 4\n",
"interpreter: 64bit\n"
]
}
],
"source": [
"%watermark -m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPython 3.4.3\n",
"IPython 3.1.0\n",
"\n",
"numpy 1.9.2\n",
"scipy 0.15.1\n",
"\n",
"compiler : GCC 4.2.1 (Apple Inc. build 5577)\n",
"system : Darwin\n",
"release : 14.3.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU cores : 4\n",
"interpreter: 64bit\n",
"Git hash : fde9b4a1c1c0cd706a09a1c37e210755c59bffae\n"
]
}
],
"source": [
"%watermark -v -m -p numpy,scipy -g"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"John Doe 06/17/2015 \n",
"\n",
"CPython 3.4.3\n",
"IPython 3.1.0\n",
"\n",
"compiler : GCC 4.2.1 (Apple Inc. build 5577)\n",
"system : Darwin\n",
"release : 14.3.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU cores : 4\n",
"interpreter: 64bit\n"
]
}
],
"source": [
"%watermark -a \"John Doe\" -d -v -m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
}
],
"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
}
| gpl-3.0 |
sbenthall/bigbang | examples/obsolete_notebooks/SummerSchoolCompareWordRankings.ipynb | 1 | 16766 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from bigbang.archive import Archive\n",
"from bigbang.archive import load as load_archive\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"\n",
"ietf_path = \"../archives/\"\n",
"ncuc_path = \"../archives/http:/lists.ncuc.org/pipermail\"\n",
"\n",
"paths = [os.path.join(ietf_path,\"6lo.csv\"),\n",
" os.path.join(ietf_path,\"5gangip.csv\")]\n",
"\n",
"archives = [load_archive(path) for path in paths]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"#tp = u'(?u)\\x08[^\\\\W\\\\d_][^\\\\W\\\\d_]+\\x08'\n",
"\n",
"tp = u'(?u)\\\\b[^\\\\W\\\\d\\_]\\\\w+\\\\b'\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def ordered_words(data,authors=None):\n",
" \n",
" if authors is not None:\n",
" ## Filter to only those emails that include given authors\n",
" \n",
" ## a series of email IDs, valued True iff\n",
" ## one of the author names is in the From field\n",
" selected = data['From'].apply(lambda x: \n",
" any([(author in x)\n",
" for author\n",
" in authors]))\n",
" \n",
" # a series of Booleans can be used to select\n",
" # only certain rows from a DataFrame\n",
" data = data[selected]\n",
" \n",
" cv = CountVectorizer(max_df=.16,min_df=5,token_pattern=tp)\n",
" \n",
" c_dtm = cv.fit_transform(data['Body'].dropna())\n",
" \n",
" feature_names = cv.get_feature_names()\n",
" feature_counts = np.array(c_dtm.sum(axis=0))[0]\n",
" \n",
" feature_order = np.argsort(feature_counts)[::-1]\n",
" \n",
" sorted_features = [feature_names[i] for i in feature_order]\n",
" \n",
" rankings = pd.Series({pair[1] : pair[0] \n",
" for pair \n",
" in enumerate(sorted_features)})\n",
"\n",
" counts = pd.Series({feature_names[i] : feature_counts[i] \n",
" for i \n",
" in feature_order})\n",
" \n",
" ## Returns a pair (a tuple of length 2)\n",
" return rankings,counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The line below creates a list of three pairs, each pair containing two [pandas.Series](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html) objects.\n",
"\n",
"A Series is like a dictionary, only its items are ordered and its values must share a data type. The order keys of the series are its *index*. It is easy to compose Series objects into a DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"series = [ordered_words(archive.data) for archive in archives]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This creates a DataFrame from each of the series.\n",
"The columns alternate between representing word rankings and representing word counts."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-4f44f4426481>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\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[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\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[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m series[2][1]],axis=1)\n",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"rankings = pd.concat([series[0][0],\n",
" series[0][1],\n",
" series[1][0],\n",
" series[1][1],\n",
" series[2][0],\n",
" series[2][1]],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'rankings' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-ae24c0150b69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# display the first 5 rows of the DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrankings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'rankings' is not defined"
]
}
],
"source": [
"# display the first 5 rows of the DataFrame\n",
"rankings[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We should rename the columns to be more descriptive of the data."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"rankings.rename(columns={0: 'ipc-gnso rankings',\n",
" 1: 'ipc-gnso counts',\n",
" 2: 'wp4 rankings',\n",
" 3: 'wp4 counts',\n",
" 4: 'ncuc-discuss rankings',\n",
" 5: 'ncuc-discuss counts'},inplace=True)"
]
},
{
"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>ipc-gnso rankings</th>\n",
" <th>ipc-gnso counts</th>\n",
" <th>wp4 rankings</th>\n",
" <th>wp4 counts</th>\n",
" <th>ncuc-discuss rankings</th>\n",
" <th>ncuc-discuss counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6824.0</td>\n",
" <td>64.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a06</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10433.0</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a0f2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>210.0</td>\n",
" <td>39.0</td>\n",
" <td>8964.0</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a0ff16b3bef68c8657</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>21921.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a17976</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>21918.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ipc-gnso rankings ipc-gnso counts wp4 rankings \\\n",
"a0 NaN NaN NaN \n",
"a06 NaN NaN NaN \n",
"a0f2 NaN NaN 210.0 \n",
"a0ff16b3bef68c8657 NaN NaN NaN \n",
"a17976 NaN NaN NaN \n",
"\n",
" wp4 counts ncuc-discuss rankings ncuc-discuss counts \n",
"a0 NaN 6824.0 64.0 \n",
"a06 NaN 10433.0 28.0 \n",
"a0f2 39.0 8964.0 38.0 \n",
"a0ff16b3bef68c8657 NaN 21921.0 6.0 \n",
"a17976 NaN 21918.0 6.0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rankings[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the [to_csv()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html) function on the DataFrame object to export the data to CSV format, which you can open easily in Excel."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"rankings.to_csv(\"rankings_all.csv\",encoding=\"utf-8\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"To filter the data by certain authors before computing the word rankings, provide a list of author names as an argument.\n",
"\n",
"Only emails whose `From` header includes on of the author names within it will be included in the calculation.\n",
"\n",
"Note that for detecting the author name, the program for now uses simple string inclusion. You may need to try multiple variations of the authors' names in order to catch all emails written by persons of interest."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(aaikman 43\n",
" abandon 1077\n",
" ability 1078\n",
" able 502\n",
" above 512\n",
" absolve 1080\n",
" abuhamad 334\n",
" ac 724\n",
" acceptable 766\n",
" access 280\n",
" accordance 1236\n",
" account 784\n",
" accountable 417\n",
" accreditation 770\n",
" acct 151\n",
" acctcrosscomm 1090\n",
" accuracy 450\n",
" achieve 835\n",
" across 310\n",
" acs 1409\n",
" act 353\n",
" action 237\n",
" actions 670\n",
" active 492\n",
" actively 1428\n",
" activities 140\n",
" activity 919\n",
" actual 916\n",
" add 1114\n",
" addition 402\n",
" ... \n",
" whole 1465\n",
" why 781\n",
" widely 1360\n",
" wiki 556\n",
" willing 1093\n",
" winston 11\n",
" wish 1116\n",
" without 14\n",
" won 938\n",
" wondering 1392\n",
" words 480\n",
" worked 1282\n",
" world 317\n",
" worldclock 514\n",
" worried 438\n",
" worst 1079\n",
" writing 1397\n",
" written 382\n",
" wsis 126\n",
" wsis10 748\n",
" year 101\n",
" years 194\n",
" yes 332\n",
" yesterday 404\n",
" yet 832\n",
" york 467\n",
" zone 292\n",
" zones 1447\n",
" zuck 1469\n",
" zzxya 1122\n",
" dtype: int64, aaikman 83\n",
" abandon 8\n",
" ability 8\n",
" able 19\n",
" above 19\n",
" absolve 8\n",
" abuhamad 25\n",
" ac 14\n",
" acceptable 13\n",
" access 29\n",
" accordance 7\n",
" account 12\n",
" accountable 22\n",
" accreditation 13\n",
" acct 40\n",
" acctcrosscomm 8\n",
" accuracy 21\n",
" achieve 12\n",
" across 26\n",
" acs 6\n",
" act 25\n",
" action 31\n",
" actions 15\n",
" active 19\n",
" actively 5\n",
" activities 43\n",
" activity 10\n",
" actual 10\n",
" add 8\n",
" addition 22\n",
" ... \n",
" whole 5\n",
" why 13\n",
" widely 6\n",
" wiki 17\n",
" willing 8\n",
" winston 141\n",
" wish 8\n",
" without 132\n",
" won 10\n",
" wondering 6\n",
" words 20\n",
" worked 7\n",
" world 26\n",
" worldclock 19\n",
" worried 21\n",
" worst 8\n",
" writing 6\n",
" written 23\n",
" wsis 47\n",
" wsis10 13\n",
" year 53\n",
" years 36\n",
" yes 26\n",
" yesterday 22\n",
" yet 12\n",
" york 20\n",
" zone 28\n",
" zones 5\n",
" zuck 5\n",
" zzxya 8\n",
" dtype: int64)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"authors = [\"Greg Shatan\",\n",
" \"Niels ten Oever\"]\n",
"\n",
"ordered_words(archives[0].data, authors=authors)"
]
},
{
"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": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| agpl-3.0 |
bsamseth/project-euler | 067/67.ipynb | 1 | 2759 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Maximum path sum II\n",
"\n",
"By starting at the top of the triangle below and moving to adjacent numbers on the row below, the maximum total from top to bottom is 23.\n",
"\n",
"3\n",
"\n",
"7 4 \n",
"\n",
"2 4 6 \n",
"\n",
"8 5 9 3\n",
"\n",
"\n",
"That is, 3 + 7 + 4 + 9 = 23.\n",
"\n",
"Find the maximum total from top to bottom in triangle.txt, a 15K text file containing a triangle with one-hundred rows.\n",
"\n",
"NOTE: This is a much more difficult version of Problem 18. It is not possible to try every route to solve this problem, as there are 299 altogether! If you could check one trillion (1012) routes every second it would take over twenty billion years to check them all. There is an efficient algorithm to solve it. ;o)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[59],\n",
" [73, 41],\n",
" [52, 40, 9],\n",
" [26, 53, 6, 34],\n",
" [10, 51, 87, 86, 81],\n",
" [61, 95, 66, 57, 25, 68],\n",
" [90, 81, 80, 38, 92, 67, 73],\n",
" [30, 28, 51, 76, 81, 18, 75, 44],\n",
" [84, 14, 95, 87, 62, 81, 17, 78, 58],\n",
" [21, 46, 71, 58, 2, 79, 62, 39, 31, 9]]\n"
]
}
],
"source": [
"triangle = []\n",
"with open('p067_triangle.txt', 'r') as f:\n",
" for line in f:\n",
" triangle.append([int(i) for i in line.split()])\n",
"__import__('pprint').pprint(triangle[:10])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7273\n"
]
}
],
"source": [
"for row in reversed(range(len(triangle)-1)):\n",
" for col in range(row+1):\n",
" triangle[row][col] += max(triangle[row+1][col], triangle[row+1][col+1])\n",
"print(triangle[0][0])\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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
antoniomezzacapo/qiskit-tutorial | community/aqua/chemistry/LiH_with_qubit_tapering_and_uccsd.ipynb | 1 | 11326 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import common packages\n",
"from collections import OrderedDict\n",
"import itertools\n",
"import logging\n",
"\n",
"import numpy as np\n",
"import scipy\n",
"\n",
"from qiskit_aqua import (get_algorithm_instance, get_optimizer_instance, \n",
" get_variational_form_instance, get_initial_state_instance, Operator)\n",
"from qiskit_aqua._logging import build_logging_config, set_logging_config\n",
"from qiskit_aqua_chemistry.drivers import ConfigurationManager\n",
"from qiskit_aqua_chemistry.core import get_chemistry_operator_instance\n",
"\n",
"# set_logging_config(build_logging_config(logging.INFO))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# using driver to get fermionic Hamiltonian\n",
"cfg_mgr = ConfigurationManager()\n",
"pyscf_cfg = OrderedDict([('atom', 'Li .0 .0 .0; H .0 .0 1.6'), \n",
" ('unit', 'Angstrom'), ('charge', 0), \n",
" ('spin', 0), ('basis', 'sto3g')])\n",
"section = {}\n",
"section['properties'] = pyscf_cfg\n",
"driver = cfg_mgr.get_driver_instance('PYSCF')\n",
"molecule = driver.run(section)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Originally requires 8 qubits\n",
"Representation: paulis, qubits: 8, size: 276\n"
]
}
],
"source": [
"core = get_chemistry_operator_instance('hamiltonian')\n",
"hamiltonian_cfg = OrderedDict([\n",
" ('name', 'hamiltonian'),\n",
" ('transformation', 'full'),\n",
" ('qubit_mapping', 'parity'),\n",
" ('two_qubit_reduction', True),\n",
" ('freeze_core', True),\n",
" ('orbital_reduction', [])\n",
"])\n",
"core.init_params(hamiltonian_cfg)\n",
"algo_input = core.run(molecule)\n",
"qubit_op = algo_input.qubit_op\n",
"\n",
"print(\"Originally requires {} qubits\".format(qubit_op.num_qubits))\n",
"print(qubit_op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the symmetries of the qubit operator"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Z2 symmetries found:\n",
"IZIZIZIZ\n",
"IIZZIIZZ\n",
"single qubit operators found:\n",
"IXIIIIII\n",
"IIXIIIII\n",
"cliffords found:\n",
"IZIZIZIZ\t0.7071067811865475\n",
"IXIIIIII\t0.7071067811865475\n",
"\n",
"IIZZIIZZ\t0.7071067811865475\n",
"IIXIIIII\t0.7071067811865475\n",
"\n",
"single-qubit list: [1, 2]\n"
]
}
],
"source": [
"[symmetries, sq_paulis, cliffords, sq_list] = qubit_op.find_Z2_symmetries()\n",
"print('Z2 symmetries found:')\n",
"for symm in symmetries:\n",
" print(symm.to_label())\n",
"print('single qubit operators found:')\n",
"for sq in sq_paulis:\n",
" print(sq.to_label())\n",
"print('cliffords found:')\n",
"for clifford in cliffords:\n",
" print(clifford.print_operators())\n",
"print('single-qubit list: {}'.format(sq_list))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the found symmetries, single qubit operators, and cliffords to taper qubits from the original qubit operator. For each Z2 symmetry one can taper one qubit. However, different tapered operators can be built, corresponding to different symmetry sectors. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of qubits of tapered qubit operator: 6\n",
"Number of qubits of tapered qubit operator: 6\n",
"Number of qubits of tapered qubit operator: 6\n",
"Number of qubits of tapered qubit operator: 6\n"
]
}
],
"source": [
"tapered_ops = []\n",
"for coeff in itertools.product([1, -1], repeat=len(sq_list)):\n",
" tapered_op = Operator.qubit_tapering(qubit_op, cliffords, sq_list, list(coeff))\n",
" tapered_ops.append((list(coeff), tapered_op))\n",
" print(\"Number of qubits of tapered qubit operator: {}\".format(tapered_op.num_qubits))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The user has to specify the symmetry sector he is interested in. Since we are interested in finding the ground state here, let us get the original ground state energy as a reference."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== GROUND STATE ENERGY ===\n",
" \n",
"* Electronic ground state energy (Hartree): -8.874303870396\n",
" - computed part: -1.078084301625\n",
" - frozen energy part: -7.796219568771\n",
" - particle hole part: 0.0\n",
"~ Nuclear repulsion energy (Hartree): 0.992207270475\n",
"> Total ground state energy (Hartree): -7.882096599921\n"
]
}
],
"source": [
"ee = get_algorithm_instance('ExactEigensolver')\n",
"ee.init_args(qubit_op, k=1)\n",
"result = core.process_algorithm_result(ee.run())\n",
"for line in result[0]:\n",
" print(line)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let us iterate through all tapered qubit operators to find out the one whose ground state energy matches the original (un-tapered) one."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lowest eigenvalue of the 0-th tapered operator (computed part) is -1.078084301625\n",
"Lowest eigenvalue of the 1-th tapered operator (computed part) is -0.509523578167\n",
"Lowest eigenvalue of the 2-th tapered operator (computed part) is -0.912078232998\n",
"Lowest eigenvalue of the 3-th tapered operator (computed part) is -0.912078232998\n",
"The 0-th tapered operator matches original ground state energy, with corresponding symmetry sector of [1, 1]\n"
]
}
],
"source": [
"smallest_eig_value = 99999999999999\n",
"smallest_idx = -1\n",
"for idx in range(len(tapered_ops)):\n",
" ee.init_args(tapered_ops[idx][1], k=1)\n",
" curr_value = ee.run()['energy']\n",
" if curr_value < smallest_eig_value:\n",
" smallest_eig_value = curr_value\n",
" smallest_idx = idx\n",
" print(\"Lowest eigenvalue of the {}-th tapered operator (computed part) is {:.12f}\".format(idx, curr_value))\n",
" \n",
"the_tapered_op = tapered_ops[smallest_idx][1]\n",
"the_coeff = tapered_ops[smallest_idx][0]\n",
"print(\"The {}-th tapered operator matches original ground state energy, with corresponding symmetry sector of {}\".format(smallest_idx, the_coeff))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, one can run multiple VQE instances to find the lowest eigenvalue sector. \n",
"Here we just validate that `the_tapered_op` reach the smallest eigenvalue in one VQE execution with the UCCSD variational form, modified to take into account of the tapered symmetries."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# setup initial state\n",
"init_state = get_initial_state_instance('HartreeFock')\n",
"init_state.init_args(num_qubits=the_tapered_op.num_qubits, num_orbitals=core._molecule_info['num_orbitals'],\n",
" qubit_mapping=core._qubit_mapping, two_qubit_reduction=core._two_qubit_reduction,\n",
" num_particles=core._molecule_info['num_particles'], sq_list=sq_list)\n",
"\n",
"# setup variationl form\n",
"var_form = get_variational_form_instance('UCCSD')\n",
"var_form.init_args(num_qubits=the_tapered_op.num_qubits, depth=1,\n",
" num_orbitals=core._molecule_info['num_orbitals'], \n",
" num_particles=core._molecule_info['num_particles'],\n",
" active_occupied=None, active_unoccupied=None, initial_state=init_state,\n",
" qubit_mapping=core._qubit_mapping, two_qubit_reduction=core._two_qubit_reduction, \n",
" num_time_slices=1,\n",
" cliffords=cliffords, sq_list=sq_list, tapering_values=the_coeff, symmetries=symmetries)\n",
"\n",
"# setup optimizer\n",
"optimizer = get_optimizer_instance('COBYLA')\n",
"optimizer.init_args()\n",
"optimizer.set_options(maxiter=1000)\n",
"\n",
"# set vqe\n",
"algo = get_algorithm_instance('VQE')\n",
"algo.setup_quantum_backend(backend='statevector_simulator')\n",
"algo.init_args(the_tapered_op, 'matrix', var_form, optimizer)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"algo_result = algo.run()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== GROUND STATE ENERGY ===\n",
" \n",
"* Electronic ground state energy (Hartree): -8.874303866224\n",
" - computed part: -1.078084297452\n",
" - frozen energy part: -7.796219568771\n",
" - particle hole part: 0.0\n",
"~ Nuclear repulsion energy (Hartree): 0.992207270475\n",
"> Total ground state energy (Hartree): -7.882096595749\n",
"The parameters for UCCSD are:\n",
"[ 0.03841346 0.0541097 -0.00571893 0.00369552 0.03835424 -0.00823223\n",
" -0.00473582 0.00365347 -0.03613082 0.05948699 -0.02738111 -0.02744031\n",
" 0.05961706 -0.11502287 -0.00593335 0.00937726 0.01207211 0.06069824\n",
" -0.09090369 -0.04738047 -0.00678526 -0.10049275 -0.02625539 -0.00075635]\n"
]
}
],
"source": [
"result = core.process_algorithm_result(algo_result)\n",
"for line in result[0]:\n",
" print(line)\n",
"\n",
"print(\"The parameters for UCCSD are:\\n{}\".format(algo_result['opt_params']))"
]
},
{
"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": 1
}
| apache-2.0 |
datitran/Krimskrams | Kaggle/Mercedes-Benz Greener Manufacturing/naive_modeling_different_seeds.ipynb | 1 | 120210 | {
"cells": [
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime\n",
"import xgboost as xgb\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\"ggplot\")\n",
"%matplotlib inline\n",
"\n",
"import itertools\n",
"from sklearn.metrics import r2_score\n",
"from sklearn import preprocessing\n",
"from sklearn.decomposition import PCA, FastICA, TruncatedSVD\n",
"from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.read_csv(\"data/train.csv\", index_col=\"ID\")\n",
"test = pd.read_csv(\"data/test.csv\", index_col=\"ID\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for c in train.columns:\n",
" if train[c].dtype == \"object\":\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(train[c].values) + list(test[c].values))\n",
" train[c] = lbl.transform(list(train[c].values))\n",
" test[c] = lbl.transform(list(test[c].values))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/datitran/anaconda/envs/kaggle/lib/python3.5/site-packages/sklearn/decomposition/fastica_.py:116: UserWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n",
" warnings.warn('FastICA did not converge. Consider increasing '\n",
"/Users/datitran/anaconda/envs/kaggle/lib/python3.5/site-packages/scipy/linalg/basic.py:1018: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
" warnings.warn(mesg, RuntimeWarning)\n"
]
}
],
"source": [
"n_comp = 12\n",
"\n",
"# tSVD\n",
"tsvd = TruncatedSVD(n_components=n_comp, random_state=42)\n",
"tsvd_results_train = tsvd.fit_transform(train.drop([\"y\"], axis=1))\n",
"tsvd_results_test = tsvd.transform(test)\n",
"\n",
"# PCA\n",
"pca = PCA(n_components=n_comp, random_state=42)\n",
"pca2_results_train = pca.fit_transform(train.drop([\"y\"], axis=1))\n",
"pca2_results_test = pca.transform(test)\n",
"\n",
"# ICA\n",
"ica = FastICA(n_components=n_comp, random_state=42)\n",
"ica2_results_train = ica.fit_transform(train.drop([\"y\"], axis=1))\n",
"ica2_results_test = ica.transform(test)\n",
"\n",
"# GRP\n",
"grp = GaussianRandomProjection(n_components=n_comp, eps=0.1, random_state=42)\n",
"grp_results_train = grp.fit_transform(train.drop([\"y\"], axis=1))\n",
"grp_results_test = grp.transform(test)\n",
"\n",
"# SRP\n",
"srp = SparseRandomProjection(n_components=n_comp, dense_output=True, random_state=42)\n",
"srp_results_train = srp.fit_transform(train.drop([\"y\"], axis=1))\n",
"srp_results_test = srp.transform(test)\n",
"\n",
"# Append decomposition components to datasets\n",
"for i in range(1, n_comp + 1):\n",
" train['pca_' + str(i)] = pca2_results_train[:, i - 1]\n",
" test['pca_' + str(i)] = pca2_results_test[:, i - 1]\n",
"\n",
" train['ica_' + str(i)] = ica2_results_train[:, i - 1]\n",
" test['ica_' + str(i)] = ica2_results_test[:, i - 1]\n",
"\n",
" train['tsvd_' + str(i)] = tsvd_results_train[:, i - 1]\n",
" test['tsvd_' + str(i)] = tsvd_results_test[:, i - 1]\n",
"\n",
" train['grp_' + str(i)] = grp_results_train[:, i - 1]\n",
" test['grp_' + str(i)] = grp_results_test[:, i - 1]\n",
"\n",
" train['srp_' + str(i)] = srp_results_train[:, i - 1]\n",
" test['srp_' + str(i)] = srp_results_test[:, i - 1]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"100.66931812782134"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train = train[\"y\"]\n",
"y_mean = np.average(y_train)\n",
"y_mean"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xgb_params = {\n",
" \"n_trees\": 500,\n",
" \"eta\": 0.005,\n",
" \"max_depth\": 4,\n",
" \"subsample\": 0.95,\n",
" \"objective\": \"reg:linear\",\n",
" \"eval_metric\": \"rmse\",\n",
" \"base_score\": y_mean,\n",
" \"silent\": 1\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dtrain = xgb.DMatrix(train.drop(\"y\", axis=1), y_train)\n",
"dtest = xgb.DMatrix(test)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-rmse:12.64\ttest-rmse:12.638\n",
"[50]\ttrain-rmse:11.092\ttest-rmse:11.1459\n",
"[100]\ttrain-rmse:10.0189\ttest-rmse:10.1351\n",
"[150]\ttrain-rmse:9.28968\ttest-rmse:9.47303\n",
"[200]\ttrain-rmse:8.80062\ttest-rmse:9.05063\n",
"[250]\ttrain-rmse:8.47291\ttest-rmse:8.78791\n",
"[300]\ttrain-rmse:8.25098\ttest-rmse:8.62768\n",
"[350]\ttrain-rmse:8.0908\ttest-rmse:8.53291\n",
"[400]\ttrain-rmse:7.95357\ttest-rmse:8.47836\n",
"[450]\ttrain-rmse:7.82536\ttest-rmse:8.4491\n",
"[500]\ttrain-rmse:7.70952\ttest-rmse:8.43563\n",
"[550]\ttrain-rmse:7.61704\ttest-rmse:8.42952\n",
"[600]\ttrain-rmse:7.53256\ttest-rmse:8.42824\n"
]
}
],
"source": [
"cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=2000, early_stopping_rounds=50, \n",
"# verbose_eval=50, show_stdv=False)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CV, 750, 1250, 1500\n",
"num_boost_rounds = 1250\n",
"model = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=num_boost_rounds)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110d824e0>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5/+bL9H1liEoppZw50pkOLZvy6quvctVVVxEdHU3g3/7c3FyMMURHRzNp0iQm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"text/plain": [
"<matplotlib.figure.Figure at 0x106756f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xgb.plot_importance(model, max_num_features=20)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred = model.predict(dtrain)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.68658199455340752"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r2_score(y_train, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-rmse:12.64\ttest-rmse:12.638\n",
"[50]\ttrain-rmse:11.092\ttest-rmse:11.1459\n",
"[100]\ttrain-rmse:10.0189\ttest-rmse:10.1351\n",
"[150]\ttrain-rmse:9.28968\ttest-rmse:9.47303\n",
"[200]\ttrain-rmse:8.80062\ttest-rmse:9.05063\n",
"[250]\ttrain-rmse:8.47291\ttest-rmse:8.78791\n",
"[300]\ttrain-rmse:8.25098\ttest-rmse:8.62768\n",
"[350]\ttrain-rmse:8.0908\ttest-rmse:8.53291\n",
"[400]\ttrain-rmse:7.95357\ttest-rmse:8.47836\n",
"[450]\ttrain-rmse:7.82536\ttest-rmse:8.4491\n",
"[500]\ttrain-rmse:7.70952\ttest-rmse:8.43563\n",
"[550]\ttrain-rmse:7.61704\ttest-rmse:8.42952\n",
"[600]\ttrain-rmse:7.53256\ttest-rmse:8.42824\n",
"576\n",
"750\n",
"1250\n",
"1500\n"
]
}
],
"source": [
"cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=2000, early_stopping_rounds=50, \n",
" verbose_eval=50, show_stdv=False)\n",
"num_boost = [len(cv_output), 750, 1250, 1500]\n",
"r2_value = []\n",
"prediction = []\n",
"\n",
"for i in num_boost:\n",
" print(i)\n",
" model = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=i)\n",
" y_pred_train = model.predict(dtrain)\n",
" y_pred_test = model.predict(dtest)\n",
" r2_value.append(r2_score(y_train, y_pred_train))\n",
" prediction.append(y_pred_test)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prediction"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_predict = model.predict(dtest)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 85.18135071, 97.18682861, 80.05259705, ..., 93.39948273,\n",
" 109.8515625 , 93.75300598], dtype=float32)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_predict"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0.61848415826468184,\n",
" 0.64248259587514533,\n",
" 0.68658199455340752,\n",
" 0.70297983205481929]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r2_value"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_values = pd.DataFrame.transpose(pd.DataFrame(prediction)).mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"output = pd.DataFrame({\"ID\": test.index, \"y\": mean_values})"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output.to_csv(\"submissions_xgb_average.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>X6</th>\n",
" <th>X8</th>\n",
" <th>X10</th>\n",
" <th>X11</th>\n",
" <th>...</th>\n",
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" <td>29.311298</td>\n",
" <td>2.542356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>49</td>\n",
" <td>20</td>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>31</td>\n",
" <td>8</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.372387</td>\n",
" <td>0.016720</td>\n",
" <td>-0.412456</td>\n",
" <td>6.412039</td>\n",
" <td>2.542356</td>\n",
" <td>0.203514</td>\n",
" <td>-0.005647</td>\n",
" <td>0.534005</td>\n",
" <td>18.929148</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>51</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>2.187761</td>\n",
" <td>-0.017054</td>\n",
" <td>-1.890669</td>\n",
" <td>8.139587</td>\n",
" <td>5.084712</td>\n",
" <td>0.996816</td>\n",
" <td>-0.012483</td>\n",
" <td>-0.702531</td>\n",
" <td>13.857533</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>50</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-2.170699</td>\n",
" <td>-0.012379</td>\n",
" <td>1.900214</td>\n",
" <td>14.435411</td>\n",
" <td>6.355890</td>\n",
" <td>-0.364884</td>\n",
" <td>-0.027055</td>\n",
" <td>0.455819</td>\n",
" <td>16.827351</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>32</td>\n",
" <td>20</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.309360</td>\n",
" <td>0.021801</td>\n",
" <td>-0.046182</td>\n",
" <td>3.499716</td>\n",
" <td>3.813534</td>\n",
" <td>0.205489</td>\n",
" <td>0.025721</td>\n",
" <td>0.900369</td>\n",
" <td>7.723744</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>43</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.558666</td>\n",
" <td>0.001382</td>\n",
" <td>3.041812</td>\n",
" <td>-3.430354</td>\n",
" <td>6.355890</td>\n",
" <td>-0.332851</td>\n",
" <td>-0.001156</td>\n",
" <td>-3.055479</td>\n",
" <td>20.151320</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>5</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.377017</td>\n",
" <td>0.020245</td>\n",
" <td>1.678514</td>\n",
" <td>9.761558</td>\n",
" <td>2.542356</td>\n",
" <td>-1.638675</td>\n",
" <td>-0.021474</td>\n",
" <td>-0.730287</td>\n",
" <td>12.687294</td>\n",
" <td>-1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>43</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.530564</td>\n",
" <td>0.000197</td>\n",
" <td>3.105998</td>\n",
" <td>-1.746055</td>\n",
" <td>6.355890</td>\n",
" <td>-0.351794</td>\n",
" <td>-0.000613</td>\n",
" <td>-3.435102</td>\n",
" <td>19.017919</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>23</td>\n",
" <td>3</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.330472</td>\n",
" <td>-0.022255</td>\n",
" <td>0.298431</td>\n",
" <td>1.775172</td>\n",
" <td>6.355890</td>\n",
" <td>0.933991</td>\n",
" <td>-0.008615</td>\n",
" <td>0.151225</td>\n",
" <td>15.636658</td>\n",
" <td>2.542356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.155564</td>\n",
" <td>0.009838</td>\n",
" <td>-0.939427</td>\n",
" <td>-1.204039</td>\n",
" <td>3.813534</td>\n",
" <td>-1.017130</td>\n",
" <td>-0.000437</td>\n",
" <td>-1.578835</td>\n",
" <td>14.693384</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.514857</td>\n",
" <td>0.030249</td>\n",
" <td>0.177333</td>\n",
" <td>8.020388</td>\n",
" <td>2.542356</td>\n",
" <td>-0.576932</td>\n",
" <td>0.004293</td>\n",
" <td>0.626014</td>\n",
" <td>7.117652</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.221697</td>\n",
" <td>-0.018617</td>\n",
" <td>0.104881</td>\n",
" <td>8.835412</td>\n",
" <td>5.084712</td>\n",
" <td>0.211328</td>\n",
" <td>-0.014430</td>\n",
" <td>0.411417</td>\n",
" <td>10.055139</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.131660</td>\n",
" <td>0.009929</td>\n",
" <td>-0.934108</td>\n",
" <td>0.387623</td>\n",
" <td>3.813534</td>\n",
" <td>-1.058603</td>\n",
" <td>-0.009120</td>\n",
" <td>-1.521472</td>\n",
" <td>14.412868</td>\n",
" <td>-1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>43</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-2.167571</td>\n",
" <td>0.002949</td>\n",
" <td>3.595895</td>\n",
" <td>-3.639964</td>\n",
" <td>5.084712</td>\n",
" <td>0.363775</td>\n",
" <td>0.010967</td>\n",
" <td>-2.657896</td>\n",
" <td>18.514652</td>\n",
" <td>5.084712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.156986</td>\n",
" <td>0.009881</td>\n",
" <td>-0.940505</td>\n",
" <td>-1.209123</td>\n",
" <td>3.813534</td>\n",
" <td>-1.016838</td>\n",
" <td>-0.000450</td>\n",
" <td>-1.585819</td>\n",
" <td>14.220430</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>52</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.296851</td>\n",
" <td>-0.006671</td>\n",
" <td>0.739267</td>\n",
" <td>-0.810420</td>\n",
" <td>7.627067</td>\n",
" <td>0.249610</td>\n",
" <td>0.022585</td>\n",
" <td>-1.393593</td>\n",
" <td>8.973012</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>51</td>\n",
" <td>19</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.502432</td>\n",
" <td>0.002563</td>\n",
" <td>0.054841</td>\n",
" <td>1.167066</td>\n",
" <td>5.084712</td>\n",
" <td>-0.146192</td>\n",
" <td>0.021908</td>\n",
" <td>-1.603956</td>\n",
" <td>8.719236</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.156986</td>\n",
" <td>0.009881</td>\n",
" <td>-0.940505</td>\n",
" <td>-1.209123</td>\n",
" <td>3.813534</td>\n",
" <td>-1.016838</td>\n",
" <td>-0.000450</td>\n",
" <td>-1.585819</td>\n",
" <td>14.220430</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.156986</td>\n",
" <td>0.009881</td>\n",
" <td>-0.940505</td>\n",
" <td>-1.209123</td>\n",
" <td>3.813534</td>\n",
" <td>-1.016838</td>\n",
" <td>-0.000450</td>\n",
" <td>-1.585819</td>\n",
" <td>14.220430</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>52</td>\n",
" <td>23</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>9</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.335471</td>\n",
" <td>0.017153</td>\n",
" <td>-0.982201</td>\n",
" <td>7.264861</td>\n",
" <td>5.084712</td>\n",
" <td>0.666863</td>\n",
" <td>-0.000245</td>\n",
" <td>-1.195631</td>\n",
" <td>7.681748</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.151297</td>\n",
" <td>0.009708</td>\n",
" <td>-0.936190</td>\n",
" <td>-1.188785</td>\n",
" <td>3.813534</td>\n",
" <td>-1.018005</td>\n",
" <td>-0.000395</td>\n",
" <td>-1.557885</td>\n",
" <td>16.112245</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>51</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.038288</td>\n",
" <td>-0.016736</td>\n",
" <td>-0.181836</td>\n",
" <td>4.278207</td>\n",
" <td>5.084712</td>\n",
" <td>-0.299314</td>\n",
" <td>0.005153</td>\n",
" <td>0.001296</td>\n",
" <td>10.184704</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>43</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.235619</td>\n",
" <td>0.001615</td>\n",
" <td>2.587240</td>\n",
" <td>-0.182639</td>\n",
" <td>5.084712</td>\n",
" <td>-0.817065</td>\n",
" <td>-0.020117</td>\n",
" <td>-2.864849</td>\n",
" <td>21.627612</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>37</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>2.861170</td>\n",
" <td>-0.021386</td>\n",
" <td>-2.835569</td>\n",
" <td>1.809420</td>\n",
" <td>5.084712</td>\n",
" <td>0.433416</td>\n",
" <td>-0.013857</td>\n",
" <td>-0.372506</td>\n",
" <td>15.526004</td>\n",
" <td>2.542356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.072904</td>\n",
" <td>-0.020223</td>\n",
" <td>-0.919104</td>\n",
" <td>-0.335273</td>\n",
" <td>2.542356</td>\n",
" <td>0.172243</td>\n",
" <td>0.011601</td>\n",
" <td>-0.174251</td>\n",
" <td>10.632803</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>32</td>\n",
" <td>20</td>\n",
" <td>37</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.638531</td>\n",
" <td>0.019319</td>\n",
" <td>-1.253382</td>\n",
" <td>-3.524436</td>\n",
" <td>3.813534</td>\n",
" <td>-0.571897</td>\n",
" <td>0.022759</td>\n",
" <td>1.167750</td>\n",
" <td>16.311535</td>\n",
" <td>3.813534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>24</td>\n",
" <td>17</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.824343</td>\n",
" <td>0.004396</td>\n",
" <td>2.122083</td>\n",
" <td>0.193996</td>\n",
" <td>5.084712</td>\n",
" <td>4.228805</td>\n",
" <td>0.025169</td>\n",
" <td>0.740460</td>\n",
" <td>11.686264</td>\n",
" <td>5.084712</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>8361</th>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.991896</td>\n",
" <td>0.024041</td>\n",
" <td>-1.136656</td>\n",
" <td>5.580933</td>\n",
" <td>3.813534</td>\n",
" <td>0.173490</td>\n",
" <td>0.000314</td>\n",
" <td>0.350638</td>\n",
" <td>5.620973</td>\n",
" <td>5.084712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8363</th>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.037404</td>\n",
" <td>0.007784</td>\n",
" <td>0.108064</td>\n",
" <td>-1.361012</td>\n",
" <td>1.271178</td>\n",
" <td>-0.630004</td>\n",
" <td>0.022961</td>\n",
" <td>-0.619932</td>\n",
" <td>6.217948</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8364</th>\n",
" <td>46</td>\n",
" <td>3</td>\n",
" <td>37</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.700086</td>\n",
" <td>-0.013676</td>\n",
" <td>-1.192935</td>\n",
" <td>1.464615</td>\n",
" <td>3.813534</td>\n",
" <td>-1.542641</td>\n",
" <td>-0.023987</td>\n",
" <td>0.477139</td>\n",
" <td>10.957880</td>\n",
" <td>2.542356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8365</th>\n",
" <td>52</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.561792</td>\n",
" <td>-0.013687</td>\n",
" <td>1.263724</td>\n",
" <td>4.795447</td>\n",
" <td>6.355890</td>\n",
" <td>-0.023743</td>\n",
" <td>0.016696</td>\n",
" <td>0.545716</td>\n",
" <td>1.832626</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8366</th>\n",
" <td>30</td>\n",
" <td>10</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.331556</td>\n",
" <td>-0.014091</td>\n",
" <td>0.453208</td>\n",
" <td>0.073163</td>\n",
" <td>5.084712</td>\n",
" <td>0.282212</td>\n",
" <td>0.007661</td>\n",
" <td>-0.451066</td>\n",
" <td>6.956368</td>\n",
" <td>-1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8370</th>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.017286</td>\n",
" <td>-0.019971</td>\n",
" <td>-0.008425</td>\n",
" <td>8.639244</td>\n",
" <td>6.355890</td>\n",
" <td>0.744845</td>\n",
" <td>-0.023673</td>\n",
" <td>0.558851</td>\n",
" <td>4.852846</td>\n",
" <td>6.355890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8372</th>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.313459</td>\n",
" <td>0.014531</td>\n",
" <td>-0.318786</td>\n",
" <td>2.679686</td>\n",
" <td>5.084712</td>\n",
" <td>-1.129103</td>\n",
" <td>0.017213</td>\n",
" <td>-0.918784</td>\n",
" <td>1.901126</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8376</th>\n",
" <td>52</td>\n",
" <td>1</td>\n",
" <td>37</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>2.067564</td>\n",
" <td>-0.015646</td>\n",
" <td>-2.055505</td>\n",
" <td>2.399907</td>\n",
" <td>5.084712</td>\n",
" <td>-0.392982</td>\n",
" <td>-0.016272</td>\n",
" <td>-0.266275</td>\n",
" <td>6.389545</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8377</th>\n",
" <td>50</td>\n",
" <td>16</td>\n",
" <td>45</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.558295</td>\n",
" <td>-0.002543</td>\n",
" <td>1.058515</td>\n",
" <td>-3.736250</td>\n",
" <td>5.084712</td>\n",
" <td>-1.033771</td>\n",
" <td>0.008687</td>\n",
" <td>-1.165563</td>\n",
" <td>14.206997</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8379</th>\n",
" <td>50</td>\n",
" <td>19</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.412253</td>\n",
" <td>0.007865</td>\n",
" <td>0.845536</td>\n",
" <td>4.883085</td>\n",
" <td>5.084712</td>\n",
" <td>-0.902703</td>\n",
" <td>0.012608</td>\n",
" <td>-1.710462</td>\n",
" <td>3.419406</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8380</th>\n",
" <td>45</td>\n",
" <td>3</td>\n",
" <td>37</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.772512</td>\n",
" <td>-0.023266</td>\n",
" <td>-1.018588</td>\n",
" <td>1.522533</td>\n",
" <td>3.813534</td>\n",
" <td>-0.655265</td>\n",
" <td>-0.006393</td>\n",
" <td>0.116385</td>\n",
" <td>11.043013</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8381</th>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.172892</td>\n",
" <td>0.002054</td>\n",
" <td>0.107133</td>\n",
" <td>3.417428</td>\n",
" <td>2.542356</td>\n",
" <td>-1.261781</td>\n",
" <td>-0.006692</td>\n",
" <td>-0.978471</td>\n",
" <td>7.339528</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8386</th>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>2.744113</td>\n",
" <td>-0.015741</td>\n",
" <td>-2.501008</td>\n",
" <td>7.991507</td>\n",
" <td>5.084712</td>\n",
" <td>1.138809</td>\n",
" <td>-0.020907</td>\n",
" <td>-0.605984</td>\n",
" <td>1.514459</td>\n",
" <td>5.084712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8388</th>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.068149</td>\n",
" <td>-0.019977</td>\n",
" <td>-0.950221</td>\n",
" <td>4.240683</td>\n",
" <td>5.084712</td>\n",
" <td>1.228884</td>\n",
" <td>-0.004383</td>\n",
" <td>0.077862</td>\n",
" <td>4.263758</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8389</th>\n",
" <td>32</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.410450</td>\n",
" <td>0.019066</td>\n",
" <td>-0.740641</td>\n",
" <td>3.138669</td>\n",
" <td>5.084712</td>\n",
" <td>0.539227</td>\n",
" <td>0.008495</td>\n",
" <td>1.134056</td>\n",
" <td>4.244269</td>\n",
" <td>5.084712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8391</th>\n",
" <td>49</td>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.198979</td>\n",
" <td>-0.008478</td>\n",
" <td>1.108685</td>\n",
" <td>8.682647</td>\n",
" <td>5.084712</td>\n",
" <td>-0.074491</td>\n",
" <td>-0.023994</td>\n",
" <td>0.638499</td>\n",
" <td>7.138845</td>\n",
" <td>2.542356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8394</th>\n",
" <td>52</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.318295</td>\n",
" <td>0.012021</td>\n",
" <td>0.011588</td>\n",
" <td>6.271418</td>\n",
" <td>6.355890</td>\n",
" <td>0.079390</td>\n",
" <td>0.025691</td>\n",
" <td>-0.817155</td>\n",
" <td>1.029160</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8396</th>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.032410</td>\n",
" <td>0.012235</td>\n",
" <td>-0.048793</td>\n",
" <td>0.018301</td>\n",
" <td>1.271178</td>\n",
" <td>-0.309524</td>\n",
" <td>0.016275</td>\n",
" <td>-0.391421</td>\n",
" <td>5.602780</td>\n",
" <td>2.542356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8398</th>\n",
" <td>23</td>\n",
" <td>19</td>\n",
" <td>37</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.664172</td>\n",
" <td>0.004929</td>\n",
" <td>-1.326393</td>\n",
" <td>0.406803</td>\n",
" <td>3.813534</td>\n",
" <td>-0.737913</td>\n",
" <td>-0.003642</td>\n",
" <td>-1.796293</td>\n",
" <td>11.362108</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8400</th>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.694680</td>\n",
" <td>-0.002265</td>\n",
" <td>0.566937</td>\n",
" <td>-4.391322</td>\n",
" <td>2.542356</td>\n",
" <td>-0.675452</td>\n",
" <td>0.025959</td>\n",
" <td>0.109204</td>\n",
" <td>6.112038</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8401</th>\n",
" <td>11</td>\n",
" <td>16</td>\n",
" <td>30</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.294331</td>\n",
" <td>0.003090</td>\n",
" <td>-0.761852</td>\n",
" <td>-1.305413</td>\n",
" <td>6.355890</td>\n",
" <td>-0.996526</td>\n",
" <td>0.018464</td>\n",
" <td>-2.537947</td>\n",
" <td>8.559801</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8404</th>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.313694</td>\n",
" <td>0.008450</td>\n",
" <td>-1.098121</td>\n",
" <td>-0.159546</td>\n",
" <td>2.542356</td>\n",
" <td>-1.093286</td>\n",
" <td>-0.013183</td>\n",
" <td>-1.251245</td>\n",
" <td>9.146901</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8407</th>\n",
" <td>41</td>\n",
" <td>13</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-1.592752</td>\n",
" <td>0.010834</td>\n",
" <td>1.532786</td>\n",
" <td>3.464167</td>\n",
" <td>1.271178</td>\n",
" <td>-0.822397</td>\n",
" <td>0.021415</td>\n",
" <td>0.119642</td>\n",
" <td>4.496499</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8408</th>\n",
" <td>36</td>\n",
" <td>10</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.728555</td>\n",
" <td>-0.010491</td>\n",
" <td>0.815011</td>\n",
" <td>1.528973</td>\n",
" <td>5.084712</td>\n",
" <td>-0.377640</td>\n",
" <td>-0.002526</td>\n",
" <td>-0.377508</td>\n",
" <td>7.341222</td>\n",
" <td>-1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8409</th>\n",
" <td>36</td>\n",
" <td>16</td>\n",
" <td>44</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.065513</td>\n",
" <td>-0.000182</td>\n",
" <td>0.531252</td>\n",
" <td>-4.071465</td>\n",
" <td>1.271178</td>\n",
" <td>-0.674307</td>\n",
" <td>0.001571</td>\n",
" <td>-1.229955</td>\n",
" <td>15.366136</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8410</th>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.131410</td>\n",
" <td>0.006054</td>\n",
" <td>0.125723</td>\n",
" <td>0.040165</td>\n",
" <td>2.542356</td>\n",
" <td>-0.198499</td>\n",
" <td>0.016121</td>\n",
" <td>-0.368980</td>\n",
" <td>5.014935</td>\n",
" <td>2.542356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8411</th>\n",
" <td>46</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.389208</td>\n",
" <td>-0.012173</td>\n",
" <td>-0.258061</td>\n",
" <td>11.596712</td>\n",
" <td>7.627067</td>\n",
" <td>0.950585</td>\n",
" <td>-0.027055</td>\n",
" <td>-0.160181</td>\n",
" <td>3.511625</td>\n",
" <td>6.355890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8413</th>\n",
" <td>51</td>\n",
" <td>23</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>-0.006041</td>\n",
" <td>0.008858</td>\n",
" <td>0.295984</td>\n",
" <td>11.141230</td>\n",
" <td>3.813534</td>\n",
" <td>-0.286020</td>\n",
" <td>-0.022914</td>\n",
" <td>-1.094539</td>\n",
" <td>5.732252</td>\n",
" <td>1.271178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8414</th>\n",
" <td>10</td>\n",
" <td>23</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.992178</td>\n",
" <td>0.023309</td>\n",
" <td>-1.051221</td>\n",
" <td>4.692869</td>\n",
" <td>3.813534</td>\n",
" <td>-0.017100</td>\n",
" <td>-0.008829</td>\n",
" <td>-0.217748</td>\n",
" <td>6.462538</td>\n",
" <td>5.084712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8416</th>\n",
" <td>46</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>2.301100</td>\n",
" <td>-0.011585</td>\n",
" <td>-1.999656</td>\n",
" <td>7.128865</td>\n",
" <td>3.813534</td>\n",
" <td>0.547094</td>\n",
" <td>-0.011092</td>\n",
" <td>-0.886460</td>\n",
" <td>1.149453</td>\n",
" <td>5.084712</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4209 rows × 436 columns</p>\n",
"</div>"
],
"text/plain": [
" X0 X1 X2 X3 X4 X5 X6 X8 X10 X11 ... pca_11 ica_11 \\\n",
"ID ... \n",
"1 24 23 38 5 3 26 0 22 0 0 ... -1.714782 0.003638 \n",
"2 46 3 9 0 3 9 6 24 0 0 ... 0.170500 -0.013091 \n",
"3 24 23 19 5 3 0 9 9 0 0 ... -0.982514 0.012243 \n",
"4 24 13 38 5 3 32 11 13 0 0 ... -1.891332 -0.007817 \n",
"5 49 20 19 2 3 31 8 12 0 0 ... 0.372387 0.016720 \n",
"8 51 1 9 4 3 30 6 18 0 0 ... 2.187761 -0.017054 \n",
"10 50 3 5 3 3 30 3 24 0 0 ... -2.170699 -0.012379 \n",
"11 32 20 5 2 3 14 3 0 0 0 ... -0.309360 0.021801 \n",
"12 15 13 43 2 3 14 9 13 0 0 ... -1.558666 0.001382 \n",
"14 41 23 19 5 3 13 5 21 0 0 ... -1.377017 0.020245 \n",
"15 15 13 43 2 3 13 3 13 0 0 ... -1.530564 0.000197 \n",
"16 23 3 26 0 3 13 11 17 0 0 ... -0.330472 -0.022255 \n",
"17 11 19 29 5 3 13 7 14 0 0 ... 1.155564 0.009838 \n",
"19 41 23 5 6 3 13 6 9 0 0 ... -0.514857 0.030249 \n",
"20 34 0 11 5 3 13 11 19 0 0 ... -0.221697 -0.018617 \n",
"21 11 19 29 5 3 13 7 18 0 0 ... 1.131660 0.009929 \n",
"22 15 13 43 2 3 13 3 7 0 0 ... -2.167571 0.002949 \n",
"23 11 19 29 5 3 12 7 14 0 0 ... 1.156986 0.009881 \n",
"26 52 10 9 0 3 12 11 2 0 0 ... -0.296851 -0.006671 \n",
"28 51 19 19 5 3 12 3 2 0 0 ... 0.502432 0.002563 \n",
"29 11 19 29 5 3 12 7 14 0 0 ... 1.156986 0.009881 \n",
"33 11 19 29 5 3 12 7 14 0 0 ... 1.156986 0.009881 \n",
"35 52 23 9 4 3 12 9 12 0 0 ... 1.335471 0.017153 \n",
"41 11 19 29 5 3 16 7 14 0 0 ... 1.151297 0.009708 \n",
"42 51 3 9 2 3 16 6 10 0 0 ... 0.038288 -0.016736 \n",
"43 15 13 43 2 3 16 9 22 0 0 ... -1.235619 0.001615 \n",
"45 23 1 37 2 3 16 6 19 0 0 ... 2.861170 -0.021386 \n",
"46 23 0 19 3 3 16 11 7 0 0 ... 1.072904 -0.020223 \n",
"51 32 20 37 2 3 16 6 0 0 0 ... 0.638531 0.019319 \n",
"53 24 17 19 5 3 16 6 0 0 0 ... -1.824343 0.004396 \n",
"... .. .. .. .. .. .. .. .. ... ... ... ... ... \n",
"8361 22 23 11 2 3 1 6 12 0 0 ... 0.991896 0.024041 \n",
"8363 9 13 19 5 3 1 8 2 0 0 ... 0.037404 0.007784 \n",
"8364 46 3 37 0 3 1 9 22 0 0 ... 0.700086 -0.013676 \n",
"8365 52 3 5 0 3 1 3 4 0 0 ... -1.561792 -0.013687 \n",
"8366 30 10 19 0 3 1 11 9 0 0 ... -0.331556 -0.014091 \n",
"8370 50 1 19 0 3 1 9 21 0 0 ... -0.017286 -0.019971 \n",
"8372 12 16 5 5 3 1 8 6 0 0 ... 0.313459 0.014531 \n",
"8376 52 1 37 2 3 1 8 18 0 0 ... 2.067564 -0.015646 \n",
"8377 50 16 45 2 3 1 9 7 0 0 ... -0.558295 -0.002543 \n",
"8379 50 19 19 5 3 1 3 5 0 0 ... -0.412253 0.007865 \n",
"8380 45 3 37 0 3 1 3 15 0 0 ... 0.772512 -0.023266 \n",
"8381 10 9 19 5 3 1 6 16 0 0 ... 0.172892 0.002054 \n",
"8386 45 1 9 2 3 1 9 22 0 0 ... 2.744113 -0.015741 \n",
"8388 23 1 19 2 3 1 11 14 0 0 ... 1.068149 -0.019977 \n",
"8389 32 17 11 2 3 1 9 7 0 0 ... 0.410450 0.019066 \n",
"8391 49 6 19 0 3 1 6 21 0 0 ... -1.198979 -0.008478 \n",
"8394 52 23 0 1 3 1 9 1 0 0 ... 0.318295 0.012021 \n",
"8396 9 13 19 5 3 1 9 4 0 0 ... 0.032410 0.012235 \n",
"8398 23 19 37 5 3 1 6 15 0 0 ... 1.664172 0.004929 \n",
"8400 10 6 19 0 3 1 9 0 0 0 ... -0.694680 -0.002265 \n",
"8401 11 16 30 5 3 1 3 6 0 0 ... 1.294331 0.003090 \n",
"8404 11 19 29 5 3 1 11 20 0 0 ... 1.313694 0.008450 \n",
"8407 41 13 19 5 3 1 3 0 0 0 ... -1.592752 0.010834 \n",
"8408 36 10 19 0 3 1 11 13 0 0 ... -0.728555 -0.010491 \n",
"8409 36 16 44 3 3 1 9 11 0 0 ... 0.065513 -0.000182 \n",
"8410 9 9 19 5 3 1 9 4 0 0 ... -0.131410 0.006054 \n",
"8411 46 1 9 3 3 1 9 24 0 0 ... 0.389208 -0.012173 \n",
"8413 51 23 19 5 3 1 3 22 0 0 ... -0.006041 0.008858 \n",
"8414 10 23 19 0 3 1 2 16 0 0 ... 0.992178 0.023309 \n",
"8416 46 1 9 2 3 1 6 17 0 0 ... 2.301100 -0.011585 \n",
"\n",
" tsvd_11 grp_11 srp_11 pca_12 ica_12 tsvd_12 grp_12 \\\n",
"ID \n",
"1 1.250838 7.046348 1.271178 3.610562 -0.021043 2.593055 26.800662 \n",
"2 0.158002 7.160356 5.084712 -0.177144 -0.024816 -1.128290 8.807027 \n",
"3 0.828812 3.644108 3.813534 3.307781 0.006142 1.492705 6.044545 \n",
"4 1.165267 1.205195 2.542356 3.900539 -0.003266 3.635485 29.311298 \n",
"5 -0.412456 6.412039 2.542356 0.203514 -0.005647 0.534005 18.929148 \n",
"8 -1.890669 8.139587 5.084712 0.996816 -0.012483 -0.702531 13.857533 \n",
"10 1.900214 14.435411 6.355890 -0.364884 -0.027055 0.455819 16.827351 \n",
"11 -0.046182 3.499716 3.813534 0.205489 0.025721 0.900369 7.723744 \n",
"12 3.041812 -3.430354 6.355890 -0.332851 -0.001156 -3.055479 20.151320 \n",
"14 1.678514 9.761558 2.542356 -1.638675 -0.021474 -0.730287 12.687294 \n",
"15 3.105998 -1.746055 6.355890 -0.351794 -0.000613 -3.435102 19.017919 \n",
"16 0.298431 1.775172 6.355890 0.933991 -0.008615 0.151225 15.636658 \n",
"17 -0.939427 -1.204039 3.813534 -1.017130 -0.000437 -1.578835 14.693384 \n",
"19 0.177333 8.020388 2.542356 -0.576932 0.004293 0.626014 7.117652 \n",
"20 0.104881 8.835412 5.084712 0.211328 -0.014430 0.411417 10.055139 \n",
"21 -0.934108 0.387623 3.813534 -1.058603 -0.009120 -1.521472 14.412868 \n",
"22 3.595895 -3.639964 5.084712 0.363775 0.010967 -2.657896 18.514652 \n",
"23 -0.940505 -1.209123 3.813534 -1.016838 -0.000450 -1.585819 14.220430 \n",
"26 0.739267 -0.810420 7.627067 0.249610 0.022585 -1.393593 8.973012 \n",
"28 0.054841 1.167066 5.084712 -0.146192 0.021908 -1.603956 8.719236 \n",
"29 -0.940505 -1.209123 3.813534 -1.016838 -0.000450 -1.585819 14.220430 \n",
"33 -0.940505 -1.209123 3.813534 -1.016838 -0.000450 -1.585819 14.220430 \n",
"35 -0.982201 7.264861 5.084712 0.666863 -0.000245 -1.195631 7.681748 \n",
"41 -0.936190 -1.188785 3.813534 -1.018005 -0.000395 -1.557885 16.112245 \n",
"42 -0.181836 4.278207 5.084712 -0.299314 0.005153 0.001296 10.184704 \n",
"43 2.587240 -0.182639 5.084712 -0.817065 -0.020117 -2.864849 21.627612 \n",
"45 -2.835569 1.809420 5.084712 0.433416 -0.013857 -0.372506 15.526004 \n",
"46 -0.919104 -0.335273 2.542356 0.172243 0.011601 -0.174251 10.632803 \n",
"51 -1.253382 -3.524436 3.813534 -0.571897 0.022759 1.167750 16.311535 \n",
"53 2.122083 0.193996 5.084712 4.228805 0.025169 0.740460 11.686264 \n",
"... ... ... ... ... ... ... ... \n",
"8361 -1.136656 5.580933 3.813534 0.173490 0.000314 0.350638 5.620973 \n",
"8363 0.108064 -1.361012 1.271178 -0.630004 0.022961 -0.619932 6.217948 \n",
"8364 -1.192935 1.464615 3.813534 -1.542641 -0.023987 0.477139 10.957880 \n",
"8365 1.263724 4.795447 6.355890 -0.023743 0.016696 0.545716 1.832626 \n",
"8366 0.453208 0.073163 5.084712 0.282212 0.007661 -0.451066 6.956368 \n",
"8370 -0.008425 8.639244 6.355890 0.744845 -0.023673 0.558851 4.852846 \n",
"8372 -0.318786 2.679686 5.084712 -1.129103 0.017213 -0.918784 1.901126 \n",
"8376 -2.055505 2.399907 5.084712 -0.392982 -0.016272 -0.266275 6.389545 \n",
"8377 1.058515 -3.736250 5.084712 -1.033771 0.008687 -1.165563 14.206997 \n",
"8379 0.845536 4.883085 5.084712 -0.902703 0.012608 -1.710462 3.419406 \n",
"8380 -1.018588 1.522533 3.813534 -0.655265 -0.006393 0.116385 11.043013 \n",
"8381 0.107133 3.417428 2.542356 -1.261781 -0.006692 -0.978471 7.339528 \n",
"8386 -2.501008 7.991507 5.084712 1.138809 -0.020907 -0.605984 1.514459 \n",
"8388 -0.950221 4.240683 5.084712 1.228884 -0.004383 0.077862 4.263758 \n",
"8389 -0.740641 3.138669 5.084712 0.539227 0.008495 1.134056 4.244269 \n",
"8391 1.108685 8.682647 5.084712 -0.074491 -0.023994 0.638499 7.138845 \n",
"8394 0.011588 6.271418 6.355890 0.079390 0.025691 -0.817155 1.029160 \n",
"8396 -0.048793 0.018301 1.271178 -0.309524 0.016275 -0.391421 5.602780 \n",
"8398 -1.326393 0.406803 3.813534 -0.737913 -0.003642 -1.796293 11.362108 \n",
"8400 0.566937 -4.391322 2.542356 -0.675452 0.025959 0.109204 6.112038 \n",
"8401 -0.761852 -1.305413 6.355890 -0.996526 0.018464 -2.537947 8.559801 \n",
"8404 -1.098121 -0.159546 2.542356 -1.093286 -0.013183 -1.251245 9.146901 \n",
"8407 1.532786 3.464167 1.271178 -0.822397 0.021415 0.119642 4.496499 \n",
"8408 0.815011 1.528973 5.084712 -0.377640 -0.002526 -0.377508 7.341222 \n",
"8409 0.531252 -4.071465 1.271178 -0.674307 0.001571 -1.229955 15.366136 \n",
"8410 0.125723 0.040165 2.542356 -0.198499 0.016121 -0.368980 5.014935 \n",
"8411 -0.258061 11.596712 7.627067 0.950585 -0.027055 -0.160181 3.511625 \n",
"8413 0.295984 11.141230 3.813534 -0.286020 -0.022914 -1.094539 5.732252 \n",
"8414 -1.051221 4.692869 3.813534 -0.017100 -0.008829 -0.217748 6.462538 \n",
"8416 -1.999656 7.128865 3.813534 0.547094 -0.011092 -0.886460 1.149453 \n",
"\n",
" srp_12 \n",
"ID \n",
"1 2.542356 \n",
"2 5.084712 \n",
"3 2.542356 \n",
"4 2.542356 \n",
"5 3.813534 \n",
"8 3.813534 \n",
"10 1.271178 \n",
"11 3.813534 \n",
"12 3.813534 \n",
"14 -1.271178 \n",
"15 3.813534 \n",
"16 2.542356 \n",
"17 0.000000 \n",
"19 1.271178 \n",
"20 1.271178 \n",
"21 -1.271178 \n",
"22 5.084712 \n",
"23 0.000000 \n",
"26 3.813534 \n",
"28 0.000000 \n",
"29 0.000000 \n",
"33 0.000000 \n",
"35 3.813534 \n",
"41 0.000000 \n",
"42 1.271178 \n",
"43 1.271178 \n",
"45 2.542356 \n",
"46 1.271178 \n",
"51 3.813534 \n",
"53 5.084712 \n",
"... ... \n",
"8361 5.084712 \n",
"8363 1.271178 \n",
"8364 2.542356 \n",
"8365 1.271178 \n",
"8366 -1.271178 \n",
"8370 6.355890 \n",
"8372 0.000000 \n",
"8376 1.271178 \n",
"8377 0.000000 \n",
"8379 1.271178 \n",
"8380 1.271178 \n",
"8381 1.271178 \n",
"8386 5.084712 \n",
"8388 1.271178 \n",
"8389 5.084712 \n",
"8391 2.542356 \n",
"8394 0.000000 \n",
"8396 2.542356 \n",
"8398 1.271178 \n",
"8400 0.000000 \n",
"8401 1.271178 \n",
"8404 0.000000 \n",
"8407 1.271178 \n",
"8408 -1.271178 \n",
"8409 0.000000 \n",
"8410 2.542356 \n",
"8411 6.355890 \n",
"8413 1.271178 \n",
"8414 5.084712 \n",
"8416 5.084712 \n",
"\n",
"[4209 rows x 436 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test"
]
},
{
"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",
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"file_extension": ".py",
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| mit |
blab/antibody-response-pulse | bcell-array/code/.ipynb_checkpoints/VBMG_infection_OAS-Equilibrium-checkpoint.ipynb | 1 | 516408 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Antibody Response Pulse\n",
"https://github.com/blab/antibody-response-pulse\n",
"\n",
"### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination\n",
"### Adaptive immune response for repeated infection"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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tA1wEEBEPA2cCW0raEEDSQsAjo0uemZnZ9OZKJzMzMzObVCtFxJ2F/0/Ln43W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G8AR7VTt5kNv4i4gZfvZLeDpI/1oO6ngD2AtpceD7uI+HdEbFh0abWZmZmZWSccdOqfq4ApOSEu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\nMjMzMzMzMzOz0v0/S62bz2J3oasAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d24a490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"'''\n",
"author: Alvason Zhenhua Li\n",
"date: 04/09/2015\n",
"'''\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from matplotlib.ticker import FuncFormatter\n",
"\n",
"import alva_machinery_event_bcell as alva\n",
"\n",
"AlvaFontSize = 23\n",
"AlvaFigSize = (15, 5)\n",
"numberingFig = 0\n",
"\n",
"# plotting\n",
"dir_path = '/Users/al/Desktop/GitHub/antibody-response-pulse/bcell-array/figure'\n",
"file_name = 'Virus-Bcell-IgM-IgG'\n",
"figure_name = '-equation'\n",
"file_suffix = '.png'\n",
"save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n",
"\n",
"numberingFig = numberingFig + 1\n",
"plt.figure(numberingFig, figsize=(12, 6))\n",
"plt.axis('off')\n",
"plt.title(r'$ Virus-Bcell-IgM-IgG \\ equations \\ (antibody-response \\ for \\ sequential-infection) $'\n",
" , fontsize = AlvaFontSize)\n",
"plt.text(0, 7.0/9, r'$ \\frac{\\partial V_n(t)}{\\partial t} = \\\n",
" + \\xi_{v}V_{n}(t)(1 - \\frac{V_n(t)}{V_{max}}) \\\n",
" - \\phi_{m} V_{n}(t) M_{n}(t) \\\n",
" - \\phi_{g} V_{n}(t) \\sum_{j = 1}^{N} (1 - \\frac{|j - n|}{r + |j - n|})G_{j}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 5.0/9, r'$ \\frac{\\partial B_n(t)}{\\partial t} = \\\n",
" + \\xi_{b} \\\n",
" + \\beta_{m} B_{n}(t) V_{n}(t) \\\n",
" + \\beta_{g} B_{n}(t)\\sum_{j = 1}^{N} (1 - \\frac{|j - n|}{r + |j - n|})V_{j}(t) \\\n",
" - \\mu_{b} B_{n}(t) \\\n",
" + m_b V_{n}\\frac{B_{n-1}(t) - 2B_n(t) + B_{n+1}(t)}{(\\Delta n)^2} $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 3.0/9,r'$ \\frac{\\partial M_n(t)}{\\partial t} = \\\n",
" + \\xi_{m} B_{n}(t) \\\n",
" - \\phi_{m} M_{n}(t) V_{n}(t) \\\n",
" - \\mu_{m} M_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 1.0/9,r'$ \\frac{\\partial G_n(t)}{\\partial t} = \\\n",
" + \\xi_{g} B_{n}(t) \\\n",
" - \\phi_{g} G_{n}(t) \\sum_{j = 1}^{N} (1 - \\frac{|j - n|}{r + |j - n|})V_{j}(t) \\\n",
" - \\mu_{g} G_{n}(t) $' \n",
" , fontsize = 1.2*AlvaFontSize)\n",
"\n",
"plt.savefig(save_figure, dpi = 100, bbox_inches='tight')\n",
"plt.show()\n",
"\n",
"# define the V-B-M-G partial differential equations\n",
"\n",
"# inverted-monod equation\n",
"def monodInvert(half_radius, i):\n",
" if half_radius == 0:\n",
" gOut = i*0\n",
" # numpy.reshape will not change the structure of i, \n",
" # so that the first element of i(unkonwn-size-array) can be setted by array_to_list[0] \n",
" array_to_list = np.reshape(i,[i.size,1]) \n",
" array_to_list[0] = 1 \n",
" else: gOut = 1 - np.absolute(i)/(half_radius + np.absolute(i))\n",
" return (gOut)\n",
"\n",
"# cross immunity\n",
"def crossI_neighborSum_X(gI, half_radius, gX):\n",
" total_neighbor_X = gX.shape[0]\n",
" I_neighborSum = np.zeros(total_neighbor_X)\n",
" # all I[xn] with neighbor-sum \n",
" ratioM = np.zeros([total_neighbor_X, total_neighbor_X])\n",
" gXX = np.tile(gX, [total_neighbor_X, 1])\n",
" gII = np.tile(gI, [total_neighbor_X, 1])\n",
" ratioM[:, :] = monodInvert(half_radius, gXX[:, :] - gXX[:, :].T)\n",
" I_neighborSum[:] = np.sum(ratioM[:, :] * gII[:, :].T, axis = 0)\n",
" if half_radius == 0:\n",
" I_neighborSum = np.copy(gI)\n",
" return (I_neighborSum)\n",
"\n",
"def dVdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dV_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" dV_dt_array[:] = +inRateV*V[:]*(1 - V[:]/maxV) \\\n",
" - killRateVm*M[:]*V[:] \\\n",
" - killRateVg*V[:]*crossI_neighborSum_X(G, cross_radius, gX)[:]\n",
" return(dV_dt_array)\n",
"\n",
"def dBdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dB_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" Bcopy = np.copy(B)\n",
" centerX = Bcopy[:]\n",
" leftX = np.roll(Bcopy[:], 1)\n",
" rightX = np.roll(Bcopy[:], -1)\n",
" leftX[0] = centerX[0]\n",
" rightX[-1] = centerX[-1]\n",
" dB_dt_array[:] = +inRateB \\\n",
" + actRateBm*V[:]*B[:] \\\n",
" + (actRateBg \\\n",
" + alva.event_recovered + alva.event_OAS_press \\\n",
" + alva.event_recoveredV + alva.event_OAS_pressV) \\\n",
" *B[:]*crossI_neighborSum_X(V, cross_radius, gX)[:] \\\n",
" - (outRateB + alva.event_OAS_slowV)*B[:] \\\n",
" + mutatRateB*V[:]*(leftX[:] - 2*centerX[:] + rightX[:])/(dx**2)\n",
" return(dB_dt_array)\n",
"\n",
"def dMdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dM_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" dM_dt_array[:] = +inRateM*B[:] - consumeRateM*M[:]*V[:] - outRateM*M[:]\n",
" return(dM_dt_array)\n",
"\n",
"def dGdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dG_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" Gcopy = np.copy(G)\n",
" centerX = Gcopy[:]\n",
" leftX = np.roll(Gcopy[:], 1)\n",
" rightX = np.roll(Gcopy[:], -1)\n",
" leftX[0] = centerX[0]\n",
" rightX[-1] = centerX[-1]\n",
" dG_dt_array[:] = +(inRateG + alva.event_OAS_boost + alva.event_OAS_boostV)*B[:] \\\n",
" - consumeRateG*G[:]*crossI_neighborSum_X(V, cross_radius, gX)[:] \\\n",
" - outRateG*G[:]\n",
" return(dG_dt_array)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"event_infect = [[ 0. 0.]\n",
" [ 4. 1.]\n",
" [ 4. 29.]\n",
" [ 0. 57.]]\n",
"event_repeated = [[ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]]\n",
"event_vaccine = [[ 0. 0.]\n",
" [ 0. 1.]\n",
" [ 0. 29.]\n",
" [ 0. 57.]]\n"
]
},
{
"data": {
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KMkx0Z48DAwODx3ryySeHrTczmpubCyYXNGKEiIiIiKRFtSYPfgG8xcx+CKwC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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cb1eed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# setting parameter\n",
"timeUnit = 'day'\n",
"if timeUnit == 'hour':\n",
" hour = float(1)\n",
" day = float(24)\n",
"elif timeUnit == 'day':\n",
" day = float(1)\n",
" hour = float(1)/24 \n",
"elif timeUnit == 'year':\n",
" year = float(1)\n",
" day = float(1)/365\n",
" hour = float(1)/24/365 \n",
" \n",
"maxV = float(50) # max virus/micro-liter\n",
"inRateV = 0.2/hour # in-rate of virus\n",
"killRateVm = 0.0003/hour # kill-rate of virus by antibody-IgM\n",
"killRateVg = killRateVm # kill-rate of virus by antibody-IgG\n",
"\n",
"inRateB = 0.01/hour # in-rate of B-cell\n",
"outRateB = inRateB/1.5 # out-rate of B-cell\n",
"actRateBm = killRateVm # activation rate of naive B-cell\n",
"actRateBg = killRateVg # activation rate of naive B-cell\n",
"\n",
"inRateM = 0.16/hour # in-rate of antibody-IgM from naive B-cell\n",
"outRateM = inRateM/1 # out-rate of antibody-IgM from naive B-cell\n",
"consumeRateM = killRateVm # consume-rate of antibody-IgM by cleaning virus\n",
"\n",
"inRateG = inRateM/10 # in-rate of antibody-IgG from memory B-cell\n",
"outRateG = outRateM/250 # out-rate of antibody-IgG from memory B-cell\n",
"consumeRateG = killRateVg # consume-rate of antibody-IgG by cleaning virus\n",
" \n",
"mutatRateB = 0.000033/hour # Virus mutation rate\n",
"\n",
"cross_radius = float(0.02) # radius of cross-immunity (the distance of half-of-value in the Monod equation)\n",
"\n",
"# time boundary and griding condition\n",
"minT = float(0)\n",
"maxT = float(7*28*day)\n",
"totalPoint_T = int(2*10**3 + 1)\n",
"gT = np.linspace(minT, maxT, totalPoint_T)\n",
"spacingT = np.linspace(minT, maxT, num = totalPoint_T, retstep = True)\n",
"gT = spacingT[0]\n",
"dt = spacingT[1]\n",
"\n",
"# space boundary and griding condition\n",
"minX = float(0)\n",
"maxX = float(3)\n",
"\n",
"totalPoint_X = int(maxX - minX + 1)\n",
"gX = np.linspace(minX, maxX, totalPoint_X)\n",
"gridingX = np.linspace(minX, maxX, num = totalPoint_X, retstep = True)\n",
"gX = gridingX[0]\n",
"dx = gridingX[1]\n",
"gV_array = np.zeros([totalPoint_X, totalPoint_T])\n",
"gB_array = np.zeros([totalPoint_X, totalPoint_T])\n",
"gM_array = np.zeros([totalPoint_X, totalPoint_T])\n",
"gG_array = np.zeros([totalPoint_X, totalPoint_T])\n",
"# initial output condition\n",
"#gV_array[1, 0] = float(2)\n",
"\n",
"# [viral population, starting time] ---first\n",
"origin_virus = int(1)\n",
"current_virus = int(2)\n",
"infection_period = 1*28*day\n",
"viral_population = np.zeros(int(maxX + 1))\n",
"viral_population[origin_virus:current_virus + 1] = 4\n",
"infection_starting_time = np.arange(int(maxX + 1))*infection_period - 27\n",
"event_infect = np.zeros([int(maxX + 1), 2])\n",
"event_infect[:, 0] = viral_population\n",
"event_infect[:, 1] = infection_starting_time\n",
"event_infect[0, 1] = 0\n",
"print ('event_infect = {:}'.format(event_infect)) \n",
"\n",
"# [viral population, starting time] ---repeated\n",
"viral_population = np.zeros(int(maxX + 1))\n",
"viral_population[origin_virus:current_virus + 1] = 0\n",
"infection_starting_time = np.arange(int(maxX + 1))*0\n",
"event_repeated = np.zeros([int(maxX + 1), 2])\n",
"event_repeated[:, 0] = viral_population\n",
"event_repeated[:, 1] = infection_starting_time\n",
"print ('event_repeated = {:}'.format(event_repeated)) \n",
"\n",
"# [vaccine population, starting time] ---first\n",
"origin_vaccine = int(1)\n",
"current_vaccine = int(2)\n",
"vaccine_period = 1*28*day\n",
"vaccine_population = np.zeros(int(maxX + 1))\n",
"vaccine_population[origin_vaccine:current_vaccine + 1] = 0\n",
"vaccine_starting_time = np.arange(int(maxX + 1))*vaccine_period - 27\n",
"event_vaccine = np.zeros([int(maxX + 1), 2])\n",
"event_vaccine[:, 0] = vaccine_population\n",
"event_vaccine[:, 1] = vaccine_starting_time\n",
"event_vaccine[0, 1] = 0\n",
"print ('event_vaccine = {:}'.format(event_vaccine)) \n",
"\n",
"#[origin-virus, current-virus, recovered-day, repeated-parameter, OAS+, OSA-]\n",
"min_cell = 1 # minimum cell\n",
"recovered_time = 14*day # recovered time of 1st-time infection \n",
"actRateBg_recovered = actRateBg*10 # activation rate of memory B-cell for repeated-infection (same virus)\n",
"inRateG_OAS_boost = 1.5/hour # boosting in-rate of antibody-IgG from memory B-cell for origin-virus\n",
"actRateBg_OAS_press = -0.000/hour # depress act-rate from memory B-cell for non-origin-virus\n",
"outRateB_OAS_slow = 0.0 # not applied in infection\n",
"event_infection_parameter = np.array([origin_virus,\n",
" current_virus, \n",
" min_cell, \n",
" recovered_time,\n",
" actRateBg_recovered,\n",
" inRateG_OAS_boost,\n",
" actRateBg_OAS_press, \n",
" outRateB_OAS_slow])\n",
"# vaccination_parameter\n",
"# vaccination_parameter\n",
"# vaccination_parameter\n",
"min_cell_v = 0.2 # minimum cell\n",
"recovered_time_v = 14*day # recovered time of 1st-time infection \n",
"actRateBg_recovered_v = actRateBg*9 # activation rate of memory B-cell for repeated-infection (same virus)\n",
"inRateG_OAS_boost_v = 2.0/hour # boosting in-rate of antibody-IgG from memory B-cell for origin-virus\n",
"actRateBg_OAS_press_v = -0.0001/hour # depress act-rate from memory B-cell for non-origin-virus\n",
"outRateB_OAS_slow_v = -outRateB/1.6 \n",
"event_vaccination_parameter = np.array([origin_vaccine,\n",
" current_vaccine, \n",
" min_cell_v, \n",
" recovered_time_v,\n",
" actRateBg_recovered_v,\n",
" inRateG_OAS_boost_v,\n",
" actRateBg_OAS_press_v,\n",
" outRateB_OAS_slow_v])\n",
"\n",
"event_parameter = np.array([event_infection_parameter, event_vaccination_parameter])\n",
"\n",
"event_table = np.array([event_parameter, event_infect, event_repeated, event_vaccine])\n",
"\n",
"# Runge Kutta numerical solution\n",
"pde_array = np.array([dVdt_array, dBdt_array, dMdt_array, dGdt_array])\n",
"initial_Out = np.array([gV_array, gB_array, gM_array, gG_array])\n",
"gOut_array = alva.AlvaRungeKutta4XT(pde_array, initial_Out, minX, maxX, totalPoint_X, minT, maxT, totalPoint_T, event_table)\n",
"\n",
"# plotting\n",
"gV = gOut_array[0] \n",
"gB = gOut_array[1] \n",
"gM = gOut_array[2]\n",
"gG = gOut_array[3]\n",
"\n",
"# Experimental lab data from (Quantifying the Early Immune Response and Adaptive Immune) paper\n",
"gT_lab_fresh = np.array([0, 5, 10, 20, 25])\n",
"gIgG_lab_fresh = np.array([0, 0.5, 4, 8.5, 8.75])*10**2 \n",
"error_IgG_fresh = gIgG_lab_fresh**(4.0/5)\n",
"gIgM_lab_fresh = np.array([0, 1.0/3, 3, 1.0/3, 1.0/6])*10**2\n",
"error_IgM_fresh = gIgM_lab_fresh**(4.0/5)\n",
"gX31_lab_fresh = gIgG_lab_fresh + gIgM_lab_fresh\n",
"error_lab_fresh = error_IgG_fresh + error_IgM_fresh\n",
"bar_width = 1\n",
"\n",
"# Experimental lab data from OAS paper\n",
"gT_lab = np.array([0, 7, 14, 28])*day + infection_period*origin_virus \n",
"gPR8_lab = np.array([2**(9 + 1.0/10), 2**(13 - 1.0/5), 2**(13 + 1.0/3), 2**(13 - 1.0/4)])\n",
"standard_PR8 = gPR8_lab**(3.0/4)\n",
"\n",
"gFM1_lab = np.array([0, 2**(6 - 1.0/5), 2**(7 - 1.0/4), 2**(8 + 1.0/4)])\n",
"standard_FM1 = gFM1_lab**(3.0/4)\n",
"bar_width = 2.0\n",
"\n",
"# Sequential infection graph\n",
"\n",
"numberingFig = numberingFig + 1\n",
"plt.figure(numberingFig, figsize = (12, 6))\n",
"plt.subplot(111)\n",
"plt.plot(gT, (gM[origin_virus] + gG[origin_virus]), linewidth = 5.0, alpha = 0.5, color = 'black'\n",
" , label = r'$ Origin-virus $')\n",
"plt.plot(gT, (gM[origin_virus + 1] + gG[origin_virus + 1]), linewidth = 5.0, alpha = 0.5, color = 'green'\n",
" , label = r'$ Subsequence-virus $')\n",
"plt.bar(gT_lab - bar_width/2, gPR8_lab, bar_width, alpha = 0.6, color = 'gray', yerr = standard_PR8\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ PR8-virus $')\n",
"plt.bar(gT_lab + bar_width/2, gFM1_lab, bar_width, alpha = 0.6, color = 'green', yerr = standard_FM1\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ FM1-virus $')\n",
"plt.bar(gT_lab_fresh - bar_width/2, gX31_lab_fresh, bar_width, alpha = 0.1, color = 'black', yerr = error_lab_fresh\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ (X31-virus) $')\n",
"plt.grid(True, which = 'both')\n",
"plt.title(r'$ Original \\ Antigenic \\ Sin \\ (sequential-infection)$', fontsize = AlvaFontSize)\n",
"plt.xlabel(r'$time \\ (%s)$'%(timeUnit), fontsize = AlvaFontSize)\n",
"plt.ylabel(r'$ Neutralization \\ \\ titer $', fontsize = AlvaFontSize)\n",
"plt.xticks(fontsize = AlvaFontSize*0.6)\n",
"plt.yticks(fontsize = AlvaFontSize*0.6) \n",
"plt.xlim([minT, 6*30*day])\n",
"plt.ylim([2**5, 2**14])\n",
"plt.yscale('log', basey = 2)\n",
"# gca()---GetCurrentAxis and Format the ticklabel to be 2**x\n",
"plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos: int(2**(np.log(x)/np.log(2)))))\n",
"#plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))\n",
"plt.legend(loc = (1, 0), fontsize = AlvaFontSize)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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gIOBoYChwQ1U/1UnX7gL2iYgdI+JlwHHA/3Szf0mSJEmSpAGnU4GizLw3MycA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\nAv/Z/+UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e2e7d50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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5Zycgd/8ThUQiIiIiF5cJGRQR1h54iDD8eQvQQFhotbGiTVUNInf/TXdf6u7LCKcffDGa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\nGBkemxgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b67ea90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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tNbN/MbMFZlbWXlt3/z6wEPiqmZ3Qk0mZ2S1m9pyZ7TOzbWb2iJnNyh4qq7yZ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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e2c1410>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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U/n9uzxcRk4APZOaUsnwMcGBmnh0R04BdM7PmMnlJUt8REZtn5srKMsW2rzmZOa2DbW1H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zs/2ElzbeT93btub9Tyfev3UfJxCSUfl0T9vijfWB69fFM5uz/dzZ4Ve2ipb4mor9hc0WX5npV7\n2LOyi7gtXcRtqaB0VSmlq0qPybjKrZWUri6ldHX0uMqtlb0f13CQuC7qexIXPvKhu7iyNWWUrSk7\n5OspTnHHU9yMGTMGRD8Vp7jeipPYuvfee5kzZw5PPvlkoOwvf/kL8+fP54knnmDUqFFHvQ/vv/8+\n+fn5vPbaa4GyzkEMNTWhvxNcf/31lJeX89Zbbx329Z544gkKCwtDduY6HHfccQdXXHHFEZ0jmr64\nJwP1HgSLj9mZeolzrt8kt/bv3098fHzIiKL4+HjGjRsX0s45x5+3/Jnmdu+XyLzUPGaMmdGbXT0q\naktqSRucRlxCXEj5npV7aK5tpqOtg4IzC0hKD50jufWNrQw/Yzipeakh5etfXE99aT2uwzF51mQG\nDR0UUr/yiZWMu2QcGQXeln/OQUcHfPrkampKGuhwMOX6qaQNzaCjw6trb4eVT29jzIVjSR2cHlK+\n5rkS6vY00uFg4lXpJGUn0NoKbW3ea90rTQzb2U5ixoGy1lbY+nYi9eXtgDGyykdSFoHzdnRA8XuZ\n5G6OJzEjtHznXwezv9rLkg+rTiLZn0vsHO27+9PB5KxNIikskb770wNxb/rjzA689qzIJWd1Eqk5\nXvvO8t2fZNFclQQGIyoSSc0Njdv11zTyVseTlhdavvODZPZXOsxgVHlcRP2OZfHkrzXS8kPLi5ca\nTRU+wFG4F9IGh9ZvX9zO0FMcg4YcKAP4/J0WmvY1A45xZR2kDw2t3/ZmI8OmZpA+LPR8W9+oo8Gf\nsJrwNzmkD00OxABsfr2G4Wekkj40OfC5AGz+nxrqy+oxgwlfzSJ9aFJI/abXqiiYnkL6sAPft2aw\n6bVK6ku9uBMuywjUB+L+VEHBmUkRcRtfLae+1OvnxMsHkT4sMSRu4yv7KDgrMVDeaeMr+wJxJwbF\nBdcfPC6tR3Fmhxd3uNc7sri9B4nbS8FZCYcVN+Ls6HGdCdOJVxza9fouLvWw4zKGh9ZveLks6uey\n4eWyA9/XUa6nOMUpTnGKGxhxElt/+7d/y0cffUR1dTW33XYb9fX1+Hw+3n333cAaN0dbRkYGycnJ\n5OfnB8reeustTjjhBL785S+HtI2Pj+c3v/kN8+bNY+bMmcTFxYWfrlsNDQ3813/9F6+88kpM+n40\n9MU9ORbuwYBLFPUn+fn5ZGVlsWPHDnbt2oVzjsLCQhISEkLard+3nk0VmwLHf3fi3xHv6z8f/b71\n+8gZn0NcYug35eqFq2mqbKKtuY1p/zCNlNxU2tuhpcV7ffT0VsZ+ZSJx6amBsrY2WPtiNbVlTbR3\nGOPKh5KUfSDR0tYGm9/0kbcFkrK8485EStG7g2isjKfDGcOr4kjKjEy0ZG1IDCRgOhMse1YMD4wA\nWdKYSHJYomXPisFklyREKc+ludZLRm1Kious35bBDnyR5fuSaO5MBJcYyWGDa6ob4rEqSA4b3NTS\n5qO13ctztrSAL+w/ddrajdZWiAudyUh7h9Hhnzba3u69Qs7bajQ3GxY26KZxv4/mFu/7rLbOaAn7\nO6e6Lh6r9NEQ1s991Qk013mFcbt9JIcN8tmzL5GGHXEkhw1G+nTtp+QmeFs+tmyNI7k8LG5nCuXx\ncSTvCSsvSqW5xutczep4ksOWpt+zNY0d7fGBxFqgfOMgmmu8Z630g4TI+g3pZO+PUr4+neYa7x9J\nu96NUr8ug+zGKOWfZdBc4yWBihdHqV+TQXZdlPLVmTTXeMmq7W9FqV+VSXZtlO/PVQfitr0VvT6m\ncSsPxG1NiV6fXXOcx/2li7jqoxdXWrWEMUH/sXC0rxdZn9VFXBbNNSkAbElWnOIGTlxR0RKSqk7s\n9/1UnOJ6K05i65xzzglsW99XTjnlFJ566imWLVvGn//8Z0pLS8nJyWHp0qVRl0e54IILmDVrFrfd\ndhuPPPJIj6/T0dHB7NmzuffeeznhhBNi+RZiqi/uybFwD/pPtmKASkhIYNy4cQwfPpySkhKGDRsW\nUt/c1sz/bv3fwPEZw89gVObRH3LYyTnH529/TnNNM821zZz8zZNJSE3AOWhuhro6eO8Puxk+Iw1S\nUmlshKYm77X2nXRqK5NpbvXxTo0PS/YSNJ12fzqU3L1xJGWEXnPP+gM/kEo/9EX+YrwnherkyPLy\n2gSa/VmLlCojOWxdvZY2n5dICZ9FFrz0VrS1+Kyb8s6wjsgG5rPo5cHDVqKc18yiX+9ocYR+BsHl\nnbqqV1zfx4mIiIiIxNDMmTOZOXNmj9t///vf5+GHH+bpp59m9uzZPYp58MEHufrqq7nqKu3gHc1A\nvwdKFMVISkoK48ePjyhftmMZ9S3ekJP0xHQuHnvxUbm+63Cse2EdEy+fSHxyPM5BfT1UVRnvvVFP\nRbmjfn8Cf61rp9WXQF2dN6oFYM/KwWRXRyZuSmtS2V/nrfGS1uBICUt++uJ9UdcEMt+B34a7SrRE\n210hOAHTVX14uc8HCQnQHtdBnDlSkjtIz/DKfT6Ii4PWrHZy89rJGBpanljawv6ERswchRPayBkF\n8fEHXsXtDQw9OYXcwpRAWUICbE6ppL6kljgfTP56FtljkgPnNYP1L5Qz6pxkcsYeKPf54LPf7aW2\nuAZwnHp9FlmjQ6dErVpQRuHMlEB5Z93KJ0up3ekN35k6J4vMUcn+z8h7rfhtKYUXp5IxIjlQ5hys\nXFBGbUkdzsEp12WTURBav/rpfYyekUb68NDyNc+WU7enHudg0t/nkD4sOTB6yzn4LKmCUV8YxKBh\noXFT4idSX7oX52DiZbkMGhpav95VM+LcQQwaElq+oa2G+rIGHHDChXkMGkJI/cb9tQyfnk7a4ANl\nAJsa6mjY24gDxk/PI23IgT4CbKmrZ/jp6aQeGPGJc7Cltp7Gfd4wqXHT8kkbHBq3taaBYadlhMQB\nbKlqCKyxNHbKgbjO2K2VDQybkkFqXmjc1orGQFzh5HzSwvqTUd7EkEkZIeXglTem+OMmtUetP1bi\nxpwUGZde3sSQkyLj0subaPQPcys8xuKcg0H7guImtnNm/oyQuEH7mhgyMfJ64XHR6hWnOMXBlCkz\n+Pydz/t9PxWnuN6KE+l0qLtu/eAHPzhKPTl+9ad7oETRUVTVVMWHOz8MHF889uIj2uWsua6Zmh01\n1JXUMfbisd6IFwe1tbB3r/H+B8bKmhbq2+OprPTW0wEo3TaE/dVewqd+awepuaHnjUuIo701crFn\nX8KB5aA6E0I+HyQlQWIitGZ3kJvbRl6hd5yY6CVSdjY20bCnljifY/yZWeSNPZBoiYuDbck1FExL\nJm9cCnFxXpnPBxtfrqSmqAqfweRvZJA3ISVQ5/PBuuf2MfLsRPJOSAkkZcxg3QuV1BTXYD7jpCtz\nyRoTuvbR1nH7yZ/cRubI0PdXPBoa9oL5fIw4B9JDB4NRXpBJWn4iKTmh5SOyRtLe3I75jLTBaYTf\n0qwbJhCfHE9c6AxEps85OZA4i0uIw8JW2zrjW1Mwn0WUn/6tUwOjUizOQtbh8eJOiXq+s26c5N03\nB4mDEgmf7XjuDRNITE+M6GfG9YXe4t8O0oakEh+6xBS5s0eRmpcS8b6HXFdAa6P3TZc1JpmE0NtA\nQfJg0guSSEwLLR+VkktLfTrOOfInJZCUHlo/Ni2T7MKEiJFrEwZ5U8+ccww/PXJq2kmZqeROiCyf\nnJVKU5X3IRac6SMlO7R+V1YyuSdGlp+clcT+Ku9GjDjbF/F9sSM7kfyTopUn0FTpZVlHnmcRz19x\nbhz5k6KXN1V4N23U+VHqc+LInxy9vKmyd+MaK7ypg6MviGFcbvS4ohwfTZVdxxXl+Bh8ci/HTbaI\nBGFRjo+m4PcXpT5qXO5B4nIVpzjFKU5xiotdnIhINBZt5MZAZWaXAT/D281tvnPut920dYfz3tva\n2oiP71l+7fm1z7OhfAMAIzJG8A+n/UPotKVD4Jzjwwc/pL6qjX21ieTMOJWKxhRKSrxpYgB71+4l\nNT+VQUNCF4Eu31RO/R5vVFPuCbmkD/d+E09IgPR0qN+6h7yCZApOziY1FVJSvFfZX7dTs7GUxPh2\nTrlyPKPOGErwWy9+t5j0gnRyxoX+Zly6upSmiiZ88T7yJ+VHLFpdW1JLclYyiWmhQ5SaqproaOvA\nfEZSelLEmkkdbR3+RInm7/RHS5YsidihSUSOjJ4rkdjSMyUSqquR/iJyfPD/HRDxC/YxM6LIzOKB\n/wS+CNQDn5jZy865ylhdwznHypUrSUhIYMyYMWRlZXXZtqi6KJAkAvjK+K/0KMHhnKN4aTHDzxhO\n4qBEWluhuBg+/9xYtnEMu7Y04zBymlrJKEgJiU0clEhb04GVkJOTIScHhiZAQ0I1GSltTDg/jsmX\npJOe7o0MMoOyz3wkpDpyQjdro3nscDrahhKXGEd8cjy+sMWQR18wOup7GHrq0G7fY+euZeFSslOi\nlnfyxfebDe9EREREREREjknHzIgiMzsXuN05N8t//DPgE+fcc120P+QRRVVVVaxevTpwnJmZGUgY\nha+v88TKJ9hZuxOAKUOmcOVJV/b4Ou8/upq6jAL2deRRXOztDAZQu6uWyq1e3ittcBr5k7xJxsnJ\nMHgwJLfWEF9dwdlXjyUvzxsVZAaN5Y3U7KwhKSOJtPw0kjKSurq0iIiIiIgcJzSiSOT4dsyPKAKG\nASVBx7uAglheYNeuXSHHNTU1bN++ndNOOy2kfGvl1kCSKM7iuKjwoohzdbR1sHfdXjJHZpKSk+Lt\nMrbW/1pZSFPVfgZPDo1JykzCzJGd1sLwzCa+fGU+I0ZAdraXEGrbn8b+mjgGDQmNS81LjZj+JSIi\nIiIiIiIS7lhKFB1VjY2NVFRURJSPHDkyYjTR4qLFgePTh59OZnJmSEzJxyUULSmipaEVKxxNZUYh\n69cfGDmUmpdG1fZqnHOYGfn5MG4cFI5JoHppNTmjBpE5Mpv8yS7k2vHJ8QxKDl2fSKS3aN0HkdjT\ncyUSW3qmREREDm5AJYrMbB5wJXAi0AS8B9ztnNsK7CZ0BNFIYHmsrl1SUhJRlpycTF5e6NYBmyo2\nsbtuNwDxvnjOH3V+RJwlxLG5KJHPduRR/X4cI87pwBd3YP2d+KQ4xhTCF85o4IwvDiIjsKSPDyae\nFnE+kf5g1apV+se3SIzpuRKJLT1TIiIiBzegEkXABcDDeAmgBOA+4E0zO8lfNsXMhuItZv1V4N9i\ndeG4uDji4uJobz+wjXxBQUHkaKLtB0YTnVlwJoMSD4zw6eiAVavg3aWDWft5G23724AOGvY2kD4s\nnWHD4LTTYNIksKZcElITIrYSF+mvqqur+7oLIsccPVcisaVnSkRE5OAGVKLIOXdp8LGZzQH2Aqc5\n5z4yszuBpYAPmO+cq4rVtceOHcuoUaPYs2cPJSUltLa2MmzYsJA2Wyq3UNZQBkBiXCJn5p3JyidW\nMmX2qWwvjuOtt2DvXgAfmaMyqdhcQbyvg5EJZfz9zV6iKGCQMkQiIiIiIiIi0rsGVKIois796SsB\nnHOvAa8drYvFx8czcuRIRowYQUNDA/HxoR/f+zveD/z59GGnk5WZRbMvhUfuraIqLnSKWn5hGuOS\ndnHWucb4LwwjY6gDIhYbFxkwioqK+roLIsccPVcisaVnSkRE5OBsoG6HaN6crz8Bac65mYcRPzDf\nuIiIiIiIiIhIDDjnIkasDOQRRb8EJgHnHU5wtA9DREREREREROR4NiATRWb2MHAZcIFzrrSv+yMi\nIiIiIiIiciwYUIki/3Szh4HLgRnOueI+7pKIiIiIiIiIyDFjQCWKgEeAq/ESRQ1mNtRfXu2c2993\n3RIRERERERERGfh8fd2BQ/QdIANYAuwOes3q6QnM7HtmVmRmTWb2oZmdcVR6KnKMMbN7zKwj7LU+\nqD7ZzB4xs3IzqzOzF80svy/7LNLfmNkFZvaamZX4n6GvhtUf9Dkys1Fm9rqZNZhZmZnNN7OB9vNc\nJCZ68EwtifKz61dhbfRMiYiIBBlQPwSdcz7nXJz/a/BrYU/izezvgQeAHwOnAWDGk2AAAAxISURB\nVGuAv5hZ7lHstsixZBUwNOj1haC6n+OtHfZ14IvAcODF3u6gSD+XCqwEvuc/Dt+Bs9vnyMzigNfx\nRgSfA9wA3Ij3c03keHSwZ8oBvyL0Z9fdnZV6pkRERCKZc8fPLvFm9lfgI+fc9/3HBuwEfu6ce6BP\nOyfSz5nZPcBXnXPTo9RlAnuBbzrnXvaXnQhsAKY75z7tzb6KDARm1gFc5pz7s//4oM+RmV0K/AkY\n7pzb529zM/DvQL5zrr0P3opIvxD+TPnLFgOfOOfu7CJGz5SIiEiYATWi6EiYWSIwDXizs8x5WbK3\n8f4HSUQO7iQz221m28xsoZkV+MtPBxIIfb42ATvQ8yXSU909R2f7i84BVnX+Quv3JpAFTOylfooM\nNDeY2T4z+8zM/s3MkoPq9EyJiIiEOW4SRUAeEAeUhZXvxRuGLCLd+whvSP6XgVuAccC7ZpaK9ww1\nOecawmLKgCG92kuRgau752hoUJvwn2NlQXUiEup3wLXADOA/8H6OPRVUr2dKREQkzEDb9UxE+ohz\n7o2gw7X+qZzFeGuptPVNr0SOS9bXHRAZKJxzvwk6XGdmJcAiM/uBc26nv1zPlIiISJDjaURROdBO\n5OiGIcCe3u+OyMDmnKsBNuONLNoDpJhZWlizIUBpb/dNZIAq5eDPUSnRf46BnjWRnlju/zre/1XP\nlIiISJjjJlHknGsBPsWbNgOAf+vTi4AP+6pfIgOVmQ0CJuAliT4FWgl9vk4ERqHnS6SnevIcfQic\namZ5QXFfAqqAjb3UT5GBbKr/a+d/En6AnikREZEQx9vUsweBp8zsU7z/UbodSAYW9GWnRAYCM/sZ\n3s4wO/C27P4J0Aw875yrNbPfAj83syqgDngYeNc5t6Kv+izS3/hHC00IKhprZlOBPc65sh48R3/B\n2wXtGTO7CxgG3Av8UrszyfGou2cKSMNbn+h1oBKYAvwceMc515kEehM9UyIiIiHM2/jr+GFm3wPu\nxFugcCVwq3Puk77tlUj/Z2a/By4AcoF9wLvAj5xzRf76JOAB4GogCfhf4LthO8mIHNfMbAawyH/o\nOLA2yj3OuX/tyXNkZqOAR/EW520AngR+6I63H+gidP9MAU8AzwAn4yWNdgIvAj8NXjRez5SIiEio\n4y5RJCIiIiIiIiIi0R03axSJiIiIiIiIiEj3lCgSERERERERERFAiSIREREREREREfFTokhERERE\nRERERAAlikRERERERERExE+JIhERERERERERAZQoEhERERERERERPyWKREREREREREQEUKJIRERE\nRERERET8lCgSEZFjlpml9XUfDoeZ3Wpm3+hh22Qzu9zMnjKzFb3QtzvN7PKjfR0RERER6RtKFImI\nyIBkZqeZ2Ve7qf97oMbM7um9Xh05M/s+kO6ce6GHIfcDvwRmH71ehfgZcIOZXdVL1xMRERGRXqRE\nkYiIDFQvAnd2Uz8CqAbe653uHDkzOx/4W+fcfT2Ncc7dBlzoP3zrqHQs9HoOLyn1/8xs7NG+noiI\niIj0LiWKRERkwDGz0UAh8G5XbZxzDzjn8pxzb/dezw6fmcUDjwH/eBjhM/1fj3qiCMA514A3iunX\nvXE9EREREek9ShSJiMhA9EX/1yV92YkYmw2UOOdWH0bsV4AmukmcHQVPAhP8o6BERERE5BihRJGI\niAxEXwRagA/7uiMx9F3g2UMN8o9Eugh4zznXEvNedcE51wa8DHynt64pIiIiIkdffF93QEREpCfM\nbDZwm/9wGlADvGtmAPc7514ws1S8xZZTgBOAWc65kqBznAF8HxgF/NY5t9DMvg5cjJd4mgS87pz7\nuZkVAncAiUAaEAfc5Jyri9I3A2YBVwMlQLK/D99xztX24L2NAU4H3jxIuyS8dZnOA4r9fXoVSCfK\ntLMo7eOBp4CXgDOcczv87RKB+UAmMBHvc9sVdJ5TgTeAy5xznwZdYjHwjJklOOdaD/Y+RURERKT/\nU6JIREQGBOfc08DTZjYSL+nxiHPuX8Ka3ecvX2dm+4D/C/wgqH4eXkLnFuC3ZnYKsMM59x0AMzsH\neN/M2oBzgVucc9X+uk3Aj8POh5llA78DcvEWoi7zl88F7gL+uQdvbwawLzipFc7M0oHXgQ7gS865\nVjM7AW9UlSMsUdRF+wnAR8AgoDyo+T8DC5xzq81sL3B72Pu8BhgC7Anr1jL/uaYBf+3B+xQRERGR\nfk6JIhERGWg6d/haHFzoTyDF+ZNEp+AlbsqC6k8FVjrn2jvbAuXOuYeDTtM5+uf/ANPDpnLVASeF\nXTMOeB44B5jQmSTya6HnU7ynANsP0uZ3eCOeJnWO3nHObTazYqA1ytpG0dpvMbOdQKNzrtH/HrKB\nfH+SaAKQR9Dn5jcD2OSc2x1c6JyrNrNaYCpKFImIiIgcE7RGkYiIDDQzgGbgg7Dy4cCj/j9/C28k\nze+D6tOBF/x//iJQ7JybH3aOU/1f/zk4SWRmCcCJwM6w9tfiTVtb6Jwr9bfNNLN/AG4EHqZnRgFV\nXVWa2VXAV4EnnXN7g8qT8ZJX7/SwfQre1LIlQc0LOLB72fWEfW7+kUnTgEVddK8CGN3tuxMRERGR\nAUMjikREZKCZAXzsnNsfXOic+ysE1uWZDbwRvM6Oc+49f30mcAberl3hLgTaiNxN7QK8dYoWh5V/\n2/8118x+hTcFrNkff65zzvXwPWUQOhUs3Pf8X18MKz8PSCJyfaKu2n8Bb82lJZ0Fzrm1QfXXAO8E\nf27+mDjCklFBKoCsbvouIiIiIgOIEkUiIjJgmNkoYAywsJtmVwI5wG+6qP8i3ojaaImPi4BPoixY\nfQ3e9vP/E1Z+MtAAXOOc6+i2891zgEWr8O9qdj7etLiPo/QXghJFB2k/Ey8R9n6U60wFCoH/CKua\n4e9feJIsEIpGKIuIiIgcM/QPOxERGUhm+L8GkhZmdpN/nZ1O38ZbdPm1zvqwc3QmV0KmUvl3ORtD\n5DSuVOAbwJ+ccw1mNtrMLvZXxwHbjjBJBF5SJ7eLuhz/dVZFGaF0EbDROVdiZiPN7BIgu5v2M4FP\n/e9jTND7AJju/xqeEJqBt7ZTV1PjcjiwtpOIiIiIDHBKFImIyEAyHW8NnY8AzCwXOL8ziWFmw/AS\nG0875zr8i1oXhJ3jImBd8No9QeUQOdLoq3g7ez3lP/4WB3b/+hBIjtZRM8sxs5/38H0V03WiaB9Q\nT9jUNP97mwa87S+6Aqh3zh2s/Uf+oq/hLdDdqfP6xUExucBpdL0+UWdcUTf1IiIiIjKAKFEkIiID\nSQVQ5Zxr9i/M/BDwo6D6zmTHUv8UrLuABzsrzWwo3k5gXU07ayJykewh/q/vmtkYYIhzbp2/7F+B\nsWZ2RtA1zMy+AjwWfO2D+AxvQesI/lFBjwKn+XdZw8wmAffhJXr2mJkPL2HWOaWsu/bl/vJzOtd1\n8lvu/3qCPyYVb1HreLpYn8i/3lMGsKaH71NERERE+jnr+TqbIiIifcvMsvCSFzV4I4vud86tCmvz\n78DpQCXw8+BkiJlNAd4ELnPOfRIW9waw2Tl3W1j5ILzd0urwplj9o3OuJqj+YuBOYBfeQtYJwHt4\nO6H16IesPwH1OXBKUBIquD4J+C+8NYSK/O/tXuArwA/8ZY875xb1oP2d/rJfO+eWhl3nB3gjk1bj\n/WfSZLzPMt851xilX5fj3Y9s51xzT96riIiIiPRvShSJiIj0A2a2HC+59HAfXT/NOdcQfAyUAS86\n5+Z0EfMQMNw5N6t3eikiIiIiR5umnomIiPQPjwLf7IsLm9kvgRozmxlU/I94U/F+2EVMAt7oo0eP\nfg9FREREpLcoUSQiItI/LATyzOxLfXDtc4DtwCYAM7sKb/e4S5xzpV3EfAvY7pwL3yVNRERERAYw\nTT0TERHpJ8zsAuDfgQucc+29eN2pwNVAEpCFt2j4fc65ii7ap+Etfn2Fc25zb/VTRERERI4+JYpE\nRET6ETP7PnCCc+57fd2XaPw7rL0IPOuce6mv+yMiIiIisaVEkYiISD9jZrcC1c65p/u6L+H8O6MV\nO+de6Ou+iIiIiEjsKVEkIiIiIiIiIiKAFrMWERERERERERE/JYpERERERERERARQokhERERERERE\nRPyUKBIREREREREREUCJIhERERERERER8VOiSEREREREREREACWKRERERERERETET4kiERERERER\nEREB4P8DZ2C9z6Qfl7cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d06b310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# step by step\n",
"numberingFig = numberingFig + 1\n",
"for i in range(totalPoint_X):\n",
" figure_name = '-response-%i'%(i)\n",
" figure_suffix = '.png'\n",
" save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n",
" plt.figure(numberingFig, figsize = AlvaFigSize)\n",
" plt.plot(gT, gV[i], color = 'red', label = r'$ V_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gT, gB[i], color = 'purple', label = r'$ B_{%i}(t) $'%(i), linewidth = 5.0, alpha = 0.5\n",
" , linestyle = '-.')\n",
" plt.plot(gT, gM[i], color = 'blue', label = r'$ IgM_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gT, gG[i], color = 'green', label = r'$ IgG_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gT, gM[i] + gG[i], color = 'gray', linewidth = 5.0, alpha = 0.5, linestyle = 'dashed'\n",
" , label = r'$ IgM_{%i}(t) + IgG_{%i}(t) $'%(i, i))\n",
" plt.grid(True, which = 'both')\n",
" plt.title(r'$ Antibody \\ from \\ Virus-{%i} $'%(i), fontsize = AlvaFontSize)\n",
" plt.xlabel(r'$time \\ (%s)$'%(timeUnit), fontsize = AlvaFontSize)\n",
" plt.ylabel(r'$ Neutralization \\ \\ titer $', fontsize = AlvaFontSize)\n",
" plt.xlim([minT, maxT])\n",
" plt.xticks(fontsize = AlvaFontSize*0.6)\n",
" plt.yticks(fontsize = AlvaFontSize*0.6) \n",
" plt.ylim([2**0, 2**14])\n",
" plt.yscale('log', basey = 2)\n",
" plt.legend(loc = (1,0), fontsize = AlvaFontSize)\n",
" plt.savefig(save_figure, dpi = 100)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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YLyIikpP6+vrtBQmvv/46y5Yty2tBQo8ePRg8eDCDBg1qMu24447tesorIoWn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1m1apXz36FZs2Z8+OGHtG/f3nmNI0eOMHHiRKZNm2Y/AarQ5GdgyuVsP/8beAP4X7ayH4HpwOx8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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d112d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Experimental lab data from (Quantifying the Early Immune Response and Adaptive Immune) paper\n",
"gT_lab_fresh = np.array([0, 5, 10, 20, 25])\n",
"gIgG_lab_fresh = np.array([0, 0.5, 4, 8.5, 8.75])*10**2 \n",
"error_IgG_fresh = gIgG_lab_fresh**(4.0/5)\n",
"gIgM_lab_fresh = np.array([0, 1.0/3, 3, 1.0/3, 1.0/6])*10**2\n",
"error_IgM_fresh = gIgM_lab_fresh**(4.0/5)\n",
"gX31_lab_fresh = gIgG_lab_fresh + gIgM_lab_fresh\n",
"error_lab_fresh = error_IgG_fresh + error_IgM_fresh\n",
"bar_width = 1\n",
"\n",
"# Experimental lab data from OAS paper\n",
"gT_lab = np.array([28, 28 + 7, 28 + 14, 28 + 28]) \n",
"gPR8_lab = np.array([2**(9 + 1.0/10), 2**(13 - 1.0/5), 2**(13 + 1.0/3), 2**(13 - 1.0/4)])\n",
"error_PR8 = gPR8_lab**(3.0/4)\n",
"\n",
"gFM1_lab = np.array([0, 2**(6 - 1.0/5), 2**(7 - 1.0/4), 2**(8 + 1.0/4)])\n",
"error_FM1 = gFM1_lab**(3.0/4)\n",
"bar_width = 1.0\n",
"\n",
"# Sequential infection graph\n",
"figure_name = '-Original-Antigenic-Sin-infection'\n",
"figure_suffix = '.png'\n",
"save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n",
"numberingFig = numberingFig + 1\n",
"plt.figure(numberingFig, figsize = (12, 6))\n",
"plt.subplot(111)\n",
"plt.plot(gT, (gM[origin_virus] + gG[origin_virus]), linewidth = 5.0, alpha = 0.5, color = 'black'\n",
" , label = r'$ Origin-virus $')\n",
"plt.plot(gT, (gM[origin_virus + 1] + gG[origin_virus + 1]), linewidth = 5.0, alpha = 0.5, color = 'red'\n",
" , label = r'$ Subsequence-virus $')\n",
"plt.bar(gT_lab - bar_width/2, gPR8_lab, bar_width, alpha = 0.6, color = 'gray', yerr = error_PR8\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ PR8-virus $')\n",
"plt.bar(gT_lab + bar_width/2, gFM1_lab, bar_width, alpha = 0.6, color = 'red', yerr = error_FM1\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ FM1-virus $')\n",
"plt.bar(gT_lab_fresh - bar_width/2, gX31_lab_fresh, bar_width, alpha = 0.1, color = 'black', yerr = error_lab_fresh\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ (X31-virus) $')\n",
"plt.grid(True, which = 'both')\n",
"plt.title(r'$ Original \\ Antigenic \\ Sin \\ (sequential-infection)$', fontsize = AlvaFontSize)\n",
"plt.xlabel(r'$time \\ (%s)$'%(timeUnit), fontsize = AlvaFontSize)\n",
"plt.ylabel(r'$ Neutralization \\ \\ titer $', fontsize = AlvaFontSize)\n",
"plt.xticks(fontsize = AlvaFontSize*0.7)\n",
"plt.yticks(fontsize = AlvaFontSize*0.7) \n",
"plt.xlim([minT, 2*30*day])\n",
"plt.ylim([2**5, 2**14])\n",
"plt.yscale('log', basey = 2)\n",
"# gca()---GetCurrentAxis and Format the ticklabel to be 2**x\n",
"plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos: int(2**(np.log(x)/np.log(2)))))\n",
"plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))\n",
"plt.legend(loc = (1, 0), fontsize = AlvaFontSize)\n",
"plt.savefig(save_figure, dpi = 100, bbox_inches='tight')\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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
nkmk/python-snippets | notebook/numpy_nan_remove.ipynb | 1 | 4690 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[11. 12. nan 14.]\n",
" [21. nan nan 24.]\n",
" [31. 32. 33. 34.]]\n"
]
}
],
"source": [
"a = np.genfromtxt('data/src/sample_nan.csv', delimiter=',')\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[False False True False]\n",
" [False True True False]\n",
" [False False False False]]\n"
]
}
],
"source": [
"print(np.isnan(a))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ True True False True]\n",
" [ True False False True]\n",
" [ True True True True]]\n"
]
}
],
"source": [
"print(~np.isnan(a))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[11. 12. 14. 21. 24. 31. 32. 33. 34.]\n"
]
}
],
"source": [
"print(a[~np.isnan(a)])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ True True False]\n"
]
}
],
"source": [
"print(np.isnan(a).any(axis=1))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[False False True]\n"
]
}
],
"source": [
"print(~np.isnan(a).any(axis=1))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[31. 32. 33. 34.]]\n"
]
}
],
"source": [
"print(a[~np.isnan(a).any(axis=1), :])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[31. 32. 33. 34.]]\n"
]
}
],
"source": [
"print(a[~np.isnan(a).any(axis=1)])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ True False False True]\n"
]
}
],
"source": [
"print(~np.isnan(a).any(axis=0))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[11. 14.]\n",
" [21. 24.]\n",
" [31. 34.]]\n"
]
}
],
"source": [
"print(a[:, ~np.isnan(a).any(axis=0)])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[11. 12. nan 14.]\n",
" [21. nan nan 24.]\n",
" [31. 32. nan 34.]]\n"
]
}
],
"source": [
"a = np.genfromtxt('data/src/sample_nan.csv', delimiter=',')\n",
"a[2, 2] = np.nan\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[11. 14.]\n",
" [21. 24.]\n",
" [31. 34.]]\n"
]
}
],
"source": [
"print(a[:, ~np.isnan(a).any(axis=0)])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[11. 12. 14.]\n",
" [21. nan 24.]\n",
" [31. 32. 34.]]\n"
]
}
],
"source": [
"print(a[:, ~np.isnan(a).all(axis=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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
hoerldavid/nis-automation | simple_overview.ipynb | 1 | 4623 | {
"cells": [
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"###################\n",
"# set up the environment, nis, and image saving path\n",
"#####################\n",
"import os\n",
"from nis_util import *\n",
"\n",
"path_to_nis = 'C:\\\\Program Files\\\\NIS-Elements\\\\nis_ar.exe'\n",
"#save_base_path = 'E:\\\\aquisition data\\\\Nicolas\\\\Overviews'\n",
"\n",
"# save_base_path = 'C:\\\\Users\\\\Nikon\\\\Documents\\\\TEST'\n",
"save_base_path = 'E:\\\\aquisition data\\\\Nicolas\\\\Overviews'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"###################\n",
"# select and name the scans\n",
"#####################\n",
"\n",
"# put the name of the optical configuration to use here\n",
"#set_optical_configuration(path_to_nis, 'Dia-4x')\n",
"\n",
"#####################################\n",
"# where to save the files, for example\n",
"# save_path_left = 'NG_Overview_024.nd2'\n",
"#\n",
"# if no slide just put None. For example:\n",
"# save_path_left = None\n",
"########################################\n",
"save_path_left = 'YX_Overview_0405016.nd2' #None\n",
"save_path_mid = 'YQ_Overview_0405017.nd2' #None\n",
"save_path_right = 'YQ_Overview_0405018.nd2' #None\n",
"\n",
"\n",
"\n",
"save_path_right = os.path.join(save_base_path, save_path_right) if (save_path_right != None) else None\n",
"save_path_mid = os.path.join(save_base_path, save_path_mid) if (save_path_mid != None) else None\n",
"save_path_left = os.path.join(save_base_path, save_path_left) if (save_path_left != None) else None"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scanning left scan.\n",
"Scanning mid scan.\n",
"Scanning right scan.\n"
]
}
],
"source": [
"############################\n",
"# run the scan\n",
"# before running the scan, you should make the focus correctly\n",
"# skips files that already exist\n",
"############################\n",
" \n",
"# the bounding boxes of the slide holders\n",
"\n",
"# left slide\n",
"do_scan_left = save_path_left != None\n",
"if (do_scan_left and (os.path.exists(save_path_left))):\n",
" print('WARNING: file {} exists.'.format(save_path_left))\n",
" do_scan_left = False\n",
"if do_scan_left:\n",
" print('Scanning left scan.')\n",
" do_large_image_scan(path_to_nis, save_path_left, 53331, 28806, -20726, 20464)\n",
"else:\n",
" print('Skipping left slide.')\n",
"\n",
"# mid slide\n",
"do_scan_mid = save_path_mid != None\n",
"if (do_scan_mid and os.path.exists(save_path_mid)):\n",
" print('WARNING: file {} exists.'.format(save_path_mid))\n",
" do_scan_mid = False\n",
"if do_scan_mid:\n",
" print('Scanning mid scan.')\n",
" do_large_image_scan(path_to_nis, save_path_mid, 13400, -7850, -20954, 18220)\n",
"else:\n",
" print('Skipping middle slide.')\n",
"\n",
"# right slide\n",
"do_scan_right = save_path_right != None\n",
"if (do_scan_right and os.path.exists(save_path_right)):\n",
" print('WARNING: file {} exists.'.format(save_path_right))\n",
" do_scan_right = False\n",
"if do_scan_right:\n",
" print('Scanning right scan.')\n",
" do_large_image_scan(path_to_nis, save_path_right, -26500, -50000, -21053, 18177)\n",
"else:\n",
" print('Skipping right slide.')"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"do_large_image_scan(path_to_nis, save_path_left, 53331, 28806, -20726, 20464)"
]
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ondrolexa/sg2 | 14_Simultaneous_deformation.ipynb | 1 | 153287 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simultaneous deformation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sg2lib import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Naive concept of simultaneous deformation\n",
"\n",
"Here we try to split simple shear and pure shear to several incremental steps and mutually superposed those increments to simulate simultaneous deformation. We will use following deformation gradients for total simple shear and pure shear:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"gamma = 1\n",
"Sx = 2\n",
"Fs = array([[1, gamma], [0, 1]])\n",
"Fp = array([[Sx, 0], [0, 1/Sx]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To divide simple shear deformation with $\\gamma$=1 to `n` incremental steps"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Incremental deformation gradient:\n",
"[[1. 0.1]\n",
" [0. 1. ]]\n"
]
}
],
"source": [
"n = 10\n",
"Fsi = array([[1, gamma/n], [0, 1]])\n",
"print('Incremental deformation gradient:')\n",
"print(Fsi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To check that supperposition of those increments give as total deformation, we can use `allclose` numpy function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_equal(matrix_power(Fsi, n), Fs)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Incremental deformation gradient:\n",
"[[1.07177346 0. ]\n",
" [0. 0.93303299]]\n"
]
}
],
"source": [
"Fpi = array([[Sx**(1/n), 0], [0, Sx**(-1/n)]])\n",
"print('Incremental deformation gradient:')\n",
"print(Fpi)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"allclose(matrix_power(Fpi, n), Fp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Knowing that deformation superposition is not cimmutative, we can check that axial ratio of finite strain resulting from simple shear superposed on pure shear and vice-versa is really different:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Axial ratio of finite strain resulting from simple shear superposed on pure shear: 4.265564437074638\n",
"Axial ratio of finite strain resulting from pure shear superposed on simple shear: 8.12695264839553\n"
]
}
],
"source": [
"u,s,v = svd(Fs @ Fp)\n",
"print('Axial ratio of finite strain resulting from simple shear superposed on pure shear: {}'.format(s[0]/s[1]))\n",
"u,s,v = svd(Fp @ Fs)\n",
"print('Axial ratio of finite strain resulting from pure shear superposed on simple shear: {}'.format(s[0]/s[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets try to split those deformation to two increments and mutually mix them:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Axial ratio of finite strain of superposed increments starting with pure shear: 4.59486578686164\n",
"Axial ratio of finite strain of superposed increments starting with simple shear: 6.342329219213246\n"
]
}
],
"source": [
"Fsi = array([[1, gamma/2], [0, 1]])\n",
"Fpi = array([[Sx**(1/2), 0], [0, Sx**(-1/2)]])\n",
"u,s,v = svd(Fsi @ Fpi @ Fsi @ Fpi)\n",
"print('Axial ratio of finite strain of superposed increments starting with pure shear: {}'.format(s[0]/s[1]))\n",
"u,s,v = svd(Fpi @ Fsi @ Fpi @ Fsi)\n",
"print('Axial ratio of finite strain of superposed increments starting with simple shear: {}'.format(s[0]/s[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is now close to each other, but still quite different. So let's split it to much more increments...."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Axial ratio of finite strain of superposed increments starting with pure shear: 5.212797654655028\n",
"Axial ratio of finite strain of superposed increments starting with simple shear: 5.246490787118572\n"
]
}
],
"source": [
"n = 100\n",
"Fsi = array([[1, gamma/n], [0, 1]])\n",
"Fpi = array([[Sx**(1/n), 0], [0, Sx**(-1/n)]])\n",
"u,s,v = svd(matrix_power(Fsi @ Fpi, n))\n",
"print('Axial ratio of finite strain of superposed increments starting with pure shear: {}'.format(s[0]/s[1]))\n",
"u,s,v = svd(matrix_power(Fpi @ Fsi, n))\n",
"print('Axial ratio of finite strain of superposed increments starting with simple shear: {}'.format(s[0]/s[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now it is very close. Let's visualize how finite strain converge with increasing number of increments:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"arp = []\n",
"ars = []\n",
"ninc = range(1, 201)\n",
"for n in ninc:\n",
" Fsi = array([[1, gamma/n], [0, 1]])\n",
" Fpi = array([[Sx**(1/n), 0], [0, Sx**(-1/n)]])\n",
" u,s,v = svd(matrix_power(Fsi @ Fpi, n))\n",
" arp.append(s[0]/s[1])\n",
" u,s,v = svd(matrix_power(Fpi @ Fsi, n))\n",
" ars.append(s[0]/s[1])\n",
"figure(figsize=(16, 4))\n",
"semilogy(ninc, arp, 'r', label='Pure shear first')\n",
"semilogy(ninc, ars, 'g', label='Simple shear first')\n",
"legend()\n",
"xlim(1, 200)\n",
"xlabel('Number of increments')\n",
"ylabel('Finite strain axial ratio');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using spatial velocity gradient\n",
"\n",
"We need to import matrix exponential and matrix logarithm functions from `scipy.linalg`"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from scipy.linalg import expm, logm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Spatial velocity gradient could be obtained as matrix logarithm of deformation gradient"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"Lp = logm(Fp)\n",
"Ls = logm(Fs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Total spatial velocity gradient of simulatanous deformation could be calculated by summation of individual ones"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"L = Lp + Ls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Resulting deformation gradient could be calculated as matrix exponential of total spatial velocity gradient"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Axial| ratio of finite strain of simultaneous pure shear and simple shear: 5.229548972272846\n"
]
}
],
"source": [
"F = expm(L)\n",
"u,s,v = svd(F)\n",
"sar = s[0]/s[1]\n",
"print('Axial| ratio of finite strain of simultaneous pure shear and simple shear: {}'.format(sar))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets overlay it on previous diagram"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"arp = []\n",
"ars = []\n",
"ninc = range(1, 201)\n",
"for n in ninc:\n",
" Fsi = array([[1, gamma/n], [0, 1]])\n",
" Fpi = array([[Sx**(1/n), 0], [0, Sx**(-1/n)]])\n",
" u,s,v = svd(matrix_power(Fsi @ Fpi, n))\n",
" arp.append(s[0]/s[1])\n",
" u,s,v = svd(matrix_power(Fpi @ Fsi, n))\n",
" ars.append(s[0]/s[1])\n",
"figure(figsize=(16, 4))\n",
"semilogy(ninc, arp, 'r', label='Pure shear first')\n",
"semilogy(ninc, ars, 'g', label='Simple shear first')\n",
"legend()\n",
"xlim(1, 200)\n",
"axhline(sar)\n",
"xlabel('Number of increments')\n",
"ylabel('Finite strain axial ratio');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decomposition of spatial velocity gradient\n",
"\n",
"Here we will decompose spatial velocity gradient of simple shear to rate of deformation tensor and spin tensor."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"L = logm(Fs)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"D = (L + L.T)/2\n",
"W = (L - L.T)/2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that decomposition give total spatial velocity gradient"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"allclose(D + W, L)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize spatial velocity gradients for rate of deformation tensor"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"vel_field(D)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize spatial velocity gradients for spin tensor"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"vel_field(W)"
]
}
],
"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,
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| mit |
QuantStack/quantstack-talks | 2019-05-22-pydata-frankfurt/notebooks/bqplot.ipynb | 5 | 70092 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# bqplot https://github.com/bloomberg/bqplot\n",
"\n",
"## A Jupyter - d3.js bridge\n",
"\n",
"bqplot is a jupyter interactive widget library bringing d3.js visualization to the Jupyter notebook.\n",
"\n",
"- Apache Licensed\n",
"\n",
"bqplot implements the abstractions of Wilkinson’s “The Grammar of Graphics” as interactive Jupyter widgets.\n",
"\n",
"bqplot provides both\n",
"-\thigh-level plotting procedures with relevant defaults for common chart types,\n",
"-\tlower-level descriptions of data visualizations meant for complex interactive visualization dashboards and applications involving mouse interactions and user-provided Python callbacks.\n",
"\n",
"**Installation:**\n",
"\n",
"```bash\n",
"conda install -c conda-forge bqplot\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from IPython.display import display\n",
"from ipywidgets import *\n",
"from traitlets import *\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import bqplot as bq\n",
"import datetime as dt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"size = 100\n",
"y_data = np.cumsum(np.random.randn(size) * 100.0)\n",
"y_data_2 = np.cumsum(np.random.randn(size))\n",
"y_data_3 = np.cumsum(np.random.randn(size) * 100.)\n",
"\n",
"x = np.linspace(0.0, 10.0, size)\n",
"\n",
"price_data = pd.DataFrame(np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,\n",
" columns=['Security 1', 'Security 2'],\n",
" index=pd.date_range(start='01-01-2007', periods=150))\n",
"\n",
"symbol = 'Security 1'\n",
"dates_all = price_data.index.values\n",
"final_prices = price_data[symbol].values.flatten()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A simple plot with the pyplot API"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from bqplot import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(1)\n",
"n = 100\n",
"plt.plot(np.linspace(0.0, 10.0, n), np.cumsum(np.random.randn(n)), \n",
" axes_options={'y': {'grid_lines': 'dashed'}})\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scatter Plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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" -5.92322606e+02]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0), title='Scatter Plot with colors'))))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(title='Scatter Plot with colors')\n",
"plt.scatter(y_data_2, y_data_3, color=y_data)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Histogram"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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" likely means that the widgets JavaScript library is either not installed or\n",
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" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.hist(y_data, colors=['OrangeRed'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Every component of the figure is an independent widget"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
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" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
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" [ 1.41117206, 2.19697589, 2.13950637, 1.74828932,\n",
" 2.68920693, 3.09441101, 3.59246342, 3.56627118,\n",
" 1.87804115, 1.76557517, 1.23308525, 1.87814053,\n",
" 2.88998296, 2.23203191, 2.70041715, 4.43629615,\n",
" 3.76858343, 5.45050517, 4.59791932, 4.62087907,\n",
" 4.60973346, 4.62123236, 3.78355432, 3.19237122,\n",
" 2.52465093, 2.85161353, 3.18164864, 5.40759297,\n",
" 6.77858198, 6.26873874, 6.59360835, 7.59072633,\n",
" 7.62132816, 7.55168658, 7.60326152, 8.47053815,\n",
" 7.62221763, 7.29654816, 7.7669813 , 8.07842838,\n",
" 8.31801113, 7.94820997, 8.92074576, 11.054614 ,\n",
" 11.4610295 , 11.2678528 , 12.02359309, 11.48446045,\n",
" 10.7347701 , 10.76757885, 8.18478222, 7.03083186,\n",
" 6.68287 , 5.32948114, 4.29683804, 3.8600897 ,\n",
" 2.21712441, 1.81105261, 1.27578245, 1.30118766,\n",
" 2.45537169, 2.6278761 , 2.64893812, 2.74839258,\n",
" 2.97578536, 1.95904671, 1.84427138, 2.15302263,\n",
" 0.78226264, 1.64791557, 2.7292916 , 2.09791561,\n",
" 1.85657782, 0.97838748, 1.67776796, 0.61654567,\n",
" 0.39406866, -0.46485124, -0.41389697, -2.20812624,\n",
" -0.8816646 , -1.84627102, -1.78637634, -1.99889938,\n",
" -2.76101389, -3.64879403, -2.71239549, -3.23803608,\n",
" -2.9668659 , -3.76836278, -4.41554421, -3.94329706,\n",
" -3.01288857, -3.18820497, -4.61012484, -2.61216876,\n",
" -3.46871807, -5.01030547, -2.41588088, -2.81991318]]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs = bq.LinearScale()\n",
"ys = bq.LinearScale()\n",
"x = np.arange(100)\n",
"y = np.cumsum(np.random.randn(2, 100), axis=1) #two random walks\n",
"\n",
"line = bq.Lines(x=x, y=y, scales={'x': xs, 'y': ys}, colors=['red', 'green'])\n",
"xax = bq.Axis(scale=xs, label='x', grid_lines='solid')\n",
"yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')\n",
"\n",
"fig = bq.Figure(marks=[line], axes=[xax, yax], animation_duration=1000)\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# update data of the line mark\n",
"line.y = np.cumsum(np.random.randn(2, 100), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e8d084f7f58a48bebc66507563ebe250",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Figure(animation_duration=1000, axes=[Axis(label='x', scale=LinearScale()), Axis(label='y', orientation='vertical', scale=LinearScale(), tick_format='0.2f')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Scatter(colors=['steelblue'], interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}, 'skew': {'dimension': 'skew'}}, tooltip_style={'opacity': 0.9}, x=array([ 0.48968491, 0.13168728, 0.39701367, 0.70440154, 0.28488552,\n",
" 0.10398808, 0.90789846, 0.70905081, 0.61527643, 0.79249891,\n",
" 0.83564604, 0.483459 , 0.88118825, 0.91641901, 0.2715511 ,\n",
" 0.60754536, 0.52658403, 0.53794578, 0.93766309, 0.3051887 ]), y=array([ 0.98343398, 0.90213121, 0.45872289, 0.81745326, 0.76904699,\n",
" 0.67789497, 0.31983389, 0.19645099, 0.6715277 , 0.8429733 ,\n",
" 0.01625279, 0.64280338, 0.44287302, 0.89808776, 0.32147293,\n",
" 0.47418481, 0.5147671 , 0.14043952, 0.7128923 , 0.83047635]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs = bq.LinearScale()\n",
"ys = bq.LinearScale()\n",
"x, y = np.random.rand(2, 20)\n",
"scatt = bq.Scatter(x=x, y=y, scales={'x': xs, 'y': ys}, default_colors=['blue'])\n",
"xax = bq.Axis(scale=xs, label='x', grid_lines='solid')\n",
"yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')\n",
"\n",
"fig = bq.Figure(marks=[scatt], axes=[xax, yax], animation_duration=1000)\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#data updates\n",
"scatt.x = np.random.rand(20) * 10\n",
"scatt.y = np.random.rand(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The same holds for the attributes of scales, axes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"xs.min = 4"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"xs.min = None"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"xax.label = 'Some label for the x axis'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use bqplot figures as input widgets"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"xs = bq.LinearScale()\n",
"ys = bq.LinearScale()\n",
"x = np.arange(100)\n",
"y = np.cumsum(np.random.randn(2, 100), axis=1) #two random walks\n",
"\n",
"line = bq.Lines(x=x, y=y, scales={'x': xs, 'y': ys}, colors=['red', 'green'])\n",
"xax = bq.Axis(scale=xs, label='x', grid_lines='solid')\n",
"yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selections"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "11816ad4473d432db421b999df0c4fa6",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Label</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Label(value='None')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def interval_change_callback(change):\n",
" db.value = str(change['new'])\n",
"\n",
"intsel = bq.interacts.FastIntervalSelector(scale=xs, marks=[line])\n",
"intsel.observe(interval_change_callback, names=['selected'] )\n",
"\n",
"db = widgets.Label()\n",
"db.value = str(intsel.selected)\n",
"display(db)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b492987cef848cc84c4811adc757a09",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Figure(animation_duration=1000, axes=[Axis(label='x', scale=LinearScale(), side='bottom'), Axis(label='y', orientation='vertical', scale=LinearScale(), side='left', tick_format='0.2f')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, interaction=FastIntervalSelector(marks=[Lines(colors=['red', 'green'], interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}}, tooltip_style={'opacity': 0.9}, x=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), y=array([[ 0.83265074, 1.15971695, 2.79131438, 3.16907355,\n",
" 3.40894065, 3.56789933, 3.76076328, 2.603746 ,\n",
" 3.37441906, 3.24397932, 5.06589442, 4.99024395,\n",
" 5.41116223, 5.65776442, 5.03220738, 6.02434421,\n",
" 7.92940785, 7.91463063, 7.61415185, 7.25912312,\n",
" 5.36676122, 5.18894808, 5.4399462 , 6.49470412,\n",
" 7.45475186, 7.03825278, 6.76142978, 7.88533509,\n",
" 7.71187119, 7.20184165, 8.5943601 , 9.63194577,\n",
" 9.65073756, 9.05696011, 7.04507979, 7.6347834 ,\n",
" 6.73841368, 4.77568167, 6.3605022 , 7.00846999,\n",
" 5.86946179, 4.65506041, 5.52602219, 4.64805158,\n",
" 5.94420144, 6.56066076, 7.09725728, 7.50195273,\n",
" 7.6934036 , 8.5739148 , 8.11983444, 8.20578642,\n",
" 8.957733 , 9.52072272, 8.32573592, 7.82532625,\n",
" 8.07812975, 7.67011505, 9.44477361, 9.05162041,\n",
" 8.88940196, 9.65883214, 9.98936489, 9.84409043,\n",
" 9.0875969 , 9.38911096, 10.4282074 , 10.90730262,\n",
" 10.1291191 , 11.86589406, 10.41931617, 8.83663052,\n",
" 9.79718775, 10.02302823, 9.47352968, 8.37495895,\n",
" 10.69575879, 10.81284967, 11.34705084, 11.66493593,\n",
" 12.09974389, 12.63983835, 13.37226236, 12.99703996,\n",
" 12.70539797, 10.96437517, 10.18407076, 10.45518356,\n",
" 11.50020693, 12.09924646, 11.75855411, 10.4953812 ,\n",
" 7.71802206, 8.86975603, 8.28052704, 7.83206204,\n",
" 7.96363601, 6.55807596, 6.20829378, 8.23176573],\n",
" [ 0.50538694, 0.8646361 , -0.71785838, 1.52574351,\n",
" 0.1029486 , 2.02527336, -0.08978266, 1.31558278,\n",
" 2.93363705, 2.10922793, 2.5318083 , 3.07928887,\n",
" 2.26549439, 0.81637678, -0.50134056, 0.03966766,\n",
" -0.04544795, -0.60974898, 0.35701903, 0.86508694,\n",
" 0.10962422, -1.0915773 , -0.56831557, -1.10589893,\n",
" -1.00669407, 0.5696049 , 1.07193314, 0.20966614,\n",
" 0.37032733, -0.58231762, 1.02620453, 0.46462579,\n",
" 0.67189653, 0.97962911, 1.13887958, -0.81966938,\n",
" -2.26609044, -2.71844072, -2.39900889, -2.5367881 ,\n",
" -3.49393557, -4.84235989, -5.24391744, -5.71239348,\n",
" -5.19955702, -5.52587549, -4.92316783, -5.5178176 ,\n",
" -5.77377527, -6.12182165, -6.90418862, -6.27906996,\n",
" -7.09266596, -7.61430747, -7.68742711, -8.98480677,\n",
" -9.30974173, -10.02104809, -10.40920228, -10.46913028,\n",
" -11.2690439 , -11.48911968, -10.18045093, -10.20624949,\n",
" -9.06098732, -8.71449288, -7.94033227, -8.71479123,\n",
" -8.60988407, -8.47597115, -9.08859689, -9.91142521,\n",
" -11.4016906 , -9.90555096, -10.87795385, -9.53173278,\n",
" -9.99922595, -10.86171925, -10.23920011, -10.87039205,\n",
" -10.30193313, -10.6347449 , -10.1543204 , -11.12250647,\n",
" -10.29115541, -9.80318272, -10.72283341, -8.07989769,\n",
" -7.53977467, -5.2493076 , -3.64903978, -3.83787456,\n",
" -4.25014631, -4.6536055 , -6.48363405, -7.17946917,\n",
" -6.93270314, -5.40674558, -6.17951746, -5.29746086]]))], scale=LinearScale()), layout=Layout(min_width='125px'), marks=[Lines(colors=['red', 'green'], interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}}, tooltip_style={'opacity': 0.9}, x=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), y=array([[ 0.83265074, 1.15971695, 2.79131438, 3.16907355,\n",
" 3.40894065, 3.56789933, 3.76076328, 2.603746 ,\n",
" 3.37441906, 3.24397932, 5.06589442, 4.99024395,\n",
" 5.41116223, 5.65776442, 5.03220738, 6.02434421,\n",
" 7.92940785, 7.91463063, 7.61415185, 7.25912312,\n",
" 5.36676122, 5.18894808, 5.4399462 , 6.49470412,\n",
" 7.45475186, 7.03825278, 6.76142978, 7.88533509,\n",
" 7.71187119, 7.20184165, 8.5943601 , 9.63194577,\n",
" 9.65073756, 9.05696011, 7.04507979, 7.6347834 ,\n",
" 6.73841368, 4.77568167, 6.3605022 , 7.00846999,\n",
" 5.86946179, 4.65506041, 5.52602219, 4.64805158,\n",
" 5.94420144, 6.56066076, 7.09725728, 7.50195273,\n",
" 7.6934036 , 8.5739148 , 8.11983444, 8.20578642,\n",
" 8.957733 , 9.52072272, 8.32573592, 7.82532625,\n",
" 8.07812975, 7.67011505, 9.44477361, 9.05162041,\n",
" 8.88940196, 9.65883214, 9.98936489, 9.84409043,\n",
" 9.0875969 , 9.38911096, 10.4282074 , 10.90730262,\n",
" 10.1291191 , 11.86589406, 10.41931617, 8.83663052,\n",
" 9.79718775, 10.02302823, 9.47352968, 8.37495895,\n",
" 10.69575879, 10.81284967, 11.34705084, 11.66493593,\n",
" 12.09974389, 12.63983835, 13.37226236, 12.99703996,\n",
" 12.70539797, 10.96437517, 10.18407076, 10.45518356,\n",
" 11.50020693, 12.09924646, 11.75855411, 10.4953812 ,\n",
" 7.71802206, 8.86975603, 8.28052704, 7.83206204,\n",
" 7.96363601, 6.55807596, 6.20829378, 8.23176573],\n",
" [ 0.50538694, 0.8646361 , -0.71785838, 1.52574351,\n",
" 0.1029486 , 2.02527336, -0.08978266, 1.31558278,\n",
" 2.93363705, 2.10922793, 2.5318083 , 3.07928887,\n",
" 2.26549439, 0.81637678, -0.50134056, 0.03966766,\n",
" -0.04544795, -0.60974898, 0.35701903, 0.86508694,\n",
" 0.10962422, -1.0915773 , -0.56831557, -1.10589893,\n",
" -1.00669407, 0.5696049 , 1.07193314, 0.20966614,\n",
" 0.37032733, -0.58231762, 1.02620453, 0.46462579,\n",
" 0.67189653, 0.97962911, 1.13887958, -0.81966938,\n",
" -2.26609044, -2.71844072, -2.39900889, -2.5367881 ,\n",
" -3.49393557, -4.84235989, -5.24391744, -5.71239348,\n",
" -5.19955702, -5.52587549, -4.92316783, -5.5178176 ,\n",
" -5.77377527, -6.12182165, -6.90418862, -6.27906996,\n",
" -7.09266596, -7.61430747, -7.68742711, -8.98480677,\n",
" -9.30974173, -10.02104809, -10.40920228, -10.46913028,\n",
" -11.2690439 , -11.48911968, -10.18045093, -10.20624949,\n",
" -9.06098732, -8.71449288, -7.94033227, -8.71479123,\n",
" -8.60988407, -8.47597115, -9.08859689, -9.91142521,\n",
" -11.4016906 , -9.90555096, -10.87795385, -9.53173278,\n",
" -9.99922595, -10.86171925, -10.23920011, -10.87039205,\n",
" -10.30193313, -10.6347449 , -10.1543204 , -11.12250647,\n",
" -10.29115541, -9.80318272, -10.72283341, -8.07989769,\n",
" -7.53977467, -5.2493076 , -3.64903978, -3.83787456,\n",
" -4.25014631, -4.6536055 , -6.48363405, -7.17946917,\n",
" -6.93270314, -5.40674558, -6.17951746, -5.29746086]]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = bq.Figure(marks=[line], axes=[xax, yax], animation_duration=1000, interaction=intsel)\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line.selected"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Handdraw"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"handdraw = bq.interacts.HandDraw(lines=line)\n",
"fig.interaction = handdraw"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.83265074, 1.15971695, 2.79131438, 3.16907355,\n",
" 3.40894065, 3.56789933, 3.76076328, 2.603746 ,\n",
" 3.37441906, 3.24397932, 5.06589442, 4.99024395,\n",
" 5.41116223, 5.65776442, 5.03220738, 6.02434421,\n",
" 7.92940785, 7.91463063, 7.61415185, 7.25912312,\n",
" 5.36676122, 5.18894808, 5.4399462 , 6.49470412,\n",
" 7.45475186, 7.03825278, 6.76142978, 7.88533509,\n",
" 7.71187119, 7.20184165, 8.5943601 , 9.63194577,\n",
" 9.65073756, 9.05696011, 7.04507979, 7.6347834 ,\n",
" 6.73841368, 4.77568167, 6.3605022 , 7.00846999,\n",
" 5.86946179, 4.65506041, 5.52602219, 4.64805158,\n",
" 5.94420144, 6.56066076, 7.09725728, 7.50195273,\n",
" 7.6934036 , 8.5739148 , 8.11983444, 8.20578642,\n",
" 8.957733 , 9.52072272, 8.32573592, 7.82532625,\n",
" 8.07812975, 7.67011505, 9.44477361, 9.05162041,\n",
" 8.88940196, 9.65883214, 9.98936489, 9.84409043,\n",
" 9.0875969 , 9.38911096, 10.4282074 , 10.90730262,\n",
" 10.1291191 , 11.86589406, 10.41931617, 8.83663052,\n",
" 9.79718775, 10.02302823, 9.47352968, 8.37495895,\n",
" 10.69575879, 10.81284967, 11.34705084, 11.66493593,\n",
" 12.09974389, 12.63983835, 13.37226236, 12.99703996,\n",
" 12.70539797, 10.96437517, 10.18407076, 10.45518356,\n",
" 11.50020693, 12.09924646, 11.75855411, 10.4953812 ,\n",
" 7.71802206, 8.86975603, 8.28052704, 7.83206204,\n",
" 7.96363601, 6.55807596, 6.20829378, 8.23176573])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line.y[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Moving points around"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Label(colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'], interactions={'hover': 'tooltip'}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}}, tooltip_style={'opacity': 0.9})"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ac44c90f608843a895cfb293cf3ac4c2",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale(), tick_format='0.2f')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Scatter(colors=['steelblue'], enable_move=True, interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}, 'skew': {'dimension': 'skew'}}, tooltip_style={'opacity': 0.9}, update_on_move=True, x=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), y=array([ 176.4052346 , 216.42095543, 314.29475384, 538.38407376,\n",
" 725.13987278, 627.41208479, 722.42092654, 707.28520571,\n",
" 696.96332053, 738.02317073])), Lines(colors=['orange'], interactions={'hover': 'tooltip'}, line_style='dashed', scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}}, stroke_width=4.0, tooltip_style={'opacity': 0.9}, x=array([0, 9]), y=array([ 546.27495987, 546.27495987]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from bqplot import *\n",
"\n",
"size = 100\n",
"np.random.seed(0)\n",
"x_data = range(size)\n",
"y_data = np.cumsum(np.random.randn(size) * 100.0)\n",
"\n",
"## Enabling moving of points in scatter. Try to click and drag any of the points in the scatter and \n",
"## notice the line representing the mean of the data update\n",
"\n",
"sc_x = LinearScale()\n",
"sc_y = LinearScale()\n",
"\n",
"scat = Scatter(x=x_data[:10], y=y_data[:10], scales={'x': sc_x, 'y': sc_y}, default_colors=['blue'],\n",
" enable_move=True)\n",
"lin = Lines(scales={'x': sc_x, 'y': sc_y}, stroke_width=4, line_style='dashed', colors=['orange'])\n",
"m = Label(value='Mean is %s'%np.mean(scat.y))\n",
"\n",
"def update_line(change):\n",
" with lin.hold_sync():\n",
" lin.x = [np.min(scat.x), np.max(scat.x)]\n",
" lin.y = [np.mean(scat.y), np.mean(scat.y)]\n",
" m.value='Mean is %s'%np.mean(scat.y)\n",
" \n",
"\n",
"update_line(None)\n",
"\n",
"# update line on change of x or y of scatter\n",
"scat.observe(update_line, names='x')\n",
"scat.observe(update_line, names='y')\n",
"\n",
"ax_x = Axis(scale=sc_x)\n",
"ax_y = Axis(scale=sc_y, tick_format='0.2f', orientation='vertical')\n",
"\n",
"fig = Figure(marks=[scat, lin], axes=[ax_x, ax_y])\n",
"\n",
"## In this case on drag, the line updates as you move the points.\n",
"with scat.hold_sync():\n",
" scat.enable_move = True\n",
" scat.update_on_move = True\n",
" scat.enable_add = False\n",
"\n",
"display(m, fig)"
]
}
],
"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": "3.6.3"
},
"widgets": {
"state": {
"0ba98b0561564e5987dba19d1b574c04": {
"views": [
{
"cell_index": 29
}
]
},
"1ffa1ac6658e4eef89e45cf62daae339": {
"views": [
{
"cell_index": 30
}
]
},
"42735872becb4c43b817ccff90973d63": {
"views": [
{
"cell_index": 15
}
]
},
"4b9a5b18593545faba761a719b2e6039": {
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{
"cell_index": 17
}
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"7fb14b25d15240ec820c565619fe4f11": {
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{
"cell_index": 7
}
]
},
"99604f1b70c54e19a508c95d705ce488": {
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{
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"bf805af773d94496888afbe9e2bd3d37": {
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{
"cell_index": 13
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},
"dc1b84a0e5fa4ed5a42454cfc52c44f5": {
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{
"cell_index": 25
}
]
},
"f30275bcf69e426c9d45a525833198d0": {
"views": [
{
"cell_index": 5
}
]
}
},
"version": "2.0.0-dev"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
google/skywater-pdk-libs-sky130_bag3_pr | workspace_setup/tutorial_files/3_analogbase.ipynb | 1 | 38666 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AnalogBase\n",
"In this module, you will learn the basics of `AnalogBase`, and how to design a source-follower layout generator using AnalogBase.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is AnalogBase?\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_1.PNG\" alt=\"Drawing\" style=\"width: 200px;\" />\n",
"`AnalogBase` is one of several \"layout floorplan\" classes that allows designers to easily develop process-portable layout generator for various electromigration-constrained circuits. To do so, `AnalogBase` draws rows of transistors with substrate contacts on the top-most and bottom-most rows, as shown in the figure above. In this floorplan, the number of current-carrying wires scales naturally with number of fingers, which is optimal for circuits with large bias currents.\n",
"\n",
"By convention, `AnalogBase` draws $N$ rows of NMOS (labeled as `nch`) and $P$ rows of PMOS (labeled as `pch`), with $N$ and $P$ being nonnegative integers, so you can only draw NMOS rows by setting $P=0$, and so on. The rows are indexed from bottom to top, so `nch(N-1)` is the top-most NMOS row, and `pch0` is the bottom-most PMOS row.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Transistor Source/Drain Naming Convention\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_3.PNG\" alt=\"Drawing\" style=\"width: 600px;\"/>\n",
"Before we talk about how `AnalogBase` draws transistor connections, we need to establish a naming convention for source/drain junctions of a transistor, since source and drain are often interchangeable. In XBase, the left-most source/drain junction of a transistor is always called \"source\", and after that source and drain alternates between each other, as shown in the above figure. This implies that for even number of fingers, the right-most junction is always \"source\", and for odd number of fingers, the left-most junction is always \"drain\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AnalogMosConn Overview\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_2.PNG\" alt=\"Drawing\" style=\"width: 600px;\"/>\n",
"To connect transistors to the routing grid, `AnalogBase` \"drops\" `AnalogMosConn`, a layout cell consisting only of wires and vias, on top of desired transistors to connect gates, sources, and drains to a vertical routing layer. For most technologies, `AnalogMosConn` draws gate, drain, and source wires on every other source/drain junction, with drain and source wires interleaving with each other. By default, the gate wires are drawn below the transistor row, to draw gate wires above the transistor row, flip the row upside down by changing the row orientation from `R0` to `MX` (we will see an example of this later).\n",
"\n",
"With this layout style, the gate wires can either be drawn in the same tracks as source wires (\"G aligned to S\"), or they can be drawn in the same tracks as drain wires (\"G aligned to D\"). The gate wire location is usually determined by source/drain wire direction. For example, in the figure above, if the source of a transistor needs to be connected to the row below it, then gate wires cannot be aligned to source, as this will cause a short between gate and source wires when the source wires is extended downwards. Because of this, when creating a `AnalogMosConn`, designer needs to specify the drain and source wire directions (whether they go \"up\" or \"down\"), and the gate wire locations will be determined automatically to avoid shorts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connecting to Horizontal Tracks\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_4.PNG\" alt=\"Drawing\" style=\"width: 300px;\"/>\n",
"In the previous section, we see that `AnalogMosConn` connects the transistor to vertical tracks. How do we connect those vertical wires to the horizontal tracks above it? If you recall from the previous module, you would need to use the `connect_to_tracks()` method with the horizontal track index. The question now becomes: how do I know which track index can be used for gate/drain/source connections?\n",
"\n",
"To get a better understanding of this problem, consider the layout shown in the figure above. The PMOS drain wires can be easily connected to track 10 with no issues, but the PMOS gate wires cannot be connected to track 10 without shorting with drain wires. In fact, the PMOS gate wires can only be connected to tracks 5, 6, and 7 without running into minimum line-end spacing rules with other wires. How can we determine what the legal track indices are? Furthermore, if our particular circuit requires more than 3 horizontal tracks for PMOS gate connections, how can we tell `AnalogBase` to space the rows further apart?\n",
"\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_5.PNG\" alt=\"Drawing\" style=\"width: 300px;\"/>\n",
"To address these issues, `AnalogBase` introduces the concept of relative track indices, as shown in the figure above. `AnalogBase` categorizes each horizontal tracks by the transistor row it belongs to, and by whether it can be connected to the gate/drain/source wires without DRC errors. \n",
"\n",
"In each row, `g0` is the horizontal track furthest from the transistor row that can be connected to the gate wires without errors, and the index increases as the wire moves closer to the transistor. `ds0` is the horizontal track closest to the transistor row (perhaps on top of it) that can be connected to the drain/source wires without errors, and the index increases as the wire moves away from the transistor.\n",
"\n",
"`AnalogBase` provides two methods to convert relative track indices to absolute track numbers, which can then be passed to `connect_to_tracks()` method to draw the connections. Using the figure above as an example, `self.get_track_index('pch', 0, 'g', 1)` will returns the track number of the horizontal track at PMOS row 0, gate type, index 1, which is track number 5. `self.make_track_id('pch', 0, 'g', 1)` will return the corresponding `TrackID` object instead.\n",
"\n",
"Finally, designer can specify the number of horizontal tracks needed for gate/drain/source connections on each row, and `AnalogBase` will automatically move rows further apart if necessary."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CS Amplifier Layout Example\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_6.PNG\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
"Now that you have a general idea of how `AnalogBase` works, lets walk through a common-source amplifier example. The figure above shows a rough sketch of the layout floorplan (**NOTE: ALWAYS DRAW FLOORPLAN BEFORE CODING!**). We have one NMOS row on the bottom, one PMOS row on the top, and we put extra dummy transistors on both sides to reduce edge layout effects. The input connects to NMOS gates from below the NMOS row, the PMOS bias connects to PMOS gates from above the PMOS row, and the output drain/source of NMOS/PMOS are connected to a horizontal track between the two rows. Finally, the supply drain/source wires are extended and shorted on top of the substrate contacts on both ends.\n",
"\n",
"The entire common-source amplifier layout generator code is reproduced below. We will walk through important sections of the code and describe what they do.\n",
"\n",
"```python\n",
"class AmpCS(AnalogBase):\n",
" \"\"\"A common source amplifier.\"\"\"\n",
" def __init__(self, temp_db, lib_name, params, used_names, **kwargs):\n",
" AnalogBase.__init__(self, temp_db, lib_name, params, used_names, **kwargs)\n",
" self._sch_params = None\n",
"\n",
" @property\n",
" def sch_params(self):\n",
" return self._sch_params\n",
"\n",
" @classmethod\n",
" def get_params_info(cls):\n",
" \"\"\"Returns a dictionary containing parameter descriptions.\n",
"\n",
" Override this method to return a dictionary from parameter names to descriptions.\n",
"\n",
" Returns\n",
" -------\n",
" param_info : dict[str, str]\n",
" dictionary from parameter name to description.\n",
" \"\"\"\n",
" return dict(\n",
" lch='channel length, in meters.',\n",
" w_dict='width dictionary.',\n",
" intent_dict='intent dictionary.',\n",
" fg_dict='number of fingers dictionary.',\n",
" ndum='number of dummies on each side.',\n",
" ptap_w='NMOS substrate width, in meters/number of fins.',\n",
" ntap_w='PMOS substrate width, in meters/number of fins.',\n",
" show_pins='True to draw pin geometries.',\n",
" )\n",
"\n",
" def draw_layout(self):\n",
" \"\"\"Draw the layout of a transistor for characterization.\n",
" \"\"\"\n",
"\n",
" lch = self.params['lch']\n",
" w_dict = self.params['w_dict']\n",
" intent_dict = self.params['intent_dict']\n",
" fg_dict = self.params['fg_dict']\n",
" ndum = self.params['ndum']\n",
" ptap_w = self.params['ptap_w']\n",
" ntap_w = self.params['ntap_w']\n",
" show_pins = self.params['show_pins']\n",
"\n",
" fg_amp = fg_dict['amp']\n",
" fg_load = fg_dict['load']\n",
"\n",
" if fg_load % 2 != 0 or fg_amp % 2 != 0:\n",
" raise ValueError('fg_load=%d and fg_amp=%d must all be even.' % (fg_load, fg_amp))\n",
"\n",
" # compute total number of fingers in each row\n",
" fg_half_pmos = fg_load // 2\n",
" fg_half_nmos = fg_amp // 2\n",
" fg_half = max(fg_half_pmos, fg_half_nmos)\n",
" fg_tot = (fg_half + ndum) * 2\n",
"\n",
" # specify width/threshold of each row\n",
" nw_list = [w_dict['amp']]\n",
" pw_list = [w_dict['load']]\n",
" nth_list = [intent_dict['amp']]\n",
" pth_list = [intent_dict['load']]\n",
"\n",
" # specify number of horizontal tracks for each row\n",
" ng_tracks = [1] # input track\n",
" nds_tracks = [1] # one track for space\n",
" pds_tracks = [1] # output track\n",
" pg_tracks = [1] # bias track\n",
"\n",
" # specify row orientations\n",
" n_orient = ['R0'] # gate connection on bottom\n",
" p_orient = ['MX'] # gate connection on top\n",
"\n",
" self.draw_base(lch, fg_tot, ptap_w, ntap_w, nw_list,\n",
" nth_list, pw_list, pth_list,\n",
" ng_tracks=ng_tracks, nds_tracks=nds_tracks,\n",
" pg_tracks=pg_tracks, pds_tracks=pds_tracks,\n",
" n_orientations=n_orient, p_orientations=p_orient,\n",
" )\n",
"\n",
" # figure out if output connects to drain or source of nmos\n",
" if (fg_amp - fg_load) % 4 == 0:\n",
" s_net, d_net = '', 'vout'\n",
" aout, aoutb, nsdir, nddir = 'd', 's', 0, 2\n",
" else:\n",
" s_net, d_net = 'vout', ''\n",
" aout, aoutb, nsdir, nddir = 's', 'd', 2, 0\n",
"\n",
" # create transistor connections\n",
" load_col = ndum + fg_half - fg_half_pmos\n",
" amp_col = ndum + fg_half - fg_half_nmos\n",
" amp_ports = self.draw_mos_conn('nch', 0, amp_col, fg_amp, nsdir, nddir,\n",
" s_net=s_net, d_net=d_net)\n",
" load_ports = self.draw_mos_conn('pch', 0, load_col, fg_load, 2, 0,\n",
" s_net='', d_net='vout')\n",
" # amp_ports/load_ports are dictionaries of WireArrays representing\n",
" # transistor ports.\n",
" print(amp_ports)\n",
" print(amp_ports['g'])\n",
"\n",
" # create TrackID from relative track index\n",
" vin_tid = self.make_track_id('nch', 0, 'g', 0)\n",
" vbias_tid = self.make_track_id('pch', 0, 'g', 0)\n",
" # can also convert from relative to absolute track index\n",
" print(self.get_track_index('nch', 0, 'g', 0))\n",
" # get output track index, put it in the middle\n",
" vout_bot = self.get_track_index('nch', 0, 'ds', 0)\n",
" vout_top = self.get_track_index('pch', 0, 'ds', 0)\n",
" vout_index = self.grid.get_middle_track(vout_bot, vout_top, round_up=True)\n",
" vout_tid = TrackID(self.mos_conn_layer + 1, vout_index)\n",
"\n",
" vin_warr = self.connect_to_tracks(amp_ports['g'], vin_tid)\n",
" vout_warr = self.connect_to_tracks([amp_ports[aout], load_ports['d']], vout_tid)\n",
" vbias_warr = self.connect_to_tracks(load_ports['g'], vbias_tid)\n",
" self.connect_to_substrate('ptap', amp_ports[aoutb])\n",
" self.connect_to_substrate('ntap', load_ports['s'])\n",
"\n",
" vss_warrs, vdd_warrs = self.fill_dummy()\n",
"\n",
" self.add_pin('VSS', vss_warrs, show=show_pins)\n",
" self.add_pin('VDD', vdd_warrs, show=show_pins)\n",
" self.add_pin('vin', vin_warr, show=show_pins)\n",
" self.add_pin('vout', vout_warr, show=show_pins)\n",
" self.add_pin('vbias', vbias_warr, show=show_pins)\n",
"\n",
" # compute schematic parameters\n",
" self._sch_params = dict(\n",
" lch=lch,\n",
" w_dict=w_dict,\n",
" intent_dict=intent_dict,\n",
" fg_dict=fg_dict,\n",
" dum_info=self.get_sch_dummy_info(),\n",
" )\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class Definition\n",
"```python\n",
"class AmpCS(AnalogBase):\n",
" \"\"\"A common source amplifier.\"\"\"\n",
" def __init__(self, temp_db, lib_name, params, used_names, **kwargs):\n",
" AnalogBase.__init__(self, temp_db, lib_name, params, used_names, **kwargs)\n",
" self._sch_params = None\n",
" \n",
" @property\n",
" def sch_params(self):\n",
" return self._sch_params\n",
"```\n",
"The layout generator code starts with the Python class definition. We subclass the `AnalogBase` class to inherit various functions described earlier. The constructor doesn't do much besides calling the super constructor and initializing a private attribute. Finally, we declare a read-only property, `sch_params`, which we will compute later. It contains the schematic parameters for the schematic generator we will see in the next module."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Parameter Specifications\n",
"```python\n",
"@classmethod\n",
"def get_params_info(cls):\n",
" \"\"\"Returns a dictionary containing parameter descriptions.\n",
" Override this method to return a dictionary from parameter names to descriptions.\n",
" Returns\n",
" -------\n",
" param_info : dict[str, str]\n",
" dictionary from parameter name to description.\n",
" \"\"\"\n",
" return dict(\n",
" lch='channel length, in meters.',\n",
" w_dict='width dictionary.',\n",
" intent_dict='intent dictionary.',\n",
" fg_dict='number of fingers dictionary.',\n",
" ndum='number of dummies on each side.',\n",
" ptap_w='NMOS substrate width, in meters/number of fins.',\n",
" ntap_w='PMOS substrate width, in meters/number of fins.',\n",
" show_pins='True to draw pin geometries.',\n",
" )\n",
"```\n",
"Next we have a class method, `get_params_info()`, that simply returns a Python dictionary from layout parameter names to a brief description of the corresponding parameter. This method should list all layout parameters, and it is used to determine to compute a unique ID to represent the generated instance. This allows XBase to avoid re-generating existing layouts when constructing a complex layout hierarchy with many duplicate layout instances."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How many fingers in a row?\n",
"Next, in the `draw_layout()` method is where all the layout generation happens. The beginning is rather straight-forward, then we get to the following section:\n",
"```python\n",
" # compute total number of fingers in each row\n",
"fg_half_pmos = fg_load // 2\n",
"fg_half_nmos = fg_amp // 2\n",
"fg_half = max(fg_half_pmos, fg_half_nmos)\n",
"fg_tot = (fg_half + ndum) * 2\n",
"```\n",
"This section computes how many fingers we need to draw in each transistor row. To get a better understanding, consider the two scenarios below:\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_7.PNG\" alt=\"Drawing\" style=\"width: 600px;\" />\n",
"Since `AnalogBase` must draw the same number of fingers for each row, we see that total number of fingers in each row depends on whether the AMP transistor or the LOAD transistor has more fingers. We resolve this by using the `max()` function to get the larger of the two."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Drawing Transistor Rows\n",
"```python\n",
"# specify width/threshold of each row\n",
"nw_list = [w_dict['amp']]\n",
"pw_list = [w_dict['load']]\n",
"nth_list = [intent_dict['amp']]\n",
"pth_list = [intent_dict['load']]\n",
"\n",
"# specify number of horizontal tracks for each row\n",
"ng_tracks = [1] # input track\n",
"nds_tracks = [1] # one track for space\n",
"pds_tracks = [1] # output track\n",
"pg_tracks = [1] # bias track\n",
"\n",
"# specify row orientations\n",
"n_orient = ['R0'] # gate connection on bottom\n",
"p_orient = ['MX'] # gate connection on top\n",
"\n",
"self.draw_base(lch, fg_tot, ptap_w, ntap_w, nw_list,\n",
" nth_list, pw_list, pth_list,\n",
" ng_tracks=ng_tracks, nds_tracks=nds_tracks,\n",
" pg_tracks=pg_tracks, pds_tracks=pds_tracks,\n",
" n_orientations=n_orient, p_orientations=p_orient,\n",
" )\n",
"```\n",
"This section specifies the layout parameters for each row, then calls the `draw_base()` method in `AnalogBase` to draw the transistor and substrate contact rows. Note that the PMOS row orientation is set to `MX` so that `AnalogMosConn` will draw gate wires on the top of PMOS row."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Is output on source or drain?\n",
"```python\n",
"# figure out if output connects to drain or source of nmos\n",
"if (fg_amp - fg_load) % 4 == 0:\n",
" aout, aoutb, nsdir, nddir = 'd', 's', 0, 2\n",
"else:\n",
" aout, aoutb, nsdir, nddir = 's', 'd', 2, 0\n",
"```\n",
"This section determines if the output should connect to drain or source of the nmos transistor, and as the result what should the nmos source/drain wire directions be. To see why this is necessary, consider the two cases shown below:\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_8.PNG\" alt=\"Drawing\" style=\"width: 600px;\" />\n",
"In both cases, we have 8 PMOS fingers, and 4 or 6 NMOS fingers, respectively. To make life simpler, we decide to always connect the output wires to PMOS drain (if you expect PMOS to always be larger, this gives you less parasitic capacitance). Furthermore, to have better symmetric, we align the center of the PMOS and NMOS transistors. Then, to minimize interconnect resistance, we should connect output to the NMOS junction that is aligned to PMOS drain. If we check the above figure, we see that the corresponding NMOS junction is drain when NMOS has 4 fingers, but it is source when NMOS has 6 fingers! This means that the correct NMOS junction to connect to actually depends on both `fg_amp` and `fg_load`. By sketching a few example, you should be able to figure out that we need to connect output to NMOS drain if the difference in number of fingers is a multiple of 4, and connect output to NMOS drain otherwise. This is exactly what this section of code does."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Drawing Transistor Connections\n",
"```python\n",
"# create transistor connections\n",
"load_col = ndum + fg_half - fg_half_pmos\n",
"amp_col = ndum + fg_half - fg_half_nmos\n",
"amp_ports = self.draw_mos_conn('nch', 0, amp_col, fg_amp, nsdir, nddir,\n",
" s_net=s_net, d_net=d_net)\n",
"load_ports = self.draw_mos_conn('pch', 0, load_col, fg_load, 2, 0,\n",
" s_net='', d_net='vout')\n",
"# amp_ports/load_ports are dictionaries of WireArrays representing\n",
"# transistor ports.\n",
"print(amp_ports)\n",
"print(amp_ports['g'])\n",
"```\n",
"Now we are ready to draw the actual transistor connections. To do so, we use the `draw_mos_conn()` function. As an example, `self.draw_mos_conn('pch', 0, load_col, fg_load, 2, 0)` creates an `AnalogMosConn` object on top of PMOS row 0, starting at transistor index `load_col` (with index 0 being left-most transistor), using `fg_load` fingers to the right, with source going up (code 2) and drain going down (code 0). Remember that the source/drain directions are used to determine gate wires location.\n",
"\n",
"The optional parameters `s_net` and `d_net` specify the net names of the source and drain of the transistor drawn, respectively. By default, if these are not specified (or set to empty strings), AnalogBase assume they connect to VDD for PMOS or VSS for NMOS. These parameters are used to infer dummy transistor schematic to simplify the process of generating LVS-clean schematics.\n",
"\n",
"the `draw_mos_conn()` method will return a dictionary from the strings `'g'`, `'d'`, and `'s'` to the `WireArray` objects for the corresponding vertical wires."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connecting Wires\n",
"```python\n",
"# create TrackID from relative track index\n",
"vin_tid = self.make_track_id('nch', 0, 'g', 0)\n",
"vout_tid = self.make_track_id('pch', 0, 'ds', 0)\n",
"vbias_tid = self.make_track_id('pch', 0, 'g', 0)\n",
"# can also convert from relative to absolute track index\n",
"print(self.get_track_index('nch', 0, 'g', 0))\n",
"\n",
"vin_warr = self.connect_to_tracks(amp_ports['g'], vin_tid)\n",
"vout_warr = self.connect_to_tracks([amp_ports[aout], load_ports['d']], vout_tid)\n",
"vbias_warr = self.connect_to_tracks(load_ports['g'], vbias_tid)\n",
"self.connect_to_substrate('ptap', amp_ports[aoutb])\n",
"self.connect_to_substrate('ntap', load_ports['s'])\n",
"```\n",
"This section used the `make_track_id()` and `get_track_index()` methods described before to get horizontal track indices from relative index. We then use `connect_to_tracks()` to connect wires to the desired tracks. `connect_to_substrate()` method is used to connect transistor junctions to the specified substrate contacts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dummies and Pins\n",
"```python\n",
"vss_warrs, vdd_warrs = self.fill_dummy()\n",
"\n",
"self.add_pin('VSS', vss_warrs, show=show_pins)\n",
"self.add_pin('VDD', vdd_warrs, show=show_pins)\n",
"self.add_pin('vin', vin_warr, show=show_pins)\n",
"self.add_pin('vout', vout_warr, show=show_pins)\n",
"self.add_pin('vbias', vbias_warr, show=show_pins)\n",
"```\n",
"After all connections are made, the `fill_dummy()` method can be used to automatically connect all unconnected transistors to corresponding substrate contacts as dummy transistors. `add_pin()` function is used to add layout pins, as seem from the routing demo module."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Schematic Parameters\n",
"```python\n",
"# compute schematic parameters\n",
"self._sch_params = dict(\n",
" lch=lch,\n",
" w_dict=w_dict,\n",
" intent_dict=intent_dict,\n",
" fg_dict=fg_dict,\n",
" dum_info=self.get_sch_dummy_info(),\n",
")\n",
"```\n",
"Finally, we compute the schematic parameter dictionary, which will be used with the schematic generator later to produce LVS clean schematic. The `get_sch_dummy_info()` method will return a data structure that describes all the dummy transistors in this AnalogBase. This data structure will be used by the schematic generator to create the corresponding transistors."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SF Amplifier Exercise\n",
"Now that you understand how the common-source amplifier layout generator works, try to complete the following source-follower amplifier class by filling in missing codes. The floorplan for the source-follower amplifier is drawn for you below:\n",
"<img src=\"bootcamp_pics/3_analogbase/analogbase_9.PNG\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
"Notice that:\n",
"* we have two rows of NMOS.\n",
"* Gate connection is on the top for second row\n",
"* To minimize parasitics, we will use leave 1 horizontal track empty between vin and VDD.\n",
"\n",
"You can evaluate the next cell (Press Ctrl+Enter) to see a preliminary layout of the source follower. It will also run LVS after generating the layout, which will fail if your layout is not correct."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"creating BagProject\n",
"computing layout\n",
"ext_w0 = 1, ext_wend=7, ytop=2880\n",
"ext_w0 = 2, ext_wend=9, ytop=3024\n",
"final: ext_w0 = 1, ext_wend=7, ytop=2880\n",
"creating layout\n",
"layout done\n",
"computing AMP_SF schematics\n"
]
},
{
"ename": "TypeError",
"evalue": "design() argument after ** must be a mapping, not NoneType",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-6182a9967843>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[0mbprj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbag\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBagProject\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m \u001b[0mdemo_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_flow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbprj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtop_specs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'amp_sf_soln'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAmpSF\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_lvs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlvs_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~/projects/bag_gen/BAG2_cds_ff_mpt/BAG_XBase_demo/xbase_demo/core.py\u001b[0m in \u001b[0;36mrun_flow\u001b[1;34m(prj, specs, dsn_name, lay_cls, sch_cls, run_lvs, lvs_only)\u001b[0m\n\u001b[0;32m 376\u001b[0m \u001b[1;31m# generate design/testbench schematics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 377\u001b[0m gen_schematics(prj, specs, dsn_name, dsn_sch_params, sch_cls=sch_cls,\n\u001b[1;32m--> 378\u001b[1;33m check_lvs=run_lvs, lvs_only=lvs_only)\n\u001b[0m\u001b[0;32m 379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 380\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlvs_only\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~/projects/bag_gen/BAG2_cds_ff_mpt/BAG_XBase_demo/xbase_demo/core.py\u001b[0m in \u001b[0;36mgen_schematics\u001b[1;34m(prj, specs, dsn_name, sch_params, sch_cls, check_lvs, lvs_only)\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[0mdsn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_design_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msch_lib\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msch_cell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'computing %s schematics'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mgen_cell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 101\u001b[1;33m \u001b[0mdsn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdesign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0msch_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 102\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 103\u001b[0m dsn = prj.new_schematic_instance(lib_name=sch_lib, cell_name=sch_cell,\n",
"\u001b[1;31mTypeError\u001b[0m: design() argument after ** must be a mapping, not NoneType"
]
}
],
"source": [
"from abs_templates_ec.analog_core import AnalogBase\n",
"\n",
"\n",
"class AmpSF(AnalogBase):\n",
" \"\"\"A template of a single transistor with dummies.\n",
"\n",
" This class is mainly used for transistor characterization or\n",
" design exploration with config views.\n",
"\n",
" Parameters\n",
" ----------\n",
" temp_db : :class:`bag.layout.template.TemplateDB`\n",
" the template database.\n",
" lib_name : str\n",
" the layout library name.\n",
" params : dict[str, any]\n",
" the parameter values.\n",
" used_names : set[str]\n",
" a set of already used cell names.\n",
" kwargs : dict[str, any]\n",
" dictionary of optional parameters. See documentation of\n",
" :class:`bag.layout.template.TemplateBase` for details.\n",
" \"\"\"\n",
"\n",
" def __init__(self, temp_db, lib_name, params, used_names, **kwargs):\n",
" AnalogBase.__init__(self, temp_db, lib_name, params, used_names, **kwargs)\n",
" self._sch_params = None\n",
"\n",
" @property\n",
" def sch_params(self):\n",
" return self._sch_params\n",
"\n",
" @classmethod\n",
" def get_params_info(cls):\n",
" \"\"\"Returns a dictionary containing parameter descriptions.\n",
"\n",
" Override this method to return a dictionary from parameter names to descriptions.\n",
"\n",
" Returns\n",
" -------\n",
" param_info : dict[str, str]\n",
" dictionary from parameter name to description.\n",
" \"\"\"\n",
" return dict(\n",
" lch='channel length, in meters.',\n",
" w_dict='width dictionary.',\n",
" intent_dict='intent dictionary.',\n",
" fg_dict='number of fingers dictionary.',\n",
" ndum='number of dummies on each side.',\n",
" ptap_w='NMOS substrate width, in meters/number of fins.',\n",
" ntap_w='PMOS substrate width, in meters/number of fins.',\n",
" show_pins='True to draw pin geometries.',\n",
" )\n",
"\n",
" def draw_layout(self):\n",
" \"\"\"Draw the layout of a transistor for characterization.\n",
" \"\"\"\n",
"\n",
" lch = self.params['lch']\n",
" w_dict = self.params['w_dict']\n",
" intent_dict = self.params['intent_dict']\n",
" fg_dict = self.params['fg_dict']\n",
" ndum = self.params['ndum']\n",
" ptap_w = self.params['ptap_w']\n",
" ntap_w = self.params['ntap_w']\n",
" show_pins = self.params['show_pins']\n",
"\n",
" fg_amp = fg_dict['amp']\n",
" fg_bias = fg_dict['bias']\n",
"\n",
" if fg_bias % 2 != 0 or fg_amp % 2 != 0:\n",
" raise ValueError('fg_bias=%d and fg_amp=%d must all be even.' % (fg_bias, fg_amp))\n",
"\n",
" fg_half_bias = fg_bias // 2\n",
" fg_half_amp = fg_amp // 2\n",
" fg_half = max(fg_half_bias, fg_half_amp)\n",
" fg_tot = (fg_half + ndum) * 2\n",
"\n",
" nw_list = [w_dict['bias'], w_dict['amp']]\n",
" nth_list = [intent_dict['bias'], intent_dict['amp']]\n",
" ng_tracks = [1, 3]\n",
" nds_tracks = [1, 1]\n",
"\n",
" n_orient = ['R0', 'MX']\n",
"\n",
" self.draw_base(lch, fg_tot, ptap_w, ntap_w, nw_list,\n",
" nth_list, [], [],\n",
" ng_tracks=ng_tracks, nds_tracks=nds_tracks,\n",
" pg_tracks=[], pds_tracks=[],\n",
" n_orientations=n_orient,\n",
" )\n",
"\n",
" if (fg_amp - fg_bias) % 4 == 0:\n",
" s_net, d_net = 'VDD', 'vout'\n",
" aout, aoutb, nsdir, nddir = 'd', 's', 2, 0\n",
" else:\n",
" s_net, d_net = 'vout', 'VDD'\n",
" aout, aoutb, nsdir, nddir = 's', 'd', 0, 2\n",
"\n",
" # TODO: compute bias_col and amp_col\n",
" bias_col = amp_col = 0\n",
"\n",
" amp_ports = self.draw_mos_conn('nch', 1, amp_col, fg_amp, nsdir, nddir,\n",
" s_net=s_net, d_net=d_net)\n",
" bias_ports = self.draw_mos_conn('nch', 0, bias_col, fg_bias, 0, 2,\n",
" s_net='', d_net='vout')\n",
"\n",
" # TODO: get TrackIDs for horizontal tracks\n",
" # The following are related code copied and pasted from AmpCS\n",
" # for reference\n",
" # vin_tid = self.make_track_id('nch', 0, 'g', 0)\n",
" # vout_tid = self.make_track_id('pch', 0, 'ds', 0)\n",
" # vbias_tid = self.make_track_id('pch', 0, 'g', 0)\n",
" vdd_tid = vin_tid = vout_tid = vbias_tid = None\n",
"\n",
" if vdd_tid is None:\n",
" return\n",
"\n",
" # uncomment to visualize track location\n",
" # hm_layer = self.mos_conn_layer + 1\n",
" # xl = self.bound_box.left_unit\n",
" # xr = self.bound_box.right_unit\n",
" # self.add_wires(hm_layer, vdd_tid.base_index, xl, xr, unit_mode=True)\n",
" # self.add_wires(hm_layer, vin_tid.base_index, xl, xr, unit_mode=True)\n",
" # self.add_wires(hm_layer, vout_tid.base_index, xl, xr, unit_mode=True)\n",
" # self.add_wires(hm_layer, vbias_tid.base_index, xl, xr, unit_mode=True)\n",
" \n",
" # TODO: connect transistors to horizontal tracks\n",
" # The following are related code copied and pasted from AmpCS\n",
" # for reference\n",
" # vin_warr = self.connect_to_tracks(amp_ports['g'], vin_tid)\n",
" # vout_warr = self.connect_to_tracks([amp_ports[aout], load_ports['d']], vout_tid)\n",
" # vbias_warr = self.connect_to_tracks(load_ports['g'], vbias_tid)\n",
" vin_warr = vout_warr = vbias_warr = vdd_warr = None\n",
"\n",
" if vin_warr is None:\n",
" return\n",
"\n",
" self.connect_to_substrate('ptap', bias_ports['s'])\n",
"\n",
" vss_warrs, _ = self.fill_dummy()\n",
"\n",
" self.add_pin('VSS', vss_warrs, show=show_pins)\n",
" # TODO: add pins\n",
"\n",
" # set schematic parameters\n",
" self._sch_params = dict(\n",
" lch=lch,\n",
" w_dict=w_dict,\n",
" intent_dict=intent_dict,\n",
" fg_dict=fg_dict,\n",
" dum_info=self.get_sch_dummy_info(),\n",
" )\n",
"\n",
" \n",
" \n",
"import os\n",
"\n",
"# import bag package\n",
"import bag\n",
"from bag.io import read_yaml\n",
"\n",
"# import BAG demo Python modules\n",
"import xbase_demo.core as demo_core\n",
"from xbase_demo.demo_layout.core import AmpSFSoln\n",
"\n",
"# load circuit specifications from file\n",
"spec_fname = os.path.join(os.environ['BAG_WORK_DIR'], 'specs_demo/demo.yaml')\n",
"top_specs = read_yaml(spec_fname)\n",
"\n",
"# obtain BagProject instance\n",
"local_dict = locals()\n",
"if 'bprj' in local_dict:\n",
" print('using existing BagProject')\n",
" bprj = local_dict['bprj']\n",
"else:\n",
" print('creating BagProject')\n",
" bprj = bag.BagProject()\n",
"\n",
"demo_core.run_flow(bprj, top_specs, 'amp_sf_soln', AmpSF, run_lvs=True, lvs_only=True)"
]
}
],
"metadata": {
"anaconda-cloud": {},
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"name": "python3"
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
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| apache-2.0 |
IST256/learn-python | content/lessons/11-WebAPIs/HW-WebAPIs.ipynb | 1 | 6275 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework: TLDL: The Gist of a Song\n",
"\n",
"## The Problem\n",
"\n",
"TLDL: Too Long didn't listen. In this assignment you are tasked with producing the gist of any song through its lyrics. The parts you need for this are:\n",
"\n",
" 1. An API to find the song given a title and artist\n",
" 2. An API to retrieve the lyrics for the song\n",
" 3. Run key phrase extraction over the lyrics to discover what the song is about\n",
" 4. Run sentiment analysis over the lyrics to identify the mood\n",
"\n",
"A bottom-up approach is suggested. Solve problems 1-4 first and then assemble into a working program that when given a title and author will provide a song LTDL gist. `{title} by {artist} is a {sentiment} song about {key phrases}`.\n",
"\n",
"For example:\n",
"\n",
" Song: First world problems\n",
" Artist: Weird Al Yankovic\n",
" \n",
" TLDL (Output): \n",
" First World Problems by Weird Al Yankovic is a mixed song about ['world problems', 'wallet small', 'corner', 'vending machine', 'water', 'hour', 'laptop screen', 'bills', 'maid', 'bathroom', 'breakfast menu', 'bread', 'tiramisu', 'pixel', 'shower', 'idiot', 'phone']\n",
" \n",
" \n",
"For style points use `@interact_manual`, of course.\n",
"\n",
"HINTS and ADVICE:\n",
"\n",
"- You can use the Musixmatch API https://developer.musixmatch.com/ to search for songs and retrieve the lyrics. \n",
" - The free version of the API provides access to 30% of the lyrics. This should be sufficient. \n",
" - Similar to Zomato, this API has a playground mode where you can try it out in the browser. https://playground.musixmatch.com/\n",
"- Take a bottom up approach! Implement each step as a function, solving that problem then putting the entire program together from the pieces\n",
"- Leverage what you learned from the lab to perform sentiment and key-phrase analysis. That code should be easy to incorporate.\n",
"- Don't worry about coding around errors, but do think about how you might handle them (there's a question about it).\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"label": "problem_analysis_cell"
},
"source": [
"## Part 1: Problem Analysis\n",
"\n",
"Inputs:\n",
"\n",
"```\n",
"TODO: Inputs\n",
"```\n",
"\n",
"Outputs:\n",
"\n",
"```\n",
"TODO: Outputs\n",
"```\n",
"\n",
"Algorithm (Steps in Program): \n",
"\n",
"```\n",
"TODO:Steps Here\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 2: Code Solution\n",
"\n",
"You may write your code in several cells, but place the complete, final working copy of your code solution within this single cell below. Only the within this cell will be considered your solution. Any imports or user-defined functions should be copied into this cell. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"label": "code_solution_cell"
},
"outputs": [],
"source": [
"# Step 2: Write code here\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"label": "homework_questions_cell"
},
"source": [
"## Part 3: Questions\n",
"\n",
"1. Does your code handle errors? Such as track not found or no lyrics? If yes, explain how you did it, if not describe how you would address this issue.\n",
"\n",
"`--== Double-Click and Write Your Answer Below This Line ==--` \n",
"\n",
"\n",
"2. In what way can we make key-phrase extraction better?\n",
"\n",
"`--== Double-Click and Write Your Answer Below This Line ==--` \n",
"\n",
"3. Do you feel the sentiment from the lyrics accurately reflects the mood of a song? Explain your answer.\n",
"\n",
"`--== Double-Click and Write Your Answer Below This Line ==--` \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"label": "reflection_cell"
},
"source": [
"## Part 4: Reflection\n",
"\n",
"Reflect upon your experience completing this assignment. This should be a personal narrative, in your own voice, and cite specifics relevant to the activity as to help the grader understand how you arrived at the code you submitted. Things to consider touching upon: Elaborate on the process itself. Did your original problem analysis work as designed? How many iterations did you go through before you arrived at the solution? Where did you struggle along the way and how did you overcome it? What did you learn from completing the assignment? What do you need to work on to get better? What was most valuable and least valuable about this exercise? Do you have any suggestions for improvements?\n",
"\n",
"To make a good reflection, you should journal your thoughts, questions and comments while you complete the exercise.\n",
"\n",
"Keep your response to between 100 and 250 words.\n",
"\n",
"`--== Double-Click and Write Your Reflection Below Here ==--` \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# run this code to turn in your work!\n",
"from coursetools.submission import Submission\n",
"Submission().submit()"
]
}
],
"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": "3.8.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": false,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": false,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
jbgalvanize/jbgalvanize.github.io | posts/Notation.ipynb | 1 | 6623 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Notation in Statistics\n",
"\n",
"Understanding mathematics and statistics when you are new to an idea or topic is difficult enough, but we (yes, myself included) need to be careful as mathematicians/statisticians to introduce ideas with consistent notation that allows a reader to focus on relating the notation to an idea rather than deciphering our inconsistent notation. \n",
"\n",
"In this set of notes, I would like to identify a few of the consistencies regarding common statistical notation. A misunderstanding in notation can frequently lead to a misconception of an idea altogether. With that, let's get started.\n",
"\n",
"#### Parameters vs Statistics\n",
"\n",
"**Parameters** are defined as numeric summaries about a population. Alternatively, **statistics** are defined as numeric summaries about a sample. In notation, we represent parameters and statistics differently. \n",
"\n",
"Parameters are frequently identified by greek symbols. For example, $\\mu$ is the symbol for a `population` mean (a parameter). However, we use $\\bar{X}$ to identify a `sample` mean (a statistic). We might also choose to represent a sample mean with the notation of $\\hat{\\mu}$, where the 'hat' suggests this is a predicted value of the parameter of interest. Let's consider some common statistic-parameter pairs:\n",
"\n",
"\n",
"| Description | Parameter | Statistic |\n",
"|:-:|:-:|:-:|\n",
"| Mean | $\\mu$ | $\\bar{X}$ | \n",
"| St. Dev. | $\\sigma$ | $s$ |\n",
"| Variance | $\\sigma^2$ | $s^2$ | \n",
"| Proportion | $\\pi$ | $p$ | \n",
"| Intercept | $\\beta_0$ | $b_0$ | \n",
"| Slope | $\\beta_1$ | $b_1$ | \n",
"\n",
"\n",
"We could also create the above table using our 'hat' notation in the following way: \n",
"\n",
"\n",
"| Description | Parameter | Statistic |\n",
"|:-:|:-:|:-:|\n",
"| Mean | $\\mu$ | $\\hat{\\mu}$ | \n",
"| St. Dev. | $\\sigma$ | $\\hat{\\sigma}$ |\n",
"| Variance | $\\sigma^2$ | $\\hat{\\sigma^2}$ | \n",
"| Proportion | $\\pi$ | $\\hat{\\pi}$ | \n",
"| Intercept | $\\beta_0$ | $\\hat{\\beta_0}$ | \n",
"| Slope | $\\beta_1$ | $\\hat{\\beta_1}$ | \n",
"\n",
"\n",
"Although you might be comfortable with either representation, the second table is very nice in its consistency in moving from a parameter to an estimate of that parameter (and maybe preferable for this reason).\n",
"\n",
"Given the above notation as a basis, notice that we provide model representations for our data (statistical distributions, linear models, etc.) using population parameters. The reason being that these are the 'true' representations of the data in the population that we believe to be true. Similarly, hypothesis testing and confidence intervals are always performed on parameters. \n",
"\n",
"Performing hypothesis tests for statistics doesn't make much sense (as statistics are calculated from our data), so the following statement is pretty ridiculous:\n",
"\n",
"$H_0: \\bar{X} = 5$ \n",
"\n",
"The value of a sample mean either is 5 upon collecting the data or not. Calculating a p-value associated with such a claim doesn't make sense. However, a statement like the following is asking the question of whether a parameter about a population is a particular value:\n",
"\n",
"$H_0: \\mu = 5$ \n",
"\n",
"Based on observed data, we can associate a probability of obtaining our data if the above claim is true.\n",
"\n",
"#### Uppercase vs. Lowercase \n",
"\n",
"In statistical notation, an uppercase and lowercase representation of the same value actually has different meaning. That is $X$ is not the same as $x$. Common practice is that if a particular letter represents a random variable, we capitalize the letter. However, if we observe an occurence of this random variable, it will be notated by a lowercase letter letter.\n",
"\n",
"For example, consider we are modeling revenue for each month. We could say that revenue (uppercase $X$) is a random variable for month to month. Then we observe the revenue of \\$100k (lowercase $x$). In the case of functions, we frequently use a lowercase letter to identify a particular function, and an uppercase letter to disucss the [cumulative distribution function (cdf)](https://en.wikipedia.org/wiki/Cumulative_distribution_function) of this function. An example of this idea is shown directly on the wiki page for cdfs. \n",
"\n",
"#### Example\n",
"\n",
"Consider we have data that follows a [Beta distribution](https://en.wikipedia.org/wiki/Beta_distribution). We might write this as:\n",
"\n",
"$X \\sim Beta(\\alpha, \\beta)$\n",
"\n",
"Notice, our data ($X$) has a capital letter associated with how we discuss it. The parameters ($\\alpha$, $\\beta$) are represented by greek symbols. If you are not familiar with a Beta distribution, it essentially models data that falls between the values of 0 and 1 with some probability. You can think of the parameters as having $\\alpha$ successes in $\\alpha$ + $\\beta$ trials as our expected success rate. We might observe $x$ (lowercase) from this distribution equal to 0.32. We might observe a number of other $x$ values from this distribution. From those values, we could try to estimate $\\alpha$ and $\\beta$ using a [method of moments](https://en.wikipedia.org/wiki/Method_of_moments_(statistics)), [maximum likelihood approach](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) or (if we are feeling a little fast and loose) [bayesian approach](https://en.wikipedia.org/wiki/Bayes_estimator) as $\\hat{\\alpha}$ and $\\hat{\\beta}$.\n",
"\n",
"If you have questions, please do not hesitate to contact me."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
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"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
K3D-tools/K3D-jupyter | examples/mesh_custom.ipynb | 1 | 2673 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import k3d\n",
"import numpy as np\n",
"\n",
"N = 100\n",
"\n",
"theta = np.linspace(0, 2.0 * np.pi, N)\n",
"phi = np.linspace(0, 2.0 * np.pi, N)\n",
"theta, phi = np.meshgrid(theta, phi)\n",
"\n",
"c, a = 2, 1\n",
"x = (c + a * np.cos(theta)) * np.cos(phi)\n",
"y = (c + a * np.cos(theta)) * np.sin(phi)\n",
"z = a * np.sin(theta)\n",
"\n",
"vertices = np.dstack([x, y, z]).astype(np.float32)\n",
"indices = (np.stack([\n",
" np.arange(N*N - N - 1) + 0, np.arange(N*N - N - 1) + N, np.arange(N*N - N - 1) + N + 1,\n",
" np.arange(N*N - N - 1) + 0, np.arange(N*N - N - 1) + N + 1, np.arange(N*N - N - 1) + 1\n",
"]).T).astype(np.uint32)\n",
"\n",
"plot = k3d.plot()\n",
"\n",
"points = k3d.points(vertices, point_size=0.05, shader='3d', color=0)\n",
"\n",
"\n",
"mesh = k3d.mesh(vertices, indices, flat_shading=False, \n",
" shader='mesh',\n",
" attribute=phi, \n",
" color_map=k3d.matplotlib_color_maps.twilight,\n",
" color=0x00ff00)\n",
"\n",
"wire = k3d.lines(vertices, indices, flat_shading=False, \n",
" shader='mesh', width=0.003,\n",
" indices_type='segment',\n",
" color=0)\n",
"\n",
"\n",
"plot += mesh\n",
"plot += wire\n",
"plot += points\n",
"\n",
"plot.display()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot.camera_reset(0.8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mesh.attribute = []\n",
"plot.colorbar_object_id = -1\n",
"mesh.colors = np.random.randint(0, 0xFFFFFF, N * N).astype(np.uint32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": []
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"version": "3.8.12"
}
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"nbformat": 4,
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| mit |
kubernetes-client/python | examples/notebooks/intro_notebook.ipynb | 1 | 8225 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Managing kubernetes objects using common resource operations with the python client\n",
"-----------------------------------------------------------------------------------------------\n",
"\n",
"Some of these operations include;\n",
"\n",
"- **`create_xxxx`** : create a resource object. Ex **`create_namespaced_pod`** and **`create_namespaced_deployment`**, for creation of pods and deployments respectively. This performs operations similar to **`kubectl create`**.\n",
"\n",
"\n",
"- **`read_xxxx`** : read the specified resource object. Ex **`read_namespaced_pod`** and **`read_namespaced_deployment`**, to read pods and deployments respectively. This performs operations similar to **`kubectl describe`**.\n",
"\n",
"\n",
"- **`list_xxxx`** : retrieve all resource objects of a specific type. Ex **`list_namespaced_pod`** and **`list_namespaced_deployment`**, to list pods and deployments respectively. This performs operations similar to **`kubectl get`**.\n",
"\n",
"\n",
"- **`patch_xxxx`** : apply a change to a specific field. Ex **`patch_namespaced_pod`** and **`patch_namespaced_deployment`**, to update pods and deployments respectively. This performs operations similar to **`kubectl patch`**, **`kubectl label`**, **`kubectl annotate`** etc.\n",
"\n",
"\n",
"- **`replace_xxxx`** : replacing a resource object will update the resource by replacing the existing spec with the provided one. Ex **`replace_namespaced_pod`** and **`replace_namespaced_deployment`**, to update pods and deployments respectively, by creating new replacements of the entire object. This performs operations similar to **`kubectl rolling-update`**, **`kubectl apply`** and **`kubectl replace`**.\n",
"\n",
"\n",
"- **`delete_xxxx`** : delete a resource. This performs operations similar to **`kubectl delete`**.\n",
"\n",
"\n",
"For Further information see the Documentation for API Endpoints section in https://github.com/kubernetes-client/python/blob/master/kubernetes/README.md"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from kubernetes import client, config"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Load config from default location."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"config.load_kube_config()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Create API endpoint instance as well as API resource instances (body and specification)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"api_instance = client.AppsV1Api()\n",
"dep = client.V1Deployment()\n",
"spec = client.V1DeploymentSpec()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Fill required object fields (apiVersion, kind, metadata and spec)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"name = \"my-busybox\"\n",
"dep.metadata = client.V1ObjectMeta(name=name)\n",
"\n",
"spec.template = client.V1PodTemplateSpec()\n",
"spec.template.metadata = client.V1ObjectMeta(name=\"busybox\")\n",
"spec.template.metadata.labels = {\"app\":\"busybox\"}\n",
"spec.template.spec = client.V1PodSpec()\n",
"dep.spec = spec\n",
"\n",
"container = client.V1Container()\n",
"container.image = \"busybox:1.26.1\"\n",
"container.args = [\"sleep\", \"3600\"]\n",
"container.name = name\n",
"spec.template.spec.containers = [container]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Create Deployment using create_xxxx command for Deployments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"api_instance.create_namespaced_deployment(namespace=\"default\",body=dep)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Use list_xxxx command for Deployment, to list Deployments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"deps = api_instance.list_namespaced_deployment(namespace=\"default\")\n",
"for item in deps.items:\n",
" print(\"%s %s\" % (item.metadata.namespace, item.metadata.name))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Use read_xxxx command for Deployment, to display the detailed state of the created Deployment resource."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"api_instance.read_namespaced_deployment(namespace=\"default\",name=name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Use patch_xxxx command for Deployment, to make specific update to the Deployment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"dep.metadata.labels = {\"key\": \"value\"}\n",
"api_instance.patch_namespaced_deployment(name=name, namespace=\"default\", body=dep)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Use replace_xxxx command for Deployment, to update Deployment with a completely new version of the object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"dep.spec.template.spec.containers[0].image = \"busybox:1.26.2\"\n",
"api_instance.replace_namespaced_deployment(name=name, namespace=\"default\", body=dep)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Use delete_xxxx command for Deployment, to delete created Deployment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": [
"api_instance.delete_namespaced_deployment(name=name, namespace=\"default\", body=client.V1DeleteOptions(propagation_policy=\"Foreground\", grace_period_seconds=5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": 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
}
| apache-2.0 |
chongxi/spiketag | notebooks/weighted_feature.ipynb | 1 | 158788 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:50:47.110000",
"start_time": "2016-06-29T12:50:47.042000"
},
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext autoreload"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:50:50.771000",
"start_time": "2016-06-29T12:50:50.767000"
},
"collapsed": true
},
"outputs": [],
"source": [
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:50:54.557000",
"start_time": "2016-06-29T12:50:54.495000"
},
"collapsed": true
},
"outputs": [],
"source": [
"%gui qt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:51:15.099000",
"start_time": "2016-06-29T12:51:13.799000"
},
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from spiketag.base import *\n",
"from spiketag.view import *\n",
"from hdbscan import HDBSCAN"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:51:19.572000",
"start_time": "2016-06-29T12:51:19.400000"
},
"collapsed": true
},
"outputs": [],
"source": [
"import phy\n",
"from phy.gui import GUI"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:51:23.734000",
"start_time": "2016-06-29T12:51:23.551000"
},
"collapsed": true
},
"outputs": [],
"source": [
"filename = 'S:/pcie.bin'\n",
"nCh = 32\n",
"fs = 25000\n",
"numbyte = 4\n",
"time_span = 1 # 1 seconds\n",
"global time_slice\n",
"time_slice = 0\n",
"span = time_span * fs\n",
"highlight = True"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:51:31.857000",
"start_time": "2016-06-29T12:51:28.208000"
},
"collapsed": true
},
"outputs": [],
"source": [
"mua = MUA(filename=filename, nCh=nCh, fs=fs, numbytes=numbyte)\n",
"spk = mua.tospk(ch_span=2)\n",
"fet = spk.tofet('pca')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:51:35.913000",
"start_time": "2016-06-29T12:51:35.712000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2566L, 6L)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fet[26].shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:51:44.797000",
"start_time": "2016-06-29T12:51:44.591000"
},
"collapsed": true
},
"outputs": [],
"source": [
"spk.tofet?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:55:07.428000",
"start_time": "2016-06-29T12:55:07.217000"
},
"collapsed": true
},
"outputs": [],
"source": [
"spk = spk[26]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:55:12.213000",
"start_time": "2016-06-29T12:55:11.976000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2566L, 20L, 5L)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spk.shape"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:59:58.714000",
"start_time": "2016-06-29T12:59:58.496000"
},
"collapsed": true
},
"outputs": [],
"source": [
"X = np.concatenate((spk[:,4:14,:].transpose(2,1,0)),axis=0).T"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:59:59.352000",
"start_time": "2016-06-29T12:59:59.128000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2566L, 50L)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:59:59.727000",
"start_time": "2016-06-29T12:59:59.529000"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:01:54.453000",
"start_time": "2016-06-29T13:01:54.080000"
},
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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AKJcAEDfxvOqtAmYppZ4H+oE7gCbAqbWuj7znaeBKwNAAAHDe/FKON/Xw/d/sTrluhWyn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\nePKGxAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4e836278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(X[2000,:],'.--');"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:03:38.871000",
"start_time": "2016-06-29T13:03:38.409000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x4a71b780>,\n",
" <matplotlib.lines.Line2D at 0x4a71b940>,\n",
" <matplotlib.lines.Line2D at 0x4a71ba58>,\n",
" <matplotlib.lines.Line2D at 0x4a71bb70>,\n",
" <matplotlib.lines.Line2D at 0x4a71bc88>]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NRcgUGjfKpCh++q1l2FoO4nV3oVJpiUq8h5jklWi04/OM63N4uFht5cK1Tiqu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\nFKULUUVfURSlC1FFX1EUpQv5f1mMDsH/0+dQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4ea1a5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(spk[15,4:14,:].squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:23:15.342000",
"start_time": "2016-06-29T13:23:15.004000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x4d9e50b8>]"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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xIp4D9nSe/hL4JnAPcAjYlplXH3N1R7F91wF27J4G3j5F0xspS9K7LWnoJiJOBMjMT3b+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4dc132e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"weight_vector = abs(spk[15,:,2].squeeze())\n",
"plot(weight_vector,'.')"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:24:27.342000",
"start_time": "2016-06-29T13:24:27.156000"
},
"collapsed": false
},
"outputs": [],
"source": [
"weight_vector = weight_vector/numpy.linalg.norm(weight_vector)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:26:32.778000",
"start_time": "2016-06-29T13:26:32.596000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.0849471 , 0.06968062, 0.04670614, 0.00711128, 0.05453867,\n",
" 0.14115332, 0.24235712, 0.33697289, 0.40319541, 0.41603884,\n",
" 0.36650273, 0.2593981 , 0.1175777 , 0.02762537, 0.14709981,\n",
" 0.22136538, 0.2443735 , 0.22796471, 0.18808866, 0.1447459 ], dtype=float32)"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weight_vector"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:26:06.293000",
"start_time": "2016-06-29T13:26:05.884000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x4c0e5668>]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Naz0MXARGnfvce7V9xtPW1ju56MU1WSwxM3o8B4asHChrIiUhgoSIYL/+t3PnWIYAmclR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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4d9ecda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(weight_vector)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:31:06.508000",
"start_time": "2016-06-29T13:31:06.172000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x4d5574a8>]"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qRq4YqqmynpHln5m6O3XmxOdLzjq3f1pGKn9dcReb/thIg/iG/KfX63Q+/+pg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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4cbb9c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(2.5 * weight_vector * spk[40,:,2].squeeze(),'r-o')\n",
"plt.plot(spk[15,:,2].squeeze(),'g-o')"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:37:35.887000",
"start_time": "2016-06-29T13:37:35.682000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5L"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spk.shape[-1]"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:41:15.199000",
"start_time": "2016-06-29T13:41:15.005000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"7/2"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:44:46.634000",
"start_time": "2016-06-29T13:44:46.452000"
},
"collapsed": false
},
"outputs": [],
"source": [
"u = arange(5/2+1)+1"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:55:10.990000",
"start_time": "2016-06-29T13:55:10.817000"
},
"collapsed": false
},
"outputs": [],
"source": [
"def f(x, a=None, p=None):\n",
" n = len(x)\n",
" if a is None:\n",
" a = float(n)/2 - 1\n",
" if p is None:\n",
" p = n/2\n",
" return (a/p) * (p - abs(x % (2*p) - p) ) + 1"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:57:12.641000",
"start_time": "2016-06-29T13:57:12.453000"
},
"collapsed": false
},
"outputs": [],
"source": [
"weight_channel = f(np.arange(5))"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:57:15.413000",
"start_time": "2016-06-29T13:57:15.230000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 1.75, 2.5 , 1.75, 1. ])"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weight_channel"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:57:25.427000",
"start_time": "2016-06-29T13:57:25.245000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20L,)"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weight_vector"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:59:06.983000",
"start_time": "2016-06-29T13:59:06.786000"
},
"collapsed": false
},
"outputs": [],
"source": [
"W = weight_channel * weight_vector.reshape(-1,1)"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T13:59:07.333000",
"start_time": "2016-06-29T13:59:07.143000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20L, 5L)"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"W.shape"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T14:00:44.718000",
"start_time": "2016-06-29T14:00:44.328000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x4e5f8198>]"
]
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Qq2wULa6BUJtL9BbSK390eIFMtsDbbprB6bhwOl1ZfLi+dsb6DIZSnxvTg7G1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\nhGyt3bKJIxUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x49038f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(W.T.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T15:13:57.919000",
"start_time": "2016-06-29T15:13:57.680000"
},
"collapsed": true
},
"outputs": [],
"source": [
" weight_vector = np.array([ 0.0149471 , 0.02968062, 0.03670614, 0.04711128, 0.05453867,\n",
" 0.14115332, 0.24235712, 0.33697289, 0.40319541, 0.41603884,\n",
" 0.36650273, 0.2593981 , 0.1175777 , 0.04762537, 0.18709981,\n",
" 0.22136538, 0.2443735 , 0.22796471, 0.18808866, 0.1447459 ],\n",
" dtype=np.float32)"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T15:13:58.432000",
"start_time": "2016-06-29T15:13:58.025000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x45a9c2e8>]"
]
},
"execution_count": 219,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jt+rMDidokvSjxPXSTgKl+eExMSaalBd7N0tvbLPmZum6WZL+jWw2G7/7saUU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\nIopI0hdCiCgiSV8IIaLI/wcSZ9qpshySCwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4ca82748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(weight_vector)"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T15:13:59.684000",
"start_time": "2016-06-29T15:13:59.488000"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.04762537"
]
},
"execution_count": 220,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weight_vector[13]"
]
},
{
"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
}
| bsd-3-clause |
jasonost/clinicaltrials | nlp/MTIextractPrep.ipynb | 1 | 2532 | {
"metadata": {
"name": "",
"signature": "sha256:f150b3bf6ab58e8c2ff76126d6fe2860e164b58f5a45f7f4dd0858b437dd926a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import codecs, string, random, math, cPickle as pickle, re"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# load trial descriptions\n",
"trial_desc = pickle.load(open('../data/trial_desc.pkl','rb'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"trial_desc.items()[:2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"[(u'NCT01025063',\n",
" (u'This study will investigate the safety and efficacy of treatment of choroidal neovascularization (CNV) due to age-related macular degeneration (AMD) with a combination of ranibizumab (Lucentis) and verteporfin PDT (Visudyne), as compared with ranibizumab monotherapy.',\n",
" '')),\n",
" (u'NCT00499928',\n",
" (u'This study will examine the biological fate of radioactive SB-751689 administered to healthy males and healthy postmenopausal women. Subjects will receive a single oral dose of radioactive SB-751689. Excreta and blood samples will be taken over the course of 7 days. This study will help determine the major route of elimination of SB-751689 in humans. It will also provide samples (blood, plasma, urine, and stools) for analysis of metabolites, if any.',\n",
" ''))]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = codecs.open('../data/MTIdescriptions.txt','w', encoding=\"ascii\", errors=\"replace\")\n",
"for nctid in trial_desc.keys():\n",
" f.write('%s|%s\\n' % (nctid,' '.join(trial_desc[nctid])))\n",
"f.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
Ryan-J-Smith/income-explorer | notebooks/MainFile-Tracts.ipynb | 1 | 15397 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Main File - Tract Level\n",
"\n",
"This file is used for getting and acquiring census data at the tract level."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os.path\n",
"from census import Census\n",
"from us import states\n",
"import requests\n",
"import geopandas as gpd\n",
"import pandas as pd\n",
"import zipfile\n",
"\n",
"# Specify state and county to download (select one)\n",
"loc_name, state_codes, county_codes = \"maryland\", states.MD.fips, None\n",
"loc_name, state_codes, county_codes = \"delmarva\", [states.MD.fips, states.DE.fips, states.VA.fips], None\n",
"\n",
"if county_codes is not None:\n",
" county_list = [\"{:03d}\".format(county_id) for county_id in county_codes]\n",
"else:\n",
" county_list = None\n",
"\n",
"# CENSUS API Stuff\n",
"CENSUS_API = #YourAPIKeyHere\n",
"c = Census(CENSUS_API) # Initialize census class with API key\n",
"\n",
"# Generate codes for census variables of interest\n",
"var_ids = [\"B19001_0{:02d}E\".format(x) for x in range(2, 18)] # Household income over 12 months\n",
"\n",
"# TIGER Stuff\n",
"TIGER_BASE_URL = 'http://www2.census.gov/geo/tiger/TIGER2013/'\n",
"TIGER_TRACT_DIR = 'TRACT/'\n",
"TIGER_BLOCKGROUP_DIR = 'BG/'\n",
"\n",
"TIGER_WATER_DIR = 'AREAWATER/'\n",
"\n",
"tiger_zip_file = 'tl_2013_{0}_tract.zip'.format(state_code)\n",
"tiger_shape_file = 'tl_2013_{0}_tract.shp'.format(state_code)\n",
"\n",
"FULL_TIGER_URL = TIGER_BASE_URL + TIGER_TRACT_DIR + tiger_zip_file\n",
"\n",
"# Local Storage Parameters\n",
"LOCAL_DATA_DIR = './data/'\n",
"GEO_SUB_DIR = 'geo/'\n",
"\n",
"ATTR_FILE_END = '_census_data.csv'\n",
"attr_outfile = LOCAL_DATA_DIR + loc_name + ATTR_FILE_END\n",
"\n",
"GEO_FILE_END = '_geo_data.json'\n",
"geo_outfile = LOCAL_DATA_DIR + loc_name + GEO_FILE_END\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"census_data = c.acs.get(var_ids, {'for': 'tract:*', 'in': 'state:{0}'.format(state_code)})\n",
"census_df = pd.DataFrame(census_data)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>B19001_002E</th>\n",
" <th>B19001_003E</th>\n",
" <th>B19001_004E</th>\n",
" <th>B19001_005E</th>\n",
" <th>B19001_006E</th>\n",
" <th>B19001_007E</th>\n",
" <th>B19001_008E</th>\n",
" <th>B19001_009E</th>\n",
" <th>B19001_010E</th>\n",
" <th>B19001_011E</th>\n",
" <th>B19001_012E</th>\n",
" <th>B19001_013E</th>\n",
" <th>B19001_014E</th>\n",
" <th>B19001_015E</th>\n",
" <th>B19001_016E</th>\n",
" <th>B19001_017E</th>\n",
" <th>county</th>\n",
" <th>state</th>\n",
" <th>tract</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>101</td>\n",
" <td>97</td>\n",
" <td>81</td>\n",
" <td>105</td>\n",
" <td>76</td>\n",
" <td>126</td>\n",
" <td>67</td>\n",
" <td>51</td>\n",
" <td>80</td>\n",
" <td>202</td>\n",
" <td>136</td>\n",
" <td>159</td>\n",
" <td>113</td>\n",
" <td>18</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>001</td>\n",
" <td>24</td>\n",
" <td>000100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>94</td>\n",
" <td>80</td>\n",
" <td>127</td>\n",
" <td>36</td>\n",
" <td>60</td>\n",
" <td>58</td>\n",
" <td>37</td>\n",
" <td>73</td>\n",
" <td>44</td>\n",
" <td>122</td>\n",
" <td>95</td>\n",
" <td>138</td>\n",
" <td>119</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>17</td>\n",
" <td>001</td>\n",
" <td>24</td>\n",
" <td>000200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>86</td>\n",
" <td>167</td>\n",
" <td>37</td>\n",
" <td>103</td>\n",
" <td>51</td>\n",
" <td>213</td>\n",
" <td>72</td>\n",
" <td>38</td>\n",
" <td>12</td>\n",
" <td>76</td>\n",
" <td>109</td>\n",
" <td>118</td>\n",
" <td>39</td>\n",
" <td>22</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>001</td>\n",
" <td>24</td>\n",
" <td>000300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>87</td>\n",
" <td>77</td>\n",
" <td>95</td>\n",
" <td>53</td>\n",
" <td>108</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>55</td>\n",
" <td>87</td>\n",
" <td>90</td>\n",
" <td>80</td>\n",
" <td>95</td>\n",
" <td>157</td>\n",
" <td>64</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>001</td>\n",
" <td>24</td>\n",
" <td>000400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>298</td>\n",
" <td>157</td>\n",
" <td>75</td>\n",
" <td>41</td>\n",
" <td>63</td>\n",
" <td>48</td>\n",
" <td>42</td>\n",
" <td>6</td>\n",
" <td>43</td>\n",
" <td>93</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>001</td>\n",
" <td>24</td>\n",
" <td>000500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" B19001_002E B19001_003E B19001_004E B19001_005E B19001_006E B19001_007E \\\n",
"0 101 97 81 105 76 126 \n",
"1 94 80 127 36 60 58 \n",
"2 86 167 37 103 51 213 \n",
"3 87 77 95 53 108 89 \n",
"4 298 157 75 41 63 48 \n",
"\n",
" B19001_008E B19001_009E B19001_010E B19001_011E B19001_012E B19001_013E \\\n",
"0 67 51 80 202 136 159 \n",
"1 37 73 44 122 95 138 \n",
"2 72 38 12 76 109 118 \n",
"3 89 55 87 90 80 95 \n",
"4 42 6 43 93 40 35 \n",
"\n",
" B19001_014E B19001_015E B19001_016E B19001_017E county state tract \n",
"0 113 18 8 0 001 24 000100 \n",
"1 119 32 40 17 001 24 000200 \n",
"2 39 22 10 7 001 24 000300 \n",
"3 157 64 14 18 001 24 000400 \n",
"4 37 36 8 0 001 24 000500 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get census (attribute) data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def build_bg_fips(record):\n",
" fips_code = record['state'] + record['county'] + record['tract'] + record['block group']\n",
" return str(fips_code)\n",
"\n",
"def build_tract_fips(record):\n",
" fips_code = record['state'] + record['county'] + record['tract']\n",
" return str(fips_code)\n",
"\n",
"\n",
"def census_bg_to_dataframe(var_list, state_code, county_codes):\n",
" fips_codes = []\n",
" all_records = []\n",
" \n",
" for county in county_codes: \n",
" census_data = c.acs.get(var_list, {'for': 'tract:*', 'in': 'state:{0}'.format(state_code)})\n",
" \n",
" for idx, record in enumerate(census_data):\n",
" # Build fips codes\n",
" fips_code = build_bg_fips(record)\n",
" census_data[idx][\"fips\"] = fips_code\n",
"\n",
" # Eliminate original code components\n",
" key_list = ['state', 'county', 'tract', 'block group']\n",
" for key in key_list:\n",
" if key in census_data[idx]: \n",
" del census_data[idx][key]\n",
" \n",
" all_records.extend(census_data)\n",
" \n",
" census_df = pd.DataFrame(all_records)\n",
" census_df = census_df.set_index(\"fips\")\n",
" \n",
" return census_df\n",
"\n",
"def census_tracts_to_dataframe(var_list, state_codes):\n",
" fips_codes = []\n",
" all_records = []\n",
" \n",
" for state_id in state_codes:\n",
" census_data = c.acs.get(var_list, {'for': 'tract:*', 'in': 'state:{0}'.format(state_id)})\n",
"\n",
" for idx, record in enumerate(census_data):\n",
"\n",
" # Build fips codes\n",
" fips_code = build_tract_fips(record)\n",
" census_data[idx][\"fips\"] = fips_code\n",
"\n",
" # Eliminate original code components\n",
" key_list = ['state', 'county', 'tract']\n",
" for key in key_list:\n",
" if key in census_data[idx]: \n",
" del census_data[idx][key]\n",
" \n",
" all_records.extend(census_data)\n",
" \n",
" census_df = pd.DataFrame(all_records)\n",
" census_df = census_df.set_index(\"fips\")\n",
" \n",
" return census_df\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This segment of code will get household income estimates for each block group in Baltimore city\n",
"census_df = census_tracts_to_dataframe(var_ids, state_codes)\n",
"census_df.to_csv(attr_outfile)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get TIGER (shape) data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Already had the file. Great.\n",
"Got the file! Copying to disk.\n",
"Got the file! Copying to disk.\n"
]
}
],
"source": [
"for state_id in state_codes:\n",
" tiger_zip_file = 'tl_2013_{0}_tract.zip'.format(state_id)\n",
"\n",
" FULL_TIGER_URL = TIGER_BASE_URL + TIGER_TRACT_DIR + tiger_zip_file\n",
"\n",
" # Check if file is in directory, else download it\n",
" if os.path.isfile(LOCAL_DATA_DIR + GEO_SUB_DIR + tiger_zip_file):\n",
" print(\"Already had the file. Great.\")\n",
" else:\n",
" r = requests.get(FULL_TIGER_URL)\n",
"\n",
" if r.status_code == requests.codes.ok:\n",
" print(\"Got the file! Copying to disk.\")\n",
" with open(LOCAL_DATA_DIR + GEO_SUB_DIR + tiger_zip_file, \"wb\") as f:\n",
" f.write(r.content)\n",
" else:\n",
" print(\"Something went wrong. Status code: \".format(r.status_code))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trim shape data to match attributes"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"state_shapes = []\n",
"for idx, state_id in enumerate(state_codes):\n",
" tiger_zip_file = 'tl_2013_{0}_tract.zip'.format(state_id)\n",
" tiger_shape_file = 'tl_2013_{0}_tract.shp'.format(state_id)\n",
"\n",
" # Unzip file, extract contents\n",
" zfile = zipfile.ZipFile(LOCAL_DATA_DIR + GEO_SUB_DIR + tiger_zip_file)\n",
" zfile.extractall(LOCAL_DATA_DIR + GEO_SUB_DIR)\n",
"\n",
" # Load to GeoDataFrame\n",
" state_shape = gpd.GeoDataFrame.from_file(LOCAL_DATA_DIR + GEO_SUB_DIR + tiger_shape_file)\n",
" \n",
" state_shapes.append(state_shape)\n",
" \n",
" # Only keep counties that we are interested in\n",
" if county_list is not None:\n",
" shapes = shapes[shapes[\"COUNTYFP\"].isin(county_list)]\n",
"\n",
"shapes = gpd.GeoDataFrame( pd.concat(state_shapes, ignore_index=True) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Eliminate unneeded attributes, export shapes to geojson"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"small_shapes = gpd.GeoDataFrame()\n",
"small_shapes[\"geometry\"] = shapes[\"geometry\"].simplify(tolerance=0.001) # Simplify geometry to reduce file size\n",
"small_shapes[\"fips\"] = shapes[\"GEOID\"]\n",
"small_shapes = small_shapes.set_index(\"fips\")\n",
"small_json = small_shapes.to_json()\n",
"\n",
"# Write to file\n",
"with open(geo_outfile, 'w') as f:\n",
" f.write(small_json)"
]
}
],
"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 |
tzjin/tf_chatbot | reddit_proc/DataProc.ipynb | 1 | 17304 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sqlite3 as sql\n",
"import pandas as pd\n",
"import codecs, re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"db = sql.connect('database.sqlite')\n",
"data = pd.read_sql('SELECT parent_id, body, id FROM May2015\\\n",
" WHERE LENGTH(body) > 10 AND LENGTH(body) < 150', db, chunksize=100000)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"REMOVE = re.compile(r'[\\*\\r\\n]*')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chunks processed:\t1 Files written:\t406\n",
"Chunks processed:\t2 Files written:\t1442\n",
"Chunks processed:\t3 Files written:\t2812\n",
"Chunks processed:\t4 Files written:\t4060\n",
"Chunks processed:\t5 Files written:\t5757\n",
"Chunks processed:\t6 Files written:\t7416\n",
"Chunks processed:\t7 Files written:\t9229\n",
"Chunks processed:\t8 Files written:\t11544\n",
"Chunks processed:\t9 Files written:\t14197\n",
"Chunks processed:\t10 Files written:\t17187\n",
"Chunks processed:\t11 Files written:\t20368\n",
"Chunks processed:\t12 Files written:\t22808\n",
"Chunks processed:\t13 Files written:\t25150\n",
"Chunks processed:\t14 Files written:\t27655\n",
"Chunks processed:\t15 Files written:\t29726\n",
"Chunks processed:\t16 Files written:\t32203\n",
"Chunks processed:\t17 Files written:\t34599\n",
"Chunks processed:\t18 Files written:\t37177\n",
"Chunks processed:\t19 Files written:\t40021\n",
"Chunks processed:\t20 Files written:\t43468\n",
"Chunks processed:\t21 Files written:\t46783\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-34-5065093f1072>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;31m# for each comment in the chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomment\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\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---> 34\u001b[0;31m \u001b[0mparent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetParent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mvalidComment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-34-5065093f1072>\u001b[0m in \u001b[0;36mgetParent\u001b[0;34m(parent_id)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetParent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_id\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[0mlvl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparent_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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----> 3\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT parent_id, body, id FROM May2015 WHERE id='\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpid\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"'\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvalidComment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomment\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_dates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 499\u001b[0;31m chunksize=chunksize)\n\u001b[0m\u001b[1;32m 500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m 1607\u001b[0m frame = _wrap_result(data, columns, index_col=index_col,\n\u001b[1;32m 1608\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1609\u001b[0;31m parse_dates=parse_dates)\n\u001b[0m\u001b[1;32m 1610\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36m_wrap_result\u001b[0;34m(data, columns, index_col, coerce_float, parse_dates)\u001b[0m\n\u001b[1;32m 137\u001b[0m coerce_float=coerce_float)\n\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m \u001b[0m_parse_date_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36m_parse_date_columns\u001b[0;34m(data_frame, parse_dates)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;31m# we could in theory do a 'nice' conversion from a FixedOffset tz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;31m# GH11216\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_col\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\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 126\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mdata_frame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_handle_date_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36miteritems\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 604\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_item_cache'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 605\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 607\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1346\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1347\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_as_cached\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[0;31m# for a chain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_set_as_cached\u001b[0;34m(self, item, cacher)\u001b[0m\n\u001b[1;32m 1356\u001b[0m \u001b[0mcacher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1357\u001b[0m \"\"\"\n\u001b[0;32m-> 1358\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cacher\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcacher\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 1359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1360\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_reset_cacher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m 2679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2680\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2681\u001b[0;31m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2682\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2683\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"def getParent(parent_id):\n",
" return pd.read_sql(\"SELECT parent_id, body, id FROM May2015\\\n",
" WHERE id='\" + comment.parent_id[3:] + \"'\", db)\n",
" \n",
"def validComment(comment):\n",
" \n",
" # ignore deleted comments\n",
" if '[deleted]' in comment:\n",
" return False\n",
" \n",
" # allow only ascii characters\n",
" try:\n",
" comment.decode('ascii')\n",
" except:\n",
" return False\n",
" \n",
" # no emoticons allowed!\n",
"# if re.match('\\[\\]\\(/[a-z]+\\)', emoticon) is None:\n",
" if '[]' in comment[:2]:\n",
" return False\n",
" \n",
" return True\n",
" \n",
" \n",
"minconvlen = 8\n",
"numchunks = 0\n",
"filenum = 0\n",
"\n",
"\n",
"with open('conv/redditComments.txt', 'w+') as f:\n",
" # for each chunk of data\n",
" for chunk in data:\n",
"\n",
" # for each comment in the chunk\n",
" for idx, comment in chunk.iterrows():\n",
" parent = getParent(comment.parent_id)\n",
"\n",
" if not validComment(comment.body):\n",
" continue\n",
"\n",
" # trace parents\n",
" conv = [comment.body]\n",
" while not parent.empty:\n",
"\n",
" if not validComment(parent.body[0]):\n",
" break\n",
"\n",
" conv.append(parent.body[0])\n",
" parent = getParent(parent.parent_id[0])\n",
"\n",
" # write long conversations to file\n",
" if len(conv) > minconvlen:\n",
" for comm in reversed(conv):\n",
" f.write(re.sub(REMOVE, ' ', comm))\n",
" f.write('\\n')\n",
"\n",
" filenum += 1\n",
"\n",
" numchunks +=1\n",
" print \"Chunks processed:\\t\" + str(numchunks), \"Files written:\\t\" + str(filenum)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello, ' \n",
" World.\n",
"hello World \n"
]
}
],
"source": [
"thing = \"hello, \\' \\n World.\"\n",
"thing2 = re.sub(r'[^A-Za-z0-9]+[\\r\\n]*', ' ', thing)\n",
"print thing\n",
"print thing2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_sql('SELECT id, parent_id FROM May2015\\\n",
" WHERE LENGTH(body) > 10 AND LENGTH(body) < 150', db)"
]
},
{
"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
}
| mit |
kyleam/seaborn | doc/tutorial/regression.ipynb | 2 | 18992 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _regression_tutorial:\n",
"\n",
".. currentmodule:: seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing linear relationships"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Many datasets contain multiple quantitative variables, and the goal of an analysis is often to relate those variables to each other. We :ref:`previously discussed <distribution_tutorial>` functions that can accomplish this by showing the joint distribution of two variables. It can be very helpful, though, to use statistical models to estimate a simple relationship between two noisy sets of observations. The functions discussed in this chapter will do so through the common framework of linear regression.\n",
"\n",
"In the spirit of Tukey, the regression plots in seaborn are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analyses. That is to say that seaborn is not itself a package for statistical analysis. To obtain quantitative measures related to the fit of regression models, you should use `statsmodels <http://statsmodels.sourceforge.net/>`_. The goal of seaborn, however, is to make exploring a dataset through visualization quick and easy, as doing so is just as (if not more) important than exploring a dataset through tables of statistics."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set(color_codes=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(sum(map(ord, \"regression\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tips = sns.load_dataset(\"tips\")"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Functions to draw linear regression models\n",
"------------------------------------------\n",
"\n",
"Two main functions in seaborn are used to visualize a linear relationship as determined through regression. These functions, :func:`regplot` and :func:`lmplot` are closely related, and share much of their core functionality. It is important to understand the ways they differ, however, so that you can quickly choose the correct tool for particular job.\n",
"\n",
"In the simplest invocation, both functions draw a scatterplot of two variables, ``x`` and ``y``, and then fit the regression model ``y ~ x`` and plot the resulting regression line and a 95% confidence interval for that regression:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.regplot(x=\"total_bill\", y=\"tip\", data=tips);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"You should note that the resulting plots are identical, except that the figure shapes are different. We will explain why this is shortly. For now, the other main difference to know about is that :func:`regplot` accepts the ``x`` and ``y`` variables in a variety of formats including simple numpy arrays, pandas ``Series`` objects, or as references to variables in a pandas ``DataFrame`` object passed to ``data``. In contrast, :func:`lmplot` has ``data`` as a required parameter and the ``x`` and ``y`` variables must be specified as strings. This data format is called \"long-form\" or `\"tidy\" <http://vita.had.co.nz/papers/tidy-data.pdf>`_ data. Other than this input flexibility, :func:`regplot` possesses a subset of :func:`lmplot`'s features, so we will demonstrate them using the latter.\n",
"\n",
"It's possible to fit a linear regression when one of the variables takes discrete values, however, the simple scatterplot produced by this kind of dataset is often not optimal:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"size\", y=\"tip\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"One option is to add some random noise (\"jitter\") to the discrete values to make the distribution of those values more clear. Note that jitter is applied only to the scatterplot data and does not influence the regression line fit itself:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"size\", y=\"tip\", data=tips, x_jitter=.05);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"A second option is to collapse over the observations in each discrete bin to plot an estimate of central tendency along with a confidence interval:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"size\", y=\"tip\", data=tips, x_estimator=np.mean);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Fitting different kinds of models\n",
"---------------------------------\n",
"\n",
"The simple linear regression model used above is very simple to fit, however, it is not appropriate for some kinds of datasets. The `Anscombe's quartet <https://en.wikipedia.org/wiki/Anscombe%27s_quartet>`_ dataset shows a few examples where simple linear regression provides an identical estimate of a relationship where simple visual inspection clearly shows differences. For example, in the first case, the linear regression is a good model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"anscombe = sns.load_dataset(\"anscombe\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'I'\"),\n",
" ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The linear relationship in the second dataset is the same, but the plot clearly shows that this is not a good model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'II'\"),\n",
" ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"In the presence of these kind of higher-order relationships, :func:`lmplot` and :func:`regplot` can fit a polynomial regression model to explore simple kinds of nonlinear trends in the dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'II'\"),\n",
" order=2, ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"A different problem is posed by \"outlier\" observations that deviate for some reason other than the main relationship under study:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'III'\"),\n",
" ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In the presence of outliers, it can be useful to fit a robust regression, which uses a different loss function to downweight relatively large residuals:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'III'\"),\n",
" robust=True, ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"When the ``y`` variable is binary, simple linear regression also \"works\" but provides implausible predictions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tips[\"big_tip\"] = (tips.tip / tips.total_bill) > .15\n",
"sns.lmplot(x=\"total_bill\", y=\"big_tip\", data=tips,\n",
" y_jitter=.03);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The solution in this case is to fit a logistic regression, such that the regression line shows the estimated probability of ``y = 1`` for a given value of ``x``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"big_tip\", data=tips,\n",
" logistic=True, y_jitter=.03);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Note that the logistic regression estimate is considerably more computationally intensive (this is true of robust regression as well) than simple regression, and as the confidence interval around the regression line is computed using a bootstrap procedure, you may wish to turn this off for faster iteration (using ``ci=False``).\n",
"\n",
"An altogether different approach is to fit a nonparametric regression using a `lowess smoother <https://en.wikipedia.org/wiki/Local_regression>`_. This approach has the fewest assumptions, although it is computationally intensive and so currently confidence intervals are not computed at all:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", data=tips,\n",
" lowess=True);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The :func:`residplot` function can be a useful tool for checking whether the simple regression model is appropriate for a dataset. It fits and removes a simple linear regression and then plots the residual values for each observation. Ideally, these values should be randomly scattered around ``y = 0``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.residplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'I'\"),\n",
" scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"If there is structure in the residuals, it suggests that simple linear regression is not appropriate:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.residplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'II'\"),\n",
" scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Conditioning on other variables\n",
"-------------------------------\n",
"\n",
"The plots above show many ways to explore the relationship between a pair of variables. Often, however, a more interesting question is \"how does the relationship between these two variables change as a function of a third variable?\" This is where the difference between :func:`regplot` and :func:`lmplot` appears. While :func:`regplot` always shows a single relationsihp, :func:`lmplot` combines :func:`regplot` with :class:`FacetGrid` to provide an easy interface to show a linear regression on \"faceted\" plots that allow you to explore interactions with up to three additional categorical variables.\n",
"\n",
"The best way to separate out a relationship is to plot both levels on the same axes and to use color to distinguish them:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In addition to color, it's possible to use different scatterplot markers to make plots the reproduce to black and white better. You also have full control over the colors used:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=tips,\n",
" markers=[\"o\", \"x\"], palette=\"Set1\");"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"To add another variable, you can draw multiple \"facets\" which each level of the variable appearing in the rows or columns of the grid:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", col=\"time\", data=tips);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\",\n",
" col=\"time\", row=\"sex\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Controlling the size and shape of the plot\n",
"------------------------------------------\n",
"\n",
"Before we noted that the default plots made by :func:`regplot` and :func:`lmplot` look the same but on axes that have a different size and shape. This is because func:`regplot` is an \"axes-level\" function draws onto a specific axes. This means that you can make mutli-panel figures yourself and control exactly where the the regression plot goes. If no axes is provided, it simply uses the \"currently active\" axes, which is why the default plot has the same size and shape as most other matplotlib functions. To control the size, you need to create a figure object yourself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f, ax = plt.subplots(figsize=(5, 6))\n",
"sns.regplot(x=\"total_bill\", y=\"tip\", data=tips, ax=ax);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In contrast, the size and shape of the :func:`lmplot` figure is controlled through the :class:`FacetGrid` interface using the ``size`` and ``aspect`` parameters, which apply to each *facet* in the plot, not to the overall figure itself:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", col=\"day\", data=tips,\n",
" col_wrap=2, size=3);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", col=\"day\", data=tips,\n",
" aspect=.5);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Plotting a regression in other contexts\n",
"---------------------------------------\n",
"\n",
"A few other seaborn functions use :func:`regplot` in the context of a larger, more complex plot. The first is the :func:`jointplot` function that we introduced in the :ref:`distributions tutorial <distribution_tutorial>`. In addition to the plot styles previously discussed, :func:`jointplot` can use :func:`regplot` to show the linear regression fit on the joint axes by passing ``kind=\"reg\"``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.jointplot(x=\"total_bill\", y=\"tip\", data=tips, kind=\"reg\");"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Using the :func:`pairplot` function with ``kind=\"reg\"`` combines :func:`regplot` and :class:`PairGrid` to show the linear relationship between variables in a dataset. Take care to note how this is different from :func:`lmplot`. In the figure below, the two axes don't show the same relationship conditioned on two levels of a third variable; rather, :func:`PairGrid` is used to show multiple relationships between different pairings of the variables in a dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.pairplot(tips, x_vars=[\"total_bill\", \"size\"], y_vars=[\"tip\"],\n",
" size=5, aspect=.8, kind=\"reg\");"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Like :func:`lmplot`, but unlike :func:`jointplot`, conditioning on an additional categorical variable is built into :func:`pairplot` using the ``hue`` parameter:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.pairplot(tips, x_vars=[\"total_bill\", \"size\"], y_vars=[\"tip\"],\n",
" hue=\"smoker\", size=5, aspect=.8, kind=\"reg\");"
]
}
],
"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-3-clause |
tpin3694/tpin3694.github.io | statistics/t-tests.ipynb | 1 | 4712 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: T-Tests \n",
"Slug: t-tests \n",
"Summary: T-tests in Python. \n",
"Date: 2016-02-08 12:00 \n",
"Category: Statistics \n",
"Tags: Basics\n",
"Authors: Chris Albon "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Preliminaries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy import stats\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a list of 20 observations drawn from a random distribution \n",
"# with mean 1 and a standard deviation of 1.5\n",
"x = np.random.normal(1, 1.5, 20)\n",
"\n",
"# Create a list of 20 observations drawn from a random distribution \n",
"# with mean 0 and a standard deviation of 1.5\n",
"y = np.random.normal(0, 1.5, 20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## One Sample Two-Sided T-Test\n",
"\n",
"Imagine the one sample T-test and drawing a (normally shaped) hill centered at `1` and \"spread\" out with a standard deviation of `1.5`, then placing a flag at `0` and looking at where on the hill the flag is location. Is it near the top? Far away from the hill? If the flag is near the very bottom of the hill or farther, then the t-test p-score will be below `0.05`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.00010976647757800537"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run a t-test to test if the mean of x is statistically significantly different than 0\n",
"pvalue = stats.ttest_1samp(x, 0)[1]\n",
"\n",
"# View the p-value\n",
"pvalue"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two Variable Unpaired Two-Sided T-Test With Equal Variances\n",
"\n",
"Imagine the one sample T-test and drawing two (normally shaped) hills centered at their means and their 'flattness' (individual spread) based on the standard deviation. The T-test looks at how much the two hills are overlapping. Are they basically on top of each other? Do just the bottoms of the hill just barely touch? If the tails of the hills are just barely overlapping or are not overlapping at all, the t-test p-score will be below 0.05."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.00035082056802728071"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.ttest_ind(x, y)[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two Variable Unpaired Two-Sided T-Test With Unequal Variances"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.00035089238660076095"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.ttest_ind(x, y, equal_var=False)[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two Variable Paired Two-Sided T-Test\n",
"\n",
"Paired T-tests are used when we are taking repeated samples and want to take into account the fact that the two distributions we are testing are paired."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.00034222792790150386"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.ttest_rel(x, y)[1]"
]
}
],
"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 |
QuantumTechDevStudio/RUDNEVGAUSS | invariance_testing/testing.ipynb | 2 | 2013 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Anaconda\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"# Необходмые команды импорта.\n",
"import sys\n",
"#import os\n",
"sys.path.append('../physlearn/')\n",
"sys.path.append('../source')\n",
"import numpy as np\n",
"from numpy import linalg as LA\n",
"import tensorflow as tf\n",
"from matplotlib import pylab as plt\n",
"from IPython.display import clear_output\n",
"from physlearn.NeuralNet.NeuralNet import NeuralNet\n",
"from physlearn.Optimizer.NelderMead.NelderMead import NelderMead\n",
"from CostFunction import CostFunction\n",
"import d1_osc\n",
"import ann_constructor\n",
"import math_util\n",
"from visualiser import Visualiser"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8,) ; 8\n",
"[1 2 3 4 5 6]\n"
]
}
],
"source": [
"a = np.array([1,2,3,4,5,6,7,8])\n",
"print(a.shape, ' ; ', a[a.size-1])\n",
"print(a[:a.size-2])"
]
}
],
"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 |
zimmermant/vpython_optics_bench | optics_bench_test.ipynb | 1 | 32579 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from vpython import *\n",
"\n",
"#This iteration projects rays from a sphere projecting to the surface of a larger spheroid.\n",
"\n",
"spikeball = sphere(pos = vector(0, 0, 0), radius = 0.01, color = color.white)\n",
"r = curve(pos = [(0,0,0), (0,0,0)])\n",
"\n",
"r.aa = curve(pos = [(0,0,0),(1, 0, 0)], color = color.yellow) #Set of rays along each axis (+/- x, +/- y, +/- z)\n",
"r.ab = curve(pos = [(0,0,0),(-1, 0, 0)], color = color.yellow)\n",
"r.ac = curve(pos = [(0,0,0),(0, 1, 0)], color = color.yellow)\n",
"r.ad = curve(pos = [(0,0,0),(0, -1, 0)], color = color.yellow)\n",
"r.ae = curve(pos = [(0,0,0),(0, 0, 1)], color = color.yellow)\n",
"r.af = curve(pos = [(0,0,0),(0, 0, -1)], color = color.yellow)\n",
"\n",
"r.ba = curve(pos = [(0,0,0),(100/141.421, 100/141.421, 0)], color = color.yellow) #Set of rays in 2D planes of two coordinate axes\n",
"r.bb = curve(pos = [(0,0,0),(-100/141.421, 100/141.421, 0)], color = color.yellow)\n",
"r.bc = curve(pos = [(0,0,0),(100/141.421, -100/141.421, 0)], color = color.yellow)\n",
"r.bd = curve(pos = [(0,0,0),(-100/141.421, -100/141.421, 0)], color = color.yellow)\n",
"r.be = curve(pos = [(0,0,0),(100/141.421, 0, 100/141.421)], color = color.yellow)\n",
"r.bf = curve(pos = [(0,0,0),(-100/141.421, 0, 100/141.421)], color = color.yellow)\n",
"r.bg = curve(pos = [(0,0,0),(100/141.421, 0, -100/141.421)], color = color.yellow)\n",
"r.bh = curve(pos = [(0,0,0),(-100/141.421, 0, -100/141.421)], color = color.yellow)\n",
"r.bi = curve(pos = [(0,0,0),(0, 100/141.421, 100/141.421)], color = color.yellow)\n",
"r.bj = curve(pos = [(0,0,0),(0, -100/141.421, 100/141.421)], color = color.yellow)\n",
"r.bk = curve(pos = [(0,0,0),(0, 100/141.421, -100/141.421)], color = color.yellow)\n",
"r.bl = curve(pos = [(0,0,0),(0, -100/141.421, -100/141.421)], color = color.yellow)\n",
"\n",
"r.ca = curve(pos = [(0,0,0), (100/173.205081,100/173.205081,100/173.205081)], color = color.yellow) #Set of rays along no explicit axis\n",
"r.cb = curve(pos = [(0,0,0), (-100/173.205081,100/173.205081,100/173.205081)], color = color.yellow)\n",
"r.cc = curve(pos = [(0,0,0), (100/173.205081,-100/173.205081,100/173.205081)], color = color.yellow)\n",
"r.cd = curve(pos = [(0,0,0), (100/173.205081,100/173.205081,-100/173.205081)], color = color.yellow)\n",
"r.ce = curve(pos = [(0,0,0), (-100/173.205081,-100/173.205081,100/173.205081)], color = color.yellow)\n",
"r.cf = curve(pos = [(0,0,0), (-100/173.205081,-100/173.205081,-100/173.205081)], color = color.yellow)\n",
"r.cg = curve(pos = [(0,0,0), (100/173.205081,-100/173.205081,-100/173.205081)], color = color.yellow)\n",
"r.ch = curve(pos = [(0,0,0), (-100/173.205081,100/173.205081,-100/173.205081)], color = color.yellow)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from vpython import *\n",
"\n",
"spikecube = sphere(pos = vector(0, 0, 0), radius = 0.01, color = color.white)\n",
"r = curve(pos = [(0,0,0), (0,0,0)])\n",
"\n",
"r.aa = curve(pos = [(0,0,0),(1, 0, 0)], color = color.yellow) #Set of rays along each axis (+/- x, +/- y, +/- z)\n",
"r.ab = curve(pos = [(0,0,0),(-1, 0, 0)], color = color.yellow)\n",
"r.ac = curve(pos = [(0,0,0),(0, 1, 0)], color = color.yellow)\n",
"r.ad = curve(pos = [(0,0,0),(0, -1, 0)], color = color.yellow)\n",
"r.ae = curve(pos = [(0,0,0),(0, 0, 1)], color = color.yellow)\n",
"r.af = curve(pos = [(0,0,0),(0, 0, -1)], color = color.yellow)\n",
"\n",
"r.ba = curve(pos = [(0,0,0),(1, 1, 0)], color = color.yellow) #Set of rays in 2D planes of two coordinate axes\n",
"r.bb = curve(pos = [(0,0,0),(-1, 1, 0)], color = color.yellow)\n",
"r.bc = curve(pos = [(0,0,0),(1, -1, 0)], color = color.yellow)\n",
"r.bd = curve(pos = [(0,0,0),(-1, 0, -1)], color = color.yellow)\n",
"r.be = curve(pos = [(0,0,0),(1, 0, 1)], color = color.yellow)\n",
"r.bf = curve(pos = [(0,0,0),(-1, 0, 1)], color = color.yellow)\n",
"r.bg = curve(pos = [(0,0,0),(1, 0, -1)], color = color.yellow)\n",
"r.bh = curve(pos = [(0,0,0),(-1, 0, -1)], color = color.yellow)\n",
"r.bi = curve(pos = [(0,0,0),(0, 1, 1)], color = color.yellow)\n",
"r.bj = curve(pos = [(0,0,0),(0, -1, 1)], color = color.yellow)\n",
"r.bk = curve(pos = [(0,0,0),(0, 1, -1)], color = color.yellow)\n",
"r.bl = curve(pos = [(0,0,0),(0, -1, -1)], color = color.yellow)\n",
"\n",
"r.ca = curve(pos = [(0,0,0), (1,1,1)], color = color.yellow) #Set of rays along no explicit axis\n",
"r.cb = curve(pos = [(0,0,0), (-1,1,1)], color = color.yellow)\n",
"r.cc = curve(pos = [(0,0,0), (1,-1,1)], color = color.yellow)\n",
"r.cd = curve(pos = [(0,0,0), (1,1,-1)], color = color.yellow)\n",
"r.ce = curve(pos = [(0,0,0), (-1,-1,1)], color = color.yellow)\n",
"r.cf = curve(pos = [(0,0,0), (-1,-1,-1)], color = color.yellow)\n",
"r.cg = curve(pos = [(0,0,0), (1,-1,-1)], color = color.yellow)\n",
"r.ch = curve(pos = [(0,0,0), (-1,1,-1)], color = color.yellow)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Full Sphere of rays (point source)\n",
"from vpython import *\n",
"import numpy \n",
"\n",
"\n",
"list_of_rays=[] # Create an empty list named 'list_of_rays'\n",
"L=1 #Define a length for the rays\n",
"\n",
"for i in numpy.arange(0,2*pi+0.1,.1):\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
" \n",
" x=L*cos(k)*cos(i) #Prints vector with endpoint along a sphere\n",
" y=L*cos(k)*sin(i) #Equation for a sphere in Spherical Coordinates\n",
" z=L*sin(k)\n",
" \n",
" pointSource = sphere(pos = vector(0, 0, 0), radius = 0.01, color = color.white)\n",
" rays = curve(pos=[vec(0,0,0),vec(x,y,z)], color=color.green)\n",
" # list_of_rays.append(new_curve)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This cell generates a beam of Parallel Rays (Uniform Step Size)\n",
"from vpython import *\n",
"import numpy \n",
"\n",
"L=10 #Define a length for the rays\n",
"\n",
"\n",
"for i in numpy.arange(0,1,.06):\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
"\n",
" x1 = 0\n",
" x2 = L\n",
" y1 = i*sin(k)\n",
" y2 = y1\n",
" z1 = i*cos(k)\n",
" z2 = z1\n",
" \n",
" beam = curve(pos=[vec(x1,y1,z1),vec(x2,y2,z2)], color=color.red)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This cell generates a beam of Parallel Rays (Step Size accounting for Gaussian distribution of beam density)\n",
"from vpython import *\n",
"import numpy \n",
"\n",
"L=10 #Define a length for the rays\n",
"\n",
"\n",
"for i in numpy.arange(0,1.15,.15):\n",
" for j in numpy.arange(0,1+(1/e*e),pow(e,-2*i)): #Taking Gaussian Function for Radial Step 'i'\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
"\n",
" x1 = 0\n",
" x2 = L\n",
" y1 = j*sin(k)\n",
" y2 = y1\n",
" z1 = j*cos(k)\n",
" z2 = z1\n",
" \n",
" beam = curve(pos=[vec(x1,y1,z1),vec(x2,y2,z2)], color=color.red)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This cell generates a beam of Parallel Rays (Uniform Step Size)\n",
"from vpython import *\n",
"import numpy \n",
"\n",
"L=10 #Define a length for the rays\n",
"\n",
"\n",
"\n",
"\n",
"for i in numpy.arange(0,1,.06):\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
" \n",
" x1 = 0\n",
" x2 = L\n",
" y1 = i*sin(k)\n",
" y2 = y1\n",
" z1 = i*cos(k)\n",
" z2 = z1\n",
" \n",
" \n",
" for x in numpy.arange(0,5,1):\n",
" beam = curve(pos=[vec(x1,y1,z1),vec(x2,y2,z2)], color=color.red)\n",
" for x in numpy.arange(5,10,.1):\n",
" beam = curve(pos=[vec(x1,y1,z1),vec(x2,y2,z2)], color=color.green)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This cell generates a beam of Parallel Rays (Uniform Step Size) and changes the color as the beam passes x = 5\n",
"from vpython import *\n",
"import numpy \n",
"\n",
"L=10 #Define a length for the rays\n",
"\n",
"\n",
"\n",
"\n",
"for i in numpy.arange(0,1,.06):\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
" \n",
" x1 = 0\n",
" x2 = L\n",
" y1 = i*sin(k)\n",
" y2 = y1\n",
" z1 = i*cos(k)\n",
" z2 = z1\n",
" \n",
" beam = curve(pos=vec(x1,y1,z1), color=color.red)\n",
" beam.append(pos=vec(5,y1,z1), color=color.red)\n",
" beam.append(pos=vec(5,y1,z1), color=color.green)\n",
" beam.append(pos=vec(x2,y2,z2), color=color.green)\n",
" \n",
"#This is plotting a new curve that changes color from red to green at a prespecified point (x = 5).\n",
"#Can we make this change point variable? or user input?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This cell generates a beam of Parallel Rays (Uniform Step Size) and changes the direction as the beam passes x = 5\n",
"from vpython import *\n",
"import numpy \n",
"\n",
"L=10 #Define a length for the rays\n",
"\n",
"\n",
"for i in numpy.arange(0,1,.06):\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
" \n",
" x1 = 0\n",
" x2 = L\n",
" y1 = i*sin(k)\n",
" y2 = y1\n",
" z1 = i*cos(k)\n",
" z2 = z1\n",
" \n",
" beam = curve(pos=vec(x1,y1,z1), color=color.red)\n",
" beam.append(pos=vec(5,y1,z1), color=color.red)\n",
" beam.append(pos=vec(5,y1,z1), color=color.red)\n",
" beam.append(pos=vec(x2,y2,z2), color=color.red)\n",
" \n",
" #Define Vectors\n",
" R = vector(1,0,0) #Vector along x-axis; just rays\n",
" P = vector(-1,-1,-1) #Normal vector for a plane somewhere\n",
" \n",
" beam.append(pos=vec(x_o + dx, y_o + dy, z_o + dz) #Using (numpy.arcsin((1/(1.5))*sin(vector.diff_angle(R,P))))\n",
" \n",
"#This is plotting a new curve that changes color from red to green at a prespecified point (x = 5).\n",
"#Can we make this change point variable? or user input?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from vpython import *\n",
"import numpy \n",
"\n",
"L=10 #Define a length for the rays\n",
"\n",
"\n",
"\n",
"\n",
"for i in numpy.arange(0,1,.6):\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
" \n",
" x1 = 0\n",
" x2 = L\n",
" y1 = i*sin(k)\n",
" y2 = y1\n",
" z1 = i*cos(k)\n",
" z2 = z1\n",
" \n",
" beam = curve(pos=vec(x1,y1,z1), color=color.red)\n",
" beam.append(pos=vec(5,y1,z1), color=color.red)\n",
" beam.append(pos=vec(5,y1,z1), color=color.green)\n",
" beam.append(pos=vec(x2,y2,z2), color=color.green)\n",
" \n",
"#This is plotting a new curve that changes color from red to green at a prespecified point (x = 5).\n",
"#Can we make this change point variable? or user input?\n",
"\n",
"plane = box(pos=vector(15,0,0), length=20,height=5, width=10, up=vec(3,4,0), color=color.white,opacity=.5)\n",
"print(plane.axis)\n",
"print(plane.up)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from vpython import *\n",
"import numpy\n",
"\n",
"class ray():\n",
" '''A light ray class'''\n",
" def __init__(self,start_point,direction_vector,color):\n",
" self.current_point=start_point\n",
" self.direction_vector=direction_vector.norm()\n",
" self.color=color\n",
" self.length=10\n",
" self.ray=curve(pos=self.current_point, color=self.color)\n",
" \n",
" #Draw all rays in Source\n",
" def draw_ray(self):\n",
" '''Draw a single light ray'''\n",
" new_point=self.current_point+self.length*self.direction_vector.norm()\n",
" self.current_point=new_point\n",
" self.ray.append(new_point)\n",
" \n",
" #Define normal as normal vector to object\n",
" #Using Snell's Law (numpy.arcsin((1/(1.5))*sin(vector.diff_angle(ray1,normal))))\n",
" def new_direction(self,new_direction):\n",
" '''Set a new direction for the ray'''\n",
" self.direction_vector=new_direction #\n",
" \n",
" #Stop when encounters an object and then reset length to 10\n",
" def new_length(self,new_length):\n",
" '''Set a new length for the vector'''\n",
" self.length=new_length\n",
" \n",
"for i in numpy.arange(0,1,.05):\n",
" for k in numpy.arange(0,2*pi+0.1,.05):\n",
" \n",
" direction_vector = vec(1,3,1).norm() #1, 3,and 1 are to be user-input, or calculated from existing ray\n",
" \n",
" beamDirection=direction_vector\n",
" pos1=vec(0,0,0)\n",
" \n",
" dir_perp1=vec(beamDirection.y,-beamDirection.x,0)\n",
" dir_perp2=beamDirection.cross(dir_perp1)\n",
" \n",
" perp1=ray(vec(0.5,1.5,0.5),dir_perp1,color.red)\n",
" perp2=ray(vec(0.5,1.5,0.5),dir_perp2,color.green)\n",
" \n",
" perp1.draw_ray()\n",
" perp2.draw_ray()\n",
" \n",
" new_start=pos1+i*(perp1.direction_vector*cos(k)+perp2.direction_vector*sin(k))\n",
" \n",
" ray1=ray(vec(new_start),direction_vector,color.blue)\n",
" ray1.draw_ray()\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from vpython import *\n",
"import numpy\n",
"\n",
"d = vec(1,3,1)\n",
"p = vec(0,1,-3)\n",
"\n",
"beam1 = curve(pos=vec(0,0,0), color=color.red)\n",
"beam1.append (pos=vec(1,3,1))\n",
"beam2 = curve(pos=vec(0,0,0), color=color.green)\n",
"beam2.append(pos=vec(0,1,-3))\n",
"beam3 = curve(pos=vec(0,0,0), color=color.green)\n",
"beam3.append(pos=vec(1,0,-1))\n",
"beam4 = curve(pos=vec(0,0,0), color=color.green)\n",
"beam4.append(pos=vec(-3,1,0))\n",
"beam5 = curve(pos=vec(0,0,0), color=color.green)\n",
"beam5.append(pos=vec(-1,0,1))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"glowscript\" class=\"glowscript\"></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")}"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<1.000000, 0.000000, 0.000000>\n",
"<-1.000000, -0.000000, -0.000000>\n",
"<0.000000, 1.000000, 0.000000>\n",
"<-0.000000, -1.000000, -0.000000>\n",
"<0.000000, 0.000000, 1.000000>\n",
"<-0.000000, -0.000000, -1.000000>\n"
]
},
{
"ename": "AttributeError",
"evalue": "'ray' object has no attribute 'oldPoint'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-6397b4d4138d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 229\u001b[0m \u001b[0mray1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgreen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 231\u001b[1;33m \u001b[0mb1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_boundaries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mray1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 233\u001b[0m \u001b[0mray2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-4-6397b4d4138d>\u001b[0m in \u001b[0;36mcheck_boundaries\u001b[1;34m(self, ray)\u001b[0m\n\u001b[0;32m 194\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcheck_boundaries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurrentPoint\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 196\u001b[1;33m \u001b[0mb_old\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moldPoint\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 197\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 198\u001b[0m \u001b[0mfrontCheck\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalFront\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m)\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[1;31mAttributeError\u001b[0m: 'ray' object has no attribute 'oldPoint'"
]
}
],
"source": [
"from vpython import *\n",
"import numpy\n",
"\n",
"scene = canvas()\n",
"\n",
"#Classes\n",
"# A class is a template for an object\n",
" #Ray class is a template for a ray\n",
" #init: Set all of the variables for the beam\n",
" #define methods\n",
"\n",
"class ray():\n",
" '''A light ray class'''\n",
" def __init__(self,startPoint,directionVector,color):\n",
" self.startPoint = startPoint\n",
" self.currentPoint = startPoint\n",
" self.directionVector = directionVector.norm()\n",
" self.color = color\n",
" self.length = 10\n",
" self.ray = curve(pos=self.currentPoint, color=self.color)\n",
" \n",
" self.draw_ray()\n",
" \n",
" #Draw all rays in Source\n",
" def draw_ray(self):\n",
" '''Draw a single light ray'''\n",
" newPoint = self.currentPoint + self.length*self.directionVector\n",
" self.currentPoint = newPoint\n",
" self.ray.append(newPoint)\n",
" \n",
" if newPoint.x > 5:\n",
" self.currentPoint.x = 5\n",
" thetaIncident = pi-diff_angle(self.directionVector,normal.directionVector)\n",
" thetaRefracted = asin((n1/n2)*sin(thetaIncident))\n",
" a1 = self.directionVector - normal.directionVector * (dot(self.directionVector,normal.directionVector))\n",
" \n",
" self.directionVector = (-1*cos(thetaRefracted)*normal.directionVector) + (sin(thetaRefracted)*a1.norm())\n",
" \n",
" newPoint = self.currentPoint + self.length*self.directionVector\n",
" self.currentPoint = newPoint\n",
" self.ray.append(newPoint)\n",
" \n",
" #Loop to check the position of newPoint. If position is greater than 5, call Snell's Law\n",
" # and assign a new direction vector\n",
" \n",
" #Define normal as normal vector to object\n",
" #Using Snell's Law (numpy.arcsin((1/(1.5))*sin(vector.diff_angle(ray1,normal))))\n",
" def newDirection(self,newDirection):\n",
" '''Set a new direction for the ray'''\n",
" self.directionVector = newDirection \n",
" \n",
" #Stop when encounters an object and then reset length to 10\n",
" def newLength(self,newLength):\n",
" '''Set a new length for the vector'''\n",
" self.length=newLength\n",
" \n",
"class beam():\n",
" '''A Beam Class'''\n",
" def __init__(self, centerRay, width):\n",
" self.centerRay = centerRay\n",
" self.currentPoint = centerRay.currentPoint\n",
" self.beamDirection = centerRay.directionVector\n",
" self.width = width\n",
" self.color = centerRay.color\n",
" self.beam = []\n",
" \n",
" \n",
" self.find_perp()\n",
" self.draw_beam()\n",
"\n",
" \n",
" def find_perp(self):\n",
" '''Find two vectors perpendicular to the center ray'''\n",
" if centerRay.directionVector.y==0 and centerRay.directionVector.x==0:\n",
" self.perp1=vec(1,0,0)\n",
" else:\n",
" self.perp1 = norm(vec(centerRay.directionVector.y,-centerRay.directionVector.x,0))\n",
" self.perp2 = norm(vec(centerRay.directionVector.cross(self.perp1)))\n",
" def draw_beam(self):\n",
" '''Draw a beam; lots of parallel rays'''\n",
" for i in numpy.arange(0,self.width,.1): #CAN WE DEFINE A BEAM USING A FOR LOOP?\n",
" for k in numpy.arange(0,2*pi+0.1,.1):\n",
" self.newStart = centerRay.startPoint + i*(self.perp1*cos(k)+self.perp2*sin(k))\n",
" \n",
" beamRay = ray(self.newStart, self.beamDirection, self.color)\n",
" \n",
" self.beam.append(beamRay)\n",
" \n",
"class pointSource():\n",
" def __init__(self, position):\n",
" self.position = position\n",
" self.color = centerRay.color\n",
" self.pointSource = []\n",
" self.source = sphere(pos=vec(centerRay.startPoint), radius=0.01, color=self.color)\n",
" \n",
" self.draw_pointSource()\n",
" \n",
" def draw_pointSource(self):\n",
" '''Draw a point source'''\n",
" self.source\n",
" \n",
" for i in numpy.arange(0,2*pi+0.1,(pi/36)):\n",
" for k in numpy.arange(0,2*pi+0.1,(pi/36)):\n",
" \n",
" x = centerRay.length*(cos(k)*cos(i))\n",
" y = centerRay.length*(cos(k)*sin(i))\n",
" z = centerRay.length*(sin(k))\n",
" \n",
" pointSourceRay = ray(self.position, vec(x,y,z), self.color)\n",
" \n",
" self.pointSource.append(pointSourceRay)\n",
" \n",
"#Class for planes\n",
"#As the ray is drawing itself, we need the ray to check its position against other possible objects\n",
" #if it encouters another object, it need to call Snell's Law and give itself a new direction\n",
"normal = ray(vec(5,5,5),vec(-1,0,0),color.red) #y-z plane at x=5\n",
"n1 = 1.0 #Refractive index of air\n",
"n2 = 1.5 #Refractive index of glass\n",
"\n",
" \n",
"centerRay = ray(vec(0,0,0), vec(1,1,1), color.green)\n",
"#beam1 = beam(centerRay, 2)\n",
"#pointSource1 = pointSource(centerRay.startPoint)\n",
"\n",
"#Define normal and refractive index--> In box class (Material attribute?)\n",
"#normal = ray(centerRay.currentPoint,vec(-1,0,0),color.red) #y-z plane at x=5\n",
"n1 = 1.0 #Refractive index of air\n",
"n2 = 1.5 #Refractive index of glass\n",
"\n",
"#print(centerRay.directionVector)\n",
"#print(normal.directionVector)\n",
"\n",
"#REFRACT CODE\n",
"#thetaIncident = pi-diff_angle(centerRay.directionVector,normal.directionVector)\n",
"#thetaRefracted = asin((n1/n2)*sin(thetaIncident))\n",
"#a1 = centerRay.directionVector - normal.directionVector * (dot(centerRay.directionVector,normal.directionVector))\n",
"#print(a1)\n",
"\n",
"#refractedRay = ray(centerRay.currentPoint, (-1*cos(thetaRefracted)*normal.directionVector) + (sin(thetaRefracted)*a1.norm()), color.yellow)\n",
"#refractedRay2 = ray(refractedRay.currentPoint, (-1*cos(thetaIncident)*normal.directionVector) + (sin(thetaIncident)*a1.norm()), color.green)\n",
"\n",
"#glass = box(pos = ((refractedRay.startPoint + refractedRay.currentPoint)/2), axis = vec(1,0,0), up = vec(0,1,0), length = 8.33, width = 100, height = 100, opacity = 0.5)\n",
"\n",
"class block():\n",
" '''an optical obstacle class'''\n",
" def __init__(self, position, axis, up, length, width, height, material):\n",
" self.position = position\n",
" self.axis = axis\n",
" self.up = up\n",
" self.material = material\n",
" self.length = length\n",
" self.width = width\n",
" self.height = height\n",
" if(self.material == \"glass\"):\n",
" self.n = 1.5\n",
" \n",
" self.draw_block()\n",
" \n",
" def draw_block(self):\n",
" self.shape = box(pos = self.position, axis = self.axis, up = self.up, length = self.length, width = self.width, height = self.height)\n",
" \n",
" self.normalFront = self.axis.norm()\n",
" self.normalBack = -1 * self.axis.norm()\n",
" self.normalTop = self.up.norm()\n",
" self.normalBottom = -1 * self.up.norm()\n",
" self.normalLSide = cross(self.axis, self.up)\n",
" self.normalRSide = -1 * cross(self.axis, self.up)\n",
" \n",
" def check_boundaries(self, ray):\n",
" b = ray.currentPoint - self.position\n",
" b_old = ray.oldPoint - self.position\n",
"\n",
" \n",
" if (abs(dot(rel_pos, self.normalBack)) < ((self.length)/2) and abs(dot(rel_old_pos, normalBack) > ((sel.flength)/2))\n",
" \n",
" frontCheck = dot(b,self.normalFront) - ((self.length)/2)\n",
" backCheck = dot(b,self.normalBack) - ((self.length)/2)\n",
" topCheck = dot(b,self.normalTop) - ((self.height)/2)\n",
" bottomCheck = dot(b,self.normalBottom) - ((self.height)/2)\n",
" lSideCheck = dot(b,self.normalLSide) - ((self.width)/2)\n",
" rSideCheck = dot(b,self.normalRSide) - ((self.width)/2)\n",
" \n",
" frontCheck_old = dot(b_old,self.normalFront) - ((self.length)/2)\n",
" backCheck_old = dot(b_old,self.normalBack) - ((self.length)/2)\n",
" topCheck_old = dot(b_old,self.normalTop) - ((self.height)/2)\n",
" bottomCheck_old = dot(b_old,self.normalBottom) - ((self.height)/2)\n",
" lSideCheck_old = dot(,self.normalLSide) - ((self.width)/2)\n",
" rSideCheck_old = dot(b_old,self.normalRSide) - ((self.width)/2)\n",
" \n",
" if(frontCheck <= 0 and backCheck <= 0 and topCheck <= 0 and bottomCheck <= 0 and lSideCheck <= 0 and rSideCheck <= 0):\n",
" print(1)\n",
" \n",
" \n",
" \n",
"b1 = block(vec(0,0,0), vec(1,0,0), vec(0,1,0), 6, 3, 3, \"glass\")\n",
"print(b1.normalFront)\n",
"print(b1.normalBack)\n",
"print(b1.normalTop)\n",
"print(b1.normalBottom)\n",
"print(b1.normalLSide)\n",
"print(b1.normalRSide)\n",
" \n",
"ray1 = ray(vec(-7,0,0), vec(1,0,0), color.green)\n",
" \n",
"b1.check_boundaries(ray1)\n",
"\n",
"ray2 = ray(vec(0,0,0), vec(0,0,1), color.blue)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
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"kernelspec": {
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| gpl-3.0 |
yyl/data-mining-repo | hw2.ipynb | 1 | 3832 | {
"metadata": {
"name": "",
"signature": "sha256:3d6753004134b9ca5debc5342da747912c824af07a15837bf20227fa3e0b53ae"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"import pandas as pd\n",
"data = pd.read_csv(\"./data1.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def computeEntropy(df, cla):\n",
" count = list(df.groupby(cla)[cla].count())\n",
" total = float(sum(count))\n",
" prob = (c/total for c in count)\n",
" return sum([-p*math.log(p, 2) for p in prob])\n",
"\n",
"def infoGain(df, cla, col):\n",
" res = computeEntropy(df, cla)\n",
" n = df[cla].count()\n",
" for typ in df[col].unique():\n",
" n_i = df[df[col]==typ][cla].count()\n",
" res -= computeEntropy(df[df[col]==typ], cla) * (float(n_i)/n)\n",
" return res"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 108
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"(a). total entropy = %f\" % computeEntropy(data, \"Class\")\n",
"print \"(b). movie ID entropy = %f\" % computeEntropy(data, \"ID\")\n",
"print \"(c). Format entropy = %f\" % computeEntropy(data, \"Format\")\n",
"print \"(d). Movie Category entropy = %f\" % computeEntropy(data, \"Category\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(a). total entropy = 1.000000\n",
"(b). movie ID entropy = 4.321928\n",
"(c). Format entropy = 0.970951\n",
"(d). Movie Category entropy = 1.521928\n"
]
}
],
"prompt_number": 81
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(e). Movie format has the lowest entropy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(f). We choose attributes based on information gain:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Info gain if splitting on:\" \n",
"print \"Format: %f\" % infoGain(data, \"Class\", \"Format\")\n",
"print \"Category: %f\" % infoGain(data, \"Class\", \"Category\")\n",
"print \"ID: %f\" % infoGain(data, \"Class\", \"ID\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Info gain if splitting on:\n",
"Format: 0.124511\n",
"Category: 0.295807\n",
"ID: 1.000000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 114
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The rule is to choose the attribute that gives you the highest information gain. In this case, although that would be \"ID\", the thing is \"ID\" will split the data into 20 nodes where each node contains only one data point. Therefore, we should not consider it as a valid classification. Without that, we should choose \"Category\" as it gives the second largest info gain."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2"
]
}
],
"metadata": {}
}
]
} | gpl-2.0 |
fedor1113/LineCodes | Decoder.ipynb | 1 | 29315 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decode line codes in png graphs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assumptions (format):\n",
"* The clock is given and it is a red line on the top.\n",
"* The signal line is black\n",
"* ..."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pypng in /home/fedor1113/anaconda3/lib/python3.6/site-packages\r\n"
]
}
],
"source": [
"# Makes sure to install PyPNG image handling module\n",
"import sys\n",
"!{sys.executable} -m pip install pypng"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import png"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"r = png.Reader(\"ex.png\")\n",
"t = r.asRGB()\n",
"\n",
"img = list(t[2])\n",
"# print(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Outline\n",
"The outline of the idea is:\n",
"\n",
"1. Find the red lines that represent parallel synchronization signal above\n",
"2. Calculate their size\n",
"3. \"Synchromize with rows below\" (according to the rules of the code)\n",
"4. ...\n",
"5. PROFIT!\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## !!! Things to keep in mind:\n",
"1. deviations of red\n",
"2. deviations of black\n",
"3. noise - it might just break everything!\n",
"4. beginning and end of image...\n",
"5. ..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A rather simple PNG we'll work with first:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Let us first define colour red\n",
"# We'll work with RGB for colours\n",
"# So for accepted variants we'll make a list of 3-lists.\n",
"\n",
"class colourlist(list):\n",
" \"\"\"Just lists of 3-lists with some fancy methods to work with RGB colours\n",
" \"\"\"\n",
" \n",
" def add_deviations(self, d=8): # Magical numbers are so magical!\n",
" \"\"\"Adds deviations for RGB colours to a given list.\n",
" Warning! Too huge - it takes forever.\n",
" \n",
" Input: list of 3-lists\n",
" Output: None (side-effects - changes the list)\n",
" \"\"\"\n",
" \n",
" #l = l[:] Nah, let's make it a method\n",
" l = self\n",
" \n",
" v = len(l)\n",
" max_deviation = d\n",
" \n",
" for i in range(v): # Iterate through the list of colours\n",
" \n",
" for j in range(-max_deviation, max_deviation+1): \n",
" # Actually it is the deviation.\n",
" \n",
" #for k in range(3): # RGB! (no \"a\"s here)\n",
" \n",
" newcolour = self[i][:] # Take one of the original colours\n",
" newcolour[0] = abs(newcolour[0]+j) # Create a deviation\n",
" l.append(newcolour) \n",
" # Append new colour to the end of the list. \n",
" # <- Here it is changed!\n",
" for j in range(-max_deviation, max_deviation+1): \n",
" # Work with all the possibilities with this d\n",
" newcolour1 = newcolour[:]\n",
" newcolour1[1] = abs(newcolour1[1]+j)\n",
" l.append(newcolour1) \n",
" # Append new colour to the end of the list. Yeah! \n",
" # <- Here it is changed!\n",
" \n",
" for j in range(-max_deviation, max_deviation+1): \n",
" # Work with all the possibilities with this d\n",
" newcolour2 = newcolour1[:]\n",
" newcolour2[2] = abs(newcolour2[2]+j)\n",
" l.append(newcolour2) # Append new colour to the end of the list. Yeah! \n",
" # <- Here it is changed!\n",
" \n",
" return None\n",
"\n",
"def withinDeviation(colour, cl, d=20):\n",
" \"\"\"This is much more efficient!\n",
" Input: 3-list (colour), colourlist, int\n",
" Output: bool\n",
" \"\"\"\n",
" for el in cl:\n",
" if (abs(colour[0] - el[0]) <= d and \n",
" abs(colour[1] - el[1]) <= d and \n",
" abs(colour[2] - el[2]) <= d):\n",
" return True\n",
" return False\n",
"\n",
"\n",
"\n",
"accepted_colours = colourlist([[118, 58, 57], [97, 71, 36], [132, 56, 46], [132, 46, 47], [141, 51, 53]]) # ...\n",
"\n",
"#accepted_colours.add_deviations()\n",
"\n",
"# print(accepted_colours) # -check it! - or better don't - it is a biiiig list....\n",
"\n",
"# print(len(accepted_colours)) # That will take a while... Heh.."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 3)\n",
"118 58 57\n",
"array('B', [255, 245, 243, 118, 58, 57, 132, 46, 47, 133, 56, 46, 97, 71, 36, 141, 165, 105, 113, 186, 105, 103, 178, 96, 138, 168, 106, 95, 76, 36, 131, 55, 41, 130, 52, 40, 96, 72, 36, 140, 165, 108, 116, 184, 107, 107, 175, 98, 144, 165, 109, 98, 74, 38, 129, 55, 44, 127, 53, 40, 97, 71, 34, 141, 165, 105, 113, 186, 105, 103, 178, 96, 138, 168, 106, 95, 76, 36, 131, 55, 41, 130, 52, 40, 96, 72, 36, 144, 169, 112, 112, 180, 103, 111, 179, 102, 141, 162, 106, 98, 74, 38, 127, 53, 42, 131, 57, 44, 98, 72, 35, 140, 164, 104, 114, 187, 106, 103, 178, 96, 135, 165, 103, 94, 75, 35, 131, 55, 41, 132, 54, 42, 98, 74, 38, 134, 159, 102, 110, 178, 101, 110, 178, 101, 145, 166, 110, 98, 74, 38, 132, 58, 47, 127, 53, 40, 97, 71, 34, 145, 169, 109, 109, 182, 101, 107, 182, 100, 135, 165, 103, 95, 76, 36, 129, 53, 39, 134, 56, 44, 98, 74, 38, 134, 159, 102, 110, 178, 101, 110, 178, 101, 145, 166, 110, 98, 74, 38, 132, 58, 47, 127, 53, 40, 97, 71, 34, 141, 165, 105, 113, 186, 105, 103, 178, 96, 138, 168, 106, 95, 76, 36, 131, 55, 41, 130, 52, 40, 96, 72, 36, 140, 165, 108, 116, 184, 107, 107, 175, 98, 144, 165, 109, 98, 74, 38, 129, 55, 44, 127, 53, 40, 97, 71, 34, 145, 169, 109, 109, 182, 101, 107, 182, 100, 135, 165, 103, 95, 76, 36, 129, 53, 39, 134, 56, 44, 97, 73, 37, 139, 164, 107, 117, 185, 108, 107, 175, 98, 141, 162, 106, 97, 73, 37, 129, 55, 44, 129, 55, 42, 98, 72, 35, 140, 164, 104, 114, 187, 106, 103, 178, 96, 135, 165, 103, 94, 75, 35, 131, 55, 41, 132, 54, 42, 97, 73, 37, 139, 164, 107, 117, 185, 108, 107, 175, 98, 141, 162, 106, 97, 73, 37, 129, 55, 44, 129, 55, 42, 98, 72, 35, 140, 164, 104, 114, 187, 106, 103, 178, 96, 135, 165, 103, 94, 75, 35, 131, 55, 41, 132, 54, 42, 98, 74, 38, 134, 159, 102, 110, 178, 101, 110, 178, 101, 145, 166, 110, 98, 74, 38, 132, 58, 47, 127, 53, 40, 97, 71, 34, 141, 165, 105, 113, 186, 105, 103, 178, 96, 138, 168, 106, 95, 76, 36, 131, 55, 41, 130, 52, 40, 96, 72, 36, 140, 165, 108, 116, 184, 107, 107, 175, 98, 144, 165, 109, 98, 74, 38, 129, 55, 44, 127, 53, 40, 97, 71, 34, 145, 169, 109, 109, 182, 101, 107, 182, 100, 135, 165, 103, 95, 76, 36, 129, 53, 39, 134, 56, 44, 98, 74, 38, 134, 159, 102, 110, 178, 101, 110, 178, 101, 145, 166, 110, 98, 74, 38, 132, 58, 47, 127, 53, 40, 97, 71, 34, 141, 165, 105, 113, 186, 105, 103, 178, 96, 138, 168, 106, 95, 76, 36, 131, 55, 41, 130, 52, 40, 96, 72, 36, 144, 169, 112, 112, 180, 103, 111, 179, 102, 141, 162, 106, 98, 74, 38, 127, 53, 42, 131, 57, 44, 97, 73, 37, 140, 164, 106, 117, 186, 106, 107, 176, 96, 141, 162, 103, 98, 73, 33, 131, 55, 39, 129, 56, 39, 97, 75, 36, 133, 161, 103, 109, 181, 107, 99, 181, 107, 113, 170, 115, 234, 255, 232, 245, 255, 241])\n"
]
}
],
"source": [
"def find_first_pixel_of_colour(pixellist, accepted_deviations):\n",
" \"\"\"Returns the row and column of the pixel \n",
" in a converted to list with RGB colours PNG\n",
" \n",
" Input: ..., colourlist\n",
" Output: 2-tuple of int (or None)\n",
" \"\"\"\n",
" \n",
" accepted_deviations = accepted_deviations[:]\n",
" rows = len(pixellist)\n",
" cols = len(pixellist[0])\n",
" \n",
" for j in range(rows):\n",
" for i in range(0, cols, 3):\n",
" # if [pixellist[j][i], pixellist[j][i+1], pixellist[j][i+2]] in accepted_deviations:\n",
" if withinDeviation([pixellist[j][i], pixellist[j][i+1], pixellist[j][i+2]], accepted_deviations):\n",
" return (j, i)\n",
" \n",
" return None\n",
"\n",
"\n",
"\n",
"fr = find_first_pixel_of_colour(img, accepted_colours)\n",
"\n",
"if fr is None:\n",
" print(\"Warning a corrupt file or a wrong format!!!\")\n",
"\n",
"print(fr)\n",
"print(img[fr[0]][fr[1]], img[fr[0]][fr[1]+1], img[fr[0]][fr[1]+2])\n",
"print(img[fr[0]])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# [133, 56, 46] in accepted_colours"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"# Let us now find the length of the red lines that represent the sync signal\n",
"\n",
"def find_next_pixel_in_row(pixel, row, accepted_deviations):\n",
" \"\"\"Returns the column of the next pixel of a given colour\n",
" (with deviations) in a row from a converted to list with RGB \n",
" colours PNG\n",
" \n",
" Input: 2-tuple of int, list of int with len%3==0, colourlist\n",
" Output: int (returns -1 specifically if none are found)\n",
" \"\"\"\n",
" \n",
" l = len(row)\n",
" \n",
" if pixel[1] >= l-1:\n",
" return -1\n",
" \n",
" for i in range(pixel[1]+3, l, 3):\n",
" # if [row[i], row[i+1], row[i+2]] in accepted_deviations:\n",
" if withinDeviation([row[i], row[i+1], row[i+2]], accepted_deviations):\n",
" return i\n",
" \n",
" return -1\n",
"\n",
"\n",
"\n",
"def colour_line_length(pixels, start, colour, deviations=20):\n",
"\n",
" line_length = 1\n",
" pr = start[:]\n",
" r = (pr[0], \n",
" find_next_pixel_in_row(pr, pixels[pr[0]], colour[:]))\n",
" # print(pr, r)\n",
" if not(r[1] == pr[1]+3):\n",
" print(\"Ooops! Something went wrong!\")\n",
" else:\n",
" line_length += 1\n",
" while (r[1] == pr[1]+3):\n",
" pr = r\n",
" r = (pr[0], \n",
" find_next_pixel_in_row(pr, \n",
" pixels[pr[0]], colour[:]))\n",
" line_length += 1\n",
" \n",
" return line_length\n",
"\n",
"\n",
"\n",
"line_length = colour_line_length(img, fr, accepted_colours, deviations=20)\n",
" \n",
"print(line_length) # !!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**We found the sync (clock) line length in our graph!**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"It is 5\n"
]
}
],
"source": [
"print(\"It is\", line_length)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the information transfer signal itself is ~\"black\", so we need to find the black colour range as well!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0, 0, 0], [0, 1, 0], [7, 2, 8]]\n"
]
}
],
"source": [
"# Let's do just that\n",
"\n",
"black = colourlist([[0, 0, 0], [0, 1, 0], [7, 2, 8]])\n",
"# black.add_deviations(60) # experimentally it is somewhere around that\n",
"# experimentally the max deviation is somewhere around 60\n",
"print(black)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The signal we are currently interested in is *Manchester code* __(as per G.E. Thomas)__."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is a self-clocking signal, but since we *do* have a clock with it - we use it)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us find the height of the Manchester signal in our PNG - just because..."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"fb = find_first_pixel_of_colour(img, black)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def signal_height(pxls, fib):\n",
" signal_height = 1\n",
" # if ([img[fb[0]+1][fb[1]], img[fb[0]+1][fb[1]+1], img[fb[0]+1][fb[1]+2]] in black):\n",
" if withinDeviation([pxls[fib[0]+1][fib[1]], pxls[fib[0]+1][fib[1]+1]\n",
" , pxls[fib[0]+1][fib[1]+2]], black, 60):\n",
" signal_height += 1\n",
" i = 2\n",
" rows = len(pxls)\n",
" # while([img[fb[0]+i][fb[1]], img[fb[0]+i][fb[1]+1], img[fb[0]+i][fb[1]+2]] in black):\n",
" while(withinDeviation([pxls[fib[0]+i][fib[1]]\n",
" , pxls[fib[0]+i][fib[1]+1]\n",
" , pxls[fib[0]+i][fib[1]+2]], black, 60)):\n",
" signal_height += 1\n",
" i += 1\n",
" if (i >= rows):\n",
" break\n",
" else:\n",
" print(\"\") # TO DO\n",
" return signal_height\n",
"\n",
"sheight = signal_height(img, fb)-1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n"
]
}
],
"source": [
"print(sheight)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Let's quickly find the last red line\n",
"..."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"def manchester(pixels, start, clock, \n",
" line_colour, d=60, inv=False):\n",
" \"\"\"Decodes Manchester code (as per G. E. Thomas) \n",
" (or with inv=True Manchester code\n",
" (as per IEEE 802.4)).\n",
" \n",
" Input: array of int with len%3==0 (- PNG pixels),\n",
" int, int, colourlist, int, bool (optional)\n",
" Output: str (of '1' and '0') or None\n",
" \"\"\"\n",
" \n",
" res = \"\"\n",
" \n",
" cols = len(pixels[0])\n",
" fb = find_first_pixel_of_colour(pixels, line_colour)\n",
" m = 2*clock*3-2*3 # Here be dragons!\n",
" # Hack: only check it using the upper line \n",
" # (or lack thereof)\n",
" \n",
" if not(inv):\n",
" for i in range(start, cols-2*3, m):\n",
" fromUP = withinDeviation([pixels[fb[0]][i-6], \n",
" pixels[fb[0]][i-5], \n",
" pixels[fb[0]][i-4]], \n",
" line_colour, d)\n",
" if fromUP:\n",
" res = res + \"1\"\n",
" else:\n",
" res = res + \"0\"\n",
" else:\n",
" for i in range(start, cols-2*3, m):\n",
" fromUP = withinDeviation([pixels[fb[0]][i-6], \n",
" pixels[fb[0]][i-5], \n",
" pixels[fb[0]][i-4]], \n",
" line_colour, d)\n",
" if cond:\n",
" res = res + \"0\"\n",
" else:\n",
" res = res + \"1\"\n",
" \n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"def nrz(pixels, start, clock, \n",
" line_colour, d=60, inv=False):\n",
" \"\"\"Decodes NRZ code\n",
" (or with inv=True its inversed version).\n",
" It is assumed that there is indeed a valid\n",
" NRZ code with a valid message.\n",
" \n",
" Input: array of int with len%3==0 (- PNG pixels),\n",
" int, int, colourlist, int, bool (optional)\n",
" Output: str (of '1' and '0') or (maybe?) None\n",
" \"\"\"\n",
" \n",
" res = \"\"\n",
" \n",
" cols = len(pixels[0])\n",
" fb = find_first_pixel_of_colour(pixels, line_colour)\n",
" m = 2*clock*3-2*3 # Here be dragons!\n",
" # Hack: only check it using the upper line \n",
" # (or lack thereof)\n",
" \n",
" if not(inv):\n",
" for i in range(start, cols, m):\n",
" UP = withinDeviation([pixels[fb[0]][i], \n",
" pixels[fb[0]][i+1], \n",
" pixels[fb[0]][i+2]], \n",
" line_colour, d)\n",
" if UP:\n",
" res = res + \"1\"\n",
" else:\n",
" res = res + \"0\"\n",
" else:\n",
" for i in range(start, cols-2*3, m):\n",
" UP = withinDeviation([pixels[fb[0]][i], \n",
" pixels[fb[0]][i+1], \n",
" pixels[fb[0]][i+2]], \n",
" line_colour, d)\n",
" if cond:\n",
" res = res + \"0\"\n",
" else:\n",
" res = res + \"1\"\n",
" \n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"def code2B1Q(pixels, start, clock=None, \n",
" line_colour=[[0, 0, 0]], d=60, inv=False):\n",
" \"\"\"Decodes 2B1Q code. The clock is not used - it\n",
" is for compatibility only - really, so put \n",
" anything there. Does _NOT_ always work!\n",
" \n",
" WARNING! Right now does not work AT ALL \n",
" (apart from one specific case)\n",
" \n",
" Input: array of int with len%3==0 (- PNG pixels),\n",
" int, *, colourlist, int\n",
" Output: str (of '1' and '0') or None\n",
" \"\"\"\n",
" \n",
" res = \"\"\n",
" \n",
" cols = len(pixels[0])\n",
" fb = find_first_pixel_of_colour(pixels, line_colour) # (11, 33)\n",
" # will only work if the first or second dibit is 0b11\n",
" ll = colour_line_length(pixels, fb, line_colour, deviations=20) # 10\n",
" sh = signal_height(pixels, fb) - 1 # 17 -1?\n",
" m = ll*3-2*3 # will only work if there is a transition\n",
" # (after the first dibit)\n",
" # We only need to check if the line is\n",
" # on the upper, middle upper or middle lower rows...\n",
" \n",
" for i in range(start, cols, m):\n",
" UP = withinDeviation([pixels[fb[0]][i], \n",
" pixels[fb[0]][i+1], \n",
" pixels[fb[0]][i+2]], \n",
" line_colour, d)\n",
" DOWN = withinDeviation([pixels[fb[0]+sh][i], \n",
" pixels[fb[0]+sh][i+1], \n",
" pixels[fb[0]+sh][i+2]], \n",
" line_colour, d)\n",
" almostUP = UP\n",
" # if UP:\n",
" # res = res + \"10\"\n",
" if DOWN: # elif DOWN:\n",
" res = res + \"00\"\n",
" # print(\"00\")\n",
" elif almostUP:\n",
" res = res + \"11\"\n",
" # print(\"11\")\n",
" else:\n",
" res = res + \"01\"\n",
" # print(\"01\")\n",
" \n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# A-a-and... here is magic!\n",
"\n",
"res = manchester(img, fr[1]+5*3, line_length, black, d=60, inv=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"ans = []\n",
"for i in range(0, len(res), 8):\n",
" ans.append(int('0b'+res[i:i+8], 2))\n",
"# print(ans)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"242\n",
"224\n",
"236\n"
]
}
],
"source": [
"for i in range(0, len(ans)):\n",
" print(ans[i])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Huzzah!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And _that_ is how we decode it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us now look at some specific examples."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"# Here is a helper function to automate all that\n",
"\n",
"def parse_code(path_to_file, code, inv=False):\n",
" \"\"\"Guess what... Parses a line code PNG\n",
" \n",
" Input: str, function \n",
" (~coinsides with the name of the code)\n",
" Output: str (of '1' and '0') or (maybe?) None\n",
" \"\"\"\n",
" \n",
" r1 = png.Reader(path_to_file)\n",
" t1 = r1.asRGB()\n",
" img1 = list(t1[2])\n",
" fr1 = find_first_pixel_of_colour(img1, accepted_colours)\n",
" line_length1 = colour_line_length(img1, \n",
" fr1, accepted_colours, deviations=20)\n",
" \n",
" res1 = code(img1, fr1[1]+5*3, line_length1, black, d=60, inv=inv)\n",
" \n",
" return res1"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"def print_nums(bitesstr):\n",
" \"\"\"I hope you get the gist...\n",
" \n",
" Input: str\n",
" Output: list (side effects - prints...)\n",
" \"\"\"\n",
" \n",
" ans1 = []\n",
" for i in range(0, len(bitesstr), 8):\n",
" ans1.append(int('0b'+bitesstr[i:i+8], 2))\n",
" \n",
" for i in range(0, len(ans1)):\n",
" print(ans1[i])\n",
" \n",
" return ans1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Manchester Code\n",
"#### _(a rather tricky example)_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a tricky example of Manchester code - where we have ASCII '0's and '1's with which a 3-letter \"word\" is encoded."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"49\n",
"49\n",
"49\n",
"48\n",
"49\n",
"49\n",
"49\n",
"49\n",
"49\n",
"49\n",
"49\n",
"48\n",
"49\n",
"49\n",
"48\n",
"49\n",
"49\n",
"49\n",
"49\n",
"48\n",
"49\n",
"48\n",
"49\n",
"49\n"
]
}
],
"source": [
"ans1 = print_nums(parse_code(\"Line_Code_PNGs/Manchester.png\", manchester))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"239\n",
"237\n",
"235\n"
]
}
],
"source": [
"res2d = \"\"\n",
"for i in range(0, len(ans1)):\n",
" res2d += chr(ans1[i])\n",
"\n",
"ans2d = []\n",
"for i in range(0, len(res2d), 8):\n",
" print(int('0b'+res2d[i:i+8], 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NRZ"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"237\n",
"243\n",
"241\n"
]
}
],
"source": [
"ans2 = print_nums(parse_code(\"Line_Code_PNGs/NRZ.png\", nrz))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2B1Q"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Warning! 2B1Q is currently almost completely broken.__ Pull requests with correct solutions are welcome :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"inputHidden": false,
"outputHidden": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"49\n",
"49\n",
"49\n",
"48\n",
"49\n",
"49\n",
"48\n",
"49\n",
"49\n",
"49\n",
"49\n",
"49\n",
"48\n",
"48\n",
"48\n",
"48\n",
"49\n",
"49\n",
"49\n",
"48\n",
"49\n",
"49\n",
"48\n",
"48\n"
]
}
],
"source": [
"ans3 = print_nums(parse_code(\"Line_Code_PNGs/2B1Q.png\", code2B1Q))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"237\n",
"240\n",
"236\n"
]
}
],
"source": [
"res2d3 = \"\"\n",
"for i in range(0, len(ans3)):\n",
" res2d3 += chr(ans3[i])\n",
"\n",
"ans2d3 = []\n",
"for i in range(0, len(res2d3), 8):\n",
" print(int('0b'+res2d3[i:i+8], 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernel_info": {
"name": "python3"
},
"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"
},
"nteract": {
"version": "0.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ituethoslab/navcom-2017 | Intro to git and GitHub.ipynb | 1 | 1904 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Intro to *git* and GitHub\n",
"\n",
"*git* is a version control system, especially for software code, and other things too such as data.\n",
"\n",
"GitHub is an online service, which provides *git* service. GitHub has also other features, such as bug tracking and also website for projects. Both *git* and GitHub are very popular among software developers and other kinds of developers too.\n",
"\n",
"## *git* on Navigating Complexity\n",
"\n",
"On *Navigating Complexity* we use *git* and GitHub simply to download exercise sheets. You are encouraged to learn to use them for coordinating your group work too.\n",
"\n",
"Some of the [Thursday exercises are distributed via GitHub](https://github.com/ituethoslab/navcom-2017). Once you have git installed, get them on your computer by running the following command once on the command line, at the beginning of the course.\n",
"\n",
" git clone https://github.com/ituethoslab/navcom-2017.git\n",
"\n",
"The above will create a directory called `navcom-2017`. Afterwards, when you run the following two commands on the command line once a week at the beginning of the exercises, you will receive updates\n",
"\n",
" cd navcom-2017\n",
" git pull\n",
" \n",
"Do that every Thursday when we work with exercises which are delivered via GitHub."
]
}
],
"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": 2
}
| gpl-3.0 |
kkwteh/cdips_hpi_forecast | ZHVI_Exploration.ipynb | 1 | 248103 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using environment in /Users/emunsing/Documents/Coding/github/cdips_hpi_forecast/env\n",
"Python version 3.5.2 (default, Oct 31 2016, 16:50:28) \n",
"[GCC 4.2.1 Compatible Apple LLVM 7.3.0 (clang-703.0.31)]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.pyplot import cm \n",
"% matplotlib inline\n",
"\n",
"import sys, os, copy\n",
"print(\"Using environment in \"+sys.prefix)\n",
"print(\"Python version \"+sys.version)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inputFiles = {'all': 'Metro_Zhvi_AllHomes.csv',\n",
" 'sft': 'Metro_MedianValuePerSqft_AllHomes.csv',\n",
" '1br': 'Metro_Zhvi_1bedroom.csv',\n",
" '2br': 'Metro_Zhvi_2bedroom.csv',\n",
" '3br': 'Metro_Zhvi_3bedroom.csv',\n",
" '4br': 'Metro_Zhvi_4bedroom.csv',\n",
" '5br': 'Metro_Zhvi_5BedroomOrMore.csv',\n",
" 'top': 'Metro_Zhvi_TopTier.csv',\n",
" 'bottom': 'Metro_Zhvi_BottomTier.csv',\n",
" 'condos': 'Metro_Zhvi_Condominum.csv',\n",
" 'single': 'Metro_Zhvi_SingleFamilyResidence.csv',\n",
" }\n",
"\n",
"# Map Zillow RegionID to metro area string\n",
"metroRegionID = {'Dallas-Fort Worth, TX':394514,\n",
" 'Atlanta, GA':394347,\n",
" 'Phoenix, AZ':394976,\n",
" 'Las Vegas, NV':394775 }\n",
"\n",
"metroString = {'Dallas':u'Dallas-Fort Worth, TX',\n",
" 'Atlanta':u'Atlanta, GA',\n",
" 'Phoenix':u'Phoenix, AZ',\n",
" 'Vegas':u'Las Vegas, NV'}\n",
"\n",
"orderedColumns = ['top','5br','4br','3br','2br','1br','bottom','condos','single','sft','all']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Goal: check how prices for each of the houses are correlated for each of the regions.\n",
"\n",
"startDate = '2003-01'\n",
"endDate = None\n",
"\n",
"# We'll have a different dataframe for each city, and pull them together in a Dictionary\n",
"allCities = {city:pd.DataFrame() for city in metroString.keys()}\n",
"allCitiesNormed = copy.deepcopy(allCities) # This will hold the normalized data\n",
"\n",
"\n",
"for houseType in inputFiles.keys():\n",
" # Read data into a temporary data file, then pull out the city and time period which we want.\n",
" df = pd.read_csv('Data/ZHVI/'+inputFiles[houseType],index_col=[1])\n",
" df = df.drop(['RegionID','SizeRank'],axis=1)\n",
" df.columns = pd.DatetimeIndex(df.columns)\n",
" \n",
" for city in metroString.keys():\n",
" allCities[city][houseType] = df.loc[ metroString[city],startDate:endDate]\n",
" \n",
"# Clean up the data\n",
"for city in metroString.keys():\n",
" allCities[city] = allCities[city][orderedColumns]\n",
" allCitiesNormed[city] = allCities[city] / allCities[city].iloc[0,:]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x111aeeda0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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1HJ197jmWG55Uaq7fUobuziUvt/WrtTCExWIhPj4+7vjx46633nprxsCBA4uh6ZYzrGlM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\n9BPGhA0Gg8Fg6Cf+P64+eJvbbobaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111aeef98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot a single city\n",
"allCitiesNormed[city].plot(colormap=cm.rainbow)\n",
"plt.ylabel('Price relative to Jan 2013')\n",
"plt.title(city)\n",
"plt.legend(bbox_to_anchor=(1.3, 1))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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9Orj8cuHa6wR//yP/jxzIUF6am8G4NVnUiyzU/z8FUWZ/DowARuey/h5glar2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p2dO3eaXnzxxVBFUdBqtfLjjz9OfeaZZ8Iudt20adNODhs2LPLdd98N7tq1a7Gb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\nKaWUUhlBGydKKaWUUkqpjPD/Af3JEKLic3HWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114895208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(12,8))\n",
"\n",
"for i in range( len(metroString) ):\n",
" twobits = '{0:02b}'.format(i)\n",
" row = int(twobits[-1])\n",
" col = int(twobits[-2])\n",
" city = list(metroString.keys())[i]\n",
"\n",
" # Normalized prices\n",
" allCitiesNormed[city].plot(colormap=cm.rainbow, ax=axes[row,col],title=city,legend=None)\n",
" axes[row,col].set_ylabel('Price relative to January 2013')\n",
" \n",
"# # Raw prices\n",
"# allCities[city].plot(colormap=cm.rainbow, ax=axes[row,col],title=city,legend=None)\n",
"# axes[row,col].set_ylabel('Home Price ($)')\n",
"\n",
"plt.suptitle('Normalized Prices for all Cities')\n",
"plt.legend(bbox_to_anchor=(1.3, 1))\n",
"plt.show()"
]
}
],
"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": 1
}
| mit |
gis4dis/poster | jupyter-notebooks/AD Experimentation.ipynb | 1 | 23402 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../src/')\n",
"sys.path.insert(0, '../')\n",
"\n",
"import django\n",
"django.setup()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from importlib import reload\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, clear_output\n",
"from ipywidgets import interact, interact_manual, Layout"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import luminol.anomaly_detector as lad\n",
"\n",
"from apps.utils.time import UTC_P0100\n",
"import apps.ad.anomaly_detection as ad\n",
"\n",
"from apps.mc.api.util import get_topics, get_property, get_time_slots, get_features_of_interest, get_aggregating_process, get_observation_getter\n",
"\n",
"from psycopg2.extras import DateTimeTZRange"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"phenomenon_date_from = pd.to_datetime(\"2019-01-01\")\n",
"phenomenon_date_to = pd.to_datetime(\"2019-03-30\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"d_topic = get_topics()[0]\n",
"d_prop = get_property(d_topic)[0]\n",
"d_feature = get_features_of_interest(d_topic, d_prop)[0]\n",
"d_time_slot = get_time_slots(d_topic)[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"pixiedust": {
"displayParams": {}
},
"scrolled": true
},
"outputs": [],
"source": [
"def detect_anomalies(\n",
" phenomenon_date_from = phenomenon_date_from,\n",
" phenomenon_date_to = phenomenon_date_to,\n",
" detector_method='bitmap_mod',\n",
" use_baseline=True,\n",
" shift=True,\n",
" extend_range=True,\n",
" detector_params={\n",
" \"precision\": 6,\n",
" \"lag_window_size\": 96,\n",
" \"future_window_size\": 96,\n",
" \"chunk_size\": 2\n",
" },\n",
" topic = d_topic,\n",
" prop = d_prop,\n",
" feature = d_feature,\n",
" time_slot = d_time_slot\n",
"):\n",
"\n",
" pt_range_z = DateTimeTZRange(\n",
" pd.to_datetime(phenomenon_date_from).replace(tzinfo=UTC_P0100),\n",
" pd.to_datetime(phenomenon_date_to).replace(tzinfo=UTC_P0100)\n",
" )\n",
" \n",
" get_func, feature_time_slots = get_observation_getter(\n",
" topic,\n",
" prop,\n",
" time_slot,\n",
" feature,\n",
" pt_range_z\n",
" )\n",
" \n",
" anoms = ad.get_timeseries(\n",
" phenomenon_time_range=pt_range_z,\n",
" num_time_slots=len(feature_time_slots),\n",
" get_observations=get_func,\n",
" detector_method=detector_method,\n",
" detector_params=detector_params,\n",
" shift=shift,\n",
" use_baseline=use_baseline,\n",
" extend_range=extend_range,\n",
" )\n",
" \n",
" anoms[\"feature_time_slots\"] = feature_time_slots\n",
"\n",
" return anoms"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def highlight(indices, alpha, color, ax):\n",
" i=0\n",
" while i<len(indices):\n",
" ax.axvspan(indices[i]-0.5, indices[i]+0.5, facecolor=color, edgecolor='none', alpha=alpha)\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"colors = ['r', 'g', 'c', 'm', 'y', 'k']\n",
"results = []\n",
"\n",
"plt.ioff()\n",
"\n",
"def plot(detectors, hlt_detector):\n",
" results = detectors\n",
" \n",
" plt.close()\n",
" fig, ax1 = plt.subplots(figsize=(20,7))\n",
" \n",
" hs = pd.DataFrame({\n",
" 'anomalies': detectors[hlt_detector][\"property_anomaly_rates\"]\n",
" })\n",
" \n",
" if detectors[hlt_detector][\"property_anomaly_percentiles\"]:\n",
" perc = detectors[hlt_detector][\"property_anomaly_percentiles\"]\n",
" color = colors[list(detectors.keys()).index(hlt_detector)]\n",
" for p in perc.keys():\n",
" highlight(hs[hs['anomalies'] > perc[p]].index, p*0.0025, color, ax1)\n",
" \n",
" first_result = detectors[list(detectors.keys())[0]]\n",
" \n",
" lns = []\n",
" \n",
" if first_result[\"property_values\"]:\n",
" ts = pd.DataFrame({\n",
" 'values': [float(n) if n is not None else n for n in first_result[\"property_values\"]]\n",
" }, index=[n.lower.strftime(\"%-d.%-m.%Y\") for n in first_result[\"feature_time_slots\"]])\n",
" \n",
" values_line = ts['values'].plot.line(ax=ax1, color='b')\n",
" ax1.set_ylabel('values', color='b')\n",
" ax1.tick_params('y', colors='b')\n",
"\n",
" lns.append(values_line.get_lines()[0])\n",
" \n",
" for i in range(len(detectors.keys())):\n",
" detector = list(detectors.keys())[i]\n",
" color = colors[i]\n",
" anomalies = detectors[detector][\"property_anomaly_rates\"]\n",
"\n",
" if anomalies:\n",
" ts = pd.DataFrame({\n",
" 'anomalies': anomalies\n",
" })\n",
"\n",
" ax2 = ax1.twinx()\n",
" anomalies_line = ts['anomalies'].plot.line(ax=ax2, color=color, label=detector)\n",
" lns.append(anomalies_line.get_lines()[0])\n",
" ax2.tick_params('y', colors=color)\n",
" \n",
"\n",
" if lns:\n",
" labs = [ln.get_label() for ln in lns]\n",
" ax1.legend(lns, labs, loc=1)\n",
" \n",
"# baseName = f\"{baserange.lower.date()}..{baserange.upper.date()}\"\n",
"# if first_result['phenomenon_time_range']:\n",
"# rangeName = f\"{first_result['phenomenon_time_range'].lower.date()}..{first_result['phenomenon_time_range'].upper.date()}\"\n",
"# plt.savefig(f\"graphs/{baseName}_{rangeName}_window-{str(window_size)}_prec-{str(detector_params['precision'])}.png\", format=\"png\")\n",
"# plt.savefig(f\"graphs/{rangeName}_window-{str(window_size)}_prec-{str(detector_params['precision'])}.png\", format=\"png\")\n",
" \n",
" return fig"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"detectors = {\n",
" \"Bitmap mod\": \"bitmap_mod\",\n",
" \"Bitmap diminishing\": \"bitmap_diminishing\",\n",
" \"Bitmap diminishing baseline\": \"bitmap_diminishing_bl\",\n",
" \"Bitmap mod shift\": \"bitmap_mod_shift\",\n",
" \"LinkedIn bitmap\": \"bitmap_detector\",\n",
" \"Default\": \"default_detector\",\n",
" \"Derivative\": \"derivative_detector\",\n",
" \"Exponential average\": \"exp_avg_detector\",\n",
"# \"Absolute threshold\": \"absolute_threshold\",\n",
"# \"Diff Percent\": \"diff_percent_threshold\",\n",
"# \"Sign test\": \"sign_test\",\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def val_bitmap_mod(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"bitmap_mod\", shift=False, feature=feature, prop=prop, time_slot=time_slot, detector_params=detector_params)\n",
"def val_bitmap_diminishing(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"bitmap_diminishing\", shift=False, use_baseline=False, feature=feature, prop=prop, time_slot=time_slot, detector_params=detector_params)\n",
"def val_bitmap_diminishing_bl(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"bitmap_diminishing\", shift=False, feature=feature, prop=prop, time_slot=time_slot, detector_params=detector_params)\n",
"def val_bitmap_mod_shift(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"bitmap_mod_shift\", feature=feature, prop=prop, time_slot=time_slot, detector_params=detector_params)\n",
"def val_bitmap_detector(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"bitmap_detector\", shift=False, use_baseline=False, feature=feature, prop=prop, time_slot=time_slot, detector_params=detector_params)\n",
"def val_default_detector(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"default_detector\", feature=feature, prop=prop, time_slot=time_slot, detector_params={}, use_baseline=False, extend_range=False, shift=False)\n",
"def val_derivative_detector(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"derivative_detector\", feature=feature, prop=prop, time_slot=time_slot, detector_params={}, use_baseline=False, extend_range=False, shift=False)\n",
"def val_exp_avg_detector(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(start_date, end_date, \"exp_avg_detector\", feature=feature, prop=prop, time_slot=time_slot, detector_params={}, use_baseline=False, extend_range=False, shift=False)\n",
"def val_absolute_threshold(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(t_from, t_to, \"absolute_threshold\", feature=feature, prop=prop, time_slot=time_slot, detector_params={}, use_baseline=False, extend_range=False, shift=False)\n",
"def val_diff_percent_threshold(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(t_from, t_to, \"diff_percent_threshold\", feature=feature, prop=prop, time_slot=time_slot, detector_params={}, use_baseline=False, extend_range=False, shift=False)\n",
"def val_sign_test(feature, prop, time_slot, start_date, end_date, detector_params):\n",
" return detect_anomalies(t_from, t_to, \"sign_test\", feature=feature, prop=prop, time_slot=time_slot, detector_params={}, use_baseline=False, extend_range=False, shift=False)\n",
"\n",
"def plot_anomalies(feature, prop, time_slot, start_date, end_date, precision, window_size, chunk_size, hlt_detector, bitmap_mod, bitmap_diminishing, bitmap_diminishing_bl, bitmap_mod_shift, bitmap_detector, default_detector, derivative_detector, exp_avg_detector):\n",
" args = [feature, prop, time_slot, start_date, end_date, {\n",
" \"precision\": precision,\n",
" \"lag_window_size\": window_size,\n",
" \"future_window_size\": window_size,\n",
" \"chunk_size\": chunk_size\n",
" }]\n",
" anomalies = {}\n",
" \n",
" if bitmap_mod:\n",
" anomalies[\"bitmap_mod\"] = val_bitmap_mod(*args)\n",
" if bitmap_diminishing:\n",
" anomalies[\"bitmap_diminishing\"] = val_bitmap_diminishing(*args)\n",
" if bitmap_diminishing_bl:\n",
" anomalies[\"bitmap_diminishing_bl\"] = val_bitmap_diminishing_bl(*args)\n",
" if bitmap_mod_shift:\n",
" anomalies[\"bitmap_mod_shift\"] = val_bitmap_mod_shift(*args)\n",
" if bitmap_detector:\n",
" anomalies[\"bitmap_detector\"] = val_bitmap_detector(*args)\n",
" if default_detector:\n",
" anomalies[\"default_detector\"] = val_default_detector(*args)\n",
" if derivative_detector:\n",
" anomalies[\"derivative_detector\"] = val_derivative_detector(*args)\n",
" if exp_avg_detector:\n",
" anomalies[\"exp_avg_detector\"] = val_exp_avg_detector(*args)\n",
" \n",
"# if absolute_threshold:\n",
"# anomalies[\"absolute_threshold\"] = val_absolute_threshold(*args)\n",
"# if diff_percent_threshold:\n",
"# anomalies[\"diff_percent_threshold\"] = val_diff_percent_threshold(*args)\n",
"# if sign_test:\n",
"# anomalies[\"sign_test\"] = val_sign_test(*args)\n",
" \n",
" \n",
" if len(anomalies.keys()) > 1:\n",
" i = 0\n",
" while hlt_detector not in anomalies.keys():\n",
" hlt_detector = detectors[i]\n",
" i += 1\n",
" return plot(anomalies, hlt_detector)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"t_from = \"2019-01-01\"\n",
"t_to = \"2019-03-30\"\n",
"\n",
"def hlt_detectors():\n",
" d = {}\n",
" if bitmap_mod_widget.value: d[\"Bitmap mod\"] = \"bitmap_mod\"\n",
" if bitmap_diminishing_widget.value: d[\"Bitmap diminishing\"] = \"bitmap_diminishing\"\n",
" if bitmap_diminishing_bl_widget.value: d[\"Bitmap diminishing baseline\"] = \"bitmap_diminishing_bl\"\n",
" if bitmap_mod_shift_widget.value: d[\"Bitmap mod shift\"] = \"bitmap_mod_shift\"\n",
" if bitmap_detector_widget.value: d[\"LinkedIn bitmap\"] = \"bitmap_detector\"\n",
" if default_detector_widget.value: d[\"Default\"] = \"default_detector\"\n",
" if derivative_detector_widget.value: d[\"Derivative\"] = \"derivative_detector\"\n",
" if exp_avg_detector_widget.value: d[\"Exponential average\"] = \"exp_avg_detector\"\n",
"# if absolute_threshold_widget.value: d[\"Absolute threshold\"] = \"absolute_threshold\"\n",
"# if diff_percent_threshold_widget.value: d[\"Diff Percent\"] = \"diff_percent_threshold\"\n",
"# if sign_test_widget.value: d[\"Sign test\"] = \"sign_test\"\n",
" return d\n",
"\n",
"def update_hlt_detectors(*args):\n",
" hlt_detector_widget.options = hlt_detectors()\n",
" \n",
"def update_property_widget(*args):\n",
" property_widget.options = get_property(topic_widget.value)\n",
"\n",
"def update_feature_widget(*args):\n",
" feature_widget.options = get_features_of_interest(topic_widget.value, property_widget.value)\n",
"\n",
"def update_time_slots_widget(*args):\n",
" time_slots_widget.options = get_time_slots(topic_widget.value)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"bitmap_mod_widget = widgets.Checkbox(value=False,description=\"Bitmap mod\")\n",
"bitmap_mod_widget.observe(update_hlt_detectors, \"value\")\n",
"bitmap_diminishing_widget = widgets.Checkbox(value=True,description=\"Bitmap diminishing\")\n",
"bitmap_diminishing_widget.observe(update_hlt_detectors, \"value\")\n",
"bitmap_diminishing_bl_widget = widgets.Checkbox(value=True,description=\"Bitmap diminishing baseline\")\n",
"bitmap_diminishing_bl_widget.observe(update_hlt_detectors, \"value\")\n",
"bitmap_mod_shift_widget = widgets.Checkbox(value=False,description=\"Bitmap mod shift\")\n",
"bitmap_mod_shift_widget.observe(update_hlt_detectors, \"value\")\n",
"bitmap_detector_widget = widgets.Checkbox(value=False,description=\"LinkedIn bitmap\")\n",
"bitmap_detector_widget.observe(update_hlt_detectors, \"value\")\n",
"default_detector_widget = widgets.Checkbox(value=False,description=\"Default\")\n",
"default_detector_widget.observe(update_hlt_detectors, \"value\")\n",
"derivative_detector_widget = widgets.Checkbox(value=False,description=\"Derivative\")\n",
"derivative_detector_widget.observe(update_hlt_detectors, \"value\")\n",
"exp_avg_detector_widget = widgets.Checkbox(value=False,description=\"Exponential average\")\n",
"exp_avg_detector_widget.observe(update_hlt_detectors, \"value\")\n",
"# absolute_threshold_widget = widgets.Checkbox(value=False,description=\"Absolute threshold\")\n",
"# absolute_threshold_widget.observe(update_hlt_detectors, \"value\")\n",
"# diff_percent_threshold_widget = widgets.Checkbox(value=False,description=\"Diff Percent\")\n",
"# diff_percent_threshold_widget.observe(update_hlt_detectors, \"value\")\n",
"# sign_test_widget = widgets.Checkbox(value=False,description=\"Sign test\")\n",
"# sign_test_widget.observe(update_hlt_detectors, \"value\")\n",
"\n",
"detector_widgets = widgets.HBox([\n",
" widgets.VBox([\n",
" bitmap_mod_widget,\n",
" bitmap_diminishing_widget,\n",
" bitmap_diminishing_bl_widget,\n",
" bitmap_detector_widget,\n",
" ]),\n",
" widgets.VBox([\n",
" bitmap_mod_shift_widget,\n",
" default_detector_widget,\n",
" derivative_detector_widget,\n",
" exp_avg_detector_widget,\n",
" ]),\n",
"# widgets.VBox([\n",
" # absolute_threshold_widget,\n",
" # diff_percent_threshold_widget,\n",
" # sign_test_widget\n",
"# ])\n",
"])\n",
"\n",
"hlt_detector_widget = widgets.Dropdown(options=hlt_detectors(), value=detectors[\"Bitmap diminishing\"], description=\"Highlight\")\n",
"\n",
"precision_widget = widgets.IntSlider(value=6, min=2, max=16, step=1, description=\"Precision\")\n",
"window_size_widget = widgets.BoundedIntText(value=96, min=4, max=256, step=1, description=\"Window size\")\n",
"chunk_size_widget = widgets.IntSlider(value=2, min=2, max=16, step=1, description=\"Chunk size\")\n",
"\n",
"topic_widget = widgets.Dropdown(options=get_topics(), description=\"Topic\")\n",
"topic_widget.observe(update_property_widget, \"value\")\n",
"topic_widget.observe(update_feature_widget, \"value\")\n",
"topic_widget.observe(update_time_slots_widget, \"value\")\n",
"property_widget = widgets.Dropdown(options=get_property(topic_widget.value), description=\"Property\")\n",
"property_widget.observe(update_feature_widget, \"value\")\n",
"feature_widget = widgets.Dropdown(options=get_features_of_interest(topic_widget.value, property_widget.value), description=\"Station\")\n",
"time_slot_widget = widgets.Dropdown(options=get_time_slots(topic_widget.value), description=\"Aggregate to\")\n",
"\n",
"start_date_widget = widgets.DatePicker(value=pd.to_datetime(t_from).date(), description=\"Start date\")\n",
"end_date_widget = widgets.DatePicker(value=pd.to_datetime(t_to).date(), description=\"End date\")\n",
"\n",
"date_widgets = widgets.HBox([\n",
" start_date_widget,\n",
" end_date_widget\n",
"])\n",
"\n",
"# ui = widgets.Tab(children=[\n",
"widget_accordion = widgets.Accordion(children=[\n",
" widgets.VBox([\n",
" widgets.HBox([topic_widget, property_widget]),\n",
" widgets.HBox([feature_widget, time_slot_widget]),\n",
" date_widgets\n",
" ]),\n",
" widgets.VBox([\n",
" precision_widget,\n",
" window_size_widget,\n",
" chunk_size_widget\n",
" ]),\n",
" widgets.VBox([\n",
" detector_widgets,\n",
" hlt_detector_widget\n",
" ])\n",
"],\n",
"# layout=Layout(\n",
"# height='300px',\n",
"# display='flex',\n",
"# align_items='center',\n",
"# justify_content='center'\n",
"# )\n",
")\n",
"\n",
"widget_accordion.set_title(0, \"General\")\n",
"widget_accordion.set_title(1, \"Detector parameters\")\n",
"widget_accordion.set_title(2, \"Detectors used\")\n",
"widget_accordion.selected_index = 0\n",
"\n",
"out = widgets.Output()\n",
"\n",
"plot_button = widgets.Button(\n",
" description=\"Plot\"\n",
")\n",
"\n",
"def click(b):\n",
" \n",
" fig = plot_anomalies(\n",
" bitmap_mod=bitmap_mod_widget.value,\n",
" bitmap_diminishing=bitmap_diminishing_widget.value,\n",
" bitmap_diminishing_bl=bitmap_diminishing_bl_widget.value,\n",
" bitmap_mod_shift=bitmap_mod_shift_widget.value,\n",
" bitmap_detector=bitmap_detector_widget.value,\n",
" default_detector=default_detector_widget.value,\n",
" derivative_detector=derivative_detector_widget.value,\n",
" exp_avg_detector=exp_avg_detector_widget.value,\n",
" # absolute_threshold=absolute_threshold_widget.value,\n",
" # diff_percent_threshold=diff_percent_threshold_widget.value,\n",
" # sign_test=sign_test_widget.value,\n",
" feature=feature_widget.value,\n",
" prop=property_widget.value,\n",
" time_slot=time_slot_widget.value,\n",
" start_date=start_date_widget.value,\n",
" end_date=end_date_widget.value,\n",
" precision=precision_widget.value,\n",
" window_size=window_size_widget.value,\n",
" chunk_size=chunk_size_widget.value,\n",
" hlt_detector=hlt_detector_widget.value\n",
" )\n",
" \n",
" with out:\n",
" clear_output(wait=True)\n",
" display(fig)\n",
"\n",
"plot_button.on_click(click)\n",
"\n",
"ui = widgets.VBox([\n",
" widget_accordion,\n",
" plot_button,\n",
" out\n",
"])\n",
"\n",
"def init():\n",
" reload(ad)\n",
" reload(lad)\n",
" display(ui)\n",
" click(None)\n",
" print(\"Aggregating process:\", get_aggregating_process(topic_widget.value, property_widget.value, feature_widget.value))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4bb85793b3f4419c91f68a13a7f53f61",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Accordion(children=(VBox(children=(HBox(children=(Dropdown(description='Topic', options=(<Topic…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aggregating process: arithmetic mean\n"
]
}
],
"source": [
"init()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Django Shell-Plus",
"language": "python",
"name": "django_extensions"
},
"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
}
| bsd-3-clause |
NeuroDataDesign/seelviz | Jupyter/.ipynb_checkpoints/Python Notebook Example-checkpoint.ipynb | 1 | 2771 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"os.chdir('/Users/Tony/Documents/Git Folder/seelviz/Jupyter/DownsampleGraphML')\n",
"\n",
"from argparse import ArgumentParser\n",
"from collections import OrderedDict\n",
"from subprocess import Popen\n",
"from scipy.stats import gaussian_kde\n",
"from matplotlib.backends.backend_pdf import PdfPages\n",
"\n",
"import numpy as np\n",
"import nibabel as nb\n",
"import networkx as nx\n",
"import os\n",
"import pickle\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.mlab as mlab\n",
"\n",
"import random"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.458981151759\n"
]
}
],
"source": [
"x = random.uniform(0, 1)\n",
"print x"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.58984995]\n",
" [ 0.95935512]\n",
" [ 0.33090203]\n",
" ..., \n",
" [ 0.35773899]\n",
" [ 0.28649891]\n",
" [ 0.10661699]]\n"
]
}
],
"source": [
"xhist = (np.random.rand(5000, 1))\n",
"print xhist"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pp = PdfPages('test.pdf')\n",
"\n",
"num_bins = 50\n",
"# the histogram of the data\n",
"n, bins, patches = plt.hist(xhist, num_bins, facecolor='green', alpha=0.5)\n",
"\n",
"plt.axis([0, 1, 0, 1000])\n",
"plt.xlabel('Values')\n",
"plt.ylabel('Counts')\n",
"plt.title(r'Histogram of Values vs Counts')\n",
"\n",
"# Tweak spacing to prevent clipping of ylabel\n",
"plt.subplots_adjust(left=0.15)\n",
"pp.savefig()\n",
"pp.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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
}
| apache-2.0 |
jni/notebooks | python-bioformats test run.ipynb | 1 | 77849 | {
"metadata": {
"name": "",
"signature": "sha256:11eb3cb1b5aab1e64af410417f84434f836ebb76370e4a49a5a33c794f344b59"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recently, the CellProfiler team at the Broad Institute [announced](https://groups.google.com/a/broadinstitute.org/forum/#!topic/cellprofiler-dev/QZJ1WpiJxuI) pre-release versions of the [python-javabridge](https://github.com/CellProfiler/python-javabridge) and [python-bioformats](https://github.com/CellProfiler/python-bioformats) packages available on PyPI. The latter enables easy import of many biological image formats into numpy arrays, without any of the [flailing around](http://ilovesymposia.com/2014/02/26/fiji-jython/) with converters in Jython that I'd fiddled with in the past.\n",
"\n",
"Before starting, you'll need Java development headers on your machine. I imagine that on Linux systems you'd do something like `sudo apt-get install java-devel`, but on OSX you head to Apple's [Developer downloads](https://developer.apple.com/downloads/index.action) page, which requires your Apple ID, and download a \"Java for OS X `<version>` Developer Package\".\n",
"\n",
"Then, simply run `pip install python-bioformats`, and you'll get both packages!\n",
"\n",
"Next, I follow the examples in the python-bioformats [documentation](http://pythonhosted.org/python-bioformats/) to try to read parts of a massive, 400+GB Leica file without giving my RAM a heart attack."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import javabridge as jv\n",
"import bioformats as bf\n",
"jv.start_vm(class_path=bf.JARS, max_heap_size=\"8G\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is imperative to specify the `max_heap_size` to be something bigger than the anemic default of 256MB. Otherwise, even reading in a small amount of metadata (in our case, a 27MB XML string) will fail. See the following post for more details:\n",
"\n",
"[https://github.com/CellProfiler/python-bioformats/issues/8](https://github.com/CellProfiler/python-bioformats/issues/8)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Change to the appropriate directory and get the file information\n",
"%cd /Volumes/LaCie/Data/zebrafish-lesion\n",
"%ls"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Volumes/LaCie/Data/zebrafish-lesion\n",
"\u001b[31m2d-post-SCI.lif-cut-PTZ-1.tif\u001b[m\u001b[m* \u001b[31msci-quant.lzf.h5\u001b[m\u001b[m*\r\n",
"\u001b[31mLong time Gfap 260314.lif\u001b[m\u001b[m* \u001b[31msci-quant.tif\u001b[m\u001b[m*\r\n",
"\u001b[31mim.png\u001b[m\u001b[m* \u001b[31mtifffile.py\u001b[m\u001b[m*\r\n",
"\u001b[31mim.tif\u001b[m\u001b[m* \u001b[31mtifffile.pyc\u001b[m\u001b[m*\r\n",
"\u001b[31mlineprofile.py\u001b[m\u001b[m* \u001b[30m\u001b[43mtime-series\u001b[m\u001b[m/\r\n",
"\u001b[31mlineprofile.pyc\u001b[m\u001b[m*\r\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filename = 'Long time Gfap 260314.lif'\n",
"rdr = bf.ImageReader(filename, perform_init=True)\n",
"reader = rdr.rdr\n",
"print(\"The number of series is %d.\" % reader.getSeriesCount())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The number of series is 179.\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next cell, we measure the size of the metadata. I'm maintaining it just for future reference."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"with open('Long time Gfap 260314.lif', \"rb\") as fd:\n",
" fd.read(9)\n",
" length = np.frombuffer(fd.read(4), \"<i4\")\n",
" print(length[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"27594197\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reader.setSeries(0)\n",
"print reader.getMetadataValue('Name')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try the XML approach again, see if it can be read with the new heap size:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"md = bf.get_omexml_metadata('Long time Gfap 260314.lif')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It works! Let's see if we can parse it..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(type(md))\n",
"print(len(md))\n",
"print(md[:10])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'unicode'>\n",
"66620949\n",
"<?xml vers\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so we have an XML string. Let's whip out an XML parser to make sense of it."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import xml\n",
"import xml.etree.ElementTree as ET\n",
"mdroot = ET.fromstring(md)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We figure out the parsing from the `xml` package doc [here](https://docs.python.org/2/library/xml.etree.elementtree.html#module-xml.etree.ElementTree). In our case, we are (for now) only interested in the `'Name'` attribute of each `Image` child of the root:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print([(a.tag, a.attrib) for a in list(list(mdroot[300]))])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[('{http://www.openmicroscopy.org/Schemas/OME/2011-06}AcquiredDate', {}), ('{http://www.openmicroscopy.org/Schemas/OME/2011-06}InstrumentRef', {'ID': 'Instrument:121'}), ('{http://www.openmicroscopy.org/Schemas/OME/2011-06}ObjectiveSettings', {'RefractiveIndex': '1.33', 'ID': 'Objective:121:0'}), ('{http://www.openmicroscopy.org/Schemas/OME/2011-06}Pixels', {'SizeT': '14', 'DimensionOrder': 'XYCZT', 'PhysicalSizeY': '0.445197265625', 'PhysicalSizeX': '0.445197265625', 'PhysicalSizeZ': '1.9912714979001302', 'SizeX': '1024', 'SizeY': '1024', 'SizeZ': '108', 'SizeC': '2', 'Type': 'uint8', 'ID': 'Pixels:121'})]\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"image_names = []\n",
"sizes_tzyxc = []\n",
"res_zyx = []\n",
"size_tags = ['Size' + c for c in ['T', 'Z', 'Y', 'X', 'C']]\n",
"res_tags = ['PhysicalSize' + c for c in ['Z', 'Y', 'X']]\n",
"for child in mdroot:\n",
" if child.tag.endswith('Image'):\n",
" image_names.append(child.attrib['Name'])\n",
" for grandchild in child:\n",
" if grandchild.tag.endswith('Pixels'):\n",
" att = grandchild.attrib\n",
" sizes_tzyxc.append(tuple([int(att[t])\n",
" for t in size_tags]))\n",
" res_zyx.append(tuple([float(att[t]) for t in res_tags]))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sizes_tzyxc[45]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"(1, 51, 1024, 1024, 2)"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for name, size in zip(image_names, sizes_tzyxc):\n",
" print(name + ' ' + str(size))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pre lesion 2x/Pos001_S001 (1, 53, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos002_S001 (1, 55, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos003_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos004_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos005_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos006_S001 (1, 58, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos007_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos008_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos009_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos010_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos011_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos012_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos013_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos014_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos015_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos016_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos017_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos018_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos019_S001 (1, 51, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos020_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos021_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos022_S001 (1, 52, 1024, 1024, 2)\n",
"Pre lesion 2x/Pos023_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos001_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos002_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos003_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos004_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos005_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos006_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos007_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos008_S001 (1, 55, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos009_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos010_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos011_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos012_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos013_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos014_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos015_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos016_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos017_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos018_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos019_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos020_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos021_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos022_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos023_S001 (1, 51, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos024_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos025_S001 (1, 52, 1024, 1024, 2)\n",
"Mark_and_Find_001/Pos026_S001 (1, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos001_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos002_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos003_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos004_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos005_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos006_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos007_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos008_S001 (34, 55, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos009_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos010_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos011_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos012_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos013_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos014_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos015_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos016_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos017_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos018_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos019_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos020_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos021_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos022_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos023_S001 (34, 51, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos024_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos025_S001 (34, 52, 1024, 1024, 2)\n",
"0 to 16.5h pSCI/Pos026_S001 (34, 51, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos001_S001 (37, 54, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos002_S001 (37, 55, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos003_S001 (37, 54, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos004_S001 (37, 54, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos005_S001 (37, 53, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos006_S001 (37, 59, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos007_S001 (37, 54, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos008_S001 (37, 55, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos009_S001 (37, 52, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos010_S001 (37, 3, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos011_S001 (37, 53, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos012_S001 (37, 55, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos013_S001 (37, 52, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos014_S001 (37, 64, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos015_S001 (37, 55, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos016_S001 (37, 55, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos017_S001 (37, 54, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos018_S001 (37, 86, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos019_S001 (37, 51, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos020_S001 (37, 52, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos021_S001 (37, 129, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos022_S001 (37, 60, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos023_S001 (37, 61, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos024_S001 (37, 52, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos025_S001 (37, 96, 1024, 1024, 2)\n",
"22.5h to 41h pSCI/Pos026_S001 (37, 71, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos001_S001 (14, 64, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos002_S001 (14, 63, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos003_S001 (14, 64, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos004_S001 (14, 70, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos005_S001 (14, 64, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos006_S001 (14, 72, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos007_S001 (14, 68, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos008_S001 (14, 62, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos009_S001 (14, 3, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos010_S001 (14, 3, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos011_S001 (14, 65, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos012_S001 (14, 66, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos013_S001 (14, 3, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos014_S001 (14, 63, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos015_S001 (14, 62, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos016_S001 (14, 70, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos017_S001 (14, 65, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos018_S001 (14, 81, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos019_S001 (14, 3, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos020_S001 (14, 3, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos021_S001 (14, 108, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos022_S001 (14, 64, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos023_S001 (14, 61, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos024_S001 (14, 3, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos025_S001 (14, 84, 1024, 1024, 2)\n",
"41h to 47.5 hpSCI/Pos026_S001 (14, 69, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos001_S001 (31, 64, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos002_S001 (31, 63, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos003_S001 (31, 64, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos004_S001 (31, 70, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos005_S001 (31, 64, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos006_S001 (31, 72, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos007_S001 (31, 68, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos008_S001 (31, 62, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos009_S001 (31, 3, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos010_S001 (31, 3, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos011_S001 (31, 65, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos012_S001 (31, 66, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos013_S001 (31, 3, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos014_S001 (31, 63, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos015_S001 (31, 62, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos016_S001 (31, 70, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos017_S001 (31, 65, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos018_S001 (31, 81, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos019_S001 (31, 3, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos020_S001 (31, 3, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos021_S001 (31, 108, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos022_S001 (31, 64, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos023_S001 (31, 61, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos024_S001 (31, 3, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos025_S001 (31, 84, 1024, 1024, 2)\n",
"47.5h to 63hpSCI/Pos026_S001 (31, 69, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos001_S001 (20, 64, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos002_S001 (20, 63, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos003_S001 (20, 64, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos004_S001 (20, 70, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos005_S001 (20, 64, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos006_S001 (20, 72, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos007_S001 (20, 68, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos008_S001 (20, 62, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos009_S001 (20, 3, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos010_S001 (20, 3, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos011_S001 (20, 65, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos012_S001 (20, 66, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos013_S001 (20, 3, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos014_S001 (20, 63, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos015_S001 (20, 62, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos016_S001 (20, 70, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos017_S001 (20, 65, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos018_S001 (20, 81, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos019_S001 (20, 3, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos020_S001 (20, 3, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos021_S001 (20, 108, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos022_S001 (20, 64, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos023_S001 (20, 61, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos024_S001 (20, 3, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos025_S001 (20, 84, 1024, 1024, 2)\n",
"63 to 72.5hpSCI/Pos026_S001 (20, 69, 1024, 1024, 2)\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(np.arange(0, 17, 0.5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"34"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"idx0 = 49\n",
"size0 = sizes_tzyxc[idx0]\n",
"nt, nz = size0[:2]\n",
"image5d0 = np.zeros(size0, np.uint8)\n",
"reader.setSeries(idx0)\n",
"for t in range(nt):\n",
" for z in range(nz):\n",
" image5d0[t, z] = rdr.read(z=z, t=t, series=idx0, rescale=False)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import vis\n",
"vis.cshow(image5d0[..., 0]) # success!"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"<matplotlib.image.AxesImage at 0x24755f990>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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f4sZmmefddaxM+A91n5diyS+ffJqzY2/SnltlgSncUsT7Z5epIhHKcmllnEYnwGw6MKlJ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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x246398c90>"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys; sys.path.append('/Users/nuneziglesiasj/projects/lesion/')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"squished = image5d0.sum(axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"squished = squished[..., 0]\n",
"squished.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"(34, 1024, 1024)"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from lesion import trace\n",
"profiles = [trace.trace_profile(img) for img in squished]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/Users/nuneziglesiasj/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.\n",
" warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n",
"/Users/nuneziglesiasj/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:65: RuntimeWarning: invalid value encountered in true_divide\n",
" ret, rcount, out=ret, casting='unsafe', subok=False)\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from matplotlib import pyplot as plt\n",
"plt.imshow(squished[-1])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def read_image(rdr, idx, sizes):\n",
" size = sizes[idx]\n",
" nt, nz = size[:2]\n",
" image = np.zeros(size[:-1], np.uint8)\n",
" for t in range(nt):\n",
" for z in range(nz):\n",
" image[t, z] = rdr.read(z=z, t=t, c=0, series=idx, rescale=False)\n",
" return image\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reader.setSeries(0)\n",
"print(sizes_tzyxc[0])\n",
"reader.getSizeZ()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1, 53, 1024, 1024, 2)\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"53"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"im = read_image(rdr, 0, sizes_tzyxc)\n",
"im.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"(1, 53, 1024, 1024)"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def parse_name(name):\n",
" pstart = name.find('Pos') + 3\n",
" pend = pstart + 3\n",
" position = int(name[pstart:pend])\n",
" if name.startswith('Pre'):\n",
" times = np.array([-1.0])\n",
" elif name.startswith('Mark'):\n",
" times = np.array([np.nan])\n",
" else:\n",
" tstart = 0\n",
" tend = min(name.find(' '), name.find('h'))\n",
" t0 = float(name[tstart:tend])\n",
" tstart = name.find('to ') + 3\n",
" tend = name[tstart:].find('h')\n",
" t1 = float(name[tstart:tstart + tend])\n",
" times = np.arange(t0, t1 + 0.01, 0.5)\n",
" return position, times"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"name = image_names[100]\n",
"tstart = 0\n",
"tend = min(name.find(' '), name.find('h'))\n",
"t0 = float(name[tstart:tend])\n",
"tstart = name.find('to ') + 3\n",
"tend = name[tstart:].find('h')\n",
"tend\n",
"print(name, name[tstart:tstart+tend])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"('22.5h to 41h pSCI/Pos026_S001', '41')\n"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"postimes = map(parse_name, image_names)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def traces_from_image(imt):\n",
" try:\n",
" return [trace.trace_profile(im) for im in imt.sum(axis=1)]\n",
" except:\n",
" return None"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"imt = read_image(rdr, 100, sizes_tzyxc)\n",
"trs = traces_from_image(imt)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"postimes[100]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 87,
"text": [
"(26, array([ 22.5, 23. , 23.5, 24. , 24.5, 25. , 25.5, 26. , 26.5,\n",
" 27. , 27.5, 28. , 28.5, 29. , 29.5, 30. , 30.5, 31. ,\n",
" 31.5, 32. , 32.5, 33. , 33.5, 34. , 34.5, 35. , 35.5,\n",
" 36. , 36.5, 37. , 37.5, 38. , 38.5, 39. , 39.5, 40. ,\n",
" 40.5, 41. ]))"
]
}
],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"squished = imt.sum(axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(trs[-2])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vis.cshow(squished[-2])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"prog = [np.min(a) / np.max(a) for a in trs]\n",
"plt.plot(prog)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_traces = []\n",
"for series_idx in range(len(image_names)):\n",
" try:\n",
" imt = read_image(rdr, series_idx, sizes_tzyxc)\n",
" trs = traces_from_image(imt)\n",
" except:\n",
" trs = None\n",
" all_traces.append(trs)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len([x for x in all_traces if x is None])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"0"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cPickle as pck\n",
"with open('Gfap-traces.pck', 'w') as trs_file:\n",
" pck.dump((postimes, all_traces), trs_file, protocol=-1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def min_over_max(tr):\n",
" return tr.min() / tr.max()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"unique_positions = len(np.unique([pos for pos, time in postimes]))\n",
"unique_positions"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"26"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = [None] * (unique_positions + 1)\n",
"for (pos, times), traces in zip(postimes, all_traces):\n",
" stats = np.array(map(min_over_max, traces))\n",
" xy = np.vstack((times[:len(stats)], stats))\n",
" if data[pos] is None:\n",
" data[pos] = xy\n",
" else:\n",
" data[pos] = np.hstack((data[pos], xy))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ex = data[2]\n",
"sel = ~np.isnan(ex.sum(axis=0))\n",
"plt.plot(ex[0, sel], ex[1, sel])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"def make_plot(position, xydata, fn=os.path.expanduser('~/Desktop/fig.png')):\n",
" fig = plt.figure(figsize=(7, 5), dpi=600)\n",
" sel = ~np.isnan(xydata.sum(axis=0))\n",
" times, ratios = xydata[:, sel]\n",
" plt.plot(times, ratios, lw=2, c='k')\n",
" for i in range(len(times) - 1):\n",
" diff = times[i+1] - times[i]\n",
" if diff > 0.5:\n",
" print times[i], times[i+1]\n",
" plt.fill_between((times[i] + 0.25, times[i+1] - 0.25), (1, 1),\n",
" color='k', alpha=0.3)\n",
" plt.xlim(-2, times.max() + 1)\n",
" plt.ylim(max(0, ratios.min() - 0.1), min(1, ratios.max() + 0.1))\n",
" plt.title('Position ' + str(position))\n",
" plt.savefig(fn, dpi=600)\n",
" plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 128
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"make_plot(1, data[1])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for position in range(1, len(data)):\n",
" make_plot(position, data[position], fn='plots/position%02i.png' % position)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Kill the VM at the end! I actually edited this first\n",
"# because it's easy to forget!\n",
"jv.kill_vm()"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
robertodias/study | python/movie_recom/model_2.ipynb | 3 | 26840 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"ratings_list = [i.strip().split(\"::\") for i in open('ml-1m/ratings.dat', 'r').readlines()]\n",
"users_list = [i.strip().split(\"::\") for i in open('ml-1m/users.dat', 'r').readlines()]\n",
"movies_list = [i.strip().split(\"::\") for i in open('ml-1m/movies.dat', 'r').readlines()]\n",
"\n",
"ratings_df = pd.DataFrame(ratings_list, columns = ['UserID', 'MovieID', 'Rating', 'Timestamp'], dtype = int)\n",
"movies_df = pd.DataFrame(movies_list, columns = ['MovieID', 'Title', 'Genres'])\n",
"movies_df['MovieID'] = movies_df['MovieID'].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>MovieID</th>\n",
" <th>Title</th>\n",
" <th>Genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Toy Story (1995)</td>\n",
" <td>Animation|Children's|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Jumanji (1995)</td>\n",
" <td>Adventure|Children's|Fantasy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Grumpier Old Men (1995)</td>\n",
" <td>Comedy|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Waiting to Exhale (1995)</td>\n",
" <td>Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Father of the Bride Part II (1995)</td>\n",
" <td>Comedy</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MovieID Title Genres\n",
"0 1 Toy Story (1995) Animation|Children's|Comedy\n",
"1 2 Jumanji (1995) Adventure|Children's|Fantasy\n",
"2 3 Grumpier Old Men (1995) Comedy|Romance\n",
"3 4 Waiting to Exhale (1995) Comedy|Drama\n",
"4 5 Father of the Bride Part II (1995) Comedy"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movies_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>UserID</th>\n",
" <th>MovieID</th>\n",
" <th>Rating</th>\n",
" <th>Timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>978300760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>661</td>\n",
" <td>3</td>\n",
" <td>978302109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>914</td>\n",
" <td>3</td>\n",
" <td>978301968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>3408</td>\n",
" <td>4</td>\n",
" <td>978300275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>2355</td>\n",
" <td>5</td>\n",
" <td>978824291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UserID MovieID Rating Timestamp\n",
"0 1 1193 5 978300760\n",
"1 1 661 3 978302109\n",
"2 1 914 3 978301968\n",
"3 1 3408 4 978300275\n",
"4 1 2355 5 978824291"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ratings_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>MovieID</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>...</th>\n",
" <th>3943</th>\n",
" <th>3944</th>\n",
" <th>3945</th>\n",
" <th>3946</th>\n",
" <th>3947</th>\n",
" <th>3948</th>\n",
" <th>3949</th>\n",
" <th>3950</th>\n",
" <th>3951</th>\n",
" <th>3952</th>\n",
" </tr>\n",
" <tr>\n",
" <th>UserID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.0</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",
" <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>2</th>\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",
" <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>3</th>\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",
" <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>4</th>\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",
" <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>5</th>\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>2.0</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>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",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 3706 columns</p>\n",
"</div>"
],
"text/plain": [
"MovieID 1 2 3 4 5 6 7 8 9 10 ... \\\n",
"UserID ... \n",
"1 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"5 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 ... \n",
"\n",
"MovieID 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 \n",
"UserID \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
"[5 rows x 3706 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"R_df = ratings_df.pivot(index = 'UserID', columns ='MovieID', values = 'Rating').fillna(0)\n",
"R_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"R = R_df.as_matrix()\n",
"user_ratings_mean = np.mean(R, axis = 1)\n",
"R_demeaned = R - user_ratings_mean.reshape(-1, 1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from scipy.sparse.linalg import svds\n",
"U, sigma, Vt = svds(R_demeaned, k = 50)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"sigma = np.diag(sigma)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"all_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) + user_ratings_mean.reshape(-1, 1)\n",
"preds_df = pd.DataFrame(all_user_predicted_ratings, columns = R_df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User 837 has already rated 69 movies.\n",
"Recommending the highest 10 predicted ratings movies not already rated.\n"
]
}
],
"source": [
"def recommend_movies(predictions_df, userID, movies_df, original_ratings_df, num_recommendations=5):\n",
" \n",
" # Get and sort the user's predictions\n",
" user_row_number = userID - 1 # UserID starts at 1, not 0\n",
" sorted_user_predictions = predictions_df.iloc[user_row_number].sort_values(ascending=False)\n",
" \n",
" # Get the user's data and merge in the movie information.\n",
" user_data = original_ratings_df[original_ratings_df.UserID == (userID)]\n",
" user_full = (user_data.merge(movies_df, how = 'left', left_on = 'MovieID', right_on = 'MovieID').\n",
" sort_values(['Rating'], ascending=False)\n",
" )\n",
"\n",
" print 'User {0} has already rated {1} movies.'.format(userID, user_full.shape[0])\n",
" print 'Recommending the highest {0} predicted ratings movies not already rated.'.format(num_recommendations)\n",
" \n",
" # Recommend the highest predicted rating movies that the user hasn't seen yet.\n",
" recommendations = (movies_df[~movies_df['MovieID'].isin(user_full['MovieID'])].\n",
" merge(pd.DataFrame(sorted_user_predictions).reset_index(), how = 'left',\n",
" left_on = 'MovieID',\n",
" right_on = 'MovieID').\n",
" rename(columns = {user_row_number: 'Predictions'}).\n",
" sort_values('Predictions', ascending = False).\n",
" iloc[:num_recommendations, :-1]\n",
" )\n",
"\n",
" return user_full, recommendations\n",
"\n",
"already_rated, predictions = recommend_movies(preds_df, 837, movies_df, ratings_df, 10)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" .dataframe thead tr:only-child th {\n",
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" 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>UserID</th>\n",
" <th>MovieID</th>\n",
" <th>Rating</th>\n",
" <th>Timestamp</th>\n",
" <th>Title</th>\n",
" <th>Genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>837</td>\n",
" <td>858</td>\n",
" <td>5</td>\n",
" <td>975360036</td>\n",
" <td>Godfather, The (1972)</td>\n",
" <td>Action|Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>837</td>\n",
" <td>1387</td>\n",
" <td>5</td>\n",
" <td>975360036</td>\n",
" <td>Jaws (1975)</td>\n",
" <td>Action|Horror</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>837</td>\n",
" <td>2028</td>\n",
" <td>5</td>\n",
" <td>975360089</td>\n",
" <td>Saving Private Ryan (1998)</td>\n",
" <td>Action|Drama|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>837</td>\n",
" <td>1221</td>\n",
" <td>5</td>\n",
" <td>975360036</td>\n",
" <td>Godfather: Part II, The (1974)</td>\n",
" <td>Action|Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>837</td>\n",
" <td>913</td>\n",
" <td>5</td>\n",
" <td>975359921</td>\n",
" <td>Maltese Falcon, The (1941)</td>\n",
" <td>Film-Noir|Mystery</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>837</td>\n",
" <td>3417</td>\n",
" <td>5</td>\n",
" <td>975360893</td>\n",
" <td>Crimson Pirate, The (1952)</td>\n",
" <td>Adventure|Comedy|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>837</td>\n",
" <td>2186</td>\n",
" <td>4</td>\n",
" <td>975359955</td>\n",
" <td>Strangers on a Train (1951)</td>\n",
" <td>Film-Noir|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>837</td>\n",
" <td>2791</td>\n",
" <td>4</td>\n",
" <td>975360893</td>\n",
" <td>Airplane! (1980)</td>\n",
" <td>Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>837</td>\n",
" <td>1188</td>\n",
" <td>4</td>\n",
" <td>975360920</td>\n",
" <td>Strictly Ballroom (1992)</td>\n",
" <td>Comedy|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>837</td>\n",
" <td>1304</td>\n",
" <td>4</td>\n",
" <td>975360058</td>\n",
" <td>Butch Cassidy and the Sundance Kid (1969)</td>\n",
" <td>Action|Comedy|Western</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UserID MovieID Rating Timestamp \\\n",
"36 837 858 5 975360036 \n",
"35 837 1387 5 975360036 \n",
"65 837 2028 5 975360089 \n",
"63 837 1221 5 975360036 \n",
"11 837 913 5 975359921 \n",
"20 837 3417 5 975360893 \n",
"34 837 2186 4 975359955 \n",
"55 837 2791 4 975360893 \n",
"31 837 1188 4 975360920 \n",
"28 837 1304 4 975360058 \n",
"\n",
" Title Genres \n",
"36 Godfather, The (1972) Action|Crime|Drama \n",
"35 Jaws (1975) Action|Horror \n",
"65 Saving Private Ryan (1998) Action|Drama|War \n",
"63 Godfather: Part II, The (1974) Action|Crime|Drama \n",
"11 Maltese Falcon, The (1941) Film-Noir|Mystery \n",
"20 Crimson Pirate, The (1952) Adventure|Comedy|Sci-Fi \n",
"34 Strangers on a Train (1951) Film-Noir|Thriller \n",
"55 Airplane! (1980) Comedy \n",
"31 Strictly Ballroom (1992) Comedy|Romance \n",
"28 Butch Cassidy and the Sundance Kid (1969) Action|Comedy|Western "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"already_rated.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
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" <th>Title</th>\n",
" <th>Genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>516</th>\n",
" <td>527</td>\n",
" <td>Schindler's List (1993)</td>\n",
" <td>Drama|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1848</th>\n",
" <td>1953</td>\n",
" <td>French Connection, The (1971)</td>\n",
" <td>Action|Crime|Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>596</th>\n",
" <td>608</td>\n",
" <td>Fargo (1996)</td>\n",
" <td>Crime|Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1235</th>\n",
" <td>1284</td>\n",
" <td>Big Sleep, The (1946)</td>\n",
" <td>Film-Noir|Mystery</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2085</th>\n",
" <td>2194</td>\n",
" <td>Untouchables, The (1987)</td>\n",
" <td>Action|Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1188</th>\n",
" <td>1230</td>\n",
" <td>Annie Hall (1977)</td>\n",
" <td>Comedy|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1198</th>\n",
" <td>1242</td>\n",
" <td>Glory (1989)</td>\n",
" <td>Action|Drama|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>897</th>\n",
" <td>922</td>\n",
" <td>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</td>\n",
" <td>Film-Noir</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1849</th>\n",
" <td>1954</td>\n",
" <td>Rocky (1976)</td>\n",
" <td>Action|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>581</th>\n",
" <td>593</td>\n",
" <td>Silence of the Lambs, The (1991)</td>\n",
" <td>Drama|Thriller</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MovieID Title \\\n",
"516 527 Schindler's List (1993) \n",
"1848 1953 French Connection, The (1971) \n",
"596 608 Fargo (1996) \n",
"1235 1284 Big Sleep, The (1946) \n",
"2085 2194 Untouchables, The (1987) \n",
"1188 1230 Annie Hall (1977) \n",
"1198 1242 Glory (1989) \n",
"897 922 Sunset Blvd. (a.k.a. Sunset Boulevard) (1950) \n",
"1849 1954 Rocky (1976) \n",
"581 593 Silence of the Lambs, The (1991) \n",
"\n",
" Genres \n",
"516 Drama|War \n",
"1848 Action|Crime|Drama|Thriller \n",
"596 Crime|Drama|Thriller \n",
"1235 Film-Noir|Mystery \n",
"2085 Action|Crime|Drama \n",
"1188 Comedy|Romance \n",
"1198 Action|Drama|War \n",
"897 Film-Noir \n",
"1849 Action|Drama \n",
"581 Drama|Thriller "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"file_extension": ".py",
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| mit |
brttstl/taco-mb25 | p1/.ipynb_checkpoints/p1-checkpoint.ipynb | 1 | 10479 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import random\n",
"import timeit"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## BUBBLESORT(A)\n",
"def bubbleSort(A):\n",
" # 1 for i = 1 to A.length - 1\n",
" for i in range(len(A) - 1):\n",
" # 2 for j = A.length downto i + 1\n",
" for j in range(len(A) - 1, i, -1):\n",
" # 3 if A[j] < A[j - 1]\n",
" if (A[j] < A[j - 1]):\n",
" # 4 exchange A[j] with A[j - 1]\u0002\n",
" A[j], A[j - 1] = A[j - 1], A[j]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## INSERTION-SORT(A)\n",
"def insertSort(A):\n",
" # 1 for j = 2 to A.length\n",
" for j in range(len(A)):\n",
" # 2 key = A[j]\n",
" temp = A[j]\n",
" # 3 // Insert A[j] into the sorted sequence A[1 .. j - 1].\n",
" # 4 i = j - 1\n",
" i = j - 1\n",
" # 5 while i > 0 and A[i]\u0002 > key\n",
" while ((i > 0) & (A[i] > temp)):\n",
" # 6 A[i + 1] = A[i]\n",
" A[i + 1] = A[i]\n",
" # 7 i = i - 1\n",
" i = i - 1\n",
" # 8 A[i + 1] = key\n",
" A[i + 1] = temp"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This is just a wrapper function for the timings\n",
"def wrapper(func, *args, **kwargs):\n",
" def wrap():\n",
" return func(*args, **kwargs)\n",
" return wrap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#### For plot 1\n",
"## Create 3 lists of 10 sublists of n random numbers to avoid skewing timing:\n",
"listsBubble5001 = [[] for _ in range(25)]\n",
"listsInsert5001 = [[] for _ in range(25)]\n",
"\n",
"n = 0\n",
"for i in range(25):\n",
" n += 2000 \n",
" listsBubble5001[i] = random.sample(range(1, 200001), n)\n",
" listsInsert5001[i] = random.sample(range(1, 200001), n)\n",
" \n",
"listLen = [len(listsBubble5001[i]) for i in range(len(listsBubble5001))]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create empty lists to store timings\n",
"# bubbleSort timings\n",
"bubble5001 = []\n",
"insert5001 = []\n",
"\n",
"# Loop through and time the 25 random lists * 10 times for each of the three lengths\n",
"for i in range(len(listsBubble5001)):\n",
" bubble5001.append(timeit.timeit(wrapper(bubbleSort, listsBubble5001[i]), number=10))\n",
" insert5001.append(timeit.timeit(wrapper(insertSort, listsInsert5001[i]), number=10))\n",
" \n",
"bubble1 = [np.mean(bubble5001)]\n",
"insert1 = [np.mean(insert5001)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"graph = pd.DataFrame({'n': listLen, \n",
" 'bubble time (random)': bubble5001,\n",
" 'insert time (random)': insert5001})\n",
"graph = graph[['n', 'bubble time (random)', 'insert time (random)']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# red dashes, blue squares and green triangles\n",
"plt.plot(graph['n'], graph['bubble time (random)'], 'r--')\n",
"plt.plot(graph['n'], graph['insert time (random)'], 'b--')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#### Plot 2\n",
"## Create 3 lists of 10 sublists of n random numbers to avoid skewing timing:\n",
"listsBubble5002 = [[] for _ in range(25)]\n",
"listsInsert5002 = [[] for _ in range(25)]\n",
"\n",
"# Create data, store in list, and sort in increasing order\n",
"n = 0\n",
"for i in range(25):\n",
" n += 2000 \n",
" listsBubble5002[i] = np.sort(random.sample(range(1, 200001), n), axis=None)\n",
" listsInsert5002[i] = np.sort(random.sample(range(1, 200001), n), axis=None)\n",
"\n",
"# Create empty lists to store timings\n",
"# bubbleSort timings\n",
"bubble5002 = []\n",
"insert5002 = []\n",
"\n",
"# Loop through and time the 25 sorted random lists * 1 times\n",
"for i in range(len(lists5002)):\n",
" bubble5002.append(timeit.timeit(wrapper(bubbleSort, listsBubble5002[i]), number=1))\n",
" insert5002.append(timeit.timeit(wrapper(insertSort, listsInsert5002[i]), number=1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Add results to dataframe\n",
"graph['bubble time (random asc.)'] = bubble5002\n",
"graph['insert time (random asc.)'] = insert5002"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(graph['n'], graph['bubble time (random asc.)'], 'r--')\n",
"plt.plot(graph['n'], graph['insert time (random asc.)'], 'b--')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#### Plot 3\n",
"## Create a list of 25 sublists of n random numbers to avoid skewing timing:\n",
"listsBubble5003 = [[] for _ in range(25)]\n",
"listsInsert5003 = [[] for _ in range(25)]\n",
"\n",
"# Create data, store in list, and sort in increasing order\n",
"n = 0\n",
"for i in range(25):\n",
" n += 2000 \n",
" listsBubble5003[i] = np.sort(random.sample(range(1, 200001), n), axis=None)\n",
" listsInsert5003[i] = np.sort(random.sample(range(1, 200001), n), axis=None)\n",
"\n",
"for i in range(25):\n",
" listsBubble5003[i][::-1].sort()\n",
" listsInsert5003[i][::-1].sort()\n",
" \n",
"# Create empty lists to store timings\n",
"# bubbleSort timings\n",
"bubble5003 = []\n",
"insert5003 = []\n",
"\n",
"# Loop through and time the 25 sorted random lists * 1 times\n",
"for i in range(len(lists5003)):\n",
" bubble5003.append(timeit.timeit(wrapper(bubbleSort, listsBubble5003[i]), number=1))\n",
" insert5003.append(timeit.timeit(wrapper(insertSort, listsInsert5003[i]), number=1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Add results to dataframe\n",
"graph['bubble time (random desc.)'] = bubble5003\n",
"graph['insert time (random desc.)'] = insert5003\n",
"graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(graph['n'], graph['bubble time (random desc.)'], 'r--')\n",
"plt.plot(graph['n'], graph['insert time (random desc.)'], 'b--')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explain Your Choices: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Explain any platform/language choices that you made for your code/plots. \n",
" - I chose python since I am most comfortable in python. For the code, I adapted the psuedo code from the book and used that psuedo code as comments in-line to highlight how I adapted the steps."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ How did you create/store your data that you used to make the plots? \n",
" - I used a for loop and numpy's random sample function to create a list with 25 randomly sampled sublists of size with increasing n from 2000 - 50000 for plot 1. For plots 2 and 3 I used similar approach but sorted in ascending and descending order, respectively to meet the constraints instructed. I took the 10 timings for each of the 25 sets of data for plot 1 and plotted in matplotlib. I only took one timing for the purposes of plots 2 & 3 per the faq posted on piazza: https://piazza.com/class/j63o8j0x6m3123?cid=18 \n",
" - For these graphs, Red represents the bubbleSort algorithm timing; whereas blue represents the insertSort algorithm."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ If you ran into any special difficulties or made any interesting observations, feel free to mention them here.\n",
" - I originally ran into some difficulty with the nested for loop with bubble sort (step 2) and getting it to iterate through the entire list but eventually got it work."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusions: \n",
"+ These two algorithms are supposed to have quadratic running time. \n",
"+ Does the first plot reflect this? \n",
"+ How do the two algorithms compare in terms of running time? \n",
"+ How about the second plot? \n",
"+ Do you think this one is quadratic? Why do you think it looks the way it does?\n",
"+ How does the third compare to the first and the second? \n",
"+ What kind of functions do you think you’re observing in the three plots (linear, logarithmic, quadratic, exponential, etc etc)? \n",
"+ Would you use these algorithms for real life data, and why?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"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
}
| apache-2.0 |
JasonTam/ndsb2015 | CNN_Features.ipynb | 2 | 17839 | {
"metadata": {
"name": "",
"signature": "sha256:64bdf6f877b93773016482a8d42438231ccf9cd01c8ee878fddab689997cf9bb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import h5py\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"import lmdb\n",
"from caffe.proto import caffe_pb2\n",
"import caffe\n",
"import matplotlib.pyplot as plt\n",
"import tools.my_io as my_io\n",
"%matplotlib inline\n",
"\n",
"\n",
"PRETRAINED = '/media/raid_arr/data/ndsb/models/pl_iter_56000.caffemodel'\n",
"MODEL_FILE = './deploy_deeper.prototxt'\n",
"MEAN_FILE = '/media/raid_arr/data/ndsb/augment/testaug_mean.npy'\n",
"LAYER = 'fc2'\n",
"LAYER2 = 'fc1'\n",
"OUTPUT = '/media/raid_arr/data/ndsb/features/pl_56000_feats'\n",
"# N_MBATCH = 1000\n",
"N = 10000 # Chunk size\n",
"TEST_IM = '/media/raid_arr/data/ndsb/augment/train/acantharia_protist/100224_rot0.jpg'\n",
"VALIDATION_DB = './data/64x64/ndsb_test_lmdb'\n",
"# TRAIN_DB = '/media/raid_arr/data/ndsb/augment/ndsb_trainaug_lmdb/'\n",
"TRAIN_DB = '/media/raid_arr/data/ndsb/ndsb_train_lmdb'\n",
"TEST_DB = '/data/ndsb/ndsb_test_lmdb'\n",
"\n",
"FEAT_OUT = '/media/raid_arr/data/ndsb/features_test.hdf5'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# image_dims = data[0][1].shape[:2]\n",
"caffe.set_mode_gpu()\n",
"# caffe.set_phase_test()\n",
"image_dims = (64, 64)\n",
"crop_dims = np.array([57, 57])\n",
"\n",
"mean=np.load(MEAN_FILE)\n",
"mean.shape\n",
"mean_resized = caffe.io.resize_image(mean.transpose((1,2,0)), crop_dims).transpose((2,0,1))\n",
"\n",
"net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
" mean=mean_resized,\n",
" raw_scale=1.0, # 255 if load from caffe.io, 1.0 if load from my_io lmdb\n",
" image_dims=image_dims)\n",
"\n",
"n_feats2 = net.blobs['fc2'].data.shape[1]\n",
"n_feats1 = net.blobs['fc1'].data.shape[1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# im = caffe.io.load_image(TEST_IM, color=False)\n",
"for k, v in net.blobs.items():\n",
" print k, v.data.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"data (10, 1, 57, 57)\n",
"conv1 (10, 96, 14, 14)\n",
"pool1 (10, 96, 7, 7)\n",
"conv2 (10, 256, 7, 7)\n",
"pool2 (10, 256, 3, 3)\n",
"conv3 (10, 384, 3, 3)\n",
"conv4 (10, 384, 3, 3)\n",
"conv5 (10, 256, 3, 3)\n",
"pool5 (10, 256, 1, 1)\n",
"fc1 (10, 2048, 1, 1)\n",
"fc2 (10, 2048, 1, 1)\n",
"fc3 (10, 121, 1, 1)\n",
"prob (10, 121, 1, 1)\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create new h5 file\n",
"f.close()\n",
"f = h5py.File(FEAT_OUT, 'w')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 219
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Make Groups\n",
"fc2_db = f.create_dataset(\"fc2\", shape=(N, n_feats2), maxshape=(None, n_feats2), dtype='f')\n",
"fc1_db = f.create_dataset(\"fc1\", shape=(N, n_feats1), maxshape=(None, n_feats1), dtype='f')\n",
"lbls_db = f.create_dataset(\"lbls\", shape=(N,), maxshape=(None,), dtype='i8')\n",
"impaths_db = f.create_dataset(\"im_paths\", shape=(N,), maxshape=(None,), dtype='S120')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 220
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# PREDICTION TIME\n",
"print 'Predicting...', TEST_DB\n",
"prediction_list = []\n",
"test_files_list = []\n",
"next_key = ''\n",
"first_run = True\n",
"while next_key or first_run:\n",
" print 'Starting at key: ', next_key\n",
" read_start = time.time()\n",
" data_chunk, next_key = my_io.load_lmdb_chunk(TEST_DB, next_key, N)\n",
" print \"Read done in %.2f s.\" % (time.time() - read_start)\n",
" chunk_len = len(data_chunk)\n",
" print 'Chunk size:', chunk_len\n",
" sys.stdout.flush()\n",
" pred_start = time.time()\n",
" \n",
" \n",
" im_paths = []\n",
" feats_fc2 = []\n",
" feats_fc1 = []\n",
" lbls = []\n",
" if not first_run:\n",
" fc2_db.resize(fc2_db.shape[0] + chunk_len, axis=0)\n",
" fc1_db.resize(fc1_db.shape[0] + chunk_len, axis=0)\n",
" lbls_db.resize(lbls_db.shape[0] + chunk_len, axis=0)\n",
" impaths_db.resize(impaths_db.shape[0] + chunk_len, axis=0)\n",
" for ii, (im_path, im, lbl) in enumerate(data_chunk):\n",
" # print im_path\n",
" # print im.shape\n",
" # print lbl\n",
" prediction = net.predict([im])\n",
" feat_fc2 = np.squeeze(net.blobs['fc2'].data.mean(0))\n",
" feat_fc1 = np.squeeze(net.blobs['fc1'].data.mean(0))\n",
" feats_fc2.append(feat_fc2)\n",
" feats_fc1.append(feat_fc1)\n",
" lbls.append(lbl)\n",
" im_paths.append(im_path)\n",
" fc2_db[-chunk_len:] = np.array(feats_fc2)\n",
" fc1_db[-chunk_len:] = np.array(feats_fc1)\n",
" lbls_db[-chunk_len:] = np.array(lbls)\n",
" impaths_db[-chunk_len:] = np.array(im_paths)\n",
" \n",
" \n",
"# im_path_chunk, images_chunk, labels_chunk = zip(*data_chunk)\n",
"# prediction = net.predict(images_chunk)\n",
"# prediction_list.append(prediction)\n",
"# test_files_list.append(test_files_chunk)\n",
" print \"Pred done in %.2f s.\" % (time.time() - pred_start)\n",
" sys.stdout.flush()\n",
" first_run = False\n",
" \n",
"# predictions = np.concatenate(prediction_list)\n",
"# test_files = list(itertools.chain(*test_files_list))\n",
"print \"Done predicting\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Predicting... /data/ndsb/ndsb_test_lmdb\n",
"Starting at key: \n",
"Read done in 1.47 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.60 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00010000_/data/ndsb/test/131890.jpg\n",
"Read done in 1.51 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.63 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00020000_/data/ndsb/test/143698.jpg\n",
"Read done in 1.46 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.60 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00030000_/data/ndsb/test/127171.jpg\n",
"Read done in 1.43 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.53 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00040000_/data/ndsb/test/9227.jpg\n",
"Read done in 1.41 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.57 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00050000_/data/ndsb/test/132350.jpg\n",
"Read done in 1.41 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.58 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00060000_/data/ndsb/test/74795.jpg\n",
"Read done in 1.38 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.54 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00070000_/data/ndsb/test/135220.jpg\n",
"Read done in 1.37 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.58 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00080000_/data/ndsb/test/56438.jpg\n",
"Read done in 1.35 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.58 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00090000_/data/ndsb/test/45426.jpg\n",
"Read done in 1.33 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.65 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00100000_/data/ndsb/test/64677.jpg\n",
"Read done in 1.31 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.60 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00110000_/data/ndsb/test/111353.jpg\n",
"Read done in 1.30 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.55 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00120000_/data/ndsb/test/145818.jpg\n",
"Read done in 1.28 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 225.57 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting at key: 00130000_/data/ndsb/test/153380.jpg\n",
"Read done in 0.06 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chunk size: 400\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pred done in 9.03 s.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Done predicting\n"
]
}
],
"prompt_number": 221
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"start = time.time()\n",
"prediction = net.predict(images)\n",
"print \"Done in %.2f s.\" % (time.time() - start)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Done in 131.15 s.\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"feats = []\n",
"y = []\n",
"tic = time.time()\n",
"for ii, (im_path, im, lbl) in enumerate(data):\n",
"# print im_path\n",
"# print im.shape\n",
"# print lbl\n",
" prediction = net.predict([im])\n",
" fc2 = np.squeeze(net.blobs['fc2'].data.mean(0))\n",
" feats.append(fc2)\n",
" y.append(lbl)\n",
" \n",
" if ii%1000==0:\n",
" print ii\n",
"# break\n",
"# prediction.shape\n",
"# fc2.shape\n",
"print \"Done in %.2f s.\" % (time.time() - tic)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n",
"1000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"3000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"4000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"5000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"6000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Done in 136.09 s."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = np.array(y)\n",
"feats_arr = np.array(feats)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"0"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import svm\n",
"from sklearn import cross_validation\n",
"clf = svm.SVC()\n",
"\n",
"scores = cross_validation.cross_val_score(clf, feats_arr, y, cv=5)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/lib/python2.7/site-packages/sklearn/cross_validation.py:413: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=5.\n",
" % (min_labels, self.n_folds)), Warning)\n"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 209,
"text": [
"[u'fc1', u'fc2', u'im_paths', u'lbls']"
]
}
],
"prompt_number": 209
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
Phelimb/cbg | example-scripts/search.ipynb | 1 | 9004 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def search(seq, threshold=1):\n",
" url=\"http://api.bigsiseq.com/search?threshold=%f&seq=%s\" % (float(threshold),seq)\n",
" results = requests.get(url).json()\n",
" samples = []\n",
" for i,j in list(results.values())[0][\"results\"].items():\n",
" samples.append(i)\n",
" return samples"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"seq=\"CGGTCAGTCCGTTTGTTCTTGTGGCGAGTGTTGCCGTTTTCTTGACCGCGACCGCCAATCTTACCTTTTTTGATAAAATCAGCCAAACCTATCCCATCGCGGACAATCTCGGCTTTGTGCTGACGATCGCTGTCGTGCTCTTTGGCGCGATGCTACTGATCACCACGCTGTTATCATCGTATCGCTATGTGCTAAAGCCTGTGTTGATTTTGCTATTAATCATGGGCGCGGTGACCAGTTATTTTACTGACACTTATGGCACGGTCTATGATACGACCATGCTCCAAAATGCCCTACAGACCGACCAAGCCGAGACCAAGGATCTATTAAACGCAGCGTTTATCATGCGTATCATTGGTTTGGGTGTGCTACCAAGTTTGCTTGTGGCTTTTGTTAAGGTGGATTATCCGACTTGGGGCAAGGGTTTGATGCGCCGATTGGGCTTGATCGTGGCAAGTCTTGCGCTGATTTTACTGCCTGTGGTGGCGTTCAGCAGTCATTATGCCAGTTTCTTTCGCGTGCATAAGCCGCTGCGTAGCTATGTCAATCCGATCATGCCAATCTACTCGGTGGGTAAGCTTGCCAGTATTGAGTATAAAAAAGCCAGTGCGCCAAAAGATACCATTTATCACGCCAAAGACGCGGTACAAGCAACCAAGCCTGATATGCGTAAGCCACGCCTAGTGGTGTTCGTCGTCGGTGAGACGGCACGCGCCGATCATGTCAGCTTCAATGGCTATGAGCGCGATACTTTCCCACAGCTTGCCAAGATCGATGGCGTGACCAATTTTAGCAATGTCACATCGTGCGGCACATCGACGGCGTATTCTGTGCCGTGTATGTTCAGCTATCTGGGCGCGGATGAGTATGATGTCGATACCGCCAAATACCAAGAAAATGTGCTGGATACGCTGGATCGCTTGGGCGTAAGTATCTTGTGGCGTGATAATAATTCGGACTCAAAAGGCGTGATGGATAAGCTGCCAAAAGCGCAATTTGCCGATTATAAATCCGCGACCAACAACGCCATCTGCAACACCAATCCTTATAACGAATGCCGCGATGTCGGTATGCTCGTTGGCTTAGATGACTTTGTCGCTGCCAATAACGGCAAAGATATGCTGATCATGCTGCACCAAATGGGCAATCACGGGCCTGCGTATTTTAAGCGATATGATGAAAAGTTTGCCAAATTCACGCCAGTGTGTGAAGGTAATGAGCTTGCCAAGTGCGAACATCAGTCCTTGATCAATGCTTATGACAATGCCTTGCTTGCCACCGATGATTTCATCGCTCAAAGTATCCAGTGGCTGCAGACGCACAGCAATGCCTATGATGTCTCAATGCTGTATGTCAGCGATCATGGCGAAAGTCTGGGTGAGAACGGTGTCTATCTACATGGTATGCCAAATGCCTTTGCACCAAAAGAACAGCGCAGTGTGCCTGCATTTTTCTGGACGGATAAGCAAACTGGCATCACGCCAATGGCAACCGATACCGTCCTGACC\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"samples = search(seq)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ERR1681620', 'ERR1163291', 'ERR1609376', 'ERR1623360', 'ERR1622314', 'ERR1656135', 'ERR1609380', 'ERR1622709', 'ERR1623341', 'ERR1163331', 'ERR1622651', 'ERR1623390', 'ERR1366473', 'SRR2053338', 'ERR1622310', 'ERR1759157', 'ERR197184', 'ERR956997', 'ERR1623239', 'ERR1681650', 'SRR1645608', 'ERR1623521', 'SRR5184275', 'SRR2767734', 'ERR1623227', 'ERR1759167', 'ERR1622655', 'ERR1623229', 'ERR1759128', 'ERR1681717', 'ERR1656450', 'ERR1623113', 'ERR715581', 'SRR3745275', 'ERR1622312', 'ERR1759166', 'ERR1622305', 'ERR1622034', 'SRR1788031', 'ERR1622541', 'ERR1407280', 'ERR1623222', 'SRR2053340', 'SRR1788026', 'ERR1681704', 'SRR1967426', 'SRR1958396', 'SRR1788027', 'ERR1681654', 'ERR1623579', 'SRR5129179', 'ERR1681785', 'ERR1622079', 'ERR1623230', 'ERR1609434', 'ERR1623215', 'ERR1407279', 'ERR1623237', 'ERR1623346', 'ERR1407278', 'ERR1622547', 'ERR1360255', 'ERR1609449', 'ERR1623228', 'ERR1622435', 'ERR1623394', 'SRR3322633', 'ERR1622708', 'ERR135710', 'SRR1957973', 'ERR1622928', 'ERR1681839', 'ERR1681783', 'ERR1623457', 'ERR1622654', 'ERR1609312', 'ERR1622881', 'ERR1609378', 'SRR3584989', 'ERR1622946', 'ERR1759207', 'ERR1623525', 'ERR1622370', 'ERR1229301', 'ERR1046133', 'ERR1622670', 'ERR1609197', 'ERR1622309', 'SRR3882972', 'SRR1788028', 'SRR3707448', 'ERR1622739', 'ERR1229297', 'ERR1609196', 'SRR4302136', 'SRR1788024', 'ERR1623088', 'ERR1623225', 'ERR1622311', 'SRR2075991', 'ERR1149371', 'ERR1218581', 'ERR1622769', 'SRR1960364', 'ERR1622930', 'ERR1229302', 'SRR5201504', 'ERR1622883', 'ERR702345', 'ERR1218720', 'ERR1623226', 'SRR1965341', 'SRR3168915', 'ERR1623212', 'ERR1623214', 'SRR4302224', 'ERR1623594', 'SRR3407159', 'SRR1788032', 'ERR1622648', 'ERR1609215', 'SRR1814872', 'SRR3170531', 'SRR2054237', 'ERR1432660', 'ERR1609201', 'ERR1622653', 'SRR3452849', 'SRR2010693', 'ERR1432659', 'ERR1622107', 'ERR1609245', 'SRR4302302', 'ERR1622060', 'ERR1681606', 'ERR1622545', 'ERR1622729', 'SRR4302214', 'SRR2015698', 'ERR1622839', 'ERR1035693', 'ERR135713', 'ERR1623241', 'ERR1609249', 'ERR1218643', 'SRR4289227', 'ERR1622589', 'ERR1544012', 'ERR1623223', 'ERR1562562', 'ERR1622058', 'ERR1681602', 'ERR1623444', 'ERR1622987', 'ERR1622770', 'ERR1623342', 'ERR1360256', 'SRR3170679', 'ERR1681601', 'ERR1622725', 'ERR1623523', 'ERR1681781', 'SRR2054248', 'ERR1229300', 'ERR1622840', 'SRR1969022', 'ERR1359224', 'ERR1681854', 'ERR1623279']\n"
]
}
],
"source": [
"print(samples)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from Bio import Entrez\n",
"import time\n",
"Entrez.email =\"[email protected]\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ERR1681620', 'ERR1163291', 'ERR1609376', 'ERR1623360', 'ERR1622314', 'ERR1656135', 'ERR1609380', 'ERR1622709', 'ERR1623341', 'ERR1163331', 'ERR1622651', 'ERR1623390', 'ERR1366473', 'SRR2053338', 'ERR1622310', 'ERR1759157', 'ERR197184', 'ERR956997', 'ERR1623239', 'ERR1681650', 'SRR1645608', 'ERR1623521', 'SRR5184275', 'SRR2767734', 'ERR1623227', 'ERR1759167', 'ERR1622655', 'ERR1623229', 'ERR1759128', 'ERR1681717', 'ERR1656450', 'ERR1623113', 'ERR715581', 'SRR3745275', 'ERR1622312', 'ERR1759166', 'ERR1622305', 'ERR1622034', 'SRR1788031', 'ERR1622541', 'ERR1407280', 'ERR1623222', 'SRR2053340', 'SRR1788026', 'ERR1681704', 'SRR1967426', 'SRR1958396', 'SRR1788027', 'ERR1681654', 'ERR1623579', 'SRR5129179', 'ERR1681785', 'ERR1622079', 'ERR1623230', 'ERR1609434', 'ERR1623215', 'ERR1407279', 'ERR1623237', 'ERR1623346', 'ERR1407278', 'ERR1622547', 'ERR1360255', 'ERR1609449', 'ERR1623228', 'ERR1622435', 'ERR1623394', 'SRR3322633', 'ERR1622708', 'ERR135710', 'SRR1957973', 'ERR1622928', 'ERR1681839', 'ERR1681783', 'ERR1623457', 'ERR1622654', 'ERR1609312', 'ERR1622881', 'ERR1609378', 'SRR3584989', 'ERR1622946', 'ERR1759207', 'ERR1623525', 'ERR1622370', 'ERR1229301', 'ERR1046133', 'ERR1622670', 'ERR1609197', 'ERR1622309', 'SRR3882972', 'SRR1788028', 'SRR3707448', 'ERR1622739', 'ERR1229297', 'ERR1609196', 'SRR4302136', 'SRR1788024', 'ERR1623088', 'ERR1623225', 'ERR1622311', 'SRR2075991', 'ERR1149371', 'ERR1218581', 'ERR1622769', 'SRR1960364', 'ERR1622930', 'ERR1229302', 'SRR5201504', 'ERR1622883', 'ERR702345', 'ERR1218720', 'ERR1623226', 'SRR1965341', 'SRR3168915', 'ERR1623212', 'ERR1623214', 'SRR4302224', 'ERR1623594', 'SRR3407159', 'SRR1788032', 'ERR1622648', 'ERR1609215', 'SRR1814872', 'SRR3170531', 'SRR2054237', 'ERR1432660', 'ERR1609201', 'ERR1622653', 'SRR3452849', 'SRR2010693', 'ERR1432659', 'ERR1622107', 'ERR1609245', 'SRR4302302', 'ERR1622060', 'ERR1681606', 'ERR1622545', 'ERR1622729', 'SRR4302214', 'SRR2015698', 'ERR1622839', 'ERR1035693', 'ERR135713', 'ERR1623241', 'ERR1609249', 'ERR1218643', 'SRR4289227', 'ERR1622589', 'ERR1544012', 'ERR1623223', 'ERR1562562', 'ERR1622058', 'ERR1681602', 'ERR1623444', 'ERR1622987', 'ERR1622770', 'ERR1623342', 'ERR1360256', 'SRR3170679', 'ERR1681601', 'ERR1622725', 'ERR1623523', 'ERR1681781', 'SRR2054248', 'ERR1229300', 'ERR1622840', 'SRR1969022', 'ERR1359224', 'ERR1681854', 'ERR1623279']\n"
]
}
],
"source": [
"## Search for a GI from genbank\n",
"## e.g. https://www.ncbi.nlm.nih.gov/nuccore/1150750917\n",
"\n",
"GI=\"1150750917\"\n",
"\n",
"request = Entrez.epost(\"nucleotide\",id=GI)\n",
"result = Entrez.read(request)\n",
"webEnv = result[\"WebEnv\"]\n",
"queryKey = result[\"QueryKey\"]\n",
"handle = Entrez.efetch(db=\"nucleotide\",retmode=\"xml\", webenv=webEnv, query_key=queryKey)\n",
"for r in Entrez.parse(handle):\n",
" print (search(r.get(\"GBSeq_sequence\").upper()))\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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
renecnielsen/twitter-diy | ipynb/02 - Descriptive Statistics-RCN-Copy1.ipynb | 2 | 1544735 | null | mit |
ioam/holoviews | doc/Homepage.ipynb | 1 | 6660 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"HoloViews is a [Python](http://python.org) library that makes analyzing and visualizing scientific or engineering data much simpler, more intuitive, and more easily reproducible. Instead of specifying every step for each plot, HoloViews lets you store your data in an annotated format that is instantly visualizable, with immediate access to both the numeric data *and* its visualization. Examples of how HoloViews is used in Python scripts as well as in live [Jupyter Notebooks](http://jupyter.org) may be accessed directly from the [holoviews gallery](http://holoviews.org/gallery/index.html) webpage. Here is a quick example of HoloViews in action:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"hv.notebook_extension('matplotlib')\n",
"fractal = hv.Image(np.load('mandelbrot.npy'))\n",
"\n",
"((fractal * hv.HLine(y=0)).hist() + fractal.sample(y=0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fundamentally, a HoloViews object is just a thin wrapper around your data, with the data always being accessible in its native numerical format, but with the data displaying itself automatically whether alone or alongside or overlaid with other HoloViews objects as shown above. The actual rendering is done using a separate library like [matplotlib](http://matplotlib.org) or [bokeh](http://bokeh.pydata.org), but all of the HoloViews objects can be used without any plotting library available, so that you can easily create, save, load, and manipulate HoloViews objects from within your own programs for later analysis. HoloViews objects support arbitrarily high dimensions, using continuous, discrete, or categorical indexes and values, with flat or hierarchical organizations, and sparse or dense data formats. The objects can then be flexibly combined, selected, sliced, sorted, sampled, or animated, all by specifying what data you want to see rather than by writing plotting code. The goal is to put the plotting code into the background, as an implementation detail to be written once and reused often, letting you focus clearly on your data itself in daily work."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More detailed example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even extremely complex relationships between data elements can be expressed succinctly in HoloViews, allowing you to explore them with ease:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%opts Points [scaling_factor=50] Contours (color='w')\n",
"dots = np.linspace(-0.45, 0.45, 19)\n",
"\n",
"layouts = {y: (fractal * hv.Points(fractal.sample([(i,y) for i in dots])) +\n",
" fractal.sample(y=y) +\n",
" hv.operation.threshold(fractal, level=np.percentile(fractal.sample(y=y)['z'], 90)) +\n",
" hv.operation.contours(fractal, levels=[np.percentile(fractal.sample(y=y)['z'], 60)]))\n",
" for y in np.linspace(-0.3, 0.3, 21)}\n",
"\n",
"hv.HoloMap(layouts, kdims=['Y']).collate().cols(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we have built a dictionary indexed by a numerical value `y`, containing a set of ``Layout`` objects that are each composed of four HoloViews objects. We then collated the ``Layout`` objects into a HoloViews data structure that can display arbitrarily high dimensional data. The result is that in **A** we can see the same fractal data as above, but with a horizontal cross section indicated using a set of dots with sizes proportional to the underlying data values, illustrating how even a simple annotation can be used to reflect other data of interest. **B** shows a cross-section of the fractal, **C** shows a thresholded version of it, and **D** shows the same data with a contour outline overlaid. The threshold and contour levels used are not fixed, but are calculated as the 90th or 60th percentile of the data values along the selected cross section, using standard Python/NumPy functions. All of this data is packaged into a single HoloViews data structure for a range of cross sections, allowing the data for a particular cross section to be revealed by moving the Y-value slider at right. Even with these complicated interrelationships between data elements, the code still only needs to focus on the data that you want to see, not on the details of the plotting or interactive controls, which are handled by HoloViews and the underlying plotting libraries.\n",
"\n",
"Note that just as the 2D array became a 1D curve automatically by sampling to get the cross section, this entire figure would become a single static frame with no slider bar if you chose a specific ``Y`` value by re-running with ``.select(Y=0.3)`` before ``.cols(2)``. There is nothing in the code above that adds the slider bar explicitly -- it appears automatically, just because there is an additional dimension of data that has not been laid out spatially. Additional sliders would appear if there were other dimensions being varied as well, e.g. for parameter-space explorations.\n",
"\n",
"This functionality is designed to complement the [IPython/Jupyter Notebook](http://jupyter.org) interface, though it can be used just as well separately. This web page and all the [HoloViews Tutorials](Tutorials/) are runnable notebooks, which allow you to interleave text, Python code, and graphical results easily. With HoloViews, you can put a minimum of code in the notebook (typically one or two lines per subfigure), specifying what you would like to see rather than the details of how it should be plotted. HoloViews makes the IPython Notebook a practical solution for both exploratory research (since viewing nearly any chunk of data just takes a line or two of code) and for long-term [reproducibility](Tutorials/Exporting.html) of the work (because both the code and the visualizations are preserved in the notebook file forever, and the data and publishable figures can both easily be exported to an archive on disk). See the [Tutorials](Tutorials/) for detailed examples, and then start enjoying working with your data!"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
jay-johnson/sci-pype | examples/example-core-demo.ipynb | 1 | 2521 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python Core Demo\n",
"\n",
"How to use the python core from a Jupyter notebook. It also shows how to debug the JSON application configs which are used to connect to external database(s) and redis server(s).\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"import os, sys, json\n",
"\n",
"sys.path.insert(0, os.getenv('ENV_PYTHON_SRC_DIR', '/opt/work/src'))\n",
"\n",
"from pycore import PyCore\n",
"\n",
"# default config: /opt/work/configs/jupyter.json \n",
"config = os.getenv('ENV_PYTHON_CORE_CONFIG', '/opt/work/configs/jupyter.json')\n",
"\n",
"print 'Initializing Python Core with Redis and Database Applications - Disabled'\n",
"core = PyCore(config)\n",
"\n",
"core.lg('')\n",
"core.lg('--------------------------------------------------------')\n",
"core.lg('Logging Demos')\n",
"core.lg('')\n",
"core.lg(' This is RED', 0)\n",
"core.lg(' This is BLUE', 1)\n",
"core.lg(' This is YELLOW', 2)\n",
"core.lg(' This is PURPLE', 3)\n",
"core.lg(' This is GRAY', 4)\n",
"core.lg(' This is GREEN', 5)\n",
"core.lg(' This is BLACK', 6)\n",
"core.lg(' Default is BLACK')\n",
"core.lg('')\n",
"\n",
"core.lg('--------------------------------------------------------')\n",
"core.lg('Here is the Central Resource JSON Configuration', 2)\n",
"print core.ppj(core.m_core_json)\n",
"core.lg('')\n",
"\n",
"core.lg('--------------------------------------------------------')\n",
"core.lg('Here are the Database Resources stored in JSON', 2)\n",
"print core.ppj(core.m_db_apps_json)\n",
"core.lg('')\n",
"\n",
"core.lg('--------------------------------------------------------')\n",
"core.lg('Here are the Redis Resources stored in JSON', 2)\n",
"print core.ppj(core.m_rd_apps_json)\n",
"core.lg('')\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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
blepfo/drp_fall_2016 | Simple_NN.ipynb | 1 | 65374 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np # For matrix/vector operations\n",
"import gzip # For un-zipping the MNIST data\n",
"import pickle # For de-serializing the MNIST data\n",
"import matplotlib.pyplot as pyplot # For graphing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import MNIST Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"\n",
"We use the serialized and zipped copy of the MNIST data available at Michael Nielson's \n",
"repository:\n",
"https://github.com/mnielsen/neural-networks-and-deep-learning\n",
"\"\"\"\n",
"# Unzip the MNIST Data\n",
"with gzip.open('mnist.pkl.gz', 'rb') as f:\n",
" # Unpickle (de-serialize) the\n",
" mnist_data = pickle.load(f, encoding='bytes');\n",
" \n",
"\"\"\"\n",
"mnist_data = 3-tuple containing (mnist_training, mnist_validation, mnist_testing)\n",
" mnist_training = 2-tuple containing (training_data, training_labels)\n",
" training_data = [50,000, 784] ndarray of data for each training example\n",
" training_labels = [50,000] ndarray of labels for the training_data\n",
" mnist_validation = 2-tuple containing (validation_data, validation_labels)\n",
" validation_data = [10,000, 784] ndarray of data for each validation image\n",
" validation_labels = [10,000] ndarray of labels for the training_data\n",
" mnist_testing = 2-tuple containing (testing_data, testing_labels)\n",
" testing_data = [10,000, 784] ndarray of data for each testing image\n",
" testing_labels = [10,000] ndarray of labels for testing data\n",
"\"\"\" \n",
"training_data = mnist_data[0][0];\n",
"training_labels = mnist_data[0][1];\n",
"validation_data = mnist_data[1][0];\n",
"validation_labels = mnist_data[1][1];\n",
"testing_data = mnist_data[2][0];\n",
"testing_labels = mnist_data[2][1];\n",
"# Concatenate lables to end of each training example to work with the implementation\n",
"# of the train() function\n",
"# Now training_examples, validation_examples, and testing_examples are all \n",
"# [50,000, 785] or [10,000, 785] arrays where the last column contains the labels\n",
"training_examples = np.column_stack((training_data, training_labels));\n",
"validation_examples = np.column_stack((validation_data, validation_labels));\n",
"testing_examples = np.column_stack((testing_data, testing_labels));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Network Class"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"\n",
"Simple_NN\n",
"Class to create a simple neural network\n",
" Learning Algorithm: stochastic gradient descent with backpropagation\n",
" Activation Function: sigmoid\n",
" Cost Function: MSE\n",
"\"\"\"\n",
"class Simple_NN(object):\n",
" \"\"\" \n",
" INITIALIZE THE NETWORK\n",
" \"\"\"\n",
" def __init__(self, layers, activation_function=\"sigmoid\", cost_function=\"MSE\"):\n",
" \"\"\"\n",
" self.layers is a list of numbers where the ith number how many neurons are in\n",
" the ith layer of the network.\n",
" \"\"\"\n",
" self.layers = layers;\n",
" self.num_layers = len(layers);\n",
" \n",
" \"\"\"\n",
" self.weights[Layer - 1, input_neuron, output_neuron] = \n",
" List of weight matrices for each layer. \n",
" self.biases[Layer - 1, neuron] = \n",
" List of vectors with biases for each neuron \n",
" FOR EXAMPLE:\n",
" self.weights[layer, j, i] = weight going into the jth neuron of the lth layer\n",
" from the ith neuron of the (l-1)st layer \n",
" self.biases[layer, k] = bias on the kth nuron of the lth layer\n",
" NOTE: layer 0 is the input layer, so self.weights[0] is the weights going into layer 1\n",
" \"\"\"\n",
" self.weights = [];\n",
" self.biases = [];\n",
" self.Z = [];\n",
" self.activations = [];\n",
" # Create matrices with correct dimensions \n",
" for layer_num in range(1, self.num_layers):\n",
" self.weights.append(np.random.randn(layers[layer_num], layers[layer_num - 1]));\n",
" self.biases.append(np.random.randn(layers[layer_num]));\n",
" \"\"\"\n",
" self.activation = string specifying what activation function the neurons will use. \n",
" The options are:\n",
" sigmoid (default)\n",
" self.cost_function = string specifying hat cost function will be used for the \n",
" network.\n",
" The options are:\n",
" MSE (default)\n",
" \"\"\"\n",
" self.activation_function = activation_function;\n",
" self.cost_function = cost_function;\n",
" self.errors = [];\n",
" self.gradC_b = [];\n",
" self.gradC_w = [];\n",
" \n",
" \"\"\"\n",
" TRAINING\n",
" Train the network using stochastic gradient descent and backpropagation.\n",
" Training data should be given in the following format:\n",
" [x11, x12, ..., x1i, y1\n",
" x21, x22, ..., x2i, y2\n",
" ...\n",
" xm1, xm1, ..., xmi, ym]\n",
" Where each row corrsponds to a training example with i data points\n",
" \"\"\"\n",
" def train(self, training_data, testing_data, batch_size, num_epochs, learning_rate):\n",
" for epoch in range(num_epochs):\n",
" # Randomize the order of training examples\n",
" np.random.shuffle(training_data);\n",
" # Separate inputs from outputs\n",
" inputs = training_data[:, :-1];\n",
" outputs = training_data[:, -1];\n",
" testing_inputs = testing_data[:, :-1];\n",
" testing_labels = testing_data[:, -1];\n",
" # For each epoch, loop through each batch to use as training data\n",
" for batch in range(len(training_data))[::batch_size]:\n",
" \"\"\"\n",
" For each batch, we calculate activations and use the backpropagation\n",
" algorithm to change the weights and biases using gradient descent\n",
" Create matrix out of all training inputs in the batch\n",
" To apply W to all input vectors, we can multiply WX where\n",
" X is the ixm matrix containing all m training examples as columns\n",
" \"\"\"\n",
" X = inputs[batch : batch + batch_size];\n",
" Y = outputs[batch : batch + batch_size];\n",
" X = np.transpose(X);\n",
" # FEEDFORWARD\n",
" self.evaluate(X, training=True);\n",
" # BACKPROPAGATION\n",
" self.backpropagate(X, Y);\n",
" # GRADIENT DESCENT\n",
" self.grad_descent();\n",
" # For each epoch, compute number of correct outputs out of testing data\n",
" print(\"Epoch %d: %s\" % \\\n",
" ((epoch + 1), self.test(testing_inputs, testing_labels)));\n",
" \"\"\"\n",
" EVALUATE\n",
" Takes an input column vector X and computes the output of the network\n",
" \"\"\"\n",
" def evaluate(self, X, training=False):\n",
" \"\"\"\n",
" self.Z[layer, neuron, training_example] = \n",
" List of vectors with weighted inputs to the neurons\n",
" self.activations[layer, neuron, training_Example] = \n",
" List of vectors with activations for each neuron\n",
" \"\"\"\n",
" self.Z = [];\n",
" self.activations = [];\n",
" # Calclate outputs going forwards through the network\n",
" for layer in range(self.num_layers - 1):\n",
" if layer == 0:\n",
" # Feed inputs to the network\n",
" prev_activations = X;\n",
" else:\n",
" prev_activations = self.activations[layer - 1];\n",
" # If training, X will be a 2D matrix [input, training_example]. In this case,\n",
" # bias_matrix where each column is a copy of the bias vector is needed\n",
" # to add bias terms for each training example.\n",
" if (training):\n",
" one_vector = np.ones(np.shape(X)[1]);\n",
" bias_matrix = np.outer(self.biases[layer], one_vector);\n",
" else:\n",
" # When evaluating single training example, we want a 1D bias vector\n",
" bias_matrix = self.biases[layer];\n",
" self.Z.append(np.dot(self.weights[layer], prev_activations) + bias_matrix);\n",
" self.activations.append(self.activation(self.Z[layer]));\n",
" \n",
" \"\"\"\n",
" BACKPROPAGATE\n",
" Using input vector X and output vector Y, calculates the following instance variables:\n",
" self.errors[layer, neuron, training_example] - error in each neuron\n",
" self.gradC_b[layer] - average gradient wrt biases over all training examples\n",
" self.gradC_w[layer, input_neuron, output_neuron] - average gradient wrt weights\n",
" \"\"\"\n",
" def backpropagate(self, X, Y):\n",
" # Calculate output error matrix so the [i, j]th entry contains the\n",
" # error for the ith neuron in the output layer for the jth training\n",
" # example\n",
" output_error = np.multiply(\n",
" self.cost_derivative(self.activations[-1], Y),\n",
" self.activation_derivative(self.Z[-1]));\n",
" # Backpropogate: we create the errors matrix which is indexed\n",
" # in the form errors[layer, neuron, training_example]\n",
" self.errors = [output_error];\n",
" # Note that in the loop, we use negative subscripts to go through the \n",
" # layers from output towards input. We therefore start at layer [-2], the\n",
" # second to last layer\n",
" for layer in range(2, self.num_layers): \n",
" # For each layer, calculate errors in previous layer\n",
" previous_errors = np.multiply(\n",
" np.dot(\n",
" np.transpose(self.weights[-layer + 1]),\n",
" self.errors[0]), \n",
" self.activation_derivative(self.Z[-layer])); \n",
" # Add previous errors to the beginning of the error matrix list\n",
" self.errors.insert(0, previous_errors);\n",
" # Calculate gradients of cost function\n",
" self.gradC_b = [];\n",
" self.gradC_w = [];\n",
" for layer in range(self.num_layers - 1):\n",
" \"\"\"\n",
" gradC_b[layer, neuron] = \n",
" Gradient of cost wrt biases for a layer is just the\n",
" vector of errors for that layer.\n",
" When we compute the average over all training examples using \n",
" np.average(matrix, 1), we get a column vector. In order to\n",
" correctly subtract this from the row-vector of biases, \n",
" we transpose the gradient vectors.\n",
" \"\"\"\n",
" self.gradC_b.append(np.average(self.errors[layer],1));\n",
" \"\"\"\n",
" sum_of_weights[layer, j, k] will contain the partial derivative \n",
" of cost wrt the weight from the kth neuron in layer - 1 to the jth\n",
" neuron in layer summed over all training examples. That is,\n",
" sum_of_weightes[layer, j, k] = [sum over training examples dC/dw_j,k]\n",
" \"\"\"\n",
" if (layer == 0):\n",
" prev_activations = X;\n",
" else:\n",
" prev_activations = self.activations[layer - 1];\n",
" sum_of_weights = np.dot(\n",
" self.errors[layer],\n",
" np.transpose(prev_activations));\n",
" \"\"\"\n",
" gradC_w [layer, input_neuron, output_neuron] is also \n",
" averaged over all traininge examples in the batch\n",
" \"\"\"\n",
" self.gradC_w.append((1 / batch_size) * sum_of_weights);\n",
" \n",
" \"\"\"\n",
" GRADIENT DESCENT\n",
" Use the gradients computed in backpropagate() to update the weights and\n",
" biases of the network.\n",
" \"\"\"\n",
" def grad_descent(self):\n",
" for layer in range(self.num_layers - 1):\n",
" self.biases[layer] = np.subtract(self.biases[layer],\n",
" np.transpose(np.multiply(learning_rate, self.gradC_b[layer])));\n",
" self.weights[layer] = np.subtract(self.weights[layer],\n",
" np.multiply(learning_rate, self.gradC_w[layer]));\n",
" \n",
" \"\"\" \n",
" ACTIVATION FUNCTION\n",
" For this network, we use the sigmoid function to calculate neuron activation\n",
" In general, we assume the input z will be a matrix where the [i,j]th entry is the\n",
" ith neuron in the jth training example\n",
" \"\"\" \n",
" def activation(self, z):\n",
" if (self.activation_function == \"sigmoid\"):\n",
" return 1.0 / (1 + np.exp(-z));\n",
" \n",
" def activation_derivative(self, z):\n",
" if (self.activation_function == \"sigmoid\"):\n",
" return np.multiply((1 - self.activation(z)), self.activation(z));\n",
" \"\"\"\n",
" COST FUNCTION\n",
" We assume that activations is a 2D matrix where each column corresponds to the activations\n",
" for a specific training example and each row corresponds to a specific neuron\n",
"\n",
" We assume for now that outputs for the network are disjoint categories, so only\n",
" one output neuron should fire at a time. The output_matrix function turns the output\n",
" vector where each entry corresponds to a training example into a matrix where each\n",
" column corresponds to a training example, with only one nonzero entry per column\n",
" corresponding to the output for that example\n",
" \n",
" Both calculate_cost and cost_derivative return a row vector where the ith entry\n",
" is the cost/cost derivative for training example i\n",
" \"\"\"\n",
" def output_matrix(self, activations, outputs):\n",
" # output_matrix[neuron, training_example]\n",
" output_matrix = np.zeros((activations.shape[0], outputs.shape[0])); \n",
" for training_example, output in enumerate(outputs): \n",
" output_matrix[int(output), training_example] = 1;\n",
" return output_matrix;\n",
"\n",
" def calculate_cost(self, activations, outputs):\n",
" outputs_matrix = self.output_matrix(activations, outputs);\n",
" if (self.cost_function == \"MSE\"):\n",
" squared_errors = np.square(activations - output_matrix);\n",
" # Average sum of squared errors over each training example\n",
" return 0.5 * np.average(np.sum(squared_errors, 0));\n",
" \"\"\"\n",
" Derivative of cost functions with respect to activations\n",
" Each entry [i, j] in the resulting matrix will be the gradient of the cost function\n",
" with respect to the activation of the ith neuron in the jth training example\n",
" \"\"\"\n",
" def cost_derivative(self, activations, outputs):\n",
" output_matrix = self.output_matrix(activations, outputs);\n",
" if (self.cost_function == \"MSE\"):\n",
" return (activations - output_matrix);\n",
" \n",
" \"\"\"\n",
" PREDICT\n",
" predict(x) - Given a data vector x, returns an integer for which class the network \n",
" predicts x belongs to. That is, the integer returned is the neuron in the \n",
" output layer with the maximum activation, indexed starting at 0.\n",
" \"\"\"\n",
" def predict(self, x):\n",
" self.evaluate(x);\n",
" return np.argmax(self.activations[-1]);\n",
" \n",
" \"\"\"\n",
" display_prediction(x) shows an image of the input with the network prediction.\n",
" Height and width can be customized, but their defualts are set for the MNIST images.\n",
" The show parameter also allows you to not immediately show the prediction, allowing\n",
" for subplotting.\n",
" \"\"\"\n",
" def display_prediction(self, x, height=28, width=28, show=True):\n",
" prediction = self.predict(x);\n",
" image = np.reshape(x, (height,width));\n",
" pyplot.title('Prediction: %d' % prediction, fontsize = 12, color = 'white');\n",
" pyplot.imshow(image, cmap='Greys_r');\n",
" if (show):\n",
" pyplot.show();\n",
" \n",
" \"\"\"\n",
" display_predictions(index) will create a figure with 9 images and their predicted\n",
" digits. The images are selected from the testing images beginning with the image\n",
" with the given index. Indices must be between 0-10,000\n",
" \"\"\"\n",
" def display_predictions(self, index):\n",
" for example_num in range(1, 10):\n",
" subplot = 330 + example_num;\n",
" pyplot.subplot(subplot);\n",
" test.display_prediction(testing_data[index + example_num], show=False);\n",
" pyplot.axis('off'); \n",
" pyplot.tight_layout();\n",
" pyplot.show();\n",
" \n",
" \"\"\"\n",
" test(testing_data, testing_labels) - predicts class for all data in \n",
" testing_data and computes accuracy as a percentage using \n",
" \"\"\"\n",
" def test(self, testing_data, testing_labels):\n",
" total_examples = len(testing_labels);\n",
" total_correct = 0;\n",
" for example in range(total_examples):\n",
" if (testing_labels[example] == self.predict(testing_data[example])):\n",
" total_correct = total_correct + 1;\n",
" accuracy = total_correct / total_examples;\n",
" return (str(total_correct) + \" / \" + str(total_examples) + \" = \" + str(accuracy))\n",
"\n",
" \"\"\"\n",
" print_network prints the weights, biases, and activations for the entire network. \n",
" An argument of 1 enables debugging mode, which prints more information, \n",
" including errors and gradients.\n",
" \"\"\"\n",
" def print_network(self, debug=0):\n",
" print(\"Number of layers: \");\n",
" print(self.num_layers);\n",
" print(\"Weights: \")\n",
" for layer in self.weights:\n",
" print(layer)\n",
" print(\"\\nBiases:\" )\n",
" for layer in self.biases:\n",
" print(layer)\n",
" print(\"\\nActivations:\")\n",
" for layer in self.activations:\n",
" print(layer);\n",
" # Print extra info about the network\n",
" if (debug == 1):\n",
" print(\"\\nWeighted Inputs:\")\n",
" for layer in self.Z:\n",
" print(layer)\n",
" print(\"\\nErrors:\")\n",
" for layer in self.errors:\n",
" print(layer)\n",
" print(\"\\nBias Gradients:\")\n",
" for layer in self.gradC_b:\n",
" print(layer)\n",
" print(\"\\nWeight Gradients\")\n",
" for layer in self.gradC_w:\n",
" print(layer)\n",
" \"\"\"\n",
" print_activations will print the output activations\n",
" \"\"\"\n",
" def print_activations(self, index=0):\n",
" image = testing_data[index];\n",
" test.predict(image);\n",
" print(\"Output Activations:\")\n",
" for i in range(0, 10):\n",
" print(\"%d: %f\" % (i, test.activations[1][i]))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1: 9166 / 10000 = 0.9166\n",
"Epoch 2: 9286 / 10000 = 0.9286\n",
"Epoch 3: 9391 / 10000 = 0.9391\n",
"Epoch 4: 9414 / 10000 = 0.9414\n",
"Epoch 5: 9456 / 10000 = 0.9456\n",
"Done Training\n"
]
}
],
"source": [
"\"\"\"\n",
"CREATE AND TRAIN NETWORK\n",
"\"\"\"\n",
"test = Simple_NN([784, 30, 10]);\n",
" \n",
"batch_size = 10;\n",
"num_epochs = 5;\n",
"learning_rate = 3;\n",
"\n",
"test.train(training_examples, validation_examples, batch_size, num_epochs, learning_rate);\n",
"print(\"Done Training\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9380 / 10000 = 0.938\n"
]
}
],
"source": [
"\"\"\"\n",
"TEST NETWORK\n",
"\"\"\"\n",
"result = test.test(testing_data, testing_labels);\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
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rxe/3Mz4+ztTUFBMTE4/tZ7FY6O3txel0PtfxjUF95XKZQCBAMBjEbref1OkLceqM8Si7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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x225d3146710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"VISUALIZE RESULTS\n",
"\"\"\"\n",
"index =1670;\n",
"test.display_predictions(index);"
]
}
],
"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
}
| gpl-3.0 |
ternaus/kaggle_wallmart | Fill with regressor.ipynb | 1 | 203341 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data has many nan values. Normally people will fill it with most common value for a classification variable or mean of some subclass for a continuous variable, but I will try to fill missed values using machine learning, treating them as target."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#import fillna \n",
"import sys\n",
"sys.path += [\"src\"]\n",
"import graphlab as gl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import fill_na_graphlab"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#import dataframe to fill"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"# from sklearn.ensemble import RandomForestRegressor"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#clf = RandomForestRegressor(n_estimators=100, n_jobs=3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weather = pd.read_csv(os.path.join(\"data\", \"weather_modified_3.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weather_temp = weather.drop([\"date\", \n",
" \"TSSN\", \n",
" 'SG', \n",
" 'PRFG', \n",
" 'GR', \n",
" 'VCFG', \n",
" 'GS', \n",
" 'SQ', \n",
" 'BLDU', \n",
" 'PL',\n",
" 'DU',\n",
" 'FU',\n",
" 'FZDZ',\n",
" 'BLSN',\n",
" 'MIFG',\n",
" 'BCFG',\n",
" 'FZRA'], 1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>station_nbr</th>\n",
" <th>tmax</th>\n",
" <th>tmin</th>\n",
" <th>tavg</th>\n",
" <th>depart</th>\n",
" <th>dewpoint</th>\n",
" <th>wetbulb</th>\n",
" <th>heat</th>\n",
" <th>cool</th>\n",
" <th>sunrise</th>\n",
" <th>...</th>\n",
" <th>DZ</th>\n",
" <th>BR</th>\n",
" <th>FG</th>\n",
" <th>TS</th>\n",
" <th>RA</th>\n",
" <th>FG+</th>\n",
" <th>TSRA</th>\n",
" <th>FZFG</th>\n",
" <th>SN</th>\n",
" <th>days</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>52</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>NaN</td>\n",
" <td>36</td>\n",
" <td>40</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>48</td>\n",
" <td>33</td>\n",
" <td>41</td>\n",
" <td>16</td>\n",
" <td>37</td>\n",
" <td>39</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>436</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>55</td>\n",
" <td>34</td>\n",
" <td>45</td>\n",
" <td>9</td>\n",
" <td>24</td>\n",
" <td>36</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>455</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>63</td>\n",
" <td>47</td>\n",
" <td>55</td>\n",
" <td>4</td>\n",
" <td>28</td>\n",
" <td>43</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>448</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>34</td>\n",
" <td>49</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>43</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>447</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" station_nbr tmax tmin tavg depart dewpoint wetbulb heat cool \\\n",
"0 1 52 31 42 NaN 36 40 23 0 \n",
"1 2 48 33 41 16 37 39 24 0 \n",
"2 3 55 34 45 9 24 36 20 0 \n",
"3 4 63 47 55 4 28 43 10 0 \n",
"4 6 63 34 49 0 31 43 16 0 \n",
"\n",
" sunrise ... DZ BR FG TS RA FG+ TSRA FZFG SN days \n",
"0 NaN ... 0 1 1 0 1 0 0 1 0 0 \n",
"1 436 ... 0 0 0 0 1 0 0 0 0 0 \n",
"2 455 ... 0 0 0 0 0 0 0 0 0 0 \n",
"3 448 ... 0 0 0 0 0 0 0 0 0 0 \n",
"4 447 ... 0 0 0 0 0 0 0 0 0 0 \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_temp.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"FZFG 0.010131\n",
"UP 0.011085\n",
"DZ 0.023638\n",
"VCTS 0.026919\n",
"sealevel 0.036532\n",
"FG+ 0.039133\n",
"SN 0.046719\n",
"TSRA 0.049958\n",
"FG 0.059648\n",
"HZ 0.067018\n",
"TS 0.083557\n",
"preciptotal 0.118162\n",
"RA 0.192189\n",
"BR 0.213887\n",
"snowfall 0.249671\n",
"stnpressure 1.551055\n",
"avgspeed 15.472094\n",
"resultspeed 17.577701\n",
"station_nbr 33.275974\n",
"depart 58.472925\n",
"cool 61.384033\n",
"resultdir 93.618431\n",
"heat 191.652813\n",
"wetbulb 289.604567\n",
"tmin 366.303442\n",
"tavg 368.423355\n",
"dewpoint 376.379194\n",
"tmax 393.921316\n",
"sunrise 2950.082229\n",
"sunset 3031.578059\n",
"days 10323.527239\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = weather_temp.var()\n",
"a.sort()\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"906 tmax\n",
"908 tmin\n",
"1469 tavg\n",
"11511 depart\n",
"666 dewpoint\n",
"1252 wetbulb\n",
"1469 heat\n",
"1469 cool\n",
"9656 sunrise\n",
"9656 sunset\n",
"7224 snowfall\n",
"860 preciptotal\n",
"929 stnpressure\n",
"1724 sealevel\n",
"589 resultspeed\n",
"589 resultdir\n",
"875 avgspeed\n"
]
}
],
"source": [
"for column in weather_temp.columns:\n",
" a = sum(weather_temp[column].isnull()) \n",
" if a > 0:\n",
" print a, column"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['station_nbr',\n",
" 'tmax',\n",
" 'tmin',\n",
" 'tavg',\n",
" 'depart',\n",
" 'dewpoint',\n",
" 'wetbulb',\n",
" 'heat',\n",
" 'cool',\n",
" 'sunrise',\n",
" 'sunset',\n",
" 'snowfall',\n",
" 'preciptotal',\n",
" 'stnpressure',\n",
" 'sealevel',\n",
" 'resultspeed',\n",
" 'resultdir',\n",
" 'avgspeed',\n",
" 'HZ',\n",
" 'UP',\n",
" 'VCTS',\n",
" 'DZ',\n",
" 'BR',\n",
" 'FG',\n",
" 'TS',\n",
" 'RA',\n",
" 'FG+',\n",
" 'TSRA',\n",
" 'FZFG',\n",
" 'SN',\n",
" 'days']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(weather_temp.columns)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features = [\n",
"# 'station_nbr',\n",
" 'tmax',\n",
" 'tmin',\n",
" 'tavg',\n",
" 'depart',\n",
" 'dewpoint',\n",
" 'wetbulb',\n",
" 'heat',\n",
" 'cool',\n",
" 'sunrise',\n",
" 'sunset',\n",
" 'snowfall',\n",
" 'preciptotal',\n",
" 'stnpressure',\n",
" 'sealevel',\n",
" 'resultspeed',\n",
" 'resultdir',\n",
" 'avgspeed',\n",
" 'HZ',\n",
" 'UP',\n",
" 'VCTS',\n",
" 'DZ',\n",
" 'BR',\n",
" 'FG',\n",
" 'TS',\n",
" 'RA',\n",
" 'FG+',\n",
" 'TSRA',\n",
" 'FZFG',\n",
" 'SN',\n",
" 'days']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Let's find columns that have only positive values\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = gl.SFrame(weather_temp)\n",
"for column in a.column_names():\n",
" a[column] = a[column].fillna(np.nan)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tmax 906\n",
"tmin 908\n",
"tavg 1469\n",
"depart 11511\n",
"dewpoint 666\n",
"wetbulb 1252\n",
"heat 1469\n",
"cool 1469\n",
"snowfall 7224\n",
"preciptotal 860\n",
"stnpressure 929\n",
"sealevel 1724\n",
"resultspeed 589\n",
"resultdir 589\n",
"avgspeed 875\n",
"HZ 0\n",
"FU 0\n",
"UP 0\n",
"VCTS 0\n",
"DZ 0\n",
"BR 0\n",
"FG 0\n",
"BCFG 0\n",
"DU 0\n",
"FZRA 0\n",
"TS 0\n",
"RA 0\n",
"PL 0\n",
"GS 0\n",
"GR 0\n",
"FZDZ 0\n",
"VCFG 0\n",
"PRFG 0\n",
"FG+ 0\n",
"TSRA 0\n",
"FZFG 0\n",
"BLDU 0\n",
"MIFG 0\n",
"SQ 0\n",
"BLSN 0\n",
"SN 0\n",
"SG 0\n",
"month 0\n",
"day 0\n",
"day_length"
]
},
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" 9656\n",
"['HZ', 'FU', 'UP', 'VCTS', 'DZ', 'BR', 'FG', 'BCFG', 'DU', 'FZRA', 'TS', 'RA', 'PL', 'GS', 'GR', 'FZDZ', 'VCFG', 'PRFG', 'FG+', 'TSRA', 'FZFG', 'BLDU', 'MIFG', 'SQ', 'BLSN', 'SN', 'SG', 'month', 'day']\n",
"filling resultdir na = 589\n"
]
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"text": [
"\n",
" Model summary \n",
"--------------------------------------------------------\n",
"Class : BoostedTreesRegression\n",
"\n",
"Number of examples : 19928\n",
"Number of feature columns : 29\n",
"Number of unpacked features : 29\n",
"\n",
"Number of trees : 10\n",
"Max tree depth : 6\n",
"Train RMSE : 9.1744\n",
"Validation RMSE : None\n",
"Training time (sec) : 2.8141\n",
"\n",
"None\n",
"filling resultspeed na = 589\n",
"\n",
" Model summary \n",
"--------------------------------------------------------\n",
"Class : BoostedTreesRegression\n",
"\n",
"Number of examples : 19928\n",
"Number of feature columns : 31\n",
"Number of unpacked features : 31\n",
"\n",
"Number of trees : 10\n",
"Max tree depth : 6\n",
"Train RMSE : 3.6864\n",
"Validation RMSE : None\n",
"Training time (sec) : 3.1361\n",
"\n",
"None\n",
"filling dewpoint na = 666"
]
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"\n",
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"\n",
" Model summary \n",
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"\n",
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"Train RMSE : 3.6689\n",
"Validation RMSE : None\n",
"Training time (sec) : 3.3023\n",
"\n",
"None\n"
]
}
],
"source": [
"reload(fill_na_graphlab)\n",
"\n",
"weather_result = fill_na_graphlab.fill_missed_all(a, features, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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\">station_nbr</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tmax</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tmin</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tavg</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">depart</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dewpoint</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">wetbulb</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">heat</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">cool</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">snowfall</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">preciptotal</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stnpressure</th>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">48.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">41.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">37.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">39.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.07</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">28.82</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">55.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">34.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">45.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.77</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">63.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">55.0</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\">28.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">43.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.79</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">63.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">34.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">49.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">31.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">43.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.95</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">72.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">48.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">60.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">54.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">56.0</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\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.15</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">50.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">34.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">42.0</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\">25.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.13</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">15</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">48.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">37.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">28.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.09</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.53</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">59.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">40.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">50.0</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\">28.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">40.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">15.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.98</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">19</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">32.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.12</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.06</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">46.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">28.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">37.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">32.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">28.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.01</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.01</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">28.51</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sealevel</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">resultspeed</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">resultdir</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">avgspeed</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">HZ</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FU</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">UP</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">VCTS</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">DZ</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">BR</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">BCFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">DU</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FZRA</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">TS</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">RA</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PL</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.91</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</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\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.47</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.9</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">31.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.48</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.47</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13.8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.18</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.6</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</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\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.52</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.4</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">32.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.89</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">17.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</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\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</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\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.49</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.9</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.79</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.6</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">15.6</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</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\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</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\">0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.62</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13.3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">GS</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">GR</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FZDZ</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">VCFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PRFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FG+</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">TSRA</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FZFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">BLDU</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">MIFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SQ</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">...</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</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\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
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" </tr>\n",
"</table>\n",
"[10 rows x 66 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tstation_nbr\tint\n",
"\ttmax\tfloat\n",
"\ttmin\tfloat\n",
"\ttavg\tfloat\n",
"\tdepart\tfloat\n",
"\tdewpoint\tfloat\n",
"\twetbulb\tfloat\n",
"\theat\tfloat\n",
"\tcool\tfloat\n",
"\tsnowfall\tfloat\n",
"\tpreciptotal\tfloat\n",
"\tstnpressure\tfloat\n",
"\tsealevel\tfloat\n",
"\tresultspeed\tfloat\n",
"\tresultdir\tfloat\n",
"\tavgspeed\tfloat\n",
"\tHZ\tint\n",
"\tFU\tint\n",
"\tUP\tint\n",
"\tVCTS\tint\n",
"\tDZ\tint\n",
"\tBR\tint\n",
"\tFG\tint\n",
"\tBCFG\tint\n",
"\tDU\tint\n",
"\tFZRA\tint\n",
"\tTS\tint\n",
"\tRA\tint\n",
"\tPL\tint\n",
"\tGS\tint\n",
"\tGR\tint\n",
"\tFZDZ\tint\n",
"\tVCFG\tint\n",
"\tPRFG\tint\n",
"\tFG+\tint\n",
"\tTSRA\tint\n",
"\tFZFG\tint\n",
"\tBLDU\tint\n",
"\tMIFG\tint\n",
"\tSQ\tint\n",
"\tBLSN\tint\n",
"\tSN\tint\n",
"\tSG\tint\n",
"\tmonth\tint\n",
"\tday\tint\n",
"\tday_length\tfloat\n",
"\tsunset_hour\tfloat\n",
"\tsunset_minute\tfloat\n",
"\tsunrise_hour\tfloat\n",
"\tsunrise_minute\tfloat\n",
"\tresultdirNAN\tint\n",
"\tresultspeedNAN\tint\n",
"\tdewpointNAN\tint\n",
"\tpreciptotalNAN\tint\n",
"\tavgspeedNAN\tint\n",
"\ttmaxNAN\tint\n",
"\ttminNAN\tint\n",
"\tstnpressureNAN\tint\n",
"\twetbulbNAN\tint\n",
"\tcoolNAN\tint\n",
"\theatNAN\tint\n",
"\ttavgNAN\tint\n",
"\tsealevelNAN\tint\n",
"\tsnowfallNAN\tint\n",
"\tday_lengthNAN\tint\n",
"\tdepartNAN\tint\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------+------+------+------+--------+----------+---------+------+------+\n",
"| station_nbr | tmax | tmin | tavg | depart | dewpoint | wetbulb | heat | cool |\n",
"+-------------+------+------+------+--------+----------+---------+------+------+\n",
"| 2 | 48.0 | 33.0 | 41.0 | 16.0 | 37.0 | 39.0 | 24.0 | 0.0 |\n",
"| 3 | 55.0 | 34.0 | 45.0 | 9.0 | 24.0 | 36.0 | 20.0 | 0.0 |\n",
"| 4 | 63.0 | 47.0 | 55.0 | 4.0 | 28.0 | 43.0 | 10.0 | 0.0 |\n",
"| 6 | 63.0 | 34.0 | 49.0 | 0.0 | 31.0 | 43.0 | 16.0 | 0.0 |\n",
"| 11 | 72.0 | 48.0 | 60.0 | 7.0 | 54.0 | 56.0 | 5.0 | 0.0 |\n",
"| 14 | 50.0 | 34.0 | 42.0 | 5.0 | 25.0 | 35.0 | 23.0 | 0.0 |\n",
"| 15 | 48.0 | 26.0 | 37.0 | 16.0 | 35.0 | 38.0 | 28.0 | 0.0 |\n",
"| 18 | 59.0 | 40.0 | 50.0 | 4.0 | 28.0 | 40.0 | 15.0 | 0.0 |\n",
"| 19 | 38.0 | 25.0 | 32.0 | 10.0 | 26.0 | 30.0 | 33.0 | 0.0 |\n",
"| 2 | 46.0 | 28.0 | 37.0 | 12.0 | 24.0 | 32.0 | 28.0 | 0.0 |\n",
"+-------------+------+------+------+--------+----------+---------+------+------+\n",
"+----------+-------------+-------------+----------+-------------+-----------+\n",
"| snowfall | preciptotal | stnpressure | sealevel | resultspeed | resultdir |\n",
"+----------+-------------+-------------+----------+-------------+-----------+\n",
"| 0.0 | 0.07 | 28.82 | 29.91 | 9.1 | 23.0 |\n",
"| 0.0 | 0.0 | 29.77 | 30.47 | 9.9 | 31.0 |\n",
"| 0.0 | 0.0 | 29.79 | 30.48 | 8.0 | 35.0 |\n",
"| 0.0 | 0.0 | 29.95 | 30.47 | 14.0 | 36.0 |\n",
"| 0.0 | 0.0 | 30.15 | 30.18 | 4.6 | 23.0 |\n",
"| 0.0 | 0.0 | 29.13 | 30.52 | 11.4 | 32.0 |\n",
"| 0.0 | 0.09 | 29.53 | 29.89 | 2.5 | 17.0 |\n",
"| 0.0 | 0.0 | 29.98 | 30.49 | 9.0 | 33.0 |\n",
"| 0.5 | 0.12 | 29.06 | 29.79 | 14.6 | 29.0 |\n",
"| 0.01 | 0.01 | 28.51 | 29.62 | 12.7 | 26.0 |\n",
"+----------+-------------+-------------+----------+-------------+-----------+\n",
"+----------+----+----+----+------+-----+\n",
"| avgspeed | HZ | FU | UP | VCTS | ... |\n",
"+----------+----+----+----+------+-----+\n",
"| 11.3 | 0 | 0 | 0 | 0 | ... |\n",
"| 10.0 | 0 | 0 | 0 | 0 | ... |\n",
"| 8.2 | 0 | 0 | 0 | 0 | ... |\n",
"| 13.8 | 0 | 0 | 0 | 0 | ... |\n",
"| 4.8 | 0 | 0 | 0 | 0 | ... |\n",
"| 11.3 | 0 | 0 | 0 | 0 | ... |\n",
"| 3.8 | 0 | 0 | 0 | 0 | ... |\n",
"| 8.9 | 0 | 0 | 0 | 0 | ... |\n",
"| 15.6 | 0 | 0 | 0 | 0 | ... |\n",
"| 13.3 | 0 | 0 | 0 | 0 | ... |\n",
"+----------+----+----+----+------+-----+\n",
"[10 rows x 66 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weather_result[\"date\"] = weather[\"date\"]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weather_result = weather_result.to_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constraints:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 23 >= sunset[\"hour\"] >= sunrise[\"hour\"] >= 0\n",
"* 0 <= sunset[\"minutes\"] <= 59\n",
"* 0 <= sunrise[\"minutes\"] <= 59\n",
"* 24* 60 >= day_length >= 0\n",
"* stnpressure >= 0\n",
"* preciptotal >= 0\n",
"* dewpoint >= 0\n",
"* sealevel >= 0\n",
"* resultspeed >= 0\n",
"* 359 >= resultdir >=0 \n",
"* avgspeed >= 0"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>station_nbr</th>\n",
" <th>HZ</th>\n",
" <th>FU</th>\n",
" <th>UP</th>\n",
" <th>VCTS</th>\n",
" <th>DZ</th>\n",
" <th>BR</th>\n",
" <th>FG</th>\n",
" <th>BCFG</th>\n",
" <th>DU</th>\n",
" <th>...</th>\n",
" <th>tavg</th>\n",
" <th>sealevel</th>\n",
" <th>snowfall</th>\n",
" <th>day_length</th>\n",
" <th>sunrise_hour</th>\n",
" <th>sunrise_minute</th>\n",
" <th>sunset_hour</th>\n",
" <th>sunset_minute</th>\n",
" <th>depart</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 51 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [station_nbr, HZ, FU, UP, VCTS, DZ, BR, FG, BCFG, DU, FZRA, TS, RA, PL, GS, GR, FZDZ, VCFG, PRFG, FG+, TSRA, FZFG, BLDU, MIFG, SQ, BLSN, SN, SG, month, day, resultdir, resultspeed, dewpoint, preciptotal, avgspeed, tmax, tmin, stnpressure, wetbulb, cool, heat, tavg, sealevel, snowfall, day_length, sunrise_hour, sunrise_minute, sunset_hour, sunset_minute, depart, date]\n",
"Index: []\n",
"\n",
"[0 rows x 51 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[weather_result[\"sunset_hour\"] < 0 ]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>tavg</th>\n",
" <th>sealevel</th>\n",
" <th>snowfall</th>\n",
" <th>day_length</th>\n",
" <th>sunrise_hour</th>\n",
" <th>sunrise_minute</th>\n",
" <th>sunset_hour</th>\n",
" <th>sunset_minute</th>\n",
" <th>depart</th>\n",
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"Empty DataFrame\n",
"Columns: [station_nbr, HZ, FU, UP, VCTS, DZ, BR, FG, BCFG, DU, FZRA, TS, RA, PL, GS, GR, FZDZ, VCFG, PRFG, FG+, TSRA, FZFG, BLDU, MIFG, SQ, BLSN, SN, SG, month, day, resultdir, resultspeed, dewpoint, preciptotal, avgspeed, tmax, tmin, stnpressure, wetbulb, cool, heat, tavg, sealevel, snowfall, day_length, sunrise_hour, sunrise_minute, sunset_hour, sunset_minute, depart, date]\n",
"Index: []\n",
"\n",
"[0 rows x 51 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[weather_result[\"sunset_hour\"] > 23]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"\n",
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},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"weather_result[weather_result[\"sunset_hour\"] < weather_result[\"sunrise_hour\"]]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1553 -0.683154\n",
"1558 -0.351302\n",
"7788 -0.790381\n",
"15055 -0.070507\n",
"16017 -0.050679\n",
"16037 -0.501580\n",
"16052 -0.766174\n",
"Name: sunset_minute, dtype: float64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[\"sunset_minute\"][weather_result[\"sunset_minute\"] < 0 ]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weather_result[\"sunset_minute\"][weather_result[\"sunset_minute\"] < 0 ] = 0"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>sunrise_hour</th>\n",
" <th>sunrise_minute</th>\n",
" <th>sunset_hour</th>\n",
" <th>sunset_minute</th>\n",
" <th>depart</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 51 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [station_nbr, HZ, FU, UP, VCTS, DZ, BR, FG, BCFG, DU, FZRA, TS, RA, PL, GS, GR, FZDZ, VCFG, PRFG, FG+, TSRA, FZFG, BLDU, MIFG, SQ, BLSN, SN, SG, month, day, resultdir, resultspeed, dewpoint, preciptotal, avgspeed, tmax, tmin, stnpressure, wetbulb, cool, heat, tavg, sealevel, snowfall, day_length, sunrise_hour, sunrise_minute, sunset_hour, sunset_minute, depart, date]\n",
"Index: []\n",
"\n",
"[0 rows x 51 columns]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[weather_result[\"sunset_minute\"] > 60 ]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"Index: []\n",
"\n",
"[0 rows x 67 columns]"
]
},
"execution_count": 47,
"metadata": {},
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}
],
"source": [
"weather_result[weather_result[\"day_length\"] < 0] "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <th></th>\n",
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"Empty DataFrame\n",
"Columns: [station_nbr, tmax, tmin, tavg, depart, dewpoint, wetbulb, heat, cool, snowfall, preciptotal, stnpressure, sealevel, resultspeed, resultdir, avgspeed, HZ, FU, UP, VCTS, DZ, BR, FG, BCFG, DU, FZRA, TS, RA, PL, GS, GR, FZDZ, VCFG, PRFG, FG+, TSRA, FZFG, BLDU, MIFG, SQ, BLSN, SN, SG, month, day, day_length, sunset_hour, sunset_minute, sunrise_hour, sunrise_minute, resultdirNAN, resultspeedNAN, dewpointNAN, preciptotalNAN, avgspeedNAN, tmaxNAN, tminNAN, stnpressureNAN, wetbulbNAN, coolNAN, heatNAN, tavgNAN, sealevelNAN, snowfallNAN, day_lengthNAN, departNAN, date]\n",
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"\n",
"[0 rows x 67 columns]"
]
},
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"metadata": {},
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}
],
"source": [
"weather_result[weather_result[\"day_length\"] > 24 * 60]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"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\">station_nbr</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tmax</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tmin</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tavg</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">depart</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dewpoint</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">wetbulb</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">heat</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">cool</th>\n",
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"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stnpressure</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sealevel</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">resultspeed</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">resultdir</th>\n",
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" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FU</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">UP</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">VCTS</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">DZ</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">BR</th>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">BCFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">DU</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FZRA</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">TS</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">RA</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PL</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">GS</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">GR</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FZDZ</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">VCFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PRFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FG+</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">TSRA</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">FZFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">BLDU</th>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">MIFG</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SQ</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">...</th>\n",
" </tr>\n",
"</table>\n",
"[? rows x 67 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use len(sf) to force materialization.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tstation_nbr\tint\n",
"\ttmax\tfloat\n",
"\ttmin\tfloat\n",
"\ttavg\tfloat\n",
"\tdepart\tfloat\n",
"\tdewpoint\tfloat\n",
"\twetbulb\tfloat\n",
"\theat\tfloat\n",
"\tcool\tfloat\n",
"\tsnowfall\tfloat\n",
"\tpreciptotal\tfloat\n",
"\tstnpressure\tfloat\n",
"\tsealevel\tfloat\n",
"\tresultspeed\tfloat\n",
"\tresultdir\tfloat\n",
"\tavgspeed\tfloat\n",
"\tHZ\tint\n",
"\tFU\tint\n",
"\tUP\tint\n",
"\tVCTS\tint\n",
"\tDZ\tint\n",
"\tBR\tint\n",
"\tFG\tint\n",
"\tBCFG\tint\n",
"\tDU\tint\n",
"\tFZRA\tint\n",
"\tTS\tint\n",
"\tRA\tint\n",
"\tPL\tint\n",
"\tGS\tint\n",
"\tGR\tint\n",
"\tFZDZ\tint\n",
"\tVCFG\tint\n",
"\tPRFG\tint\n",
"\tFG+\tint\n",
"\tTSRA\tint\n",
"\tFZFG\tint\n",
"\tBLDU\tint\n",
"\tMIFG\tint\n",
"\tSQ\tint\n",
"\tBLSN\tint\n",
"\tSN\tint\n",
"\tSG\tint\n",
"\tmonth\tint\n",
"\tday\tint\n",
"\tday_length\tfloat\n",
"\tsunset_hour\tfloat\n",
"\tsunset_minute\tfloat\n",
"\tsunrise_hour\tfloat\n",
"\tsunrise_minute\tfloat\n",
"\tresultdirNAN\tint\n",
"\tresultspeedNAN\tint\n",
"\tdewpointNAN\tint\n",
"\tpreciptotalNAN\tint\n",
"\tavgspeedNAN\tint\n",
"\ttmaxNAN\tint\n",
"\ttminNAN\tint\n",
"\tstnpressureNAN\tint\n",
"\twetbulbNAN\tint\n",
"\tcoolNAN\tint\n",
"\theatNAN\tint\n",
"\ttavgNAN\tint\n",
"\tsealevelNAN\tint\n",
"\tsnowfallNAN\tint\n",
"\tday_lengthNAN\tint\n",
"\tdepartNAN\tint\n",
"\tdate\tstr\n",
"\n",
"Rows: Unknown\n",
"\n",
"Data:\n",
"\t[]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[weather_result[\"stnpressure\"] < 0 ]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>16711</th>\n",
" <td> 9</td>\n",
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" <th>16787</th>\n",
" <td> 9</td>\n",
" <td> 11</td>\n",
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" <td> 7</td>\n",
" <td>-14.082035</td>\n",
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" <td> 9</td>\n",
" <td> 8</td>\n",
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" <td> 13</td>\n",
" <td> 51</td>\n",
" <td> 19</td>\n",
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" <td> 2014-07-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19637</th>\n",
" <td> 8</td>\n",
" <td> 74</td>\n",
" <td> 64</td>\n",
" <td> 69</td>\n",
" <td> 8.498392</td>\n",
" <td> 64</td>\n",
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" <td> 1</td>\n",
" <td> 2014-09-18</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 67 columns</p>\n",
"</div>"
],
"text/plain": [
" station_nbr tmax tmin tavg depart dewpoint wetbulb heat \\\n",
"16711 9 10 -6 2 -13.994639 -5 2 63 \n",
"16787 9 11 2 7 -14.082035 -3 5 58 \n",
"18184 9 8 -7 1 -14.764138 -7 0 64 \n",
"18322 13 51 19 35 -1.531664 23 28 30 \n",
"19637 8 74 64 69 8.498392 64 66 0 \n",
"\n",
" cool snowfall ... stnpressureNAN wetbulbNAN coolNAN heatNAN \\\n",
"16711 0 -0.038727 ... 0 0 0 0 \n",
"16787 0 -0.038727 ... 0 0 0 0 \n",
"18184 0 -0.038727 ... 0 0 0 0 \n",
"18322 0 -0.028028 ... 0 0 0 0 \n",
"19637 4 -0.024997 ... 0 0 0 0 \n",
"\n",
" tavgNAN sealevelNAN snowfallNAN day_lengthNAN departNAN date \n",
"16711 0 0 1 1 1 2014-04-24 \n",
"16787 0 0 0 1 1 2014-04-28 \n",
"18184 0 0 0 0 1 2014-07-07 \n",
"18322 0 1 0 0 1 2014-07-14 \n",
"19637 0 0 1 1 1 2014-09-18 \n",
"\n",
"[5 rows x 67 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[weather_result[\"snowfall\"] < 0 ]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weather_result[\"snowfall\"][weather_result[\"snowfall\"] < 0 ] = 0"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"Empty DataFrame\n",
"Columns: [station_nbr, tmax, tmin, tavg, depart, dewpoint, wetbulb, heat, cool, snowfall, preciptotal, stnpressure, sealevel, resultspeed, resultdir, avgspeed, HZ, FU, UP, VCTS, DZ, BR, FG, BCFG, DU, FZRA, TS, RA, PL, GS, GR, FZDZ, VCFG, PRFG, FG+, TSRA, FZFG, BLDU, MIFG, SQ, BLSN, SN, SG, month, day, day_length, sunset_hour, sunset_minute, sunrise_hour, sunrise_minute, resultdirNAN, resultspeedNAN, dewpointNAN, preciptotalNAN, avgspeedNAN, tmaxNAN, tminNAN, stnpressureNAN, wetbulbNAN, coolNAN, heatNAN, tavgNAN, sealevelNAN, snowfallNAN, day_lengthNAN, departNAN, date]\n",
"Index: []\n",
"\n",
"[0 rows x 67 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[weather_result[\"preciptotal\"] < 0 ]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weather_result[\"snowfall\"][weather_result[\"snowfall\"] < 0 ] = 0"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], name: resultdir, dtype: float64)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# weather_result[\"resultdir\"][weather_result[\"resultdir\"] < 0]\n",
"weather_result[\"resultdir\"][weather_result[\"resultdir\"] < 0]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], name: resultdir, dtype: float64)"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[\"resultdir\"][weather_result[\"resultdir\"] > 355]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], name: resultspeed, dtype: float64)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[\"resultspeed\"][weather_result[\"resultspeed\"] < 0]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], name: sealevel, dtype: float64)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[\"sealevel\"][weather_result[\"sealevel\"] < 0]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], name: avgspeed, dtype: float64)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[\"avgspeed\"][weather_result[\"avgspeed\"] < 0]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>station_nbr</th>\n",
" <th>HZ</th>\n",
" <th>FU</th>\n",
" <th>UP</th>\n",
" <th>VCTS</th>\n",
" <th>DZ</th>\n",
" <th>BR</th>\n",
" <th>FG</th>\n",
" <th>BCFG</th>\n",
" <th>DU</th>\n",
" <th>...</th>\n",
" <th>stnpressure</th>\n",
" <th>wetbulb</th>\n",
" <th>cool</th>\n",
" <th>heat</th>\n",
" <th>tavg</th>\n",
" <th>sealevel</th>\n",
" <th>log_snowfall</th>\n",
" <th>day_length</th>\n",
" <th>depart</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 47 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [station_nbr, HZ, FU, UP, VCTS, DZ, BR, FG, BCFG, DU, FZRA, TS, RA, PL, GS, GR, FZDZ, VCFG, PRFG, FG+, TSRA, FZFG, BLDU, MIFG, SQ, BLSN, SN, SG, month, day, log_resultspeed, resultdir, dewpoint, log_preciptotal, log_avgspeed, tmax, tmin, stnpressure, wetbulb, cool, heat, tavg, sealevel, log_snowfall, day_length, depart, date]\n",
"Index: []\n",
"\n",
"[0 rows x 47 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_result[weather_result[\"log_resultspeed\"] < 0]"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#save to file\n",
"weather_result.to_csv(os.path.join(\"data\", \"weather_filled_gl_default_3.csv\"), index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jorisvandenbossche/DS-python-data-analysis | notebooks/case4_air_quality_processing.ipynb | 1 | 18261 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p><font size=\"6\"><b> CASE - air quality data of European monitoring stations (AirBase)</b></font></p>\n",
"\n",
"> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:[email protected]>, <mailto:[email protected]>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**AirBase** is the European air quality database maintained by the European Environment Agency (EEA). It contains air quality monitoring data and information submitted by participating countries throughout Europe. The [air quality database](https://www.eea.europa.eu/data-and-maps/data/aqereporting-8/air-quality-zone-geometries) consists of a multi-annual time series of air quality measurement data and statistics for a number of air pollutants."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some of the data files that are available from AirBase were included in the data folder: the **hourly concentrations of nitrogen dioxide (NO2)** for 4 different measurement stations:\n",
"\n",
"- FR04037 (PARIS 13eme): urban background site at Square de Choisy\n",
"- FR04012 (Paris, Place Victor Basch): urban traffic site at Rue d'Alesia\n",
"- BETR802: urban traffic site in Antwerp, Belgium\n",
"- BETN029: rural background site in Houtem, Belgium\n",
"\n",
"See http://www.eea.europa.eu/themes/air/interactive/no2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Processing a single file\n",
"\n",
"We will start with processing one of the downloaded files (`BETR8010000800100hour.1-1-1990.31-12-2012`). Looking at the data, you will see it does not look like a nice csv file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\") as f:\n",
" print(f.readline())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we will need to do some manual processing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just reading the tab-delimited data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\", sep='\\t')#, header=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above data is clearly not ready to be used! Each row contains the 24 measurements for each hour of the day, and also contains a flag (0/1) indicating the quality of the data. Furthermore, there is no header row with column names."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
"\n",
"<b>EXERCISE 1</b>: <br><br> Clean up this dataframe by using more options of `pd.read_csv` (see its [docstring](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html))\n",
"\n",
" <ul>\n",
" <li>specify the correct delimiter</li>\n",
" <li>specify that the values of -999 and -9999 should be regarded as NaN</li>\n",
" <li>specify our own column names (for how the column names are made up, see <a href=\"http://stackoverflow.com/questions/6356041/python-intertwining-two-lists\">http://stackoverflow.com/questions/6356041/python-intertwining-two-lists</a>)\n",
"</ul>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Column names: list consisting of 'date' and then intertwined the hour of the day and 'flag'\n",
"hours = [\"{:02d}\".format(i) for i in range(24)]\n",
"column_names = ['date'] + [item for pair in zip(hours, ['flag' + str(i) for i in range(24)]) for item in pair]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing1.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing2.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the sake of this tutorial, we will disregard the 'flag' columns (indicating the quality of the data)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
"\n",
"**EXERCISE 2**:\n",
"\n",
"Drop all 'flag' columns ('flag1', 'flag2', ...)\n",
"\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"flag_columns = [col for col in data.columns if 'flag' in col]\n",
"# we can now use this list to drop these columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing3.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we want to reshape it: our goal is to have the different hours as row indices, merged with the date into a datetime-index. Here we have a wide and long dataframe, and want to make this a long, narrow timeseries."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\">\n",
"\n",
"<b>REMEMBER</b>: \n",
"\n",
"\n",
"Recap: reshaping your data with [`stack` / `melt` and `unstack` / `pivot`](./pandas_08_reshaping_data.ipynb)</li>\n",
"\n",
"\n",
"\n",
"<img src=\"../img/pandas/schema-stack.svg\" width=70%>\n",
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
"\n",
"<b>EXERCISE 3</b>:\n",
"\n",
"<br><br>\n",
"\n",
"Reshape the dataframe to a timeseries. \n",
"The end result should look like:<br><br>\n",
"\n",
"\n",
"<div class='center'>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BETR801</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1990-01-02 09:00:00</th>\n",
" <td>48.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990-01-02 12:00:00</th>\n",
" <td>48.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990-01-02 13:00:00</th>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990-01-02 14:00:00</th>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 20:00:00</th>\n",
" <td>16.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 21:00:00</th>\n",
" <td>14.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 22:00:00</th>\n",
" <td>16.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 23:00:00</th>\n",
" <td>15.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p style=\"text-align:center\">170794 rows × 1 columns</p>\n",
"</div>\n",
"\n",
" <ul>\n",
" <li>Reshape the dataframe so that each row consists of one observation for one date + hour combination</li>\n",
" <li>When you have the date and hour values as two columns, combine these columns into a datetime (tip: string columns can be summed to concatenate the strings) and remove the original columns</li>\n",
" <li>Set the new datetime values as the index, and remove the original columns with date and hour values</li>\n",
"\n",
"</ul>\n",
"\n",
"\n",
"**NOTE**: This is an advanced exercise. Do not spend too much time on it and don't hesitate to look at the solutions. \n",
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reshaping using `melt`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing4.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reshaping using `stack`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing5.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing6.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Combine date and hour:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing7.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing8.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing9.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_stacked.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our final data is now a time series. In pandas, this means that the index is a `DatetimeIndex`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_stacked.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_stacked.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Processing a collection of files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have seen the code steps to process one of the files. We have however multiple files for the different stations with the same structure. Therefore, to not have to repeat the actual code, let's make a function from the steps we have seen above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
"\n",
"<b>EXERCISE 4</b>:\n",
"\n",
" <ul>\n",
" <li>Write a function <code>read_airbase_file(filename, station)</code>, using the above steps the read in and process the data, and that returns a processed timeseries.</li>\n",
"</ul>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def read_airbase_file(filename, station):\n",
" \"\"\"\n",
" Read hourly AirBase data files.\n",
" \n",
" Parameters\n",
" ----------\n",
" filename : string\n",
" Path to the data file.\n",
" station : string\n",
" Name of the station.\n",
" \n",
" Returns\n",
" -------\n",
" DataFrame\n",
" Processed dataframe.\n",
" \"\"\"\n",
" \n",
" ...\n",
" \n",
" return ..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing10.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test the function on the data file from above:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"filename = \"data/BETR8010000800100hour.1-1-1990.31-12-2012\"\n",
"station = os.path.split(filename)[-1][:7]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"station"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test = read_airbase_file(filename, station)\n",
"test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now want to use this function to read in all the different data files from AirBase, and combine them into one Dataframe."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
"\n",
"**EXERCISE 5**:\n",
"\n",
"Use the [pathlib module](https://docs.python.org/3/library/pathlib.html) `Path` class in combination with the `glob` method to list all 4 AirBase data files that are included in the 'data' directory, and call the result `data_files`.\n",
"\n",
"<details><summary>Hints</summary>\n",
"\n",
"- The pathlib module provides a object oriented way to handle file paths. First, create a `Path` object of the data folder, `pathlib.Path(\"./data\")`. Next, apply the `glob` function to extract all the files containing `*0008001*` (use wildcard * to say \"any characters\"). The output is a Python generator, which you can collect as a `list()`.\n",
"\n",
"</details> \n",
"\n",
" \n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing11.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
"\n",
"**EXERCISE 6**:\n",
"\n",
"* Loop over the data files, read and process the file using our defined function, and append the dataframe to a list.\n",
"* Combine the the different DataFrames in the list into a single DataFrame where the different columns are the different stations. Call the result `combined_data`.\n",
"\n",
"<details><summary>Hints</summary>\n",
"\n",
"- The `data_files` list contains `Path` objects (from the pathlib module). To get the actual file name as a string, use the `.name` attribute.\n",
"- The station name is always first 7 characters of the file name.\n",
"\n",
"</details> \n",
"\n",
"\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing12.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbtutor-solution"
]
},
"outputs": [],
"source": [
"# %load _solutions/case4_air_quality_processing13.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"combined_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we don't want to have to repeat this each time we use the data. Therefore, let's save the processed data to a csv file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# let's first give the index a descriptive name\n",
"combined_data.index.name = 'datetime'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"combined_data.to_csv(\"airbase_data_processed.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"formats": "ipynb,md:myst"
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| bsd-3-clause |
nilutz/Connectfour | notebooks/connectfour-withMinmax.ipynb | 1 | 20160 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Minmax(object):\n",
" \n",
" '''\n",
" This is a simple MinMax algorithm with score-Hashing and Alpha Beta pruning.\n",
" The score evaluates a heuristic.\n",
" '''\n",
" \n",
" DIC ={}\n",
" \n",
" def __init__(self):\n",
" \n",
" #load a DIC with some precalculated values values\n",
" try: \n",
" self.DIC = self.load_obj('scores')\n",
" except (RuntimeError, TypeError, NameError, OSError, IOError):\n",
" self.DIC = {}\n",
" print '--> no scores.pkl available. Now it takes way longer to build the tree!!'\n",
"\n",
" def save_obj(self, obj, name ):\n",
" import pickle\n",
" '''\n",
" save a data object\n",
" '''\n",
" with open(name + '.pkl', 'wb') as f:\n",
" pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
" def load_obj(self, name):\n",
" '''\n",
" loads a data object\n",
" '''\n",
" import pickle\n",
" with open(name + '.pkl', 'rb') as f:\n",
" return pickle.load(f)\n",
" \n",
" def get_hash_key(self, board):\n",
" '''\n",
" This function creates a hashkey for a board so it can be stored in the dictionary.\n",
" '''\n",
" b=np.ndarray.flatten(board)\n",
" string_flat=np.char.mod('%d', b)\n",
" key = \"\".join(string_flat)\n",
" return key\n",
" \n",
" \n",
" #heuristic to evaluate the next best move\n",
" def score(self, state, depth, cur_player):\n",
" '''\n",
" heuristic function that uses Hashing for the scores if they are already calculated\n",
" \n",
" state: current gameState\n",
" depth: current depth\n",
" cur_player: current player\n",
" \n",
" we are using a simple heuristic here,\n",
" just counting and punishing good and reward good moves with a weight.\n",
" \n",
" score = num of(four in row)*1000+ num of(three in row)* 100+ num of(two in row)*10\n",
" - num of opponent(four in row)*100000- num of opponent(three in row)*100- \n",
" num of opponent(two in row)*10\n",
" \n",
" returns the score\n",
" '''\n",
" \n",
" if cur_player == 1:\n",
" oponent = 2\n",
" else:\n",
" oponent = 1\n",
" \n",
" hash_key = self.get_hash_key(state)\n",
" \n",
" # Score already calculated\n",
" if hash_key in self.DIC:\n",
" return self.DIC[hash_key]\n",
" \n",
" # else calculate\n",
" else:\n",
" \n",
" #counting the number of good four/threes/twos in a row/column/diag\n",
" _ , b = zip(*(move_was_winning_move(state, cur_player, e) for e in range(2,5)))\n",
" _ , c = zip(*(move_was_winning_move(state, oponent, f) for f in range(2,5)))\n",
" \n",
"\n",
" score = b[2]*1000+b[1]*100+b[0]*10-c[2]*100000-c[1]*100-c[0]*10\n",
" \n",
" #and put in DIC\n",
" self.DIC[hash_key] = score\n",
" return score\n",
" \n",
" def listofmoves(self, gameState):\n",
" '''\n",
" returns a list of possible moves = column not full and orders it with middle first\n",
" \n",
" gameState: current gamestate\n",
" \n",
" return: list of possible moves\n",
" '''\n",
" l=[]\n",
" for i in range(gameState.shape[1]):\n",
" if 0 in gameState[:,i]:\n",
" l.append(i)\n",
" \n",
" m=sum(l)/len(l)\n",
" return sorted(l, key=lambda x:abs(x-m))\n",
"\n",
" \n",
" def min_play(self, board, depth, alpha ,beta , cur_player):\n",
" '''\n",
" recursively building a the tree, min part of minmax\n",
" minimzing the score, moving the oponent\n",
" \n",
" board: a gameState \n",
" depth: depth parameter of search tree\n",
" alpha: alpha for alphabeta pruning\n",
" beta: beta for alphabeta pruning\n",
" cur_player: the current_player's number\n",
" \n",
" return best score\n",
" '''\n",
"\n",
" #eval current player\n",
" if cur_player == 1:\n",
" oponent = 2\n",
" else:\n",
" oponent = 1\n",
" \n",
" #termination\n",
" #max depth\n",
" if depth == 0:\n",
" return self.score(board, depth, cur_player)\n",
" #all full\n",
" if not move_still_possible(board):\n",
" return self.score(board, depth, cur_player)\n",
" #or winner\n",
" \n",
" winningmove, _ = move_was_winning_move(board, cur_player, 4)\n",
" if winningmove:\n",
" return self.score(board, depth, cur_player)\n",
" \n",
"\n",
" \n",
" best_score = np.inf\n",
" \n",
" #get all available moves\n",
" moves = self.listofmoves(board)\n",
" best_move = moves[0]\n",
" \n",
" if len(moves) == 0:\n",
" return self.score(board, depth, cur_player)\n",
" \n",
" for eachmove in moves:\n",
" #copy board and move oponent\n",
" boardcopy = board.copy()\n",
" \n",
" board_i, _ , _ = move(boardcopy, oponent, eachmove)\n",
" \n",
" #build recursivley max\n",
" score = self.max_play(board_i, depth-1,alpha ,beta , cur_player)\n",
" \n",
" #compare scores MIN\n",
" #if score < best_score:\n",
" # best_move = eachmove\n",
" # best_score = score\n",
" #print 'if', best_move, best_score\n",
" \n",
" #with alpha - beta\n",
" if score < beta:\n",
" beta = score\n",
" #best_move = eachmove\n",
" if beta <= alpha:\n",
" return beta\n",
"\n",
" \n",
" return beta#, best_move\n",
" \n",
" \n",
" def max_play(self, board, depth, alpha ,beta , cur_player):\n",
" '''\n",
" recursively building a the tree, max part of minmax\n",
" maximizing the score\n",
" \n",
" board: a gameState \n",
" depth: depth parameter of search tree\n",
" alpha: alpha for alphabeta pruning\n",
" beta: beta for alphabeta pruning\n",
" cur_player: the current_player's number\n",
" \n",
" return best score\n",
" '''\n",
"\n",
"\n",
" #eval current player\n",
" if cur_player == 1:\n",
" oponent = 2\n",
" else:\n",
" oponent = 1\n",
" \n",
" #termination\n",
" #max depth\n",
" if depth == 0:\n",
" return self.score(board, depth, cur_player)\n",
" #all full\n",
" elif not move_still_possible(board):\n",
" return self.score(board, depth, cur_player)\n",
" #or winner\n",
" winningmove, _ = move_was_winning_move(board, cur_player, 4)\n",
" if winningmove:\n",
" return self.score(board, depth, cur_player)\n",
" \n",
" \n",
" best_score = -np.inf\n",
" \n",
" #get all available moves\n",
" moves = self.listofmoves(board)\n",
" best_move = moves[0]\n",
" \n",
" if len(moves) == 0:\n",
" return self.score(board, depth, cur_player)\n",
" \n",
" for eachmove in moves:\n",
"\n",
" #copy board and move player\n",
" boardcopy = board.copy()\n",
" \n",
" board_i, _ , _ = move(boardcopy, cur_player, eachmove)\n",
" \n",
" #build recursivley min\n",
" score = self.min_play(board_i, depth-1,alpha ,beta , cur_player)\n",
" \n",
" #compare scores MAX\n",
" #if score > best_score:\n",
" # best_move = eachmove\n",
" # best_score = score\n",
" #print 'if', best_move, best_score\n",
" \n",
" #with alpha-beta\n",
" if score > alpha:\n",
" alpha = score\n",
" #best_move = eachmove\n",
" if alpha >= beta:\n",
" return alpha\n",
" \n",
" return alpha #, best_move\n",
" \n",
" \n",
" def minmax(self, board, cur_player, depth=4, alpha=-np.inf, beta=np.inf):\n",
" '''\n",
" recursively building a the tree with alpha beta pruning\n",
" may not be the best choice but everyone keeps saying: memory is cheap\n",
" \n",
" board: a gameState \n",
" depth: depth parameter of search tree\n",
" cur_player: the current_player's number\n",
" \n",
" return best score, best move\n",
" '''\n",
" \n",
" #eval current player\n",
" if cur_player == 1:\n",
" oponent = 2\n",
" else:\n",
" oponent = 1\n",
" \n",
" best_score = -np.inf\n",
" \n",
" #get all available moves\n",
" moves = self.listofmoves(board)\n",
" best_move = moves[0]\n",
"\n",
" #for each move do\n",
" for eachmove in moves:\n",
" #print eachmove\n",
" #copy board and move\n",
" boardcopy = board.copy() \n",
" \n",
" #build recursivley\n",
" board_i, _ , _ = move(boardcopy, cur_player, eachmove)\n",
" \n",
" score = self.min_play(board_i, depth-1 ,alpha ,beta, cur_player)\n",
" \n",
" #compare scores\n",
" if score > alpha:\n",
" alpha = score\n",
" best_move = eachmove\n",
" if alpha >= beta:\n",
" return alpha\n",
" \n",
" return alpha, best_move"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is a Minmax vs Random Connect Four simulation: \n",
"Difficulty: 3\n",
"m moves\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']]\n",
"r moves\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' 'r' 'm' ' ' ' ' ' ']]\n",
"m moves\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' ' ' 'r' 'm' ' ' ' ' ' ']]\n",
"r moves\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' 'r' 'r' 'm' ' ' ' ' ' ']]\n",
"m moves\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' 'r' 'r' 'm' ' ' ' ' ' ']]\n",
"r moves\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' 'r' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' 'r' 'r' 'm' ' ' ' ' ' ']]\n",
"m moves\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' 'r' ' ' 'm' ' ' ' ' ' ']\n",
" [' ' 'r' 'r' 'm' ' ' ' ' ' ']]\n",
"player m wins after 7 moves\n"
]
}
],
"source": [
"#import numpy as np\n",
"\n",
"\n",
"def move_is_correct(grid,num):\n",
" '''\n",
" @param grid: 6x7 grid containing the current game state\n",
" @param num: column\n",
"\n",
" returns True if move is allowed on that column\n",
" '''\n",
"\n",
" #if 0 is in column\n",
" if 0 in grid[:,num]:\n",
" \n",
" #move is allowed\n",
" return True\n",
"\n",
" else:\n",
"\n",
" return False\n",
"\n",
"def move_still_possible(S):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" returns True if grid contains no 0, therefore no move possible anymore\n",
" '''\n",
" return not(S[S==0].size == 0)\n",
"\n",
"\n",
"def move(S,p,col_num):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" @param p: current player\n",
" @param col_num: column number\n",
" \n",
" sets the player's number on the grid and returns the grid\n",
" '''\n",
" \n",
" #sanity check\n",
" if 0 in S[:,col_num]: \n",
" y = np.where(S[:,col_num]==0)[0][-1]\n",
" S[y,col_num] = p\n",
" return S , y, col_num\n",
" else:\n",
" return S, None, None\n",
" return \n",
"\n",
"def move_at_random(S):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" moves at random\n",
" '''\n",
" return np.random.randint(0,S.shape[1])\n",
"\n",
"\n",
"#neat and ugly but the fastest way to search a matrix for a vector is a string find\n",
"player1=[' ','1', '1 1', '1 1 1', '1 1 1 1']\n",
"oponent=[' ','1', '2 2', '2 2 2', '2 2 2 2']\n",
"\n",
"\n",
"def move_was_winning_move(S, p, num):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" @param p: current player\n",
" @param num: how many occurences to count\n",
" \n",
" combines all the allowed formations of the grid and string_finds with \n",
" the currents player vector. Returns true if match.\n",
" \n",
" return: True or False whether move was winning move or not,\n",
" and count of occurences\n",
" '''\n",
" if p == 1:\n",
" match = player1[num]\n",
" else:\n",
" match = oponent[num]\n",
"\n",
" l=[]\n",
" #for every possible diag\n",
" for i in range(-2,4):\n",
" l.append(np.diag(S,k = i))\n",
" l.append(np.diag(np.fliplr(S),k=i))\n",
" #left to right\n",
" l.append(S)\n",
" #top to bottom\n",
" l.append(np.rot90(S)) \n",
" \n",
" #convert to string\n",
" stringmatrix =''.join(np.array_str(e) for e in l)\n",
" \n",
" #count the occurences\n",
" counter = stringmatrix.count(match)\n",
" \n",
" #print stringmatrix\n",
" \n",
" #if four in a row\n",
" if num == 4 and counter == 1:\n",
" return True, counter\n",
" return False, counter\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# relate numbers (1, -1, 0) to symbols ('x', 'o', ' ')\n",
"symbols = {1:'m', 2:'r', 0:' '}\n",
"\n",
"# print game state matrix using symbols\n",
"def print_game_state(S):\n",
" B = np.copy(S).astype(object)\n",
" for n in [1, 2, 0]:\n",
" B[B==n] = symbols[n]\n",
" print B\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
" # initialize 6x7 connectfour board\n",
" gameState = np.zeros((6,7), dtype=int)\n",
"\n",
" # initialize player number, move counter\n",
" player = 1\n",
" mvcntr = 1\n",
"\n",
" # initialize flag that indicates win\n",
" noWinnerYet = True\n",
" \n",
" m = Minmax()\n",
" le = len(m.DIC)\n",
" print 'This is a Minmax vs Random Connect Four simulation: '\n",
" difficulty = int(raw_input('Difficulty: '))\n",
"\n",
" while move_still_possible(gameState) and noWinnerYet:\n",
" \n",
" while True:\n",
" # get player symbol\n",
" name = symbols[player]\n",
" print '%s moves' % name\n",
" \n",
" #move with Minmax\n",
" if player == 1:\n",
" _ , col_num = m.minmax(gameState, 1, difficulty, -np.inf, np.inf)\n",
" \n",
" # let player move at random\n",
" else:\n",
" col_num = move_at_random(gameState)\n",
" \n",
" if move_is_correct(gameState, col_num):\n",
" gameState, _ , _ = move(gameState,player,col_num)\n",
"\n",
" # print current game state\n",
" print_game_state(gameState)\n",
"\n",
" # evaluate game state\n",
" winningmove, _ = move_was_winning_move(gameState, player, 4)\n",
" if winningmove:\n",
" print 'player %s wins after %d moves' % (name, mvcntr)\n",
" noWinnerYet = False\n",
"\n",
"\n",
" # switch player and increase move counter\n",
" if player == 1:\n",
" player = 2\n",
" elif player == 2:\n",
" player = 1\n",
"\n",
" mvcntr += 1\n",
"\n",
" break\n",
"\n",
"\n",
"\n",
"\n",
" if noWinnerYet:\n",
" print 'game ended in a draw' \n",
"\n",
" #save new DIC for better Hashing\n",
" if le < len(m.DIC):\n",
" m.save_obj(m.DIC,'scores')\n",
" "
]
}
],
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| mit |
zipeiyang/liupengyuan.github.io | chapter2/homework/computer/middle/201611580308.ipynb | 16 | 3674 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * \n",
" * * \n",
" * * * \n",
" * * * * \n",
" * * * * * \n"
]
}
],
"source": [
" for i in range(5):\n",
" print(\" \"*(5-i),end=\"\")\n",
" print(\"* \"*(i+1))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 * 1 = 1\t\n",
"2 * 1 = 2\t2 * 2 = 4\t\n",
"3 * 1 = 3\t3 * 2 = 6\t3 * 3 = 9\t\n",
"4 * 1 = 4\t4 * 2 = 8\t4 * 3 = 12\t4 * 4 = 16\t\n",
"5 * 1 = 5\t5 * 2 = 10\t5 * 3 = 15\t5 * 4 = 20\t5 * 5 = 25\t\n",
"6 * 1 = 6\t6 * 2 = 12\t6 * 3 = 18\t6 * 4 = 24\t6 * 5 = 30\t6 * 6 = 36\t\n",
"7 * 1 = 7\t7 * 2 = 14\t7 * 3 = 21\t7 * 4 = 28\t7 * 5 = 35\t7 * 6 = 42\t7 * 7 = 49\t\n",
"8 * 1 = 8\t8 * 2 = 16\t8 * 3 = 24\t8 * 4 = 32\t8 * 5 = 40\t8 * 6 = 48\t8 * 7 = 56\t8 * 8 = 64\t\n",
"9 * 1 = 9\t9 * 2 = 18\t9 * 3 = 27\t9 * 4 = 36\t9 * 5 = 45\t9 * 6 = 54\t9 * 7 = 63\t9 * 8 = 72\t9 * 9 = 81\t\n"
]
}
],
"source": [
"for i in range(9):\n",
" i=i+1\n",
" for j in range(i):\n",
" j=j+1\n",
" print(i,\"*\",j,\"=\",i*j,end=\"\\t\")\n",
" print(\"\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def zhi(n):\n",
" i=2\n",
" flag =1\n",
" while(i<n):\n",
" if(n%i==0):\n",
" flag=0\n",
" i=i+1\n",
" if(flag):\n",
" return 1\n",
" else:\n",
" return 0\n",
" \n",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n=int(input('pls enter'))\n",
"\n",
"m = n+2\n",
"for i in range(n) :\n",
" i=i+1\n",
" print(i,\"[\",end=\"\")\n",
" for j in range(5):\n",
" j=j+1\n",
" if(i<=2):\n",
" if(i==j):\n",
" print(i,\"*\",\",\",end=\"\")\n",
" else:\n",
" if(j==5):\n",
" print(j,end=\"\") \n",
" else:\n",
" print(j,\",\",end=\"\")\n",
" else:\n",
" if(i==i+j-3): \n",
" print(i,\"*\",\",\",end=\"\")\n",
" else:\n",
" if(j==5):\n",
" print(i+j-3,end=\"\")\n",
" else:\n",
" print(i+j-3,\",\",end=\"\")\n",
" \n",
" print(\"]\")"
]
},
{
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
hfoffani/deep-learning | face_generation/dlnd_face_generation.ipynb | 1 | 1295062 | null | mit |
Molns/pyurdme | examples/yeast_polarization/2D_periodic_test.ipynb | 5 | 354880 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pyurdme\n",
"from matplotlib import cm as CM\n",
"from IPython.display import display, clear_output\n",
"import time"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num_vox = 30\n",
"class testDiffusion2D(pyurdme.URDMEModel):\n",
" def __init__(self):\n",
" pyurdme.URDMEModel.__init__(self,\"test1D\")\n",
" X = pyurdme.Species(name=\"X\", diffusion_constant=0.001)\n",
" self.add_species([X])\n",
" self.mesh = pyurdme.URDMEMesh.generate_unit_square_mesh(nx=num_vox, ny=num_vox, periodic=True)\n",
" self.set_initial_condition_place_near({X:num_vox*num_vox*10}, (0.1,0.1))\n",
" #self.set_initial_condition_scatter({X:1000})\n",
" #self.set_initial_condition_distribute_uniformly({X:1000})\n",
" self.timespan(range(101))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model = testDiffusion2D()\n",
"result = model.run()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_vals = model.mesh.coordinates()[:, 0]\n",
"y_vals = model.mesh.coordinates()[:, 1]\n",
"z_vals = result.get_species(\"X\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for tndx in model.tspan:\n",
" heatmap, xedges, yedges = np.histogram2d(x=x_vals, y=y_vals, weights=z_vals[tndx,:], bins=num_vox+1)\n",
" #heatmap\n",
" fig = plt.figure(figsize=(8,6), dpi=100)\n",
" plt.imshow(heatmap)\n",
" cb = plt.colorbar()\n",
" cb.set_label('molecule population')\n",
" plt.title('t={0}'.format(tndx))\n",
" clear_output()\n",
" plt.show()\n",
" time.sleep(.1)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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IuYoNGV8TRWaEhvdJgZkGFWoyZHGkEphTQaZG9YZaLVMciSmQo6MmvUcLiJUwq/XZVuyQ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JtRO/Gd73wnPvGJT+ADH/gAVBV/8id/gu/7vu977Nc/UThRiNGJMZ9atiVzAOuQQkbUOgeP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\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tLZ/Ytfq0fc7X2mecuWrW6s5c5cQsJ67upNBvOTM4lWlyko/g5ZkO2PxBmLamuk4ogqMy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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1139fe150>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for tndx in model.tspan:\n",
" fig = plt.figure(figsize=(8,6), dpi=100)\n",
" plt.hexbin(x=x_vals, y=y_vals, C=z_vals[tndx,:], gridsize=int(num_vox*.75), cmap=CM.jet, bins=None)\n",
" plt.axis([x_vals.min(), x_vals.max(), y_vals.min(), y_vals.max()])\n",
" plt.title(\"t={0}\".format(tndx))\n",
" cb = plt.colorbar()\n",
" cb.set_label('molecule population')\n",
" clear_output()\n",
" plt.show()\n",
" time.sleep(.01)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Ek+w6KHhnCBP0oxxUlYWXUVsjcJtCeLrme1TWyghF8QlbfW062RsPg61rD6Gp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\npDAs7QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10797ea90>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
}
],
"metadata": {}
}
]
} | gpl-3.0 |
LFPy/LFPy | examples/LFPy-example-02.ipynb | 2 | 76490 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 2: Extracellular response of synaptic input\n",
"This is an example of **``LFPy``** running in a **``Jupyter notebook``**. To run through this example code and produce output, press **``<shift-Enter>``** in each code block below.\n",
"\n",
"First step is to import ``LFPy`` and other packages for analysis and plotting:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.gridspec import GridSpec\n",
"import LFPy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create some dictionarys with parameters for cell, synapse and extracellular electrode:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"cellParameters = {\n",
" 'morphology': 'morphologies/L5_Mainen96_LFPy.hoc',\n",
" 'tstart': -50,\n",
" 'tstop': 100,\n",
" 'dt': 2**-4,\n",
" 'passive': True,\n",
"}\n",
"\n",
"synapseParameters = {\n",
" 'syntype': 'Exp2Syn',\n",
" 'e': 0.,\n",
" 'tau1': 0.5,\n",
" 'tau2': 2.0,\n",
" 'weight': 0.005,\n",
" 'record_current': True,\n",
"}\n",
"\n",
"z = np.mgrid[-400:1201:100]\n",
"electrodeParameters = {\n",
" 'x': np.zeros(z.size),\n",
" 'y': np.zeros(z.size),\n",
" 'z': z,\n",
" 'sigma': 0.3,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, create the **`cell`**, **`synapse`** and **`electrode`** objects using the\n",
"**`LFPy.Cell`**, **`LFPy.Synapse`**, **`LFPy.RecExtElectrode`** classes."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"cell = LFPy.Cell(**cellParameters)\n",
"cell.set_pos(x=-10, y=0, z=0)\n",
"cell.set_rotation(x=4.98919, y=-4.33261, z=np.pi)\n",
"\n",
"synapse = LFPy.Synapse(cell,\n",
" idx = cell.get_closest_idx(z=800),\n",
" **synapseParameters)\n",
"synapse.set_spike_times(np.array([10, 30, 50]))\n",
" \n",
"electrode = LFPy.RecExtElectrode(cell, **electrodeParameters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the simulation using **`cell.simulate()`** probing the extracellular potential with \n",
"the additional keyword argument **`probes=[electrode]`**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"cell.simulate(probes=[electrode])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then plot the **somatic potential** and the **prediction** obtained using the `RecExtElectrode` instance \n",
"(now accessible as `electrode.data`):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'time (ms)')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 6))\n",
"gs = GridSpec(2, 3)\n",
"\n",
"ax0 = fig.add_subplot(gs[:, 0])\n",
"ax0.plot(cell.x.T, cell.z.T, 'k')\n",
"ax0.plot(synapse.x, synapse.z, \n",
" color='r', marker='o', markersize=10,\n",
" label='synapse')\n",
"ax0.plot(electrode.x, electrode.z, '.', color='g', \n",
" label='electrode')\n",
"ax0.axis([-500, 500, -450, 1250])\n",
"ax0.legend()\n",
"ax0.set_xlabel('x (um)')\n",
"ax0.set_ylabel('z (um)')\n",
"ax0.set_title('morphology')\n",
"\n",
"ax1 = fig.add_subplot(gs[0, 1])\n",
"ax1.plot(cell.tvec, synapse.i, 'r')\n",
"ax1.set_title('synaptic current (pA)')\n",
"plt.setp(ax1.get_xticklabels(), visible=False)\n",
"\n",
"ax2 = fig.add_subplot(gs[1, 1], sharex=ax1)\n",
"ax2.plot(cell.tvec, cell.somav, 'k')\n",
"ax2.set_title('somatic voltage (mV)')\n",
"\n",
"ax3 = fig.add_subplot(gs[:, 2], sharey=ax0, sharex=ax1)\n",
"im = ax3.pcolormesh(cell.tvec, electrode.z, electrode.data,\n",
" vmin=-abs(electrode.data).max(), vmax=abs(electrode.data).max(),\n",
" shading='auto')\n",
"plt.colorbar(im)\n",
"ax3.set_title('LFP (mV)')\n",
"ax3.set_xlabel('time (ms)')\n",
"\n",
"#savefig('LFPy-example-02.pdf', dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
nkmk/python-snippets | notebook/urllib_parse_query_string.ipynb | 1 | 13121 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import urllib.parse"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url = 'https://www.google.co.jp/search?q=%E6%A1%9C&tbm=isch'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ParseResult(scheme='https', netloc='www.google.co.jp', path='/search', params='', query='q=%E6%A1%9C&tbm=isch', fragment='')\n"
]
}
],
"source": [
"print(urllib.parse.urlparse(url))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"qs = urllib.parse.urlparse(url).query"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"q=%E6%A1%9C&tbm=isch\n"
]
}
],
"source": [
"print(qs)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(qs))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"qs_d = urllib.parse.parse_qs(qs)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'q': ['桜'], 'tbm': ['isch']}\n"
]
}
],
"source": [
"print(qs_d)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'dict'>\n"
]
}
],
"source": [
"print(type(qs_d))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['桜']\n"
]
}
],
"source": [
"print(qs_d['q'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n"
]
}
],
"source": [
"print(type(qs_d['q']))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"桜\n"
]
}
],
"source": [
"print(qs_d['q'][0])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(qs_d['q'][0]))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"qs_l = urllib.parse.parse_qsl(qs)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('q', '桜'), ('tbm', 'isch')]\n"
]
}
],
"source": [
"print(qs_l)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n"
]
}
],
"source": [
"print(type(qs_l))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('q', '桜')\n"
]
}
],
"source": [
"print(qs_l[0])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'tuple'>\n"
]
}
],
"source": [
"print(type(qs_l[0]))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"桜\n"
]
}
],
"source": [
"print(qs_l[0][1])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(qs_l[0][1]))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d = {'key1': 'value / one', 'key2': 'バリュー2'}"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d_qs = urllib.parse.urlencode(d)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key1=value+%2F+one&key2=%E3%83%90%E3%83%AA%E3%83%A5%E3%83%BC2\n"
]
}
],
"source": [
"print(d_qs)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(d_qs))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = [('key1', 'value / one'), ('key2', 'バリュー2')]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l_qs = urllib.parse.urlencode(l)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key1=value+%2F+one&key2=%E3%83%90%E3%83%AA%E3%83%A5%E3%83%BC2\n"
]
}
],
"source": [
"print(l_qs)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(l_qs))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key1=value+%2F+one&key2=%E3%83%90%E3%83%AA%E3%83%A5%E3%83%BC2\n"
]
}
],
"source": [
"print(urllib.parse.urlencode(d))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key1=value%20%2F%20one&key2=%E3%83%90%E3%83%AA%E3%83%A5%E3%83%BC2\n"
]
}
],
"source": [
"print(urllib.parse.urlencode(d, quote_via=urllib.parse.quote))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key1=value+/+one&key2=%E3%83%90%E3%83%AA%E3%83%A5%E3%83%BC2\n"
]
}
],
"source": [
"print(urllib.parse.urlencode(d, safe='/'))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key1=value%20/%20one&key2=%E3%83%90%E3%83%AA%E3%83%A5%E3%83%BC2\n"
]
}
],
"source": [
"print(urllib.parse.urlencode(d, safe='/', quote_via=urllib.parse.quote))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'q': ['桜'], 'tbm': ['isch']}\n"
]
}
],
"source": [
"print(qs_d)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"q=%5B%27%E6%A1%9C%27%5D&tbm=%5B%27isch%27%5D\n"
]
}
],
"source": [
"print(urllib.parse.urlencode(qs_d))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"q=%E6%A1%9C&tbm=isch\n"
]
}
],
"source": [
"print(urllib.parse.urlencode(qs_d, doseq=True))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search?q=%E6%A1%9C&tbm=isch\n"
]
}
],
"source": [
"print(url)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search?q=%E6%A1%9C&tbm=vid\n"
]
}
],
"source": [
"print(url.replace('isch', 'vid'))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def update_query(url, key, org_val, new_val):\n",
" pr = urllib.parse.urlparse(url)\n",
" d = urllib.parse.parse_qs(pr.query)\n",
" l = d.get(key)\n",
" if l:\n",
" d[key] = [new_val if v == org_val else v for v in l]\n",
" else:\n",
" d[key] = new_val\n",
" return urllib.parse.urlunparse(pr._replace(query=urllib.parse.urlencode(d, doseq=True)))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search?q=%E6%A1%9C&tbm=vid\n"
]
}
],
"source": [
"print(update_query(url, 'tbm', 'isch', 'vid'))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search?q=%E6%A2%85&tbm=isch\n"
]
}
],
"source": [
"print(update_query(url, 'q', '桜', '梅'))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search?q=%E6%A1%9C&tbm=isch&new-key=yyy\n"
]
}
],
"source": [
"print(update_query(url, 'new-key', 'xxx', 'yyy'))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def remove_query(url, key):\n",
" pr = urllib.parse.urlparse(url)\n",
" d = urllib.parse.parse_qs(pr.query)\n",
" d.pop(key, None)\n",
" return urllib.parse.urlunparse(pr._replace(query=urllib.parse.urlencode(d, doseq=True)))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search?q=%E6%A1%9C\n"
]
}
],
"source": [
"print(remove_query(url, 'tbm'))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search?q=%E6%A1%9C&tbm=isch\n"
]
}
],
"source": [
"print(remove_query(url, 'new-key'))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def remove_all_query(url):\n",
" return urllib.parse.urlunparse(urllib.parse.urlparse(url)._replace(query=None))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://www.google.co.jp/search\n"
]
}
],
"source": [
"print(remove_all_query(url))"
]
}
],
"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
}
| mit |
amirziai/learning | reinforcement-learning/Options.ipynb | 1 | 2163 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Standard RL assumes flat states and action spaces with no hierarchy\n",
"* Represent sequences of **primitive actions** to achieve subgoals\n",
"* \n",
"\n",
"> Sequence of actions that can be treated as a whole are called macro-operators. Once a problem is solved, the learning component takes the computed plan and stores it as a macro-operator. The preconditions are the initial conditions of the problem just solved, and its post conditions correspond to the goal just achieved. The problem solver efficiently uses the knowledge base it gained from its previous experiences. By generalizing macro-operators the problem solver can even solve different problems. Generalization is done by replacing all the constants in the macro-operators with variables.The STRIPS, for example, is a planning algorithm that employed macro-operators in it’s learning phase. It builds a macro operator MACROP, that contains preconditions, post-conditions and the sequence of actions. The macro operator will be used in the future operation.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References\n",
"- http://www.cs.utexas.edu/~pstone/Courses/394Rspring13/resources/week8a-nick.pdf\n",
"- http://intelligence.worldofcomputing.net/machine-learning/learning-with-macro-operators.html#.WgNlfxNSzUI"
]
}
],
"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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
rasbt/algorithms_in_ipython_notebooks | ipython_nbs/essentials/greedy-algorithm-examples.ipynb | 2 | 4937 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext watermark"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
"last updated: 2016-06-08 \n",
"\n",
"CPython 3.5.1\n",
"IPython 4.2.0\n"
]
}
],
"source": [
"%watermark -a 'Sebastian Raschka' -u -d -v"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# More Greedy Algorithm Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For an introduction to greedy algorithm, see the related notebook, [Introduction to Greedy Algorithms](greedy-algorithm-intro.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set Cover Problems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set cover problems are problems where we want to find the minimum number of subsets such that their set union contains all items in a target set. This target set is typically called the \"universe.\" To borrow an example from the excellent [Wikipedia page](https://en.wikipedia.org/wiki/Set_cover_problem) on set cover problems, let's assume we have the universe \n",
"\n",
"- $U=\\{1, 2, 3, 4, 5\\}$\n",
"\n",
"and are given the collection of sets \n",
"\n",
"- $C=\\{\\{1, 2, 3\\}, \\{2, 4\\}, \\{3, 4\\}, \\{4, 5\\}\\}$\n",
"\n",
"The task is to find the minimum number of sets in $C$ so that their union equals $U$.\n",
"\n",
"Note that set cover problems are NP-complete, thus no computationally efficient solution exists. However, we can use greedy algorithms to approximate the solution; this solution may or may not be globally optimal.\n",
"\n",
"The greedy strategy we are going to employ is very simple and works as follows:\n",
"\n",
"- While not all elements in U are covered:\n",
" - For all uncovered sets in C:\n",
" - Pick the set that covers most of the elements in U"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['set_1', 'set_2', 'set_4']\n"
]
}
],
"source": [
"collection = {'set_1': {1, 2, 3},\n",
" 'set_2': {2, 4}, \n",
" 'set_3': {3, 4}, \n",
" 'set_4': {4, 5}}\n",
"sets_used = []\n",
"elements_not_covered = {1, 2, 3, 4, 5}\n",
"\n",
"\n",
"while elements_not_covered:\n",
" elements_covered = set()\n",
" for set_ in collection.keys():\n",
" \n",
" if set_ in sets_used:\n",
" continue\n",
" \n",
" current_set = collection[set_]\n",
" would_cover = elements_covered.union(current_set)\n",
" if len(would_cover) > len(elements_covered):\n",
" elements_covered = would_cover\n",
" sets_used.append(set_)\n",
" elements_not_covered -= elements_covered\n",
" \n",
" if not elements_not_covered:\n",
" break\n",
" \n",
"print(sets_used)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a result, we can see that 3 sets are required to cover the universe U. In this case, the greedy algorithm has not found the globally optimal solution, which would be `'set_1'` and `'set_4'`. Note that this is just a trivial example, and greedy algorithms can be very useful approximators for solutions that are computationally infeasible.\n",
"\n",
"For instance, an exhaustive solution to this problem that would guaranteed to find the global optimum (remember that set cover problems are NP-complete) would involve iterating over a power set, which has $2^n$ elements, where $n$ is the number of sets in the collection. For example, a collection of 30 sets would already require comparing the solutions of $2^{30}=1,073,741,824$ million possible combinations!\n",
"\n",
"(Note that the greedy approach may have found the globally optimal solution in this simple example if it had iterated over the dictionary in a different order -- for example, if we had swapped the positions of {2, 4} and {4, 5})"
]
}
],
"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"
}
},
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}
| gpl-3.0 |
LorenzoBi/courses | TSAADS/tutorial 9/.ipynb_checkpoints/Untitled-checkpoint.ipynb | 1 | 108472 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import fsolve\n",
"%matplotlib inline\n",
"\n",
"def sigmoid(x):\n",
" return 1. / (1 + np.exp(-x))\n",
"\n",
"def df(x, w=0, theta=0):\n",
" return w * sigmoid(x) * (1 - sigmoid(x))\n",
"\n",
"def f(x, w=0, theta=0):\n",
" return w * sigmoid(x) + theta\n",
"\n",
"def g(x, w, theta):\n",
" return f(x, w, theta) - x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.1 \n",
"We can see in the plot the fixed points. In the central point the function has a slope bigger than one, so it is not a stable point. For the other two is the opposite."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fe04fd80d68>]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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9roPB9zg3SiWs+GNVjAHGAKustc/UvCSR8DF7XS53TVzGMe0b8cjZ3dzb+ahkD3xxFyx9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\nGQW7iEiYUbCLiISZ/weXdVaQU6BEowAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe04fe060b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(-5, 5, 100)\n",
"theta = -3.5\n",
"w = 8.\n",
"plt.plot(x, f(x, w, theta))\n",
"plt.plot(x, x)\n",
"plt.xlabel('x')\n",
"\n",
"FP = []\n",
"for x_0 in range(-4, 4):\n",
" temp, _, ier, _ = fsolve(g, (x_0), args=(w, theta), full_output=True)\n",
" if ier == 1:\n",
" FP.append(temp)\n",
"FP = np.array(FP)\n",
"plt.plot(FP, f(FP, w, theta), '*')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe04de1cc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for theta in np.arange(-10, 0, .05):\n",
" x = np.arange(-10, 10, dtype='float')\n",
" stable = []\n",
" unstable = []\n",
" for i in range(len(x)):\n",
" temp, _, ier, _ = fsolve(g, (x[i]), args=(w, theta), full_output=True)\n",
"\n",
"\n",
" if ier == 1:\n",
" x[i] = temp\n",
" if abs(df(temp, w,theta)) < 1:\n",
" stable.append(temp)\n",
" else:\n",
" unstable.append(*temp)\n",
" else:\n",
" x[i] = None\n",
" plt.plot(theta * np.ones(len(stable)), stable, '.', color='green')\n",
" plt.plot(theta * np.ones(len(unstable)), unstable, '.', color='red')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-9.99963669])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fsolve(g, (-1), args=(w, -10))"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f7ee3b19160>]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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G7876HWclnEXFZ8+zfMVgDrsGkZweRcJ5idz6/mZ6xobw9i/k9pFoHyQwCHGG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\nDdwxmJlZA3cMZmbW4P/RH0516e1QvQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7ee3b19128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y = np.linspace(-10, 10, 100)\n",
"for i in np.arange(-5, -3, .5):\n",
" plt.plot(y, f(y, w, i))\n",
"plt.plot(y, y)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x, di, ler, s = fsolve(g, (-10), args=(w, -10), full_output=True)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-9.99963669])"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ler"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f7ee3569e48>]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jtrN60EyDvkRE/kuD/sTRGDMRmAgQHR19Us/RIyKUwdFtmX7ZQHpEhPoynoiI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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7ee4044470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.arange(3), [1, 2, None])"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def l(x, r):\n",
" return x * np.exp(r* (1-x))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f7ee35fa710>]"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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k+t0uRUTEFZ4K9pauIHtONbNy1li3SxERcY2ngv3Vow1ELaycpdUwIpK8PBXs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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7ee35fa4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y = np.linspace(-1, 4, 100)\n",
"\n",
"plt.plot(y, l(y, np.exp(1)))\n",
"plt.plot(y, y)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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+JdVWm3Lj3wNCxDdbcCo6gpWlaX91NTr+awfTfAOMbALOYH2AGRZsrDIxw+0u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\nlUnhjoiozD839eJTzGKPDAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7edb202860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Npre = 200\n",
"Nplot = 100\n",
"x = np.zeros((Nplot, 1))\n",
"for r in np.arange(1, 25, .5):\n",
" x[0] = np.random.random() * 100\n",
" for n in range(Npre):\n",
" x[0] = l(x[0], r)\n",
" for n in range(Nplot - 1):\n",
" x[n + 1] = l(x[n], r)\n",
" \n",
" plt.plot(r * np.ones((Nplot, 1)), x, '.')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#hj = set()\n",
"hj.add(100)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1, 2, 100}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hj"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ nan]),\n",
" {'fjac': array([[ nan]]),\n",
" 'fvec': array([ nan]),\n",
" 'nfev': 13,\n",
" 'qtf': array([ nan]),\n",
" 'r': array([ nan])},\n",
" 5,\n",
" 'The iteration is not making good progress, as measured by the \\n improvement from the last ten iterations.')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" fsolve(g, (x[0]), args=(w, theta), full_output=True)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'nan'"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'nan'"
]
},
{
"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 |
teoguso/sol_1116 | cumulant-to-pdf.ipynb | 1 | 7125 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Best report ever\n",
"\n",
"Everything you see here is either markdown, LaTex, Python or BASH."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## The spectral function\n",
"\n",
"It looks like this:\n",
"\\begin{equation}\n",
" A(\\omega) = \\mathrm{Im}|G(\\omega)|\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## GW vs Cumulant\n",
"\n",
"Mathematically very different:\n",
"\n",
"\\begin{equation}\n",
" G^{GW} (\\omega) = \\frac1{ \\omega - \\epsilon - \\Sigma (\\omega) } \n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"\\begin{equation}\n",
" G^C(t_1, t_2) = G^0(t_1, t_2) e^{ i \\int_{t_1}^{t_2} \\int_{t'}^{t_2} dt' dt'' W (t', t'') }\n",
"\\end{equation}\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"---\n",
"BUT they connect through $\\mathrm{Im} W (\\omega) = \\frac1\\pi \\mathrm{Im} \\Sigma ( \\epsilon - \\omega )$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"## Implementation\n",
"\n",
"Using a multi-pole representation for $\\Sigma^{GW}$:\n",
"\n",
"\\begin{equation}\n",
" \\mathrm{Im} W (\\omega) = \\frac1\\pi \\mathrm{Im} \\Sigma ( \\epsilon - \\omega )\n",
"\\end{equation}\n",
"\\begin{equation}\n",
" W (\\tau) = - i \\lambda \\bigl[ e^{ i \\omega_p \\tau } \\theta ( - \\tau ) + e^{ - i \\omega_p \\tau } \\theta ( \\tau ) \\bigr]\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## GW vs Cumulant\n",
"\n",
"- GW:\n",
"\\begin{equation}\n",
" A(\\omega) = \\frac1\\pi \\frac{\\mathrm{Im}\\Sigma (\\omega)} \n",
" { [ \\omega - \\epsilon - \\mathrm{Re}\\Sigma (\\omega) ]^2 + \n",
" [ \\mathrm{Im}\\Sigma (\\omega) ]^2}\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Cumulant:\n",
"\n",
"\\begin{equation}\n",
" A(\\omega) = \\frac1\\pi \\sum_{n=0}^{\\infty} \\frac{a^n}{n!} \\frac{\\Gamma}{ (\\omega - \\epsilon + n \\omega_p)^2 + \\Gamma^2 }\n",
"\\end{equation}\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now some executable code (Python)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have implemented the formulas above in my Python code. \n",
"I can just run it from here., but **before** let me check\n",
"if my input file is correct..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!gvim data/SF_Si_bulk/invar.in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I can run my script:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"%cd data/SF_Si_bulk/\n",
"%run ../../../../../Code/SF/sf.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not very elegant, I know. It's just for demo pourposes. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cd ../../../"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have first to import a few modules/set up a few things:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# plt.rcParams['figure.figsize'] = (9., 6.)\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next I can read the data from a local folder:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sf_c = np.genfromtxt(\n",
" 'data/SF_Si_bulk/Spfunctions/spftot_exp_kpt_1_19_bd_1_4_s1.0_p1.0_800ev_np1.dat')\n",
"sf_gw = np.genfromtxt(\n",
" 'data/SF_Si_bulk/Spfunctions/spftot_gw_s1.0_p1.0_800ev.dat')\n",
"#!gvim spftot_exp_kpt_1_19_bd_1_4_s1.0_p1.0_800ev_np1.dat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I can plot the stored arrays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(sf_c[:,0], sf_c[:,1], label='1-pole cumulant')\n",
"plt.plot(sf_gw[:,0], sf_gw[:,1], label='GW')\n",
"plt.xlim(-50, 0)\n",
"plt.ylim(0, 300)\n",
"plt.title(\"Bulk Si - Spectral function - ib=1, ikpt=1\")\n",
"plt.xlabel(\"Energy (eV)\")\n",
"plt.grid(); plt.legend(loc='best')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating a PDF document\n",
"\n",
"I can create a PDF version of this notebook from itself, using the command line:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!jupyter-nbconvert --to pdf cumulant-to-pdf.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pwd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!xpdf cumulant-to-pdf.pdf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python [conda env:py3]",
"language": "python",
"name": "conda-env-py3-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 |
ljinke/MachineLearning | exercises/import and package.ipynb | 1 | 4216 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python27.zip',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-darwin',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac/lib-scriptpackages',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-tk',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-old',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload',\n",
" '/usr/local/lib/python2.7/site-packages',\n",
" '/Library/Python/2.7/site-packages',\n",
" '/usr/local/lib/python2.7/site-packages/IPython/extensions',\n",
" '/Users/liujin/.ipython']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"sys.path"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['/usr/local/lib/python2.7/site-packages/tensorflow']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
"sys.modules\n",
"tf.__path__"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python27.zip',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-darwin',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac/lib-scriptpackages',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-tk',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-old',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload',\n",
" '/usr/local/lib/python2.7/site-packages',\n",
" '/Library/Python/2.7/site-packages',\n",
" '/usr/local/lib/python2.7/site-packages/IPython/extensions',\n",
" '/Users/liujin/.ipython',\n",
" '/Users/liujin/Github/tensorflow/tensorflow/examples']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import tensorflow\n",
"from tensorflow.examples import *"
]
},
{
"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": 1
}
| mit |
NLP-Deeplearning-Club/Classic-ML-Methods-Algo | ipynbs/appendix/ensemble/voting.ipynb | 1 | 5057 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 投票\n",
"\n",
"投票是最简单最基本的集成方式,核心思想也很朴素:大家伙投票决定结果.\n",
"\n",
"其原理是结合了多个不同的机器学习分类器,并且采用多数表决(硬投票)或者平均预测概率(软投票)的方式来预测分类标签.这样的分类器可以用于一组同样表现良好的模型,以便平衡它们各自的弱点.\n",
"\n",
"## ***使用sklearn做投票***\n",
"\n",
"sklearn提供了用于投票的接口`sklearn.ensemble.VotingClassifier`.下面的例子可以大体了解如何使用投票接口"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.ensemble import RandomForestClassifier, VotingClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 自己构造一组随机的数据"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
"y = np.array([1, 1, 1, 2, 2, 2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 初始化多个分类器模型"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clf1 = LogisticRegression(random_state=1)\n",
"clf2 = RandomForestClassifier(random_state=1)\n",
"clf3 = GaussianNB()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 初始化投票器"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 训练模型 "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eclf1 = eclf1.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 预测"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1 1 2 2 2]\n"
]
}
],
"source": [
"print(eclf1.predict(X))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 投票器的设置\n",
"\n",
"投票器可以设置\n",
"\n",
"+ voting \n",
"\n",
" hard表示直接以多数原则投票确定结果,soft表示基于预测概率之和的argmax来预测类别标签\n",
" \n",
"+ n_jobs\n",
"\n",
" 并行任务数设置\n",
" \n",
"+ weights\n",
"\n",
" 不同分类器的权重\n",
" \n",
"+ flatten_transform(0.19版本接口)\n",
"\n",
" 仅当voting为'soft'时有用.flatten_transform = true时影响变换输出的形状，变换方法返回形为(n_samples,n_classifiers * n_classes).如果flatten_transform = false,则返回(n_classifiers，n_samples，n_classes)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eclf3 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],n_jobs=3, voting='soft', weights=[2,1,1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eclf3 = eclf3.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1 1 2 2 2]\n"
]
}
],
"source": [
"print(eclf3.predict(X))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3, 6, 2)\n"
]
}
],
"source": [
"print(eclf3.transform(X).shape)"
]
}
],
"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 |
tensorflow/docs-l10n | site/en-snapshot/addons/tutorials/optimizers_cyclicallearningrate.ipynb | 2 | 14858 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Tce3stUlHN0L"
},
"source": [
"##### Copyright 2021 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "tuOe1ymfHZPu"
},
"outputs": [],
"source": [
"#@title 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": "markdown",
"metadata": {
"id": "qFdPvlXBOdUN"
},
"source": [
"# TensorFlow Addons Optimizers: CyclicalLearningRate"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MfBg1C5NB3X0"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/addons/tutorials/optimizers_cyclicallearningrate\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/addons/blob/master/docs/tutorials/optimizers_cyclicallearningrate.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/addons/blob/master/docs/tutorials/optimizers_cyclicallearningrate.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://storage.googleapis.com/tensorflow_docs/addons/docs/tutorials/optimizers_cyclicallearningrate.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xHxb-dlhMIzW"
},
"source": [
"## Overview\n",
"\n",
"This tutorial demonstrates the use of Cyclical Learning Rate from the Addons package."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IqEImEhBJWFv"
},
"source": [
"## Cyclical Learning Rates\n",
"\n",
"It has been shown it is beneficial to adjust the learning rate as training progresses for a neural network. It has manifold benefits ranging from saddle point recovery to preventing numerical instabilities that may arise during backpropagation. But how does one know how much to adjust with respect to a particular training timestamp? In 2015, Leslie Smith noticed that you would want to increase the learning rate to traverse faster across the loss landscape but you would also want to reduce the learning rate when approaching convergence. To realize this idea, he proposed [Cyclical Learning Rates](https://arxiv.org/abs/1506.01186) (CLR) where you would adjust the learning rate with respect to the cycles of a function. For a visual demonstration, you can check out [this blog](https://www.jeremyjordan.me/nn-learning-rate/). CLR is now available as a TensorFlow API. For more details, check out the original paper [here](https://arxiv.org/abs/1506.01186). "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MUXex9ctTuDB"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "t-p545dluzjI"
},
"outputs": [],
"source": [
"!pip install -q -U tensorflow_addons"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RPF3aDZZu8le"
},
"outputs": [],
"source": [
"from tensorflow.keras import layers\n",
"import tensorflow_addons as tfa\n",
"import tensorflow as tf\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"tf.random.set_seed(42)\n",
"np.random.seed(42)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XLOnLLrlR-ti"
},
"source": [
"## Load and prepare dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uAHLo_Ffvie3"
},
"outputs": [],
"source": [
"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
"\n",
"x_train = np.expand_dims(x_train, -1)\n",
"x_test = np.expand_dims(x_test, -1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AfUS_s-uSBvx"
},
"source": [
"## Define hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qumJ7KpwvvwE"
},
"outputs": [],
"source": [
"BATCH_SIZE = 64\n",
"EPOCHS = 10\n",
"INIT_LR = 1e-4\n",
"MAX_LR = 1e-2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G-x3E7RWSXWc"
},
"source": [
"## Define model building and model training utilities"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vni6Gz3Dv9Db"
},
"outputs": [],
"source": [
"def get_training_model():\n",
" model = tf.keras.Sequential(\n",
" [\n",
" layers.InputLayer((28, 28, 1)),\n",
" layers.experimental.preprocessing.Rescaling(scale=1./255),\n",
" layers.Conv2D(16, (5, 5), activation=\"relu\"),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" layers.Conv2D(32, (5, 5), activation=\"relu\"),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" layers.SpatialDropout2D(0.2),\n",
" layers.GlobalAvgPool2D(),\n",
" layers.Dense(128, activation=\"relu\"),\n",
" layers.Dense(10, activation=\"softmax\"),\n",
" ]\n",
" )\n",
" return model\n",
"\n",
"def train_model(model, optimizer):\n",
" model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n",
" metrics=[\"accuracy\"])\n",
" history = model.fit(x_train,\n",
" y_train,\n",
" batch_size=BATCH_SIZE,\n",
" validation_data=(x_test, y_test),\n",
" epochs=EPOCHS)\n",
" return history"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RlRKWRWrSk_t"
},
"source": [
"In the interest of reproducibility, the initial model weights are serialized which you will be using to conduct our experiments. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-JxnpsIzwCgj"
},
"outputs": [],
"source": [
"initial_model = get_training_model()\n",
"initial_model.save(\"initial_model\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oNF33-tBSuFG"
},
"source": [
"## Train a model without CLR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Q4dEJtQzwjei"
},
"outputs": [],
"source": [
"standard_model = tf.keras.models.load_model(\"initial_model\")\n",
"no_clr_history = train_model(standard_model, optimizer=\"sgd\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eaK0PAN-Sy6l"
},
"source": [
"## Define CLR schedule\n",
"\n",
"The `tfa.optimizers.CyclicalLearningRate` module return a direct schedule that can be passed to an optimizer. The schedule takes a step as its input and outputs a value calculated using CLR formula as laid out in the paper. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ne0b8aGNyc3v"
},
"outputs": [],
"source": [
"steps_per_epoch = len(x_train) // BATCH_SIZE\n",
"clr = tfa.optimizers.CyclicalLearningRate(initial_learning_rate=INIT_LR,\n",
" maximal_learning_rate=MAX_LR,\n",
" scale_fn=lambda x: 1/(2.**(x-1)),\n",
" step_size=2 * steps_per_epoch\n",
")\n",
"optimizer = tf.keras.optimizers.SGD(clr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "icVL3hsUTwXV"
},
"source": [
"Here, you specify the lower and upper bounds of the learning rate and the schedule will *oscillate* in between that range ([1e-4, 1e-2] in this case). `scale_fn` is used to define the function that would scale up and scale down the learning rate within a given cycle. `step_size` defines the duration of a single cycle. A `step_size` of 2 means you need a total of 4 iterations to complete one cycle. The recommended value for `step_size` is as follows:\n",
"\n",
"`factor * steps_per_epoch` where factor lies within the [2, 8] range. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5JV_ESYqUb4d"
},
"source": [
"In the same [CLR paper](https://arxiv.org/abs/1506.01186), Leslie also presented a simple and elegant method to choose the bounds for learning rate. You are encouraged to check it out as well. [This blog post](https://www.pyimagesearch.com/2019/08/05/keras-learning-rate-finder/) provides a nice introduction to the method. \n",
"\n",
"Below, you visualize how the `clr` schedule looks like. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b_WRGfDx4Wwc"
},
"outputs": [],
"source": [
"step = np.arange(0, EPOCHS * steps_per_epoch)\n",
"lr = clr(step)\n",
"plt.plot(step, lr)\n",
"plt.xlabel(\"Steps\")\n",
"plt.ylabel(\"Learning Rate\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bBlKaAqNjHP1"
},
"source": [
"In order to better visualize the effect of CLR, you can plot the schedule with an increased number of steps. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gjhoyk-Li368"
},
"outputs": [],
"source": [
"step = np.arange(0, 100 * steps_per_epoch)\n",
"lr = clr(step)\n",
"plt.plot(step, lr)\n",
"plt.xlabel(\"Steps\")\n",
"plt.ylabel(\"Learning Rate\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ObYcy5NRkF4V"
},
"source": [
"The function you are using in this tutorial is referred to as the `triangular2` method in the CLR paper. There are other two functions there were explored namely `triangular` and `exp` (short for exponential). "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-OV_8QVIe5m_"
},
"source": [
"## Train a model with CLR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zRSglElvy_fF"
},
"outputs": [],
"source": [
"clr_model = tf.keras.models.load_model(\"initial_model\")\n",
"clr_history = train_model(clr_model, optimizer=optimizer)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8rhTLQJdnGfP"
},
"source": [
"As expected the loss starts higher than the usual and then it stabilizes as the cycles progress. You can confirm this visually with the plots below. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LyHEgnv6e8lX"
},
"source": [
"## Visualize losses"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wg0JjLwH2RTl"
},
"outputs": [],
"source": [
"(fig, ax) = plt.subplots(2, 1, figsize=(10, 8))\n",
"\n",
"ax[0].plot(no_clr_history.history[\"loss\"], label=\"train_loss\")\n",
"ax[0].plot(no_clr_history.history[\"val_loss\"], label=\"val_loss\")\n",
"ax[0].set_title(\"No CLR\")\n",
"ax[0].set_xlabel(\"Epochs\")\n",
"ax[0].set_ylabel(\"Loss\")\n",
"ax[0].set_ylim([0, 2.5])\n",
"ax[0].legend()\n",
"\n",
"ax[1].plot(clr_history.history[\"loss\"], label=\"train_loss\")\n",
"ax[1].plot(clr_history.history[\"val_loss\"], label=\"val_loss\")\n",
"ax[1].set_title(\"CLR\")\n",
"ax[1].set_xlabel(\"Epochs\")\n",
"ax[1].set_ylabel(\"Loss\")\n",
"ax[1].set_ylim([0, 2.5])\n",
"ax[1].legend()\n",
"\n",
"fig.tight_layout(pad=3.0)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2EwZuz_pkqLM"
},
"source": [
"Even though for this toy example, you did not see the effects of CLR much but be noted that it is one of the main ingredients behind [Super Convergence](https://arxiv.org/abs/1708.07120) and can have a [really good impact](https://www.fast.ai/2018/08/10/fastai-diu-imagenet/) when training in large-scale settings. "
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "optimizers_cyclicallearningrate.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
sudhanshuptl/Machine-Learning | Kaggle/Titanic/.ipynb_checkpoints/Analysing Data-checkpoint.ipynb | 1 | 237043 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysing Data"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 101,
"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>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>892</td>\n",
" <td>3</td>\n",
" <td>Kelly, Mr. James</td>\n",
" <td>male</td>\n",
" <td>34.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330911</td>\n",
" <td>7.8292</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>893</td>\n",
" <td>3</td>\n",
" <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>363272</td>\n",
" <td>7.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>894</td>\n",
" <td>2</td>\n",
" <td>Myles, Mr. Thomas Francis</td>\n",
" <td>male</td>\n",
" <td>62.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>240276</td>\n",
" <td>9.6875</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>895</td>\n",
" <td>3</td>\n",
" <td>Wirz, Mr. Albert</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>315154</td>\n",
" <td>8.6625</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>896</td>\n",
" <td>3</td>\n",
" <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3101298</td>\n",
" <td>12.2875</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass Name Sex \\\n",
"0 892 3 Kelly, Mr. James male \n",
"1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
"2 894 2 Myles, Mr. Thomas Francis male \n",
"3 895 3 Wirz, Mr. Albert male \n",
"4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
"\n",
" Age SibSp Parch Ticket Fare Cabin Embarked \n",
"0 34.5 0 0 330911 7.8292 NaN Q \n",
"1 47.0 1 0 363272 7.0000 NaN S \n",
"2 62.0 0 0 240276 9.6875 NaN Q \n",
"3 27.0 0 0 315154 8.6625 NaN S \n",
"4 22.0 1 1 3101298 12.2875 NaN S "
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test=pd.read_csv(\"test.csv\")\n",
"test.head() #visualising last 10 data\n",
"# print g_model.loc[417,\"Survived\"] #individual visualisation"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22</td>\n",
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" <td>0</td>\n",
" <td>A/5 21171</td>\n",
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" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n",
"2 Heikkinen, Miss. Laina female 26 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n",
"4 Allen, Mr. William Henry male 35 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mData=pd.read_csv(\"train.csv\")\n",
"mData.head()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 73.1+ KB\n",
"_________________________________________________\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 418 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
"PassengerId 418 non-null int64\n",
"Pclass 418 non-null int64\n",
"Name 418 non-null object\n",
"Sex 418 non-null object\n",
"Age 332 non-null float64\n",
"SibSp 418 non-null int64\n",
"Parch 418 non-null int64\n",
"Ticket 418 non-null object\n",
"Fare 418 non-null float64\n",
"Cabin 91 non-null object\n",
"Embarked 418 non-null object\n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 31.0+ KB\n"
]
}
],
"source": [
"mData.info()\n",
"print \"_________________________________________________\"\n",
"test.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Drop Non Required Coloumn"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <td>894</td>\n",
" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>895</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.6625</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>896</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>12.2875</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>897</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>14.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.2250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>898</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>30.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.6292</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>899</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>29.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>900</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>18.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2292</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>901</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>21.0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>24.1500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>902</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>903</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>46.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>904</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>23.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>82.2667</td>\n",
" <td>B45</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>905</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>26.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>906</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>61.1750</td>\n",
" <td>E31</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>907</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>24.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>27.7208</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>908</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12.3500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>909</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>21.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>910</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>911</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>45.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>912</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>55.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>59.4000</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>913</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>9.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3.1708</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>914</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31.6833</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>915</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>21.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>61.3792</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>916</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>48.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>262.3750</td>\n",
" <td>B57 B59 B63 B66</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>917</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>50.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>14.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>918</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>61.9792</td>\n",
" <td>B36</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>919</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>920</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>41.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.5000</td>\n",
" <td>A21</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>921</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>21.6792</td>\n",
" <td>NaN</td>\n",
" <td>C</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",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td>1280</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>21.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td>1281</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>6.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>1282</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>23.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>93.5000</td>\n",
" <td>B24</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>1283</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>39.4000</td>\n",
" <td>D28</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>1284</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>13.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>20.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>1285</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>47.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>1286</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>29.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>22.0250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>1287</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>18.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>60.0000</td>\n",
" <td>C31</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>1288</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>1289</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>48.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>79.2000</td>\n",
" <td>B41</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td>1290</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>1291</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7333</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>1292</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>30.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>164.8667</td>\n",
" <td>C7</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401</th>\n",
" <td>1293</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>21.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402</th>\n",
" <td>1294</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>59.4000</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403</th>\n",
" <td>1295</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>17.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47.1000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404</th>\n",
" <td>1296</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>43.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>27.7208</td>\n",
" <td>D40</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>1297</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.8625</td>\n",
" <td>D38</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406</th>\n",
" <td>1298</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>23.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>10.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407</th>\n",
" <td>1299</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>50.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>211.5000</td>\n",
" <td>C80</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>408</th>\n",
" <td>1300</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7208</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>409</th>\n",
" <td>1301</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>13.7750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>410</th>\n",
" <td>1302</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>411</th>\n",
" <td>1303</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>37.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>90.0000</td>\n",
" <td>C78</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>412</th>\n",
" <td>1304</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>1305</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>414</th>\n",
" <td>1306</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>108.9000</td>\n",
" <td>C105</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>415</th>\n",
" <td>1307</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>38.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>416</th>\n",
" <td>1308</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>1309</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>22.3583</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>418 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass Sex Age SibSp Parch Fare \\\n",
"0 892 3 male 34.5 0 0 7.8292 \n",
"1 893 3 female 47.0 1 0 7.0000 \n",
"2 894 2 male 62.0 0 0 9.6875 \n",
"3 895 3 male 27.0 0 0 8.6625 \n",
"4 896 3 female 22.0 1 1 12.2875 \n",
"5 897 3 male 14.0 0 0 9.2250 \n",
"6 898 3 female 30.0 0 0 7.6292 \n",
"7 899 2 male 26.0 1 1 29.0000 \n",
"8 900 3 female 18.0 0 0 7.2292 \n",
"9 901 3 male 21.0 2 0 24.1500 \n",
"10 902 3 male NaN 0 0 7.8958 \n",
"11 903 1 male 46.0 0 0 26.0000 \n",
"12 904 1 female 23.0 1 0 82.2667 \n",
"13 905 2 male 63.0 1 0 26.0000 \n",
"14 906 1 female 47.0 1 0 61.1750 \n",
"15 907 2 female 24.0 1 0 27.7208 \n",
"16 908 2 male 35.0 0 0 12.3500 \n",
"17 909 3 male 21.0 0 0 7.2250 \n",
"18 910 3 female 27.0 1 0 7.9250 \n",
"19 911 3 female 45.0 0 0 7.2250 \n",
"20 912 1 male 55.0 1 0 59.4000 \n",
"21 913 3 male 9.0 0 1 3.1708 \n",
"22 914 1 female NaN 0 0 31.6833 \n",
"23 915 1 male 21.0 0 1 61.3792 \n",
"24 916 1 female 48.0 1 3 262.3750 \n",
"25 917 3 male 50.0 1 0 14.5000 \n",
"26 918 1 female 22.0 0 1 61.9792 \n",
"27 919 3 male 22.5 0 0 7.2250 \n",
"28 920 1 male 41.0 0 0 30.5000 \n",
"29 921 3 male NaN 2 0 21.6792 \n",
".. ... ... ... ... ... ... ... \n",
"388 1280 3 male 21.0 0 0 7.7500 \n",
"389 1281 3 male 6.0 3 1 21.0750 \n",
"390 1282 1 male 23.0 0 0 93.5000 \n",
"391 1283 1 female 51.0 0 1 39.4000 \n",
"392 1284 3 male 13.0 0 2 20.2500 \n",
"393 1285 2 male 47.0 0 0 10.5000 \n",
"394 1286 3 male 29.0 3 1 22.0250 \n",
"395 1287 1 female 18.0 1 0 60.0000 \n",
"396 1288 3 male 24.0 0 0 7.2500 \n",
"397 1289 1 female 48.0 1 1 79.2000 \n",
"398 1290 3 male 22.0 0 0 7.7750 \n",
"399 1291 3 male 31.0 0 0 7.7333 \n",
"400 1292 1 female 30.0 0 0 164.8667 \n",
"401 1293 2 male 38.0 1 0 21.0000 \n",
"402 1294 1 female 22.0 0 1 59.4000 \n",
"403 1295 1 male 17.0 0 0 47.1000 \n",
"404 1296 1 male 43.0 1 0 27.7208 \n",
"405 1297 2 male 20.0 0 0 13.8625 \n",
"406 1298 2 male 23.0 1 0 10.5000 \n",
"407 1299 1 male 50.0 1 1 211.5000 \n",
"408 1300 3 female NaN 0 0 7.7208 \n",
"409 1301 3 female 3.0 1 1 13.7750 \n",
"410 1302 3 female NaN 0 0 7.7500 \n",
"411 1303 1 female 37.0 1 0 90.0000 \n",
"412 1304 3 female 28.0 0 0 7.7750 \n",
"413 1305 3 male NaN 0 0 8.0500 \n",
"414 1306 1 female 39.0 0 0 108.9000 \n",
"415 1307 3 male 38.5 0 0 7.2500 \n",
"416 1308 3 male NaN 0 0 8.0500 \n",
"417 1309 3 male NaN 1 1 22.3583 \n",
"\n",
" Cabin Embarked \n",
"0 NaN Q \n",
"1 NaN S \n",
"2 NaN Q \n",
"3 NaN S \n",
"4 NaN S \n",
"5 NaN S \n",
"6 NaN Q \n",
"7 NaN S \n",
"8 NaN C \n",
"9 NaN S \n",
"10 NaN S \n",
"11 NaN S \n",
"12 B45 S \n",
"13 NaN S \n",
"14 E31 S \n",
"15 NaN C \n",
"16 NaN Q \n",
"17 NaN C \n",
"18 NaN S \n",
"19 NaN C \n",
"20 NaN C \n",
"21 NaN S \n",
"22 NaN S \n",
"23 NaN C \n",
"24 B57 B59 B63 B66 C \n",
"25 NaN S \n",
"26 B36 C \n",
"27 NaN C \n",
"28 A21 S \n",
"29 NaN C \n",
".. ... ... \n",
"388 NaN Q \n",
"389 NaN S \n",
"390 B24 S \n",
"391 D28 S \n",
"392 NaN S \n",
"393 NaN S \n",
"394 NaN S \n",
"395 C31 S \n",
"396 NaN Q \n",
"397 B41 C \n",
"398 NaN S \n",
"399 NaN Q \n",
"400 C7 S \n",
"401 NaN S \n",
"402 NaN C \n",
"403 NaN S \n",
"404 D40 C \n",
"405 D38 C \n",
"406 NaN S \n",
"407 C80 C \n",
"408 NaN Q \n",
"409 NaN S \n",
"410 NaN Q \n",
"411 C78 Q \n",
"412 NaN S \n",
"413 NaN S \n",
"414 C105 C \n",
"415 NaN S \n",
"416 NaN S \n",
"417 NaN C \n",
"\n",
"[418 rows x 9 columns]"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mData.drop([\"PassengerId\",\"Name\",\"Ticket\"], axis=1)\n",
"test.drop([\"Name\",\"Ticket\"], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting Some data"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x91999d0>"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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1FApXpGCRNpYh4D5ymQxZc2KRNScW/8tmh+HWvT2qWmCzf7lHVWfvIE6er8PJ83WIjQxG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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8b053f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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6cOCAqqurNWnSJNntds2bN8/TsQD4CZ7sAQAAuEhwcDATsgDwGJc/2bPb7UpJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8cd7870>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('Embarked','Survived', data=mData,size=4,aspect=3)\n",
"\n",
"#divide screen in 3\n",
"fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(15,5))\n",
"sns.countplot(x='Embarked', data=mData, ax=axis1)\n",
"sns.countplot(x='Survived', hue=\"Embarked\", data=mData, order=[1,0], ax=axis2)\n",
"\n",
"#Below Is Wow Feature\n",
"# group by embarked, and get the mean for survived passengers for each value in Embarked\n",
"embark_perc = mData[[\"Embarked\", \"Survived\"]].groupby(['Embarked'],as_index=False).mean()\n",
"sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# #Apply some priproccesing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fare"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# only for test, since there is a missing \"Fare\" values\n",
"test[\"Fare\"].fillna(test[\"Fare\"].median(), inplace=True) #replace missing value by median\n",
"\n",
"# convert from float to int\n",
"mData['Fare'] = mData['Fare'].astype(int)\n",
"test['Fare'] = test['Fare'].astype(int)\n",
"\n",
"# get fare for survived & didn't survive passengers \n",
"fare_not_survived = mData[\"Fare\"][mData[\"Survived\"] == 0]\n",
"fare_survived = mData[\"Fare\"][mData[\"Survived\"] == 1]\n",
"\n",
"# get average and std for fare of survived/not survived passengers\n",
"avgerage_fare = pd.DataFrame([fare_not_survived.mean(), fare_survived.mean()])\n",
"std_fare = pd.DataFrame([fare_not_survived.std(), fare_survived.std()])"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0xbff3e70>"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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m//7v//TAAw/ogw8+0N69ezV48GBmZCGDBw9WUVGRioqK9P7776tv374aP348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\n5Irm9wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x91470b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot\n",
"mData['Fare'].plot(kind='hist', figsize=(15,3),bins=100, xlim=(0,80))"
]
},
{
"cell_type": "code",
"execution_count": 143,
"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>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Survived</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21.690346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>47.991228</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0\n",
"Survived \n",
"0 21.690346\n",
"1 47.991228"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avgerage_fare.index.names = std_fare.index.names = [\"Survived\"]\n",
"avgerage_fare"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0xc667470>"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0xc6674b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"avgerage_fare.plot(yerr=std_fare,kind='bar',legend=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Age"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\python27\\lib\\site-packages\\ipykernel\\__main__.py:29: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"c:\\python27\\lib\\site-packages\\ipykernel\\__main__.py:30: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0xd292170>"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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fV0ZGhjIyMpxNVEZGhiTplltu0auvvqqioiKdOHFCf//735074J49e6p58+Z67bXXVFpa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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc71eb90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
"axis1.set_title('Original Age values - Titanic')\n",
"axis2.set_title('New Age values - Titanic')\n",
"\n",
"\n",
"# get average, std, and number of NaN values in titanic_df\n",
"average_age_titanic = mData[\"Age\"].mean()\n",
"std_age_titanic = mData[\"Age\"].std()\n",
"count_nan_age_titanic = mData[\"Age\"].isnull().sum()\n",
"\n",
"# get average, std, and number of NaN values in test\n",
"average_age_test = test[\"Age\"].mean()\n",
"std_age_test = test[\"Age\"].std()\n",
"count_nan_age_test = test[\"Age\"].isnull().sum()\n",
"\n",
"# convert from float to int\n",
"mData['Age'] = mData['Age'].astype(int)\n",
"test['Age'] = test['Age'].astype(int)\n",
"\n",
"# plot original Age values\n",
"mData['Age'].hist(bins=70, ax=axis1)\n",
" \n",
"# generate random numbers between (mean - std) & (mean + std) ## WOW\n",
"rand_1 = np.random.randint(average_age_titanic - std_age_titanic, average_age_titanic + std_age_titanic, size = count_nan_age_titanic)\n",
"rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test)\n",
"\n",
"# fill NaN values in Age column with random values generated\n",
"mData[\"Age\"][np.isnan(mData[\"Age\"])] = rand_1\n",
"test[\"Age\"][np.isnan(test[\"Age\"])] = rand_2\n",
"\n",
"\n",
" \n",
"# plot new Age Values\n",
"mData['Age'].hist(bins=70, ax=axis2)\n"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0xd6de190>"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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HG7BFhrBketx5PXfA18921+vUD9Rg0UUwP3opZp3lMkUqROCFm7UsnpqE2x3P\ngYZTODVHqdHv518/OUGGupjb5swnJVaKRSGEECJYSJEoxHnq7HHzx+2laDUKNy1KRacd/fDQjsE2\n3mp4mU5vO3EhicyJXIROIx9DMTEYjVoWpk3G7U1jr+sgLSGVnFZ28EjxUVL98yiYm8209GiZ0VcI\nIYQIMDk6FeI8qKrKH7eX0jvgJX9WAtYI06if29BfyzuuV3H7B5hkzmFq+Ew5GBYTklFnZGniAtrc\nmXzWuoc+awO13m089cEp7LuyWDU/lflTHOd1AkYIIYQQl44UiUKch10H6zlS2UZqrJlZWaNfKPxk\n91F2Nm1DRWVmxDzSwjIvY5RCjA3RRivXxt3A6d5THOs6jJJ2jLaeev74/lReK4rhhgUpLJsRj1Gv\nDXSoQgghxIQiRaIQo9TQ0suWwlOEGLTcMD91VL2Aqqqyt/1D9rZ/iE7RMz96qax/KMSXaBQNGeZs\n4k1JlHTup4FaQnI/pq8xlRff7+etD0+zcl4y+bMSCA3RBzpcIYQQYkKQIlGIUfD6/Gx6+xhen8qN\nC5MJDz33warX76WweRtlPccJ1YSxMOZqLPqIKxCtEGOPSRvK/OiluAYaONzxGX2xp4mwNzNQMYU3\nir1s/6SK5bMTWTk3mYgwQ6DDFUIIIca1c17wUVxczPXXX8/KlSvZtGnTWds8/PDDrFy5ktWrV3P8\n+HEAnE4nGzZsoKCggBtvvJHnnntuuH1HRwd33HEH1113Hd///vfp6uq6RJsjxOXx5oenqWnsYVp6\nNFlJkeds3+/r442GFyjrOU6UPoarbddJgSjEKMSGxJNvL2BSWA4eTQ+aSXtJml2BRu/l3U9r+Kf/\n+YjNO07S1jUQ6FCFEEKIcWvEItHn87Fx40aefvpptm3bxrZt26ioqDijTVFREdXV1ezYsYONGzfy\n4IMPAqDT6bjvvvvYtm0bL730Es8///zwczdt2sSiRYt47733WLBgwTcWn0IEg7LaDrZ/Wk1EmJ7l\nsxLP2b57sJNX6/6My11PQkgyS60rMGpDrkCkQowPOo2O3IiruNp6PRZdJC3aU4RM/5Cr5gwSatTx\n/oF6fvH7T/jze1IsCiGEEJfDiEViSUkJycnJJCYmotfrKSgooLCw8Iw2hYWFrFmzBoAZM2bQ1dVF\nS0sLNpuNnJwcAMLCwsjIyKCpqQmA999/f/g5a9asYefOnZd8w4S4FPrdXv7w9jFQoWBh6jkn0Gjz\ntPBK/XP2dnqIAAAgAElEQVR0eNvIDMthbtRitIpMuiHEhYgyRHON7XqmhM/A4x/ghKaQxHknWT4v\nBrNJz66DUiwKIYQQl8OI1yQ2NjYSF/fFQuEOh4OSkpIz2jQ1NREb+8VEHLGxsbhcLqxW6/Df6urq\nKC0tZfr06QC0trYOP261Wmltbb34LRHiMthSeIrWLjcLpjhItJlHbOsaaOAt50u4/QNMDZ9JVviU\nKxSlEOOXRtGQHT6VeFMSB9v3cLqvjHqlikXLlkNrEh8fa2TXwXqKDzewdEY8BQtSiImQnnshhBDi\nYoxYJI52DTdVVb/xeb29vdxzzz3cf//9hIWFnfU9Rvs+Nlv4qNqJK2O85+PTo052lziJiwll1ZL0\nEddsq+wqZ2vDC3hVLwsdS8iMyLqCkX7BbJaD42Ai+bh0zIQQG3EjpzpPcqD5Mz5o/Qup5jTuvO1m\naqp9vL+/hg8O1rP7cAPXLUjh76/NJtry9X//8b7fGoskJ8FF8hF8JCciEEYsEh0OB06nc/i+y+XC\n4XCc0cZut+Nyuc7aZnBwkHvuuYfVq1ezYsWK4TYxMTE0Nzdjs9loamoiOjp6VME2N3ePqp24/Gy2\n8HGdj85eD/+55QBajcIN85Pp6f7moWynekrZ0fgWAPOilhCrTaKn58oPfTObQwLyvuLsJB+XR7wu\nlSi7nYMde6nqOc0fjv8XC2Ou5nvXz+JETQcfHXGx/eMq/rq3mhVzkli1IIWwz5fOGO/7rbFIchJc\nJB/BR3ISXCZSwT7iNYm5ublUV1dTV1eHx+Nh+/bt5Ofnn9EmPz+frVu3AnDo0CEsFgtWqxVVVbn/\n/vvJyMjge9/73hnPWb58OW+88QYAW7duPaOAFCLQVFXlT++W0tPvJW9GPNYI0ze2PdJ5gL80bkWD\nhkUx1xBvSrqCkQoxMZm0oSyMzmNO5CIURcPu1p284Xye+AT4PzdOYeXcJAw6Le9+WsM//8/HvPNx\nFW6PL9BhCyGEEGOGon51rOhXFBUV8cgjj+D3+1m3bh0//OEP2bJlCwDr168H4KGHHmL37t2YTCYe\nffRRpk6dyr59+/jOd75Ddnb28HDSe++9l2XLltHR0cFPfvITnE4nCQkJPPnkk1gslnMGK2dSgsd4\nPrNVfLiBP717gmS7mb9fnnnW4dCqqrKv42M+bSvGoBhZHHMNkYbR9YhfLtJzFVwkH1eG2zfAoc7P\naBioRYOWBdFLuSpyPj4fHDzVzKfHGxnw+AgP1fOt6yYzKyMGve6cqz+JK2Q8f5eMRZKP4CM5CS4T\nqSfxnEViMJEPSfAYrzutpvY+/r//txcU+P4NOVjOsmi3qqp82FrIoc7PMGlCWWxdTrju3Cc5Ljcp\nSoKL5OPKqu+v4XDHZ7hVNzaDgxX2G7Ea7bg9Pj472cRnJxoZ9KrEWIysvTqDeTkONKO8Hl5cPuP1\nu2SsknwEH8lJcJlIRaKcThXic36/yh/ePo7H6+faOUlnLRD9qp/3m9/lUOdnhGst5NlWBkWBKMRE\nl2BKZoXjRpJMaTR7Gnmp7hn2tO1Gp4cl0+L44U1TWTw9jo4eD5veOs7GZ/dxsqY90GELIYQQQUmK\nRCE+9+6eaioaushOimRKStTXHvepPv7a9DbHuw8ToYtiqXUFJm1oACIVQpyNQWNkTtRCFkbnYdAY\n2dv+IVtq/0jjgJPQED0Fi9O5syCHycmRVLu6+bcXDvLb10pwtvYGOnQhhBDjjN/v5+GHH2b9+vV8\n5zvf4dvf/ja9vRf3fXPvvffi9/sv6jV+/vOfs3fv3nO2G3F2UyEmimpXN28UVxIWomPl3KSvXYfo\nU738xfUmlX1lROtjWBRzDXrN13sahRCBFxuSwAp7AUe7DlHVV84r9c9yVeR8VlpWEmk2snpxGnOy\ne9l1sI5Dp1ooKW8h76oEbl6cdtYRBEIIIcT5+vDDD2lvbx+ey6Wnp4eQkHMvjeXz+dBqtWd97Ikn\nnrjouEa7/KD0JIoJzz3o43/fOopfhVULUjAZzzx3Mugf5B3nq1T2lWE12Fkcs1wKRCGCnF5j4KrI\neSyJycekCeVAx6f877H/or6/FoB4axjfWpHFmqVDheGuA/X84vcfs+2TKga9MhOqEEKIixMaGkp1\ndTWlpaX4/X7MZjP79u3jn/7pn4bb5OXlAbBnzx6+973vce+99/LrX/+ab33rW8PLELa2tnLrrbcC\nQytE+Hw+fvazn7Fv3z5gaMnBVatW4fV62bdvHxs2bOC73/0uP/nJTxgYGJob4be//S3r1q3jnnvu\nweVyfW2N+7ORnkQx4b30fjmutn5mZ9lIizvz+kKP383bzldoGKjFYYxnfvQStIp8bIQYK2xGB/n2\nAo53H6ai9ySvN2xmumU2C2OuxqAxMCkxkvT4CA6Xt/DRESevFVXywcF61udPYlaWbVRnW4UQQoiv\nmjNnDhs2bOA//uM/KC8vJz8/f8Rl/5xOJ5s2bcJgMPDKK6/w2muvcffdd/PWW29xyy23DLdTFIW1\na9fy2muvMWfOHHbt2sXSpUvR6XT867/+K3/+85+JjIzk2Wef5cUXX2ThwoXs2bOHV155BY/Hw6pV\nq6QnUYhzOVjWzAcH67FGhJA3M/6MxwZ8/Wxt2ELDQC3xIUksiF4qBaIQY5BOo2N6xGyuT7oRszac\nkq79PF/zB2r6KgHQahRmZdn4wU1TmJNto73bzX+/cZT/ePEgtU09AY5eCCHEWHXzzTfzxz/+kZ07\nd1JbW8vBgwfPePzLPXq5ubkYDEMj1VatWsV7770HwDvvvMPq1avPeN6CBQs4evQofX19vP7666xb\nt462tjacTif/+I//yIYNG3j33XdpaWmhsrKS6dOnoygKRqORqVOnjip2OeIVE1Z7t5s/bi9Fq1G4\naVEqOu0X50z6fX1srX+RlsEmkkxpzIqcj0aRcypCjGU2k53l9lWc6D7KqZ7jvOl8icnmXJZY8zFp\nQwkx6Fg+K5EZmVZ2HaznRE0HDz6zl7wZ8dyyLB1LqAwzF0IIMTpNTU2YTCbCw8MxGAxERERgsViG\nh5G6XC7a2tqG22s0XxxnhoWFkZuby6ZNm0hKSiIyMvKM19ZoNFx33XU8++yzdHR0MGnSJFRVJTEx\nkd/97neYzWZgaChqRUUFL7zwAqqq4vF4OH78+KjilyJRTEh+VeXpd47TO+BlxexEbJGm4cd6vN1s\nrX+Bdm8baaGZzIiYK0POhBgntIqWqZYZJJiSONC+hxM9R6nqK2eZ9VqyzFNRFIUYSwjr8jKobOji\n/QN1fHCogT3HG7l5SRrLZyeecUJJCCGEOJvGxkYeffRRFEXB6/WSmprK+vXrOXjwILfffjvTpk0j\nKmpoNv2zTSazdu1a/uEf/oHf//73w3/7cptbb72VlStX8utf/3r4sX/5l3/h7rvvxu/3oygK3//+\n98nLy2Pu3LmsW7eOhIQEkpOTRxW/oo7mysUgIYuJBo+xvrjrX/bU8PKuctLjLaxdlj78oesa7OSN\nhufp8naSGTaZXMtVY6ZAlMXbg4vkI/h8NSd+1U9F70mOd5Xgx0eyKY1rbNdj0X9xxtbnVzl0qpkP\njzhxD/qJjTbx7ZXZTE2NDsQmjDtj/btkvJF8BB/JSXCx2cIDHcIVI6dDxYRT7ermtaIKQo1abpif\nPFwEdnjaeLX+z3R5O5lszh1TBaIQ4vxpFA2TzDmssBdgM8RS03+a52v+wMGOvfjVoXWotBqF2dl2\nfnDTVGZmWnG19fObLYf479eP0NLZH+AtEEIIIS4PKRLFhPK35S58fpVVC1IJC9ED0Opp5tX6P9Pr\n62Zq+ExyLNOlQBRiggjTmVkccw2zIxeiKBo+bC3k5bpnaXY3DrcJNQ6tofrd67KJjwllf1kz9//h\nU9766LQsmSGEEGLckSJRTCgvFZ4aXu4iPX5ouYsmt4vX6jfT7+9jesRsssKnBDhKIcSVpigKyaFp\nXGu/kSRTKs0eFy/VPcPulkI8fvdwu9joUL59bRYFC1LQazVs3X2a+zZ9ysFTzaNad0oIIYQYC6RI\nFBPGgbJmPjjUgO1Ly104B+p4o/553P4BroqcT0ZYdoCjFEIEklEbwpyoRSyKvgaTJoxDnXv5c/X/\ncqqndLgIVBSFqWnR/OCmqcNLZjz12hH+78uHaWzrC/AWCCGEEBfvnEVicXEx119/PStXrmTTpk1n\nbfPwww+zcuVKVq9efca0qr/61a9YtGgRN9100xntn3rqKZYtW8Ytt9zCLbfcQnFx8UVuhhAja+92\n88zflrtYPLTcRV1/NVsbXmRQHWRu1CJSQzMCHaYQIkg4QuJY4Shgcvg0Bvz9/KVxK1sbXqTd0zrc\nxqjXsnxWInfckEOyw8zR0238+uk9vFZUgdsjQ1CFEEKMXSMWiT6fj40bN/L000+zbds2tm3bRkVF\nxRltioqKqK6uZseOHWzcuJEHH3xw+LG1a9fy9NNPf+11FUXhjjvuYOvWrWzdupVly5Zdmq0R4iy8\nPj//88YRege8LJ+VgDXCRFVvOW81vIRf9TMvaimJptRAhymECDJaRUtO+DTy7QU4jHHUDVTzQu3T\nfNJaxKB/cLhdTEQIf39NJjcvTiXUqGXbJ9Xct+kT9p1okiGoQgghxqQRi8SSkhKSk5NJTExEr9dT\nUFBAYWHhGW0KCwtZs2YNADNmzKCrq4vm5mYA5syZg8ViOetryxenuFJe/aCCioYuJidHMjPTSnnP\nCba5XkNFZUH0MuJNiYEOUQgRxMy6cBZGX838qKUYNEb2dXzM5ppNVPaeGm6jKArZyVHceeMUFkxx\n0Nnr4X+2HuXxLYdwtvYGMHohhBAT0WhGg45kxCKxsbGRuLi44fsOh4PGxsYz2jQ1NREbGzt8PzY2\n9mttzmbz5s2sXr2a++67j66urvONW4hR2X+yiR2f1RIdbuS6eckc6zrEu41voEHD4phrcITEBzpE\nIcQYoCgK8aYkrrXfxCTzFHp93WxzvcqbDS/R5mkZbmfQaVk2I57vr8ohNTac0up2Hnh6Dy/vKqff\n7Q3gFgghhJgoRjMa9Fx0Iz042iUAvtoreK7n3X777fz4xz8G4Mknn+Sxxx7jkUceOef7TKQFLMeC\nYM9HQ3MPf9xeil6n4Ts35FDm+YwPWgoxaozkJ15HTIg10CFecmZzSKBDEF8i+Qg+lyInCywLyHFP\nZm/TJ9T0V/JC7Wlm2eaSF7ecMH0YAJGRofwgKYrSqjbe+bCSv+ypYc/xRr6/Ope8qxJkiZ0vCfbv\nkolG8hF8JCfifH15NCgwPBo0I2P082+MWCQ6HA6cTufwfZfLhcPhOKON3W7H5XKN2OarYmJihn+/\n7bbb+NGPfjSqYJubu0fVTlx+Nlt4UOfDM+jj4ef20e/2sWpBMnvbd3K4cx8mTSiLrcsxes309AwE\nOsxLymwOGXfbNJZJPoLPpcyJlhAWRF6Ny9TA0c4D7G/eS0nLIeZFLWZG5By0ytDXa3yUiTtuyGFP\naSN7jjfym+f383ZROd9emU2S3XxJYhnLgv27ZKKRfAQfyUlwuZCC/Y9vH+Ojw/WXNI7FMxL4/k1T\nv/Hxs40GLSkpOa/3GHG4aW5uLtXV1dTV1eHxeNi+fTv5+flntMnPz2fr1q0AHDp0CIvFgtU6cg9N\nU1PT8O87d+4kKyvrvIIW4lw2/7WMuuZeZmRG0xD6EYc79xGutZBnW0m47uzXyQohxPlQFIW4kATy\n7QVMt8wG4KO2XV9bMkOv07BkWhx3FuSQmRBBWV0nDz6zl+d3lNE7MDjSWwghhBDn7VKMVhmxJ1Gn\n0/HAAw9w55134vf7WbduHRkZGWzZsgWA9evXk5eXR1FREddeey0mk4lHH310+Pn33nsve/fupaOj\ng7y8PO655x7Wrl3L448/TmlpKYqikJiYyEMPPXTRGyLE3+w+3MCHJU7s0XoGEj6lpqeSKH0Mi2Ku\nxqAxBjo8IcQ4o1E0ZJizSQpN40T3ESp7y/hL41ZijQksteYTG5IAQKTZyK3L0jnt7GLnvloKD9Tx\n6XEX667OYOn0eDQaGYIqhBDjzfdvmjpir9/lMJrRoOeiqGNomlHpbg8ewTr8oaaxm4ef24dW78U+\n6ygtXid2Qxzzo5ei04x4TmTMk+GNwUXyEXyuVE56vF0c7TyE010HQGpoJguil2EzfvEF7fP52Xey\nmY+POhn0qaTEhvPtFVlkJkZc9viCSbB+l0xUko/gIzkJLmPl+lCv18v111/Pn/70J+x2O7fddhtP\nPPHEpbsmUYixpG/Ay3+/fgSvpp+o6Ydp8baTGJLC7KgFaBRtoMMTQkwQZp2FBTHLaHE3cqzrMFV9\n5VT1lZMRls386KXEGGxotRrmT3EwJTWKDw41UFrdziOb9zN/ioPbrs4g2iKTHgkhhLgw3zQa9HxI\nT6K4IMF2ZsvvV/mvN45wuO404VMPMqjpIz0si+mW2RNmFkHpuQouko/gE4icqKpKk9vJ8a4SOrxt\nAGSZpzAvaglRhi8mcatr7qFwfx2N7f3odQoFC1K5bn4yRv34PsEVbN8lE53kI/hIToLLWOlJvBSk\nJ1GMC698UE5J83FCph5mUONjSvgMssxTJkyBKIQIToqi4AiJx26Mw+Wup7SrhLKe45zqKWVyeC5z\no5YQoY8k0Wbmu9dlc/R0G0WHG9j64WmKDtfz98snMXeyXfZlQgghrigpEsWYt+tAHYU1xRgnnUSj\naJkTtYQEU3KgwxJCiGFDM6EmEmtMoGGgjtKuw5R2H+FE91GyzFO4KnIeNmMs09JjyEqK5NNjjew7\n2cTv3zzGzn213L4ii7Q4mZlZCCHElSFFohjTDlc28VLZ6+iT6zAQwiLr1UQZogMdlhBCnJWiKCSY\nkogPSaR+oJoTXcc42TN0SwxJYVbUApJNaeTNjGdGZgy7DtZzqq6Tjc/uY8EUB7cuS8caaQr0Zggh\nhBjnpEgUY9YpZzP/e+QZtPZWwohkqeNqTNrQQIclhBDnpCgKiaZUEkJSaHI7KesppW6gmjpnNdF6\nK7Mi55MVPpU1S9Opbuxm18E6Pj0+1Lu4Yk4SNy5MITREH+jNEEIIMU7JxDXiggT6QuqKlgb+72d/\nQDX2EqHGsyxuybhf4uJcZKKU4CL5CD7BnpOOwTbKe05Q11+NikqoJoyZkXOZapmJURPC8ep2ig83\n0N03SKhRy81L0rlmVgI6rSbQoV+wQH+XiDNJPoKP5CS4TKSJa6RIFBckkDuto81l/P7Qs6jaQSLd\nWVydOnFmMB1JsB8ATzSSj+AzVnLS5+2lovckVb3lePGiQUumOZuplpk49IkcKGvhk+MuPIN+bBEh\nrLsmkznZtjG5H5QD4OAi+Qg+kpPgMpGKxInd9SLGFL/q572qXbxTuQNVgYiOmVydkzMmD4yEEOKb\nhOrCmBYxi8nhuVT1VVDVW05Zz3HKeo4ToYtkatxVbEjN4VBpNwdPNfO7rUdJcZi5NS+D3LRo2ScK\nIcQE96tf/YqioiJiYmJ4++23L+g1pCdRXJArfWarw93Js8e2UNZRgeoxEto8m2tnpaDVysHQ34yV\nXpKJQvIRfMZqTlRVpdXTTFVfOfX9Nfjxo6AhPWwSKbqplJ/Uc7KmE4BJiRGszcsgKykywFGPjvSS\nBBfJR/CRnASXsdKTuG/fPkJDQ/nFL35xwUWi9CSKoHe0pZTnjr9Er7cPX7sdfeN0rllilQJRCDEh\nKIqC1WjHarQzPWIOtX2nqeotp6L3JBWcJCzBzIyUSbTVRnOqqoPHnj9Ablo0t+alkxory2YIIcRE\nM2fOHOrq6i7qNaRIFEHL6/fyZsW7vF+7G0XV4KnOQd+ZwjVLIzAaxu5EDUIIcaEMGgMZ5mzSw7Jo\nH2ylqq+C+r4aynwHwQ7RjnDoiONYbSdH/9TKrCw7a5amkWAzBzp0IYSYkP586DU+rT1wSV9zQdIs\nNsxce0lf86vOWSQWFxfzyCOP4Pf7WbduHXfdddfX2jz88MMUFxcTEhLCY489xpQpU4BvHg/b0dHB\nT3/6UxoaGkhISODJJ5/EYpGzneILTX0t/PHo89T21GMinPZj0zD6IshfYsFi1gY6PCGECChFUYg2\nWIk2WJkRMYcmt5P6/hoa+uvwRZQREgEaTxglzQ4OvlDJVUlp3LQojZTYsTFUSgghRGCNWCT6fD42\nbtzIM888g8PhYN26deTn55ORkTHcpqioiOrqanbs2MHhw4d58MEHefnllwFYu3YtGzZs4Be/+MUZ\nr7tp0yYWLVrED37wAzZt2sSmTZv4+c9/fhk2T4xFe10HePHE63j8HhyadKr2pmPU6clfYiEiXApE\nIYT4Mq2iJS4kkbiQRHyRXhoHnNT1V+OiHn1CJfqESo6593Nkl41kUwZr58xjcqI10GELIcSEsGHm\n2sve63c5jFgklpSUkJycTGJiIgAFBQUUFhaeUSQWFhayZs0aAGbMmEFXVxfNzc3YbLZvHA/7/vvv\ns3nzZgDWrFnDhg0bpEgUNPe18uqptzjaWopeoydHv4gDH1kwGhTyl4QTYZECUQghRqJVdMSbkog3\nJeH1e3G562nor8WlOvE5ammglt+eKCb0SCzzE3NZnjmbGFNUoMMWQggRZEYsEhsbG4mLixu+73A4\nKCkpOaNNU1MTsbGxw/djY2NpbGzEZrN94+u2trZitQ6dxbRarbS2tl5Q8GJ88Pg8vFe9i79Wf4BP\n9ZFojiNhcC4f7PZgNED+knAiLXL5rBBCnA+dRkeiKYVEUwp+1U+bp4WK9lpcnnr6jQ180NzAB807\niNTFcFVsDtnRmWRGpmPShQQ6dCGEEBfh3nvvZe/evXR0dJCXl8c999zD2rXn15s54pH3aNda+uoq\nGuezRpOiKLKm0wSlqiqHmo/yatlbdHg6CdOFkZe4kL5GO+/ubsOgh+WLLVIgCiHERdIomqEZUmPt\nwGxqWzo45qylR+ui3dLKrroP2VX3IQoKKeGJZEdPIisqg/SIVAxafaDDF0IIcR6eeOKJi36NEY++\nHQ4HTqdz+L7L5cLhcJzRxm6343K5RmzzVTExMcNDUpuamoiOjh5VsGNlbZKJ4mLyUdfl5Jn9L3Ok\n6QQaRcOS5LksTZnHkdI+3i1yYjQo3HitlZgoOTg5H2az9AAEE8lH8JGcDMkxx5KTGktr+yAlpZ1U\ntjhRwtvQR7ZRpdZR1V3Le9Xvo9PoyI5JZ4p9Ejm2TCbFpGPUGS5pLPLdHlwkH8FHciICYcQiMTc3\nl+rqaurq6rDb7Wzfvv1rlWl+fj6bN2+moKCAQ4cOYbFYhoeSfpPly5fzxhtvcNddd7F161ZWrFgx\nqmBlMdHgcaGLu/Z7+3m3qpBdNR/ix09KeBJ5iYuINEawo6iFD/d1DvUgLgrHqPfR0+O7DNGPT2N1\nofDxSvIRfCQnX2fUw9zpZqb2Z1B2OoFTZQMM+rzoItqxJ3ehmls51lzGseYyADRoSApPIDMyjYzI\nNDIiUzHrwy74/WWh8OAi+Qg+kpPgMpEKdkX96ljRrygqKjpjCYwf/vCHbNmyBYD169cD8NBDD7F7\n925MJhOPPvooU6dOBc4cDxsTEzM8Hrajo4Of/OQnOJ3O81oCI1Afkr/9E8mw2C+c706rpb+VD+o+\n4uP6vbj9Hix6M3lJi0mzpOD1qWz/oJXSij7CTAp5C+UaxAshB8DBRfIRfCQn5+b1qlTWuDlR3k9P\n39B3X2KCQlJmH0pYOw19Tpr7WvDzxaGDI9Q+VDRGpJIWkYLNFDPq70s5AA4uko/gIzkJLlIkBqnL\n8SEZ8Lqp73HS7u6g29Pz+a2b7sEeutzddHl66PH0MKh6UVDQKho0X7lpFS06RYvFaCHSaCHibzfD\n0M+hv0WMq8kARrPTUlWV8o7T7KrdTUnLcVRUwnShzLRPY6YtF51GR0+vl9fea8bZ7MEapWXZgnBC\njJortBXjixwABxfJR/CRnIyeX1Wpdw5SVjlAY4sXgNAQDTNzzEzJDqFf00ZDj4v6HifO3ka8qnf4\nuWZ9GBmRaaRHpJAekUpSeAJ6zdlP/MkBcHCRfAQfyUlwmUhF4oTqrun3DlD3/7d378Fx1ffdx9/n\nsvertNKuZMmyLfkCtmNCkycX0oaC4WkZFzJunEzSpJ3SNjCZZmjitGkIyQwNBDOTPmk6nc40DinT\nlEwJECxIDA9MbB4bQoCaxjbE5mLjyJZ1WUmr1d4v5/L8sauV5ItsE/AeWd+X53B295yzOusv8u5n\nf5eTPcnx7ElOZE8ykBkkWRw76/4qKj7dS9QTwaW5sG0bCwvLtrEsEwsby7awbYuiWSVVTmNz9swd\n0P0kAnE6A3ES/jgd9XWrN4qqXDrByLAM/id5kF3H9zKYGwIg7mvjyvgGVkV70dTapSxGxys8/OQo\nuYLFiqVuPvDeAJomrbVCCNFsqqKwdImbpUvcZLImb/6mxFsDZZ7/VYZf7s/Qt9THlevW8oGVv4ON\nzVhxguHcCEP5EYZyIxwYe5UDY68CoCsaPaHuOcEx6H77XVSFEEK8+y7plsRsJcevJ17jcOoNBqZO\nMFaae6kNl+oi7msj7m8j4gnj1/34XV78uh+f7sOjuS+oi6llWxSNEvlqnly1QL6aJ19f56oFJkuT\nZCq504KkS9Fp97exJNhBV6Cztg52EvVEHNvF9dRvtizbYiAzyKsTh/nFyRfJVnMoKPRGlnNl/D0s\nCXTMeS1vHCvw+K4xDBOuWOtj7SqvY1/rQiGtJM4i9XAeqclvxzBsBk5WePNYiVS6Nl48FFB5z+og\n69cEaY3MTDSWqWQZzo0ynB/hZG6EiVJqzntf3NdGX3QFV3RfRlxNEPe3y3uAA0irlfNITZxlMbUk\nXlIh0bZtRgpJXhk/xMGxQxzLDDS2uVUXcX87cX9bbe1rJ+oJX/Q3JcMySZenmCylSZUnSZXSpIqT\nTJbTmPbcSVp8mpeuYCddoc5GeOwIxPHpvot6zmfS3h7iyOAQh1Ovc2jidQ5PvEHBLAK1v+t1scu5\nonOgod4AACAASURBVH0dEc/csaa2bfPigQz/78U0mgZXvS/I0iXv7Ex5i5V8AHYWqYfzSE3eOam0\nwZvHygycLGPUe5p2Jdy8Z3WQy/oCpw0bqJgVRvLJWktjfoSR3CjVWV1UA7q/MRHOqmgv3cEljV4n\n4uKRQOI8UhNnkZDoUGf6JTEtkyPpY7wycYiDyV8zUZ4EQEGhIxCnN7KcFeFltHqjjv6W0rZtMpUs\n48UJxospxkspxgvjpCuZ0/YNuYJ0BhIkAnE6/HESgXY6/PF3teVx+vxG8klem3yT19NvMjA12Nge\n0P2siPSwLLyUnlA3bu304JcvmPzfZyd48zdFfF6Fqz8UojW6qHo8v6vkA7CzSD2cR2ryzjMMm8Hh\nCm8dLzMyVgt9mgarl/t5z+ogy7q8ZxxGYNkWE8UUk1aKo+MnGMoOkzPyje1u1cWKyDJWRlewMtrL\n8nCPXK/xIpBA4jxSE2eRkOhQs39JkoUxnh/6b345/N/kqrU3FpfqYll4Kb2RZSwPL3VEi9tvq2pV\nSZXSjfA4WUqTKqXIVvOn7etW3UQ9YaKeCGFPiLA7RMQTJuwONRav7qFW8VrZp/87/X9BxaowXkwx\nUUrV1sUJxgoTTJQm50xMoCkqSwKdLI/0sCy09Jwh/PCRPE89N0GpbBOP6XzkfwXxeS+dcZhOIB+A\nnUXq4TxSk3dXoWhx7ESZtwbKZPMWAF6PwmW9AS7r9dOzxIuqzn2fiEb9pNMFoNZFdXoynOkJ5aZp\nisrSUDerW/pYFe2lN7Icr+65eC9ukZBA4jxSE2eRkOhQJ0dS7B97hV+cfJEjU8cA8KhuVreupC+y\ngu5g56LpnlK1qkyWZrqt1sLjJPlqgZJZfkd/lkd1E27M2BqiK9jJuu6VFLLVcx5bKJo89VyK198q\noKnw3nV+Vvd6HN2qu1DJB2BnkXo4j9Tk4rBtm4lJk9+cKHN8qEKpXPuY4fOqXNbr57K+AEs7PKiq\nMicknqpoFOuhcYSTuSHGihONcY0qKj2hLla19LGqpY++yDK8l9AM4s0igcR5pCbOIiHRgX7w8oPs\nOfZCIwB1B5ewPnY5fdHl6GeZWnuxMi2TglEkXy1QMAr1yXNqi2HVWgNPDWkKtfuaqhFxhwi7w0Q8\ntfWZvq2d74192mtv5Xnq2QmKJZu2Fo0Pvy9IKLg4QnwzyAdgZ5F6OI/U5OKzbJuxcYOBkxVODFUo\nV2ofOfw+lTUr/Fy5roXWiIJ+HjNbV8wKQ/laS+OJ7BDJwlgjNCoo9IS6WN2yktUtffRFV+A5w7AH\nMT8JJM4jNXEWCYkO9Mkffx6/7mNd7DLWxtYQ9USafUqL2rzf/pZMnn4uxeGjtdbDK9b6Wd3nQZXW\nw3eVfAB2FqmH80hNmsuybJKNwFimUu+M4tIVent8rFrmo6/Hh897fl8mVswqw/kRBnNDDNZDo1UP\njZqisiy0lDWttdC4IrwMl4xpPCcJJM4jNXEWCYkO9OihJ2nXEpfU9QQXsjOFRMOw+dXhLM//T5pi\nySbWovHh3wkSDknr4cUgH4CdRerhPFIT57Asm7EJg+SExbHjBXKFeougAt0JDyuX++ld6qWtxXXe\nwxOmQ+OJ7BAnsoNzuqfqisaKyDLWtKxiTWsfy0JLF83wlAshgcR5pCbOIiHRgX6dfOOc3RvFxTM7\nJFqWzSuv53ju5TTZvIWuw/o1Pi5b6ZXWw4tIPgA7i9TDeaQmzhMMeslmi2SyFoMjFQaHK0xMzlwO\nKuBT6V3qY8VSH8u7vPh95x/symaZk7lhBrNDHM+eZKKUamxzqy5WRntZ07qSNS0r6Qp2ypfQSCBx\nIqmJsyymkCiD+cTbZts2h48W2Pvfk6QzJqoKl6/0cvkq72nXyBJCCCHORFEUImGNSNjHutU+iiWL\n4dEqw2NVhpMVXnkjzytv1Gb0TrS56O32sazbS1fCg0s/+3uNR/PQG1lOb2Q5UJsIZzA3zGD2JCcy\nJzmUep1DqdcB8Gs+Vrf2NcY0dvjjMsGaEGJRk5ZEccFs22Z43OKJZ0YZn6yiKLByuYd1q334fRIO\nm0VaSZxF6uE8UhPnOVdNbNtmcspkOFllOFllbMJoXLJJVaGz3U3PEi89nV66Ojy4Xef/HpSr5muB\nMTvE8exg43JaAEFXgDUtK1nV0sfqlj7ivrZFERql1cp5pCbOsphaEiUkivNWLJm8+mae/YeyTKRr\ns6SuWOrmPZf5CAZkbEezyQdgZ5F6OI/UxHkutCZVwyY5XmV03GB0rMrk1EzXVFWBRLubnk4v3R0e\nliQ8BM6ze6pt20xVMgxmhxjMDXEie5KCUWxsD7tCjUlwVkZ7affFLsnQKIHEeaQmziIhcZa9e/dy\nzz33YFkWW7Zs4ZZbbjltn7vvvpu9e/fi9Xq59957Wbt27bzH/su//AsPP/wwra2tAGzdupWPfvSj\n856ohMTmsG2bgaESB1/L8dpbBSyrNrFAb4+Xy1Z6iMikNI4hH4CdRerhPFIT5/lta1Kt2oylqiTH\nDUbHq6TSJrM/1URCGl2JWmDsSniIt7rRzuNyG7Ztky5PcSI31GhtLJkz5xl2hVjV0ltbor0kLpHu\nqRJInEdq4iyLKSTOOybRNE3uuusu7r//fhKJBFu2bGHjxo309fU19tmzZw8DAwM8/fTTHDhwgDvv\nvJOHHnpo3mMVReHmm2/m5ptvftdfoHh7cnmDV97Ic+BwlnS29k1tOKjSt9zDiqUe2mJ++bAlhBCi\nqVwuhSUJN0sStWsiVg2biZTBWMpgYrLKeMrg0JECh47UvmTWNOhoc9PR7iHR5qajzU0s6jotOCqK\nQos3Sos3yoa2tdi2Tao0yWBuqDEZzsvJA7ycPABAQPezqqWPldEVrIyuYEmgQ2ZPFUIsaPOGxIMH\nD9LT00N3dzcAmzZtYteuXXNC4q5du9i8eTMAV1xxBZlMhrGxMQYHB+c9doH0cl00bNtmdKLKW8eL\nHBkoMDxWwbZBU2tdSlcu99DWql8S35QKIYS4NLl0hY64i464C/Bh2zbZvMV4ymBi0mBsospQssLJ\n0UrjGE2F9piLjjYPHW1u2mNu2lpceNwz4xsVRSHmayXma+WK9vWNlsbZoXH/2CvsH3sFALfqZkWk\nh97Icvoiy1ke6cGney/2X4cQQrxt84bE0dFROjs7G/cTiQQHDx6cs08ymaSjo6Nxv6Ojg9HRUZLJ\n5LzHPvDAA/T397N+/Xq++tWvEg6Hf+sXIy5MuWLxm8ESR08UOTpQIF+0GtvaW3WWdbtZ3u3G7ZbJ\naIQQQiw8iqIQDmqEgxq9PR4ADNMmnTGZTBuk0iaptEFyvMrIWHXOseGgRjzmpr3F1QiO062Os1sa\n31NvacxUsgzmhhjOjzKUG+H1ySO8Pnmkdh4oLAl00BddzorIMpaHey7ZcY1CiEvDvCHxfP/xutBW\nwU9/+tP89V//NQDf/e53uffee7nnnnsu6DnEhbFtm6mcyfBomaGxMkOjZYbHKlj1XOh2w/KlbroS\nLjrjLgmGQgghLkm6ptDWotPWMvMRyLRspjImk2mTdMYgnTFJZ0yODBQ5MjAzgY2iQDSk0RqtdVON\nRXVaoy5aoy7C3hDrYpexLnYZAEWjxHB+lOH8CEO5EUYKSU7mh9l78pdA7bIbyyNLWR7uYXmkh2Xh\npQRdgYv7lyGEEGcxb0hMJBIMDw837o+MjJBIJObsE4/HGRkZmbNPR0cHhmGc9dhYLNZ4/BOf+ASf\n//znz+tko1H/ee232Nm2TTZnMDJe5sRwiRNDBU4MFee0FCpArFWnp8tLT5eXtlYXqnph32gGg9J1\nxmmkJs4i9XAeqYnzOKUmkTD0dM99rFiabnGskqqvpzIGk8eLHD1enLOv16MQa3HT1uKpBceIm9aW\nHnq7VxIO6thYDGeTDGaGOZkZYXBqmEOpNziUeqPxHPFAG6tiy+ltWUZvaw8rokvxu30X4+U3LKaJ\nORYKqYlohnlD4vr16xkYGGBwcJB4PM4TTzzBd77znTn7bNy4kQceeIBNmzaxf/9+wuEwbW1tRKPR\nsx6bTCaJx+MA/PznP2f16tXndbIyu+lcpmmTzhiMp6uk0lUm0lXGJytMpA2q1bmtu36vQs8SN7EW\njViLTmtUR9enQ6FFoVC+oJ8tswQ6j9TEWaQeziM1cZ6FUJNwEMJBneXdOlALtOWKRSZrkslZZHMm\nmazJVLbWW+fkyOnvp6oKkaBGJOQiHGonHOzgyuD78bYZlPUUGWuCZDHJaD7JL47v4xfH9zWObfO2\nsiy8lKWhLpaGuugJdeF3vTtfmstMms4jNXGWxRTY5w2Juq7zjW98g7/8y79sXMair6+PBx98EIBP\nfepTXH311ezZs4frr78en8/Htm3b5j0W4B//8R85fPgwiqLQ3d3NN7/5zXf5ZS48tm1TrtjkCgZT\nWZNMzmAqazTWU1mDXME67ThVgVBQJdyuEwlptEZ1Yi06Pq90HxVCCCHeCR63SntMpT0293HbtikU\nLXIFi1zeIpc3a7dzJrmCyWTGPMOzaUAcv7eDYEAlGimjBTOYninK2iTpUorx0sxMqgAt7gjd4S66\ng510BZfQFeykzdeKqsh7vRDinXHO6yQ6xaVynUTbtimWLPJFk3zBJF80628itSWbN8nlDXIFE+NM\n7yXUxkT4vAoBv0YooBIJaYRDtYH5gYCKehEGwi+Eb38XG6mJs0g9nEdq4jyLrSaGUQuR+aJFoTD9\nWcAiX7AoFE2KJRvztO9/bRR3ESWQQQtmcIUy4M1i63NbLHVctHvjdAU76Ql3sSyyhCXBDvyu8++u\nKq1WziM1cRZpSRQXrFqt/WM/Hfby9SVXMMkXLXJ5g3zRpFC0sM4Ry70ehWBAxe/T8HlVAv764qut\nfV71gscPCiGEEKK5dF2pfakbOvM1FG3bplq1KZQsCkWLYqn2xXKh6KVUilDMWhTHLEplG0svo/oz\nqL4sij+L5c8yZA8xXDrJvvGZ7qqq4cNrtRBWW2lxtZPwJVgSjBMN+An5XYQDboI+F7omrZBCiBkS\nEs/Btm1KZYtMziSbN8jmzfpi1MYh5A1yeZNKdf7kp6m18NcSrQU/r0etrxV8XrWxeD2KBEAhhBBi\nEVIUBbdbwe1Wic5zZTDbtqlUbUqlOMWyRbFkUSpbFNMGeXOKAhnK6hSmK4vpyVJwD1FgiBEbDhfA\nzoNd8mMXQ1jFIHYxiNsM0+KOEfR4CAfchAJuwn43Yb+LkN9NOFBf/C58HrlushCXOgmJQKVqMZkx\nmMrUxvqls9Pj/qqks+Zpk8DM5nKB36vSWg9/Pq+K16viq4e/2m0VXT//S4oIIYQQQpyNoih43Aoe\nN0Q4tVVybnc427bJl0tMlNKkK2myxhR5e4qSJ4PlG0VjtLFvylaYKPmxi0Gs0Vp4tIpB7FIA7JmW\nRk1VCPldRAJuIkFPLUwG3EQCM2Gyts2NXwKlEAvSogmJpmmTztansZ4ymJyamRF09qUhZtM1CPhV\ngjEdv0+dWbwqvvp6ZoZQIYQQQghnURSFoNdH0OtjGZ2Nx23bpmyVyBhTZKq18JizMqSVNMYp4RFb\nwWOF0I0ISjmEWQhQzfo5OeFhYHT+bqqaqhAOuIgEPESDHiJBdyNcTgfJaKD2uHR5FcI5LrmQaFk2\nqSmD8ckK46kq45NVxlIVJqeMM44F9PsUEu064aBG0K8S8GsEA7Wxf26XIt9+CSGEEOKSoygKXs2H\nV/MR93QAtYmEstkiJatIpjpF1pgiY0yRrabJVDOUtQx4gDDQAW4U2rQoYT2G327BbUbQKmGsUoBS\nySJXqpIv1pbjySy/GZl/ApaAVycScBMNzQTK6QAZDXqIBmvh0uM685hOIcQ7Z0GHxHzRJDlRITle\nITlRZXSiTGrKwDqlYVDXoSWqEQlphIK1WUBDQZVgQEPXJAQKIYQQQkAtPPo0Pz7NT+KUlseSVSRb\nrQdHY4pMtbZMmZMzT6CBElCIRKLEPHG63G20uttoccXxE6VUsskVqzMBsmSQqwfJbLHC+FSJoYn5\nZ7P3utVay+R0mDylRXK6lTLgla6uQrxdCyYkjqXKvPlWnuREhdGJCqPjFQqndBPVNIiGNaLhWiCM\n1Nd+nyr/SAghhBBCvE2zw2P8fMKjMUU6P8nR/Oszz4FCRG8h5mmnNdxGrK2dPlcbUXcHmjLTOlg1\nLPKlaiM85kpGbd1YKmTyFUYni/Oes6YqhP0uIsG5XV3D05PyzBpH6XVr8llRiFkWTEj8P99/a859\nv1ehq8NFNKzREtFpidS6icovuBBCCCHExXG+4XF2t9W0kTotPEb1FmKeOK31lsdWdxudgVY0JXjW\nn21aNoXSdHA05gTLfCNYVjiezJ2zq6uuTU/GU5/ddXpWV//07dr9kM9F0O/C45JQKS5tCyYkrlrh\nIxRQaIloRCMaHrcMbhZCCCGEcKJzhcfZYx4z1TTZ6hSTRgrys56jHh5bPe214OiqhceoqxVd1euz\nrLoJ+d3znott25SrJvlivWtrqUqhZFAo14LldKDMl6ocT+awznVBa2qhMujVCdZ/frAeHoNeF0Gf\ni4BPr6/r970u/F4dVYKlWCAWTEi85iMt5HKlZp+GEEIIIYR4m8415jFTTc/ptjodHk9teQzrEVrd\n7bS4Y7S6YrS4Y7S4Yng07xl/ptet43XrxCKnb5/Ntm0qhkWhZFAs14JkLVBWG48VKyaFUpVi2SA5\nWWRwLD/vc86cB/g9OgGvXg+P7lqY9NbC5MzjtVAZ8On4Ah4s25ZwKS66BRMShRBCCCHEpWlueFzS\neLzRbdXIzIx7rK+nCm9yrPDmnOfxq4FacKyHxqi7lairlZAeQVXO3QtNURQ8Lg2PS6Ml5DmvczdN\nqx4cDUqVWogsluu3yyal8vRjVYplk1yxyvhU6Yyz7p/5nGbCZa3Fst5yOaulMlRfB/212wGfSy4p\nIn4rEhKFEEIIIYQjzem2Wr9UB9Rb/KxyLTwaGbLGVCNIniwd52Tp+JznUVEJ61Fa6qEx6mol4moh\n6mohoIfOK0CejaapBH0qQZ/rvI+xbZuqYVGqmBQrBqVyfV2ZCZWlsoFhQyZXpliukr3AcOl1q/Uw\n6Z4zznJ6XGWoPt4yXB976XHLpUXEDAmJQgghhBBiQVEUBY/mxaN5afPE52wzLIOcmSVbnSJvZskZ\nWbLVDDkzS7qQOu25VFSCepiIq4WIK0pEjxJ21Rc9gkf1vuOT1CiKgtul4XZphANnH1MZjfpJp2cu\nCWLbNpWqRbFS7/o6HS7LBoWySalS6yJbrHeRLZZNUpnseQVLl64Q8tWCYyToOW3ynumgOX3fpUuo\nvJSdMyTu3buXe+65B8uy2LJlC7fccstp+9x9993s3bsXr9fLvffey9q1a+c9Np1O86UvfYmhoSG6\nurr47ne/SzgcfodfmhBCCCGEWGx0VSeq1loJZ5tufczVg2PeyJI3c7W1kSdjpDlxhqtq6OgE9RAh\nV4SQHiZYX0J6iKAeJqAFcaueizLbqaIoeNwaHrdGNHh+3WHPNs5y5na1NolPsbYeHKswMJo75/N6\n3WojVIYDM6FydpCcPamPxyWhciGZNySapsldd93F/fffTyKRYMuWLWzcuJG+vr7GPnv27GFgYICn\nn36aAwcOcOedd/LQQw/Ne+z27du56qqr+NznPsf27dvZvn07f/u3f/uuv1ghhBBCCLE4zW59jLnb\nT9tuWFXyZp68kaNg5sibOQpGnoKZJ2/mSRuTZ31uDQ2/FsCvBwnoQQJaAL8WxK8H8Kl+vJqvtqi1\n9ezrQr7bLnSc5exQeerEPTOPVeszw1aZyJSw7PkvMQK1lsqgd3ZXV3d90h69MVFP4JRJfPweXcZW\nNsm8IfHgwYP09PTQ3d0NwKZNm9i1a9eckLhr1y42b94MwBVXXEEmk2FsbIzBwcGzHrt7924eeOAB\nADZv3syf/umfSkgUQgghhBBNo6suImqUiCt6xu2GZVC0ChTNAkUzT9EsUDALlM0iJbNIySqRLA9j\nl8/dt9OluPGqXryaH6/mxaN6catu3KpnztJih6gWwaW40FUXuuLCpeiN27qivytdYS80VJYqZq2b\na9mYEy6L5ZllevtIqsCJ5PnNCAu1cOlz6/i9Oj6Pht+n4fPUWlM9HgWPS8XtUvC4a2uXS8HtUnHp\nCpqmoOsKuga6rqAoNihg2Xb93C1sbCzbxsbGtm3qWxqvbbZr2j9w3ue90M0bEkdHR+nsnJmeOJFI\ncPDgwTn7JJNJOjpmBhJ3dHQwOjpKMpk867ETExO0tbUB0NbWxsTExG//SoQQQgghhHiX6KpOSA0T\n0s8+RGq6S2vZKlGySpTMIhWrPGupULbKVMwSFatC3kxiYZ39h46d+7w0dDRFQ1M01PpaQ0NTa+vp\nxxRFQUFFVVQUlNPWNcqs/wLKzBa79gKZiVGzbttgUwtctm5j6xaW32b6Uc22CNgWfmws28KyTUzL\nwrRrizVrsev72FjYig1YVLFJKzZTZ8rD1fpSOMO2d9g1l0tIBDjvbyZOTdln2+dMz6coynn9nMzY\nuFwn0UGsolfq4TBSE2eRejiP1MR5pCbOIvV4Z3nR8RICQqdvVOsLYNomJkb9j4lhV6k9UkVzK+TL\nJSzM+p96wMKsPWabGJjY9YBl2FWqlLHqgc1qBC1nUGylEUiVM/zR6rlAQUNBR0Gdu4eioNgqtg3Y\nCratgg22rWJbSi2s2kr9tjLntmUp2BZYdm09vd2q38aeG5QbGn99i+talfOGxEQiwfDwcOP+yMgI\niURizj7xeJyRkZE5+3R0dGAYxpxjR0dHicdrs0/FYjHGxsZob28nmUzS2tp6zhP9zP++4fxekRBC\nCCGEEEKIt23ekaDr169nYGCAwcFBKpUKTzzxBBs3bpyzz8aNG+nv7wdg//79hMNh2tra5j322muv\nZceOHQD09/dz3XXXvRuvTQghhBBCCCHEBVLsc/QV3bNnz5zLWNx66608+OCDAHzqU58C4Jvf/CbP\nPvssPp+Pbdu2sW7durMeC7VLYHzxi19keHhYLoEhhBBCCCGEEA5yzpAohBBCCCGEEGLxkAuPCCGE\nEEIIIYRokJAohBBCCCGEEKJBQqIQQgghhBBCiIZ5L4HhBHv37p0z+c0tt9zS7FNadG6//Xb27NlD\nLBbjpz/9KVCbfOhLX/oSQ0NDMvnQRTY8PMxXvvIVUqkUiqLwyU9+kj/7sz+TmjRRuVzms5/9LJVK\nhWq1ysaNG/nyl78sNWky0zT5+Mc/TkdHB//2b/8m9Wiya6+9lkAggKZp6LrOI488IjVpskwmw9e/\n/nXefPNNFEVh27ZtLFu2TGrSBG+99RZbt25t3D9x4gR/8zd/w0033ST1aKLvfe97PP7446iqyurV\nq9m2bRuFQmFR1MTRLYmmaXLXXXdx3333sXPnTnbu3MnRo0ebfVqLzsc//nHuu+++OY9t376dq666\niqeeeooPfehDbN++vUlnt/jous7XvvY1du7cyY9//GN+9KMfcfToUalJE3k8Hn74wx/y2GOP8fjj\nj/Piiy+yb98+qUmT/fCHP6Svr69xX+rRfP/5n/9Jf38/jzzyCCA1abZvfetbfPSjH+XJJ5/k8ccf\np7e3V2rSJL29vfT399Pf38+jjz6Kz+fj+uuvl3o00eDgIA899BA7duzgpz/9KaZpsnPnzkVTE0eH\nxIMHD9LT00N3dzcul4tNmzaxa9euZp/WovP+97//tG9Idu/ezebNmwHYvHkzP//5z5txaotSe3s7\nl19+OQCBQIC+vj5GR0elJk3m8/kAqFarmKZJJBKRmjTRyMgIe/bs4ROf+ETjMalH8506obrUpHmy\n2Sz79u1jy5YtQO0LyFAoJDVxgOeff56enh46OzulHk0UDAbRdZ1isYhhGJRKJeLx+KKpiaND4ujo\nKJ2dnY37iUSC0dHRJp6RmDYxMUFbWxsAbW1tTExMNPmMFqfBwUEOHz7Mhg0bpCZNZlkWH/vYx7jq\nqqv44Ac/yKpVq6QmTXTPPffwla98BVWdeZuTejSXoijcfPPN/PEf/zEPPfQQIDVppsHBQVpbW7n9\n9tvZvHkzX//61ykUClITB9i5cyebNm0C5HekmaLRKH/xF3/B7//+7/N7v/d7hEIhPvKRjyyamjg6\nJCqK0uxTEOdBURSpVRPk83luu+027rjjDoLB4JxtUpOLT1VVHnvsMfbu3cu+fft44YUX5myXmlw8\nzzzzDLFYjLVr157WcjVN6nHx/dd//Rf9/f3cd999/OhHP2Lfvn1ztktNLi7DMDh06BCf/vSn2bFj\nBz6f77Ruc1KTi69SqfDMM89www03nLZN6nFxHT9+nP/4j/9g9+7dPPvssxQKBR577LE5+1zKNXF0\nSEwkEgwPDzfuj4yMkEgkmnhGYlosFmNsbAyAZDJJa2trk89ocalWq9x2223cdNNNXHfddYDUxClC\noRBXX301v/71r6UmTfKrX/2K3bt3c+211/LlL3+ZF154gb/7u7+TejRZPB4HoLW1leuvv56DBw9K\nTZqoo6ODRCLBhg0bAPiDP/gDDh06RFtbm9Skifbu3cu6desaf+/yO9I8r776KldeeSUtLS3ous71\n11/P/v37F83viKND4vr16xkYGGBwcJBKpcITTzzBxo0bm31agtosdTt27ACgv7+/EVTEu8+2be64\n4w76+vr48z//88bjUpPmSaVSZDIZAEqlEs8//zxr166VmjTJ1q1b2bNnD7t37+Y73/kOH/rQh/j2\nt78t9WiiYrFILpcDoFAo8Nxzz7F69WqpSRO1t7fT2dnJsWPHAPjlL3/JypUrueaaa6QmTbRz507+\n6I/+qHFffkeap7e3lwMHDlAqlbBte9H9jij22friOMSePXvmXALj1ltvbfYpLTpbt27lpZdeIp1O\nE4vFuO2229i4cSNf/OIXGR4evqSn/3Wiffv28dnPfpY1a9Y0ujhs3bqVDRs2SE2a5PXXX+erbFyj\npgAAA2dJREFUX/0qlmU1xib+1V/9Fel0WmrSZC+99BL//u//3rgEhtSjOU6cOMEXvvAFoDZz+Y03\n3sitt94qNWmy1157jTvuuINqtUpPTw/btm3DNE2pSZMUCgWuueYadu3a1RhGIr8jzfX973+f/v5+\nVFVl7dq13H333eTz+UVRE8eHRCGEEEIIIYQQF4+ju5sKIYQQQgghhLi4JCQKIYQQQgghhGiQkCiE\nEEIIIYQQokFCohBCCCGEEEKIBgmJQgghhBBCCCEaJCQKIYQQQgghhGiQkCiEEOKSMDU1xYYNG/jW\nt77V7FMRQgghFjQJiUIIIS4JP/vZz/jd3/1dnnzySarVarNPRwghhFiw9GafgBBCCPFO+MlPfsId\nd9zB9u3b2bVrF3/4h39INpvla1/7GkeOHCGRSBCPx4nFYvz93/89lUqFf/qnf2Lfvn1UKhXWrFnD\nnXfeid/vb/ZLEUIIIZpKWhKFEEIseK+99hq5XI73ve99fOxjH+MnP/kJAP/6r/9KNBrlySef5J//\n+Z95+eWXURQFgPvuu49wOMzDDz/MY489Rnt7O9/73vea+TKEEEIIR5CWRCGEEAveI488wk033QTA\nxo0b+Yd/+AdGR0d56aWX+MY3vgFAJBLhuuuuaxyze/du8vk8Tz31FACVSoXLL7/84p+8EEII4TAS\nEoUQQixolUqFn/3sZ3g8Hh599FEADMNgx44dANi23dh39m2AO++8kw9+8IMX72SFEEKIBUC6mwoh\nhFjQdu3aRV9fH3v27GH37t3s3r2bH/zgB+zYsYMPfOAD9Pf3A5DJZNi9e3fjuGuvvZb777+fcrkM\nQC6X4+jRo015DUIIIYSTSEgUQgixoD366KPceOONcx5773vfi23bXHfddaRSKW644Qa+8IUvsH79\nekKhEAC33HILa9asYcuWLdx000185jOf4dixY814CUIIIYSjKPapfW+EEEKIS4RhGFiWhdvtJpfL\n8Sd/8ifcfvvtfPjDH272qQkhhBCOJWMShRBCXLKmpqb43Oc+h2VZlMtlbrzxRgmIQgghxDlIS6IQ\nQgghhBBCiAYZkyiEEEIIIYQQokFCohBCCCGEEEKIBgmJQgghhBBCCCEaJCQKIYQQQgghhGiQkCiE\nEEIIIYQQokFCohBCCCGEEEKIhv8PI+G9TC0x2XwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc76ed90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0xd6c5e70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# .... continue with plot Age column\n",
"\n",
"# peaks for survived/not survived passengers by their age\n",
"facet = sns.FacetGrid(mData, hue=\"Survived\",aspect=4)\n",
"facet.map(sns.kdeplot,'Age',shade= True)\n",
"facet.set(xlim=(0, mData['Age'].max()))\n",
"facet.add_legend()\n",
"\n",
"# average survived passengers by age\n",
"fig, axis1 = plt.subplots(1,1,figsize=(18,4))\n",
"average_age = mData[[\"Age\", \"Survived\"]].groupby(['Age'],as_index=False).mean()\n",
"sns.barplot(x='Age', y='Survived', data=average_age)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Family"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\python27\\lib\\site-packages\\pandas\\core\\indexing.py:117: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self._setitem_with_indexer(indexer, value)\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.text.Text at 0xda7d530>, <matplotlib.text.Text at 0xda91ab0>]"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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VvPiGNqOBAwfqo48+UteuXWWxWNSlSxedOHFC+/btO2/C79ixoyor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\nvIlPhno0ggAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xdb08c10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Family\n",
"\n",
"# Instead of having two columns Parch & SibSp, \n",
"# we can have only one column represent if the passenger had any family member aboard or not,\n",
"# Meaning, if having any family member(whether parent, brother, ...etc) will increase chances of Survival or not.\n",
"mData['Family'] = mData['Parch'] + mData['SibSp']\n",
"\n",
"mData['Family'].loc[mData['Family'] > 0] = 1\n",
"mData['Family'].loc[mData['Family'] == 0] = 0\n",
"\n",
"test['Family'] = test['Parch'] + test['SibSp']\n",
"test['Family'].loc[test['Family'] > 0] = 1\n",
"test['Family'].loc[test['Family'] == 0] = 0\n",
"\n",
"# drop Parch & SibSp\n",
"mData = mData.drop(['SibSp','Parch'], axis=1)\n",
"test = test.drop(['SibSp','Parch'], axis=1)\n",
"\n",
"# plot\n",
"fig, (axis1,axis2) = plt.subplots(1,2,sharex=True,figsize=(10,5))\n",
"\n",
"# sns.factorplot('Family',data=titanic_df,kind='count',ax=axis1)\n",
"sns.countplot(x='Family', data=mData, order=[1,0], ax=axis1)\n",
"\n",
"# average of survived for those who had/didn't have any family member\n",
"family_perc = mData[[\"Family\", \"Survived\"]].groupby(['Family'],as_index=False).mean()\n",
"sns.barplot(x='Family', y='Survived', data=family_perc, order=[1,0], ax=axis2)\n",
"\n",
"axis1.set_xticklabels([\"With Family\",\"Alone\"], rotation=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sex"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YJEm+vr7y9/ev0747d+5UXFycYmNjlZqaWuP7rKwsjRw5UqNHj9bdd9+t3bt3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\nOfdhBLLLlJ+frwcffFB9+vT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"text/plain": [
"<matplotlib.figure.Figure at 0xe1dffd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Sex\n",
"\n",
"# As we see, children(age < ~16) on aboard seem to have a high chances for Survival.\n",
"# So, we can classify passengers as males, females, and child\n",
"def get_person(passenger):\n",
" age,sex = passenger\n",
" return 'child' if age < 16 else sex\n",
" \n",
"mData['Person'] = mData[['Age','Sex']].apply(get_person,axis=1)\n",
"test['Person'] = test[['Age','Sex']].apply(get_person,axis=1)\n",
"\n",
"# No need to use Sex column since we created Person column\n",
"mData.drop(['Sex'],axis=1,inplace=True)\n",
"test.drop(['Sex'],axis=1,inplace=True)\n",
"\n",
"# create dummy variables for Person column, & drop Male as it has the lowest average of survived passengers\n",
"person_dummies_titanic = pd.get_dummies(mData['Person'])\n",
"person_dummies_titanic.columns = ['Male','Female','Child']\n",
"person_dummies_titanic.drop(['Male'], axis=1, inplace=True)\n",
"\n",
"person_dummies_test = pd.get_dummies(test['Person'])\n",
"person_dummies_test.columns = ['Male','Female','Child']\n",
"person_dummies_test.drop(['Male'], axis=1, inplace=True)\n",
"\n",
"mData = mData.join(person_dummies_titanic)\n",
"test = test.join(person_dummies_test)\n",
"\n",
"fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))\n",
"\n",
"# sns.factorplot('Person',data=titanic_df,kind='count',ax=axis1)\n",
"sns.countplot(x='Person', data=mData, ax=axis1)\n",
"\n",
"# average of survived for each Person(male, female, or child)\n",
"family_perc = mData[[\"Person\", \"Survived\"]].groupby(['Person'],as_index=False).mean()\n",
"sns.barplot(x='Person', y='Survived', data=family_perc, ax=axis2, order=['male','female','child'])\n",
"\n",
"mData.drop(['Person'],axis=1,inplace=True)\n",
"test.drop(['Person'],axis=1,inplace=True)"
]
},
{
"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-2.0 |
mne-tools/mne-tools.github.io | dev/_downloads/5b9edf9c05aec2b9bb1f128f174ca0f3/40_cluster_1samp_time_freq.ipynb | 1 | 11850 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Non-parametric 1 sample cluster statistic on single trial power\n\nThis script shows how to estimate significant clusters\nin time-frequency power estimates. It uses a non-parametric\nstatistical procedure based on permutations and cluster\nlevel statistics.\n\nThe procedure consists of:\n\n - extracting epochs\n - compute single trial power estimates\n - baseline line correct the power estimates (power ratios)\n - compute stats to see if ratio deviates from 1.\n\nHere, the unit of observation is epochs from a specific study subject.\nHowever, the same logic applies when the unit of observation is\na number of study subjects each of whom contribute their own averaged\ndata (i.e., an average of their epochs). This would then be considered\nan analysis at the \"2nd level\".\n\nFor more information on cluster-based permutation testing in MNE-Python,\nsee also: `tut-cluster-spatiotemporal-sensor`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Alexandre Gramfort <[email protected]>\n# Stefan Appelhoff <[email protected]>\n#\n# License: BSD-3-Clause"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.stats\n\nimport mne\nfrom mne.time_frequency import tfr_morlet\nfrom mne.stats import permutation_cluster_1samp_test\nfrom mne.datasets import sample"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set parameters\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = sample.data_path()\nmeg_path = data_path / 'MEG' / 'sample'\nraw_fname = meg_path / 'sample_audvis_raw.fif'\ntmin, tmax, event_id = -0.3, 0.6, 1\n\n# Setup for reading the raw data\nraw = mne.io.read_raw_fif(raw_fname)\nevents = mne.find_events(raw, stim_channel='STI 014')\n\ninclude = []\nraw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more\n\n# picks MEG gradiometers\npicks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,\n stim=False, include=include, exclude='bads')\n\n# Load condition 1\nevent_id = 1\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), preload=True,\n reject=dict(grad=4000e-13, eog=150e-6))\n\n# just use right temporal sensors for speed\nepochs.pick_channels(mne.read_vectorview_selection('Right-temporal'))\nevoked = epochs.average()\n\n# Factor to down-sample the temporal dimension of the TFR computed by\n# tfr_morlet. Decimation occurs after frequency decomposition and can\n# be used to reduce memory usage (and possibly computational time of downstream\n# operations such as nonparametric statistics) if you don't need high\n# spectrotemporal resolution.\ndecim = 5\n\n# define frequencies of interest\nfreqs = np.arange(8, 40, 2)\n\n# run the TFR decomposition\ntfr_epochs = tfr_morlet(epochs, freqs, n_cycles=4., decim=decim,\n average=False, return_itc=False, n_jobs=None)\n\n# Baseline power\ntfr_epochs.apply_baseline(mode='logratio', baseline=(-.100, 0))\n\n# Crop in time to keep only what is between 0 and 400 ms\nevoked.crop(-0.1, 0.4)\ntfr_epochs.crop(-0.1, 0.4)\n\nepochs_power = tfr_epochs.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define adjacency for statistics\nTo perform a cluster-based permutation test, we need a suitable definition\nfor the adjacency of sensors, time points, and frequency bins.\nThe adjacency matrix will be used to form clusters.\n\nWe first compute the sensor adjacency, and then combine that with a\n\"lattice\" adjacency for the time-frequency plane, which assumes\nthat elements at index \"N\" are adjacent to elements at indices\n\"N + 1\" and \"N - 1\" (forming a \"grid\" on the time-frequency plane).\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# find_ch_adjacency first attempts to find an existing \"neighbor\"\n# (adjacency) file for given sensor layout.\n# If such a file doesn't exist, an adjacency matrix is computed on the fly,\n# using Delaunay triangulations.\nsensor_adjacency, ch_names = mne.channels.find_ch_adjacency(\n tfr_epochs.info, 'grad')\n\n# In this case, find_ch_adjacency finds an appropriate file and\n# reads it (see log output: \"neuromag306planar\").\n# However, we need to subselect the channels we are actually using\nuse_idx = [ch_names.index(ch_name)\n for ch_name in tfr_epochs.ch_names]\nsensor_adjacency = sensor_adjacency[use_idx][:, use_idx]\n\n# Our sensor adjacency matrix is of shape n_chs \u00d7 n_chs\nassert sensor_adjacency.shape == \\\n (len(tfr_epochs.ch_names), len(tfr_epochs.ch_names))\n\n# Now we need to prepare adjacency information for the time-frequency\n# plane. For that, we use \"combine_adjacency\", and pass dimensions\n# as in the data we want to test (excluding observations). Here:\n# channels \u00d7 frequencies \u00d7 times\nassert epochs_power.data.shape == (\n len(epochs), len(tfr_epochs.ch_names),\n len(tfr_epochs.freqs), len(tfr_epochs.times))\nadjacency = mne.stats.combine_adjacency(\n sensor_adjacency, len(tfr_epochs.freqs), len(tfr_epochs.times))\n\n# The overall adjacency we end up with is a square matrix with each\n# dimension matching the data size (excluding observations) in an\n# \"unrolled\" format, so: len(channels \u00d7 frequencies \u00d7 times)\nassert adjacency.shape[0] == adjacency.shape[1] == \\\n len(tfr_epochs.ch_names) * len(tfr_epochs.freqs) * len(tfr_epochs.times)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute statistic\nFor forming clusters, we need to specify a critical test statistic threshold.\nOnly data bins exceeding this threshold will be used to form clusters.\n\nHere, we\nuse a t-test and can make use of Scipy's percent point function of the t\ndistribution to get a t-value that corresponds to a specific alpha level\nfor significance. This threshold is often called the\n\"cluster forming threshold\".\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The choice of the threshold is more or less arbitrary. Choosing\n a t-value corresponding to p=0.05, p=0.01, or p=0.001 may often provide\n a good starting point. Depending on the specific dataset you are working\n with, you may need to adjust the threshold.</p></div>\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We want a two-tailed test\ntail = 0\n\n# In this example, we wish to set the threshold for including data bins in\n# the cluster forming process to the t-value corresponding to p=0.001 for the\n# given data.\n#\n# Because we conduct a two-tailed test, we divide the p-value by 2 (which means\n# we're making use of both tails of the distribution).\n# As the degrees of freedom, we specify the number of observations\n# (here: epochs) minus 1.\n# Finally, we subtract 0.001 / 2 from 1, to get the critical t-value\n# on the right tail (this is needed for MNE-Python internals)\ndegrees_of_freedom = len(epochs) - 1\nt_thresh = scipy.stats.t.ppf(1 - 0.001 / 2, df=degrees_of_freedom)\n\n# Set the number of permutations to run.\n# Warning: 50 is way too small for a real-world analysis (where values of 5000\n# or higher are used), but here we use it to increase the computation speed.\nn_permutations = 50\n\n# Run the analysis\nT_obs, clusters, cluster_p_values, H0 = \\\n permutation_cluster_1samp_test(epochs_power, n_permutations=n_permutations,\n threshold=t_thresh, tail=tail,\n adjacency=adjacency,\n out_type='mask', verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View time-frequency plots\nWe now visualize the observed clusters that are statistically significant\nunder our permutation distribution.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Talking about \"significant clusters\" can be convenient, but\n you must be aware of all associated caveats! For example, it\n is **invalid** to interpret the cluster p value as being\n spatially or temporally specific. A cluster with sufficiently\n low (for example < 0.05) p value at specific location does not\n allow you to say that the significant effect is at that\n particular location. The p value only tells you about the\n probability of obtaining similar or stronger/larger cluster\n anywhere in the data if there were no differences between the\n compared conditions. So it only allows you to draw conclusions\n about the differences in the data \"in general\", not at specific\n locations. See the comprehensive\n [FieldTrip tutorial](ft_cluster_) for more information.\n [FieldTrip tutorial](ft_cluster_) for more information.</p></div>\n\n.. include:: ../../links.inc\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"evoked_data = evoked.data\ntimes = 1e3 * evoked.times\n\nplt.figure()\nplt.subplots_adjust(0.12, 0.08, 0.96, 0.94, 0.2, 0.43)\n\nT_obs_plot = np.nan * np.ones_like(T_obs)\nfor c, p_val in zip(clusters, cluster_p_values):\n if p_val <= 0.05:\n T_obs_plot[c] = T_obs[c]\n\n# Just plot one channel's data\n# use the following to show a specific one:\n# ch_idx = tfr_epochs.ch_names.index('MEG 1332')\nch_idx, f_idx, t_idx = np.unravel_index(\n np.nanargmax(np.abs(T_obs_plot)), epochs_power.shape[1:])\n\nvmax = np.max(np.abs(T_obs))\nvmin = -vmax\nplt.subplot(2, 1, 1)\nplt.imshow(T_obs[ch_idx], cmap=plt.cm.gray,\n extent=[times[0], times[-1], freqs[0], freqs[-1]],\n aspect='auto', origin='lower', vmin=vmin, vmax=vmax)\nplt.imshow(T_obs_plot[ch_idx], cmap=plt.cm.RdBu_r,\n extent=[times[0], times[-1], freqs[0], freqs[-1]],\n aspect='auto', origin='lower', vmin=vmin, vmax=vmax)\nplt.colorbar()\nplt.xlabel('Time (ms)')\nplt.ylabel('Frequency (Hz)')\nplt.title(f'Induced power ({tfr_epochs.ch_names[ch_idx]})')\n\nax2 = plt.subplot(2, 1, 2)\nevoked.plot(axes=[ax2], time_unit='s')\nplt.show()"
]
}
],
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
simonelanucara/script | sentinelsat.ipynb | 1 | 110449 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"from shapely.geometry import MultiPolygon, Polygon\n",
"import sentinelsat\n",
"from sentinelsat import SentinelAPI\n",
"import folium\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#Bisogna essere registrati sul sito di copernicus per poter utilizzare questa API\n",
"user = '' #nome utente\n",
"password = '' #password\n",
"\n",
"api = SentinelAPI(user, password, 'https://scihub.copernicus.eu/dhus')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#SHP da utilizzare come bbox\n",
"bbox=gpd.read_file('C:/Users/Utente/prova/bbox.shp')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<folium.folium.Map at 0x1c4d706a2e8>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#mostra il bbox sulla mappa\n",
"map = folium.Map([44.53,11.9], zoom_start=8)\n",
"folium.GeoJson(bbox).add_to(map)\n",
"map"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#prendi la geometria del bbox\n",
"footprint = None\n",
"for i in bbox['geometry']:\n",
" footprint = i"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#trova le immigini passando i parametri desiderati\n",
"products = api.query(footprint,\n",
" date = ('20200504', '20200628'), #range di date da cercare\n",
" platformname = 'Sentinel-2',\n",
" processinglevel = 'Level-2A',\n",
" cloudcoverpercentage = (0, 10)) #percentuale di nuvolosità desiderata, 10% in qusto caso"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"e2c955e7-7554-4369-a7ad-b73ec1513905 Annotation(10.9533, 44.6422, 'e2c955e7-7554-43...\n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 Annotation(12.2121, 44.6137, 'c2cf4b17-3a23-45...\n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 Annotation(12.45, 44.5815, '711af836-befe-4648...\n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 Annotation(12.2121, 44.6137, 'ebbb20af-6168-42...\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 Annotation(10.9533, 44.6422, '59aeacff-a1e9-4d...\n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 Annotation(12.4529, 44.5813, '9bca3003-b71f-40...\n",
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"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc Annotation(10.9533, 44.6422, '25771ec4-223a-47...\n",
"dtype: object"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1224x1224 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#stampa i tile trovati \n",
"tile = api.to_geodataframe(products)\n",
"ax = tile.plot(column='uuid',figsize=(17, 17))\n",
"tile.apply(lambda x: ax.annotate(text=x.uuid, xy=x.geometry.centroid.coords[0],size=20,ha='center', fontsize='14'),axis=1)\n",
"\n",
"#il testo sembra stampato male, ma in realtà sono i nomi dei tile che sono sovrapposti uno sull'altro"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#tile + bbox\n",
"f, ax = plt.subplots(1,figsize=(15,15))\n",
"tile.plot(ax=ax,column='uuid',cmap=None)\n",
"bbox.plot(ax=ax,color='red')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>link</th>\n",
" <th>link_alternative</th>\n",
" <th>link_icon</th>\n",
" <th>summary</th>\n",
" <th>beginposition</th>\n",
" <th>endposition</th>\n",
" <th>ingestiondate</th>\n",
" <th>orbitnumber</th>\n",
" <th>relativeorbitnumber</th>\n",
" <th>...</th>\n",
" <th>size</th>\n",
" <th>s2datatakeid</th>\n",
" <th>producttype</th>\n",
" <th>platformidentifier</th>\n",
" <th>orbitdirection</th>\n",
" <th>platformserialidentifier</th>\n",
" <th>processinglevel</th>\n",
" <th>identifier</th>\n",
" <th>uuid</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>59aeacff-a1e9-4dd3-9853-66f1a568ddd1</th>\n",
" <td>S2B_MSIL2A_20200622T100559_N0214_R022_T32TPQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-06-22T10:05:59.024Z, Instrument: MS...</td>\n",
" <td>2020-06-22 10:05:59.024</td>\n",
" <td>2020-06-22 10:05:59.024</td>\n",
" <td>2020-06-22 19:49:23.296</td>\n",
" <td>17208</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>1.13 GB</td>\n",
" <td>GS2B_20200622T100559_017208_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2017-013A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2B</td>\n",
" <td>Level-2A</td>\n",
" <td>S2B_MSIL2A_20200622T100559_N0214_R022_T32TPQ_2...</td>\n",
" <td>59aeacff-a1e9-4dd3-9853-66f1a568ddd1</td>\n",
" <td>MULTIPOLYGON (((11.62274 44.13525, 11.66762 45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2</th>\n",
" <td>S2A_MSIL2A_20200627T101031_N0214_R022_T32TQQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-06-27T10:10:31.024Z, Instrument: MS...</td>\n",
" <td>2020-06-27 10:10:31.024</td>\n",
" <td>2020-06-27 10:10:31.024</td>\n",
" <td>2020-06-27 17:55:25.745</td>\n",
" <td>26188</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>1.01 GB</td>\n",
" <td>GS2A_20200627T101031_026188_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2015-028A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2A</td>\n",
" <td>Level-2A</td>\n",
" <td>S2A_MSIL2A_20200627T101031_N0214_R022_T32TQQ_2...</td>\n",
" <td>c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2</td>\n",
" <td>MULTIPOLYGON (((12.86999 44.09977, 12.93610 45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c36bb7ec-fc52-437e-aae7-ee2d35f9f807</th>\n",
" <td>S2A_MSIL2A_20200525T100031_N0214_R122_T32TQQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-05-25T10:00:31.024Z, Instrument: MS...</td>\n",
" <td>2020-05-25 10:00:31.024</td>\n",
" <td>2020-05-25 10:00:31.024</td>\n",
" <td>2020-05-25 17:38:03.648</td>\n",
" <td>25716</td>\n",
" <td>122</td>\n",
" <td>...</td>\n",
" <td>627.31 MB</td>\n",
" <td>GS2A_20200525T100031_025716_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2015-028A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2A</td>\n",
" <td>Level-2A</td>\n",
" <td>S2A_MSIL2A_20200525T100031_N0214_R122_T32TQQ_2...</td>\n",
" <td>c36bb7ec-fc52-437e-aae7-ee2d35f9f807</td>\n",
" <td>MULTIPOLYGON (((12.86999 44.09977, 12.93610 45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e2c955e7-7554-4369-a7ad-b73ec1513905</th>\n",
" <td>S2A_MSIL2A_20200627T101031_N0214_R022_T32TPQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-06-27T10:10:31.024Z, Instrument: MS...</td>\n",
" <td>2020-06-27 10:10:31.024</td>\n",
" <td>2020-06-27 10:10:31.024</td>\n",
" <td>2020-06-27 18:07:06.113</td>\n",
" <td>26188</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>1.12 GB</td>\n",
" <td>GS2A_20200627T101031_026188_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2015-028A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2A</td>\n",
" <td>Level-2A</td>\n",
" <td>S2A_MSIL2A_20200627T101031_N0214_R022_T32TPQ_2...</td>\n",
" <td>e2c955e7-7554-4369-a7ad-b73ec1513905</td>\n",
" <td>MULTIPOLYGON (((11.62274 44.13525, 11.66762 45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9bca3003-b71f-4024-b03e-9a7ab376ade8</th>\n",
" <td>S2B_MSIL2A_20200619T100029_N0214_R122_T32TQQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-06-19T10:00:29.024Z, Instrument: MS...</td>\n",
" <td>2020-06-19 10:00:29.024</td>\n",
" <td>2020-06-19 10:00:29.024</td>\n",
" <td>2020-06-19 20:17:03.923</td>\n",
" <td>17165</td>\n",
" <td>122</td>\n",
" <td>...</td>\n",
" <td>607.86 MB</td>\n",
" <td>GS2B_20200619T100029_017165_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2017-013A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2B</td>\n",
" <td>Level-2A</td>\n",
" <td>S2B_MSIL2A_20200619T100029_N0214_R122_T32TQQ_2...</td>\n",
" <td>9bca3003-b71f-4024-b03e-9a7ab376ade8</td>\n",
" <td>MULTIPOLYGON (((12.86999 44.09977, 12.93610 45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25771ec4-223a-47ed-a7ab-6f53e5d9e9cc</th>\n",
" <td>S2B_MSIL2A_20200523T100559_N0214_R022_T32TPQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-05-23T10:05:59.024Z, Instrument: MS...</td>\n",
" <td>2020-05-23 10:05:59.024</td>\n",
" <td>2020-05-23 10:05:59.024</td>\n",
" <td>2020-05-23 20:16:48.199</td>\n",
" <td>16779</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>1.12 GB</td>\n",
" <td>GS2B_20200523T100559_016779_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2017-013A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2B</td>\n",
" <td>Level-2A</td>\n",
" <td>S2B_MSIL2A_20200523T100559_N0214_R022_T32TPQ_2...</td>\n",
" <td>25771ec4-223a-47ed-a7ab-6f53e5d9e9cc</td>\n",
" <td>MULTIPOLYGON (((11.62274 44.13525, 11.66762 45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ebbb20af-6168-4218-a8c6-c225a0f207d8</th>\n",
" <td>S2B_MSIL2A_20200622T100559_N0214_R022_T32TQQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-06-22T10:05:59.024Z, Instrument: MS...</td>\n",
" <td>2020-06-22 10:05:59.024</td>\n",
" <td>2020-06-22 10:05:59.024</td>\n",
" <td>2020-06-22 19:51:44.137</td>\n",
" <td>17208</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>1.04 GB</td>\n",
" <td>GS2B_20200622T100559_017208_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2017-013A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2B</td>\n",
" <td>Level-2A</td>\n",
" <td>S2B_MSIL2A_20200622T100559_N0214_R022_T32TQQ_2...</td>\n",
" <td>ebbb20af-6168-4218-a8c6-c225a0f207d8</td>\n",
" <td>MULTIPOLYGON (((12.86999 44.09977, 12.93610 45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>711af836-befe-4648-bb90-5b5bd2eab5b2</th>\n",
" <td>S2A_MSIL2A_20200624T100031_N0214_R122_T32TQQ_2...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
" <td>Date: 2020-06-24T10:00:31.024Z, Instrument: MS...</td>\n",
" <td>2020-06-24 10:00:31.024</td>\n",
" <td>2020-06-24 10:00:31.024</td>\n",
" <td>2020-06-24 17:39:50.107</td>\n",
" <td>26145</td>\n",
" <td>122</td>\n",
" <td>...</td>\n",
" <td>684.45 MB</td>\n",
" <td>GS2A_20200624T100031_026145_N02.14</td>\n",
" <td>S2MSI2A</td>\n",
" <td>2015-028A</td>\n",
" <td>DESCENDING</td>\n",
" <td>Sentinel-2A</td>\n",
" <td>Level-2A</td>\n",
" <td>S2A_MSIL2A_20200624T100031_N0214_R122_T32TQQ_2...</td>\n",
" <td>711af836-befe-4648-bb90-5b5bd2eab5b2</td>\n",
" <td>MULTIPOLYGON (((12.86999 44.09977, 12.93610 45...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" title \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 S2B_MSIL2A_20200622T100559_N0214_R022_T32TPQ_2... \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 S2A_MSIL2A_20200627T101031_N0214_R022_T32TQQ_2... \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 S2A_MSIL2A_20200525T100031_N0214_R122_T32TQQ_2... \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 S2A_MSIL2A_20200627T101031_N0214_R022_T32TPQ_2... \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 S2B_MSIL2A_20200619T100029_N0214_R122_T32TQQ_2... \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc S2B_MSIL2A_20200523T100559_N0214_R022_T32TPQ_2... \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 S2B_MSIL2A_20200622T100559_N0214_R022_T32TQQ_2... \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 S2A_MSIL2A_20200624T100031_N0214_R122_T32TQQ_2... \n",
"\n",
" link \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"\n",
" link_alternative \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"\n",
" link_icon \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 https://scihub.copernicus.eu/dhus/odata/v1/Pro... \n",
"\n",
" summary \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 Date: 2020-06-22T10:05:59.024Z, Instrument: MS... \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 Date: 2020-06-27T10:10:31.024Z, Instrument: MS... \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 Date: 2020-05-25T10:00:31.024Z, Instrument: MS... \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 Date: 2020-06-27T10:10:31.024Z, Instrument: MS... \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 Date: 2020-06-19T10:00:29.024Z, Instrument: MS... \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc Date: 2020-05-23T10:05:59.024Z, Instrument: MS... \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 Date: 2020-06-22T10:05:59.024Z, Instrument: MS... \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 Date: 2020-06-24T10:00:31.024Z, Instrument: MS... \n",
"\n",
" beginposition \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 2020-06-22 10:05:59.024 \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 2020-06-27 10:10:31.024 \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 2020-05-25 10:00:31.024 \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 2020-06-27 10:10:31.024 \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 2020-06-19 10:00:29.024 \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc 2020-05-23 10:05:59.024 \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 2020-06-22 10:05:59.024 \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 2020-06-24 10:00:31.024 \n",
"\n",
" endposition \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 2020-06-22 10:05:59.024 \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 2020-06-27 10:10:31.024 \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 2020-05-25 10:00:31.024 \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 2020-06-27 10:10:31.024 \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 2020-06-19 10:00:29.024 \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc 2020-05-23 10:05:59.024 \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 2020-06-22 10:05:59.024 \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 2020-06-24 10:00:31.024 \n",
"\n",
" ingestiondate orbitnumber \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 2020-06-22 19:49:23.296 17208 \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 2020-06-27 17:55:25.745 26188 \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 2020-05-25 17:38:03.648 25716 \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 2020-06-27 18:07:06.113 26188 \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 2020-06-19 20:17:03.923 17165 \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc 2020-05-23 20:16:48.199 16779 \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 2020-06-22 19:51:44.137 17208 \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 2020-06-24 17:39:50.107 26145 \n",
"\n",
" relativeorbitnumber ... size \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 22 ... 1.13 GB \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 22 ... 1.01 GB \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 122 ... 627.31 MB \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 22 ... 1.12 GB \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 122 ... 607.86 MB \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc 22 ... 1.12 GB \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 22 ... 1.04 GB \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 122 ... 684.45 MB \n",
"\n",
" s2datatakeid \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 GS2B_20200622T100559_017208_N02.14 \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 GS2A_20200627T101031_026188_N02.14 \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 GS2A_20200525T100031_025716_N02.14 \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 GS2A_20200627T101031_026188_N02.14 \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 GS2B_20200619T100029_017165_N02.14 \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc GS2B_20200523T100559_016779_N02.14 \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 GS2B_20200622T100559_017208_N02.14 \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 GS2A_20200624T100031_026145_N02.14 \n",
"\n",
" producttype platformidentifier \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 S2MSI2A 2017-013A \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 S2MSI2A 2015-028A \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 S2MSI2A 2015-028A \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 S2MSI2A 2015-028A \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 S2MSI2A 2017-013A \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc S2MSI2A 2017-013A \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 S2MSI2A 2017-013A \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 S2MSI2A 2015-028A \n",
"\n",
" orbitdirection \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 DESCENDING \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 DESCENDING \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 DESCENDING \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 DESCENDING \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 DESCENDING \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc DESCENDING \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 DESCENDING \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 DESCENDING \n",
"\n",
" platformserialidentifier \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 Sentinel-2B \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 Sentinel-2A \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 Sentinel-2A \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 Sentinel-2A \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 Sentinel-2B \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc Sentinel-2B \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 Sentinel-2B \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 Sentinel-2A \n",
"\n",
" processinglevel \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 Level-2A \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 Level-2A \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 Level-2A \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 Level-2A \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 Level-2A \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc Level-2A \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 Level-2A \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 Level-2A \n",
"\n",
" identifier \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 S2B_MSIL2A_20200622T100559_N0214_R022_T32TPQ_2... \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 S2A_MSIL2A_20200627T101031_N0214_R022_T32TQQ_2... \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 S2A_MSIL2A_20200525T100031_N0214_R122_T32TQQ_2... \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 S2A_MSIL2A_20200627T101031_N0214_R022_T32TPQ_2... \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 S2B_MSIL2A_20200619T100029_N0214_R122_T32TQQ_2... \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc S2B_MSIL2A_20200523T100559_N0214_R022_T32TPQ_2... \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 S2B_MSIL2A_20200622T100559_N0214_R022_T32TQQ_2... \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 S2A_MSIL2A_20200624T100031_N0214_R122_T32TQQ_2... \n",
"\n",
" uuid \\\n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 59aeacff-a1e9-4dd3-9853-66f1a568ddd1 \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 c36bb7ec-fc52-437e-aae7-ee2d35f9f807 \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 e2c955e7-7554-4369-a7ad-b73ec1513905 \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 9bca3003-b71f-4024-b03e-9a7ab376ade8 \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc 25771ec4-223a-47ed-a7ab-6f53e5d9e9cc \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 ebbb20af-6168-4218-a8c6-c225a0f207d8 \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 711af836-befe-4648-bb90-5b5bd2eab5b2 \n",
"\n",
" geometry \n",
"59aeacff-a1e9-4dd3-9853-66f1a568ddd1 MULTIPOLYGON (((11.62274 44.13525, 11.66762 45... \n",
"c2cf4b17-3a23-454e-a8e3-f4c1ab1717c2 MULTIPOLYGON (((12.86999 44.09977, 12.93610 45... \n",
"c36bb7ec-fc52-437e-aae7-ee2d35f9f807 MULTIPOLYGON (((12.86999 44.09977, 12.93610 45... \n",
"e2c955e7-7554-4369-a7ad-b73ec1513905 MULTIPOLYGON (((11.62274 44.13525, 11.66762 45... \n",
"9bca3003-b71f-4024-b03e-9a7ab376ade8 MULTIPOLYGON (((12.86999 44.09977, 12.93610 45... \n",
"25771ec4-223a-47ed-a7ab-6f53e5d9e9cc MULTIPOLYGON (((11.62274 44.13525, 11.66762 45... \n",
"ebbb20af-6168-4218-a8c6-c225a0f207d8 MULTIPOLYGON (((12.86999 44.09977, 12.93610 45... \n",
"711af836-befe-4648-bb90-5b5bd2eab5b2 MULTIPOLYGON (((12.86999 44.09977, 12.93610 45... \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#crea un GDF e lo ordina in base alla % di nuvolosità\n",
"tile_gdf = api.to_geodataframe(products)\n",
"tile_gdf_sorted = granule_gdf.sort_values(['cloudcoverpercentage'], ascending=[True])\n",
"tile_gdf_sorted"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(granule_gdf_sorted)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#scarica il primo tile\n",
"api.download(['59aeacff-a1e9-4dd3-9853-66f1a568ddd1'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import zipfile\n",
"\n",
"#estrae la cartella della zip e la espota nel path desiderato\n",
"with zipfile.ZipFile('C:/Users/Utente/S2B_MSIL2A_20200622T100559_N0214_R022_T32TPQ_20200622T134211.zip', 'r') as zip_ref:\n",
" zip_ref.extractall('C:/Users/Utente/Desktop/2B_MSIL2A_20200622T100559_N0214_R022_T32TPQ_20200622T134211')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": 4
}
| gpl-3.0 |
tarmstrong/nbdiff | scripts/example-notebooks/diff/5/after.ipynb | 1 | 180773 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How Long Does It Take To Review an IPython Pull Request?"
]
},
{
"cell_type": "markdown"
"metadata": {},
"source": [
"by [Tavish Armstrong](http://tavisharmstrong.com)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fernando Perez [wrote](https://twitter.com/fperez_org/status/383739057651466240):\n",
"\n",
"> IPython: 8 minutes from bug on my box to reviewed/merged PR. We're doing something right... https://github.com/ipython/ipython/pull/4294\n",
"\n",
"This got me thinking: how long does it usually take to do a review on the IPython project?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import json\n",
"import itertools\n",
"import collections\n",
"import re\n",
"import arrow\n",
"rcParams['figure.figsize'] = (15, 3)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I recently released a python package called [git2json](https://github.com/tarmstrong/git2json) for parsing git logs. It isn't well written, but it does what it says on the tin."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!git2json --git-dir=/home/tavish/code/ipython/.git > ipython-log.json"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can parse the resulting JSON file and take a peek at the data structure."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"log = json.load(open('ipython-log.json'))\n",
"print log[0]\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'committer': {u'date': 1380325308, u'timezone': u'-0700', u'name': u'Fernando Perez', u'email': u'[email protected]'}, u'author': {u'date': 1380325308, u'timezone': u'-0700', u'name': u'Fernando Perez', u'email': u'[email protected]'}, u'tree': u'ea7fbb2694175423d776f649104ba42520db7cea', u'parents': [u'795c9e30ee8ad2e1a4e780c439fc50b565670e26', u'796a90bdeef377041c353019fdae19aaa72a76d3'], u'commit': u'b8295e9af6745b9c466d11bb31a6eef221e231c1', u'message': u\"Merge pull request #4294 from minrk/tornado-2don't require tornado 3 in `--post serve`\", u'changes': []}\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Number of commits = \", len(log)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of commits = 12382\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we answer the question posed at the beginning of this article, I'll answer some easier questions. First off: which files in IPython have been committed to the most? This is a simplified version of \"code churn\" which is [reasonably](https://research.microsoft.com/apps/pubs/default.aspx?id=69126) [effective](http://google-engtools.blogspot.ca/2011/12/bug-prediction-at-google.html) for predicting bugs. (More complicated models include lines modified or [take semantic differences into account](http://dl.acm.org/citation.cfm?id=1985456)). Since this is Friday night and I'm not trying to get a paper into ICSE, I'll just take the number of commits for each `.py` file."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"file_changes = lambda: itertools.chain.from_iterable(\n",
" [change[2] for change in commit['changes'] if re.match(r'^.*\\.py$', change[2])]\n",
" for commit in log\n",
")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rcParams['figure.figsize'] = (15, 6)\n",
"fchanges = file_changes()\n",
"fchange_count = collections.Counter(fchanges)\n",
"a = average(fchange_count.values())\n",
"most_common = fchange_count.most_common(20)\n",
"df = pd.DataFrame(most_common)\n",
"df.head()\n",
"df.index = df[0]\n",
"df = df[[1]]\n",
"df.head()\n",
"p = df.plot(kind='bar', legend=False)\n",
"p.set_title('Most-changed Files in IPython')\n",
"p.set_ylabel('Commits')\n",
"# Draw a red line at the average\n",
"hlines(a, 0, len(df), colors='r')\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.collections.LineCollection at 0x80e5f50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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l5ubWRxYAAAAAwBnwaVfMyy67TK1atdIvfvELSScO3Nu6dWudhwMAAAAAeOe1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\nSHPc2BEREREREWmOGzsiIiIiIiLN/R9KvioFbUpO+gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff3910>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Someone more familiar with IPython development will know how obvious the above graph is.\n",
"\n",
"Next, I'll make a simple plot showing weekly commit counts over time, similar to the plots GitHub would show you. I'll create a data frame from a list in the format `[(date_rounded_down_to_week, commit_id)]` and then `groupby()` the date."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def weekly_date_resolution(ts):\n",
" ar = arrow.arrow(ts)\n",
" day_of_month = ar.timetuple().tm_mday\n",
" week = int(day_of_month) / 7\n",
" new_day = (week*7)+1\n",
" assert new_day > 0\n",
" assert new_day < 30\n",
" try:\n",
" day_adjusted = ar.replace(day=new_day)\n",
" except ValueError:\n",
" new_day = day_of_month # just keep the original\n",
" day_adjusted = ar.replace(day=new_day)\n",
" return day_adjusted.date()\n",
"\n",
"commit_times = lambda: (\n",
" (weekly_date_resolution(commit['committer']['date']), commit['commit'])\n",
" for commit in log\n",
")\n",
"dfct = pd.DataFrame(list(commit_times()), columns=['date', 'id'])\n",
"dfct = dfct.groupby('date').aggregate(len)\n",
"dfct.head()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2005-07-01</th>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-07-15</th>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-08-08</th>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-08-15</th>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-08-22</th>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
" id\n",
"date \n",
"2005-07-01 1\n",
"2005-07-15 6\n",
"2005-08-08 2\n",
"2005-08-15 4\n",
"2005-08-22 1"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A few more lines gives us a basic plot."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = dfct.plot(legend=False)\n",
"p.set_title('Weekly commits on IPython')\n",
"p.set_ylabel('Commits')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<matplotlib.text.Text at 0x80e2b10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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0devWgh4YAAAAsotisLMVuzCcwAeJip3R3i4dOtT18jAFfreKXX19Ye/TM9g9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AqFQqFQKBR1hog7dgBwww03YO/evdi/fz+effbZSG9O0OTk5OC6665D7969kZ6ejjfffBMAUFJS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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x80debd0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get back to the original question: *how long does it usually take to do a review?*\n",
"\n",
"I'll make a distinction between two different kinds of commits:\n",
"\n",
"* **Merge commits**: The commit that merged the pull request branch into the main branch.\n",
"* **PR commits**: The `HEAD` (or most recent) commit on the pull request's source branch."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def is_pull_request_merge(message):\n",
" '''Match commit messages that start with \"Merge pull request #\"'''\n",
" return re.match(r'^Merge pull request #', message)\n",
"\n",
"\n",
"def pull_request_number(message):\n",
" '''Extract the PR # from the commit message.'''\n",
" return int(re.match(r'^Merge pull request #(\\d+)', message).groups()[0])\n",
"\n",
"\n",
"# Create a table for the merge commits.\n",
"merge_commits = [\n",
" [\n",
" commit['parents'][1], # the second parent seems to be the source branch\n",
" commit['committer']['date'] - int(commit['committer']['timezone'])*60*60,\n",
" pull_request_number(commit['message']),\n",
" ]\n",
" for commit in log if is_pull_request_merge(commit['message'])\n",
"]\n",
"# Also create a set of merge commits for use in the next cell.\n",
"merge_commit_parent_ids = [mc[0] for mc in merge_commits]\n",
"merge_df = pd.DataFrame(\n",
" merge_commits,\n",
" columns=['merge_commit', 'merge_timestamp', 'pr'],\n",
" index=merge_commit_parent_ids,\n",
")\n",
"merge_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>merge_commit</th>\n",
" <th>merge_timestamp</th>\n",
" <th>pr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>796a90bdeef377041c353019fdae19aaa72a76d3</th>\n",
" <td> 796a90bdeef377041c353019fdae19aaa72a76d3</td>\n",
" <td> 1382845308</td>\n",
" <td> 4294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0d6287174b62de9b89190b868317d12b5bc4ae4</th>\n",
" <td> b0d6287174b62de9b89190b868317d12b5bc4ae4</td>\n",
" <td> 1382843422</td>\n",
" <td> 4270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>07b4962bd787068896f0e2b5dfb977441c7b391a</th>\n",
" <td> 07b4962bd787068896f0e2b5dfb977441c7b391a</td>\n",
" <td> 1382821596</td>\n",
" <td> 4278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</th>\n",
" <td> e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</td>\n",
" <td> 1382791407</td>\n",
" <td> 4282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68b0f9d2d5f29c540f7873141cb09026c492d0ef</th>\n",
" <td> 68b0f9d2d5f29c540f7873141cb09026c492d0ef</td>\n",
" <td> 1382735023</td>\n",
" <td> 4279</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" merge_commit \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 796a90bdeef377041c353019fdae19aaa72a76d3 \n",
"b0d6287174b62de9b89190b868317d12b5bc4ae4 b0d6287174b62de9b89190b868317d12b5bc4ae4 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 07b4962bd787068896f0e2b5dfb977441c7b391a \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 68b0f9d2d5f29c540f7873141cb09026c492d0ef \n",
"\n",
" merge_timestamp pr \n",
"796a90bdeef377041c353019fdae19aaa72a76d3 1382845308 4294 \n",
"b0d6287174b62de9b89190b868317d12b5bc4ae4 1382843422 4270 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 1382821596 4278 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 1382791407 4282 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 1382735023 4279 "
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create a table for pull request commits.\n",
"pr_commits = [\n",
" [\n",
" commit['commit'],\n",
" commit['committer']['date'] - int(commit['committer']['timezone'])*60*60,\n",
" len(commit['changes']), # let's sneak in another basic churn metric\n",
" ]\n",
" for commit in log\n",
" # Do we have a merge commit for this commit?\n",
" if commit['commit'] in merge_commit_parent_ids\n",
"]\n",
"commit_ids = [prc[0] for prc in pr_commits]\n",
"pr_df = pd.DataFrame(pr_commits, columns=['commit', 'commit_timestamp', 'churn'], index=commit_ids)\n",
"pr_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>commit</th>\n",
" <th>commit_timestamp</th>\n",
" <th>churn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>796a90bdeef377041c353019fdae19aaa72a76d3</th>\n",
" <td> 796a90bdeef377041c353019fdae19aaa72a76d3</td>\n",
" <td> 1382845245</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</th>\n",
" <td> e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</td>\n",
" <td> 1382682595</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68b0f9d2d5f29c540f7873141cb09026c492d0ef</th>\n",
" <td> 68b0f9d2d5f29c540f7873141cb09026c492d0ef</td>\n",
" <td> 1382678115</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>07b4962bd787068896f0e2b5dfb977441c7b391a</th>\n",
" <td> 07b4962bd787068896f0e2b5dfb977441c7b391a</td>\n",
" <td> 1382673904</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</th>\n",
" <td> d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</td>\n",
" <td> 1379379263</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" commit \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 796a90bdeef377041c353019fdae19aaa72a76d3 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 68b0f9d2d5f29c540f7873141cb09026c492d0ef \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 07b4962bd787068896f0e2b5dfb977441c7b391a \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa \n",
"\n",
" commit_timestamp churn \n",
"796a90bdeef377041c353019fdae19aaa72a76d3 1382845245 1 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 1382682595 1 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 1382678115 3 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 1382673904 1 \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa 1379379263 1 "
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have these two data frames, we can merge them together."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"both_df = pr_df.join(merge_df)\n",
"both_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>commit</th>\n",
" <th>commit_timestamp</th>\n",
" <th>churn</th>\n",
" <th>merge_commit</th>\n",
" <th>merge_timestamp</th>\n",
" <th>pr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>796a90bdeef377041c353019fdae19aaa72a76d3</th>\n",
" <td> 796a90bdeef377041c353019fdae19aaa72a76d3</td>\n",
" <td> 1382845245</td>\n",
" <td> 1</td>\n",
" <td> 796a90bdeef377041c353019fdae19aaa72a76d3</td>\n",
" <td> 1382845308</td>\n",
" <td> 4294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</th>\n",
" <td> e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</td>\n",
" <td> 1382682595</td>\n",
" <td> 1</td>\n",
" <td> e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</td>\n",
" <td> 1382791407</td>\n",
" <td> 4282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68b0f9d2d5f29c540f7873141cb09026c492d0ef</th>\n",
" <td> 68b0f9d2d5f29c540f7873141cb09026c492d0ef</td>\n",
" <td> 1382678115</td>\n",
" <td> 3</td>\n",
" <td> 68b0f9d2d5f29c540f7873141cb09026c492d0ef</td>\n",
" <td> 1382735023</td>\n",
" <td> 4279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>07b4962bd787068896f0e2b5dfb977441c7b391a</th>\n",
" <td> 07b4962bd787068896f0e2b5dfb977441c7b391a</td>\n",
" <td> 1382673904</td>\n",
" <td> 1</td>\n",
" <td> 07b4962bd787068896f0e2b5dfb977441c7b391a</td>\n",
" <td> 1382821596</td>\n",
" <td> 4278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</th>\n",
" <td> d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</td>\n",
" <td> 1379379263</td>\n",
" <td> 1</td>\n",
" <td> d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</td>\n",
" <td> 1382669814</td>\n",
" <td> 4131</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
" commit \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 796a90bdeef377041c353019fdae19aaa72a76d3 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 68b0f9d2d5f29c540f7873141cb09026c492d0ef \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 07b4962bd787068896f0e2b5dfb977441c7b391a \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa \n",
"\n",
" commit_timestamp churn \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 1382845245 1 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 1382682595 1 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 1382678115 3 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 1382673904 1 \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa 1379379263 1 \n",
"\n",
" merge_commit \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 796a90bdeef377041c353019fdae19aaa72a76d3 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 68b0f9d2d5f29c540f7873141cb09026c492d0ef \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 07b4962bd787068896f0e2b5dfb977441c7b391a \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa \n",
"\n",
" merge_timestamp pr \n",
"796a90bdeef377041c353019fdae19aaa72a76d3 1382845308 4294 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 1382791407 4282 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 1382735023 4279 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 1382821596 4278 \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa 1382669814 4131 "
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can take the *delta* by subtracting the merge timestamp from the commit timestamp. This is the difference between the commit-under-review's timestamp and the merge timestamp, in seconds. We can do some basic math to get the difference on other scales. The HTML version of the table shows the results."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rcParams['figure.figsize'] = (5, 10)\n",
"both_df['delta'] = both_df['merge_timestamp'] - both_df['commit_timestamp']\n",
"delta = both_df['delta']\n",
"both_df['delta_weeks'] = delta/60.0/60.0/24.0/7.0\n",
"both_df['delta_days'] = delta/60.0/60.0/24.0\n",
"both_df['delta_hours'] = delta/60.0/60.0\n",
"both_df['delta_mins'] = delta/60.0\n",
"both_df['delta_secs'] = delta\n",
"both_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>commit</th>\n",
" <th>commit_timestamp</th>\n",
" <th>churn</th>\n",
" <th>merge_commit</th>\n",
" <th>merge_timestamp</th>\n",
" <th>pr</th>\n",
" <th>delta</th>\n",
" <th>delta_weeks</th>\n",
" <th>delta_days</th>\n",
" <th>delta_hours</th>\n",
" <th>delta_mins</th>\n",
" <th>delta_secs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>796a90bdeef377041c353019fdae19aaa72a76d3</th>\n",
" <td> 796a90bdeef377041c353019fdae19aaa72a76d3</td>\n",
" <td> 1382845245</td>\n",
" <td> 1</td>\n",
" <td> 796a90bdeef377041c353019fdae19aaa72a76d3</td>\n",
" <td> 1382845308</td>\n",
" <td> 4294</td>\n",
" <td> 63</td>\n",
" <td> 0.000104</td>\n",
" <td> 0.000729</td>\n",
" <td> 0.017500</td>\n",
" <td> 1.050000</td>\n",
" <td> 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</th>\n",
" <td> e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</td>\n",
" <td> 1382682595</td>\n",
" <td> 1</td>\n",
" <td> e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77</td>\n",
" <td> 1382791407</td>\n",
" <td> 4282</td>\n",
" <td> 108812</td>\n",
" <td> 0.179914</td>\n",
" <td> 1.259398</td>\n",
" <td> 30.225556</td>\n",
" <td> 1813.533333</td>\n",
" <td> 108812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68b0f9d2d5f29c540f7873141cb09026c492d0ef</th>\n",
" <td> 68b0f9d2d5f29c540f7873141cb09026c492d0ef</td>\n",
" <td> 1382678115</td>\n",
" <td> 3</td>\n",
" <td> 68b0f9d2d5f29c540f7873141cb09026c492d0ef</td>\n",
" <td> 1382735023</td>\n",
" <td> 4279</td>\n",
" <td> 56908</td>\n",
" <td> 0.094094</td>\n",
" <td> 0.658657</td>\n",
" <td> 15.807778</td>\n",
" <td> 948.466667</td>\n",
" <td> 56908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>07b4962bd787068896f0e2b5dfb977441c7b391a</th>\n",
" <td> 07b4962bd787068896f0e2b5dfb977441c7b391a</td>\n",
" <td> 1382673904</td>\n",
" <td> 1</td>\n",
" <td> 07b4962bd787068896f0e2b5dfb977441c7b391a</td>\n",
" <td> 1382821596</td>\n",
" <td> 4278</td>\n",
" <td> 147692</td>\n",
" <td> 0.244200</td>\n",
" <td> 1.709398</td>\n",
" <td> 41.025556</td>\n",
" <td> 2461.533333</td>\n",
" <td> 147692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</th>\n",
" <td> d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</td>\n",
" <td> 1379379263</td>\n",
" <td> 1</td>\n",
" <td> d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa</td>\n",
" <td> 1382669814</td>\n",
" <td> 4131</td>\n",
" <td> 3290551</td>\n",
" <td> 5.440726</td>\n",
" <td> 38.085081</td>\n",
" <td> 914.041944</td>\n",
" <td> 54842.516667</td>\n",
" <td> 3290551</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
" commit \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 796a90bdeef377041c353019fdae19aaa72a76d3 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 68b0f9d2d5f29c540f7873141cb09026c492d0ef \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 07b4962bd787068896f0e2b5dfb977441c7b391a \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa \n",
"\n",
" commit_timestamp churn \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 1382845245 1 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 1382682595 1 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 1382678115 3 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 1382673904 1 \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa 1379379263 1 \n",
"\n",
" merge_commit \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 796a90bdeef377041c353019fdae19aaa72a76d3 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 68b0f9d2d5f29c540f7873141cb09026c492d0ef \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 07b4962bd787068896f0e2b5dfb977441c7b391a \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa \n",
"\n",
" merge_timestamp pr delta \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 1382845308 4294 63 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 1382791407 4282 108812 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 1382735023 4279 56908 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 1382821596 4278 147692 \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa 1382669814 4131 3290551 \n",
"\n",
" delta_weeks delta_days delta_hours \\\n",
"796a90bdeef377041c353019fdae19aaa72a76d3 0.000104 0.000729 0.017500 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 0.179914 1.259398 30.225556 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 0.094094 0.658657 15.807778 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 0.244200 1.709398 41.025556 \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa 5.440726 38.085081 914.041944 \n",
"\n",
" delta_mins delta_secs \n",
"796a90bdeef377041c353019fdae19aaa72a76d3 1.050000 63 \n",
"e51d4d021c57c846ff1ad439d20b3fcc6b1ebc77 1813.533333 108812 \n",
"68b0f9d2d5f29c540f7873141cb09026c492d0ef 948.466667 56908 \n",
"07b4962bd787068896f0e2b5dfb977441c7b391a 2461.533333 147692 \n",
"d4d6bc63d6c6dd34c5fe1270a90caec2e0208bfa 54842.516667 3290551 "
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first thing that stands out is (as of this writing) commit `796a90bdeef3770` has a delta of one minute. Fernando reported 8 minutes, which is much more plausible. This makes me wonder if some people have out-of-date clocks. **Update: Fernando says that the review time was 1 minute; 8 minutes was from bug to pull request to commit.**\n",
"\n",
"Anyways, let's make a box plot. Box plots are fun."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"both_df[['delta_days']].boxplot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"{'boxes': [<matplotlib.lines.Line2D at 0x83afe50>],\n",
" 'caps': [<matplotlib.lines.Line2D at 0x83af450>,\n",
" <matplotlib.lines.Line2D at 0x83af950>],\n",
" 'fliers': [<matplotlib.lines.Line2D at 0x83b3890>,\n",
" <matplotlib.lines.Line2D at 0x83b3d50>],\n",
" 'medians': [<matplotlib.lines.Line2D at 0x83b3390>],\n",
" 'whiskers': [<matplotlib.lines.Line2D at 0x83a9c50>,\n",
" <matplotlib.lines.Line2D at 0x83a9e90>]}"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x827d0d0>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on this plot, we see that the IPython folks are pretty fast when it comes to reviewing and merging pull requests \u2014 the ones that get committed, anyways. There are some outliers, though: some take up to half a year before getting committed.\n",
"\n",
"We can filter out all of these ranges that are larger than a month and ask: if a review takes less than a month, how long is it likely to take?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"both_df[both_df['delta_days'] < 30][['delta_days']].boxplot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"{'boxes': [<matplotlib.lines.Line2D at 0x82f2410>],\n",
" 'caps': [<matplotlib.lines.Line2D at 0x82f09d0>,\n",
" <matplotlib.lines.Line2D at 0x82f0ed0>],\n",
" 'fliers': [<matplotlib.lines.Line2D at 0x82f2e10>,\n",
" <matplotlib.lines.Line2D at 0x82fa310>],\n",
" 'medians': [<matplotlib.lines.Line2D at 0x82f2910>],\n",
" 'whiskers': [<matplotlib.lines.Line2D at 0x82f0210>,\n",
" <matplotlib.lines.Line2D at 0x82f0450>]}"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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73vc+9Xfu3r1bF1xwgXK5nH7729+WP18qlbRixQpddNFFuuiii/T3v/9dktTc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\n",
"text": [
"<matplotlib.figure.Figure at 0x83da3d0>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on this, it looks like most review periods are between -4 days (whatever that means) and about two weeks. The median is (I'm eyeballing this) around 1 day. Not bad at all.\n",
"\n",
"If we look closely at the pull requests outside this range, we might see any number of things: pull requests that are large and hard to understand or pull requests that are blocked by some other task. I don't think pandas would help us much: we'd have to use our brains and look for patterns.\n",
"\n",
"OK, one more question. Can we see any relationship between the review time and the number of files modified by the last pull request commit? This is a sad way to do it \u2014 since I'm not taking into account all commits on a pull request, I'm missing a lot of useful information \u2014 but it might be fun."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The data is skewed, so i'll use the spearman correlation\n",
"both_df[['churn', 'delta']].corr(method='spearman')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>churn</th>\n",
" <th>delta</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>churn</th>\n",
" <td> 1.000000</td>\n",
" <td>-0.001523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>delta</th>\n",
" <td>-0.001523</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" churn delta\n",
"churn 1.000000 -0.001523\n",
"delta -0.001523 1.000000"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There's almost no correlation, and the correlation that is there is probably just noise. Should we conclude that the number of files that you change in a pull request doesn't affect how long it will take to be reviewed? I don't think so, for two reasons:\n",
"\n",
"1. I'm completely ignoring pull requests that get ignored or closed. My professor has looked at [how long it takes patches to get accepted on large open source projects like linux](https://research.microsoft.com/apps/pubs/default.aspx?id=193432) and he found that on certain projects like Linux, large patches were ignored or rejected. No one had time to understand them and review them. This might be the case for IPython. (Does anybody from the IPython project think this is true?)\n",
"2. I'm only processing the tip of the pull request branch. A pull request can have many commits, and I should incorporate those into my churn metric."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I didn't really make any claims here, but let's pretend this is a real paper.\n",
"\n",
"## Threats to Validity\n",
"\n",
"1. Dates are user-system-reported and thus inaccurate.\n",
"2. It is 1am on a Friday night.\n",
"3. The churn metric only represents the tip (ha) of the pull request iceberg.\n",
"4. This analysis doesn't look at how review time changed over time. Can we get a less skewed picture of pull request length by looking at only the period in which the project was funded?\n",
"\n",
"## Conclusions\n",
"\n",
"1. The IPython folks are very prompt. Send them a decent pull request and they'll probably merge it within a day.\n",
"2. The IPython Notebook is pretty cool.\n"
]
}
],
"metadata": {}
}
]
}
| mit |
moizumi99/CVBookExercise | Chapter-9/CV Book Chapter 9 Exercise 2.ipynb | 1 | 42563 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from PIL import Image\n",
"from numpy import *\n",
"from pylab import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The data is still available from web archive\n",
"# https://web.archive.org/web/20161203110733/http://research.microsoft.com/en-us/um/cambridge/projects/visionimagevideoediting/segmentation/grabcut.htm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.misc import imresize\n",
"import graphcut\n",
"graphcut = reload(graphcut)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"im = array(Image.open('229036.jpg'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scale = 0.1\n",
"im = imresize(im, scale, interp='bilinear')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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8Sdbu3P0g+HjrWEelRs7+y15P76Tc29d/MyeGCVVKJXlMxomR5iv6PfNifRPnHsmIe78/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\nCNCsASAC/wunKc3nc73QAwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7fbab55610>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(32, 48, 3)\n"
]
}
],
"source": [
"figure()\n",
"imshow(im)\n",
"axis('off')\n",
"show()\n",
"print im.shape"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fg = [[16, 24], [32, 32]]\n",
"bg = [[0, 0], [48, 8]]"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_msr_labels(size, fg, bg):\n",
" \"\"\" Create labels matrix for training from\n",
" user annotations. \"\"\"\n",
" \n",
" labels = zeros(size)\n",
" \n",
" for xi in arange(bg[0][0], bg[1][0]):\n",
" for yi in arange(bg[0][1], bg[1][1]):\n",
" labels[yi, xi] = -1\n",
"\n",
" for xi in arange(fg[0][0], fg[1][0]):\n",
" for yi in arange(fg[0][1], fg[1][1]):\n",
" labels[yi, xi] = 1\n",
" \n",
" \n",
" return labels"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"labels = create_msr_labels(im.shape[:2], fg, bg)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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UegBw+dJpAMDufY4d6yjuUNK4rqrabVuXoMmBII7wwhnf4Qghpsr4RWNjRfd7qlabHFIB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\ni+9whBD+4jscIYS/+A5HCOEvvsMRQvwPQI7VN4pA5LsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ae6049090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure()\n",
"gray()\n",
"subplot(1, 3, 1)\n",
"imshow(im)\n",
"axis('off')\n",
"subplot(1, 3, 2)\n",
"imshow(labels)\n",
"axis('off')\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pygraph.classes.digraph import digraph\n",
"from pygraph.algorithms.minmax import maximum_flow\n",
"import bayes\n",
"\n",
"\n",
"def build_bayes_graph(im, labels, sigma=1e-2, kappa=2, weight=1):\n",
" \"\"\" Build a graph from 4-neighborhood of pixels.\n",
" Foregraound and background is determined from\n",
" labels (1 for foreground, -1 for background, 0 othewise)\n",
" and is modeled with naive Bayes classifiers. \"\"\"\n",
"\n",
" m, n = im.shape[:2]\n",
"\n",
" # RGB vector version (one pixel per row)\n",
" vim = im.astype('float')\n",
" vim = vim.reshape((-1, 3))\n",
"\n",
" # RGB for foreground and background\n",
" foreground = im[labels == 1].reshape((-1, 3))\n",
" background = im[labels == -1].reshape((-1, 3))\n",
" train_data = [foreground, background]\n",
"\n",
" # train naive Bayes classifier\n",
" bc = bayes.BayesClassifier()\n",
" bc.train(train_data, labels)\n",
"\n",
" # get probabilities for all pixels\n",
" bc_lables, prob = bc.classify(vim)\n",
" prob_fg = prob[0]\n",
" prob_bg = prob[1]\n",
"\n",
" # create graph with m*n+2 nodes\n",
" gr = digraph()\n",
" gr.add_nodes(range(m*n+2))\n",
"\n",
" source = m*n # second to last is source\n",
" sink = m*n+1 # last node is sink\n",
"\n",
" # normalize\n",
" pos = m*n/2-100\n",
" for i in range(vim.shape[0]):\n",
" vim[i] = vim[i] / linalg.norm(vim[i])\n",
"\n",
" # go through all nodes and add edges\n",
" lb = labels.copy()\n",
" lb = lb.flatten()\n",
" for i in range(m*n):\n",
" # add edge from source\n",
" if lb[i]==1:\n",
" gr.add_edge((source, i), wt=weight)\n",
" elif lb[i]==-1:\n",
" gr.add_edge((i, sink), wt=weight) \n",
" elif (prob_fg[i]>prob_bg[i]):\n",
" gr.add_edge((source, i), wt=(prob_fg[i]/(prob_fg[i] + prob_bg[i])))\n",
" else:\n",
" # add edge to sink\n",
" gr.add_edge((i, sink), wt=(prob_bg[i]/(prob_fg[i] + prob_bg[i])))\n",
"\n",
" # add edges to neighbors\n",
" if i % n != 0: # left exists\n",
" edge_wt = kappa*exp(-1.0*sum((vim[i] - vim[i-1])**2)/sigma)\n",
" gr.add_edge((i, i-1), wt=edge_wt)\n",
" if (i+1) % n != 0: # right exists\n",
" edge_wt = kappa*exp(-1.0*sum((vim[i] - vim[i+1])**2)/sigma)\n",
" gr.add_edge((i, i+1), wt=edge_wt)\n",
" if i//n != 0: # up exists\n",
" edge_wt = kappa*exp(-1.0*sum((vim[i] - vim[i-n])**2)/sigma)\n",
" gr.add_edge((i, i-n), wt=edge_wt)\n",
" if i//n != m-1: # down exists\n",
" edge_wt = kappa*exp(-1.0*sum((vim[i] - vim[i+n])**2)/sigma)\n",
" gr.add_edge((i, i+n), wt=edge_wt)\n",
" \n",
" return gr\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-1. -1. -1. ..., -1. -1. -1.]\n",
" [-1. -1. -1. ..., -1. -1. -1.]\n",
" [-1. -1. -1. ..., -1. -1. -1.]\n",
" ..., \n",
" [ 0. 0. 0. ..., 0. 0. 0.]\n",
" [ 0. 0. 0. ..., 0. 0. 0.]\n",
" [ 0. 0. 0. ..., 0. 0. 0.]]\n"
]
}
],
"source": [
"print labels"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g = build_bayes_graph(im, labels, sigma=1e-2, kappa=2, weight=100)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res = graphcut.cut_graph(g, im.shape[:2])"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QTOZG8c//eIMEW8wJEmshDoKBF56eJtjpLqMHoRCzSEute8XKVLPRmelU0Qj/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\nrIUQIgBIrIUQIgD8fwxNfyupop9VAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ae6049e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure()\n",
"graphcut.show_labeling(im, res)\n",
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
jGaboardi/Facility_Location | CPLEX_v_Gurobi/Cplex_v_Gurobi_pMedian_constant_matrix.ipynb | 2 | 1247933 | null | lgpl-3.0 |
boffi/boffi.github.io | dati_2015/ha04/03_Rayleigh.ipynb | 1 | 31401 | {
"metadata": {
"name": "",
"signature": "sha256:f9f1154d7009d1308dad26248b6ea0022790e92c78d209d8e5b426a6578c5ea4"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import *\n",
"init_printing(use_latex=True)\n",
"from IPython.display import display\n",
"from numpy import array\n",
"from scipy.linalg import eigh\n",
"from IPython.display import HTML\n",
"HTML(open(\"00_custom.css\", \"r\").read())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<style>\n",
"@font-face {\n",
" font-family: 'Charis SIL';\n",
" src: url('fonts/CharisSILEur-R.woff') format('woff');\n",
"}\n",
"@font-face {\n",
" font-family: 'Architects Daughter';\n",
" font-style: normal;\n",
" src: url(https://fonts.gstatic.com/s/architectsdaughter/v6/RXTgOOQ9AAtaVOHxx0IUBM3t7GjCYufj5TXV5VnA2p8.woff2) format('woff2');\n",
" unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215, U+E0FF, U+EFFD, U+F000;\n",
"}\n",
".prompt{display: None;}\n",
"div.cell{margin: auto;width:900px;}\n",
"\n",
"div #notebook { /* centre the content */\n",
" background: #fff9f0;\n",
" margin: auto;\n",
" padding: 0em;\n",
" padding-top: 1em;\n",
"}\n",
"div #notebook_container {width: 960px!important;}\n",
"#notebook li { /* More space between bullet points */\n",
" margin-top:0.2em;\n",
"}\n",
"\n",
"div.text_cell_render{\n",
" font-family: \"Charis SIL\", Cambria, serif;\n",
" line-height: 155%;\n",
" font-size: 130%;\n",
"}\n",
".CodeMirror{\n",
" font-family: Consolas, monospace;\n",
" font-size: 140%;\n",
" background-color:#fffcf6!important;\n",
"}\n",
"\n",
".output_area {\n",
" font-family: Consolas,monospace;font-size: 120%;\n",
" background-color:#fffcf6!important;\n",
" margin-top:0.8em;\n",
"}\n",
"\n",
"div.output_latex{overflow:hidden}\n",
"\n",
"h1 {\n",
" font-family: 'Architects Daughter', serif;\n",
" font-size: 48pt!important;\n",
" text-align: center;\n",
" text-shadow: 4px 4px 4px #aaa;\n",
" padding-bottom: 48pt;\n",
"}\n",
"\n",
".warning{color: rgb( 240, 20, 20 )}\n",
"</style>\n",
"<script>\n",
"MathJax.Hub.Config({\n",
" TeX: {extensions: [\"AMSmath.js\", \"begingroup.js\"]},\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {styles: {'.MathJax_Display': {\"margin\": 4}}}\n",
" });\n",
"</script>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7fcb8d805790>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Rayleigh Quotient"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to use a little symbolic algebra, hence we'll need the following base symbols:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x1, x2, x3, k, L, m = symbols('x1, x2, x3, k, L, m')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The single degrees of freedom have to be collected in a vector,"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = Matrix((x1,x2,x3))\n",
"x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\left[\\begin{matrix}x_{1}\\\\x_{2}\\\\x_{3}\\end{matrix}\\right]$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAACUAAABLCAMAAADAvr4bAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nCT8lSBs6vn2GPwAAAABJRU5ErkJggg==\n",
"prompt_number": 3,
"text": [
"\u23a1x\u2081\u23a4\n",
"\u23a2 \u23a5\n",
"\u23a2x\u2082\u23a5\n",
"\u23a2 \u23a5\n",
"\u23a3x\u2083\u23a6"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to compute the (double of the) strain energy, to do so we need the relative rotations, that we can compute if we know in advance the rotations of each individual bar... "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"phi1, phi2, phi3 = x1/L, (x2-x1)/L, (x3-x2)/L\n",
"phi21, phi32 = phi2-phi1, phi3-phi2\n",
"W2 = x2**2*k + phi21**2*k*L*L + phi32**2*k*L*L\n",
"print latex((W2/k).expand())\n",
"W2.expand()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5 x_{1}^{2} - 8 x_{1} x_{2} + 2 x_{1} x_{3} + 6 x_{2}^{2} - 4 x_{2} x_{3} + x_{3}^{2}\n"
]
},
{
"latex": [
"$$5 k x_{1}^{2} - 8 k x_{1} x_{2} + 2 k x_{1} x_{3} + 6 k x_{2}^{2} - 4 k x_{2} x_{3} + k x_{3}^{2}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAbQAAAAZBAMAAACr78bxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nGHtyjen0lpogvSdm74bExp5cYz62pf8BOanS4Q4fHQcAAAAASUVORK5CYII=\n",
"prompt_number": 9,
"text": [
" 2 2 2\n",
"5\u22c5k\u22c5x\u2081 - 8\u22c5k\u22c5x\u2081\u22c5x\u2082 + 2\u22c5k\u22c5x\u2081\u22c5x\u2083 + 6\u22c5k\u22c5x\u2082 - 4\u22c5k\u22c5x\u2082\u22c5x\u2083 + k\u22c5x\u2083 "
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By comparison with the previuous expression, we have the stiffness matrix"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"K = k*Matrix(((5,-4,1),(-4,6,-2),(1,-2,1)))\n",
"K"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\left[\\begin{matrix}5 k & - 4 k & k\\\\- 4 k & 6 k & - 2 k\\\\k & - 2 k & k\\end{matrix}\\right]$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAKsAAABLCAMAAAA4coepAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nWwgHPzSwNn+B9oTgC6em30JYq/4Bf2I6t6ynixsAAAAASUVORK5CYII=\n",
"prompt_number": 6,
"text": [
"\u23a15\u22c5k -4\u22c5k k \u23a4\n",
"\u23a2 \u23a5\n",
"\u23a2-4\u22c5k 6\u22c5k -2\u22c5k\u23a5\n",
"\u23a2 \u23a5\n",
"\u23a3 k -2\u22c5k k \u23a6"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To compute the (double of the) kinetic energy, we have to compute the rotatory inertia and the displacements of the centers of the bars, as we have already computed the bars' rotations,"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"J = m*L*L/12\n",
"xg1, xg2, xg3 = x1/2, (x1+x2)/2, (x2+x3)/2\n",
"T2 = m*(xg1**2+xg2**2+xg3**2) + J*(phi1**2+phi2**2+phi3**2) \n",
"print latex((T2/m).expand())\n",
"T2.expand()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\\frac{2 x_{1}^{2}}{3} + \\frac{x_{1} x_{2}}{3} + \\frac{2 x_{2}^{2}}{3} + \\frac{x_{2} x_{3}}{3} + \\frac{x_{3}^{2}}{3}\n"
]
},
{
"latex": [
"$$\\frac{2 m}{3} x_{1}^{2} + \\frac{m x_{1}}{3} x_{2} + \\frac{2 m}{3} x_{2}^{2} + \\frac{m x_{2}}{3} x_{3} + \\frac{m x_{3}^{2}}{3}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAAvBAMAAAAWUbwpAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAF3klEQVRoBdWYXWgcVRiG39nsZH+ym2xzUxE1\nS0IrtBCKVeiF0tAbL4p0vKlUCkmQ3rQB14oNaqCLCEERUlCxVcHVC6O0kICg4CpdI20VC40VLxRL\ngnjlRU3A2FIT1zNz/mbOnDM/ditkIJnzfef5vrxzztnMmwCb9zr+gLN5xQO5Svf8ZtZfrGRXN7X+\nunVzM+sHymtEf//Qg51/Cmtw90jnu6odiy3AHsPJujpx2/FB5P6HvT1OdHY7KJy6bb1qgyPAGTXX\n8bi3SloWGuj+s+Ot3waeHul4V6XhNF4GetbuhP7zzp3Xn/3y2kPeExXX8ie2XJ7IT+1RHtAfSuTw\nxbsutfxT+vGcMz1+z8XW9Nd1/byXFYh9efy1qQjQnVIkFNrtFa9ippbrehij3+CniAYCsWu954rz\nyMa8+/I3UNvdstfr5ahPl0CewO+ts8gNbUshgaMf4jEiaKCGP3hGcxdIzsmtlSvZ7Q0N5EsVx7KL\nZ2HdQC9dJN+UHErkOZx0vsczOCYnQyNFAp/PVOGM1rHk4Cme0iyEQGz07CJcuSFg7WAQtn3TfcN0\nuzS9QoZLIg4+Icz7OFDhcPiuSpja7rjQAvlaAnYA74oazUJIpK9FuBj9pSpBVpBpoODS3qUxXD5k\nnTAfYaBOWe33oITMcsZbyCq2YCcwi/yqzcs0CyGRAyMEi9F/FVYlM49iDTMVb5VIicZwSSRLf4mT\nU2C+ghIyNe9o7gO+wlsgm52Zv5fXahaCI2XnCkpOjP5sFaVKoQZyLndai6xrMWy4BPJjaQUXCHeO\nK9DdFQlw19+andzeIEaOPEtPrSar1IUQyMziXtwXt/53Tx4/6p4F8hvhs5xs6hkuGUIg+Y2uFWuZ\nePqqf1YZqxJwaIS8v9rtdiPfQGkM5WES80tdCIEcvHx4eDlO//l2+xZeBS4CV3/jLckJasmxN+KI\nPXFh4TrJ/KrMB0JVQumLwHQgiFwIl4w5/4FmInANV9SVr+VHouaVOe/zq+RYGLkQLlM+pS+MyvZW\no2bJ3P5rPzgxiH/a2vBH/rFciF/8aTm2dvzVkhEflTU5PkfunuFyYxP3Xrvtw/nQIOHQiPUPR9S7\nXAhDsVpAY5MuOisMl1G/vqtBQrFSelNfAMiFMBTrC6P1C8PVGf3ZqROVsAzVCEbrV7yoUX9SztOT\nToLyCNQIymSkfuZFha8x6Wec6GriKMC86MIRxkdKED3ZgBpBmY0spl5U+hqTLsrJpiaOElTCMIZY\nQaQE8vryXSugRhD7vdoXm82Pm81P3bEPEkNQL8p8Ta7Z/PydZnOMwH0C8QaMA90nM+fBXML91M7H\nSXC1Ba91Eu47ynORD0+UtshbVfga87q6XPw+8R/qSkCy9ecl4k6N4M88jtHveVH6jyRSYdbvctJ/\nmjnvx1IJ9htMQ4wERrEbM4LJ9DMvKnyNSRflkuyTq4JJ+K7FJKXRz41gMv3Mi4L7GpN+znH/aeI8\nwVzCfzo/3Agm08+8qPA1Jl2Mi90nTz+XgIFFugHx629NPstYtmUw6g+jRl+zdfIR3o7djfsURsnp\nH1imZar+sIR+lBUrJPQ/rkgIo5C+xva/0K0qlpgC1kPsU4Ajk1YIdf+W/9ahdfESriziFmX5d6Gf\nJ/g9jEL6Gg5599IG+hqBjPCfgSwJwijw6KVxFWNxWMIrdfvvIGz8V1YYDRbKKH8ao/MyhG+f/Fl3\nHEJVIBDrJCjnp39uIlDiDxTUP6WOg+fHsE+0KIiqjZQ4LOHJmoKYw+SoPWvuosykQEllSMLWD9hn\nRWmrCZOj1gvbNPXaVAqU1Gsk5E5r++qSKdDn67oG2lwKlDipsNoziTcAydGC8Q+80DOkQEmtImEL\nsHcx1FKbSI5aFXQZ/0EQ7J0CJYVhCW0nsf7kaN9aYv0pUKI/LOF1YK4SXBJTlBwt7EJx1dQmmE+B\nksKwhOsorwc7GqPkaO8yllrGPoGJFCipC0vIDw7VAw3NQQr0pcE95j7BmRQoeVv71P4Lu7dgmDnC\n/OYAAAAASUVORK5CYII=\n",
"prompt_number": 8,
"text": [
" 2 2 2\n",
"2\u22c5m\u22c5x\u2081 m\u22c5x\u2081\u22c5x\u2082 2\u22c5m\u22c5x\u2082 m\u22c5x\u2082\u22c5x\u2083 m\u22c5x\u2083 \n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\n",
" 3 3 3 3 3 "
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, by comparison with the kinetic energy we can derive the mass matrix,"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M = m*Matrix(((4,1,0),(1,4,1),(0,1,2)))/6\n",
"M"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\left[\\begin{matrix}\\frac{2 m}{3} & \\frac{m}{6} & 0\\\\\\frac{m}{6} & \\frac{2 m}{3} & \\frac{m}{6}\\\\0 & \\frac{m}{6} & \\frac{m}{3}\\end{matrix}\\right]$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAIUAAABNCAMAAAC/v1fXAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nef/GV8USwvjWmPfuD0MyPuoXcyCyAAAAAElFTkSuQmCC\n",
"prompt_number": 11,
"text": [
"\u23a12\u22c5m m \u23a4\n",
"\u23a2\u2500\u2500\u2500 \u2500 0\u23a5\n",
"\u23a2 3 6 \u23a5\n",
"\u23a2 \u23a5\n",
"\u23a2 m 2\u22c5m m\u23a5\n",
"\u23a2 \u2500 \u2500\u2500\u2500 \u2500\u23a5\n",
"\u23a2 6 3 6\u23a5\n",
"\u23a2 \u23a5\n",
"\u23a2 m m\u23a5\n",
"\u23a2 0 \u2500 \u2500\u23a5\n",
"\u23a3 6 3\u23a6"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Having computed, as requested, the structural matrices we are ready for the Rayleigh estimates, but first we define a trial vector and an auxiliary matrix, that we'll use in the following to simplify our lengthy expressions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x0 = Matrix((1,2,4))\n",
"D = K.inv()*M"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the expression for the energies, normalized with respect to $Z_0^2/2$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w2 = 1\n",
"V0 = (x0.T*K*x0)[0] * w2**0\n",
"T0 = (x0.T*M*x0)[0] * w2**1\n",
"V1 = (x0.T*M*D*x0)[0] * w2**2\n",
"T1 = (x0.T*M*D*D*x0)[0] * w2**3\n",
"V0, T0, V1, T1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\left ( 5 k, \\quad 12 m, \\quad \\frac{355 m^{2}}{12 k}, \\quad \\frac{31523 m^{3}}{432 k^{2}}\\right )$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAUEAAAA1BAMAAAAt5ERhAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRU5ErkJggg==\n",
"prompt_number": 13,
"text": [
"\u239b 2 3\u239e\n",
"\u239c 355\u22c5m 31523\u22c5m \u239f\n",
"\u239c5\u22c5k, 12\u22c5m, \u2500\u2500\u2500\u2500\u2500\u2500, \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u239f\n",
"\u239c 12\u22c5k 2 \u239f\n",
"\u239d 432\u22c5k \u23a0"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Rayleigh estimates are as follows, fractions first and then floating point numbers"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"display(V0/T0, T0/V1, V1/T1)\n",
"map(N,(V0/T0, T0/V1, V1/T1))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\frac{5 k}{12 m}$$"
],
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAACcAAAAsBAMAAAAUSrzFAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text": [
"5\u22c5k \n",
"\u2500\u2500\u2500\u2500\n",
"12\u22c5m"
]
},
{
"latex": [
"$$\\frac{144 k}{355 m}$$"
],
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAADIAAAArBAMAAADIyMbRAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nUm4FH8cAAAAASUVORK5CYII=\n",
"text": [
"144\u22c5k\n",
"\u2500\u2500\u2500\u2500\u2500\n",
"355\u22c5m"
]
},
{
"latex": [
"$$\\frac{12780 k}{31523 m}$$"
],
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAEcAAAAsBAMAAADFrbi1AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ndSfF9LsBWuRbLtjaZxiG9kcK+ov4CT0f9r29Uz1bAAAAAElFTkSuQmCC\n",
"text": [
"12780\u22c5k\n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"31523\u22c5m"
]
},
{
"latex": [
"$$\\left [ \\frac{0.416666666666667 k}{m}, \\quad \\frac{0.405633802816901 k}{m}, \\quad \\frac{0.40541826602798 k}{m}\\right ]$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAAyBAMAAAAqxyvjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRK5CYII=\n",
"prompt_number": 10,
"text": [
"\u23a10.416666666666667\u22c5k 0.405633802816901\u22c5k 0.40541826602798\u22c5k\u23a4\n",
"\u23a2\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500, \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500, \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u23a5\n",
"\u23a3 m m m \u23a6"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to be sure, a check with the results of a library function..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for x in eigh(\n",
" array(((5,-4,1),(-4,6,-2),(1,-2,1)))/1.,\n",
" array(((4,1,0),(1,4,1),(0,1,2)))/6., eigvals=(0,0)): print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.40540716]\n",
"[[ 0.2843138 ]\n",
" [ 0.60585859]\n",
" [ 1.11964382]]\n"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
} | mit |
chengsoonong/didbits | Estimation/SVM_kernels.ipynb | 1 | 161707 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machine with Kernels\n",
"\n",
"This follows the example from ```scikit-learn``` ([here](http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html) and [here](http://scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html)) very closely."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import svm\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparison of the support vector machine (SVM) with different kernels on a 2D toy dataset.\n",
"\n",
"This example shows how to plot the decision surface for four different kernels.\n",
"\n",
"Both linear models have linear decision boundaries (intersecting hyperplanes)\n",
"while the non-linear kernel models (polynomial or Gaussian RBF) have more\n",
"flexible non-linear decision boundaries with shapes that depend on the kind of\n",
"kernel and its parameters.\n",
"\n",
"While plotting the decision function of classifiers for toy 2D\n",
"datasets can help get an intuitive understanding of their respective\n",
"expressive power, be aware that those intuitions don't always generalize to\n",
"more realistic high-dimensional problems.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def make_data():\n",
" \"\"\"Create some toy data\"\"\"\n",
" X = np.c_[(.4, -.7),\n",
" (-1.5, -1),\n",
" (-1.4, -.9),\n",
" (-1.3, -1.2),\n",
" (-1.1, -.2),\n",
" (-1.2, -.4),\n",
" (-.5, 1.2),\n",
" (-1.5, 2.1),\n",
" (1, 1),\n",
" # --\n",
" (1.3, .8),\n",
" (1.2, .5),\n",
" (.2, -2),\n",
" (.5, -2.4),\n",
" (.2, -2.3),\n",
" (0, -2.7),\n",
" (1.3, 2.1)].T\n",
" Y = [0] * 8 + [1] * 8\n",
" return (X,Y)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def make_meshgrid(x, y, h=.02):\n",
" \"\"\"Create a mesh of points to plot in\n",
"\n",
" Parameters\n",
" ----------\n",
" x: data to base x-axis meshgrid on\n",
" y: data to base y-axis meshgrid on\n",
" h: stepsize for meshgrid, optional\n",
"\n",
" Returns\n",
" -------\n",
" xx, yy : ndarray\n",
" \"\"\"\n",
" x_min, x_max = x.min() - 1, x.max() + 1\n",
" y_min, y_max = y.min() - 1, y.max() + 1\n",
" xx, yy = np.mgrid[x_min:x_max:300j, y_min:y_max:300j]\n",
" return xx, yy\n",
"\n",
"\n",
"def plot_contours(ax, clf, xx, yy):\n",
" \"\"\"Plot the decision boundaries for a classifier.\n",
"\n",
" Parameters\n",
" ----------\n",
" ax: matplotlib axes object\n",
" clf: a classifier\n",
" xx: meshgrid ndarray\n",
" yy: meshgrid ndarray\n",
" \"\"\"\n",
" Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
" Z = Z.reshape(xx.shape)\n",
" ax.pcolormesh(xx, yy, Z > 0, cmap=plt.cm.Paired)\n",
" ax.contour(xx, yy, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n",
" levels=[-.5, 0, .5])\n",
"\n",
"\n",
"def plot_data(ax, X0, X1, y, xx, yy, title):\n",
" \"\"\"Plot the data\n",
" \n",
" Parameters\n",
" ----------\n",
" ax: matplotlib axes object\n",
" X0: first (horizontal) dimension\n",
" X1: second (vertical) dimension\n",
" y: label\n",
" xx: meshgrid ndarray\n",
" yy: meshgrid ndarray\n",
" title: text to display above figure\n",
"\n",
" \"\"\"\n",
" ax.scatter(X0, X1, c=y, cmap=plt.cm.bwr, s=50, edgecolors='k')\n",
" ax.set_xlim(xx.min(), xx.max())\n",
" ax.set_ylim(yy.min(), yy.max())\n",
" ax.set_xlabel('first feature')\n",
" ax.set_ylabel('second feature')\n",
" ax.set_xticks(())\n",
" ax.set_yticks(())\n",
" ax.set_title(title)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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xcM1fn+T39z3OtvVrWPrNDJZ+O4Oigp0A1FQ52bZuDSf1GySNQY5BQ10tAMFywe2YFFQ2Mne7ky1l9Wwvr8fZ4Oss+vzodnSMc2BvKqLax4YQHWIjqmnOW0hT4Xtmh2gGZkRgVQqlwKs1Hi/Yrb47YcOyo8iKDqay0YOz3kN5nZt6txerxff41PWlLN7tK6ZjQqxkRYfQNcHBJd2NH4nTkklBJkQrs+GHJezduZ1zr7vN8Njff/0ZDXW1jG5v/NXy2VsqsCqkmYcQJ2j/kkTt9fKfB/9MdWUFwyZcwrBzLyW7aw+z0wsYFouFDt1706F7by6//QE8bjcAS2Z9wdtP/ZX45DSGnXcpw8+9lJgEc5Z4BxJXYyPguwsjDq/R42VdUR2rCqsZmBFB14RQyupcTNtQSruoYPqmhZMVHUxGVDDpkcEADMyIYOCvjKeJC7UTF3rkUQMdYkPoEHvk/eJ/OCWJUe2j2eVsYIezke3l9fy4r+ZAQfbcwgLsVkWPpFBOTgojIaxtjjWQgkyIViYvdxKh4ZH0H3G24bG/e/URU+Z/uTxe8vIr6ZcWTrRD3taEOB51NdXMmfoRi2d9ziPv5mIPCubOf7xFUkYWIY5Qs9MLaEqpA3vrhoy/iNCISPKmT+bTV18g982X6DP8TG56/EXsQcEmZ9pyeb2+OzsWqcf+h8ujWbK7iu93VbGysJp6t8ZuUaRFBtM1IZSuCaFMvrCT4SNo9ksIs5MQZufU1P8fur5/P5rWGqtFsaqwhnn5vj2C6ZFBjO8cy5gObWvfpXxyEaIV2T//a9iESwzvapb/8zo2mzT/a2lBNZUNHka3b1tv4EI0h/raGmZNfpcZH75OtbOCk/oNorK8jLiklAODlkXzsQcFM3DMeAaOGc/enduZM/Uj9u7cfqAY27Z+DVmdu2Oxyn6bg+kDTSWkIvNqTWmt+8DdpNdX7MVmUQzNiqJfajg9kkIPFGA2i8LWwqpYS9P/Q6UUd52WitaaHc4GVhfWsLKwBnfTRrbqRg8frClmUGYE3RNDD7yuNZKCTIhWZMPKpbhdjZxxwRWGx87LnWTa/K81e2tICLXRKyXM8NhCBLLiPbt56KpzqCwvpfegEZz3xz/ToUfv336haBbJmdlcccdDB74uL97HI9ecT1xyKuOvuZnBZ19w4M5aW7e/ENtfmLVFtS4Pc7Y5mbm5HK+GV8/JwW5V/H10Folh9gP7tgKNUoqs6BCyokP+Zw/4jooG5uc7mbWlgjiHjWFZkYzIiSYtMsjEbP1DCjIhWpG+w8/kX18vJyYhydC4DXV1LJqZy2kZEabM/7qpbzLl9Z5WffVMiOaitaaoYCdJ6e2IT0nj9LHnMmD0eDr2PMXs1Nq8qNh4bnnqFT5751+8+dg95L75Euf98c8MOedCrLa2/ZHNavX9/G6vyYmYoKLOzWc/l/HV5grq3F66xDsY2yEarQEFKRGtr0ABOCkxlA/O68iygmrm5TvJ3VjG1A1lvD4uh+Tw1vUzt+2/biFaEa/Xi8ViMbwYA1g6Zwa11ZWM7p9peGyv1liUIlb2jgnxm/btyue9Zx9i46pl/GP6fGISkrnq7kfMTks0sVit9BtxFn3PGMvqRXlMff0fvP3kX+jSux8p7XLMTs9U9mDfkk6Xp+1VZBtK6pi+sYyBGRGc1yWWjnGBN2j9eAXbLAxuF8ngdpGU17lZVVhzoBh7d1URUcFWxnSIDvj5dPIJRohW4u0n/0pNlZM/P/uq4Wvs86ZNJDXCzkkJxp4kPF7NrTO3M65zDGM7ykBoIY7E7XIx48PXmfbmi9hsdi6+6R4iomU8REullKL3oDPodfpwdm7ecKAY+/TVF+h52jA6nXyqyRkaLyjEd35p8LT+JYtur2bGpnIUML5LLP3Tw/nP2Tmt9k7Y0Ypx2Dgjx7ctwqs1O50NrCys4ZP1pZzVMYYJXWKIDA7M0kaGOQjRCtRWV/H919MJDY8wvBgr2L6Fn1cvZ1ROtOGxV+yppqCqUe6OCfErGuvrePjqCUz517P0HjSC56flMfaK62RvUgBQSh1orFLtLCdv+iQeueZc/vXArZQVFZqcnbEcob49wnWtfM3i+uJabv9qO++sKmJDSR26aRVIWy/GDmVRioeHZfCPMVn0Sg5j6vpS/vj5NpburjI7teMin2KEaAUWz/qchrpahp97meGx502fhFXBGdnGN/P4ZmsFMQ4bfQ5qpyuE+F9BIQ56DBjChGtvod+Is8xORxyn8KgYXpj+HZ+/+29mfPA6K+d/w4U33sWYS69pE/vLHOG+WVm1Lo/JmfhHvdvLB6uLmLG5gsQwG/cPTqNfWrh0lfwN7WNDuG9QGjudDXyyrpScGN/YnfI6N+FB1gMDrFs6uUMmRCuQlzuRjA6dDe+O5mps4LsvPjVl/ldJrYsfCmsYkR0VsJ2lhPCXamc5L95zA9vWrwHg0lv/IsVYKxDiCOXim+7huU++pXPvfkx78yWqKyvMTssQYRG+i37Vja3zDll+RQNfb6ngnE4xvHJWDv3TjV/xEsgyo4K567RUEsLsaK154fs93PbVdlbuqTY7taMiBZkQAS7/53VsW/8jw8+73PA37x/mzaaqoozRJgxwnLPNiVfDyBzj78wJ0ZLlb1zL/ZefxQ/zv6Fg2xaz0xF+kJSRxb0vv8/Tk74mKjYer9fL3GkTcTU2mJ2a30RExwJQ1dC67pBtLasHoEu8g9fGteePpyYRYtIQ59bk3C6xgObR+bt5dmEBpbUus1P6VfJ/XIgAF5uYwqW3/ZVBZ51neOy5uZN887+SjZ//1T89nOtOSZR19UIcZMnsL3jk2vPwej08/M5UBp9zgdkpCT9RSpGQmg7AumULeeuJ+3joqnHs3LzB5Mz8I9jhIDjEgbPebXYqzcLj1by2fC93zspnQ3EtAIlhsq+zOSil6JMWzstjs7miZzwr9lRz68ztB37PLZEUZEIEuMiYWMZffRPhUcZ2Gdy3ewdrly5gZPtoU+Z/ZUWHMK5zrOFxhWip1nw/j5f/chNZXXrw5Ecz6NBdBjy3FT0GDOGuf75DRWkxD155Dl9NfLtVDlCOjEugvD7w75DVu708+d1uvtpSwXldYunUhtrYG8lutXDxSfG8NDabfmnhZDftL2uJfxtSkAkRwH5a8h2LvsrF6zH+BDX/sykoi8WUJYMzNpXzc0md4XGFaMm69xvElXf+jQdem0RUXILZ6QiDnTp0FM998i09Bgzhw+cf4e0n/2p2Ss0uJiGJsrrAvkNW0+jh4bxdrNpbw5/6JnF170TZB+1nqRFB3D4wlRCbhQa3lwfn7mxxe8ukIBMigE1740Wmvv5PlMXYP2WP2828z6bQ67ThxIcau8Siot7N2yv3sXBnpaFxhWiJPG43k15+mvLivVhtNs668o/Yg4LNTkuYJDImjrtffIff3fMop4891+x0ml1sQlKL3wv0W5bsrmJLWR33nJ7KmR1kfqbRnA0eqhq8PDp/Nx/9WIzH2zLulklBJkSA2j//a/i5lxnezGP1orlUlBQxVm00NC7A3O1OPBpGtze+kYgQLYnH7ebfD97GF+/9h5XzvzU7HdFCKKU487Jr6XrqAAA+fe0fzJs+2eSsmkdcchqlde4WueTsaI3IieZfZ+VwWkak2am0SYlhdv4+uh0jc6L4ZF0pj83fTXWj+ctgpSATIkDNmz4Jq83GkHEXGh577rRJpsz/0lrzzVYnXeMdZETJXQDRdnm9Xl5/9G6WzP6Cy/78ACMuvNLslEQL5HG72fzjCt547B7ef+5veNyBvdwvPiWNRo/GGWD7yLxa8+ryvWwq9S21l2ZU5gq2Wbi1fwo390tmbVEN/1pq/pD11j9JUIhWaP/8r1OHjjZ8r0hZUSFrFn7L+V3jDF/3vr64jj1VjVzULcXQuEK0JFpr/vuPx1g4YyoX/ukuxv3+RrNTEi2U1Wbj3pc/YOJLT/LVf9+icMc2bnv2VUKbhiwHmoTUDAD21bgMn315IiavLeHrLRWkRgRJA48WZHT7aDIigwzfenE4codMiABUuncPUXHxDD/3UsNjz/tsimnzv0pr3SSH2zk9MzA/TAjRHOpra1i7bCFjLruW8677s9npiBbOarNx1V0P88eHnmPtsoU8ecMlpjSCag6J6ZkA7K1uNDmTo7eioJopa0sZkR3F+M6yZ6yl6ZoQSkKYHY9X88/Fe1heYE6zj8C5vCCEOCA5M5tnPzZ+z4jX62Xe9Cn0TAo1ZcnFkKxIBreLMHzPnBAtiSMsnEfenU6wI1T+FsRRG37eZcQlp1JZXobFajU7neOSmJaJUorC6sBo7FFa6+LFJXvIjg7mhj5J8vfagtW5veyqbOTpBbu587RUBmUau8dP7pAJEWBqqpzU19WilDL8zX3t0gWUFO42paFGaa0Lr9ZyQhNt1r5d+bzx6N3U19XiCAvHYnB3VRH4eg4cyqCzzgNgxbxZbFv/o8kZHZug4BBik1LYUxkYd8hmbq6g0aO5+/RUgm3y99qShQdZeeKMDDrHO3jh+z0s2GFsJ2f51yFEgPny/de45cx+1NUYf1t9bu4kIoKsDEg3vpnHo/N28+zCAkPjCtFSuBobeOm+P7E872uqykvNTkcEOLfLxaSXn+bJGy9l05oVZqdzTFLatWdPVWAUZFf0jOe5Ue1Ij5QmVIEg1G7lb0Mz6BLv4B+L97B0d5VhsaUgEyKAeNxu5n/+MZ1P7oMjzNiiyFlWwg/zZjM8OxK71di3js1l9exwNtA7JczQuEK0FB//+znyN67lxkf/caCxgRDHy2a3c/9/JhIVG8/TN13Bhh+WmJ3SUUtpl8PuysYW3fq+utFDRZ0bi1JkxYSYnY44Bg67hYeGpnNyUhgRwcYt7ZWCTIgAsmrhHCpKihh2nvHNPL774lM8bpcpyxVnb60g2KoY0k7mtoi2Z8MPS5j50ZuMvOgqTh062ux0RCsRl5zKQ29+QlxSKs/d9nt+Xr3c7JSOSnpOR+rcXsrqWm4L/4/XlnDLzG3UtID5VuLYhdqtPDI8g24JoQDUuvz//1EKMiECSF7uZKLjEzll8EhD42qtmTd9kinzv2pdHhbsqGRwu0hC7YG5EV2I46W15r//fJz41Awu//MDZqcjWpmYhCQeeH0ysYnJrPwuMIaLp+V0AmCXs2UuWyyqcTFjczn90yMIC5JzVqCbsamcP3+VT0W9fy8ASJdFIQKEs7SY1YvmMu73f8JqM/ZPd+PKpRTu2MbF/Y2f/7V4VxX1bs0oE+7MCWE2pRR3/uMtKkqKCQmVJbui+cUkJPHo+58RFuEbZaJbePOk9Pa+gmyHs4FeLXAZ+yfrSgDFpd3jzU5FNINOcSFU1Lt5dmEBjw3PxG71z9+G3CETIkBExSXw5EczGHPp1YbHzsudRGh4pCnzv4ZmRfHwsHQ6x8k6fNG2NNbXobUmNjGFnG49zU5HtGLhkdEopdi9bROPXns+pfsKzU7piCJj4oiMjWdHRYPZqfxCaa2LududjMyJIiHM/GHD4sR1jHNwa/8U1hfX8d7qIr/FkYJMiACS1aU7MQnJhsasdpaz9OtchqZYTGnba7MoTkkJb9FXbIXwh7efup9nbr6yRTcvEK2L2+Vi19afee7Wq6itMrbt97HI6NCFHc6WV5CtLKzBq+G8rrFmpyKa0ZB2kYzrHMOXm8pZvMs/nRelIBMiAKxbtohX/3YHlSa0u1701XRcXm1KM4+P15Y0Lf8Qom0p2L6FhTOnkdmxi1yMEIbJ6nwSd/z9Dfbkb+XFe2/E7WqZA5gzO3Zhp7MBj7dlXawY1T6aN8a1Jzk8yOxURDP7/cmJdEtwUNXgnwYfUpAJEQC++fRDVi2cY3ire601c6dNpH1sCDkGt+5tcHuZvrGMnS1047YQ/vTZO/8iKDiEcVffZHYqoo3p3n8Q1z34LGuXLuCD5x82O53DatepG40eTWF1yzk/7L+TLUsVWye7VfHkiExGd/DPxWkpyIRo4Xzzv2Yx+OwLsAcZ2+Fw69rV7NqykdHtowyNC/D9ripqXF5TYgthptK9e/j+6+mccd5lRMbEmZ2OaIOGjr+Ycb//EwXbNtNYX2d2Or/QrvNJAGwrbznLFp9dWMDrK/aanYbwI4sGNU1QAAAgAElEQVRSaK1ZsKOSedudzXps6bIoRAvnm//l5ozzLjc89tzciabN/5q9tYKUcDvdE0MNjy2EmeZM+y9aa868/A9mpyLasEtuuQ+v14vN3vLu+KRld8Bmge3l9S1iPmVVg4dlBdWM7yx7x9qC2Vsr2FJWT8/kMGIdzVNKyR0yIVqw/fO/OvXqS1pOR0Nj19VUs3jW56bM/9pd2cD64jpGtY+W/TOizRl/9U3c+/L7JKRmmJ2KaMMsVis2ux1naTGv/PVmKkr812HuWNnsQaR36s7mqJPMTgWA5QXVeDSmdCIWxlJKcVPfZFwezTsr9zXbcaUgE6IFa6yvp9egMxhrwpXyxbM+p6Gu1pRmHlrDaRkRnJEtyxVF2xMSGsbJpw0zOw0hgKZl8/Nn86/7b8Xr8U9Dg+OR1fkkdvy8vkV0IV2+p5pYh40OsTKepS1IiQji/G6xLNhZxbqi2mY5phRkQrRgwQ4HV931MP1Hnm147Lm5E8mMCqKTCfO/MqKCuW9QGjHNtBRAiEAx7c0XmTP1v2anIcQBmR27cs1fnmT9iu+Z/vYrZqdzQFaX7lSWl1JS6zY1D49Xs2ZvDaekhMmKjjbkgq5xxIfaeGdVEd5muCggBZkQLVR1ZQVrly7E6/UaHnvHpvVsW7eG0SYsGdxeXk9BZcvpnCWEUdyuRmZ8+AZb164yOxUh/seQcRcx6KzzmfrGP9m4apnZ6QCQ3dU3LH1reb2peTR6NOM7xzI0y/y9bMI4wTYL15+axPldY2mOT0lSkAnRQi2amctTf7qM3Vt+Njx2Xu4k7EHBDMsyfsnge6uLeGTezma54iREIPl59QrqqqvoPWSk2akI8T+UUlz9lydISE1nyivPmJ0O4LtzZ7Fa2VpmbkHmsFu4tEc8PZPCTM1DGK9/egSnZ0Y2y4VrWQ8kRAu0f/5XdtceZHbqamjshro6Fk37gIGp4UQEG9vMY191I6v31nJZj3gssvRDtDFrly7AYrVyUt/TzU5FiF8IDY/g7hffJTo+0exUAN+S/rTsDmwt221qHvnl9SSG2w1vfiVaBpfHS+6GMjKighmYcfxNXeQOWQvSUFdHSWEBjQ3mXu0R5ts//8uMVvdL58wwbf7XN9ucWBSMMLCZR53LS3GNC5fH+KWhQhxs48qlZHftQWi4dGo7nIqSIipKilpEE4e2Kj2nE+GR0bhdjRRs32J2OmR37cnm+hBT/0089t1uXl3efN32WhKtNaW1Lirqzd2n15JZLYqFO6uY9FPJCa3skTtkLUBdTTXvPfskS2Z/jrKEA7UMHX8xV9xxD0HB0rGnLZqbO5HgEAcDz5xgeOx5uZNMmf/l8WrmbHPSOyWMhDD/z72pbPDw/vK9LCmoJlxBg4LRHWK4+OQEbBa5OyeMpbUmJDSM7K49zE6lxVm7dCHvPvMsxYU7UEB8SiZX33cvPQYMMTu1Nuu1R+5i4w9LeO7TuaZeQMju2pPvvviEktpoQ84bh3LWuymtddM+Jtjw2P62bHcVk1cWUVbnxgNkRgRxZd8kuibIbNCDWZTi3K6xvLSkkNWFNZySGn58x2nmvMQx8nq9PHH9NSyebcXVuInG+gIa639k3mfFvHDHrWanJ0zg9XrZuHIpA8eMN/xEV7B9CxtXLTNl/ld+RQNVDR5D2uy7PJrHvsmnc0EV+V5NoUez3K0p2VzO64v3+D2+EIdSSnHfvz7k4pvvNTuVFmXjyqU8f8dtFO54GHdjMa7GEgp3PMoLd9zO+hWLzU6vzTrzsmspLyli8itPm5pHTjdfY48tJu0jy69oACArpnVdPF9RUM2b3+/h3zUuSr2aUq/mAWcDz+btMu133ZINzowkxmHj85/Lj/sYUpCZ7Kcl31G4ow534ztAUtN3M3A1TOHn1evZtv5HM9MTJrBYLDz3yRwuv/0Bw2PPmz4Zq8KU+V/tY0N499wO9DnOq0vHYvGuKhLr3Lzihbj98YHPPJoVu6sprJIuj0K0BP/950s01r8AnAtY8X1smUBjw0v898WXzE2uDevQvTdnXnoNcz79iM0/rjQtj3adfI09zCoSdlX6CrLMqNZzh0xrzZSV+3jHoxkDKHzL6S4FHvNocte0nAHhLYXdqjizQzSr9tYc9+cHKchMtnrRQuprL+GX/yvseFwXsHbpAjPSEibyejzY7HbCo2IMjet2NfLdF5/QLy3c8PlfHq9v3XVEsNWQ5YJrC6q40q1/0ao2DDhLwZp9NX7PQYiDffXft/jrpWNwu+RiwH5ej4dt678HLj7MoxeQv2EpbpfL6LREkwv/dDfR8Ym88/T9eNzm7DEKCnGQntPJtE6LBZWNhNotxIS0noYe1Y1e9tS6GHuYxy4DVhXVGZ1SQBiVE0X/9HBc3uPbRyYFmcnsQTaUOvyHP2WtwWo3fk20MM+OTeu57ZyBplxxXDFvNlUVZYwyYMngoaZuKOXe2fk0GtRYw2pVHKnkqlXIHjJhuD35Wykr2ovNHmR2Ki2HUiiLDTjcB8B6lLJgscjHGLM4wsK56q6H0V4vzrJi0/LI6daTreX1pjT2GNMhmtsHpLSqgdBWC3g1HO7SUA1glz+5w4oLtXP/4PTjvlsqv1aTDRh1FvbgD+AXHw/LUHxK3+FnmpGWMMncaROpKi8jpV22KbHjQ230SjZ2lopXa77Z6iTYZiHIasxbUv92UbxpU7844RQA33qhrwHLJoU4WEVpEdHxCWan0aJYLBZOPm0MSr32ywfV6/QYOAaLtfXcmQhE/Uedw1MTvyI2McW0HLK79qCywUNxrfF36bKiQ+if3rq6oobarXSLDeH9wzz2HwUDW9nP29wKqxrZcxzLFqUgM1lOt570HT6Y4JCRwFygBJhJcMgwRl50GYlpmSZnKIzSWF/Hopm59B0x1vDlikUFO1m7dAGjcqKxGnx3aM3eWopqXIbemeuZHEpCQihjrIqF+P7qcoGhVsX53eOICpEGtMJY1RXlhv/dB4Ir77wLR9iLKMvfgHwgH4vlERyhz3PVXXeZnJ1QSmGxWqmurOCH+bNNySG7qbGH0csWtdYs2lnJvurWt8z4ir7J3G9TPA3sArYA9yh4127hgp5y4ehI3F7NXbPy+WRdyTG/VgqyFuBPjz/NFXdOICnjdoJCOpKW/RB/eOA6rrzzPrNTEwZa+u1MaqsrGX7uZYbHnjd9MspiYUSOCbPHtlYQEWRhYLpxd6UsSnHnkHQ69Ezg6lAbHayKR6KDuWhgKuedFG9YHkLsV19XiyNM7sweKqVdDk9OnMqgsdtwhA/AEdafgWM289TEqaRmtTc7PdHk01df4MV7bmTf7h2Gx87s2BWrzWZ4Y48al5fnFu1h8a4qQ+MaIScmhMdGZzE3I4JeNgun2S38nB3F02OzTRkvEChsFkX/9HCW7q7G5Tm2JbRyGbgFsFgsjLzwSkZeeKXZqQgT5eVOJCkji259Bhoa1+N2M//zjzkl2WH4G62z3s3SgirO6hiD3aDlivtZLYpxXWIZ1yXW0LhCHE7nXn2JSUj67Se2QUnp7fjT48+YnYb4FROuvZl5n03mk1ef55YnXzE0dlBwCGnZHdlattPQuPuHJRvdBMsoGVHB3Doozew0As6A9Ajmbq9kbVEtvVOOfgtI6/xXJEQAmvCHW3E1Nhi+OXj1ormUF+9j9GDj33iDbRauPzXZ8CHUQrQ0V9/3uNkpCHHcYhKSOfOyP/DFe/9hwjU3k9Ghi6Hxs7v2YPWsTWitDTuHVtR7AIiWJe7iIL2SwwiyKpYXVB1TQSZLFoVoIU4+bRh9ho0xPO7caZOIjk80ZP7XoUJsFsZ0iCYtUjrLCSFEIDvndzcQEhbOp6/9w/DYWV164GzwUFpnXGOPqgZfQRYZLI1lxP8Ltlk4OSmUVXuPbXyOFGRCmMzV2MDH//k7xXt2Gx67rKiQNQu/ZUSC2/BW75tL65ixqZwGtzGt7oVoyZ6//RpevOcGs9MQ4riFR8Uw9rI/4PV6DJ8Pl9XlJAC2lRu3j6yq0VeQhQdJQSb+1x9OSeLZUVnH9Bq5zyqEyX6Y/w3T33qZzif3ISE13dDY8z//GK/GlNljX24qZ1lBtSmNRIRoaTweD+XFu8xOQ4gTcsGNd5oyk6tdp24oYFt5A/3SjGnL3i8tnLQRma12D5k4fikRx77qR/4VCWGyvNyJxCen0WPAEEPjer1e5k2fQs+k0ON68zgR1Y0evt9VxYjsKEJscqNeiIioGAq2bTY7DSFOyP5ibO/O7QSFhBg2nywkNIzkdjlsju8IGNPpMTrEJvvHxBF9vaWcetfRrwCST0JCmKioYCc/LVnA0AmXGD7gdN2yhRTv2cWoHOPvjs3Pr6TRo025MydESxSblEJ58T68XlnCKwJbbVUl910yis/f/Y+hcTM7dWPHpg2GxdtQXMuCHZWGxROBZc3eWmZsrjjq50tBJoSJ9s//GjbhEsNjz5k2kYggKwMzjG3mobVm9tYK2seG0D42xNDYQrRUCSnpeNwuyov2mp2KECckNCKSgaPHMf/zj6mpchoWt13HrhQX7KTW5TEk3tztTt5auc+QWCLwdIl3UFRz9HsppSATwkSNDfX0HX4mccmphsZ1lpXww7zZDM+ONHz+V43LS6jdwuj2sndMiP2SM7MBKNyxzeRMhDhxZ15+HQ11tcybPtmwmJmdugGwo6LBkHiNHk2wwedPETg6xR3bBWf5lySEia6882/8+bnXDI+74MtP8bhdpiwZDA+y8vTIdoyR5YpCHJDevhMAu7b+bHImQpy4rM4n0blXX7799CPDluFmduwKQL6BBZndanwDExEYcmJCOJbm1VKQCWGSksICAMM7UmmtyZs+mS7xDjKjgg2N3eD24qz3zYkxoxOXEC1VVFwCz306h9EX/97sVIRoFiMuvIqSwgJ2G3SRIS45FUd4hGF3yFxejd3gcTEicATbLOTEHP1dMinIhDBBWVEhfx53GrMmv2d47I0rl1KYv9WUJYPz8yu59rMtFFQ2Gh5biJYuPacTVpt0bROtQ/+RZ/HvWSsO3LnyN6UUGe07sz2ikyHx3B6NTe6QiV/x/Oh2R/1cKciEMMH8zz9Ge730On2Y4bHzcifhCI/g9MxIw2PP3lZBSngQqRF2w2ML0dLt3raJt5/8K86yErNTEeKE2YOCiYyJBXwrM4yQltOJ3ds3GxLvpn7J3DXQ2P3fIrAcy0ogKciEMNj++V8n9T2dpIwsQ2NXV1awdM4MhiVbDZ//lV9ez+bSeka3j5blikIcRl11NXOmfsSGFYvNTkWIZlFT5eSRa89n7rSJhsRLb9+J6opynA3+77SYGGY3fIanaL2kIBPCYGuXLqB4zy6Gn3+Z4bEXzZyOq6GB0SY01Ji9tQKbRTEsW7orCnE4Od16EhoeyY9LvjM7FSGaRWh4JDXOChbNnGZIvLTsDgDsNmBZ/KKdlSzeVeX3OKJtkIJMCIN998UnhEdF03f4mYbG1VqTlzuRrC49yDF4/pfbq/luRyUD08OJDDZ2ALYQgcJqs9Fj4BBWL5wrA6JFq6CUYuCZ49m4ahml+wr9Hi8tuyMAu5z+b+zxxc/lzNxc7vc4om2QgkwIg117/9Pc/eK72IOM7XC4dd1qdm7ewJnhRYbGBbBZFM+PyeLyngmGxxYikJw6ZBQVJUVs+WmV2akI0SwGjBoHwPK5X/k9VkxiMsEhDgqq/H+HTAOy+F40FynIhDBYaHgEnU7uY3jcvNxJBFsVQ7OMb+YBkBweRKqstxfiV/UeMoKE1AycpcZfOBHCH1Kz2pPevhMr8r72eyyLxUJyZjZ7DCjIhGhOUpAJYRCtNf+8648sN+CkdKi6mmoWz/qcQZmRhNqNXTJYUNnIE9/tllb3QhyFsIgoXvxiEX3PGGt2KkI0m3N+/yf6jzzbkFhJmdkUSkEmAowUZEIYZOPKpSzP+5q6auM3AS+Z/QX1tTWMMmH22OytFfywp5pQu7zdCHE0lFJ4PR6qKmR/imgdhpxzIaMMGnqenJHFvmoXHq//W98b08xftAUygVIIg+yf/9V/5DmGx56bO5HMqCC6xDsMjevyaPK2O+mXFk6MQ95uhDgaWmsevOocEtMyuf3vr5udjhDNwllazL7dO/y+ZD8pIwuPhuJaF8nh/lsm/5dBabKHTDQbuWQthAH2z/8aNPY8gh3GFkU7N21g69rVpsz/WlZQhbPBwygT2uwLEaiUUnTrM5Af5s+mvHif2ekI0Sw+eP4RXrznBr8PbU5KbwfA3mqXX+PEOGxEy4VG0UykIBPCAItm5uJqaGD4ecbPHpubOxGbRTE0y4zlik7iQ230Tg4zPLYQgWzkhVfh9XiY8+lHZqciRLPo3n8wFSVFFGzf7Nc4CakZABT5uSCbn+9k7nanX2OItkMKMiEMkJiWyRnnX0FWl+6Gxm2sr2PhzFxOy4gwfP6X1pruiQ4u6BaH1SILO4Q4FsmZ2fQaNIJvP/2Qxvo6s9MR4oR16zMQgPXLv/drnLikFCxWK/tq/FuQfbvNyawtFX6NIdoOKciEMEDvwSO47sFnDI+79NuZ1FY5TWnmoZTiopPiOatjjOGxhWgNzvnd9VSWl7L025lmpyLECUtMyyQ2MZmNq5b5NY7FaiU2MYViPxdkFqXw+nn5pWg7pCATws9+XDyfyvJSU2LnTZ9EUkYWPRJDDY3r8WoW76rC5ZGTlRDHq8spA7j/1UmcftZ5ZqcixAlTStHp5D5s+XGl32PFJ6dSXOvfgsxqATnFieYiBZkQflRXU80/776eyS8/bXjsPflb2bhyKaNjawxv5vFDYTXPLCzgh8JqQ+MK0ZoopejefxAWi8XvjRCEMMIFN97F/a9N8nuc2ORUSmrdfo1hVQqvAa31RdsgBZkQfrRk9hc01NUy7Fzjm3nk5U7CqmBEtjnNPKJDrPRJDTc8thCtzfzPP+b+y8fidsmwWxHY0rI7kJyZ7fc4cUmplNW5/bqk0KpA6jHRXKQgE8KP5uZOJC2nEx17nmJoXLerkQVffkpfE+Z/lda6+GFPNSOyo7BJMw8hTlhUXAI7fl5HXq7/7ywI4W9zPv2IH+Z/49cYsYnJuL2aygaP32LcNiCFZ0Zl+u34om2RgkwIP9k//+uM8y4zfMnginmzqSwvZbQJ87/mbHfi1cjsMSGaycmnDaPLKf2Z+saL1NXIMmAR2GZOfIv5n0/xa4zo+EQAyuv8t2wx1G4l1G5s92LReklBJoSfbFi5BHtwMIPOvsDw2Hm5k4gPtdHLhPlf64vq6JEYSkpEkOGxhWiNlFJcfvsDVJaV8MX7r5qdjhAnJLNjV3Zu3uDXGEYUZIt3VfHJuhK/HV+0LVKQCeEnYy69hldmLiMi2ti278V7drF26QJG5kSZMv/r4WHp3DsozfC4QrRmHbr35rQzJzDzozeodpabnY4Qxy09pxPFBbv8Ol9vf0FWVu+/gmxVYQ1fbpK/RdE8pCATwg88bt9JIDIm1vDYedMno9CMzDF+yaDHq1FKGT6EWoi24LLb7ueB16YQHiWz/UTgSs1qj9aavTvz/RYjKi4BAGe9//aQ2Szglq4eoplIQSaEHzxz85W8/9zfDI/rcbuZ/9kUeqeEkRBmNzS2s97NNdO3sHhXlaFxhWgr4pJTDzQIamyoNzkbIY5PSrscAIoLd/ktRogjlGBHKBV+vENmt1pk1qZoNlKQCdHM9uRvZd3yRcQkJBkee8338ygv3scoE+6O5W134mzwkBYpe8eE8Kfct17igSvOxtXYYHYqQhyzjA5deGfRz5w6dLRf40TGxPn5DpnCJXfIRDORgkyIZpaXOwmrzcbQ8RebEHsi0SFW+qYZO/9La80325x0iXeQGRVsaGwh2pqcrj0p2LZJGnyIgGS12QhxhPo9TkRMLE4/tr23WxVe7VuqL8SJkoJMiGa0f/7XKYNHHljDbpTy4r2sWjiXM0yY/7WhpI7dlY2Mam/8EGoh2pqTTx/OgFHnMP2tV9iTv9XsdIQ4Zl9NfJvpb73s1xiRMXFUNvhvyeKF3eKYdklnU5pnidZHCjIhmtEP87+hsryU4edfbnjs+Z99jNfjMWX22OwtFThsFgZlRhoeW4i26Hf3PEpQiIO3nrgPr9drdjpCHJP1K75n8ezP/RojIjqWKj/eIbNZlBRjotlIQSZEM+p08qlcfvsD9BwwxNC4Xq+XvM+m0K3PaabM/zq7Uww39k0ixCZvKUIYITo+kSvveJBt69ewa8tGs9MR4pjEJiRTVrTXrzHCo6KpavRfQbaptI5Xl+/F6cfGIaLtkE9PQjSjmIRkzvndjVisxrZ9X7dsIcUFOxlrzzc07n4d4xwMy5LlikIYaeiES3hh2nzadepmdipCHJOYhCRqKp1+7RYaERVDvVvT6PHPHeS91S6+3lLh131qou2Qgky0ClpraqsqcbsaTcthwZdTWTbnK1Niz82dRESQhYEZxjfzmPhTMfnl0oJbCKMppYhLTkVrzZrv58nSRXFEblcjtVWVaN0yGlDs32NdWVbqtxjh0b55ff5athhi8y1XbHDL3504cVKQiYCmtWbutMncPGYYN4zoz7WDevLSfXdSUVJkaB4et5vJrzxN3vRJhsYFqCwvZUXeLIZlR2G3GvsnvaWsnilrS9lQUmdoXCHE//tx8XyeveUqvvn4A7NTES1MefE+Xrr3Dq4d1JMbRvTnljOHk5c7xey0iIpLICI6lroa/82tDI/07aeubvRPweRoWqJf55KCTJw4KchEQPvsndf58Pl3qCj5AI+7ErcrnxVz2/HglRdTW23cgOLVi/IoL97HGeddZljM/RZ8+Sket8ucZh5bKwiyKoa0k2YeQpil58ChnHzaMCa9/CSFO7aZnY5oIWqqnDx45UUsz8vB7dqBx11JefF7fPD3t/js3ddNze2UISN5fe4aMjp08VuMsKj9BZl/7pA57L6tCXVyh0w0AynIRMCqq6lm+lv/pqH+K+B0QAHxeDx/p7qyN/Omf2xYLnm5E4mKS6D34JGGxYSmO4S5k+hswvyvOpeX73ZUMSgzkrAgY/fMCSH+n1KKP/7tOexBwbz60O143NJkQMDcaZOpqeyH1/MsEIfvHDmIhvoZ5L75b+rrak3O0L/CInz7mv1VkIXZLdgsikZPy1gGKgKbFGQiYG1avRyrrSfQ7hePNdb/jsWz8wzJo7x4L6sX5TF0/MXY7HZDYu7386plFOZvZbQJ878W7qyk3u01JbYQ4n/FJqZwzV+fZMvaVXz27r/MTke0AEu+mU9jw1WHeSQHq+0kNq9ZYXhO+9VWVfLPu69n5Xff+i1GeNMdMn91WkwOtzP1ks4MlhUiohlIQSYClq+T4ZGuBLuxWIz5511SuIekjCyGTbjEkHgHm5s7ybT5X/VuL13jHXSJdxgeWwjxS6eNmcDoS64mo31ns1MRLYDF8ivnSO1qetwcVpud5XO/Yve2TX6LER7l36YeSskMMtF8bGYnIMTx6tyrL17vz8AGoOtBj2iCQ95k8DmjDcmjY89TeH5qnuFvzjVVTpZ++yUjsiJNmf81rnMs53SKkZOSEC3I1fc9bnYKooUYdNYIdm15i8b68fiWK+63Dq230vHkU81KjaCQECxWK3V+3OvtCAvHovzX1APg9RV7yY4OYXQH4/dwi9ZF7pCJgBUU4uCKO+4lKGQs8BlQD2zBZr+W2KQCBp99gd9zKC/eR2N9nSlFyaKZubgaGkxp5rGvuhGttRRjQrRQMz58nfeefcjsNISJhk64mNjEfGz264At+M6RuQSFnMUVd9xHUHCIabkppQhxhFFfW+PXGOHRcZR3Heu3GCsLa/ipqHXvxRPGkIJMBLSRF17OLU89REaHJ1GWCBxhpzPigmAe/2ASwQ7/L6X74PlHuO+S0YbPdtFak5c7iZyYYNrHGntSbXB7uXNWPm+vMna0gBDi6FWWlTJ7ynusmDfL7FSESUIcoTz+4WTOuMCOI+w0lCWCjI7PcOvTjzDigkvNTo9gh4MGPzcWiYiOpcpZ7rfjR4XYqKiXJjrixMmSRRHw+gwbQ59hYwyPu3/+1+iLf2f4naKt61azY9N6buyTZGhcgMW7qqhu9NIv1dgh1EKIo3fRTXfz09IFvPnYvbQ/qRcxCca/VwjzhUVEcfW9D3L1vQ+ancovpLRrT2iEf/c/h0dFU11RBgn+OX5MiJWCykb/HFy0KXKHTIjjtH/+13ATZo/l5U4mOMRhyvyvb7ZVkBxup3tSqOGxhRBHx2YP4uYnXqahvpbXH7kLr1dmJYmW5cE3pnDlnX/za4zImDiqKsr8dvxYh42yOrlDJk6cFGRCHAffksHJdOx5KukGdzSrr61h8ReTOT0lyPD5XwWVjawtqmNU+2gsBt0VrHd72eVsoNJPnbKEaK3Scjpyxe0P8tPSBWz5aZXZ6QhhuPDoWJxlpX47fnJ4EBHBVlwe8y54FNe42F3ZgMcr89ACmSxZFOI4bF23mj35W7jhkRcMj7141uemzf+al+/EomBEtv9juzxePlpZxNztTuIVFHvh5EQHfxiQSqxD3rqEOBojL/odnXv1I7NT199+shAGmvTSU9TX1XLNX57wW4yoWN8dMq+O88tFxAldYpnQJbbZj3s0NpfW8e7SvRRWNRJugXqL4oIeCYzpGC0NtwKQ3CET4ji0P6kXj73/Of1HnWN47Lm5E0mPDDJl/tfFJ8Xz1IhMYgwoiP7z/R4atztZ79FsdWsKvJpB+2p5dHY+DW5ZfiXE0VBKHSjGNq5ahtsl+11Ey7B76ya2/LTSrzGi4hLRXq/fZpGZpaCykafm7uQvzgb2eTU73JpZjV6+XV3E15v818RE+I8UZEIcB6UUHXr0JsRh7D6qnZs2sHXtaka3N+cKmN2q6Jrg/5+5oLKRNYU1fOrRpDV9LwJ4WkNOo4dFO/03u0aI1mj3tk08ft2FfPraP8xORQgA7MHBNDY0+E7/51IAACAASURBVDVGdLyvm4e/9nk1uL08nLeLOdsq/HL8I/liXQm3ejRX8v9L3XoBUz2aqT+V4JbliwFHCjIhjlHe9Mm8+fi9uBr9eyI5fOxJ2CyK4QYsGTzUv5cV8tVmY668rS2qZSxwuHuAl7o16/dUG5KHEK1Fek4nhp17KV+89x82rlpmdjpCYA8KxtVY79cY+7uL+qsgC7IqNpfW8XOpf3+OQ63bW8PFh6m5egARWkvnxwAkBZkQx0BrzezJ77J9w1rsQcGGxm6sr2PBjGkMTA8nMtjYZh5FNS6+2eo0bN5KkFXhPMINQCdgt8n6eCGO1ZV3/o341Axef/hOvw7kFeJo2IODaaz3d0GWDEBprX/OXUopMqKC2eU09gJtkNWC8zDf9wLV2ncOFYFFCjIhjsG29WvYsWk9Z5xvfKv7ZXO+orbKyej20YbH/marbznGyBxjYvdNC2e+hu2HfL8OeN2mGGjCHUIhAp0jLJwbH3mBooKdTHrpKbPTEW1cUno7UrM6+DVGTEISSimKa11+i9EuOpidzga0Nm6Z4IDsKF6yKA6NOA2ICbWRHG43LBfRPKQgE+IYzJ02ieAQB6eNmWB87NyJpsz/8ng1c7Y56Z0SRkKYMW/y4UFWLuuZwBCr4gMgH5gFDLcqMpLD6JEoM9CEOB5dTx3A+GtuJjXbvx+EhfgtE669hQffmOLXGDa7nZjEZIpr/FeQZUcHU93opdhPd+EO56zOMawItXG5RbEM2Aw8A1xvVVzTP0W6LAYg6R0txFGqr61h8azPGDB6HKERxg5kLtyxjY0rl3JVzwTD5n/tt7KwhtI6N388NcnQuGd1iSU5Koh/ryvlPmcDsSE2hneOYUSOtPQV4kRccst9ZqcghGES0zLZt+tHvx2/c5yD7omh1Lo8gDEXLcOCrDw+JosvN5Zx6XYnDR5Nt8RQHu0eT7toY7dTiOYhBZkQR6mxoZ4h4y5i0FnnGx47L3cSVpuNM3KMX6oXGWxlWFYkfdPCDY99Sko4p6QYH1eItmDhzFyKdu/g/OtvNzsV0QZ9//V0vnj/NR5+eyohoWF+i5OU3o4f16/w2/FzYkN4ckSm345/JGFBVi7pmcAlPRMMjy2anxRkQhylyJg4rr7vccPjul2NLJjyJn2THaYMRO4c7+D/2LvvsKiO7oHj39nG0ps0QRSw995j16jRxJZeTPKaxDRTfdMTTUwvpvfENFss2GIPgg2NLfaugCgC0uvW+/sDzc/XmIjK3gvLfJ6HR2HhniPCzs6dmXOaaNDzTJIk1zq0cwu/z5tOmx59iGvRVut0pFqmpKiQ1IN7KSspdumELKJ+LEnlDkptDryMriuIZbE78TDIk0DSlZE/OZJUCZnpqezftknVQ7vnbEtaRYHFoUkxjz1ZpWQWu3f53PRCCztPl5DjwkPfklQd3fLocwQEh/DtlGdx2NU7/yJJAJ7evgCUlbi2jUlEgzgA0l1YCn7Z4Txun3eYEqt7NaC22J3szSplf3ap7G12BS6nIbmckElSJSyf8R1vPnQ7JYXqNn8ESJg/gzpeBtqGu+4O4sU4FYWPN2XwyebTqsZVS0aRlReWHee15SmsWH+SJxcfY+ra9LPnACTJ/Xn5+HLn05NIPbiXVXN+0jodqZbx8jk3IStyaZyo2MYApOW7rjR9lJ8Jm1Nhd1apy2Kobcn+HO6ff5h5a9OZnpjO+PmHWZtysWL70j/5YmvlXz/JCZkkXcK5/l+d+g3Gxz9Q1djZp06wZ/M6BsT6o9epW8hiV2YpmSU2Bsa5X4n5UpuDyatSuS/fwgmHwnqbkxNOhdiMYqYmpWudniSppsuA62jVtRdzvniP4gJ1Gr9LEvBXcaySQte+yA+Lqo+H2ZMUF07ImtbxwtOgY/sp9+jvt/pIPom7z/CHQ2Grzckuu5OlNie//HGaPzPc49+ohuN5lf+ZkxMySbqEc/2/+o28TfXYiQtng6Ko1v/rfCuP5uNj0tGtnq/qsV0tKaWQDnYnE/j/g7Q+wFdOSM8t51iua5uVSlJ1IYRg7H9f5aHXPsTbT/3nGan2CqwTStP2XTCZXXtGWafXE924OUfzXPe8btQL2kV4s/lkEY4avrXPqSgs2J3NDw6FRud9vCPwgUNh4a5srVKrUUptDjKKKr9NVk7IJOkSEuJnEBZVn2Ydu6ka12G3k7RwNu1V7P91TkG5nc3pRfRt4I9J735PE0czSxnh+PugaQAGAwdzylTPSZK0UrdBHB16D0IIock5Wal2CqvXgJe/nUvTdp1dHiu2eWuO5pa7dLLULcqX/HIHB87U7PGjyOKg0OrkYq94rgP258sblpVxJKf8b427/437vdKSpCpUXJjPqeNH6DvyVnQ6dX9ddm5MJDfrNAM1KOZxbkIyqKF73jH3NOk59Q+PnRICL6N8apRqn8U/fsmHEx/QOg1JqnINW7XD4lBIdeG2xU6RPoxtE0JdX5PLYqjBbNBhQ+FiJ/syAB83vEnrCgcu88au/K5K0r/w9vXnk2WbGXTLParHXhM/gwCzXpP+X50jfflxZCOi/d2zweQ1cf58qRdceGJmN7BRUTT5nkuS1oSALQnL2L9tk9apSLXEa/fdyE/vTnJ5nCZtK1bh9ma7ruiGp1HHqObBBGrQnqYqeRh0dAzzZuoFx9YV4G0dXBPjfufKXaFjhA/3dQit9OfLCZkk/QOnw4GiKBhNHpg9vVSNnZd9mh3rE+gX449B5WIe50rb+phc169Fa42DPekS608XvWAakAy8LaCfXnBf53CX9qqRpOpq0E1jCagTytwv39c6FamWKC8t4VTKEZfHqRMRSUjdeuxxcRVEh1NhbWphjS98cWencL4y6blbJ0gAlgEj9IL1XkZGt6qjdXo1QmyQmWGNgyr9+XJCJkn/YMua5TxxfU+yTqapHjtp0RycDm16j332RwZTkk64/VmSsR3CuKVHJN+FeXGfr4mEer48P6A+vRrIu39S7WQyezL87ofYv20TB3b8oXU6Ui1QJzySnNP/tIG8arXs0pPdmaUuPUemEzBz9xl+2ZVdo8fQUG8j71wXg715MI/7m3g+wIPQ1iFMGRzj1jdrq0pGkZXtGcXYLnJW/Z/ICZkk/YOE+TNwOh3UCY9UNa7T6SRx4SxahnoRofJe9GKrg/VpRQR5GRFC3ZU5tQkh6Bjpw3/7RfPGsFge7hFJXJBZ67QkSVP9Rt6Gb0AQi3/4QutUpFogJLIeWafScDqdLo/VpnsfSmxOlxbdEEIwvEkgh3PL2V/Di3v4eRi4qVUdpgyNZfKQGIY3DcJTnq+ulITjBbyWlI7FUfmfa/mdlaSLONf/q/f1N6PTq3s3aO+WDWSlpzFIg/5fSSmFWB2KJitzkiRpz8PTk3EvvsWY8U9qnYpUC4RHx2CzWMjNzHB5rFZde2Ewmtic7tpG1P1j/PHz0DN3b45L40jV19ZTxTQJ9rys1UQ5IZOki1izYBYAfW64Wf3Y82fg4x+gev8vRVFYdTSf2EAPGsqVIkmqtTr1G0JMs1ZapyHVArHN29BjyEicTofLY3l6+9Cicw+S04tdup3Qw6DjhiZBbMso4WANXyWTLl92iY1jeRY6X2ZxMDkhk6QLOOx21i76ldbd+1AnQt3tioV5uWxZs4KeQ0er3v/rSG45x/MtcnVMkiTSjx1i2psvYLXInkOS68Q2b83Dr39MaGS0KvG6DhxGVomNQzmu/bke2jiAJsFmLHbXb8WUqpeNJypWYC/3prqckEnSBYQQ3PPc64wcN0H12Ot+m4vDbmNofoLqsSN8TdzfIYxe9f1Ujy1JUvWSm5nBqjk/8ed69Z+LpNpFURRKigpUidWp72BMesGa466N52XU886gBrQO93ZpHKn62Z1ZQmygx2XXAJATMkm6gE6vp0PvQTRu01HVuIqikBg/iyZ1PKkfoH7/Lx+TnusaB+ItKyhJUq3XolMP/IND2Lh8odapSG7uy1ee5MU7hqkSy8vXj46DbiApU1Fl9crqcLJgf45cKatFnrsmihd7RV3218kJmSSdJy/7NL9+9g4FOdmqxz745xZOHj/MoFj1i3lsOVnMiiP5Li0HLElSzaE3GOjUbzA7N6yR2xYll4qKbUzmiRQK89QpgtF/1G2UFhWwLrXQ5bGO5JYz7c9sZu054/JYkvYURUGvEwR7GS/7a+WETJLOk7TwVxZ89wmlJcWqx14TPxNPg46eGmwZ/HXvGRYdzEXlHtSSJFVj7XsNxFJexv5tm7RORXJjTdp1BuDA9s2qxGvaviv1GjZhyaE8l/cKax7ixYBYfxYcyOVQjizw4c5sDiePLUshMeXKtsPKCZkkneV0OlmzcDbNO3YjIjpG1dglRQVsXr2EXg38MBvU/bVMzbdwKKecQXEBbtV7TFEUVh/N59nfjnHPnEO8uPQ4a1MLa3SzTklSU7P2XQmpW4/igjytU5HcWGzz1pi9vNm9eZ0q8YQQDLltHMfzLfx5utTl8e5tF0qQp4GpyRmU2arP1sXsEhtfb87g/nmHuH/eIb7adIrMYqvWadVY69OKSC2w4O9huKKvlxMySTpr75YNZJ9Mo+/IW1WPvWFpPNbyck0qHK48mo9BJ+gb417FPL7ZnMGGbZlMLbSyx+5kSoGFpZszmPFnltapSVKN4OHpyUdLNtJjyEitU5HcmMFopEXnHuxcv0a1G2Y9ho4kKDScX/eecXlMb5Oex7tGkFFk5bsdmS6NVVmni608t+w4LY4XsM7qZL3VSdvjhTy/PIWThXJSdrkURWHhgVyi/Ey0Dfe6omvICZkknbVm/gy8/fzp1G+IqnEVRWHNglma9P+yOpwkphTQNcoHvyu8q1MdHcstZ1taEYkOhWuBCOB6YJ1DYdWhfHkXUJIkqRoZevs4bn/yJRSnOitIRpMHw+9+mH3ZZezMdP0qWaswbx7oGMawxoEuj1UZs7dnMcHm5G0FGgENgdeBp21OZm2vHpPGmmR7RgnH8y2MbBp0xTuN5IRMkqiYFBmMJvqOuBWTh7qTouP7d5F6cK8mq2O5ZXbq+prcrvdYclohYx0KF3YBCQZGo7ApXf0zgpJUE+3ZvJ4J13XjVMpRrVOR3FizDt3oMuA6dHr1qvz2HXkLdcIj+XlnNk4VVuaGNAqkQYAZRVHIKbW5PN4/URSFDaeKeegij40HNp0ukQW+LoOiKMzem0OIl4HeDa68KJuckEkSFXvKH5ryEbc+9rzqsRPmz8SkF5r0/wr3MfHuoAa0DruyJfbqyu5Q+Kfvpq+zYmVQkqRLM5k9OZORzum041qnIrm57FPp/PbzVzhVWiUzeZi58aGJHMktZ22K6ysunjN3Xw6PLdNua6AC2BW4WIc0r7OPy/lY5Qkh+E+7UMZ3DMeov/Jz+HJCJtV6iqKQcfbFhtpFLcpLS0hesZCe0b6q9/8qtNgpsjgA9f/drtYqwpuZBsGFw7odmKcXtA6TzTolqTJC6kYCkJN5SuNMJHd3cMcfTJ86RbVqi1Bxliy2RRumHXFQanOoErNntB86AZMST2iyUqYTgjZBZuZe5LF4oJm/x1VNLGqjJnU86Rjpc1XXkBMyqdY7uOMPnhrRi21Jq1SPnbxyMWUlxZpsGZy/L5dxi46qNgipqW2EN8LXxDgdnOsodwq4VS+ICPakcbC621IlqabyC6wDQFFersaZSO6uU78hePn6kzB/umoxdToddz/zGgU52fyyS51eYRG+Jl7qHUWhxcEra05QaLGrEvd8Y9qF8phesBhwnn1bCjysF4xpF6p6PjXV0sN5fPpHBrYq2HUjJ2RSrZcQPxNPbx9adOqueuw18TOJjGlE0zqeqsa1ORQSjhfQJswLL6O6K3Nq0AnB8/2jyajnR5xOEG0QNNMLrA38eKp3lNutCEqSq+gNBkxmM2UlRVqnIrk5D09Peg8fw+bVS8nNylAtbsOW7Rh401iWHs5nf7brC3wANAr25IVekWSW2JiUmK76ma0WoV5MuCaKp3yMhOoFoXrBY95GHuoZSZtwuYOkMvLK7PyyM5vMYhuGKmji6j5l1STpCvzV/2vYjZi91H0SSju8nyO7t3Nvu1CECFI19pZTRRRYHG5XzON8XkY9D3avy1ibg4JyB4GeBtV7vEmSO+jc/zrqNmiodRpSLTDolntYPmsaK2ZOU/VM982PPMP2tav5aFMGHw6JUWWsaB3mzYu9oii1OdFXwQv6y9U2wps2w2LJKbOjKFDHyyBvVl6Gb7ZlYnEoPNAxrEq+b/LViVSrbVgaj81i0aT32JoFszTr/7XySAHBXgbaRbj/nTAvo54IX5OcjEnSFXrotQ81eY6Uap+wqPp0v/YGSorUK7IB4Ontw/jJH3C6xM53KpZ9bxPuTbd6FfWAk08UkV5oUS02VJwfr+NlJMTbKCdjl2FDWiEbThRxc8tgovw8quSacoVMqtXWL42nQdOWxDZvrWpcq6Wc9b/N06T/V36ZnZ2ZJdzYIliTu3KSJEmS9E8efO1DdDr1b6A179iN4Xc/xKJpn9EqzFvVyscWu5NvtmdisTt57pooWoa6V+Vjd2J3KvzwZzaNgsyMbhZcZdeVt4ylWu2/n/zIg69OVT3uH78vo6SwQJMtgwGeBj4ZGsPQhtWjQaUkSdVXYV4u43q1IHHBLK1TkWqJc5OxlIN7yctWt0nxmPFP0bhtJz77I4O0AvVWqzwMOt7sH02A2cAra9JYeSRftdjS5THoBFP61ePpHnWr9Kb2JSdkosIdQoiXz74fLYToXGUZSJKGfPwCqNewqepx18TPINzHSCuN+n9F+XkQ4CkXyCXparn7GJmbeYrS4kK8fNXfWi3VXoV5ubwy9gZmf/qWqnENRiOPvf05ZoOON9amU2xVrwpxmI+JtwfWp1WoN59tOc3nW06jqNCwWqq8PzNKUBSFMB8T4T6mKr12ZVbIPge6Aec2kBcBn1VpFpKksvLSEqbcfxP7tiarHjsj7Tj7t21iYGwAOpX3bG8/Vcxb60+SX6Z+mV1JclNuPUaeawgdGlVf40yk2sQvMIjBt/2HtYvncmjnVlVjB4aE89jnc8gqh7fXn8TmUG9S5GPS81LvKEY3C8LXpJfnuqqR5UfyeCXxBEkuaiJemQlZF0VRHgbKARRFyQOqdlooSSpLXrmYfVuT0RvUXyVKjJ+JTkC/WH/VYy8/ks++rFLVm1BLkhtz6zEy7fB+dHo9dRvEaZ2KVMuMHDeBoLAIvnv9Oew2q6qxm7TtxH0vv8OuzFLVV6r0OsFdbUO5o3VFD8A9WaX8fixfrpZpaOvJYr7amkmHut5c46KzhZWZkNmEEHpAARBChFDRQ06Saqw182cQGdOIxm06qhrXbrOStHgOnSJ9CFJ5y2BumZ0tp4rpH+uPUS/vuklSFXHrMfLInj+pF9cEk4dspi6py+zlzT3PTuHEkQMs+uFz1eP3GjaG0Q88ScLxAn7ema16/HOrYyuO5PPx5tO8KXe3aGJvVilvbzhJTKCZp7tX7bmx81VmQvYxEA+ECiFeB9YDb7gkG0lSQdrh/RzZs4O+I29VfTvA9rWrKcw9o0kxj4RjBTgVGOjGvcckSQNuPUZ26D2QfqNv1zoNqZbq0HsQvYaPwWjS5obAqPsfp/+YO5i3P5e5+3I0yeHxrhHc3TaE7adKeGTpMRKOF8jVMpWU2hy8uS6dUG8jr/SOwsvout1Fl7xFryjKdCHENqA/IIARiqLsd1lGkuRiaxbMwmA00fO60arHTpg/g6CwCNqFq9v/y6korDqWT8tQT+r6us1uKknSnLuPkdfeco/WKUi13AOTPtDsLJUQgnuemUJZcRE/L1+IUSe4oWmQqjnodYKRzYLpFOnDJ5tP89GmDLyMOrpG+aqaR23kZdTzVPdI6gd44G927a6mf726EEIH7FIUpSVwwKWZSJJKGrVqh49fAH6B6j6pZp86we7kJG5qqX7/L5tDoVd9PxoFy21HklRV3H2MPLZvJ+H1YmSFRUlT5yZj25JWsX9bMnc8+bKq8XV6PeMnT8Vus/H970tRFIURVdh/qrKi/Dx4c0A0ySeK6BzpA8C+7FLq+3vIc+FVLPF4AQrQN8afdhHq3ED/1y2LiqI4gZ1CiGhVspEkFXQfPILRDzyhetzEhbMBGBCr/pZBD4OO21uH0DlS3lGTpKrizmOk0+HggyfH8dWkp7RORZKAihsES3/5hjUa9MQzGI088sandBk4jGl/ZvPr3jOabBvUCUGPaD90QmCxO3ljbToPLjnGb4fysDvlNsar5VQUpu/KZuqmDBJT1N0aWpkzZBHAXiHE70KIRefeXJ2YJLnCplVLKC5Uv+Gi0+Fg7fTPaBfhTai3UdXYhRY7m9KLcMgna0lyBbccI/du2UBu1mm6XXu91qlIEgCj73+CVl2vYdqbL3Bk9w7V4xuMRh55/RN6Xjea6bvOMG1HFk4Nz3J5GHRM6htNPX8Pvt6WySO/HSMppUCO9VeoyOLg9bXp/Lo3h/4x/rzYq56qW2UrsyFyssuzkCQVZKQd5+NnHuTmR57hhnsfUTX2zo2J5JTaua99mKpxAdYcL+T7HVl8PCSG+gEeqseXJDfnlmPk7/Nn4OMfQPveA7VORZKAiq2Dj7zxGS/eOYwPnhrHaz8tJji8rqo56A0Gxk/+AG9fPxbOmkZeuYMJXSI0q1zcMMjMlH712JZRws87s/kgOYO6viYaBXtqkk9NVWpz8OSK4+SW2bm/QxhDGwWofm6xMkU9ktRIRJJcLTF+Jjq9nl7Db1Q9dkL8DPw99HQ6u+9bLYqisPJoPk2CzXIyJkku4I5jZE5mBtsSVzD41ntluXupWvENCGTih9/zyj0jWffbPEb851HVc9DpdNw1cTIBdUKZ/enb5JbZee6aSHw0OsclhKBjXR/aR3izJ6v0r8nYzN3ZBHoa6NvAHw9DZTbE1T4Op4JeJ/Ay6hnaKJCWoV6aTWYv+T8khCgSQhSefSsXQjiEEK5pUy1JLnKu/1f7XgMIDFF3lSovO5Md636nf6w/BpWLeRw4U0Z6oVWTMvuSVBu44xi5Y91qFEVh0M13a52KJP1NVFwT3pixTPWdLucTQnDDvY/w0JSPOZBnZ+LKVE4Wqtu8+kI6IWgdVlGAwuFU2JVZyhdbMhm36CjTd2WTU2rTNL/qZuvJYh767Rj7s0sBGNksWNOVxUtOyBRF8VUUxe/smxkYDXzq+tQkqepsS1pFYe4Z+o68TfXYSYt+xelwaNL/a+XRfMwGHT2iZZU0SXIFdxwjB4y5kw8WrCWkbj2tU5GkiwqLqo8QgpPHj/Dz+5NxOhya5NFz6Eie/2IGRXofJq5MYUdGiSZ5XEivE7zRP5op/erRpI4nc/bmcN+ioyQcL9A6Nc2l5lt4NfEEr61Nx6ATmrVUuNBlr2EqirIA6OeCXCTJZY7u+ZPg8Lq06dZb1bhOp5PEhbNoGeqlev8vh1PhaK6FXvV98TTK7QqSpIaaPkaWFhcBEBrpdoUjJTe0KzmJZdO/ZdpbL2rWLLlp+y689vNiguo3ZvLak8wKGVUtGjcLIWgV5s2LvaL4Ylgsw5sE0eTsCtDerFJm7T5DZrG2q3pq+3ZbJo8tO86BM2Xc0zaEDwfH0LRO9Thvd8kzZEKIUee9qwM6Atr/pEnSZbjt8RcY8Z9H0enV3eO9b8tGstLTuKNbhKpxoeIO2YdDGlBud6oeW5JqC3caI/PPZPH0qL7c8dTL9LnhZq3TkaRLGnLbfyjIyWbRtM/w8PTk9ide0mTFIzQymsk/LuTrSU8x8+M3SIny4dEuEdWmP1iEr4l72oX+9f6erFJm7TnDzD1naFrHk57RvnSv50uwl7pVoF1NURQO5ZTTMMiMXicI8TZyQ9MgxjQPxtejevzfnFOZKovDz/u7HUgBbnBJNpLkAnabFYPRpElz04T4GXj7+dOtnrr9vxRFwe4Eo77isKokSS7jNmPkvK+mYikvpUm7zlqnIkmVdvMjz2ApL2PpL9+g0+m59bHnNZmUmT29ePStz4lr1Y6ZH71ByooU/tsjktig6lcY5+aWdegX48/a1EKSUgr5dnsWiw7m8fXwWIQQ5JXZCTDrq812vstVYnWwLq2QFUfyOZZn4b896tIj2o8bmgZpndo/qsyE7FtFUTac/wEhRA8gyzUpSVLVcTocPD26L31H3Kr6AeDCvFy2rlnB4BhvTHp1twwezbMwOfEEz18TSbMQL1VjS1It4xZjZNrh/STEz2DQTWOJiI7ROh1JqjQhBHc9PQmnw8GR3dux26wYTdpUFRZCcN0d9xPXoi2fPvcwE1elcm+7UE3KqF9KiLeR0c2DGd08mPRCC2dK7AghcDgVJiw7jtkgaBvuTdtwb1qGeuFvrsyUQVulNgcfbz7N1pPF2JwK9f09GN8xjPYR6la4vhKV+e5+ArSvxMckqdrZuTGRrPQ0IurHqh573W9zsdusDIqLVD32yqP5lNud1POXpe4lycVq/BipKAo/vTsJb18/Rj/whNbpSNJlE0Jw9zOvYbOUYzR5UF5WisnDjE6nzfnppu0688bMFXz5yhN8vT6BHadLeLRzeLWd1ET5eRDlV/F6wako3N66DttPlbA+rYiVRysKgdzVJoTRzYOxOZycLrZR19eEXuXK0edzOBWO5ZWzO7MUBIxqFoynQUdumZ1rGwbQp4EfDYPM1W4i/E/+8SdDCNEN6A6ECCGePO8hP0DugZJqhIT4GfgF1aF9rwGqxlUUhaRv39ak/1e53cnalEJ6Rvtq1hdFktydO42RJ44c4OCfWxj738n4+AdqnY4kXREhBCazJzarhbcfvoOwevW576V30Ru0mQT5BQYx8aMfWD7jO2Z+/CaPri3gsZYedKhbvVdrjHodgxsGMrhhIA6nwpHciklPy9CK3TZHcst5dnUaZoOgQUDFa5xofxPdolx3Bs3mcGI8u9No6eE8NqQVcSS3jHJ7xXHd7o3GMwAAIABJREFUNmFejGoWjBCCdwbWd0kOrvZvP6UmwOfs55x/AKYQGOPKpCSpKpzr/3XdHfdjMKpb4fDgn1tIL7TySOdwVeMCrE8tpMzu1KTMviTVIm4zRkY3asZ78xJkmXvJLRiMJlp2uYZ5X31AWXExD7/xiWYNzoUQDLl9HC069eCzFx/l1aSDDG4YwN1tQ2tE9WO9TtCkjidNzqtEGOFr4rEuERzJK+d4XjnrUwspsTmJDTQT7GVkQ1ohP+3MJtjTQJCnAX+zAT8PPUMaBeDnYeBkoZX0Qgs6IRCAQ1GwOxU6R/pi1Au2ZxSz/VQJeeV2zpTaySqxUWx1MPvGxuiE4FShlXK7k34x/jQL8aJlqBdBntVz5fFy/OO/QFGUJCBJCPGDoiipKuYkSVXiXP+vPiNvVT32mviZmA06emrQ/2vl0Xyi/Ew0qyalXCXJHbnLGJl6aB/1GzcnrF4DrVORpCohhGD0A0/g7efPT+++wjuP3sWT73+rSWGvc6IbN+O1n5cw5/N3Wfrz1+zIKGFC14i/Vp1qkgCzgX6x/vTDH6jYEZRX7sDHVDHB9PXQ0zjYk5xSG0dyy8kvd1Bmd9KngR9+HrApvYifdmb/7bo/jmhIgKeBA2fKWH2sgEBPPUGeRtqGexPmY8TmUPAwCMZ1CFP136uWykwpS4UQ7wItgL9uMSiKUmP7rEi1Q48hI/APqqP6AfXSokI2r15C3/p+mtwBu7tdKOU2Z43ZNy1JNVyNHSP3bU1myv03Mf7VqfQaVqMW9STpkgbfei8+/oF8NelJvnjlCZ764DtN8zF5mLn9iZfo0HsQX016ihd+T2VoowDualMzVsv+iRDif1aoWod50zrM+38+x+5UOHfcrH+sP23DvXGe16vNqBf4nC1Df2vLOtzWKsT1iVczlZmQTQdmA8OA8cBY4O9TW0mqZkLq1qOvBqtjG5bFYy0vZ2CcNndxmsuqipKkpho5RpaXlfLNqxMJi6pPl/7XaZ2OJLlEz6Ej8QsMIjhc/eJa/6Rp+y68OXslv376Dstmfc/WU8U82DGc9tX8bNnVMJxX/CPAbCDgX4qb1NabyZWZkgcrivIdYFMUJUlRlHuBri7OS5KuypKfvmTnxkTV4yqKQkL8TGICPGiocu8Rq8PJd9szySiyqhpXkmq5GjlGzv7kLTLTU7n/lXfx8JTbmyX31bpbbyJjGqIoCtOnTuHA9s1ap4TZ04u7Jk7i5e/mYQhpwOSkdN7Jb0Z+uV3r1CSNVGZCZjv7Z4YQ4johRDsgyoU5SdJVKczL5dfP3uXPDWtUj318/y5SD+5lYJz6PUeSTxSx6GAeWSW2S3+yJElVpcaNkXu3bGTFrGlce8s9NOvQTet0JEkVJUUFbF+3mjcevI3kFYu0TgeAJm078eas5Yy87zE2rVrCwwlnWHU0/3+280m1Q2UmZFOEEP7AU8DTwLeAbFQiVVvn+n/102C7YsL8mZjMZno30KKYRwFh3kZahckti5Kkoho3RpaVFBHXsi03P/qs1qlIkmp8/AKYPC2ehi3b8slzD7Pw+09RqsHEx2jy4MYHn+bNWcuJim3Mp3+c5vnVaaTkl2udmqSiS54hUxRlydm/FgB9XZuOpAW7zUbyikUkxC+hvLSUtj06c+0tdxJQJ1Tr1C6boiismT+Thq3aU69hU1Vjl5eWkLxiIT3CTar3/zpVZGVPVil3tK6Drpbuv5YkLdTEMbJjn2vp0HtQrT2rcbky01NZ+stPHPxzD36B/gy8aRQdeg/SrOmwdOV8/AN59vPpfD35aWZ/+jb5OdmMnThZ67QAiIptzIvfzGHt4jnM+Oh1nliewrDGgdzSsg7esqeo27vkhEwI0Rj4AghTFKWlEKI1cL2iKFNcnp3kcjarhdfH/4fUg3YsZY8CQZw8Po9Vc4YyadoMomIba53iZTm0cyunUo5w/8vvqh5708rFlJUUM6hbtOqxVx3NRyegX4y/6rElqTarSWPktqRVZJ86waCb75aTiUravy2Zdx59ELv9Xhz214A0Du/6gPa9VvPIG+/KSW0NZPIw89CUjwmNqk+jVu21Tud/6HQ6+txwMx37DGLWJ2+zOH4Ga1MLGds2lL4N/OTPmxurzDPyN8BznN0nryjKLuAWVyYlqef3eTNJOWDEUpYE3AwMxG79ktLiV/j8xRe0Tu+ylRQWUL9xc7oOGq567IT4mZr1/9IJQa/6fgR7GVWPLUm1XI0YIwtyz/DNqxPP9meUhQMqw+lw8NEzE7GU/4LD/g7QD7gbS9kmtq/dw451v2udonSFdDodNz00kXbX9Adg/dJ4sk6maZzV//PxD2Tci2/x6k+LqNOwNR9tyuCZ1WkczinTOjXJRSozIfNSFOWPCz4mn83dxOo58VjLnwEuWA5XxnHy+BHOZJzUJK8r1b7XAN6ctQKzl/elP7kKnThygCO7tzNIg2IeAHe2CeGJbnVVjytJUvUfIxVF4ZtXJ1JWUszDUz7CYDRpnVKNcGjnVqyWAGDwBY94YSl7nNVz5muRllTFSouL+Pm9Sbx81/Uc2rlN63T+R1yLtkz6YQEPTHqf04oPE1el8X5xK3LLqtVTjFQFKjMhOyOEiAMUACHEGCDDpVlJqikpygcu1p/DiMEQSklRgdopXbHMEynYrBZNYq+Jn4XBaKJvjPrFPE4UWKrFwWRJqqWq/RiZuGAW29eu5pZHnyUqronW6dQYxYX5CP7pRlcUhXk1Z3yU/pmXjy+vfD8fT28fXn/g5mpTgfEcnU5H7+tv4v34JK678wE2LFvAg0uO8uveM1jsTq3Tk6pIZSZkDwNfAU2FECeBx6lofim5gYat2iDEsos8koLDkUF4vRjVc7oSiqLw4X/H886jY1WPbbWUs+63uXTsMwg/j8r0Wq86WSU2Hl16nMUH81SNK0nSX6r1GFlcmM8vH7xK847dufbWe7VOp0aJadYau20TUPy3xwzGpTTv2Eb9pCSXqNsgjsk/LiK2eWs+ee5hFk37TOuU/sbLx5fbHn+Bd+f+TqtrrmX6rjM89NsxElMKZJl8N/CPEzIhxGNn/xqhKMoAIARoqihKT0VRUlXJTnK5kePGYfR4C1h33kez8DDfybW3jK0xDUPP9f/q3H+I6rH/+H0ZJYUFDLHvVD326mP5AHSJ8lE9tiTVZjVljPTxC+DJD75j/OQPZCGPyxQcFkG7Xv0xetwDFJ39qALMx2CcybW33KFhdlJV8wsM4vkvZ9J9yAiEqL6/K+HRMTzx/je89M2v+EQ3Y2pyBk+vTGVXZonWqUlX4d9+4u45++cnAIqilCiKUvQvny/VQLHNWzPhrXfw8b8Fs3c7PH36YjQ1oveIltz88ONap1dp5/p/9RgyUvXYiQtmEuZtpLXK/b8cToXVxwpoG+5NmI88EyJJKqv2Y2RBTjYALTp1p07ExbamS5fy0Ktv0KGXDoMpGk+fgZi9mhEU+gzPff4dweHy3K67MZo8eHjKxwwbW7HIfWzfTkqLq9Wv9V+adejGlF+W8OCrH5JvCuKlhBO8mnSC1Hxtjm5IV+ff9lftF0KkACFCiF3nfVwAiqIorV2amaSa9r0G8MWqPhzetQ1LWSlxLT/Gxz9Q67QqrbyslOQVC+k6cBhevuqe4cpIO86+rcma9P/acbqEnFI7/2lX8/rFSZIbqNZjZNqh/bw0djj3v/yuJjeq3IXJ7MmEt98nL/s0qYf24+MfQFyLtrL8uBs7939bXlrC24/cRWBIGP/95EeCQiM0zuzvdDod1wwbTZcBQ1kx6wcWfv8pjy1P4Zpho7lL7CDEW1Zerin+cUKmKMqtQohwYAVwvXopSVrQGww0bd9F6zSuyPa1qygrKabvyNtUj524YBY6Af1jA1SPnZRSiL+Hns6RvqrHlqTarjqPkXablS9eeQJPb19ade2ldTpuITAknMCQcK3TkFRk9vLm4dc/5sOJDzDp7pE88+nPRMY20jqtizKZPRl+94P0GXELi6Z9ysrZP5JstzKkUQBjmgfjb1b3fLt0+f51k6yiKKcVRWmjKErqhW9qJShJl9Jt0PW8+uMiGrfpqGpcu83G2sVz6FTXhyBP9Z/sHu0SzqS+9TDq5Z1aSdJCdR0jF3z7CakH9zLuxbfwCwzWMhVJqtFad+vNy9/OxW63Mfk/ozi8a7vWKf0r34BAbn/iJd6PT6LH8JtYfLiABxYfY+bubEptDq3Tk/5F9T21KFUZq6WcZTO+56mR1/PgwN58+N8nSD20T+u0qowQgoat2qm+hWT72tUU5GQzKE791TEAk15HbKBZk9iSJFVPKQf3snDap/QcOoqOfa7VOp0a4cSRA3z8zFM8NKgPT44Yzm8/f4O1XDbglSo0aNqSV76fj7evP7/P+0XrdCqlTkQk97/yHu/8upqWva5l1p4c7l90jPn7c2Sp/GpKTsjcnM1qYcp99zD702QyUt+jIGcJWxI688rdt7MrOUnr9K7anC/e4+f3J2vSh2tN/AyCvQy0i1C3CbVTUZi05gTrUgtVjStJUvV34vABAkPCuWviJK1TqRH2btnIy3fdyubf25F/ZhGn06Yy5/NtTLr3TqyWcq3Tk6qJsKj6TP5xIeNefAsAh71mNGaOjG3EE+99zZRffiOmQ09+/DObBxYfZcmhXGwOOTGrTuSEzM2tWzKfE0eMWMt/A/oBzVCcE7GWz+SLl1/E6ay5v5BWSzkrZ/9AXtZp1VfHzmScZFdyEgNi/dHr1I29O7OUHadLcMi+I5IkXeCaYaN5Pz6xRhVm0oqiKHzx0gtYyn9EcT4LNAf6YLUs4lRKAIkLZmudolSN+AUGYzCaKMzL4fnbhrBhWbzWKVVabPPWPPvZL7z83TzCW3Tim21Z3J9YyNLDeXJiVk3848EXIcRiKhpuXJSiKNXqELN0cQnxS7CUP8Hf5979sZR5cmzfThq2bKdFaldtS0JF/6++o7Qp5gHQP8Zf9dirjubjY9LRLUoW85AkrVS3MTIj7TgnDh+gc/8hGE0eaoausVIP7aOkGODC/pU6rOWPkRD/GoNuHqtBZlJ1ZvIw4xsQyOcvPY6iQM+hNaeKadN2nXnx61/Z+8cG5n71AV9t3cK8fTmMaR7MgFh/jHq5TqOVf6tE8N7ZP0cB4cC5jbO3AikuzEmqQpbSUiDoIo8IhC4AS1nN3Se/Jn4mIZHRtOjUQ9W4ToeDxEWzadW1F2E+marGLrQ4SE4v5tqGAXgY5BOnJGmo2oyRiqLw/evPcXz/bpp37CpXxyrJUlaKTgRQ0angQoFYSmWjXenvzF7ePP3RD7z32D188fLjGAwGug4arnValSaEoGWXnrTo3IPdm9Yy76upfLl1G3P35TD67MTMJCdmqvvH77iiKEmKoiQB7RRFuVlRlMVn324DeqqXonQ1WvfojN447yKPnMJu20dM05aq51QVzvX/6jviFnQ6dZ84diYnkZuZwWDdYVXjAqw5XoDdqTAoTv2VOUmS/l91GiM3Ll/A3i0buPnRZ+Rk7DLUb9ICh+MokPa3x/SG+bTuXjNbwUiuZ/b04umPptGkTUc+e3FCjTyTL4SgdbfeTJoWz7Of/UJQo7Z8tTWTBxYfY/HBXFn8Q2WVeSUbIoSIPfeOECIGCHFdSlJVGnrbWEymWcC3wLlDqMfxMI9m0E1jVW+kXFX0ej19R95G7+tvUj32mvgZmvX/qh/gwfAmgTQIkNUVJama0HSMLC0uYvrUKcS1bEv/UberFdYtmD29GHrHf/AwjwaOnv2oHfgRo+kHht11j4bZSdVdxaTsB7oPHkG9hk21TueKnZuYvfL9fF74chZhzTvy7fYs7lt0lHn7cmS5fJVUpnnSE0CiEOLY2fcbAA+4LCOpSgWH1+Xl737mi5deIiPteQyGUJzODIbcdg9jHpygdXpXLDQymvteelv1uHnZmWxPXMGIpkGa9P9qG+5N23B1qzpKkvSvNB0j5389lYKcbJ7+8Ht0er1aYd3GjQ9OQK83sOSnLuh0oTjsOYRG1ePBV38kpG49rdOTqjkvH18efHUqUHGcoaggD/+gOhpndWWEELTo3IMWnXtwYPtm4r/9mJ82rWXuMSvDog0MbxKIn4dsMO0ql/zOKoqyXAjRCDg3/T+gKIrFtWlJVal+4+a8NXse2adOUFJUSN36sZjMnlqndcWO7duJw+7QpPfY2sVzcCowIFb93mOb04uIDTQT4m1UPbYkSRen9RgZ27w119/zMLHN26gV0q0IIRj9wMMMH3svGWnH8fT2ITQyWuu0pBroy1eeJOXgHib/sBBPbx+t07kqTdt34bnPp3N0758s+v4zfl2znIUHchkQF8DIpkHydYgLVHaq24GKu34GoI0QAkVRfnJZVpJLhNSt5xZ7Ted8/l5FI8/fNiFUvCPsdDpZs2AmLUM9ifQzqRYXoMTq4L2Np+jTwI+HO0eoGluSpEvSbIzsPniEGmHcnsnsSf3GzbVOQ6rBeg2/kQ3LF/DtlGd45I1PVb9h7ApxLdryxPvfcPLYYRb/+DnLl8xl+eE8rqnvx8imQTQIlMcnqsolz5AJIX6moppUT6DT2beOLs5Lki4q+1Q6u5KT6H3Dzapvz9m3ZSNZ6WkMjFN/dWxtaiFWh8IgDWJLkvTPtBojD+z4g6W/fIPNKjesSFJ10LJLT256aCLJKxaRuNC9ethFxjZi/OSpTF2yiYG3/IfkTDuPLU9hcuIJdp4uQZF9Ua9aZVbIOgLNFfndlqqBpLNPcn1uuEX12GsWzMTHpKN7PfWLeaw8mk9MgAcNg+TdKEmqZlQfIxVFYcbUKeRmn2bAmDvUCitJ0iUMv/shdm9ax8/vTaJl556E1I3SOqUqVScikrsmTmLU/Y+xes7PLJ81jZfXnCAmwIMbmgbRM9pPk/P17qAyVRb3UNFjRZI0dX7/L7Wf5Iry89iSsJw+DdTvz3Ekt5xjeRYGxgW4xRYISXIzqo+R25JWcmTPDkY/8GSNPg8sSe5Gp9PxwKT3CakbRUFuttbpuIyPfyAjxk3g49+Suf/ld7EG1efDTRmMS8hj9p4zFJTbL30R6X9UZoWsDrBPCPEH8NfeCEVRrndZVpJ0ERmpx7CUldJv1G2qx163ZC52m5WBcXVVj304pwxPg47eDWpmiwJJcnOqjpFOp5O5X7xPeHQMvYaNcUUISZKuQkjdKN6avapW3EA1eZjpM+IWel1/E7s3rWX5jO+YsTGROQeL6Hbt9YywbiFO7uyplMpMyCa5OglJqozI2EZ8tmIrOp26Z8cURWHNglk0DjZr0v9rSKNAejfww8soS1pLUjU0Sc1g29euIu3wfh589UP0BlmCWpKqIyEEpcVFbF+7mp5DR2qdjsvpdDradO9Dm+59OHnsMCtmT2PdknmsLSulSR1PhjQMoGe0L0aVdxjVJJf8ziiKkgQcAHzPvu0/+zFJUo3NakFRFEweZgxGdcutHt61jZPHDmlSUMPmcALIyZgkVVNqj5G+AUF0HzKC7oNvcFUISZKqQNLC2Xz+4gRSD+3TOhVVRcY24t7n3uCz5Vu486lXKPAK58NNGdy78Cg//plFRpFV6xSrpcpUWbwJ+AO4EbgJ2CyEkPskJFUt/eUbnh7Vh/LSEtVjJ8yfgdnLm57R6m8ZfDHhBJ/9kXFV1zhRYGHu3hx+3XOGwzllVZSZJEmg/hjZpG0nHnn9E7k6JklVwG6zsmnlYuZ+8T6/z5tOSVFBlV2753Wj0RuMrP9tfpVdsybx8vVjyO3jeD8+iee+mEGT7gNZcKiA8UuO8VJC2tnq0U6t06w2KvOM/gLQSVGULAAhRAiwGpjrysQk6Ryn00niglkEhUVg9vJWNXZpUSGbls6lbwN/PI3qLrWnFVg4cKaMblFX1mDSqSh8+kc+61ItOJy34VTMzN03nRahpTx/TaDcOiBJVUO1MXLtkrm06tKTwBBZZ0uSrlb6sUNMue9urJY4ykuvwcO8jZ/ff5sJb31A+14Drvr6vgGBNOvQlV3Jidz+xItVkHHNJISgVZdraNXlGvKyT5O08FcS4mfy/sZ0vHz96RMu6B8bQFygR604d/dPKvOKTHduoDkrp5JfJ0lVYv/WZDLTU+k7Uv1iHhuWL8DqUBgY56967JVH8jHooG/MlcVefqSA9alhWB1pOJTPUHgfiyONPVlt+Xln1d0FlKRaTpUxMjM9la9eeZJVc36u6ktLUq3jdDh488FxFOZPprx0DfAqlvJfsZYv5+NnniQ36+p2ppzTuE1H0o8dxloud6cABIaEM2LcBD5cvIHnPp9O2x59WJVaxlMrUnj4D4UZwTeQXWLTOk1NVGaFbLkQYgUw8+z7NwPLXJeSJP2vhPgZePn607nfYNVjr4mfqUn/L6vDSWJKAV2ifPE3X9nWpPn7rFgcH1NxrOUcE1bHF6w42pqxbQPQ62rv3ShJqiKqjJEJ82cgdDrZd0ySqsDuTWspKwkC5Z4LHumMU7mZhPm/Mmb8Y1cdJzQqGsXpJDfrNOHRMVd9PXeh0+lo1bUXrbr2orgwn80rl7Bu6Xxmf/o2s4HmIZ70jPajWz1fgjxrx/bsS/4rFUWZKIQYBfQEBPC1oijxLs9MkoDCvFy2JCyn/+jbVe+3c2zfLlIO7OH+DmGqL6NvOlFMkdXJoNgrLySSW1YIdLrII3E4nDqKrY4rnuxJklRBjTHSbrOxdvEc2vcaQFBoRFVeWpJqpcz0VBz2i42PYLd2Iv3oiiqJ07n/UNp0641vQFCVXM8d+fgF0H/MHfQfcweZ6akkL1/IxhUL+XrbIb7ZlknzEE+61fOlc6QPYT4mrdN1mUu+GhNCxABLFUWZf/Z9TyFEA0VRUlydnCR5+fjyyBufUK9hU9Vjr4mfiUkvNOn/1TbCi/Edw2gd7nXF1wgw+5BTtgPoccEjKeiEA2+TrNwoSVdLjTFy58Y1FORk0+eGW6rqkpJUq4XUrYfesATbRQr+GYzbqRsTVSVxzJ5emD2vfByvbcKi6jNi3ARGjJtA+rFDbFq5hC0JS/l2+0G+3Z5FgwAPOkX60CHCm8bBnm61y6cy+9znAOeXQXGc/ZgkuZzBaKRz/6FE1I9VNW55WSkbly+gRz1ffDSYuPh5GBjSKBDdVazMjWjqgYf+caD0vI/aMeoeZUCsHwY3eiKTJA25fIw8dfwoQaHhtOnepyovK0m1VutuvTGZM/j/ncbn/IlON4N+I2+qkjjLZnzH7/OmV8m1apuo2MaMGf8kb/+6mg8WrOX2J17EHNOGeQfyeXZ1Gnf8lsGb69JZejiP9MKK1kg1WWX2KxkURfnrHoKiKFYhhPuuGUrVxrF9O/lz/RoG33ovXr7qrlJtWrmYspJiBnWLVjUuVBTzMOkFfa6wmMc51zUO4GBOGltO1sfquAcFM2bDD8QGljG2rdw+IUlVxOVj5PC7H2TwbffKUveSVEX0BgPPff4NU+4fi932C5ayXhg99iFYxPhX36JORORVxygvLWHuF+/Rqd8Q+o++vQqyrr3Co2O47s4HuO7OByguzGfP5vXs3rSW3clr2bT1JAB+gcE0btuRFnnbaRLsSVyQGbOh5tQgrMyze7YQ4npFURYBCCFuAM64Ni1JgpWzf+CP35cx9I77VI+9Jn4mUX4mmtVR99ya3akwfXc2jYM9r3pCptcJJvYI4mhuOcknfsDuhE6RnjQPCa7VpWUlqYq5dIx0Ohzo9HqMJo+quqQkSUD9xs35dNlaNq1aTNrhwwSHx9BzaCJ+gcFVcv3ffv6aspJiBoy5s0quJ1Xw8Qug68BhdB04DEVRyDyRwv5tmzj45x8c3LGFrenZAOgE1PU1ERPgQXSAB/X8PKjnbyLM24RR77rXQA6nQnapjfQCK6kFlkp/XWUmZOOB6UKIzwAFSAfuurI0JalySosK2bRqCT2GjFK991j60YMc3rWNe9qGqD5x2XKymPxyB4PirryYx4XigszEqVwlUpJqEZeOkV+/+jQlRYU89cF3VXVJSZLO8vD0pPf1VbM98XwpB/awaNpndB04jIat2lX59aUKQgjCo2MIj46h78hbASjIPcOR3Ts4vm8XKQf3sP/wAdbtSv/ra3QCgj0NhHgbqeNlJNCsJ8BswNdDj7dJh5dBj4dBYNTrMIiKGAoKTifYnAoWh5Mym5MSm5Mii4NCi4PcMjs5pTayS21kldixOy9/+2RlqiweBboKIXwAoShK0WVHkaTLtGH5Aqzl5fQbdavqsRPmz0RvMF5x/6+rsepoPsGeBtpHqDsJlSTpyrhyjHQ6nexYlyDPjklSDWK1lDP16fvxDQzi7mdf1zqdWsc/qA4deg+kQ++Bf32srKSYUylHyEg5xum042SdTONMRjoHsk+Tn5qFtbz8iuPp9HoCgkMICo2lXkQkHSKjCY+OoW6DOCJjG3F/n1aVuk5lqiyGAW8AdRVFGSKEaA50UxRF3q5TUdbJNBIXzCX7VBYxzRvRa/hofPyqbhWlulkTP5P6jZsT27yNqnGtlnLWL51H17pm1UvCZ5fY2J5Rwo0tgt2qcpAkuTNXjpGpB/dSlJ9L6269rzpPd+Z0ONi+bjXbEteiN+jpdu1gmnfsJrdmS5oweZi5/YkXCavXAL9AeV67OvD09iGuRVviWrT922OKomApK6UoP4/S4kLKSoqxlJVit1lx2O04nU50Oh1CCAwmDzzMnpi9vPH08cXXPwAvX390uqs/q1aZV5w/ANOAF86+fwiYDcgJmUrWxM/mh3fewOm8E4etJ1vWJDHvy3489/l3brkUXl5WSp2ISNp066P6gLp1zXKKC/IZ1L6eqnEB8svtxAaZGRCr/sqcJElX7AdcNEbu25YMQIvO3a/2Um6rtLiI1+67m8wTCuWlt4EoY8Oyl2nWvhFPTf1EFkKRVKEoCkkLZ2Mym+k+eASd+w/VOiWpkoQQmL28VT8ec6HKTOnqKIryK2fL+iqKYqeirK+kgoyrYSClAAAgAElEQVS04/z4zpvYLJtw2D4ExmMtn0lZybe8M2E8dptN6xSrnNnTiyff/5b+Y+5QPXbC/JmEREbTOkz9viGNgj354NoGbt34UJLckMvGyEM7thAaFU1gSHhVXM4t/fTeW5w83pzy0k3A46A8h6VsJ/u2lbF0urxvLLleRuox3nzodr5+dSIbli6o8eXXJW1UZkJWIoQIpuKwMkKIrkCBS7OS/pIw71ccjnuBRhc8cj12e312bkxUPykXspSVkXkiRZPYp9OOs2/rRgYFl15V/68rkVVio8Qq73NIUg3ksjGyTY++DLlN/SqzNYXNaiF5xQLs1jf435czHljLp7B8xiytUpNqgdysDL6d8iwTx/Tj6J4d3PPsFJ768Hu5VVa6IpVZy38SWATECSE2ACHAGJdmJf0l80QGDvvwiz7mdLQkL/u0yhm5VvLKRXw9+WnemrWS6MbNVI29ZsEsdAL6a1DM47vtmRzPs/DV8Fj5ZC5JNYvLxsh+o26risu4rdKiQlCMQMRFHm1BUX6G2ilJtYDdZsVgNJF5IpWkRb/Sf/QdjLrvMfyDQ7ROTarBKlNlcbsQojfQBBDAQUVR3G+fXDVQWlRIfk42gSFheHr7ANCgWRw7N27AZh17wWcr6HQbqdtg4N8vVIOtiZ9J3QYNqdeoqapx7TYbaxfPoWNdH4K9jKrGziuzs+VkMdc3CZKTMUmqYVw1RhYX5mMtLyMwJFw+L1BxRudMRjqKohBStx5CCHz8A9Ebddish4DGF3zFRupENNQiVckNFeblkLxiEWsXz6Fhy3bc89zrNG3fhY9/2yi3FEtV4pJbFoUQNwKeiqLsBUYAs4UQ7V2eWS1SUlTAR/99kgcHdufFO/7D+AFd+XLSC1jKyug38mZ0+vlA4nlfoSB0U/EPUmjWoatGWVe9c/2/+o66VfUXIDvWraYgJ7tK+39V1u/HC3AoMCBOFvOQpJrGVWPkhqULeGRwZ/LPZF11jjXdjvUJPDbsWiaOHsV/x4xmwnUD2b52NXqDgUE334HJ/DBQet5XnMHDPJGR4+7RKmXJTWxcsZC3Hr6DhwZ14Md3XsbpdNKgWUUZcyGEnIxJVaYyZ8heUhSlSAjRE7gW+BH4wrVp1R5Oh4NX/zOWbUlB2KzHKC89gs1ykOQVVt6Z8CD+wSE8PfUzzF43YvYahMH4GGav9tQJ/5rnv/zWre6cromv6P91zXWjVY+dED9Tk/5fTkVh1dF8WoR4EuXnoWpsSZKqhEvGyKyTqXiYPQmoE3rVCdZke//YwEcTJ3ImYypWyymsllPknP6Yj595lt2b13Hj+Am0vyYIo0cDjKb7MXmMxWhqxMCb+9HzulFapy/VIA67nUM7t7Jo2mc4nU4A9m1J5vSJ4wy7azxv/7qKN2cup++IWzTOVHJHlTlDdq7SwHXAF4qiLBRCTHJdSrXLzo2JZJ9SsNu+oGK3C0AYNsvPHNvXmKN7/qRF5x58uXoj25JWkZedSVTc07Tsck2V9D2oLpwOB8krF9Ox77X4BQarGvtMxkl2bUzkxuZBqvf/OppbzuliG7e2rKNqXEmSqoxLxsic06cIDo90q5tuV2L6hx9htXxIxVz3nEFYLZ8wfer7vDXrGia8/T6nUo6yKzkJvcFA+14rCQ672LkySfpf2afS2bx6Cfu2JnNwxx+UlRQjhKBDn2uJjGnInU+9gslsrvW/h5LrVWZCdlII8RUwAHhbCOFB5VbWpErYmbyR8tIb+f/J2DkGbNaR7N2ygYat2mEye9Lt2uu1SFEVOr2et2avory0WPXYiQtnA2jS/6tRsCcfD4kh3Efdc2uSJFUZl4yRedmZBIaEXXVyNZnT4SD14B/AxXZNjODE4dv+KrBQt0EcdRvEqZ2iVIMU5uVyZPd2Du/aRpcB19GgaUvSjx5kxoevE9Egju5DRtCyc0+ad+yOb0AgAB6enhpnLdUWlZmQ3QQMBt5TFCVfCPF/7N11eBzl9sDx78z6Jht3l0qaursrlApFi0NxudjlYkWKXZyLXlwvbYG6ABWaurtbUknaNI3r+s7vj9D+kAIFmpnd5P08Dw90t+05tMnOnHfe95x44IGGTavpMJmMSHIViu/X7+l0VRiMCeonpZGQ8AjVp9r7vF6WzfmK9rFWzeZ/pYaJrYqCEMAa5BpZU1lOasvWfzu5QCbJMrJswOutAX55bahFknXIsk6L1AQ/pygKkiRRVV7Kp89PJHf3doqPHQXqF4BjklJJy2pD6669eHvBxia/+CFo72y6LNYBM37y40JA9JI9R3qOuIAFX03A5XgICPnJO8XAdLoN/k6jzNRz4ugh3n3yfq5/6BlSW2SrGnv72uWUnjjOhN7qF77fHShnd7Gdf3SPw6ATD50FIRA11DXyolvuJSS8aW9lliSJjv3OZ9Oyt1F8j/38PfkdOvQ5D1knCrKmrqaqgiN7d3Fo7w4O79vFoT076NhnEFfd9zjWYBuH9+0iPasNgy+6kuZtO5GR3f70ky+j2YLRLJ6CCdo7mydkQgNKa9maXsOHsmbhQJz2p4B2wHpMlokMH38dUfGJWqfY4HJmTeXgjs2qPx0DWP7C7YSER9It0aZqXEVR+PZAOSa9LIoxQRB+pdeIsVqn4BeuvPc+9my6GEdtFV7vDQDIuk8xWz/jqnu/0Tg7QW0VJSc5vG8XbpeTrgNHAPCviwef7kYaGZdAWsvWpLaof7qsNxh5ddZyzfIVhLMlCjI/cNPjT5HVeTrzPnuSspMFxCSmM3bCvXQbfL7WqTW4U/O/OvYZpHr72IqSk6w/VsPolkYMOnUP7O4rdXC00sUd3UTLXEEQfu3wvl3YwiKafHOK2KRUnp86m1kfvcf6JSNAgS4DB3HhjbObxIKlAD9M+x+bli/i8N6dpwuv+NSM0wXZNf98EqsthLSstpos7ArCuSAKMj8gSRL9LriYfhdcrHUqqjs1/2vghVeoHnv53G/wKWgye2zhwQrMepm+KSF//JMFQWhyJl1/IUMuuZor733sj39yIxcZl8CERycx4dFJWqciNJDKshJyd24lb9c28vZspyj/MC9Nz0GWZfL27KCksIA23fuSntWGtKw2Pzve0GPYKA0zF4RzQxRkgqaWzJxCREwc7XsNUDWuz+cjZ+YUWkdbSAxRt5lHndvLyqNV9E8LwWIQ2xUFQfg1vdGIx+3WOg1BOOfcLieH9uwkLas1RpOZ2R+/xVdvvQDUN3JJTG9G83adcDnsmK1B3DjxedF2Xmj0REHWSCiKwrG8/VRXlJGY0UL1WV5/haIodOk/jK4DR6DTq/uluGfjGooKjnBFD/W3A3l8MLJFOH3E0zFBEH6DyWLFXqv+GJDGylFXy+G9OzGYTKS3ateo5nj6u7qaanZvXM2+LRs4sH0Tebu343G7ePzDaWR16k7rbr254p5HyWzdgfRWbTFbg37260UxJjQFoiBrBI4e2MMbD/6L0hOlyPpkPK5d9Bg6igkTn8BoMmud3m+SJInBF1+lSewlMycTZJDpmaxuMw+AEJOOazvEqB5XEITAEWQLpbaqQus0Ap6iKEx77y3mf/Y+OkMLFF8VJouDW598iva9B2qdXqNUWVrMnk1rSUhrRkqLVuQf3Mur992I3mAkvVVbhl9+PS3adya5eRYAzdp0pFmbjhpnLQjaEgVZgKssK+GpCVdTV/MccB3180jLWbd4Ak7nw9zz4mvaJvgbfF4vy+dNo9vg87EGq1sUVVeUs2HJ9wxPC8GkV3eV9FiVixM1LjrEBaGTxaqfIAhnFhoZTWVpidZpBLw5n7zH/M8X4XJuBWcqoOCoW8JrD1zO4x9+RkZ2O61TDHg+r5dtq5eyY+0Kdq5fQUHufgAuuOZWrmjxKBnZ7Xj8o+lkZLfz60ViQdCSKMgC3A/TJuN2XQDc8JNXw3E5v2DL8mSKjxcQnZCkVXq/aduaZbw/6Z9YgoLpPmSkqrFXzp+Ox+1iWKb6s8fm7ivjh0OVfDK2GcFGMT9HEIQzG3PDnSg+n9ZpBDSP28WcTz7A5VgGpP74qgQMxuV8lBkffMA/X3tTwwwDk6Io5B/cS2VpMW179ANJ4r9P3IvTXkfLDt3oc/44srv0Ii2rDQAGo4msjt00zloQ/JsoyALcjrVbcLvuPMM7QegNfcnbvc0vC7KlM6cQEh5J5/5DVY2rKAo5s6bQPNJMWri6K3UOj49lR6rolWwTxZggCL+rdddeWqcQ8IoKjqIoNiDr128qo9i/7VXVcwpULqeDXRtWs3n5Iras+IGyokJik1J5bc5KZFnmsfe/JjY5TTwBE4S/SBRkAS4oJBgo+o13i7AEBauZzlmpKDnJ5hWLGTF+AnqDuh0OD2zfREHufk3mf606WkWd26dJm31BEAKLvbaGvZvXkZbVhvDoWK3TCUiWoCC8nnLABfzyWnMCi1U0Vvo99toazNYgJEnik38/yrI5X2O2BtG2e18uuuU+OvQecPrnJjc7Q9ErCMJZE22GAtzgi8ZisrwJ2H/xziok+TDZXfxvlXX53G/wejwMvHC86rGXzJiMWS/TJ0X9Zh4LcytJtBnJjraoHlsQhMBSUXKSl+6+jm2rl2qdSsCKiIknKbMlSB//4h0Fg+llBl44WpO8/FldTTUr5k3n5Xuu55ZB7TmWV38ebNhl1/Hgm5/z3pJt3PvKBwwceznh0eovbApCYyWekAW4Dn0G0aH3PLau6oPT/i8gDVlehN74Onc88zJ6g0HrFH/l6MG9ZHXqTkJapqpx66qrWLdoHv1TbVgN6m4ZrHF5Kalzc0GLcNHCVxCEPxSbnIYl2Eburq0MGHOZ1ukErNueeponrr8Sl3MvXvfFQCVG81vEp5Zx3hUvap2e3yg+XsD/Xn2KrSuX4HY5iYxLYOil12CyWAFIb9VW4wwFoXETBVmAkySJu55/hXWL5rHgq/epKiujedvWjLpuav3KoB+689k3cdjrVI+7esFsnA47wzLVbzkfbNTx/qhMvIqiemxBEAKPLMs0a9OR/Vs3aJ1KQEvKbMmL38zl2y8/ZeuKuzGazQy6cCT9R1+C0dx0dysoisL+bRvxuFy07taboJBQDu/dyaCLrqTnsNE0a9tRzGoTBBWJgqwRkGWZnsNH03O4/2+/cDnsGM0WzD+uuqlpyYwppDRvRbMIdTuXeXz1RZheltAhno4JgnB2srv05Ku3XqCyrITQiCit0wlYkbHxXH3fw1x9n9aZaK+8+ATL505j2ZyvOXH0EC06dKV1t95Yg238Z+4qsYNDEDQilj8E1VRXlHPbsM4sn/uN6rEP7dnB4b07GBFSpvoFZ3V+NRNmH6Sw2qVqXEEQAlvbHv0A2LVhlcaZCI3B1++8xF3n9+Crt14gLCqGm594mQff/Pz0+6IYEwTtiCdkgmpWzp+Ovab69GwSNS2ZORmjTqJ/mvpdtRblVmDUycQG+995PkEQ/FdaVhuem/I9qS2ytU5FCECVZSUsnTWVgRdeQUh4BGkt2zDyqpsZcOF44lPStU5PEISfEAWZoApFUVgycwrN2nQkpXkrVWM77HWs/m6WJvO/CqtdbC+q48p2Uchi9VEQhD9BlmXSWrbWOg0hwOTu2sqCKZ+wdtE8PG4X0QlJ9Boxlm6Dz6Pb4PO0Tk8QhDMQBZmgigPbN3Esbz83PaZ+V6u1C+dir61hWI8U1WMvyq1AlmBweqjqsQVBCHwOex2fv/QEbbr1pteIsVqnI/gxl9PBc7eOZ/+2jZitQQy8cDzDLr2WxIzmWqfmV7weD/baauy1Ndhra3DU1eK01+F02HE57LgcDtwuJ26XC4/Hjcftwuvx4PV48Hm9KIoPn8+H4vt5ky5JAkmWkSQZWadDp9Mh6/To9Xp0egN6gwG90YjBYMRgMmEwmjGazRhN9f82mS2YLFZMFitmSxBGs1lsI21CREEmqGLJjMmYrUGaNB5ZOmuqJvO/PD6FJYcq6ZwQTKRVbFcUBOHPM5kt7Nu6gWN5B0RBJvyKvbaGPZvW0qnfEIwmM8nNs+gxbBT9Rl2CNVj9eZta8Ho8VJScpLy4iPLiE1SUFlNZWkJVeQnVFeVUl5dRU1lOTVUFddVVOOpq/1IcnQQ6WUKi/rydLHG6TZcCKAooKPgUfvyn/r//KkmWMVuDsAQFYwmyYQkKxhpsw2oLIcgWSlBIKEG2UIJDwwgKCcMWFoYtLAJbWATBoeHo9OIWP5CIvy1BFWMm3EWnfkMxW4NUjVuQt5/92zZyfYdo1VeaZAnu65mAxSB65wiC8NdIksSQi67ii1cmcWjPDjEPSgCgvLiIBVM/YfE3X+Cw1/Lmt+sIj45lwiP/1jq1c87n81FWVEhR/mGKCo5w8thRio/nU3y8gNITx6gsKTpj4WMzyoSY9ISYdMSZdARbdQSHGQkyWggyyFgMMhZ9/T9mg4xJJ2PSSxh1MgZZwqCTMMgSevnH4usv3EP4FAWvD7yKgsen4PYquH/8t8vrw+VVcHp9uDz1/3Z4fDg8CnZP/X/b3T7s7jrq3DXUnfRSd8xHidtHrctLrduL53eaRtuMMraEDEIiIgmNjCYsMpqwqBjComIIj44lIiaOiJh4LME28STOD4iCTFBFfEq6JoeIc2ZOQS/DQA22DMqSRLs4dQtQQRAan/6jL+Wb/77M/P+9z53Pvql1OoKGyotPMP29/7B87jd4vR66DjqPC665hfDoWK1T+9t8Xi9FBUfIP7iX/IP7OJa3nxMbFnG82oXL+/8Vl16GKKuBmCADKcEGomIiibIaiLDoibDoCTPrCDXr0cvaFxmyJCHrwNAAI28URcHhUahxeal2eal2eqk6/Y+HSqeXSkcRFfnHyD/gZYfDQ5371xWcWS8TbdUT0aYXkXGJRCckEZ2QRExiKjFJqdjCwkXBpgJRkAkN7svXnqbzgOFkdeymaly3y8mK+dPpnmgj1Kzul3pxrZvZ+8q4MCtCbFcUBOFvsdpCGHzRlXw3+SMuvuU+4kSHvCbH43ahNxjx+RTWLJhNv1GXcME1twTs14LX4+FY3gFyd2+jYPKzHLBkkH9gD06HHah/GhWdmEyKVU/7WCsJIUYSgo3E2YxEWvTo/KDY0pokSVgMEhaDTHTQ2d1nOD0+yuweyuweSu0eSuvclNZ5KK5zU1ReSt7u7VRXlP3s11iDQ4hLSSMuNYOEtEwSUjNJzGhOXEo6BqOpIf7XmiRRkAkN6vDencz/4n2i4pNVL8g25HxPTUU5QzskqxoX4Ie8SubuK2dUi3DVYwuC0PiMvPoWHPY61bd9C9o6tGcHsz58A3tdLY/8dzKRsfG8vWBjwH0d1FZXsn/bJvZtWc+B7ZvI270dp70OAKtBJiN8D8NSzKSFh5IaaiIl1IRJLwNWbRNvZEx6mXibkXib8QzvuqFDDHZ3FCdr3ZyocVFU46awxsXxsoMcPLKbNd97UZT6p5U6vZ64lAxSmmeR2iKb1JatSW/VlpDwSHX/pxoJUZAJDeqHGV9iMJnofb76h9FzZkwhJshA+zh1P9C9PoXFeRV0iLMSG3ymDz1BEIQ/JywqplGeDxLOLHfXVma8/zpbVizGGhzCiPE34PN6kXW6gCjGHHW17Nm8jt0bVrFrwxqO7NuJoijoJEgPNzMk2UKLyFCaR1iItxnEWBg/YjHIpIaZSA379dMvp8fHsWoX+ZVOjla6OFJ5nNyVeaxZMOf0z4mKTyKjdXuat+1I83adSW/VVjxJOwuiIBMajMNex+rvZ9N98EiCQ8JUjV2Uf5hdG1ZxZVv1539tPVFLcZ2H6zvGqBpXEITGL2/3dlbMn841/3xSnOtopNYsmMObD99BcGgYl97+AMMuuw6rLUTrtH6XoigU5O5j68octq1eyv7Nq/H4wCBLtIyycHnrSLKjLbSIsmDWi0ZXgcqkl8kIN5MRbv7Z6zUuL3nlDnLLHBwoq+TAmoWsXzwfAIPJRLM2HWnVuSetu/aiebtO6A1isfqXREEmNJi1C+dir6lm4IXjVY+dM2sqkiwzOEP9Zh6LcisINenoltg0Wg4LgqCeI/t2sWDKx6RntaHfqEu0Tkc4R+q38NXSqnNPOvQZxPi7H2XIxVdhCQrWOrXf5PV42LtlPRuXLmDLnC84WesGIC3MxKgWEXSID6JVlOXHrYdCYxZs1NEuNoh2sf//9Lbc7mFviZ1dxXXsztvCrM1rmfH+a5j1Etm9BtOhzyA69hlMZFyChpn7D1GQCQ1GlmXadO9LVqfuqsb1uN0sn/sNHfsMItJaoGpsRVGwmXQMbxaGQSdWrwVBOLf6j7mMZXO+5otXJtGuZ3/CosST+EB2ZP9upr37CpuWLqR5u85M+nQWlqBgRl17q9apnZHX42H3pjWsXTiPjTnfU11RhsFkokOkkUuyI+mcECQaWQkAhFv09Ey20TO5fnG6xuVl58k6thbWsjF3P5uXLwYgLast3QaNoPuwUZp04/YX0qnDeedSRnY75dkvvz3nv68gnI0NOd/z2v038Wi/RM2eUimKIrYTCU3GmCl7NymK0kXrPALF371GHj+cy8Pjh9O6a28eeP1T8VkTgI7lHeCbd19h/eL5WINDOP/qmxgxfoJfDnNWFIVDe3awcv4M1iycQ2VpMWa9TNeEIHql2OgUHyy2IQp/iqIoHKt2sb6ghrXHathXUt9dMy2rLX3Ov5De540lNDJa4yzPjSs6JZ/V9VE8IRMaRP7BvcSnZqI3qL9SljNzChEWPZ3j1d3qoSgKhyucpIebxQ2SIAgNJiEtkyvufpTPXnyc1d/Povd5F2qdkvAnHdqzgx1rlnHhTXdz/lU3EWRTf3v9H6kqL2XFvGksm/M1Bbn70csSXROC6Ncngc7xwWIrovCXSZJEUoiJpGwT47IjKalzs+poNcuOHOB/rz7FlDeeo1PfIQy++CradO+LLDf+rzVRkAnnnMvp4OmbL6XLgOHc/PhLqsYuPXGcbauXckmrcNXnlOw8WcfEJfk83DeRHkn+t8opCELjMeyy6zCaLXQfMlLrVISzUHy8gJkfvk5iRnNGXnUzvUaMoX3vgdjC/Gs0iqIo7N6wmsXT/8fGnAV4PW5aRpq5rWssfVJCCDbqtE5RaISirAbGZEUwJiuCo5VOfsirZMnqRWzI+Z4Em4Fhtz9Ov9GXYrY03jEIoiATzrmNP87/6jlslOqxl87+CsXnY4gGzTwW5lYSZJDpGOf/LYkFQQhskiQxcOzlAFSWleBxu4mMjdc4K+GXyk4WMvujt1gycwqyLDP6+jsAkHU6vyrG7LU1rJg3jcX/fZqCKhc2o8zIzFCGZoaREipalgvqSQk1cX3HGK5qF8Wq/Grm7y/n0xceY/prTzD8hnsZPv56v3yi/HeJgkw453JmTiU6MYXW3fqoGtfn9bJ09lRN5n9VOb2szq9mWGao2MYhCIJqfD4fz99+JYqi8PhH0/3yDFJT9cO0//H5y0/i83kZMPZyxk74h98VzaUnjvP9lI9ZOvUDat0+mkeYubtHPL2TbeJaJmjKoJMZkBbKgLRQ9hTXMX1PGdPefYXvPnqN0VkRDHlnhV93If2zREEmnFMnjh5i14ZVXHL7P1Xf87tj7XJKTxxnQm/1W6guO1yJx6cwPFPdeWuCIDRtsiwz/u5HeOnu63jl3ht48K0vMJrMf/wLhQZRVV4GQEh4BAnpzeg5fDTjbr6HmMQUjTP7ufyDe5n72busWTAbRVHolRjE6JYRtIyyaJ2aIPxKq2grE6Ot5JU7mLqzhCk7Spg/pi+X3v4AA8ZchqwL/K20YvlDOKfWLZ6PrNMxYMxlqsdeMmMyIRrN/1pxtJrmkWbSwsWNkCAI6mrXsz+3TnqVvZvX8Z8HbsHtcmqdUpNTU1nO12+/yD2jejHj/dcAaNW5B7dOetWvirG83dt59f4befDSoWxcMIPzM228NzKNB3onimJM8HsZ4WYe6ZvEi0NTSdLV8uEzDzLx6gs4sH2z1qn9beIJmXBOjbrudjr0GUR4dJyqcStLi9m8YjGjmoVqMv/rqYHJlNZ5VI8rCIIA0Pu8C3HU1fHRsw/x9dsvcuW9j2mdUpNQU1nOt19+yIIpH+Ooq6X7kJEMveQardP6lbzd25n+3mtsWbGYYKPM5W0iGdkighBT4D9ZEJqellEWnhucwsqj1XyyZS9PXDeGoZdcw/i7H8FsDcxz/KIgE84pWZZJbZGtetxlc7/B6/EwNFObg55mvUxiiLrn1gRBEH5q8EVXYg22qX5+tymb8sa/yZk5hW5DRnLRzfeQ3CxL65R+piB3H9+88zIbcr4n2ChzZbsoLmgRjtXQOAsxt1dBJ4MsSVTYPZysc+PxKXh8CqfG7mZHWzDoZErr3FQ4vJj0Ema9jNUgY9bLyGJsTUCQJIm+qSF0Tgjiy+0lzP/mc7Z9O4VbX59CVqfuWqf3p4mCTDhn3nnsHhLSMhk74S5V4yqKwrKPXyY72kJSiLrdoOrcXh5fks9V7aPpILorCoKgsZ7DRwPgcbv4/OUnueCaW/1qy1ygqyg5yfwv3qf3eWNJy2rD2Al3MWL8DX5XiJWeOM7MO4aSc6gKi0FmfNsoRrcM3EJMURTK7B5O1LhJDzdhNejYUljL/P1llDu8VDo8VLu8ODwK716QQbzNyJJDlXy2rfhXv9enY5sRbpH5/mAFX+8q/dl7sgRfXtQcq0HHgoMVbCuqJcKiJ8qiJybYQGyQkfRwkyja/IjVoOOmzrH0TrHx+tpCnrnpYsbeeA/jbr4noM6WiYJMOCdKTxxn1XczGXPDnarH3r1xDYU1bi5rE6V67OVHqjhQ5sAiulEJguBHCo/ksfr7OaxbNJ+7X3yX7C49tU4poBUfz2feZ++ydPZXeDxuIuMSSMtqQ3RCstap/UxdTTVzPnmb7yZ/CG4XY7IiuCg7MqC2JiqKgiRJHC53MGNPGflVTo5Xu3B46h9xPTc4hdYxVlxeHyV1HsItepJDjOU0nHAAACAASURBVNhMOmxGHVZD/fW4Z7KN5FATBp2ETuJ0EXVqllr/tBAyI8w4PT6cHoVat5dat+/09bza5eVQuYNNxz2nY5t0El9d0gKAGXtKOVHjJjXURFqY6XShKGgjO9rKayPS+GBTETM++A/7tm3krn+/TUh4hNapnRVRkAnnxNLZX4GinJ6Lo6acmZMJMsj0Sla/mcfC3EpSQ020iBTNPARB8B/JzbJ4+vM5vHzvDTx323guveNfXHDNrap3v20MPnl+Ij9M/x+SJNNv1MWMuvY24lLStU7rZ+rHvnzF1++8RFVZCQPSQriyXRIxQQatU/tdHp9CbpmDPcV17C11sL/UznXtY+iXFoLLp7CzuI6UEBOto60khBiJDzaSGla/E6Z7ko3uSb993Y+3GYm3/fZRgqQQ0+/uqrk4O5KLsyNRFIVat4+TNW6qnF6kHwu7kzVuVh6pYoHbB4AEtI+zMmlg/RPp0jo34Ra9eJqmIqtBx909EmgdbeXdjat47OoLeOD1T0jKbKl1an9IFGTC33Zq/leb7n1UXy2sqSxnw8I5msz/yitzkFvm4KbOMac/oAVBEPxFfGoGT38+lw+e/hdT3/g3pYXHuP7hZ7VOy+8pisLezeto2bEbsiwTFhXN8Muv5/yrbva7OWIA+7Zu4LMXH+fw3p1kR1u4YVgqzSP9s2OioijYPT6sBh0Vdg+3zMs9/fQpNshAdpSFCGv9rWmLSAsfj2mmZbpA/VmlYKOO4IifP/26tWsct3SJpczu4VC5k9xyBwb5/+8F/rXoCE6PQptYK+1jrXSKD1J9RmpTNSQzjJQwE88tL+CJ6y/kvpc/oHW33lqn9btEQSb8bdt/nP911X2Pqx57xfwZuH0KQzWY/7UwtwKDLDEgrfFNjBcEoXGwBtv4x/PvsKRrb9Kz2wHgdjnRG4xiIekXXA47q7+fzXeTPyL/4F7+9ebndOg9kAtvvFvr1M6osqyEKa8/y/K504i06rm/VwJ9U2x+9/fq9irsOFnLuoIaNhyvoXW0lft7JRBq1nFBiwgywk20irYSYQm8W1JJkoi0Goi0GuiS+P9Din2KwpVto9lxso7tRbWsya8G6p+6Xd0+GkVRUEA8PWtALSItvDQsjaeW5fPCXddw53Nv0W3weVqn9ZsC76tf8DtRcQkMveQaOvcfqmpcRVHImTmZ5hFm0jWY/9UhPoiYIMPp/eiCIAj+SJIkBl981ekff/biExTlH+a6B58mMaO5hpn5B0ddLTM/fIOcWVOoqSgnuVkWNz/xMq279tI6tTPy+XzkzJjM1Jcexen1cVGrCC5tE4XZD88yf771JN8frKDW7cOsl+gYF0TXHwsXSZK4un20xhk2DFmSGJQRyqCMUBRF4Vi1iy2FtTSLqL9XOVzh5OnlBfROtjEgLZSMcJPfFdKNQXSQgX8PSeWpZQW88dBt3P7M6/QaPkbrtM5IFGTC35aU2VKTbTAHtm+mIHc/d3RVd+bZKT1+Z++6IAiCv0rPasPahXN56PJhDL3kWi686W5sYeFap6Uqr8fDifzDJKY3w2A0sWbBHFp17M6wy68nu0tPv705Lsjdx4fPPMT+bRtpG2Pl1q6xqncX/j1HK52sOFLF5W2i0Mn17eS7JQbTK8VGh7ggjDr/KxobmiRJvzqvplA/5PjbA+XM2VdOWpiJIRmhDMkIw2Joen9GDSnYqOPJAUlMyovgnYl3o9cb6Db4fK3T+hVRkAl/y/Y1y7CFRZDeqq3qsXNmTsasl+iTqn5h9ENeBZ3igwkPwC0WgiA0bYMvvooug0Yw7b+vsOCrT1g292vueOYNOvUbonVqDe5Y3gGWz5vGinnTAHjz23Xo9Hpenr4Eo9k/z11B/RiD2R+/zayP3sQq+7i7ezwD00P8onB0e32syq9mwcEKdhfb0cv1C5aZEWYu1aD7cSDICDczsV8SVU4vK49W8UNeJf/bXszgjPojEA6Pzy+feAYqq0HHkxnlPHnUyFuP3MWDb4b63ZkycTcp/GWKovDJ8xMJj4rh8Y+mqxq7rqaatQvn0i81RPU2s0crnbyx7gTXdohmXKtIVWMLgiCcC6ERUUx49N8Mv/w6pr33GsnN6ruQFR7Jw2y1Eh6tzc6DhrJj7XK+eusF8nZvR9bpaN9rIAMvvPx0QePPxdihPTt494n7yD+4l36pIdzYKYZQs3/cvh2vdvHw4iNUOLzEBxu4rkM0g9JD/SY/fxdi0nF+83DObx5OaZ0bq0GHoig8tOgIEVY9l2RH0iraqnWajYLFIPNY/yQeWXyUV++/kUmfzvKr7oviO0b4y3ZvXENR/mHG3XSP6rHXfD8bp8POsMwY1WMvyq1AL8OgdNHMQxCEwJaU2ZJ7Xnz39I//9+rTbF+zlK6DzmPwuCtp1aVnQLbKLyk8xsalC2jbvS+JGc1RFAWfT+Gq+x6n93ljCY30/7NLHreLmR++wewPXyfMrOfRfol0S9R+q3xJnZv8Shcd44OIDTLQJSGY3j9uSRRNKv66SGv9iAKvAr2SbczdX85Di4/SPtbKVe2jaeGnnTMDSbBRx+MDkvjngsO8dM8NPPPFPL/Zri0KMuEvy5k5GastlO5D1N+Lu2TmZFJDTTSPULeZh9vrI+dQFd0SbYSJFUBBEBqZax54koVTP2HZ3GmsXTiX6IRkLrj2VoZeco3Wqf0un9fLwZ1b2Loqhy0rfuDIvl0AXHHPoyRmNKdtj36069lf4yzPXv7Bvbzz2D0c2beLgWkh3Ng5VvMGUqV1br7ZVcqivEqCjTIfjWmGXpa4q7v/jQIIZHpZ4tI2UYzOimDBwQqm7S7lgYVHeLx/Ep0Tgv/4NxB+V5TVwCP9knh48VHevaIn98/b7ReLTuKOUvhLqivKWf/Ddwwad4XqWz0O793JoT07uLlzrOr759cW1FDt8jJMgzb7giAIDS02KZWr//kkl935IOt/+I7l86ZRU1kOgNNuZ9q7L9OuZ39aduiq6TY/j9vNkf27cNrtZHfpic/n5d+3X4nL6aBl+y6Mv/tRugwYRnxqBoBfnLU6Gz6fj+8mf8hXb76A1RbCI30Tf3f4sRpqXF6m7Spl/oFyfIrC4IwwLmoVgV4OjD/TQGXWy4zJimBoZiiLcitpHxcEwOFyB4khRgxNsEHKudIi0sJNnWP474Yi5n32X0Zff4fWKYmCTPhrju7fjcFkYuCF41WPvWTmZAwmE/3TQlSPfbTSSVywgfZx6uzpVhSF3cV2VuVV4HD5aB5npX96qOrn5gRBaFqMZgt9Ro6jz8hxKEr94N78g3tZMPVT5n/xPnqDkYzW7WnRrjODL7qS2OS0Bs9p49IF7NqwmkN7dnB47w5cDgepLVvz7ynfozcYefDNz0lq1pLgkMBcMCsvPsF/H7+PnetW0C0xmDu6hfnFTowjFU5m7S2jf1oIV7SN8qvhxrUuLzmHKskrqsNi0tEvM4yWUY1ra5/VoGNMVgQATo+PJ5bmYzPq+EePeLGN8W8YnhnG9qI6vnnredr26KdJc7qfkk590J5LGdntlGe//Pac/76Cf3E57KqvkDrsddw5oBXdEoO5t2eCqrFPcXp8mFTofqQoCu+vK2T30Wpu8yrEADN0Ehv1Mk8OSyXOjy6KQtM2ZsreTYqidNE6j0ARyNdIh72OfZvXsXP9KvZtWc+hvTt58pMZZLbuwNqFc5nz6TvEJKUSHZ9ERGw8YZHRdOw3BLPFSnVFOfbaanS6+iLD5/PidrmIT81AkiRyd20lb/d2KkuKKS06TvHxAupqqnlucv2f1X/+dSvbVi4hNasNmdntaN6uMy07dm0UDUg2LVvEe5Pux11dwY2dYhmaGarpU71dJ+vILXMw+sdCoKjG5VeFGEBBlZNJi47Qx6sw2qtwDPivTqJrRijXarCDRi0bj9fwzoYTlNs9XN4miouzI9GJp5V/SbXTy13fHSI4sRnPfvkteoPhnMe4olPyWV0ftV96EQKOw16HyWzRZLvK+sXzqXP7NNkyeKoNrRrFGMCq/GqOHq1mh1fh1K7x67wKL/u8vLvqOE8OT1MlD0EQhFPMFivtew+kfe+BALhdztMFlskaRGhEFEf372bL8sW4XU4A3l6wEbPFyoIpHzPjg//86vf8ZNV+TBYLq7+bxXeTP0KSJEIjo4lOSCYpswVejwedXs+NE5/HGmRD1jWeHQJul5PJ/3mWBVM/ISPcxP0j0jSdK1bj8vLJlpMszqskLtjAiOZhGHWy3xVjAG+uOMYkl49bf/La7V6FbnmVbEoIpksjPW/VJSGYN89L592NRUzeUcL2ojom9ksS88v+AptJx21dYnluxV7mf/EeY264U7NcREEm/GmfvfAYx/IOMOmz2aqvQC2ZOYVEm5HsaHWLQY9P4fZ5eQxrFsblKs1VWbq3jIk/KcZO+YcCL1Q6OVHjEk/JBEHQlMH4/8VDxz6D6NhnEFD/hL+6opyqsmJCI+o/M7sMGkF0YjI+rxcASZYxGI3o9PUF1ugb7uSCa2/FFhZ5xpXqQN2K+FtOHD3EGw/dweG9OxjVIpxrO0Rrei5oXUE1/91wgkqnl3GtIrisTZTfDnI+XOGgptbNTb94PQx40Kvw6b6yRluQAQQZddzfK4GOcUFsK6rFrBdPyP6q7kk2uicFM+vDN+gz8iIiY7VpUiMKMuFPOTX/q9d5Y1Uvxo7lHWD/1g1c2yFa9dgbj9dQaveQHqbeymW5w0OLM7xuBFJkiXK7RxRkgiD4JUmSCAmPICQ84vRraS1bk9ay9W/+mlOFW1Ow/odveW/SP5Fl2S8ad5TUuXlx1XGSQ4w8PiCZjHB1Oxj/WeV2LxmyhM7762M3LYHKOo/6SWlgUEYog34cJn2y1s2xqvpxBMKfM6FjDHcsOMbXb7/IbU+9pkkO/rn0IfitU/O/tGrmodX8r4W5FYRb9KquuCWHmVl2htfLgYNehQSbKMYEQRACicft5ouXn+Q/D9xCssnNa/0jNS3GimpcQH0r8KcHJfPSsDS/L8YAkkONbPcq1J7hvRwJElUeieMPvthWzNPL8ll1tErrVAJObLCRCzKCWDl/OgV5+zXJQRRkwp+yZOZkUpq3IrN1B1Xjul1OVsyfTncN5n8V17rZUljLkIxQVQ/OnpcdyXM6iV0/ec0F3CpL9EgKJtQPum8JgiAIZ6e8uIhnbrmM7yZ/xMgW4Tw3OFWzs1mKojB3Xxm3zctjbUE1ANnRVgy6wNj6FmWt73Z8pyzh/snrW4DXZYnzsiJ+65c2Wrd2iaVFpIWXVx9ndb4oyv6scdkRmHQSc+8bq0l8UZAJZ+3U/K+BF45Xfcvghpzvqako16SZxw+HKvEpMDRD3SdzLaMsXNU1ll46ieF6mat1Ekk6ieMxFm4UgzgFQRACxt4t63l0THeO7NjI/b0SuLlzrGbFj9Pj49U1hXy4+SSdE4JpE6POGJdz7dZeCeyJspCik7hGJzFELzFILzGhRzxpAfCU71wLMup4YkAyLSItvLL6ONtOnOn5ofBbQkx6zm8exqr8aoryD6seXyyxC2ctIb0Zd/77Ldr17K967JyZU4kJ0tNOpflfPzUiM4z4YIMmK5kD0sPonhTCxuM12D0+Ho+ykBKqXQcuQRAE4ewpisLibz7n8xcmEhts4OlBKZp+hpfUuXl2eQGHyp1c3S6acdkRyAHaHt5q0PHI4BQOlzvYX+og3ihzS0Kwap2Q/ZHFIPNY/yQeWXyUKTtKaBdrbbTt/xvCqJYRzNlXxoKpn3LNA0+qGlsUZMJZM5rM9Bo+RvW4RQVH2LV+JVe2jdLkwhFm0dM/Tf1za6dYDDJ9U9Ufgi0IgiD8dW6Xk0+en8jSWVPpkhDEvT0TCDZq27J/58k6CqvdTOyXRJfExtGFMC3c3CSfiP2WYKOOJwYkYdbLohj7kyIsenolh7B87jdcdueDmCzqdfRuussIwp+ycekCZn/8Fh63S/XYOTOnIMkyg1XeMggwdWfJ6f31giAIgnA2KkpO8uwtl7N01lQuaR3Jo/2SNC3Galz1owYGpIXy7qiMRlOMCWcWaTUQZNTh8PhYklepdToBZXizMOpqqtiw5DtV44qCTDgr8z5/j+Vzv0GnP/dTzH+Px+1m+dxv6BJvJdKqbuwKu4evd5awp9iualxBEAQhcB3eu5PHrhnFkX27eKB3Ale1i9Z0W+Ca/GpunpPL/tL6a5najbEE7Sw4WMHr6wpZJxaWz1p2tIXYIAMrv52halxRkAl/qCBvP/u3btCkmceWlT9QUXJS9YYaAEsOVeJVYGimdtsVBUEQhMCxIed7Jl19PlSd5Pn+sfRJ0Xa7+YojVby46hhJIUYxKqUJOr95OKmhJt7bWITD49M6nYAgSxK9U2zs2rCKmspy9eKqFkkIWDkzp6DTG+h7wcWaxA6PjlV1/hfUH8RelFdBdrSFpBDRREMQBEH4bYqiMPfT//Kf+28iJczEK8PSyNB4Ftaqo1W8uuY4WVEWnhyYrPn5NUF9Bp3EbV1jKbV7mLGnVOt0AkbPZBtej4etK3NUiykKMuF3nZr/1XnAMEIjolSNXVpUyPZVSxgS41F1/hfUH3w+Xu1mqAZt9gVBEITA4XG7+fCZh5jyxnP0SrHx7KAUwi3abgs8UGrnldXHaRlp4fH+yVgNohhrqlpFW+mVbGP23nKqnB6t0wkIzSLMhJp0bF2tXkEmNhILv6u6oozM1h0YPO4K1WMvm/2VJvO/ANw+hawoC72TbarHFgRBEAJDXU01bzx4G9vXLOPi7EiubKdNN+Bfygg3c3nbKEY2D8diEGvvTd34tlG4fQq1Lh9i088fkyWJ9nFBbFu/CkVRVDmuIwoy4XdFxMTz4Jufqx7X5/WydNZU2sdaNZn/1Sk+mE7x/tGFyutT2Fdqx+721a/aiAPZgiAImis7WciLd13LsYN7uLNbnF/sqCiqcWHQyURY9FzaWt1dLVopt3vILXcQZJBpGWXxi4LY36SEmpjYL0nrNAJKmxgry4+c4ET+YeJT0hs8nrizE35TZWkxLqeT6AT1v4l3rF1OyYljXN87QfXYRyudRFsNfrGquKWwlvfWHCfSqxAlwWtehQFpIVzXNQ69yts4BUEQhHoFeft5/o6rqCst4rH+yXSMD9I6JercXp5eVoBelnh1RFqjL0zcXh8frTvBqvxqOuskChWFGr3Mbb0TaRNj1To9v3S82oXXp5Cs4XDyQNEysv4MaO7OraoUZNrfcQp+67vJH3Hf2H6qdpk5ZcnMKYSYdHRPVHfLoKIovLzqOM8sL1A17pkcqXDy+ooC/uf0ssPjI8ft47BPofZIFV9uLtI6PUEQhCZp75b1PHn9OJTKk/x7SIpfFGOKovDG2kKOVbu4vmNMoy/GAD5efwK5oJqjPoUlbh+7PQrvO7y8vDSfwmr1Z6b6O69P4aFFR5i6s0TrVAJCUqgJgyxxZN9OVeKJgkw4o1Pzv9r17E9waLiqsStLi9m8fBGD0kMx6NS9qOwvdXCk0km/VG1bFQN8u7uUe30Kg37yWgTwpVdhcV4ltT8O+hQEQRDUsXHpAp6/+WLCsPPC0FQywrXtpHjKnH3lrCmo4dr20bSP075AbGgVDg8rj1bzhVfh1ClzCTgPuNmn8N3eMg2z8086WaJrYjBbCmvx+BSt0/F7elkiOdRI/sF9qsQTBZlwRqfmfw0aN1712MvnTsPr8Wgy/2thbgVmvUTfVO2beeSW2Bl5hs/MOCBTlsivEiuAgiAIalk6ayqv/fNmUsNMvDA0RZPzzWeSV+bg820n6Z4YzJisCK3TUcXhciftdRJnWi4+X4G8k3Wq5xQIOsYHUev2kVvm0DqVgJAUYqLwSJ4qsURBJpxRzswphEXF0KH3oD/+yeeQoigsmTlZk/lfdW4vK49W0SclxC9aBAcbZc60cdILnPApBBvFt68gCIIa5n/xHu8/9QDtYyw8PTCFEJP/HMGPtOoZkBbKnd3jVekG5w+CTTqOKQpnes5TAASZtL+G+6Ps6PqzdftK7BpnEhhigw2UFh3H62n4cQH+84ki+I2aqgp2rV/FyKtvRqdX90tkz6a1FOUfZnyPeFXjAmwurMXhURjmB52yAPo0D+f5TUWc51Uw/OT1L4DQIMM5KVjL7R7m7S1l69FqkKBTSggXZEWITo6CIAjULxJ+885LzProTXon27i3Z4LqW+n/SKhZz13d1b9maikz3IRi1DHD4+Gin7zuAF7USwxtfm6OWmw+XsOC3aUU1riJCdIzpFUk3RODA7bwjbDoCTPrOFzh1DqVgBBp0ePzeqksKyYipmG/x8Rdl/ArwSFhvD5vNbJO/RWmJTMnY7WF0kuD+V99UkJIDjGREuof21AGpoey8UgVPUvt3OtRiAFmyPCNLDOx19/vPnmy1s3EBYe5yO1lsg8U4IN9ZTx0qJJnhqcRaTX84e8hCILQWPl8Pr54+UkWTP2EoZmh3NYlDp0fdbc9Wunkg01F3Nktzm+2T6pFkiRu653ITTlHWe5TGOWD48DLeonI2CB6p/z9e4gZO0tYuruUSV6FrsA2u4cnVh8nt3kYV3aM/du/v1Ye659MhMaDywNF2I+L05WlJaIgE7QRFhWjesyaynI2LJjN0MxQTHpttuOlhvlPK1idLPHAgGRW5VfzzsEKHG4vzeOCeKlF+DkpliZvKuIWl5dJP9nz0c0HDzq9fLW1mNvPQdEnCIIQiHxeLx8++xBLZ01ldMtwbugY41dPRRRF4cNNReSWOTBrdL3UWssoCy+dn8H3+8r4V1EdQSYd5zcPo0eS7W93mSyudTNrVym7fQqnroTZwDCvQssDFfTLCAvY1vHNIvyjEU0gsP249bW2qrLBY4mCTPiZbauXMvfTd7jtqf8QGafuDfmK+TNw+xRNhmt+suUkdo+P27rE+tVFVydL9EsNOeddH70+hdXHa5h+hg349ymQnl/NbSpNpxcEQfAnXo+H9yb9k5Xzp3Np60iuaBvld5+Fmwtr2VZUx42dYpr0FvPoIANXdzr3T6tW51czDvjlXVAkcI1PYdWRKi5vF33O46phX4md3HIH55+jbZ2NmeXHxQ57XU2Dx2qayyrCb1oyYzL5B/cREhGpalxFUVg6awrNIsyqtxF2enwsyq3A4fb53UW3oXgVBZ8CZyrzwgGn78yHpQVBEBozr8fDO4/dzcr507mqXRRXtov2u+uCoih8sa2YuGADI5qJm+qG4PT4iP6N1vDRCjjdPpUzOnfWH6vhg01ilunZOHVe1ONq+K7WoiATTjs1/6vfqIsxGNV9FH9g+2byD+7TpKHGqvxqat0+Tdrsa8Wok8m0GfjuDO/NBtqEmZrEYFFBEIRTvB4Pbz96F2sWzOHaDtFc0jpK65TOaMPxGg5VOLmsTZTfNRhpLLJjrMzQS/yy7FKAaXqJrFirFmkJKjt1ZFRRGr4AFwWZcNqyud/g9XgYMFb92WNLZ03RbP7XotwKEmwG2sQ0rQ/YizrGcLNOYu2PP1aAlcBdOolxHdQ/QygIgqAVr8fD2xP/wdpF87i+QzTjWqm7S+TPaB1t5abOMfQ/x1vZhf/XOtpCUIiJm2U4dXqoBrhPghqLgS4JwVqm97e4vD6MOnH7fzZOPSSVpIb/82q6G4+Fn1EUhZyZU8jq2I3E9Gaqxq6rqWbNgjn01WD+V36lk93Fdq7t4H/bUhpat0Qbjm5xjNt8EovXhw9w62Wu6xxLx/ggrdMTBEFQhc/r5d0n7mPtwrlc1yGasX5cjAEEGXVc0KJpDIDWiiRJPDgwmQ/XFpJSWEuKTiLfq9Au1spjPeP9qtvmn1Xj8oo5pmfJ462vyPTGhu9iKgoyAQCvx82wy64jLjlN9dhrFszB6bAzLFP9pzIWg8zoluEMSm862xV/ql9aKL1TQsivdIIEKaFiq6IgCE2Hz+fjw2ceZNV3M7mqXRQX+nkxNndfGcFGHQOb6DVLTUFGHXf3S6LS4eFkrZtoq4GwRtAuvszuIbwJN4L5M+ye+q2KZmvDL1KLvxEBAL3ByHlXTNAkds7MyaSGmmgRqX4r1iirgQkN0KEpkOhkiTSVG6kIgiBoTVEUvnhlEktnf8WlrSP99szYKV6fwlc7S2kfZxUFmYpCzfpG1cnyX70TqQ3gpiRqqnV7AbAGN/xxGvHMUqCmspyls6bisNepHvvwvl3k7d7OsGahqm8Z3FNcx9YTtfgU0U9QEAShqZn27issmPIxo1uGc0Vb/y7GAHaerKPa5aWvODsm/A1BRh0xQX9/lmlTUOWoL8hCwhv+ybkoyARWzJ/B+089QFH+YdVj58yYjEGWGJCm/mrf5B0lvL2+UPW4giAIgra++/JDZn7wOkMyQv1u6PNv2XC8BoMs0SFOnPEV/ppD5Q4+23qScrtH61QCQumPf06hkQ0/c04UZE3cqflfGa3bk9oiW9XYTrudVd/NoleyjWCjus08TtS42F5Ux5CMMHFmShAEoQlZ+e1MvnhlEj2Tgrm9a1xAFGMA20/U0Sraglkvbt2Ev2bDsRpm7CkL6KYkaiqucxMaGY3R1PDHOsR3dRN3cMcW8g/uY6AGre7XLZ5HXU0Vw5qpP3tsUW4lsgSDM8Q+fEEQhKZix7oVvPfk/bSJsXJfr4SAuTH1+BSMeon2seLpmPDXbTlRS3qYiRCTuovggaqw2k1MYooqsRrPKUXhL8mZORmTxUrP4aM1iD2FuJR0Wkc3fDvRn/L6FH44VEmn+CCirGIftSAIQlNw9MAe/vPPm0kKknm4b2JAzWLSyxIvD0vTOg0hgFU5Pewtsfv1jD1/c6zKSdvuGarECpxPI+GcUxSFooIj9Bw+WpUOMj917NBB9m3dwPDwGtW3ixTWuEBRGJap/pM5QRAEQX3lxSd48R/XYvE5eHxAkurb5AVBa6vzq/Ep0DtZ3fu9QFXl9FLu8JKU2UKVeOIJWRMmSRKPffANbpdT9dg5MyejC4CBqgAAIABJREFUk2CQBlsGk0JMfDimGWqVgTUuL6uOVlPu8JAeZqJLQnDAbJMRBEEIdE67nVfunUBtVSXP90sKyJ0RM3aXsuF4Dc8NTgmYM29nQ1EUDpQ52HaiFr0s0TPZRlywurtmmoo6l4/mEWbSw01apxIQDpU7AFTrryAKsibMXluDJSgYg1Hdb063y8nyedPonmQjTOXZHk6PD70soVepIFpztIp31hYyFGjpVfhBL/OFQWbikBRx0REEQWhgiqLw3qT7ObR7Ow/3TSQjIjBnLh6rdnGixt2oijGnx8eryws4XmLnYq9CrQwP7yhhULMwrgqQzpeBZFx2JGNbRYg/17N0sKy+IEvLaqtKPLFlsYk6vG8Xtw3tyI61y1WPvTFnATUV5QzNVP/p2Ky9Zdw8Nxe7CkMRT9S4eHdtIUu9CtO8Cs8Caz0+7rd7eCknH0XMPxMEQWhQcz99h7UL53J1+2i6JwXuVq06tw+roXHdsn25+SQJJXYOehVeAd71wQGvwp6DFSw7UqV1eo1KYbULRVFEV+k/YV+JndjkNGxh4arEa1zf3cJZy5kxGUVRSM9up3rsJTOnEBOkV32Wik9RWJxXQYLNiEWFC9uiA+Vc71Po+IvX/wHg8LCnxN7gOQiCIDRV29cs46u3XqBvio1xrSK0Tudv8foUDI1oq7vT4yPncCVveBV+uoE0AnjBq7BwV6lWqTU6J2vd3DE/j1l7y7ROJWD4FIXdxXW07NBVtZiiIGuCTs3/6j54JMEh6ja2KCo4wq71KzWZ/7X1RC0naz0MV6nN/skKJ93P8BBMArpSv2IlCIIgnHslhcd465E7SQkxcmf3+IDfpiVL4G1EuyoqHB6CgcQzvNcVOFbnVjmjxuvrXSVIEvRJCdE6lYBxuMJJtctHdtdeqsUUBVkTdGr+18AL1Z89tnTWVM3mfy3MrcRm1NE9MViVeJE2I1vOcA+gANuQiAkKvIPlgiAI/s7jdvPmw3fgra3iob6JjWKQckaEmWYBev7tTELNeqoVKDrDe9uAWItocXAu5Fc6+SGvkhHNwokW9xxnbWthLQBtu/dRLWbgf0oJf1rOzCnEp2aQ1am7qnG9Hg/L5nxNZw3mf1U4PKwvqGZQeggGlWbPDGkRzgeyxL5fvP4pUGuUaR1jVSUPQRCEpmTau69wYPsm7uweR4KtcTRPurR1FHf3SNA6jXPGrJfpm2LjAVnipye6a4CHdRJDsgJ7i6k/UBSFjzafxKKXubS1mD32Z2wqrCGleSvCo+NUiymWIJqgCROfp7K0RPUtHFtW/kBFyUmG9j3TJoWGZTPqmNg/iUQVL85JISau7hxL901FXKxAS5/CYr3ENp3MxAHJ4nCtIAjCObZrw2rmfvI2wzJDG9UWrQq7h61FtXSOD8Zmahwz1K7uEsvzVU7aVrkY71GoAT7XSXRMtjFEzAn928rsHg5VOBjfNopQlTtaB7Iqp5fdxXYuGDlI1bjib6gJSspoQVKGOoPufipnxhTComLonKDOlsGf0skSneLVjzsoM4z28UEsO1zJaruXVhFmbky2YWoEW2gEQRD8SV11Fe8+cS/xNiMTOsVqnc45VVDt4rU1hTzeP0mTa2hDsBp0PDksje0n6thSWINelvlXqo2M8MazNVNLkVYDb4/MwCLuN/6U9cfqB2h3HTRC1biiIGtC3C4nHz/3CMPH30Bay9aqxi4tKmTr6hwuygpXbQbYKXtL7KzNr+bi1pEEG9VfWYy0GhiXHaV6XEEQhKbkf68+RXnRcZ4fktoozo39VEa4CQnYX2pvNAUZgCxJdIgPokO8ul2XGzNFUVidX02PJJsm9zyBbsWRaqITU8jIbq9q3Mb1iSX8rk1LF7JsztdUlharHnvZ7K9QfD5NZo99u7+chbkVjaplsCAIgvD/dq5bydLZXzGuVSQtoyxap3POWQ06MsJNbC+q0zoVwc/9cKiSF1cdJ+dQpdapBJwKu4ftRbX0HDZK9WM9oiBrQpbMnEJUfBJte/RTNa7P52P5F6/TPtZKXLC6B6yrnV5W51fTPy1EbBMUBEFohFwOOx89+xDxwQYua9N4mxd0jA9mb4mdaqdX61QEP3W4wsF7G4toG2NlYLr6C+CBbtmRKnwK9Bk5TvXY4g61iSgqOMLOdSsYMPYyZFndv/Yda5dzstbDUA0O6S47Uonbp2gSWxAEQWh4C27pRVHBEW7vGodRpS66WuiZbMOnwM6T4imZ8GvVTi//XnGMIKOO+3sloBO7gv4URVFYUBlGZpsOmvRZaLyfXMLPLJ01FUmW6T/6MtVj58ycgs2oo0eSuvveFUVh4cFKMiPM4pCwIAhCI1RSeIzpB2ronWKjXVzjPoeUGW7ig9GZ9Ez+P/buOzzKKu3j+PfMpJNKEmpI6IhYsKCigh1U7G3Vteuurm111wbqCir2Xtb6rg3Fig1BUREREFGkIyCBQEgjvdeZ8/4xA4aYAAmTmZTf57pykTzlPHeGJ3Pmfk6LCnQo0gY9tTCTvIo6bj+iF3Fax63ZVudWkrFhHcee9deAXF//Y51EZEwsR512HvHde/r1usUFeSz+fhanDPDf+l/bVLssfWND2a+H1vsSEemIPnjhMay1XDa8W6BDaXXGGLp5F/d1ua1aQGQHF+ybyLH9ahiaqM88LTHj90IiIqMZOfa0gFxfCVknMe7iqwNy3bmff4irLjDdFcOCHPzr8I6zkKaIiPxhS+pa5k3/kNP36ro9UekM/vtzNkWVdUwYnRToUCTArLWsyq1kn24RDOwaxsCu6g3UErnltSxIL+XEv/6NsPDAJLTqstgJrF+5BLfL/4OArbXM+WQqeyeG0ycm1K/Xrqx1k1ZU5ddrioiI/3z40pOEBTk4e++OO5FHYxIjgvgpo4zVuRpL1plZa3lnRR53fruZxZllgQ6nXZu+rhAcTsaef3nAYlBC1sHl52Rxz2Vn8Nlrz/v92mt+XUjWpg0BaR2bu6mEf85MI61QSZmISEeTmZbKz998wbjBcUSHdq61lk4d0pX48CBeXZyDy20DHY4EwLZk7P1V+RzfP4YDtI5bi5XVuPhyfRGHnXAKib36BCwOJWQd3Lb1v0aeeLrfrz172lTCI6M4IgADkGelFpESE0pKrH9b5kREpPXNmPIKQQ7DqUPiAh2K34UFObj8gG6kFlZ7nuxLp+K2lv8t2bo9GbvukB44/LxmVkfy+doCqurcnHbZdQGNQwlZB+Z2uZjz6Xvsc+gouiel+PXaZcWFLPp2Bkf3dPp9/a+NhVWsL6jihAExfl/YT0REWld5aTHzZnzEUX2jiQ3rnEPhj0yOYkTvSD5dU0CNyx3ocMSP1uVX8dnaQsYNjlMytofKalx8vraQg44eQ/LgoQGNpXO+k3USK376gbysLVxw43i/X3v+zE+oralmzAD/zuoIntaxYIfh6L5aFFFEpKNZMPMTaqqqOGlQj0CHEjDGGK4f0YNat+3Qa6/JH7bNrLlXQjiPnJDC4PgwPXTeQ5+sKaC81s051/w70KGohawj+3n2l0TGxnHwMWP9el1rLXNemszAAKz/5XJbftpSxuF9oojqZOMKREQ6g7nTP6RvbGinn1EuNjyIxC7BuK1l7qYS3FbjyTqqLSXV3PxlGstzygEYkhCuZGwPFVTW8dmaAg4bcyopg/cOdDhqIevIrhg/mXEX/53gEP+Oo1q/Ygmbiqu5doT/n146HYbnxvWjolZdOEREOpqtGZtJXbmUS/dPDHQobcYvmWU8viCTjYVdubQTrMfW2SzKKOXJH7MIchicSsJ8ZuqKXOrclvOuvTXQoQBKyDo0h9NJz5T+fr/ud59MJSzIMCrF/5N5AEQEO4kIVuuYiEhHs3jOLAAOTw5M/dIWjegVyYkDY5n2WwFRIU7O6mTLAHRUdW7L28tzmfZbAQPiQrljVFKnWm+vNaUVVvHNhmLGDYqjR3K/QIcDqMtih2StZdIVZ/HtR2/7/dqV5WX8+NVnHJkc7fekaEtJNTfN3Ehqgaa6FxHpiJYvnEuvvgPpERkS6FDaDGMMfz+oO6OSo3hjWS7TfssPdEjiA/M2lTDttwLGDozloRNSlIz5iLWWV37NISI6llNf+j7Q4WynhKwDWvPrQtYu/ZngUP9P+b7gy0+prqxgTADWHvs6tZjNxdXEh6vhV0Sko3HV1bH2pznsH5wX6FDaHKfDcNPIXhyZHMXby/PIKasJdEjSAtZasko9/3ej+0Zz37F9uHZED03c4kNzN5Wwcmsl5113G5ExbWfZDH1y7YBmT5tKRGQ0hx43zu/X/u7jd0iJCWVwvH8HW9e63MzeWMwhSVHEKiETEelw0tevoarOsndCRKBDaZOCHIZ/jezFhsIquntbELfNzCdtX05ZDf/9OZvf86v477j+xIYHsV93LfjsS2U1Ll5bspX+w/bn2DMvDHQ4O1DK3cFsW//riJPOIDQ83K/X3rRuNRtWLw/I+l8/ZZRRUu1iTH9NdS8i0hGlrVkJwEA/P/BrT5wOw6B4T90/J62Y8d9uJr+iNsBRyc7UuS3TfsvnhhkbWZNXxcX7JxIdpnHwreHNpbkU1ViuGP8ADmfbeo3VlNDBzJvxMbU11Rxzlv8z/9nT3gnY+l+z1heRGBHE/j30NElEpCPKTEsl2GHorrE0uyXEadhUVMXNX6Zx88heHNBT9WNbU1Hr4vavN7G5uIYRvSO5+qDuJOr+bhXLc8r5KrWIcRf/nf577xfocP5ECVkHM2i/AznjyhvoO2SYX69bXVnJ/BkfB2z9r2P7xWBBXTNERDqorZnpdOsSrPf53XR4n2j6RIfyyPwMJs5J59TBcVy8fyKhQeocFWgl1S6iQz0zQh/QowsX75/IIb01c2hrqax18+xP2fSMDOaca24JdDiNUkLWwQwYNpwBw4b7/bqLvp1BRVkJYw5N9vu1rbUc3U9dFUVEOrKivK101RjhZukTE8pjY/ryxtKtfL6ukAN6duGgXpGBDqvTyq+o5b1V+Xy3sZgnxvalT0woVxzYPdBhdXivLdlKbkUdd7/6od+H8+wuvbN1IAu+/IQ+A/eiz8C9/H7t2dPepldUMMMS/Xuju9yWmesLGZ0STXSobmcRkY6qvKSY+BC17jRXaJCDvx/cgzEDY+kb6xl/t3BLKUPiw4lTgusX+RW1fPxbAV+uL8JiGTMgNiC9iTqjn7aU8lVqEade+g/2OuCQQIfTJP0ldhCV5WW8ct/tjBx7Gn//z6N+vXbGxvWsXfozlw5P9PtkHr9klvHK4q0kRARzWJKa+0VEOqramipN/70HtiVj5TUunvoxC4Cz9+7KaUO6qhtjK6qqc3PDjI1U1rk5pm8Mf9knfvssmNK68itqefanbPrutQ/n/OPfgQ5np5SQdRDb1v865owL/H7tOZ9MxRkUxLEBmMzj69Qi4sKcHKwuGCIiHZq14Odnfh1SlxAnT5zYl9eXbGXK8jy+WFfIucMSOGFAjBJeH7DWsjq3ksWZZVwyvBthQQ6uGdGDwfFhWtDcj1xuyxM/ZlHjCOG6yc8SHOL/tXmbQwlZB/HdJ1PpM3AIA/c9wK/Xra2pZu7nH3LQUWOIDd/g12vnV9SyOKucs4bGE6RB3iIiHVpISCjVdTbQYXQIvaJCmDA6idW5FUxZlsurv+YwvEcXekcrYWipiloX36eV8OX6ItKKqokKcTBucBzxEcGMTokOdHidzrsr81i5tYJrJj1B734DAx3OLikh6wA2rVvNhlXLuOTWSX7vMrh4zixKiwo4qW454N9Wqm83FOO2cLzWHhMR6fAioqIpz8kIdBgdyt6JEUw+Lpn0kprtydiTP2YSFerk5EFx9IpSgrY7VudWMPG7dKpdln6xoVw3ogdH9Y1WV9AA+SWjjA9W5TP61HMZfeq5gQ5ntygh6wDS168lMjaOI08+0+/Xnv3x1ICt/5VTXsv+3SPoqQpDRKTDi41PJCttRaDD6HCMMSTHeLpzudyeFsgZ6wr5fG0h+3WP4Pj+MRyWFKXkwstay4bCauZtLqF3dAjH94+lX2wYx/SL4bj+MQzqGub3h+Pyh6zSGh5fUkrykGFcMX5yoMPZbUrIOoAjTz6Tw04YR1CwfxOTrRmbWfnTD1ywb0JA1oW54dCe1LrUfUVEpDNI7N2HZXNrcVuLQx94W4XTYbh5ZC8uHd6Nb1KL+HpDMU/8mMWlw+s4a2g8tS43YAh2dr7X/7fcChZuKWPhllKyy2pxGBg3OA6A8GAH/xjRI8ARSkWti8lzt+BwduHmx14mJKxtTnHfGCVk7VxFaQnhkVF+T8YAvvt4Kg4TmC6D5TUuuoQ4O2WlICLSGfXuP5galyWnrFY9I1pZ1/AgztsngXOGxbNqawVJ0Z4WtAXppbz4Sw4H9ezCwb0jObBnlw675ExueS3rC6oY2cczg/O7K/NZubWC/bpHcPbe8RyWFEW0pq5vM7ZN4pFR7mL88y/Qrbf/18XdEx3zr6gTefSflxETn8hNj77k1+u66ur4/rP3ObBnFxIigv167aKqOv72WSp/O6g7YwbE+vXaIiISGP322geA3/OrlJD5icMY9u3+x5CEXlEhHN4nip8zyvhhcykGGNA1jPuPTSY82IHLbQPSY8YXcstrWZZdzurcSlblVpBdVosB3jxrINGhQVxzcHdiwpxEBCsJa4veWLqVnzPKuPyO+xl2yBGBDqfZlJC1Y9vW/7rgxgl+v/bS+bMpytvKmFG9/X7t2RuLqXFZ9kpoP03RIiKyZ/oM3IuwIAercysY3Vez1gXCoPhwBsWH47aW3/OrWJJdTnpxNeHBnvFlj/+YSXpxNYO6hjOgaxj940JJiQ1tU0lMrcuSUVrNpqJqNhZWc/LgOLp1CeaXzDJe/CWHqBAne3cLZ9ygOPbrEUFUiCd2PQRou75YV8inawsZe/7lnHDepYEOp0WUkLVj29b/Gn3qOX6/9uxpU4kLc3KQn9f/stbydWoxQxPCtw9CFhGRjs8ZFMTQw45hcdrvWGs1cUIAOYxhSEI4Qxo8GB2WGEFVrZtFGWV8u7EYgMHxYTw6pi8AH63OJ8Rp6NYlmMQuwcSHBxEV6vTpmEC3tZRWu8ivrCO3vJaU2FB6RIaQWlDFYwsyyS6rwTt3CUEOw77dI+jWJZgjkqPYp1sESdEhurfakR/TS3n11xwOOuoELv73PYEOp8WUkLVT9df/iolP9Ou1C7ZmsXT+bM7eK87v63+tzq0ks7SGc/bW4FkRkc7mgNHHsWTet6SX9NNDuTZo3OA4xg2Ow1pLbkUdGwurcNZLbqavK6Sgsm6Hc45KieZfh/cC4J7v0glxGsKDHIQGGYKdDvbrHsFhSVHUuizvrMjFbaHW5abGbamusxyWFMkRydHkltdyy6w0Sqpd2xMugCsP6MZpe3UlOtRJSkwIRyRH0Sc6hJTYUJKiQ7d/jokODeqw4+E6qlVbK3h8UR79hx3AdQ88h8PZdlpim0t3Xju1bf2vY868wO/XnvPpe1i3mxMG+H8yj1mpRXQJdnBksrqriIh0NgcfPZbXHrqLuZtKuGg//z6MlN1njKcVrFuXHceY/+/0AZRUu8gpryWvopb8irrtXQFdbkuty01xlZvKOjfVdW5qXJbwIAeHJUXhspbP1xbiMJ6WrZAgB2FOw+D4MAAiQ5yM6B1JTGgQsWFO4iOCSIgI3r6WWmKXYO4YleTfF0JazYaCKu77qZDEXn245enXCQuPCHRIe0QJWTt1wKjjuf7B59j3sNF+va7b7WbOJ++xX/cIekT6vz/1xfsnMjpFiy2KiHRGsQnd2O+w0cxeOo8L9gnMkivScsYYYsKCiAkLYnD8jt0dnQ7DA8enNHluWJCDD/8ypMn94cEOrj+kp89ilbZrc3E198xJp0tcD8a/8A7RcV0DHdIe06fadio0PJzDx56Ow+Hf/8KVP/1AXtYWTgjQ7IYJEcF+H7cmIiJtxzFnXkh+RR0/Z5YFOhQR8bOMkhr+M3szjqh4xr84lfjuHSMJV0LWDn39/hvMfPvVgFx79sdTiYyNY2SS/yfzeOHnbJZll/v1uiIi0rYcdNQJJPTozSe/FQQ6FBHxo8zSGu76uQJXeCwTXpxKz+R+gQ7JZ5SQtTOuujo++b9nWfnTPL9fu7ggj19nf8Gx3SHY6d9bZ11+FV+uLyKrrMav1xURkbbFGRTEuEuu5re8Spbn6CGdSGeQUVLDnYsqqK2pYcKLU0nqPzjQIfmUErJ2Zun82RTm5nDMWf6fzGPu5x9S5yYg3RW/3lBEqNMwOkWTeYiIdHbHnHE+cYndmbI8D2vtrk8QkXZrc3E14xeWUldby50vvUvyoKGBDsnnlJC1M7OnTSU2oRsHHHmcX69rrWXOJ1MDsv5XRa2LHzaVMColuk0tLikiIoEREhbO2Vf/i7V5lSxILw10OCLSSn7Pr2TCN5swxnD3Kx90yGQMlJC1K/k5nvW/Rp96Ls4g/06QuebXhWRt2hCQqe7nbSqlqs4yJkATiYiISNtz9Ol/IWXw3vxfKlTWugMdjoj42PKccu6anU5YQm/u+d80evcfFOiQWo0SsnaksqyEYSOOCMjaY7OnTSUi2MERAVj/KyzIwaFJkdvXGhEREXE4nVw+fjL52Zm8vSI30OGIiA/N31zCpB9yiE8eyD3/+4juSU0vidARKCFrR5IGDGHCC+/4/aYsKyli0bczOColmrAArP81um80E0YlYYzWmxERkT8M3v9gjj/3YqavK2LV1opAhyMiPjB9XQGPLsii/977cc//fUjXbh1javudUULWTmRv3khhbnZArj1/xsfU1lQHpMvg7/mVVNepK4qIiDTughsnkNi7D0+sdlNW4wp0OCLSQi635f9+zeGVxVs58KgTGP/fd4iMiQt0WH6hhKydePe5h7nzr+Nwu/xb2VhrmT3tHQZ0DaN/V/92Gayuc3PPd+m8vDjHr9cVEZH2I7xLJNfd/yyFudk891OWZl0UaYcqa908PC+Dz9YWMvb8y7n50ZcJDQ8PdFh+o4SsHSguyGPxnFmMHHsaDqd/ZxlMXbmU9PVrGBOAyTwWpJdSXuvmqL6a6l5ERJo2aL8DOf+GO/hxSxmfrNGC0SLtSW55LeO/2cTPmWVcetu9XHrbvX7/vBto/p2qT1pk7ucf4qqrDcxkHh+/Q2hYeEDW/5qVWkTPyGD27Rbh92uLiEj7cvJFf2f9yqW8+c10UmJCObBXZKBDEpFdWLW1gofnZVAb3IVbn3mF/Y84JtAhBYRayNq4bet/DR4+wu+rkleWl7Fw+nsc2TPE7+t/bSmpZnVuJScMiNVkHiIiskvGGK6+5zH6DB7GI78UsbGwKtAhiUgTrLVMX1fA3bM3E9EjhUlvfNppkzFQQtbmbUldS3Z6Gseccb7fr71y0TzP+l8D/T+Zx6ItZTgNHNvP/10lRUSkfQqL6MItT71GeGQUT2+K0ngykTZq1rBreWXxVvYfdTz3vfk5vfsNDHRIAWVa483KGJMLbPJ5wSIi0halWGsTAx1Ee6E6UkSk09it+rFVEjIRERERERHZNXVZFBERERERCRAlZCIiIiIiIgGihExERERERCRAlJBJh2WMudEY85sx5m1jzGnGmDuacW5fY8yFO9n/qDFmlTHm0RbENdwYc3JzzxMREfEF1Y8ibYsm9ZAOyxizBjjJWrtxJ8cEWWvrGtl+NHCLtfaUJs4rARKttdUtiOsy4GBr7fXNOMfg+Xt1N/d6IiIi9al+FGlblJBJh2SMeRG4AlgL/A8oxPsmb4x5HSgADgB+BT4DnvaeaoHRwNfAUGAj8Ia19sl6ZX8GjANWAA8Cs4EXgWTvITdZa+cbYw4BngLCgUrgcm95673bMrznDwXKrLWPectfCWyr6GYC3wEjgTOAIcAkIBRIBS631pbt8QsmIiKdgupHkbZHCZl0WMaYNDyVTF79p27eCicBON1a6zLGfA485K0kIoEq4Eh2/gSwzFob6f3+HeC/1tp5xphk4Ctr7VBjTDRQYa2tM8YcD/zDWnt2wyeAxpiJNF3hbAAOt9YuNMYkANPwPNUsN8bcDoRaa+/16QsnIiIdmupHkbYlKNABiATIB9Zal/f7+cATxpi3gWnW2i2eHhC77Xhg73rnRBtjooAY4A1jzCA8TxaDWxDnJmvtQu/3hwF7A/O91woBfmxBmSIiIk1R/SjiZ0rIpLMq3/aNtfYhY8wXwMnAQu/TuuZwACOttZX1NxpjngW+s9aeaYzpC8xp4vw6dpxgJ6yxOAEDfG2tvaCZ8YmIiOwu1Y8ifqZZFqXTM8YMsNausNY+DPwC7AWUAlG7WcQsYPsAZGPMcO+3MXj6wQNcVu/4hmWnAQd6zz0Q6NfEdRYCRxhjBnqPjTDGDN7NGEVERJpF9aOIfyghE4GbjDErjTHL8AwungksB+qMMcuMMTfv4vwbgYONMcuNMauBa7zbHwEeNMbMB5z1jv8OTxeOpcaYvwAfAV2NMUuBfwDrGruItTYXT8U11RizHE8FtFcLfl8REZHdofpRxA80qYeIiIiIiEiAqIVMREREREQkQJSQiYiIiIiIBIgSMhERERERkQBRQiYiIiIiIhIgSshEREREREQCRAmZiIiIiIhIgCghExERERERCRAlZCIiIiIiIgGihExERERERCRAlJCJiIiIiIgEiBIyERERERGRAFFCJiIiIiIiEiBKyERERERERAJECZmIiIiIiEiAKCETEREREREJECVkIiIiIiIiAaKETEREREREJECUkImIiIiIiASIEjIREREREZEAUUImIiIiIiISIErIREREREREAkQJmYiIiIiISIAoIRMREREREQkQJWQiIiIiIiIBooRMREREREQkQJSQiYiIiIiIBIgSMhERERERkQBRQiYiIiIiIhIgSshEREREREQCRAmZiIiIiIhIgCghExERERERCRAlZCIiIiIiIgGihEzDLd02AAAgAElEQVRERERERCRAlJCJiIiIiIgEiBIyERERERGRAFFCJiIiIiIiEiBKyERERERERAJECZmIiIiIiEiAKCETEREREREJECVkIiIiIiIiAaKETEREREREJECUkImIiIiIiASIEjIREREREZEAUUImIiIiIiISIErIREREREREAkQJmTTKGJNsjCkzxjh3cow1xgz0QyyvG2Pub+3rNIcxZoIx5tXdPHan8RtjEo0xa40xYU3sn2iMmdLSWNsKY8wiY8ywney/zBgzz58xNcVf97aItE+qI3dOdWTzqY7s3JSQBZAx5khjzAJjTLExpsAYM98YM8IYM9IYU26MiWrknCXGmOuNMX29fxC/NtifYIypMcak7Uls1trN1tpIa63LW+4cY8xVe1JmR2KtfcBa66vX4w7gNWttlY/KCwhjzKXGmMXGmBJjzBZjzCPGmKB6hzwG3Buo+ESkfVEd2X6pjvwzY8z53sSy2Biz1RjzhjEmut4hqiM7MSVkAeL9I5wOPAt0BXoDk4Bqa+2PwBbg7Abn7APsDUytt7mLd/s2FwIbWzF08SFjTChwKeD3p3sNkiVfiABuAhKAQ4HjgFvq7f8MOMYY09PH191BK/xeIuJnqiMFOlwdOR84wlobA/QHgoD6LYOqIzsxJWSBMxjAWjvVWuuy1lZaa2dZa5d7978BXNLgnEuAL6y1+fW2vYXnzar+MW82dVFjzCRjzLPe74O9Txkf8f4cboypMsbE1Xu6GGSMmQyMAp7zdtF4rl6RxxtjfjfGFBpjnjfGmCauO9EY86Ex5j1jTKkx5ldjzP719g/1PmEsMsasMsac1kQ5K40xp9b7OdgYk2eMGV4v5kuNMZu92++sd2yoMeYpY0ym9+sp75s9xpijva06t3mfXGUZY84wxpxsjFnnfTo7ocHvM6Xezx8YY7K9T77mmp10O2jgUKDIWrulXln9jDHfe1+nr/EkOPVfg8O8T42LjDHLjDFHNzh3rvfcb7z/J1O8+7a9PlcaYzYDs3ejvBhjzP95X48MY8z9pokuOtbaF6y1P1hra6y1GcDbwBH19lcBi4Exu/PCGGMeNcbMM8bEeH++whjzm/de+8oYk1LvWGuMuc4Y8zvwe71t1zR1f+6sPBEJONWRqiOhY9WR6dbavHqbXMDAevtVR3Zm1lp9BeALiAby8VQqJwFxDfb3AWqBZO/PDjxPBM/w/twXsN5/0wEnMBRYCxwPpDVx3WOBFd7vDwdSgZ/q7VvWoPwg789zgKsalGXxPMGMBZKBXODEJq470fv7nAME42k52ej9PhhYD0wAQrxxlAJDvOe+Dtzv/f424L165Z5e7/fZFvMrQDiwP1ANDPXuvxdYCHQDEoEFwH3efUcDdcB/vPH8zfv7vANEAcOAKqB/vd9nSr04rvAeFwo8BSytt297/I28Ltfh+QBRf9uPwBPeskZ7X4sp3n298dw3J3vviRO8PyfWO/cx7+t4JFBS79xtr8+bQBfva7Sr8j4BXvIe3w1YBFy9m/f4J8BDDbY9AzzRxPGXAfO8cbwCfAVEePed4b1HhuJ5qngXsKDBvfg1nifp4bu6P3ezvIGBfp/Ql7466xeqI1VH2o5XR3qvWey9TjkwpsF+1ZGd9CvgAXTmL++N/jqeSqQOT3N193r7vwEmeL8/AcgDgr0/b3vjCPIeNxZ4CLiTnVc24XjeNOPx9Mue4L1+JJ7uIM80LN/78xwar2yOrPfz+8AdTVx3IrCw3s8OIAvPU8VRQDbgqLd/KjDR+/3r/FHZ9MLz5hvt/flD4LYGMSfVK2cRcL73+1Tg5Hr7xm57nfBUNpWA0/tzlLesQ+sdv5g/KvuJ1KtsGvyusd5zYxrG38ixdwLv1vs52XsvdKm37R3+qDBuB95qUMZXeJ4Abzs3ot6+Kfy5sulfb//OyuuOp7IOr7fvAuC73bi3L/feVwkNtk8G/tfEOZcBPwHvAR8BIfX2zQSubHD/VAAp9e7FY3f3/tzN8lTZ6EtfAfxCdaTqyI5bR/b2vkaDG2xXHdlJv9RlMYCstb9Zay+z1iYB++B5I32q3iH1u2RcDLxjra1tpKg38fyhXsAu+llbayuBX4Cj8DxZ+h7PU7AjvNu+b+avkV3v+wo8lVZT0uvF4cZTyfXyfqV7t22zCc8bVsP4M/H0wz7bGBOL58np27sZUy9vufWv0avez/nWO0AbT8UDkFNvfyWN/H7GGKcx5iFjTKoxpgRI8+5KaHhsIwrxVGzb9AIKrbXlDeLcJgU419t1osgYU4TniVtP77kF1tqKesen82f1t+2svBQ8T0Kz6u17Cc9TwCYZY87A88HnJLtj9wy8v2vRTk4fiOeJ7iRrbU2DOJ+uF0cBYNjxHmnsd23qXtid8kQkgFRHqo6kA9aRANbTrf9L4N0Gu1RHdlJKyNoIa+0aPE+J6g8+ngb0NsYcA5xF0/3ePwLGARustZuaOKa+7/F0eTgA+Nn781jgEGBuUyHuRrm70mfbN8YYB5AEZHq/+ni3bZMMZDRRzhvARcC5wI/eN7bdkYnnTab+NTJ389yduRDPG+TxQAyep2zgefPaleV4x0p4ZQFxxpgu9bYl1/s+Hc/Tuth6X12stQ95z+1qjImod3wf/qz+/+XOykvH8/Qvod6+aGvtzqblPRFPV4pTrbUrGjlkKLCsqfOB3/C0rs00xgxpEOfVDeIMt9YuaOL32pXdKU9E2gjVkaojvdp1HdlAEDCgwTbVkZ2UErIAMcbsZYz5tzEmyftzHzxP7xZuO8b7BOhD4DVgk7X2l8bK8h53LLC7U8x+j+ep4mrvE5Y53nM3WmtzmzgnB8+sQHviIGPMWcYzw89NeN7IFuJpgi8HbjOeAchHA6fy5ydH23wCHAj8k50Mzm7EVOAu41nTJAFPX3hfzNwUhed3yccz0+ADzTh3ERBrjOkN4P2w8AswyRgTYow5Es9rsc0U4FRjzFjvU8cw4xlsnVTv3Inec0c2OLcxOysvC5gFPG6MiTbGOIwxA4wxRzVWkDHmWDxPYs+21i5qZH8ocBCefuxNstZOxdNN6BtjzLbK6kVgvPEOBPcOpD53F7/bzvi6PBHxIdWRqiO9OlId+VfjWb/OeCfImAx8W2+/6shOTAlZ4JTimT3oJ2NMOZ433ZXAvxsc9waeJ1Y7fVO11v5irU3dzWsvwNNPftuTvtV4+sw39eQP4GngHOOZbeeZ3bxOQ58Cf8HTBeFi4Cxrba23wjsNT9eKPOC/wCXeJ6J/4u1S8hHQD88T0t11P5434+XACuBXdpxytqXexNNlIgPPa7lw54f/wfu7v47naeY2F+K5NwqAe6j3f2+tTcfzpHECngG46cCt/PG3/FdgJJ6K7348fc2rd3L9XZV3CZ7Bz6vx/L99iKerRmPuxvP0c4bxzDRWZoyZWW//acAcb5eanbLWvoFngPlsY0xfa+3HwMPAu94uLyvx3C8t4uvyRMTnVEeqjuxodeTeeO6tMjzdStfimRxlG9WRnZix1het7CI7Z4yZiGcA6EW7OnY3y/sPnsGwPikvkIwxicAPwAHeitSXZb8HrLHW3uPLclsYy094BgmvDHQsIiJtierIpqmOlM5Ai8NJu2OM6QpciecJYrvn7QKzly/KMsaMwPPUcCOetUxOxzPBRsBZaw8NdAwiIh2d6simqY6UtkpdFqVdMcb8DU+XgZnW2p11H+mseuAZ71CGZz2Tf1hrlwQ0IhER8QvVkbukOlLaJHVZFBERERERCRC1kImIiIiIiARIq4whi4rtahN7JbVG0c1WkJNFSVEB3XsnEx4ZtesTGsjNTKeitJTuEQ4iQpwtiiGnrIaKWjeJEcFEhrasDBGRtiq1oCrPWpsY6Djai7ZUR4pI44rzc3EGBREZEwdAXV0tWWmpJIay/fNgUWUdpTUu+kSHgNmdZdWks9nd+rFVErLEXklMfntGaxTdbPk5WTx20+Wceuk1HH7iGc0+P2fLJp669Wo2rV3FecOiOX+fBJyO5v3RZZfV8OSPWazJq2RIQjhXH9ydyBYmdyIibc3pU9fszmK74tWW6kgR2T1L53/HIzdcwg2jkxnWzbO29NMLs1iaXc4TJ/YLcHTSVu1u/djhuyzGd+/J/W9N356Mpa5aitvl2u3zuyelMOm1jxl96rm8vyqfe7/fQkl1XbNi6BEZwgPHJfPXfROYt7mEG2duZENBVbPKEBEREZHWV11ZScM5FtLXrwUgOSZ0+7bc8loSuwT7NTbpmDp8QgbgDPI0BOakpzHx8rN44t9XUVVRvtvnh4SFc/XEx7nqrodZmV/LTQsqWJffvKUwnA7Defsk8MgJKfSMDCahi1YcEBEREWlr3n/+Yf45biRut3v7ti2pa+narQdR9Yae5FbU0i1Cn+dkz3WKhGyb7n36cskt97Bk3mwmXXk2+TlZu32uMYZjz7qQia99jDEO7pidyYzfC//0BGVXBsWHM/m4FKJDg3C5LU8vzCRVrWUiIiIibcLaZb+Q0CsJh+OPj8mZ8z8n2VG6/We3teRVqIVMfKNTJWQAJ5x3Kbc+/Tpbt2ziP5ecyobVy5t1fv+99+OBd2aw72GjeOmXHJ74MYvKWveuT2xETnktS7MruHVWGu+vzMPl1hIEIiIiIoFSVVnBprWrGLz/wdu3uV0u0ktqSIkJ2eHYJ0/sx8mD4vwdonRAnS4hAxh+xDHc879pOJ1BLP5+VrPPj4yJ45anXuPca2/hh82l3DorjfTi6maX0ysqhGdO6sfhfaJ4e0Ued3yziS0lzS9HRERERPZc6sqluOrqGHLAIdu35WRspsZlSY79Y/yYwxiSY0LVQiY+0SkTMoDkQUOZ/M5Mzr76XwAU5mY3q/uhw+HgzKv+yR3/fZtiZxS3zEpj7qaSZscRFerkliN6c8vhvcgsreHxBZnN7gYpIiIiIntu3bKfARi034Hbt21ZvwbYcUKPtKIqvlhXSEXt7k8UJ9KUTpuQAUTFxuFwOCjOz2XChSfzyn23UVdb06wy9j10FA9MnUnyPgfz+IJMXvwlm1pX87swjkqJ5pmT+nHTYb0wxlBZ62ZreW2zyxERERGRlhk24gjOv3E8kdGx27dt2bAOgD7RfyRkK3IqeHlxDnUabiI+0KkTsm2i4uI55swLmPPJuzx03UWUFRc26/yu3Xpy10vvMe7ivzPz9yJuWxlGTlnzEjuA+IhgUrzN4W+vyOWGGRuZtb5ILWYiIiIifjB4/4M57bJrd9i2JXUd3boEEx78x8fmvIo6QpyGKK0rKz6ghAxP98Pzrr2Va+97mnXLFvOfS08na9OGZpURFBzMX2++m5sff4XszWnc/GUaP20p3fWJTThtSFcGx4fx/M/ZTJqzhbwKtZaJiIiItJaivK2sW7aYutodP3NlbPidPg0m9CiorKNreBDGGH+GKB2UErJ6jhx3Fne+9C7lJcV8/MrTLSpjxDEnMvmdGSQOGMYDP2Tw2pKtLWrO7tYlmEnH9OHvB3VndW4FN87YyOLMshbFJCIiIiI79/PsmUy8/AzyczK3b3O7XGRt2rBDd0WAvIpa4sO1Bpn4hhKyBoYMH8H9U6ZzxYQHAM9q7c3VPSmFia9N44RzL+GTNQVM+HYzuS0YD+YwhnGD43jm5H4MSQinV1TIrk8SERERkWZbu+wXYhO60a138vZtORmbqa2pJrmJFjIRX1BC1ojEXn0Ii+hCdWUlk648m7cen4Tb1bxZdEJCw7h8/GRuePB5NlU4uOn7fH7JaFkLV4/IEO45ug89o0Kw1vLkj5l8naqxZSIiIiK+sm7Jzww54JAduiFmbJvQI2bHFrLHx/blqoO6+zU+6biUkO1EUHAwQ4YfzMy3X+Wxm6+goqz5Y8JGjj2Nye/MJL5Hb+6bu4XXW9iFcZvKOjd5FbU8tyibiXO2aCZGERERkT2Un51JXnYGQ4aP2GF7xobfAUiK3rGFLDLESWyYWsjEN5SQ7YQzKIhLb7uXKyY8wPIfv2fi5WeQm5ne7HJ6Jvdj0uufcNw5F/HxHnRhBIgIdnLfsclcfXB31uRVcMOMjcz4vRC3WstEREREWmStd/2xwfsfvMP2jA2/Ex8RRETwH7Mp5lfU8ubSrWwpqfZrjNJxKSHbDcefczF3PDeFgq05vDTx3y0qIyQ0jCsnPMgNDz7P5qJqbvpyY4tnYXQYw8mD4njmpH7slRDGeyvzqKht/tpnIiIiIgIHHzWGu195n5TBe++wPTNtPX0atI5lltbw0W8F5FXU+TNE6cDU1rqb9jn0SO578zMcTs8TErfLtf375hg59jT6Dd2XZ+64jgd+WMGpQ+K4dP9uBDubP21q98gQJh7dh9yKOiJDnLjclm83FnNcvxicDk3DKiIiIrI7QsLCGXrQyB22WWvJTEtlaNKO48eKqjzzCsSpy6L4iFrImqFnSn+6J6VgreW5O2/gnacmN3uyD4Aeyf2Y9PrHjD3/cj5fW8jt32wiq7T5C0kDGGPo1iUYgEUZZTy/KJtbZqWxoaCqReWJiIiIdCYVZaW8++xDZKal7rC9YGs2VRXlf2ohK672tIzFhmlRaPENJWQt4Ha56BIdw/Q3X+SJf19FZXnzZ08MDgnl0tvu5V9PvEp2XSg3f5nG3LSSPYprZJ8obj+yF4WVdfx7VhqvL9lKdZ26MoqIiIg05fdlv/DZa89TkJO1w/bMtPUA9G6QkBVVuXAYiApVQia+oYSsBZxBQVw54UEuu/0+ls7/jomXn8HWjM0tKuvgo8fy4NSv6DPsQB7/MZMnK/anag+SqMP7RPPcuP4c1z+Gj9cU8Oj8jBaXJSIiItLRrV36Mw6nk4H7HrDD9syNnhazpAaLQpdVu4gKceIwGh4ivqGEbA+M+ctl3P7cW+TnZPPwDZe0qPsiQELP3tz9ygecceUNfP/Z+/z7qzQ2Fra8y2FkiJPrD+nJ/cf24bx9EgCoqHVRXKXBpyIiIiL1rVv2CymD9yYsossO2zPT1hMe5CCuQdfEqw/uziunDfBniNLBKSHbQ/seOor73vyMv931MA6ns8WLNTuDgjjvutuY8MJUykJiuWXWJqavK9ijxZ/37d6FwfHhAExZnsd1X2zgmw1aUFpEREQEoK62hvUrlzBk+CF/2pe1aQO9o0N2WCgaPOP3Q4P0EVp8R3eTD/RM6c9eBx4KwKf/9yxvPHoPrrqWtUYNO+QIHnrva/Y9/FheWbyVyXO3+KRl68SBsSRFh/LsT9ncNXuz1s4QERGRTm9rRjpBwaEMOWDEn/Zlb9pA76iQP21/Y+lWZm8o9kd40kkoIfMhay2lxYV8NfV/PHLjJZSVFLWonOi4rtzy9GtcfMtEluTWcuMPJSzNLt+j2JJjQnng+GSuO6QHaUXV/HPmRuak6c1EREREOq9efQfw8nfLOfjosTtsr6mqJC87408TegB8u6GYNfmV/gpROgElZD5kjOHif9/D3/7zKKt/Wcg9l55Oxsb1LS7rpAuv5L63PqdLVAz3fJfO60u2UutqeXdDhzGMGRDL8+P6c3TfGPZK8HRnrHFpJkYRERHpnBwOB86gHdcUy05PA6BngxYyay1lNS4ig/URWnxHd1MrOOaM87nzpXcpLylm0pVnUVFW2uKyUgbvzf1TvuC4sy/i4zUF3P512h53N4wNC+KGQ3vSIzIEay0Pz8vgwR+2kFteu0flioiIiLQX1lomXXEWc6d/+Kd92Zs3Avypy2JlnRuXhUhNeS8+pISslex1wCHcP+ULLr/9fiIio/aorNDwcK6880FufvwVcmwE//oyjVnrfTM5hwWGJkbwa1Y5132xgY9W5+9RK5yIiIhIe5C5cT1rl/7c6Lj/bQlZz6jgHbaX13h6FUUGKyET31FC1ooSevZm5NjTAFj8/SxevOdmaqpbPp39iGNO5OH3ZjHo4CN5/udsHvwhg5LqPZvww2EM5+wdz/Pj+jO8ZxfeXJbLP2duZHOxJv0QERGRjmvNkkWA5yF6Q9npacSEOolokHhVu9xEBDuIDNVHaPEd3U1+kpm2gbmff8i9V51DwdasXZ/QhLjEHtzx/BT++q+7Wby1hhtnbOTXrLI9jq9bl2AmjEriP0clERXqpGu4py+1W1Pki4iISAe0dunPRHdNoEdyvz/ty0lPo1cjMywmRYcy9ZzBHN4n2h8hSiehhMxPTr30Gm5+/BUyN67nrotO4fflv7a4LIfDwbiL/u6Z8KP3ACbN2cLz7sOortvzyTkO6hXJwyekEBnixOW23P71Jt5dmeeTskVERETairVLFjFk+Ig/rTMGni6LDbsrirQWJWR+NOKYE5n0+ieEhIVx39/ObfEMjNukDN6b+9+azokXXMms917nX1+lsb6g5V0iG6qsc5MYEczUFXlcP2MjC9JLtKi0iIiItHu1NdUMGT6Cg44e86d91ZWVFObm0CPyzy1kq7ZW8Mj8DAoq93yNWJFtlJD5WZ+Be3H/W9O58KY76dV3wB6XFxIWziW3TmT8f9+mIqwrt81K4/1Vebjce544RYY4ue3I3tx3bB/Cgxw8PC+Tu2anU+SDhapFREREAiU4JJRr73+a0aec86d9uZmbAejZSEKWUVrD/M2lPvmcJbKNErIAiIyJ48QLrsAYw5bUtTx+8xWUFBbsUZn7Hjaah96bxYgTTuXt5XmM/2YTWaU1Pol3v+5dePLEvlx9cHestUSFeAa46s1IRERE2qOykqZnq87ZsgmAHo10Wayq9QzhCNc6ZOJDupsCLDNtA8t/nMtdF40jbe2qPSorMiaOGx/6L9c/8CzpNaH885ssZv5e6JNuhk6H4eRBcUw+Lhmnw1BR6+L6GRv4+Ld8arWwtIiIiLQj9111Ds+Ov67RfVu3eFrIekQ2kpB5P/OEOvURWnxHd1OAHXLcSfzn/z7E7apj4uVnsOCrT/e4zMNPPIOH3/+awcNH8OIvOUycs4W8Ct8s+rxt4GtlrZteUSG8vjSX677YyPzNGl8mIiIibV9ZSRHp69fSZ+CQRvdvzdhMeJfI7T2C6quuszgMBDv/PBGISEspIWsDBgwbzuS3Z9Bvr315bvz1/Dx75h6XGd+9J3c8P4XL77if33IruGF2Ht9tLPZZ0hQfEczdR/Vh0tF9CAty8Mj8TO74ZjMVtS6flC8iIiLSGtYt/QVofP0xgNzMdBJ6JjU6+2JokGm05UxkTyghayNi4hO586V3+evNd3HAqON8UqYxhhPOu5QHP/qepAGDeWphFg/+kEGhD2cGGt7TM77sukN60DMyePsCimU1SsxERESk7Vm7ZBHOoGAGDBve6P7czC0k9kpqdN95wxJ44ZQ9n5RNpD4lZG1IUHAI4y6+mqDgEEqLCpl8zQVsWrd6j8vtkdyP/7zyARfedCe/5tZyw4yNzN3kuy6GTodhzIBYbhrZC4Ccshqu/DSVl3/J1oyMIiIi0qb89utPDBi2PyFh4X/aZ60lN20dPbMXByAy6ayUkLVRhbnZZKWt557LTmfBl5/scXkOp5NTLrmGB6Z+SbfB+/L4gkwmb+1PUSusoxEW5ODovtHMXF/E1Z+nMnVFrroyioiISJtwyiXXcOpl/2h0X3lJEVV1brp3abxb4oer83n2p6zWDE86ISVkbVTyoKFMfnsG/Yfux3MTbmDKE/fiqtvz5Kl3v4FM/N/HnH/jeJb88C3Xzcn3aWsZQExYEP8Y0YPnTu7PgT0jeXdlPtdO30BVnWZjFBERkcA65LiTOOioPy8IDZ7uigDdmkjINhRW8VtuZavFJp2TErI2LCY+kQkvvsvY8y9nxpRXeO+5h31SrjMoiNMuu5YHps6ke1IKjy/I5MF5vl91vnd0CLcf2ZvHxqRw9t7xhAV5brclWeVaw0xERET87vflv7LxtxVN7s/LzgAgISKo0f1ut0Uz3ouvNX63SZsRFBzMpbfdy6D9DmTYiCMAT//mxmb+aa6k/oOZ+L+PmfnOq3zwwmNc/8UGrjywG8f2i/FJ+dsMig9nULynn/bGwiomzkmnV1Qw5++TwJHJ0TgdmjpWREREWt/7zz9CWUkRD079stH9+dmZACQ20ULmtuDw4WckEVALWbtx+IlnEBOfiKuujkduuITZ097xSbnOoCBOueQaHnp3FknDDuKZn7KZOGcLOWU1Pim/ob6xoUwY1Ztgh4Mnfszipi89a5i5tYaZiIiItKK62hrWr/iVvQ48tMlj8rMzCXYYYkL/vAYZgBtQOia+poSsnamuqsRay6v3387Lk26hprrKJ+X2TOnP3a9+yGW338eaErjhm2w+X1vg866FxhgOTYriqZP6cusRvXBbeG5RNhW1Gl8mIiIirWfD6hVUV1UydGcJWU4m8RFBTfYUSggPomdUSGuFKJ2UErJ2JiIyitueeYMzrrqROZ++x6QrziI3M90nZTscDsb85TIe+eBb9jrgEF79dSt3fLOJTUXVPil/h2sZw5HJ0TxzUj8ePD6ZyBAnbmt5bH6GWsxERETE59Ys+Qlgpy1kBVuzmxw/BnDNiB7cfmRvn8cmnZsSsnbI4XRy3rW38u8n/4+c9E08devVPp0lMaFnb2579k2uvf8ZslwR3PzlRqYsz6XG5ftWLKfD0Dc2DIDCyjo2FFbzyPxMbpixkTlpxZr8Q0RERHxi7a+L6NV3INFx8U0eU5ibQ9fwxsePibQWTerRjh101BjunzKd6qpKjDHU1dbicDpxOPY8zzbGcOTJZ7LfyKOY8sS9fPDFR8zfXMI/Du7Bfj26+CD6P4uPCObZk/uxIL2UD1bl8+SPWUxdkcc9R/ehl7oHiIiIyB644aHnt0/a0RhrLUW5OcT3b/pzzlvLcsmrqOXmkb1aI0TppNRC1s71SO5HyuC9AZjyxL08euOllBYV+qz86LiuXHvfU4x/4R1cMb24+7t0nvoxk+Iq3y8oDZ4Ws1Ep0Tx1Ul/Gj+pNSkwoiRGeJ1XrC6q0wLSIiIi0SFYzHLcAACAASURBVFhEF3r3H9Tk/vKSImprqokLb7q9IrO0hvUFvhm/L7KNErIOpM+AIaz6eQF3XngSqauW+rTsfQ8dxcPvf83pV1zP3C0VXDs7j69Ti1ptrJfDGA5LimLC6CSCnYY6t2Xy3C1c9Wkqby3LpcjHa6aJiIhIx7Xo2xl8+MLjuOqa/vxQmJsDQNedJGRBDs9nEhFfUkLWRlhrWbNkEfNmfEzqqqUtGhN23DkXcc//PgJg0hVn8/UHb/p0bFlIWDh/uf52Hpz6JUn9B/PcomwmfLOZtKLWf1IU5DCMH9Wb/bpH8NHqfK76LJXnFmWRVdo60/NL5+C2lhU55cxJK2ZjoZ54irRVZSVF/PTNFyz8ejplxb7rBSKdxw9ffMS8mdNwBjWdbBXl5wJKyLYpqqpj3uYSfkwvVQ+lVqYxZG1Axsb1PHLDtZQWAeyLtU+R0DOG2599gYSezZvJZ8Cw4Ux+Zyb/vfufvPPk/Rw46njie/i2n3PSgCHc9coHzP38A955ejI3z0rnpAuv5KLK74gIbnzdDl8YHB/OHaOSyCyt4dM1BczeWMwRfaLpGRVCjctNsMP4dEFr6dhSC6p4Yu4WYmvd7AW8by2JMaHcPDqJ2J1UxiLiP9Zapr30PJ+98SLOoCMwGOpq7+CUS//OOdfcoPd82S1ut5u1SxZx0FFjdnpccZ4nIYsJa/qzTLDTUOvq2AmZtZa3l2zlq9+LGOUwVAP/tZa/7JvIKUO7Bjq8DkmfOgKspqqSe6+6mNKi/4C9Cs9yg26y0h7jvr9dypOffoXD2bwkJyo2jluffp0tqWuJ79ELay0FW7OJ797TZ3E7HA6OPv0vHHTUGN599iG+eOtlfgwP4vIDujEqOapVK8leUSH8Y0QP/rpfAlEhntfm7eV5LMkq55QhcRyVEk1okBp/pWml1S4mz97MC7VuzsHzV+cC7iys4pE56Uw+sa8+6Im0Ad9/9gHT3/yc2upV1FZve0CZyRdvjSW+ezeOPev8gMYn7cOW1LWUFRftdLp7gOICT0IWG9b0x+NekcH0jQ31aXxtzee/FbBhfRHr3ZZEb2vgRuDYFbnERwYzsk9UYAPsgPSpNcAWfj2dmqp9wf6NP9Z+d+B230ZpURTLFsxpUbkOh4PkQUMB+GH6h9xy5lH8MP0jX4S8g6jYOP5298NMev1TolOG8viCTO6enc7mYt+vXdZQdOgfCzcO6BqGw8Dzi7K58tNU3li6la3lta0eg7RPszcUcYLbci5//NU5gQcsVJTWsCavMoDRicg2015+heqq54D6vUV6UVP1PNNefjlQYUk789tiz/pjQw86bKfHlRTkE+QwdAlu+uPxGUPjuffYZJ/G15a43Jbpv+XzqsuSWG97P+BJl2X6itxAhdahKSELsPUrVlFdeXyj+6qrjmPT2lV7fI19DxtN/2H788J/buLle2+lpsr3HzYH7Xcg97/1OZePn8yGqmD+OXMjr/6aQ3mNf/ocj06J5skT+zL5uGT26RbOJ2sKeHv5H28avhxLJ+3f5rxKxjTS5cQBHAektcJi6CLSPK66OvKy1wFHN7J3FIV5adTW6G9Vdq2irJje/QfTrffOE6mSwnxiQp2duodEaY2LmjrL8Eb2HQekaux+q1BCFmBxifEEBac2ui8kdAPRXRN8cI3uTHhhKqdfcT1zPnmXuy85jcy0xq+5JxxOJyecewmPfzyXo8/8K9PXFXHNd4XMSi3yywLPxhj26RbBHaOSePnUAVy4r+e121BQxTXTNzBtdX6rTdcv7Ut0RBDrm6hvUw1Eh7beWMiOJL+ili0l+kAsrcPhdBISGg1samRvOsHB4QQFa41K2bUzr/onj3zwzS6PKyks2OX7/4L0Eq7/YgNFHfTzRESwgxogr5F9G4DYVpwroDNTQhZgo045C+OYCjRMkJZh3V9z6AnjfHIdZ1AQf7n+dm5/9k2K8raStcn3Cdk20XFduequh7h/yhf0SO7H84uyuXVWGqtzK1rtmg0ldgmme6Snoq6zlq7hQbyxLJcrPl3PI/MzWJpV3mpT9kvbd/SAOF52GBouDzoPWIphRO/IQITV5pVU1zF/cwkv/pzNP6Zv4IpPU3lzqbqvSOswxnDUaecSFDIRqP9+bQkKnsSoU87p1C0Zsnu29ZDZnXultKiAqF0kZLUuS3pJDeU1bp/E19aEOB0cnhTJ/WbHvzo3MNFhOHpATKBC69CUkAVYQs/eXPSvOwgJOxyHcyLwMc6gOwgJPY5r7n2ILlG+vfH3P+IYnvxs3vaZhpYtmEN1ZeuMl+k3dF/u+b+PuP6BZymqdjH+m808Mj+DnDL/NncPjg/nweNTePbkfpw0KI7l2eU8NC+D6jrPW011Xcd8U5WmpcSGcuqweA50Gh4w8DFwkwNOcxpuPLIXIU69NQKU1bhYtfWPByn3fr+FR+Zn8l1aCT2jgrl8eCIX7rfnrfgiTfnL9TfTPWkZoeHHAm8BbxEafjzdkn7hghv/HejwpB349qMpjL/gRMpLi3d5bHlJ0S4Tsi7eycTK/DQkIxAuPqg7n0UEc6LT8A7wGnB4kGFjbChnDtN7fmvQLIttwAnnXsiQ4Qcw672pZG1aSJ+BfRl7/jR6pvRvletFRHpmxynMzebxm6+kR3Jfbnz4BZL6D/b5tYwxHH7iGRx01Fg+f+MFpr/5Aou+2MhpQ+I4Z1h8q06T31ByTChXHdidS/ZPZFNRNeHBDqy13DprEzFhTo7pF8PIpCjCdzKYVzqOM4YlMKxHF2b/XsiM8lp6dQ3n4UGx21tWO6PSahercitYubWClTkVpBVVE+w0vH32IEKcjv9n776jo6q2B45/7/SZTHqvpFESSEKHUEMXKQIWwIKo6FMU9dlAn71XLA99tmf3YfmpWEClSJHeey+hJIEU0stkyv39EYwNJcAkk7I/a2UtMjP3nj0kmXP3Pefsw6TUYAw6DYkBJnQaGZkQ9c9i9ebJ//0fq+d/x4rvPwOg1wUj6Tl0FAajycPRiaZg+9oVlBUXYrH6nPG1ZcVFeAf9/XXJL9WdS5txQuZr0vHMhXEsPVzC60dK0GoU+sb6kh7tLZ/99UQSskYipnUSU+5/tEHb9A8O466X3uG1B27n/itHcPU9j5Fx0fh6mQJiNJu55MY7GDB2Ip/OeoYv5n7BwoPFTEgJYmiCX4P+gRu0GloHmgFwqtArxpvFh4p5eXUOr2uPkx7tzcg2/rWvEc1X60Bzi/45F1Y62JFXQedwLyx6Ld/vL+TjrfkYtAptA81MSAkiJcSC9tRnQmqYl4cjFi2R3mCk78iL6TvyYk+HIpoYVVXZvWE1qb0yznhto6oqFWUleIX/feL2yxqzUlvzTcgAjDoNQxP8GJrg5+lQWgRJyFq41PT+PP3Jj7x6/2289ejd7Fy3kqmPv1xv8/IDQ8OZ+thLXDDxGj6a+RhvrF/DN/lWJkdX0SPS2uDrAXQahQkdghjfPpBd+ZUsOVTC8iMldA73onWgmaJKBzll1bQNMqORtQqiiSu1OVlzrJSdeZXszKsgp6xma4j7+0XRLdJKRqwv7YMttAk0oZdpm0KIJu7YgT2UFBaQ3DX9jK+1VVXidDhqpyT+FV+TlrRQS+1ImRDuIAmZwC8ohHtf/Yhv3nsNqNvC1/MVn5zGA299zsZlC5j98lM89XMWSUFmru4YTFKwpd7b/yNFUUgOtpAcbOH6LiG1jy/OLOa9zXkEWXT0ivamd4wPbQJNkpyJRs/uVDlYWMXu/Eri/Y2khHpRUGnn32uP423QkBRs4YJEP5JDLMT710z9CvHSE+Kl93DkQgjhHjvXrwaoU0JWWVYK1FQZ/DsWvbZZ70MmPEMSMgHUlBcec9202u+3rFzC4b07GTnpRjSa+rlTrigKXfoPpWPvgSyZ8wlfzryfGQuP0D3SypWpwbTyM9ZLu2fy25GBYYl++Jt1LD9Syrx9RXyzp5AQLz2vjYiTEQTR6DhdKh9uyWNPQSX7Cqqwn9pu4uKkAFJCvYjxNfLvC+OI8jHITQUhRLMXFhPLoIuvPOP+YwCV5WXAmRMyIeqDJGTitDYuW8iCz95n2+qfmfrYi/gHh9VbW1qdjkGXXEnvEeP44eO3+faD17nt+0P0j/VhYkoQYR4ssmDRa8mI9SUj1pfyaifrssrIKq2uTcaeWZ6FUavQPcpKxzCvBi1SIlquaqeLg4U29uZXsregEh+Tjhu6hKLVKKzJKsPboGF4az/aBZlJCrYQYK75qNcoCjG+nrnRIYQQDS2tVwZpvTLq9NqqinIATLozJ2TPrcjC5nBxf//o8wlPiFqSkInTmjz9MWLbtueD5x5i+vih/OOh52tL5dcXk9nCmCm3MuiSq/j2vdf48dN3+fnwQYYk+HFp+0CCLJ6dSuVl0JIR9+s2BC5VxaTTsDarlMWZJeg00CHEwvDW/vSM8vZgpKI5cbpU8ivstdUfX16dw9LMYpynNogJtOjo8Zt902ZdGIdWqmAJIVq4ksKT2G1VBIZF1On1tsqahMxch4RMBbJKG3YLH9G8SUImTktRFAaMnUjbTt2Zdd8tvPDP63jgrc9J6tKz3tv29vPn8tv/xfArruOrt19hwVefsOhIJcNiLVycHFh7t9/TNIrCbT3DcbrC2JVfybqsMtZnl3H81Id0qc3Jx1vzSAvzIiXUglUWAIs6yCu3syOvggMnq9h/soqDhVW4VPjkkjZoNQqJASb8TFraBJppE2gi8A83KiQZE0IIWD73Cz6a+Siv/riuTrN8bFVVQE11wTMJtuhZe6wMl6rK9G/hFo3jylY0WhGxCTzy3hxWfD+Hdp17ADXzrM1e1jMcef78g8O49t4nGXX1TXz19ivM+/ZzfjxcwQWxFsYlNZ7ETKtR6BBioUOIhWs6heA8tW7nSLGNxZklfL+/CI0CCQEm0kK9uCDRj2ApnNDi2Z0ujhZXc7CoioMnq7g8NRirQcuiQ8XM3lZTej7O38SgOF8SA824VBUtCiPa+Hs6dAAq7S62nChHLwmgEKIR2rFuJaHRsXVeclF9KiEzaM/8mRZq1WN3qRRVORvNtYho2uS3SJyR3mAk46LxAORmHeH+q0YyctKNjLzqH2i09T/qExwRzQ0PPsdF19zMV2+/wtzvPufH/UUMSfBjXFKAx6cy/tEvIxTtQyx8NK41ewsq2Xy8nK0nKvhqVwEDT017XJ9dxs7cCpKDLbQLNssIWjOlqiqFVU5MOgWLXsu2E+W8tSGXYyW22mmHJp2GAXG+tA40MyjOl55RVqJ9jI1utOtEWTXrs8tZl1XGttwKHC6VjrI3mRCikXE6HOzetIb0oaPrfIy9uiYh09chIQs7dVP1eFm1JGTCLeS3SJwVi9Wb5K69+OSVp9iyYjE3PvIiwRFRDdJ2aHQsNz4yk7FTbuXrd2bxw9wv+HF/IQPjfBmXFEi4t+eKf/wVvVahfYiF9iEWrgAq7M7a+en7T1YxZ/dJvth1EoAYXwPtgszc2DWs0V2Ii7orq3by8+ESjhbbOFxczeEiG6XVTv6ZHk5GrC/eBi1BFh3dIq3E+hmJ9zcR7q2vnfYS7KUnmMZxk8HhUtmVV8GG7HLWZ5dxtKRmOm5YTBxDJg6ic9/BtO3UnUk94j0cqRBC/OrQrm1UlpXSvluvOh9jr675fKvLqH+0r5EBsT5Y6jC9UYi6kIRMnBWrrz+3PfMffv7u/3j/2QeZMWEoV9/zKP1GXtJgMYRGx3LDQ88z9vrb+fb911j89WcsPHiQPjE+jEsKIO7UnkqN0W+rME7oEMTYdgHsKahkd14lu/MrySyy1SZjz6/IoqDSQWKAiQR/E/EBJiK9DZKseZiqquSW28kqqSartJpjJdUcK7HRO8aHC1v7Y3O4eH39CUw6Da18DaRHW2nlZ6RtoBmAWH8TD2Y03spcJysdbMwpY0N2OZuPl1Nhd6HV6WnXuQdX9hlIp36DCY+J83SYQgjxl7avXQ5A8lkkZE6HAwBdHfrYYC89t6fXrViIEHUhCZk4a4qi0G/UpSR16cl/HridPZvWNmhC9ovgiCiuvfdJxk65jXkfvcWiLz5i2eFMOoV5MS4pgJRQS4Nscn0+jDoNqaFepIb+edpXpI+B3HI7P+wvovrU3LbUUAuPndqQ8qdDxQSYdET7Gggw6xr9e21KfqlsmFNm53hZNTmldiK8DQxL9MOlwk3fHaydbmg1aIj0MdbeVQ0w63h7dAJBlqbxM3G6VPYUVLIhu5wN2WUcKrIB4B8cSvcRI+jYeyAdevTBYpXKoUKIpiHjovFEJbTBNyCozse4nDUJmbaOn9uqqlJud8lyA+EWkpCJcxYcEc39b36Gw14zzH9o1zZO5ubUe3n8P/IPDuWKf97PRdfdwsLPP+SH2e/wwOKjxLZLYax/Pn1ifOp0x6uxmZgSzMSUYJwulWMl1RwsrKrdH8XpUnl17XEcpwqIWPQaIr0NDE7w5YJEf1RV5UChjTCrXjqL07A7VU5W2smrcJBbbiev3I5Fr2FU2wAAps49yPEye+3r9RqF/rE+DEv0Q6tR+Gd6BAFmHZE+BnyN2t8lXoqiNPqiLXnldjYdL2djTjlbChUqykrQaLW0Se3C+N4D6NhnIDGtk5pEQimEEH/kFxRC14xhZ3WM61R/WtePvZdW57Anv5LXRyWcbXhC/IkkZOK8aLRaDNqaqVhzP3yDlT98Tb9Rl3DVXQ/j5e17hqPdy+rjx5jrpnHhldezYt5XzP3wTV5clcMHm/O4sI0/QxP88DE2veREq1Fo5WeklZ/xd4/996IEjhTbOFpcM2XuWEk1TlfN80VVTu78MRMAL72GEC89IV56hiX60SXCSpXDxYGTVQSYdQSYdXUq89uUHCuxkV1aTVGlk8IqBycrHSgK3Ni1ptrWQ4uPsCOv8nfHtA821yZkFycFggJhVj3hVgOBFt3vShv3beXTcG/GDWwOFztyK9h4vJzNOeW1a8ECzTq6D7uEtF4ZdOjRp8H/ZoUQwt0y9+xg7+Z19B15yVlWhP4lIatbRhZuNbA0swSbw9Xs+lDR8CQhE25z4yMzCYlqxTfvvsr2tSu4/oFnSeuV0eBxGIwmBoydSP+LxrNl5RK+//htPlzzM59uz6d/rA8jWvs36nVmdeVn0uFn0p12uqNZr2FGn0iOl1WTW24nt9xOTlk1ZdVOAI4W27hv0ZFfX6/T4GfSMqVLKF0jrGSXVjN/fxFWgxYvgwYvvRaLQUObQDM+Ri02h4uyaidGnQajVkGnUc5rNMWlqtidKnqtgkZRKK5yUFDhoNLhqvmy13wNTvBFoygsySxmQ3Y5pTYnpdVOSmxO7E4X741tDcCn2wtYdrik9vw+Ri1RPr8WfRnVNoCBcU6CLHqCvHSEeOkxaH/tUIcm+p3ze2kMXKpKZqGNzcdr1oHtzKvE7lLRaxTade9Dv/T+pKb3JyqhrYyCCSGaldXzv2Xuh2/Q95yXUqh1elWsvxEVyCyy0TbIfI5tCVFDEjLhNjq9gcum3k2X/kN4/cE7eOaWq7jzxXfo0n+IR+LRaDR06jOQTn0GcnT/bn6Y/S5Lv/+SBQcySQ42MzzRn/RoK3pt87uzZdJpSI/+6zU/Ed4GHs6IprDSwckqB0WVDgqrHLUjiMdLq5m7r7B27dovHhsQTWqYF2uzynh+ZfbvntNrFJ4aHEPrQDNLDhXz3uZcFEXht5f7TwyKIdzbwNy9hXy8NQ+HS8XhUmvXY703JhF/s47v9hby2Y6CP8Xdp5U3Fr2W46V29hZU4m3U4mPUEultwMekrd2k89L2gYxu61+btP6xjPHf/d80VbnldraeSsC2nqig2FaTfEcntmXw4L6kpvenXaceGM1y4SCEaL62rV5G65TO57Bfak0/odYtHyPu1KyVg4VVkpCJ8yYJmXC7hPYdeeJ/81j4+Ye1I2RlxYVYfT23oW10Yjuuf+AZJt46g6Xffs7Czz7ghVWH8d2oZVC8L0MT/Bpl2fz64mXQ0in8r/eP6hxh5fPL2taOhFXYa0aqIk/9HyUGmLipWyg2h0q100W1syax+mU/lmAvPd0irbj+0LH9Mq0j2sdARpwvOgX0Wg16jYJeq2DU1XSIfVv5kBBgwqTVYNbXfFn0mto1dBNSgpiQ8teLtWN8jX/5XHNRXOVgW24FW49XsOVEee2aN7+gEFIGD6VDjz6k9OiLf3CohyMVQoiGUVpUSObu7Vz8jzvO+litruaGpLOOCVmIlx5vg5b9J6vOui0h/kgSMlEvDEYTF155PQBlJUVMv2wIKT37ceWdD2L18dx0MKuvPyOuvIHhl09h2+plLPriI+YsW8iXu06SEmJhSIIv6dHev5u+1pIZdRqMOg2Bf3g83NvwtwnsL3uv/ZXUMC9S/2ZD4RhfY4tIqs5GWbWTHbkVbDtRwbbcCjJPVUM0e1lp17k/Q3r0oUP33jINUQjRYu1YuxxVVenQo+9ZH6vV1RRjcv7xTuJfUBSFSWnBhDTyIk6iaZCETNQ7o8lMv1GX8u37/2HrqqVMnv443QcN92hMGo2GtF4ZpPXK4GRuDsu++Zyl77/EzFU5eK0/Qd9WPgyM86VNoEkuboVHlFU72ZVXyfbcmgTsUGEVLhUMWoV2QWYuu/kekrv2IqF9GlqdfJQLIUTOkYN4+fiS0D7trI/V62tuMtrrmJBB019vLBoP6cVFvdMbjIy/ZTrdB13Im4/ezUt330D3QRcy9bGXMJg8P+86ICScMVNuZfS1t7Br/SqWfvMZP/00jx/2FxHhbWBArA/9Yn0Is7acKY2i4RVXOdiZV8mOvAp25FZwqNCGSs3avMROPRhzSTrJXdNJTOmEwejZojS2yko2r/iJlT98TUhk493kWgjRsoydchsXXnHDOd2k0htrZmVU/1KuuA6cLpUDhVX4GLVyjSDOiyRkosHEJaXw2AffMu+jNzmwfTN6D19U/pFGo6F99960796byWWPs2bhXJbP/YKPN6zm4235tA000beVD71jfGrXSglxLlRVJbfczs68SnbmVbAzr5Jjp0rR641GWqd0YVyXniR37UlCB88nYL/YtnoZy779PzYsnU9VRTm+gcHEtE7ydFhCCFHrXAsX/XKD2Oao+wiZ3aUyY8FhxiYFclVa8Dm1KwRIQiYamE6vZ/Q1N6OqKoqikJd9lP8+cS9X3fkQkfGtPR1eLYvVmwFjJjBgzATyc7JY+ePXrPx+Dm9v3MV/N+bSPsRMr2gfekZZCbTI/HHx9xwulYOFVezOq2RXfs1XYaUDAIvVhzYd+9CnU3fadupOQvs0dPrGcafV6XCwe9NakrumoygKqxd8x5aVi0kfNpr0oaNJ7pqORqvlizdmejpUIUQLt+j/PmLtT/O444X/nlNSZjLXrHuudNR9hMyk05AYaGZbbsVZtyfEb0lCJjzil3VZOYcPcmDHZmZMGMaoyTcx5tpbGsU0xt8KCo9k9OSpjJ48layD+1i94DtWL/iONzfs5c0NJ2gbaKJnlDfdo6xE+UghCgEFFXb2FlSxO7+SXaYEDu7ait1WU4QjKDyKpIxBtE3rStuO3YlKbItG03iKyLicTnZtXMPq+d+ydtE8SotO8vhH3xGfnMaEafdyzYzHG03CKIQQv9iwbAF52cfOeYTMbK3ZDqXSXveEDCA11MIXOwsor3biZdCeU9tCSEImPCo1vT/Pf7mEj2c+xpy3X2HVD18zefpjpPUe4OnQTisyvjUX/+OfXPyPf5J1cB/rfvqetT/9wPtbtvH+ljzCYuLo1HcQ6dk/khxs+dP+V6L5qbA72X+yin0FVewK7MqBHZs5eSIHqNmbL7adk8GXXEXr1C60SetCQEi4hyP+a0f27uLpW66kKD8Xo8lMp36DSR82mqiEtgB4+3lu6wohhPgr1bYqdq1fRb/Rl53zOby8fQAor3ae1XEdw7z4fEcB23Ir6BnV/Pa4FA1DEjLhcb4BQUx9/GX6j76Md57+FxuWLWi0CdlvRca3JjK+NWOm3ErB8Ww2LJ3Ppp8XseCzD/jeXo1JpyE11ELHMC86hXsRbtVLxcYmrtLu4mBhFQdOVnGgsIr9J6vIKqnmlxUHIVE7aNepOwkdOpHYoROx7dqjNzTOUVOXy8XeLetZs3AuEa3iGXLZ1YTFxJLUpSfdBlxAx76DaqfwCCFEY7Zn01psVZV0PI9rB69TW/KUnmVC1jbQjFmnYVNOuSRk4pxJQiYajfbde/P0Jz/idNSsrdm7ZQO7N65m+BVTGu1F7S8CwyIYOn4yQ8dPpqqinB3rVrB5xWK2rlrG2g1HAAi26EgJ9SIl1EKHEIvsXdLIFVU6OFRk41BhFQcLqzhYaCO79Nfkyy8ohPiO3enZPo2E5DTiktPw8Q/waMx1sXfLBlbN/4Z1i+ZxMvc4eoORoeMnAzWL2qc99apnAxRCiLO0ecVi9AYjyV17nfM5zF5WtDo9JbazS8j0WoUnB8UQ5StTucW5k4RMNCp6g7E2+dq4bAHfvPsqS77+lEl3P3Jed74aksniRZf+Q+nSfyiqqnLiaCbb1vzMjrUrWLt+JT8dqpnOFmzRkRxsISnYTFKQmWhfI1qNjKA1NJvDxbGSag4X2ThcbCOzyEZmURVFVb92ykFhkbTq0p70pBRi23YgLikF/+BQD0Zddy6nk8w9O4hPTgXg63dmsX3Nz6T1zmDibffRud8QzF5WD0cphBDnLiq+DUPHX33O68egZm27b0AgxVWVZ31sfEDjqIQrmi5JyESjNWHaDJI69+CD5x/m2WmT6NR3MFfe8QDhreI9HVqdKYpCWEwcYTFxDLl0Ei6Xi2P797Bzwyp2b1rL5k1rWXr4BFCTyCVaVdoEmkgMMJEQYCLUS6Y5ukuF3UlWSTVHS6o5VmzjaEk1R4ttHC93oKo14156g5HI+NakdksipnUSrdq2p1WbJKy+TWvtlNPhYNeG1axZNJf1i3+kuCCPT+yH6QAAIABJREFUf89bQ2BYBJOnP4rV11+SMCFEszFg7ES3nMc3MJjCooPndOyn2/OxGrSMaNO0+gvROEhCJhq1tN4DeKZ7b77/3zvMeftl1i+Zz6irb/R0WOdMo9EQ0yaJmDZJXDDx2pr9qI4dZt/WjezbtpED2zfx9d5dOB0nAbB4+xLbNpn4kt3E+hmJ8TUS7WvAopdKTqdT5XBxvKya42V2skurORI3hBNHD5GdeYCi/Nza12l1OsKi44ju2Yb0+DZEJbQhOrEdYdGx57ShaGOyc/0qXrrnH5QVFWI0menYdxA9Bl1YW5AjOEI2chZCNB+5WUfw9gtwy02mgJAw8rP3ntOxO/MqOFFm58LWfnIjVZy1pn3lIVoEnd7AqKtvpO+IcVh9axbdrl00j6L8XAaOuwKdvumuxVIUhdDoWEKjY+kzYhwA9mobR/fv5tDObRzavZ0je3ey4EARNuevm1UGWnREeRuI9DEQ4W0g3GogzFtPiJceg7bxlFB3J1VVqbC7yK9wkF9hJ7fcTl65nbwKByfKqjlRbv/dNEMA78wfCIuJIzW9PxGxCYS3iicirjWhUa2a9O/NL6oqK9i6cglrf/qejr0G0GfEOCJiE0hLz6D7oOGkpmec1xQeIYRo7N558j4K847zzGcLz/tcAaER7K6wn9OxvWN8eHXtcQ4U2kiUKYziLElCJpoMv6CQ2n+v++kHVnz/FfM/fZ8Jt86gS/+hzeaOlN5gJD45jfjktNrHXC4XeVlHOLJ/N1kH95F9aD/ZmftZfOQQlftyf3e8v0lLsJeeIIueQIuOQLOOALMOP1PNl69Ji7dB2yjWq6mqSqXDRanNSYnNSbHNSUmVkyKbg6JKB4VVTgorHRRUOjhZ6aDqDxt2anU6AkIjCImPpmNkNCER0YREtyI0qhVhMXF4eft66J3VH1VVWT73S9Yt/p4tK5dgt9nw9gsgoX1HoObv5OYnXvFskEII0QCqKsrZuX4VQ8df7ZbzBYVHUW53UVbtxHqWe4r1ivbmjfXHWZJZLAmZOGuSkIkmaerjL9Nz6Ej+9/KTzLxjCu06defKOx+qLVzQ3Gg0mtqRtG4DLqh9XFVVSgoLOHH0MLnHDpObfYT87GPk5WRx6EQ2Gw7nYPuLBcpeeg3eRi1eBi1eeg0WvQazToNJr8Go1WDUKRg0GvRaBZ1GQasBjaKgUeC3qZyqgksFp6ricKnYXSoOp0q1S6Xa4cLmVKlyuKi0u6h0uKiwu6iwOymrdlFe7eQ3A3+/YzCZ8AsKwS8ilOjgUNKCQwkIDScgJJzAsAgCwyLxDwpBo23+0zcLjmeTuWcHXfoPQVEUfvzkHYrycxkwZiLdBg6nXafuTX6qpRBCnK1ta37GYa+mU9/BbjlfaFQMACfK7FgDzq5vsRq0dIuwsiyzhMkdQ9A1gpueoumQHlw0SYqi0KX/UDr2HsjiObP54o0Xyc/JIj45FVVVm81o2ZnUVIUKwjcgiDZpXf70vKqqVJaVUpifS3FBHsUFeZQUnqS06CRlxYWUlRRRXlJc85ryUirLyqgqqsBWWY7dZju/2DQaDEYTRrMFo9mCyWzB7GXFy+pNiLcPZqs3Xt6+WH39sPr64+0XgI9/ID7+AfgEBGGyeLWYn+MfqarK0X27Wb/0RzYsmc+hXdvQG4y8sXgrJrOFu195Hx//wBb7/yOEEAAbly7AYvWhbcdubjlfWHQcAFml1SScwyjX4AQ/VKDM5sTPLJfYou7kt0U0aVqdjsGXXEWfERdjNNWslZnz31c4fiSTcTfcTmhUKw9H6FmKomDx9sHi7UNkXOJZHauqKvZqG47qahwOO06HA6fDgaq6aqsS1iQEChqtFq1Wi1anR28woNMb0Op0kjCcBYe9Zt2CTq9n3sdv8fHMxwBITOnMhFvvpUv/obUbNfsGBHksTiGEaAxcTicbly2kY58BblsTHBYTi6IoZJdUn9PxXSOsdI2QCrbi7ElCJpqFXy5UAVSXyuoF37Lyhzn0G3UZY66bRnBElAeja5oURcFgNGEwylz4+lJWUsTWlUvYuGwhm1cs4aZHZ9Kl/1A69xuCyexF536Dm8x+Z0II0ZAUjYb73/zUrTf+DCYzwZHRHCkuPK/z5JRWY9ZpZJRM1Jn8pohmZ9wNtzNg7ATm/HcWi7+azbJvP2fS3Q8z5NJJng5NCADKigt56e5/sHvTWlxOJz7+gXTNGIp/cBgA4TFxhMfEeThKIYRovBRFITqxndvPG53QlsOblp7z8cVVDm6ee5Ax7QKY1DHkzAcIgSRkopnyDw7jmhmPM3ryVL5591XiklKAmuII1baqJrW5tGja7NU2dm9cw+blP2G2+nDJjXfg5eOHVqdj5KQb6dxvMIkdOrWI4iRCCOEOqqrywfMPkz509GnXT5+PVm3as2nZAqocLky6s99Gxteko3uklfkHihjfIQjjOZxDtDySkIlmLTAsgmvufaL2+6/efoXFX/2P7oNHMOrqm5ptVUbheavnf8uKH+awfc1ybJUV6A1G0oeNBmru7N772v88HKEQQjRNmbu38+Psd4hpneT2hCwuOQWXCocKq0gKtpz5gNMY1TaAVcfK+OlQMcNb+7s1PtE8SUImWpRLb7oTq68fCz7/gDULvqN99z6MnnwTKT37eTo00YTZq23s3byeHetXcsmNd6LRaNixbiWHd++gz4hxdOoziORuvX631lEIIcS5WbNwLhqtli79h7r93L/s6bi34NwTsuRgM60DTczZfZKhCX6NYt9P0bhJQiZaFN/AYCZMm8HoyVNZ9MXH/DD7v6z96XtSevarqSpoq8JwqlqjEH+nMO84axbOY9uqZexcvxJbVSVanZ6+Iy4mvFU8V935INfe96RUmhRCCDdSVZU1C+eS3LUXPv4Bbj+/X1AIIV56dudXctE5nkNRFC5OCuSFldkcLKyidaBcV4i/JwmZaBZO5uaQuXsHVl8/ElM6o9H8/Zxti7cPoybfxPArrqOqsgKAPZvXMfOO6xgwZiKDL72K4IjohghdNBGlRYXsWLucVm3bE94qnsN7d/HBcw8RGh1Lv9GXkZren/bdemGyeAFIYi+EaBRcLhf7t22krLiIuKQOtcWDmqrDe3Zw4mgmIyfdWG9ttO43ku1rl5/XvqY9oqy8PTpBKi2KOpHfEtGkVVdV8vpD97Nh6Xx0hh6ormOYLBXc9uwLddooUqc3YNUbALBYvUnqks7cj97kuw9ep2OfQQy+5ErSemVIwYUWyGG3s3P9SnasXcG2Ncs5vGc7qqpyyU13Mu7620nums6L3yxv8XvdCSEar90b1/DKjLuoqrCiaCJwVK+lS8Ywbnzk8Sa7pUlhfi4hUTF0H3RhvbWR1KUnK77/imMlVqJ9jed0Do2i4GfWoaoqpdVOfIxyyS3+mvLLBq/uFJ+cqj7x8Ty3n1eIP3pl+p1sWObEbnsX8AZU4DuM5mt59vNvz2n/sYLj2Sz68mMWfzUbl9PJrB/WojcYsVVWYjTLqEdzZa+2sX/bJuzVNlLT++OwVzOlf3ucDietUzvToUcfUnr0Iz45Fa1OOtbfurxz9AZVVbt6Oo6mQvpI0RBys44w/bLR2CrfBy4EFKAUvfFqumYYmfbUcx6O8Nydz8hVXZw4dph/ju7D9V1CGNnm/KZFvrb2ONtyK5h1YZysJWuBLpq9u079o1xViCbrZG4OG5bOx159hJpkDGo6nFE4HVfxw+wPuerOe8/6vIFhEVw29W7GXX872Zn70RuMuFwuZowfQmBYJP1GXUK3gcMxe1nd+XaEB+zbupFtq5exc/0q9m3bgN1mIy4phdT0/uj0Bh5463Mi49tIMQ4hRJPz/ccf4LBfA4z4zaPe2G3vsX5xKwrz7mpyG8+XlRRhtljr/aZYaFQrQqNj2ZiTd94JWZcIL348UMSig8UMTfRzU4SiuZHNEUSTlbl7BzpDD35Nxn7lsA9n14Yt53V+nV5PTOukU+erpt/oSyk4nsXrD93BTUM68e97b+bgzvNrQzSc8tJiNq9YzDfvvVb72NwP3+CLN2ZSXlrM4Euu4o6Zb3Pvf34tR5/QvqMkY0KIJmn3xm04HcNP84wPOkNXDu/d2eAxna9PXnmau8Zl4HI6672tjn0Gsu1EBTaH67zO0z3SSrsgM//bnk+l/fzOJZovGSETTZbV1w/VdYyaaYp/nAZwDB9/992JMhhNjJ1yG2Ouu5W9W9azfN6XrFk4l/Rho4lPTiMv+yhH9u0ipUdfKebQiOzbupFl333O3s3rOXZgD6qqotXpyLhoAj7+AVzxz/uZ8sAzWH3krqUQonnx9vcFjp7mGRXVdQwvb9+GDum8VFdVsnr+t3TuN7hB1nV37jeYH2e/w6bj5fSM+vON37pSFIVrOoUwfcFhvtpdwOUpwW6MUjQXkpCJJisxpTMmSwVVFd8Bo37zTAVG80yGXPZPt7epKAptO3ajbcduXH33o7Vz2JfP+5LPX3seo9lCWq8MOvcbTMc+A/HxD3R7DOLPKkpLOLBzCwe2bWL/9k1MvPU+IuNbk3PkICt/+JrEDp3oMXgEbdK6kpjauXbUSyppCiGaq8GXjGXf1pnYKi8Dfnuj8GvMVjsJHTp6KrRzsn7pfCrKSug36tIGaS+pc0+8fHxZdbT0vBIygHZBZvrGeLM0s4TL2gehk7Vk4g8kIRNNlkaj4bZnX+Dpm6/F6ZiEw34BcAyjeSad+ybTud+Qem1fp9fX/nvU1TcRn5zG+iU/smHJfNYumofBZOKtJdvRG4wUnMjBNyDod8eIc1NZXobL5cTL25cje3fx8vQbyTl8sPb58NgEik/mExnfml7DRtNn+FipkimEaHG6DhhGx97z2byiJ7bK24EotPp56HQfcdszb51xe5jGZunXnxIUFklyt14N0p5Or6drxjDWzPs/bA4XRt35/X9d3yUUvVaRZEycliRkoklr27Ebz37+DT/870N2bXwEH38/Bl96O136D23QDXl1egNpvTJI65XBNTOeIHP3drIO7kNvqCmX+9LdN5B9aD9tO3UnuWs67Tr3ILZtB0nQzsBht7N97XKO7N1J5p4dHN6zg+NHDnHp1LsZc900/IJDiYhLpO+Ii0no0JG45NTfTT/UndrSQAghWhqNRsO0p19gw9L5LPq/LygpLCKpaxoXTDi3CsSelJd9lG2rf+aSG+9s0ESy1/AxLP3mM9Znl9E7xue8zuVrqrnktjtV8ivshHtL/yR+JWXvhWgAaxd9z7Y1NdX8cjIPANBzyEhufeY/AGxesZiY1u3wDw5r0ESyMVBVlZLCArIP7ePYgb0cO7CXsFbxDL/8OpwOB9f0bovDXk1wRDSt2iYT27YDHfsMID45zdOhi1Ok7P3ZkT5SiLPjcrnYuW4lkfGtG7QypMvpZNqFPUjQl/FAf/dMcX/y52McLrLxyvC48x51E42flL0XohHpPmg43QfVVLsqzDvBnk1r8farKaVbXJDHs9MmAeAbGExs2/bEtE6ix5CRxCeneixmd1JVlfKSIk4cO8yJY4dBVel1wRgA7pt4we+qfZmt3vQZPhYArU7Hw+9+SUhUKym8IYQQLZRGo6FDjz4N365WS9+Rl/Dde6+SX2EnyHL+s1pGtfHn/p+O8sn2fK7uGOKGKEVzIAmZEA3MPziUnkN/LULi5ePLw+/O4dCurRzatZXDe3ayY91KwmMTiE9OJXPPDmbeMYXwVnGERscSEhlDUHgUSV164hsQ5MF3UkNVVSrKSijKz6MoP5fC3OPYqioZdPEVALz12D2sXvAdlWWltceExcTVJmT9LxoPQHireKIS2hIQ8usooaqqOOx21iyYi39w6Kn9wWSapxBCtBSrF3zHvq0buGzqPRjNDV/FeMDYiXz77qvMP1DklgqJKaFeDI73Zc7uk/SK9qZ14Lm/p/JqJxuyy6lyuugQYiFCpkE2WZKQCeFhOr2BNmldaJPWpfYxh92Oy1Wzz4pWq6VNWheOH81k5Q/fUFFaDMC9r31MSs9+rFv8A+8+9S98A4Px8Q/E2z8Aq48fIyfdSFB4JFmH9nNwxxaMZjN6gxGdXo9WpyexQ0cMJjMFJ3LIyz6K6nLhcjqx26uxV9vo1GcQOr2eXRtWs2fTWioryqkoLaG8pIiKslKmz/oQRVF467F7WDLnk9+9J6PJzMBxl6MoClEJbelrMBIcGU1IZEzNhptRsbWvvWDitaf9fzmZm8O/p16JPecYfVWVn7Va3tfqmPrSO7Tt2M3NPwUhhBCNjaqqfPPua9htVVx5x4MeiSE0qhWpvQcwf+PPXJochF57/ssKrukUwsaccv695jgvDGuFXnv2UxcX7Cvkg0259FHAT4X7gbRwL6b2ijin8wnPkoRMiEaoZhSoZiQoOrEdtzw5q/a58tJi8nOyCImMAcA/KJSOfQZSXJBPaWEBJ45lUl5SzOBLrwJg+5qfef/ZP3dkL327gpDIGJbP/YJPZz3zp+dfX7QZH/9Atq5aytfvzEKnN2CxeuPl44uXjx92WxUGk5nugy4kMi4R38AQ/IND8Q8O/d0o1/DLrzvr96+qKq/cdDmXHznEg05n7S5z3wNX3HIVT36zvFGMDgohhKg/uzasJnP3Nq677ymPrq8eNuEanl2xmOVHShgQd/77t1kNWm7uHsZHW/IoqnIS7HV2CdS2E+V8sSmX9U6V1qceqwIuyynnww25XNs97LxjFA1LEjIhmhgvb9/fbeiZmNKJxJROf/n6fqMuJbVXBvaqKqqrq3DY7TgdDvyCauau12xunYqiaNDqtOj0RvQGAxZrTUWpsVNuZdwNt9dWjPyjjr0H0LH3ADe+Q9izeR3O49m/S8YAhgMjnU6WfTWbUddNc2ubQgghGpev35mFT0AQfUde7NE4UtP7Exnfhi8LtGTEutySHHaNsNIpzAvtOZTBn7ejgMd+k4wBmIA3nSptM4uZ0CkYi162e2lKJCETopkze1kxe1n/8vmQyJja0bbTMZgafs7+sQN76OdycbpuapCtivd2bm3wmIQQQjScgzu3sm31MiZMm+GRfui3NBoNoybfxOsP/pMN4VF0jfzrPvVsaDUK5dVOZm/PZ2KHILwMdUuiDhfZ6Heax8OACEUht8xOrL8kZE2JTDIVQjQ6voHB7NGe/n7Rbq0Wn/DIBo5ICCFEQ/Ly8aX/6MsYfMlVng4FgF7DLiI4IppPtufjzi2jskqrmbu3kP+sO17n8waYdOw9zeMVQI5Lrd3zTDQdkpAJIRqdTn0GskerYcEfHj8MvKXT03fcFZ4ISwghRAMJjWrFPx5+AYv3+W3I7C46vZ4xU25l38kq1maVue28bQLNTEwJ4ucjpSw8WFynYzLa+fOIVqHyD48/q0BykBl/syRkTY0kZEKIRkenNzB15n+5zGzhOoORj4B/abV0MZoYNW06kfGtz3gOIYQQTY+qqnzy76fJOrjP06H8Sd8RFxMWE8dHW/Nwutw3SnZxUiCpoRbe3HCCzKKqM75+YJwvPhFW0nQKLwDvAsO1Cu+YdExJD3dbXKLhSEImhGiUkrr05Mmvf6b8ult4u99gtl16NdM/nsvQy6d4OjQhhBD1ZN1P3/PNu6+ya+NqT4fyJzq9nvG3TOdIcTU/HarbaFZdaDUKd6RH4KXX8Pq6E2ecuqhRFKb1jmBSvygWxfrwcaSVhC6hvDAy3i2bV4uGJ2OaQohGyy8ohIuuv93TYQghhGgAtspKPn7xcaIT2zJgzERPh3Na3QddSOvULny0dTO9Y7zdVs3Q36zjvn5R+Jt0dariqCgKqaFepIZ6uaV94VkyQiaEEEIIITzuy7deIi/7KFff8xhaXeMcM1AUhUl3P0xRlZNPtxe49dxtAs0Ee+lxqSq78ircem7RuElCJoRosSpKS/jpy//xxRsvsnrBdzjs1Z4OSQghWqTMPTuY++EbZFw0nuSu6Z4O528ltO9IxpgJfLu/pE5rvs7W17tPct+iI2zKKXf7uc/GvoJKPtuRzxc7CzhWYvNoLM2dJGRCiBZp47KF3DmsKwUvPEK7N2ay4ZG7uGd4D7IO7fd0aEII0eKEx8QzctKNXH77vzwdSp1MmHYvZqs3s7ICcbmxDD7ABYn+RPsYeW5FFtmlDX+j0O508dySo7y06AghW/Px3prHgz9k8vaaHLe/V1FDEjIhRItzMjeHt2dMZX5VJV9WVvA4sLyinEcLC3j5pstxuVyeDlEIIVqM6qpKjGYzE6bNwOrr7+lw6sTHP4Cr7niIfVs3MG9foVvPbdZr+Fe/SDSKwhPLjlFW7XTr+c/k0y15+OZWcMCpMhOYpcJBp0rW4RLm7y9q0FhaCknIhBAtztIv/8d4l5Puf3h8iqoSUFbC9jU/eyQuIYRoaZZ8/SnTxw+l4ESOp0M5a31GjCOtVwYfbM4jx80jWaFWAzP6RpJTWs1Lqxvu/8bhUll4oIhXnCqG3zzuAzzvVJm/62SDxdKSSEImhGhx8vfvoUf1nztPBejudHLiaGaDxySEEC3Nrg2r+e8T9xIUHoVfYLCnwzlriqIw5f5n0Jq9eXl1jlv3JgPoEGLh1h7hjEsKcOt5/05ZtRNcEH+a57oBWRX2BoulJZGETAjR4gTEJrBRbzjtc5u0WoIiohs4IiGEaFmO7N3FzDunEBrVitue/U+jrap4JoFhEUye/hi78iv5cpd7qy4CZMT5khxsAWiQwhpeei0uBY6c5rnNQJi5af6cGjtJyIQQLU6/cZfzoUbD1j88/gmQZTKT2rOfJ8ISQogWITvzAE9OvRyj2cL0WR9g9fHzdEjnpfeFY+k5ZCSzt+WzJ7+yXtpYcaSEafMOsepoab2c/xd6rcKAOF/u0Co4fvN4BTBdqzC4bcON1rUkkpAJIVqc4Ihorn74BfoaTVxtMPIscIHZwjQfX2577eMme6dWCCGaAt/AIOKTU7nvP7MJbgYzEhRF4br7n8Y/LIrndrjqpQhH1wgriQEmZq7KZnc9JX2/uLxTCJn+JpJ1Cg8D9wJttAqWCCsXtmkaRVeaGkWth/KV8cmp6hMfz3P7eYUQwp2KC/JYPvdLik9kE9EmmfShozGazZ4Oq8m5vHP0BlVVu3o6jqZC+kjRUm36eRHJXXs128/ZfVs38uiUi+kaZuLePpEoiuLW8xdXOZi+4DBl1S6eHhJDlI/Rref/LVVV2Z5bwabsMrQahR7RPiQGmOqtvebqotm769Q/ygiZEKLF8g0MZsSkf3D53Y+QcdH4ZnuRIIQQnmSvtvH+cw/x3G2T+e6D1z0dTr1pndqZibfex5pjZXy12/3VCH1NOh7KiEajwCNLjlHlqL8tWhRFISXUi0mdQrkiLUSSsXom83KEEEIIIUS9OLhzK288fAdH9+/hgonXcdG1N3s6pHo1/Iop7Nu2kQ8XfkeCv4m0MC+3nj/c28AD/aPILLJh0sm4SnMhP0khhBBCCOF2S+Z8wgOTRlFaVMjdr7zPpLsfRvcXFW6bC0VR+MdDzxMR34bnVmRzosy9+5MBtA40MyShphDKwZNVVDvrb6RMNAxJyIQQQgghhFtUlJZwMrdmI+OkLj0ZetnVPP/FYjr1GejhyBqOyeLFHS+8jdNo5YllWVTY3V/kA6Co0sG9iw7z/Ipst++BJhqWJGRCCCGEEOK85GUfY/bLT3LriHTeefI+AEKjY7n6nkexePt4OLqGFxYTx23PvMbRMgczV7l/02gAP7OOSWkhrMkqY9ba47jqoVCfaBiyhkwIIYQQQpyTrauW8uMn77F5xU8oikK3gcMZPXmqp8NqFFJ69mPSXQ/z3jMP8O7mXKZ0DnV7GyPa+FNqczJ7ez5eBg3XdQpxe3VHUf8kIRNCCCGEEHXisFezY91KkrumozcY2bt5PYd2bWX05KkMHHcFwRFRng6xURk6fjI5Rw7x7ex3CLcaGFEP+3iN7xBIabWTb/cUkhRkpndMyxuRbOokIRNCNGsVZaUUn8zHPygEk8W91a6EEKIlKCk8ydZVS9m4bAFbVi6hsqyUO198hy79hzBy8k2Mvf42tDq5pPwrV93xIPnZR3nr50UEWXT0iPJ26/kVReG6ziEk+JvoeRbnVlWV/AoHKhBs0cnImgfJX48QolmqKC3h48dnsG7pjwRo9RS6HPQaNoYJ0x+T/caEEOJvOOx2qirLsfr4kXVwH/dcOghVVfENDKbH4BF0zRhGhx59ADCZLR6OtvHTaLXc/OQsnvjHeJ5fuYXHBsbQLsi9/ZBGURgY7wtAfoWdPfmVfztStjG7jI/Wn6C4yoECeBu1XN41jG6RVrfGJepGEjIhRLPjcrl44fpL6XVoP5/bqwmkmuPAbT/OYdaxTO5463O5EyiEEL+Rm3WELSuXsHXVUnauX0X60NFMuf9pwmMTGD9tBu279SIuKRWNRurBnQuT2cJdL73Lw9eM5bG1RTzTR0OUj7Fe2pq9LZ+fDhWjAn1Ok5Rtz63g1eVZvO9UuQBQgPkVDq5ckYW+bxQdw2U2SUOTvyohRLOzddVSdMcO86a9msBTj4UBH9ts5O3axv5tmzwZnhBCeJz6m4p8T9w4kdtH9ebdp/7Fkb27SB86mm4DLwBAo9EwevJUEtp3lGTsPPkGBDHj1Y/Q6vQ8tPgo+RX2emnnhi6htAsyM3NlNmuzSv/0/BebcnnZqXIhNYmAAgwDXnOq/N/m3HqJSfw9+csSQjQ7u1YuZUJF+Z8+4HTAJTYbO9at8ERYQgjhUfZqGxuWLuA/D/6TGeOH4nLVbCjcpf8QrrrrYV74aikvfbuCKfc/TVqvDM8G20yFRrVi+r8/oFwx8fDio5TY3L9HmVGn4YH+UcT7m3h2eTZbjpfXPudSVbacrOLi0xw3BthdbMPulPL5DU2mLAohmh2dyUSxRguuP3d0xVotemP9TBMRQojGKDvzAD/OfoeVP35NeUkxFm9fuvQfQlVFORarNxdMvNbTIbYose04a6h5AAAgAElEQVQ6cOeL7/DMLVfx6NKjPDogGote69Y2LHotD2ZEc/+iI8zelk9qqAVFUVAAnQLlKvyxJ6ygZrRMIzP6G5yMkAkhmp1uQ0fxgcFAyR8ezwf+T1HoPnC4J8ISQogG43I6sVVWAnDiaCZLvvmUtF4Z3P3K+7y+cCM3PfoiFqt7q/2Jukvums60p1/lQJGdp37OotrpcnsbPkYtjw6I5v7+UbXrphVFoU+klVmnSbr+A6SHe6GVjKzBSUImhGh2Ytu2p+Owi+hnMjMPOAZ8BfQ2mRk08VqCI6I9HKEQQtQPVVVZ+cMc7r5kIF+9/TIAqen9efXH9dzy5Cw69RmITm/wcJQCoGvGMG546Hm2nqjg6dxYHC73TxX0M+uwGrRUO128uCqbzKIqxncKYZZOw10K7Ab2ADMUeEGvYUI9bF4tzkymLAohmqWrHnyWnzt35673/0Ne7nHCI2O44Jqb6TFkpKdDE0KIenF0/27eefI+9mxeR0zrJFqndgFAq9Nh9fHzcHTidPqNvITKsjLef/YBXmnlw209w+tlhKq4ysnWExVsyinnycExPDU8jm925DPwaBkq0DXKypPtAwm1SrLuCZKQCSGaJUVR6DfqUvqNutTToQghRL1b+cMcXn/oTsxeVq5/8Dn6j75MqiI2EcMmTKaqooxPZz2DSafhpm6hbt+aJdhLz2MDo7lv4REeXHyUpwbFcF33cK7r7tZmxDmSv1QhhBBCiCauVdsOdB0wjOe++IkBYyZIMtbEXHTtLYy+5mZ+PFDEO5tyf7ctgbtE+Rh5ZEA0NoeLBxcfpaCeyu6Lsyd/rUIIIYQQTZDL5WL53C9RVZXIuERuffo1fPwDz3ygaJTG3zKdYROv5Zs9hfxvW369tBHnb+KhjGhUtWYao2gcJCETQgghhGiC5vz3FV574Da2r13u6VCEGyiKwlV3PkTGmAl8tqOA2UFj6qWdNoFmXhsZT3yACQB7PVR4FGfnjAmZUuNKRVEePPV9jKIoMuNUCCFEiyd9pPCUzD07+PLNl+g9fCwduvfxdDjCTTQaDVP+9TS9ho/hk38/zXd7T9ZLO7pThUM+3Z7Pv346SqVdkjJPqssI2WtAOjDx1PelwKv1FpEQQgjRdEgfKTzis1efxcvbl6unP+r2AhDCszRaLTc+PJOuA4bx1oZcFh4oqre2YnyN7Cuo5Kmfj8lImQfVJSHroarqzUAVgKqqhYDUxBRCCCGkjxQekJd9lM3Lf2Lo+MlSzr6Z0un1THvqVVLT+zNr7XGWHS6pl3bSo72Z1j2cLScqeH5lNs562AtNnFldEjK7oihaQAVQFCUYkBRaCCGEkD5SeEBpUSFtO3aj9/D6WWMkGge9wcg/n3+Ltp178NKqbNYcK62XdgbG+zKlcwirj5Xx2rrj9dKG+Ht1ScheAb4CQhRFeQJYDjxZr1EJIYQQTYP0kaLBxSen8tA7XxIWE+fpUEQ9M5rN3PXSu7RKTuO5NflsOV5eL+2MahvAFSlBtA+x1Mv5xd87Y0KmqurHwD3AU0AOMEZV1c/rOzAhhBCisZM+UghR3yxWb2bM+pCwmDie/PkYe/Ir66WdyzoEMTDOF4C8ctmjrCH9bUKmKIpGUZTtqqruVlX1VVVVZ6mququhghNCCCEaK+kjhae8Mv0mnpp6uafDEA3I6uvPjFc/wje8FY8uPUpmUVW9tbUnv5KbvjvIgnosJiJ+728TMlVVXcAWRVFiGigeIYQQokmQPlJ4it5oIuvQfk+HIRqYf3Ao9/3nfxj8Qnh48VFySqvrpZ14fxMpoRZeW3e83tatid+ryxqycGCHoiiLFEX55pev+g5MCCGEaAKkjxQNLjIukZMncigrLvR0KKKBBUdEM+O1j7EbvXl4yVEKKx1ub0OvVZjeJ5KEABPPr8xmV16F29sQv6erw2seqfcohBBCiKZJ+kjR4Np07AbAjnUr6TF4hIejEQ0tKr4N97zyAU/cOIGHdhp4KsWOl0Hr1jZMOg0P9ItixsLDPLHsGC9eEEewl96tbYhfnTEhU1V1aUMEIoQQQjQ10kcKT2id0hlvvwDWLJwrCVkLlZjSidufe4Pnb7+GJ4sMPJwRhV5bl4lvdedr0vFQRjQ/HSom0FKXMRxxrs74k1MUpVRRlJJTX1WKojgVRamf3emEEEKIJkT6SOEJWp2OcTfcLslYC5fWK4N/PDyT7bkVvLg6B5fq/k2dw6wGLk8JRqMo5FfYsTlkm8X6UJcRMu/ffq8oyhige71FJIQQQjQR0kcKTxk24RpPhyAagT4XjqUw9zizX3mSYEse13QKqZd2qhwu7pl/mMRAEzP6RKJRlHppp6U667FNVVXnAAPrIRYhhBCiSZM+UjSkitISvnzrJYoL8jwdivCgkVffyJDLrmbO7pN8v69+Cr2YdBrGJgWw5lgZH2yW3zd3O+MImaIo437zrQboCrh/TFQIIYRoYqSPFJ5UfDKfr956mfycLG548DlPhyM8RFEUJt31MPk5Wby5fCGhVj2dw61ub2dkG3+ySqr5avdJon0NDIr3c3sbLVVdRshG/eZrGFAKXFSfQQkhhBBNhPSRwmPCW8Uz/PIpLJnzCbs3rvF0OMKDtDodtzz5b6JbJ/PcimyOFtvc3oaiKEzpEvr/7N13eFTV1sfx75k+yaT3SggttAChI71jARRE7L3fe1/7FRULit2rXiuiWECxXLEjSEd67y1AIKQX0pPJtPP+EcCuUTM5mWR9nicPJCeZ8yNMss+avc/adDu1R9nBopoGP0dLVZ+C7E1VVa8+9Xa9qqozgXbeDiaEEEL4ABkjhaYuuPF2ImITmPXwndirq7SOIzRk9bdx5/NzMASEMnN1FpUOd4Ofw6BTuPusOEYlBxMfaGrwx2+p6lOQvVTPjwkhhBAtjYyRQlMWqx83z3ieguxM3n/hMa3jCI2Fx8Rx+7NvUGCH59bl4PY0/ArqALOem3pH42/S43B7cLql8+Lf9Zv3kCmK0h8YAEQoinLHjw4FAg27+5wQQgjhQ2SMFE1JSlpfrp42k449+2sdRTQBHbr35qp/P8pbM+9l/p4iLkuN8Mp5nG4P05Zmkhxi5tY+MV45R0vxezNkJsBGXdEW8KO3cmCy96MJIYQQTZaMkaJJGTn5cuJat0VVVem6KBgx6VKGTpzKJ3uL2ZJd6ZVzGPU6ukX58d2RMpYfLfPKOVqK35whU1V1FbBKUZR3VFU93oiZhBBCiCZNxkjRVH340pOsW/Q5j839mqAw78yMCN9w1T0zyNi/m+d3ZvNCsJkIf2ODn+PS1AgOFtt5bUsebcMsJAaZG/wcLUF97iGrVhTlGUVRFiqKsvz0m9eTCSGEEE2fjJGiSek/+jwqS0t49vZrqK2RLngtmcli5V9PvYbL6eT59d65n0yvU7hzQCxWg46n12RT65L7yf6K+hRk7wMHgNbAI8AxYLMXMwkhhBC+QsZI0aQkpXTh1sdf4ujenbx8/z/wuBu+057wHTGJrbn63pnsLaxhwf5ir5wj1Grgjv6x6HUKpXaXV87R3NWnIAtTVfUtwKmq6ipVVa8B+nk5lxBCCOELZIwUTU6voWO44u4ZbF35He8+85DWcYTGBp5zAf1Gn8f8faUcLbF75RzdY/z5z5gkomzSCv+vqE9B5jz1Z66iKOcoitIDiPdiJiGEEMJXyBgpmqQxU69i0o13kDZohNZRhMYUReGaaTOxBYXw3w25uLywdBHqli9WO93M3ppPmcyU/Sn1KcgeUxQlCLgTuAt4E7jdq6mEZlTVOz+kQgjRTMkY2UL44vg46cbb6XbWMAByMzM0TiO0ZAsK4Zr7HiejtJbP95/02nmKql0sOlzKa1vyvXaO5ugPCzJVVb9WVbVMVdU9qqoOU1W1p6qqXzZGONE4SgrzeG36vVzZrxOX9U7mgcsvZu/mdVrHEkKIJk/GyOZv07KF3DVpPJf2SuKagT1456nHqK4o1zrWn7Jr/SruumAoKz6br3UUoaHew8bSe/g4PtpbRH6lwyvnSAwyc3HXcNafqGDdCd/6OdHSHxZkiqK0VxRlmaIoe069n6ooygPejyYaQ9nJIqZdPIm1i2JxOg6geio4uvdmnvm/f7F11XdaxxNCiCZNxsjmbdH8d3l1+pPkZMwA1Y69egPLF9Ty4JUX47D7TgfDTr36k9pvMG8+9m/WLfpc6zhCQ1fc9TA6BeZsL/DaOc5PCaVNiJlZW/KpdEhTmfqoz5LF2cA0Tq2TV1V1FzDVm6FE4/n63beorhiHx/00EAtYgEtw2D9gzuMz8XikfakQQvwOGSObKXtNNR++9BwO+2LgXMAItMHlnENxXizff/OZxgnrz2A0cdszb5CS1pdXp9/GlpWLtY4kNBIWHcv4G+5iQ1YlewqqvXIOvU7h1j4xlNe6+WB3kVfO0dzUpyDzU1V1088+JnfqNRMbvluKy3ndrxwZRnWli9xjRxo9kxBC+BAZI5upA9s2otd3Bdr97IhCrf1a1nzjW6tIzFYrd73wNskdU/nvv28hT+4pa7HOvux6QqNimJMf6rV7I9uEWri1TzQTOoR45fGbm/oUZEWKorQBVABFUSYDuV5NJRqNqnoA/a8cUQDDqeNCCCF+g4yRzZTq+a3xEcDgkytIrP427nn5Pa6eNpPoxNZaxxEaMVmsTLrhdo7s2cHWnCqvnWdkcvCZNvi+2BSnMdWnILsVmAWkKIqSDdwG3OTVVKLR9Bo2DL3hvV85sh6z1U1sUttGzySEED5ExshmqkOPPrhd24HMXxwzWd6l/5jhjR+qAdgCgxk2sW5V7bGDezm6b5fGiYQWBp07mYjYBD7eV+zVYqm81sUjK0+wPqvCa+doDn6zIFMU5f9O/TVGVdWRQASQoqrqQFVVjzdKOuF146++FovfRyi6x4EywAMsxGS5kMvvvBud/rdeHRRCiJZLxsjmz88WwMRrb8FsGQesoW4StAC9/nYCQ/YxZPxkjRP+PaqqMuvhO3nqH5eTnXFY6ziikRmMRs6+7AYOFtVwsNg7m0UD+Bv1FFU7mbuzCLeX9j9rDn5vhuzqU3++BKCqapWqqlLeNjOhkTE8Nvdjup+1Ab0hBp3en+jE+/jH449w1rgJWscTQoimSsbIFmDCtTdxxd1XExJxFXq9DYOxDf1GF/DYvI+x+tu0jve3KIrC/z31Gjq9niduuYTivBytI4lGNmT8hVhtASw8VOK1c+h1CpemRpBT4WDlsTKvncfXGX7n2H5FUY4BEYqi/Hg+WwFUVVVTvZpMNJqohCTufvFVXE4HLqcTi5+/1pGEEKKpkzGyBVAUhWHnT2XoxIuwV1dhMlvQG37v0sm3RCe25t5X5jHjusk8cculPDRnAQHB0oShpbD4+XPWuImsXjCPKocbf5N3VkX1jbORHGLm473FDE0KQq9TvHIeX/abM2Sqql4M9AMOA+f96O3cU3+KZsZgNEkxJoQQ9SBjZMuiKApWf1uzKsZOa9W+E3c9P4eC7Ey+fPsVreOIRjb43AtxuFU2ePEeL0VRmNI5nLxKp9xL9ht+9zeLqqp5QLdGyiK8zF5TTW1NNQHBoeh09ennIoQQ4rfIGNl8qKpKRelJTGZLi3xhsmPPfjzwxsckd+qqdRTRyNp06U54dBwbs8oYkRzstfP0ibNxfc9IUqNa3s9XfTS/l3rELxTmnGDO4zPZs2klOp0JP1sQk2+6heGTpqIoMm0shBCi5Vr5+cd8/OrLVJaXonocdOo1mGvuu5+o+FZaR2tU7bv1BKC8pJgda5Yz+LwLNU4kGoOiKHQfNJw1n83D5VExeGk5oV6ncG77UK88dnMg0yTNXHlJMQ9cNoVdG/riduXgdBRTdnI+8/4zh6/fe1PreEIIIYRmFn3wDu88/TqlRXNxOYpxu3LZs2kQ0y+fQmlRgdbxNPHNe7N4/aE72Lz8W62jiEbSqdcA7C6VIye9123xtLWZ5Xx54KTXz+NrpCBr5r77aC726tGonulAIHX3m/ej1v4VC954mdqaGo0TCiGEEI3P6ajl41f/i8P+BXAWdeNjAKpnGvbq8Sya/2t7dDZ/k2++kzZduvP6Q3eQe/yo1nFEI2jbpQcAhxuhINuUXcmHe4pwuH1vY3Vv+r19yL5SFOXL33przJDir9u07Hucjkt/5UhbdLq2HN23o9EzCSGEr5Mx0vdl7N+DosQBHX9xzOW8jE3LVjV+qCbAaDJz29Oz0BuNvHjPTThqvX+RLrQVFh2Ln1FHVnmt1881JCmQKqeHbblVXj+XL/m9GbJngeeADKAGmH3qrRLY4/1ooiHUdYT69V+mKnb0BmPjBhJCiOZBxkgfZzAaUNXfugC1N8uOivUVFh3LzTNeIDN9P5/NflHrOMLLFEUhonUKhVVOr58rNcofm0nHRum2+BO/+dtGVdVVAIqiPKqq6uAfHfpKUZTVXk8mGsSgc0eTe+wNHLXjqFuOcdpm9LoCkjtJgzAhhPizZIz0fUkdumA0VWCvXkvdksXTVEzmWQw6d4xW0ZqEHgOHc8ODz5A2ZJTWUUQjCAwNo8RsAaq9eh6DTiEtxsbWnCpUVZXmcqfU5x6yCEVRkk+/oyhKayDCe5FEQxp+/lRCo45gMF4N7AdKgfcxWSZw9bQHMBhlhkwIIf4GGSN9lE6v59r7H8RkngS8B5QAB9Ebryc4fDcjJ//acv+WZejEqQSGhOFxu3E5HVrHEV5kNJlxOry/ZBEgLcafMD8DZXZ3o5zPF9RnPv52YKWiKKfv7EwCbvRaItGgLH7+PDr3Qxa88SqrvxpJbU05SR3SmHLrf+jSd6DW8YQQwtfJGOnD+owYhy0omI9feZWj+27FZLEx6JwJTLrpE/xsAVrHaxIctXYeu+EiuvQdyJRb7tY6jvASt8uFXq8HvF8kDWsdxLDWQV4/jy/5w4JMVdVFiqK0A1JOfeiA+tuLrkUT5B8QxOV3TuPyO6dpHUUIIZoVGSN9X6de/Xn47f5ax2iyTGYLUfGt+Oa9WQybOJWI2AStIwkvqD64gUCjDkjUOkqLVN+29z2BzkA34CJFUa7wXiQhhBDCp8gYKZq1qf+aBgp88uqzWkcRXlJQ6STcr/FuY3l9Sx6Prc5qtPM1dX9YkCmKMpe6blIDgd6n3np5OZcQQgjR5MkYKVqCsKgYxk69hrXffkb20XSt44gGVlKYR1mtm8Qgc6Od0+lWOVwse+GeVp97yHoBnVRVVb0dRgghhPAxMkaKFuGcK27ku4/fZfFHb3PNtMe1jiMa0P6tGwDoGGFttHOGWg2U2t24PSp6nXRarE9BtgeIBnK9nEUIIYTwNTJGihYhMCSM+16bT6sOnbSOIhrYlhWLCTLraRNiabRz2kx6VKDG5cFm0jfaeZuq+hRk4cA+RVE2AWduVFZVdbzXUgkhhBC+QcZI0WK0S03TOoJoYJVlJWxd9g2j2gQ16kyVSV93LqdbFhdA/Qqyh70dQgghhPBRD2sdQIjGtH7xl6z84iP+/fJcdLr69oYTTdXiD9/B6VEZ0za4Uc8bZTPSJ86GUS/LFaF+be9XKYoSRd2NygCbVFUt8G4sIYQQoumTMVK0NC6nk90bVnN49zbad5P+Nb6s7GQRC+e9Qd94G0nBjbdcESAtxkZajK1Rz9mU/WFBpijKFOAZYCWgAC8pinK3qqr/83I2IcTf5LDXsGbhZ3z/9Xe43R76jhzEsIkX4RcQqHU0IZoFGSNFS9Nz6GgMRhObli30+YIs6+ghvn1/LscOHCY8OooxF19Ep14tZ0+6uc89gqO6kiuGtm70c6uqiqLI7Nhp9VmyeD/Q+/QrfoqiRABLARlshGjCaqoqeeiqSyjMjqDWfhNgIjN9Hgvnnctj8z4hJCJK64hCNAcyRooWxc8WQOfeA9i2eimX3fGg1nH+sk3LvuXV6ffhct6Mx30JGQf2s2v93Yy55AKm/uMOreN53brFX7Du28+5uEs48YGN1+7+tKfW5lDtcDNjuGxEDfXbGFr3s+UXxfX8OiGEhha88Qr5JzpSa18ETAbG47B/TNnJi3jnqSe0jidEcyFjpGhxUgcMJS8zg8KcE1pH+UvsNdW8Nv3fOOyL8LhnAKNA/Re19s0sen8+xw7u1TqiVx07uJfZM+4hJdzK5M5hmmTIKLHjL90Vz6jPoLFIUZTFiqJcpSjKVcA3wLfejSWE+LtWfvEpTscD1K2i+oHHfTfbv1+Mwy4bMgrRAGSMFC1Oar/B9BlxNg67Xesof8m21UtQdH2Bnj87EoHLeT0rPlugRaxGkX/iGM9ccy42xcE9Z8Vi0GAPsJIaF3mVTtqFNe59a01ZfZp63K0oygXAQOqu7N5QVfUzrycTQvwt9upSIOFXjoSAYsBeU4PJ0nibQArRHMkYKVqiuOR23PbMLK1j/GVVZaW4Xb++VM7jaUX5yYONnKhx5B4/ysybpuJye3h8ZCvC/Iya5NieVwVAapS/JudviurT1KM1sFBV1QWn3rcqipKkquoxb4cTQvx18cndOH7o9HLFH9uMny0IW1DjtrgVojmSMVK0ZFUVZfgHBGkd409r3SkVne5NwMPPF4uZrd+SktZdk1zedHDHZp6741r0NeU8NjyRxKDGv2/stPUnKgi1GkgO0S5DU1OfJYufUPeMPc196mNCiCbswltuxGS5E9j3o4+ewGS5jguuv1H2jxGiYcgYKVqkj195mlvH9MLj8fzxJzcxbTp3J7Z1DHrD7fywn7sHlDcxGNcx6NxJWsZrUKqqsuTjd5l541QC3ZU8NaoVSSHaLhUcmhTIpanh6KTL4hn16bJoUFXVcfodVVUdiqKYvJhJCNEA0gaP5PI7Cpn3/GB0ug6AGZdrG+ddcROjplymdTwhmgsZI0WLFB4Tj8Nupyg3i8g43+qUpygK974yixfuvoPDexIwGHrj8RwkOMzCnS/Mw88WoHXEBlFeUsycx+9j07KF9Izx5/b+sQSYtW+kcVaibL3zc/UpyAoVRRmvquqXAIqiTACKvBtLCNEQRky+mEHnTuTA9k243W46dH+l2Qw0QjQRMkaKFik6sW7vqvys4z5XkAEEBIcwffbb5GZmkJORTkhEFK07pjaLvbFUVWXdos+ZN+N2qpweruwewcSUUM1npKocbr46WMI57UOaRGHYlNSnILsJeF9RlFcAFcgCrvBqKiFEgzFZrKT2H6J1DCGaKxkjRYsUERsPQFFOlsZJ/p6YxNbEJDb+xsjecvzQPt575iH2b91Au1AL/+wbQ6vgpnGv1sd7i/niwEn6xNukIPuZ+nRZPAL0UxTFBiiqqlZ4P5YQQgjR9MkYKVqqkIgoAEoK8zVOIqCunf2ns55n7bef4R8UzM29oxiVHIxeg7b2v+ZoiZ2vDp5kRHIQyRrfw9YU1afLYhTwOBCrquo4RVE6Af1VVX3L6+kE8MPU88L3P6a0qIDEtu2ZcM1VpKT11TqaEEK0aDJGaq84P5ev332Lrau+R6fXM/Ds0Yy95CpsgdJJ1psMRhOTb7qTDj36aB2lRcs6eogv336VdYs+x4CH81NCmNQpDFsT2nTZ4fbw4oZcbCY9V3WP1DpOk1SfJYvvAG8D9596/xDwESCDTSNQVZXXHpzG5mV7qbU/AHSgpGA1+7f9kyvvvoNh50/VOqIQQrRk7yBjpGZyMzOYfvmF1NZcjNv1LmDnq3feYOUX5/P4B58SGBKqdcRm7YIbbtM6Qovk8XjYs/F7Fs9/m+1rlmHWK5zTNpgLOoYRaq3PpX3jmrezkGOltUwfEi9LFX9Dff7XwlVV/VhRlGkAqqq6FEVxezmXOOXgjs1sXr6eWvtOwHbqo11w2EfwztN96TvybPwCpFuNEEJoRMZIDc2ZOZOayrtQ1bvOfMzpGEjZyVv4dNbLXH3vgxqma/4qSktwOWsJiYjWOkqLUHayiDXffMqKN58mp8JBsEXPxV3CObt9MIHmpleInXZ2+xAibUZ6xdr++JNbqPr871UpihJG3c3KKIrSDyjzaipxxuovv6TWfgM/FGOndUCvH8S275cx8OzztYgmhBBCxkjN1FRVcmD7GlT1018ccztvZ+3CQVKQednzd16HotMxfbZsvectTkctO9Ys5/tvPmX798twu1ykhFu5qEsMZyUEYNQ33T1Fj5y00zrETLTNxLntZbb699SnILsD+BJooyjKWiACmOzVVOKMqopqUMN/9ZjHE0FtdVUjJxJCCPEjMkZqxOmoRdGZAL9fORqOo1bGR28zmi3UVEofm4bmcjrZt2Ud6xd/yebli6iuLCfYoue8tkGMSA4iMahpdE38PWszy3l2XQ6Xd4vggo5hWsdp8urTZXGboihDgA6AAhxUVdXp9WQtjMftZu+WdRTlZhOdkERKWl8URaH7wD7sWr+A2pprf/YVDlAX0iFNNvgVQgityBjZOMqKC9m1fjUAXfsNIjg8koDgUAKCwygpWA38fGuPz2nbVRpfeZvBYMTlcmkdo1moralh14ZVbFmxmG2rl1BVXoafUUffOBuDe8XTLcq/yXRM/COLD5fy+pY8UsKtjG0rzXXqoz5dFi8EFqmquldRlAeANEVRHlNVdZv347UMmen7eeofN1JTFYyqdkVR5hAYAtNefYMBYybwyauv4KydicdzJ2ABijCabqVjz17EJ7fXOr4QQrRYMkZ6l6qqzP/vcyye/w56wwhAwe16iNEXXcElt93Nxf/6F28+djUO+wKgO3UrR5djstzLlFtf0zZ8S6AonFqtK/6C4vxcdny/jO1rlrFn0xocdjv+Rh2942wM6BZHjxh/TE14SeLPuT0q83YVsmD/SXrG+HPPwDgsBt/Jr6X6LFmcrqrqJ4qiDATGAM8CrwHy0lMDqK2p4dHrr6Sq/Gng9GyXSq39JR69/ipe/HopM979kJfvv5eMff/BYErE5cig76hzufa+57WMLoQQQsZIr1q+YD5LPlqJ03EIp+N0uy2KQyoAACAASURBVOxCln4yhsi4GEZNuZzamlrm/3ccHncIqmrHalO47v6nSJF27N6nqiiKb8zaNAUup4NDO7eya/0qdq5dwfFD+wCI8jcyKsGfPvERdIn0w+AjM2E/l13h4KuDJYxrG8z1PaN8ZkavKahPQXa6W9Q5wGuqqn6hKMrD3ovUsqxf/AVuZ09+KMYAFFTPv6iunM+OtcvpOWQ0D8+ZS3FeDmUnC4mMS8QWFKJVZCGEED+QMdKLPpv9JrX2t4Af710UQa39ZT5/6wpGTbmcEZOmMmT8JLKOHERvMBCX3B6dTl6VbwzDJ12CXe5l/135WcfZtX4Vu9avYu+mtdirq9AbDLTv1osru0fQK9ZGQqDJpwvbzLJaEoPMJAaZ+e/ZrYkNMGkdyefUpyDLVhRlFjASeEpRFDMgv+kayNF9B7DXDPvVY7U1QzmRfoCeQ0YDEBYdS1h0bGPGE0II8ftkjPQSt8vFycIjwMBfOdqf0uJMnI5ajCYzBqORpJQujR2xxTt9fSJ+UF1Zwd7Na9m9YTW7168mP+s4AJH+BobE2kiLiSM1yg8/Yyng280uqp1u3t1RyKLDpdw/OI4+cQFSjP1F9SnIpgBjgWdVVS1VFCUGuNu7sVqOsOhIDKZDuBy/PGayHCIovH/jh/JRqlq3jl1RlJ/8XQghvEjGSC/R6fWYLcHU1hwF2vzs6HGMJn8MRrn401Lu8aOYLFbComK0jqIZj9vN0f272LV+FbvXryZ99zY8bjcWg46uUX5M6BlF92h/YgOMzeaaRFVV1p2o4K1tBZTYXUzoEEK3KH+tY/m0+nRZrAYW/Oj9XCDXm6FakkHnns+C2aOB24CUHx3ZgqquoO+IRzVK1rgctXbKigux2gKwBQZzsiCXNd8soLKslMryUqoryqmurOCC6/+PlLS+7N28jpem3YrTUYvL6cDtcuFxu7n3lXmk9h/CpmULefGem1B0OgwGIwaTCaPJzF0vzKFtlx7sWr+Kb99/E7+AQGyBwQQEhxIYGka/0eMJDAnFYa9BpzdgMBq1/tYIIZowGSO9R1EUhk2cwtJPp+NyzOOHiUcPBuN0hoyf0mwucH3VC3ffSFRCK+547k2tozSq8pJidq5dyc71K9m9fjUVpSdRgDahFi7oEEz3aD9Swv0w6pvn8/M/63NZfbyc1sFm/j0wjg7hVq0j+bymu613CxEaGcM10x7i7ScG4XZfi9vVDYNxIzr9XP75xLP4BQRqHfFvU1WVsuJCCrIzCQoNJyohieL8XN587N+czM+lpDCPyrJSAK574CmGX3AJZcVFfPjSkxhNZvwDg/APDMbqb8N9qr1ucFgEvYeNPbNURW8woDcYiYpvBUBccjsm3XgHbpcTl7PuzemoJTCkbnmA0+GgoqyEvBPHqCovPXP+rv2HEBgSypL/zeWD5x8jJCKKiNgEIuISiU5IYuwl1+JnC0CVG5mFEMLrptx6Gwe2XUnuiUHUVl8FgMX6LpEJDqb+8z1twwns1VVY/Gxax/A6VVXJTN/PttVL2P79co7s3oYKBJn19Izxp0fHGHpE+xNkab6X1bkVDsL9DBj1dV0gT7e0l8YdDUM5vbSrISV3SlVnvr+wwR+3Ocs9fpSln3xIzrFsEtu1ZuSFU4mIjdc61p9SVVGGw24nJCIKe001r02/jfwTx8jPOk5tTTUA51//f1x4811UlJbwxC2XEBYVQ0hkNCERUYSER9EhrS8xia1xu1y4nE7M1sZ51cXtclFZVoItKAS9wUD6rm3sXLuCorwsCrJPUJCdSWlRAXO+34/JYuWDF2ayefm3JLRLoVX7ziSldCYppSuhkdFSqIkW55K0hK2qqvbSOoevkDHyz3E5nWxd9R3rFi1FVVUGjBlBr2FjZQVDE3D90C4MGDuRq+99TOsoDc7jdnNwx2Y2L1/Eti/fpaCq7gXh5M7d6KvLplesjeQQM7pmPubnVjj4375ilmeUcV1aFOe0l6Zyf8aE+QfqNT4231Lex8S0Subyu+7TOsafsvKLjzh+aB9ZRw6RffQQpUUFDD7vQm565D+YLVYKsjMJi4qlc+8BRMYnERmfSGK7jgAEBIfw+Aff/uZj1814Nd7TU28wEBQWceb9dqlptEtN+8nnOGrtmMwWAJJSOlOYm0Vm+n62rvwOVVWxBYcwa9lOALavWY7Vz5/kzt3OfI0QQog/z2A00nfkOfQdeY7WUcSPeDweqivK8W8GK3lO83g8HNy+ifXffcmmZd9SfrIIo8lM93AzUzqH0yvWRoi1FgjXOqrXpRfX8Nn+k6zPqkCvKJzdLoT+CQFax2q2pCATv6u0qICMA3s4fmAPxw7tw88WwA0PPgPAN+/Noigvm/g27UntP4S41u1o373uRQBFUXhi/iItoze4HxdWA8ZOZMDYiQDYa6rJPLSfspOFZ2bHPnj+MbIz0jEYTSR37kannv3oMWjkL4o8IYQQwhdVV5TVvRjZDLbhyTp6iO+//pR1iz6nOC8Hk16hd6yNAR1jSYvxx8+o1zpio/jx7Rizt+ZzotzBxJRQxncIJcQqJYM3yXe3magsK2HDkq+pKD1JUkpXuvUfgk7/536BlJecJCcjnZS0uv1MX7j7RjYt+2FZTVR8Kzr3+aH98PQ3P8EWFNLi93uxWP1o363nTz42/c3/kb5rCwe3b+bAto18+c6rnCzIpV1qGqqqsuzTeXTqNYDYpJ93DhNCCNGQVFUlfdc29m1Zh8lsoc+IswmPidM6ls8zmizcPOMFkjulah3lL7FXV7F+8Zes+Gw+h/dsR6dAWow/V/aPoU9cAFZjy7m2yat0sORIGauPl/PcmFYEmg38q18MoVZDiylGtSYFWTOwZuHnzH70ARRlLM7aJMzW/xIY8gTTZ7/7u/uW5R4/ys51Kzm8ZztHdm8nP+s4eoOBt77fXzdojTybDt17k5TShVYdOuNn++lU9ekGGeKXAkNC6Tlk9Jk9WqorK7BXVwKQl5nBnMfrlqdGJ7YmbfBIeg4ZTYfuvf90ES2EEOK32WuqeerWGzl2MBNn7fnoDDl89PI4zrn8GqbcepvW8Xya2Wpl0LmTtI7xp+UcO8KSj99l9f/epcblITHIxDU9IhmSFEhwM27K8XN2l4d1mRUszyhjd0E1OgV6xvhT6fAQaIb4QLPWEVuUlvPMa6ayjh7izUcfwVm7BqjbFNNeDQ77TJ75v3/w5Ed13ZgrSks4tHMzh3Zu5ZzLbyAwJIytq77jgxdmEhoZTZsuPRh+waW06dIdna6uKBgwZoJW/6xmx88WcKagjWmVzItfr2PHmuVsXbWE7z56l4XzZnPbM7PoM+JsamtqznSOFEII8dfNeXwGR/fF4XQsBfR4HAAz+fb9QbTulELvYWM1Tui7ivNzKcrNIrlTKkZT0754V1WVA9s2suihq9icU4VBp3BWQgDj2gWTEm5tMc24nG6VCoebUKuBMruLFzfmEm0zcknXcEYkBxHuJ41ytCJXfD5u0fz3cblu4nQxdprHcy95x1/h+btuICfjMNkZ6QDoDUZS+w+hc+8BDD5vCv3HTGjRGzpqJSI2gVFTrmTUlCupqapk57qVpPYfAsDiD+ewcN5s+o46l7PGnU+71LQWM1gIIURDqa4oZ+OSr3A6DgM/Xn0QSa39Ub6Y87IUZH/DlhWLePfpB3lt6XaCQptmQaaqKrs3rGbB7Bc5tGMzgWY9U7uEMa5tCMEt5J4op9vDrvxq1mZWsCGrgi5Rftw3KJ4om4kXxiaRFGyWa4wmoGU8G5uxrMPH8LjPB44DK4CVwHnAJBR9Z3auXUHHXv0ZeM4FdOjem+ROqZgsda3kA0NCNcstfmD1t9Fv1Lln3m/TpQcde/Vn5RcfsuTjd4mMT2TI+ClMvPZf8ktTCCHq6WRhHnpDNE5HxK8c7UPBiaONnqk5OVmQh95gJCC4aV5LHNi+iQ9fepJDOzYT7mfghp5RjEwOwmxoOfeGzdtVyDeHSqh2evAz6ugTZ2NI0g9dMVuHSBfopkIKMh/mdrkoK8kELgdKT300DEgDPKhqBve9Pp/23WR7IF/SufcAOvceQHVlBVtWLOL7bxZweNf2M8XY3s3raN+tZ5NfIiKEEFoKDo/E7cqnbnwM/tnRnYRGJ2iQqvkoKcgjJCKqyTX2ysvM4IMXZ7JlxWJCrAZu6lVXiBn1TStnQyuocrI5u5LteVXcc1YsJr2OAJOeAQkB9IsPoHu0X7P/HvgyKch8RHVFOfu2rmfPxjXo9Xouv+th9AYDZosJRVeD6nkQmAx0BnQoyquERlhpl9rzDx5ZNFV+tgAGn3chg8+7EJfTCUBRbjaP3zQV/6Bghpw3hRGTLiU6sbXGSYUQoumxBQbTfeBItn9/Py7ny8DpFQZlmK0Pce4V12oZz+cV5+cQ2oRueaitqeGLOS/x9XuzMKguLk0NZ0KH0GY9I5ZX6WBReinbcqs4XlYLQGyAiYIqJ/GBZiakNM3ZS/FLUpA1cSs+/5CVn3/Ikb078LjdmC1Wegweeeb4kx8uZvGH7/HBi8+ieopwOVtj8fsOk+Ugd/93rixxayYMxrobbUOjYrj3lXks+/R9Fs1/i2/mzqLbgKFcftfD0kJfCCF+5voHH2bGtVdQlNsLe/VkdLpSDKa5nHX2OM4aN1HreD6tOD+Htl16aB0DgD0b1/DWXVeQX+VkaFIgV3aPJLQZ3iOWX+lge14VbUIstAuzUl7r5qtDJ+kY7sfV3SPoHRdAXKBJ65jiL2h+z1YfVphzgl3rVrFn81puefQFjCYzBVnH8XjcjL/6Vrr2HUi71J4YjD/9YRsz9QrSBg9nzTefUVp8iLZdzqHvqFd/spGxaB50Oh1d+w2ma7/BlBTms+Kz+az84qMzHRzzs44TFBqOxc9f46RCCKE9W2AwT374GTvWrmD3xg2YLWYGjHmXxPYdtY7m826e8YLm1xn26io+eHEmSz+ZS2yAkceGJ9A1qvmMf26PyvbcKrbnVbEtt4qcCgcAkzqG0i7MSttQC3MvaCd7hTUDiqqqDf6gyZ1S1ZnvL/zjTxTkHj/Kko/fY+f6leQeOwJAeHQc9776PrFJbX6ya7oQv8bj8ZxZwz/j2klkHj7IyMmXMWbq1YRERGmcTrQEl6QlbFVVVW5WrScZI4X4+zL27+alabeSf+IYEzqEcEnXcJ9fnuhRVTJKaimxu+gVa0NVVa76/DDVTg9dIv3oEe1Pjxh/4gNNcm3oIybMP1Cv8VFmyBpZYc4JdqxZTpsu3Unu1I2K0hKWLZhHp579GTnpMlIHDCU2qc2ZHzT5gRN/5Mc3VE/91zQWzpvNV++8ysJ5sxl07mTOu/Imuc9MCCFEgynKzSZ911a6DRiKX0DgH39BA1JVlaX/m8vcpx4gyKxnxrB4Un14VuxkjYvtuZXsyKtmZ14VZbVuIv0N9Dyv7lrw0eGJRNuMmKQhR7MmBZmXeTweDm7fyLbVy9ixdgXZRw8BcMH1t5HcqRttu3Rn9ordZ1rRC/F3tO/Wi/bdepGXmcE3c99g9VefEN+mPeMukZvXhRBCNIx9W9bx+kN38Nxnqxq1IHPU2pnz+H2s/uoTesb4c1v/GALNvnUp63R7OFBUQ+dIP3SKwge7C1lypIxgi54eMf50j657O/2CfGKQdFRuCXzrWewjKstKKMjOJLlTN1BVXrznZqoqyklJ68uwiVPpPnA4Ma2SAdDp9Zj0UoyJhhWd2Jpr73+CSTfejtW/7v6yVV9+zLbVS7nghtto1b6TxgmFEEL4qvys4yg6HRGx8Y12zvKSYv5zx3Uc2rmFKZ3DuLhrODofWUVUWOVkW24VW3Mq2Zlfhd2l8vzYJJJDLExMCeWcdiGyQXMLJwVZA8k9fpRtq5ewddUSDu3cQlhULC98tRadXs89L71HTKtkrP42rWOKFiY4PPLM32trqtmzaQ2bl39L7+HjmHzTHSS0TdEwnRBCCF+Uf+IY4dFxv2gy5rXzZR3nyVsv42RBLvecFctZiY27TPLP8qgqTreK2aBjZ14VD644AUCEn4GhSUH0jPUnLqDuexcfKDNgQgqyv8zjdqPodCiKwgcvzOTr914HILFdR8ZfdQtpg0ed+dzkTqlaxRTijNEXXcWAcRNZ9P5bfDv/LbasWMSEa/7BlFvv0TqaEEIIH5KXmUF0YlKjnCszfT9P3HIpbpeTRwdF0THCr1HO+2c53Sq786vYkFXJpuwKxrUL4aIu4XQIt3Jl9wh6xdpIkGYc4jdIQfYnOOw17Nm0li0rF7Nt9VLuf30+CW1TSBsyirDoWNIGj2rU6Xsh/ixbYDCTb76TsZdcw9fvzaLNqT1kaqoqqa6sIKwJbfIphBCi6VFVlbzMY42yj9uxA3t4/OaLMTkqmTksgYQmeD+Vqqq8vCmPdScqqHZ6sBh0pMX40y6sbksAi0HHBR3DNE4pmjopyOqhKDebef+Zwc51K6mtqcZqC6DbgKFnXuVI6dGHlB59tA2pMY/HQ3VlORWlJVSWllBVUUZVeSnVFeVUV1ZQU1VJTVUltTVV1Nrt1NZU47DbcTkdOB21uJxO3C4XHo8bj8fNj3djUBTQ6fTo9Qb0BgMGoxGjyYzRbMFssWC2+mG2+mP198fqH4CfLQD/wCD8A4MJCA4lMCSUwNBwrP42eWXqFFtQCFP/ee+Z97+Z+wZfvfsqYy++lvFX34J/QJCG6YQQQjRlT3y4CEXxbte/Ywf3MvOmqfi5qnhsZCLRtqax4bHbo7Izv4r0YjsXdQlHURTcHpX+8QH0TwigW7SfdEQUf5oUZL+ipDCfLSsXExgSRt+R5+AfGETG/t0MPPsCeg0bQ+feAxpt3bTW3C4XJYX5nCzI5WR+LiWF+ZQWFVBaXEjFloWU2t2U2V2U1brx/M6WdnoFrEYdFkPdm1mvYNLrsOgVAvQKBp2CQVHQKaA3/LRoUtW69dhuF7gdKi6PitOjUuvyUOVWsbs82F0ealwe7K7fDmHSKwRbDIRaDQT1GEFoZDTh0XGERccRERtPRFwCtsDghvrW+ZQh4y+kIDuTr999jRWffcD51/0fo6Zc0WKe50IIIepHURQiYhO8eo6cY0d48pZLsbqqmDkikagmUIwdK7GzPKOMVcfLKbW78TfqOLd9CP4mPbf1j9U6nvBxUpCdUpCdyebl37J5+SLSd21FVVX6jjqXviPPwepv44Wv1jbL2RWP201xfg4FWZkU5JygIOs4hblZFOVmU5SbRWlB7i8KLYPRRHBYBCFOD5H+RtqFWQgyGwg06wk06wkw6wkw6fE36bAZ9ViNOkx6pVG+f26PSpXTQ5XDTaXDTXmtm7LauqKx1O6mpMbFyRoXxw/uZfv3S3HY7T/5ev/AIKITWxOTmEx0q9bEJrUlPrkd0Ymtm3VxEhGbwC2PvsDZl13PBy/MZO5zj3D80F5ueuR5raMJIYRoQg5s38Th3dsYfdFVmMyWBn/8ksI8nrzlUlAUZgxrGsXYyowynt+Qi0EHPWNtDDvVmENmwkRDadEFWXFeDmHRda9qvDXzXnZv+J5WHToz6aY76DN8HHHJ7c98ri8XY6qqUlqUT86xo+QeP0Lu8aPkHc8gL+sYBVmZuF3OM5+rUyDcz0ikv5FufgYiOoUR4WckzM9AmJ+BUKuRAJPu1PcjQLt/1G/Q65QzheEfUdVWVDjcFFa5yK9ykF/pJLfSSW7hIQ4e3cOaatcPj2swEJ2YTGK7FFq170SrDp1p3bErgSHNa114UofOTHv1fXatW0noqfvJThbkUlZcROuOXbUNJ4QQQnNbV37Hdx+9w9mXXt/gj11TVcnT/7ySyvJSHh8UQVygNsVYRomdhekldIn0Y0hSEGmxNq7vGcngVkH1ur4Q4s9qUQWZqqqcOHyATUsXsnHZQnKOHebVxVsICovgktsewOLnT1R8K61j/mWqqlKUm03WkYNkHTlEdsYhso6mk3vsCDVVlWc+z6RXiA0wkRRgon+7AGJsJqJtRqJsRsL9jOh1vlt8/hmKohBoNhBoNtAm9Jev8tldHnLKHWSW13KizMHxshyOrDnK+sVfnvmc0KgYkjul0qZzd9p26UGbLt2x+Pk35j+jwSmKQrezhp15/7PZ/2X5gvcZMuEiLvrHvwkKDdcwnRBCCC3lZBwmOrE1On3DFiYet5tX7v8nJ9L3MX1w/K+Oy97k9qhsyq7kq4Mn2VtYg0mvnLlvLdCs59z2oY2aR7QsLaYgO7RzC7MeuYvcY0dQdDpSevRh5OSHMZjqfth8baPc6opyMtP3n3o7wInDB8g6cvAnhVeI1UBCoIlhsSbiA6OICzQRF2AizM/gM5spasli0JEcaiH5Z4NCpcPN0RI7GSW1HD5ZRfrm5WxZsRiom2FMaN+Z9t161TV7SetLSESUFvEbzNR/3YvZamXxh2+zcek3TLrhdkZfdBUGo1HraEIIIRpZVkY6bbywnc8nrz3LttVLuaFnFGmxjb9v61NrstmYXUmkv4Gru0cwsk0wNpPMhonG0SwLMlVVOX5oHxu++4p2qT3pOWQUYVGxhEbGMPbia+gzfBxBYRFax6y3ksJ8jh3YQ8aB3WR/NYuM0lryKn9YZmgz6UgKNjMs1kxiUBRJwWYSgszyi8RLbCY9qVH+pEb9MBNWUesmvbiG/UU1HCjK4PsF+1jy8bsAxAYYSRk1hc69B9C5z1k+t8zRPyCIy+54kOEXXMrcZx9m3n9mUF5S/JMujUIIIZq/2poainJOMOS8Cxv0cbesXMwXc15mZHIQZ7drnOZatS4PS4+WMax1IH5GPWPaBjM4KZD+8QEtZqWQaDqaVUF27OBeNi75mg1Lvib/xDF0ej0TrvlHXUEWHcv9r8/XOuIfKjtZxNG9Ozm6bxeZX79Beq0fpUUFZ45HJSTRNqSYkclBtA6xkBRsJsxq8Ol73JqDALOetFjbmVf13B6VoyV29hZUs7ugmvXffcnyBe8DkJTSldT+g+k2YAjtUnv5zExTbFIb7nnpPbau+o52XdMAyM3MwGL18/lZQCGEEH+sMCcTFIXY1m0b7DELsjN5/cE7aBtq4cZeUV6/nnG6VZYeLeWjvcWU1LiwGnUMbx1ETw1m5YQ4zacLMlVVKSnMIzSyrvnAa9NvIzsjnU69BnDelTfTa9hYAkOa7prf2poaMvbv4vCe7Rzes4Oje3dSlJsFgALEB5roHqqjbWIkySEWWoeY8TPqgThNc4s/ptcptAuz0i7MysSOYbg9KodP2tmZX8XOvMN8885uvnz7Fay2ALr2G0zaoBF0HziiST9foe7+sl5Dx5x5/63H/k3G/t1ceMvdjJ5yZYPfUyCEEKLpiG/TgbfXHmywosnldPLStFsBuOesWK92LVRVlXUnKnhvZyF5lU46RVi5a0AsXSL9vHZOIerLJwuy7KPprP/uKzYu+Zri/BxeW7Ids9XKzTOeJzQqtkle1KqqSv6JY6Tv2kb67q0c3r2dzPT9eNxuACL9jaSEWWjXPYJ2YVaSQyxYjdJOtbnQ6xQ6hFvpEG5lSudwqp1uduZVszW3kq1rF7Np6TcoOh3tU3vSa9gYeg8fR2Rcotax/9B1DzzFO09N571nHmL1V//jugeeILlTN61jCSGE8JKGbHW/YPYLHNmzg3vOim2U9vaLDpdi1uuYPiSenjH+srpINBmKqv7Obr5/UXKnVHXm+wsb/HH3blrL3OceITN9P4qikJLWl36jz2PQuZOxWJvWKxxORy0Z+3dzcMcW0ndt4dDOrZSfLALAatDRPsxC+3Br3Z9hVoItPlkbiwagqipHSmrZlF3BpqxKMkprAUhK6UKfEWfTd9S5xCS21jjlb1NVlY1Lvua9Zx+m7GQRdz0/hx6DRmgdSzSiS9IStqqq2kvrHL7CW2OkEN72v9eew2qzcc7lN/7tx0rftY1Hrp7A0KQg/q9fTAOk+6VKh5v5u4s4v2Mo4X5GyuwubCa93CMmGs2E+QfqNT426SqgOD+XDUu+okP33rTt0gOrLQCz1Y/L73qYfqPOISQiWuuIZ1RXVpC+cwsHtm/i4I7NHN25CYe7rtiNsRlJC7eSkhxFx3ArCUFm+WUgzlAUhbahFtqGWrikawR5lQ7Wn6hgfdYRPn7laT5+5WmSUroyYOx4+o+ZQFiUdwauv0pRFPqNPo/U/kNYOG82nfucBUBFaQkBwSEapxNCCNFQvl+4gLZduv/tx3HYa3j94TsIsxq4vmdkAyT7pXUnynljSz5ltW7ahFgYnhxEkLz4LZqoJvfMLC8pZuPSb1i/6AsObN8EwKQb76Btlx4kd0rlkXc+1zhhnYrSEg5s38iBbRs59O08MkrseNS6tudtQiyMbRtMpwg/OoZbCbY2uW+zaMKibSbO7xjG+R3DKKxysjaznDWZ6XzwwkzmvzCTzpFW+t80g74jzsYvIFDruGf4BQQy+eY7AbDXVPPAZefQulMqV90zg+Bw7wy4QgghGkdNVSWF2ZkMnTDlbz/Wp2+8QO6xIzwyLOHUvfENp8zuYtaWfNaeqCA5xMwDQxJo28h7mgnxZzWJSsHjdqPT61FVlfsuHsvJgjziktsz+eY76T9mQpNYrlVZVsL+rRvYt3UD+7esJzN9PwBGs5kOQTomdwqjc6QfHcKscu+XaDAR/kYmdgxjYscwciscrDpezqpjZcyecTfvPPUAvYeNZcj4KXTuMxCdruk87wwGA8POv5jPZr/I3k1ruOyOBxl83oWyXl8IIXxU1pFDACS0Tflbj3P80D6+mTuLkclBdI/2/+Mv+JPm7yliY3YFl6dGcH7HUFmRJHyCZgWZo9bOjjXLWbfoCzLT9/PsgpXodDqunvY4ETHxJLRL0fTirbqygoPbN7F381r2bl5H5qF9qKqKSa+QEm5lUNdwOkf60T7MgtGLXYGEOC0mwMTULuFc1DmMQ8V2VhwrY/Wyr1i36AvCo+MYPH4KQydcRHiM9l04DUYTtxFODgAAIABJREFUE6/9J31GnM3sGXcz6+E7WbfoC/7vqdea1KyeEEKI+jn9QnRiu45/+TE8Hg9vzZxGgAGu6t5wKyecbpXyWhdhfkYuS41gbJtgkkJkVkz4jkYvyI4f2se3789m84rF1FRWEBQWQb/R5+Gw12Dx86fnkFGNHQmoa8JxaOdW9m5aw55Nazm6ZxseFYy6ugLs4i5hdI3yo12oFaNeXm0R2lGUHzo2XtMjko1ZlSw9WspnbzzPZ288T+pZwxgx6VJ6DByB3qDtJHhsUhumv/k/ln7yHjvXrcLiL/u8CCGEL3I5nUTFtyI8Jv4vP8aqLz7i8O5t3NYvhgBzwyxVLKxy8tTabFxulefGJGEz6bGZZAsW4Vu8frWmqipH9uwgJCKKsOhYTubnsnnFYnoPG8tZ4ybSqdcATS4aPR4Pmen72bPxe3Zv+J6DOzbhsNvRKdAu1MKkTmGkRvrRIdyK2SAzYD/ndKt4VPXM9+ZYqZ1al4rD7cHlAZdHJdRqoM2pddurj5fj+VFHTwWICzTTNtSCqqpszanCoFMw6uveTHqFEIuBIIsBVVVRAZ0sd/sFk17HoFaBDGoVSEGVkyVHSlm67Xv+s3YFYdGxDL/gEoZNvFjTe7h0Oh2jL7qKUVOuRFEUSgrzeOfJ6Vx6x3SfaO0vhBACxky9ijFTr/rLX19VUcZHT0+jU4SVoUkNs1JiV14Vz6zLwelW+Ve/aFmeKHyW1yqhnGNHWPft56z99jPys44z8dp/MuXWe0jtP4TXlmxr0H0s6qukMI9d61ez/+1H2JlXRVlt3R5gCYEmRif60y06jC6Rfg1+g6kvOFRcQ1G1k/JaNxW1biodHsKsBsan1O3p9vCKE2RXOKhxuqlx1RVd/eNt3Duo7pWyB5adoMLh/sljDk0K5Pb+sQC8uCEXl+enWyyc3S6YtqHRuDzw6OqsX2S6oGMoV3aPpMrp4bJP07EadfgZdfgb9dhMOsa2DWFwUiDVTjeLD5cSZDEQbNETYjEQYjUQaNa3qCIu0t/IpakRTO0SzuacSr5NL+GTV5/ls9efZUBCICNnzKNt1x6a5Tu9BPnE4YPs2bSGf08ZxaW3P8CISZfJvWVCCNHMff7mf6modXN9WlSD/M7/Nr2EN7bmExtg4r5B8cQFen8fMyG8xSsFWc6xI9x1wVAURaFT77OYcO0/6TN8HAB6g6HRZsQctXYObNvIrvWr2LV+1ZkbUoMtenrE+NMt2p9uUX6E+RkbJU9jqnF6KK91ndlocenRUtKL7ZyscVFS4+Kk3UW41cDTo5MAmLUln8Mn7We+3qRX6BHtf6Ygiw0wEmTR1xVFBh0Wo47EIPOZz7+9f10rduOpWS6DTiHwR8sR/juuNad//6oqqKj4nyp89Tp4elQr3KqKw63idNfNtMUG1GXXKXBh5zBqnB6qnB6qnG6qHG7q5s2gqNrFOzsKf/E9uKlXFOPahZBb4WDerkIi/Y1E+huJ8jcSaTMS5W9qlstP9TqFfvEB9IsPILvcwcL0EpZnlLH6yvG06dKdcZdeR5/hZ2MwavO8T+0/hKc+XsobM+5mzuP3sWnZt9z40LOERcdqkkcIIcTvK87L4clbL+OKux+ma7/Bf/rr808cY9H8txmRHERyA3Q8dLo9fJteSlqMP3cOiG2RL6SL5sUrlZHV38bkm+9kwJjxjbpXmKqq5GVmsGPtCnatW8n+bRtw2O0YdAqdIqyM6B5Bj2h/WgWbfX7mpNblobDaSXxgXVG09Egpm3IqKaxyUljlpMLhIdii593z2wGwObuSvQU1hFoNhFoNJASZiAv8oaC6tXc0KBBk1hNg1mP6WaOSG3r9/v9jz9jfvzfo91650p26J+q3+Bn1XJoa8ZvHEwJNzJ/cjjK7mxJ7XcFZYnfRObJus/DyWjeHT9rZkFWBy/PD190/OI4+cQEcPmln6dFS4gJMxAeaSQgyEWY1NItZm7hAE9f3jOLS1HBWZJTz9aG9vDztH4RGxTD24msYfv4lmjTZCI+JY9qr77P80/eZ9/yjfPrG89zw4DONnkMIIcQfO35oH9kZ6Zitfn/p6z965WkMqut3x/L6cLo9eFQwG3Q8NiIBf6Ns8iyaB68UZCERUZxz2Q3eeOhfsNdUs2/zOnauXcGOdSspzM4EIC6gbhliWkxdN0SLD94HVuvyYNQr6BSFbbmVrDleQW6lg9xKJyU1LhTgkyntMep15FQ4yCl3EGkz0iHMSri/kQi/H/57/z0w7neL0IZ4xUoriqLgZ9TjZ9QTE/DLwq9DuJVZ57XBo6qU1LjIr3KSX+mkXWhdEfj/7d13dFRV18Dh35mSSZn0XkilB0JHigrSkWLjxYqoKJZPUV7EBogIFlREfe0Ve1cUFQSR3nsJPSGBhCSk9zpzvz8SImiQlEkj+1mLteIt557EZO7d95yzd0peCWvjcsgv/StaczDoeHFYCK1cTSTkFJNdZCHM3dRs38I5GvWMauvOyDZu7DiVz0+HMvjilWf48b1XGXTtTYy8+U48fBq24LRSisHjbqFTn8twqggKUxLisXd0wtXDq0H7IoQQ4vzij0QDtUt5H3tgD5uXL2F8pCcedajLWlhq5dl1CTgadTx2aSAupiZRuUkIm2iWv81JJ46ze/2f7F7/J4d2bqG0pBh7g6KzrxPX9fSlu79T5VS95iIlr4Rdyfkk5JSQkF1CYm4xqfllvDMmHF+zHSezS9iZlIe/sx3d/JzwNxvxM9txZlXWrV19uPVfUsg29xFBW9AphaejEU9HIx3PeknXP9iFfq2cyS6ycDKnmJM5JZzMLsbbqXxK38rYbH44mAGAv9lIuIc9rd3tGdPOo9lNedQpRa9AM70CzRzLKOKnQxks/exdfv/8XS4dcz2jJ95LQGhEg/bJNygEKB/hfmvWQySfjOOuWfPpMWBYg/ZDCCFE1eIPH8C3VSgOtciU+80bL+Jsp+eaDh61vn5+iYWn1yRwJL2QB3r7XxQzWIQ4W7MIyEpLijm0cwu71v3JniWfkJRXCkCQix0jw5zoEeBDpLdDk68HVlBq4UR2CSeyionPLuZEdjETunjT1tOBYxlFvLUtBZNeEehiR3svRwaH21VOHRzbzp2r2tf+w0z8O6UUbg4G3BwMdPY9t1DlVe08iPRxJDaziNiMIo6mF7E3uaDy5vL53lQyCsto7+VAey8HglzsmsXNorWHPdP6BXBLlBeLD2Xwxy/fsOanr+k9dDRX3XE/oe0iG7Q/SikmzXiON2c9xIKpk7jimhuZMG029o62LxwqhBCi+uIPRxNSi3vCoV1b2btpDRO7eNd6hkl+iYWnVp8kJqOI6f0D6NdKalmKi0+TDciy01PZtW4lu9atZN+WdRQV5GO0M9HZ044x7TzoEeCEXxMdBbNqGil5pcRlFRPoYkewq4kj6YVMXx5feYy9QdHK1URxxaKmbv5OvDc2Ai9HQ5WjWc3hAf9i5eZgoKeDmZ5nrZMrLLVW/j/JLbawJSGXP2KzAXC203N5iHPlujtN05r0/z9fsx139/Tj+k5eLDmcya+b1rBlxS90u2wI19w1hdadGi4zY6vW7Zn7yc989/bLLFn0Jge2beTR1z/FLziswfoghBDiL1arlXbdetOuW+8an/v92y/j6unNqLbutb7+SxtPEZNRxKOXBnJJkHOt2xGiKWsyAZmmacQfOcDOtSvYufYPYqP3AODpaGBAgJleAUFE+To22ZpgxWVWPtmTSmxmEccziymsCLTGR3pyc5Q3QS523BzlRYiriVA3E95OxnMCrzNroETz4GD86/fwnl5+3N3Tl8TcEg6lFnIgtRCniqKUmqZx7y+xBLmaiPJxpIufE8GuTXMEzc3ewIQu3lzTwcJvR0z8vOVPnlz3B136DeTayVNpE9W9QfphMNpxwwOP0bX/FSz5+G08fBouMZAQQohz6XQ67pnzco3PO7x7G9HbNnBHN586PbvdEuXN8NZuEoyJi1qjBmSlJcUc2LaRHWtXsGvdStKTT6GANp723BzlRc8AM2Fupibz8GqxaiTklHAso5BjGUXEZBTR2sOeyT39MOoVG07m4uNkZGCYC2Fu9oS5mwipSA3vaNQzPlISFVyslFIEuZgIcjExJMKtcntRmUaUrxN7U/LZlpgHgLu9noldfbgizLWxuvuvzHZ6xnfyYnQ7d5YezWLxjnXMvm01Xfpfwbh7/ktEZNcG6Uf77pfQvvslABTk5fLhs4/zn/umV645E0IIUf+KCwuxs7ev8bPYj++9iou7JyNau1344L+xWDV2JuXTK9BMhIc9Ec048ZgQ1dHgAVluVia71q9k55oV7F2zjKIyK/YGRVc/J27q7UfPADNudcjCYyuapnE6v5T0wjI6epeneZ32exzHs4oBsDfoCHc3VSYP0SnFR1dFNJngUTQNDkYd9/UuH+FJzS9ld3I+e5LzKzNNHUkv5N0dKfQMKJ8SGe7edEoyOBr1XNfRkyvbuPPr0UwWb1vDrA2r6DFgKP+5dzrBbTs0WF8SYo6we/0qdq1bye2PPcOlo65tsGsLIURL9vELszi8exsLflxT7XOOH9zH3k1rmNDFu1ajYx/sOs2vRzJ5dnBwZQkbIS5mDRL5pJyMY/ua5ez+9CUOphVi1cDdwcCAUBd6B5qJ8nX8R92rxnAso4jdSfkcTi/kSHohWUUW3Oz1LLq6NUopxrbzQKnyZAiBLnb/eHCWYEz8G28nI0Mj3Bh61ghaiUVDB3y1L40v96Xh4WCgV6CZmzt74Wrf+C8moDyoHNfRkyvbuPHL4UwWb1zJY2tW0Hf4WMbd+zD+DbC+q22XHjz31e+8OXMKb856kD0bV3P748/gaJYpLEIIUZ+OH9qPl39Qjc5ZsuhNHI06RtZidGzJ4Qx+PZLJ2HbuEoyJFqPenvhiD+xh++rl7Fj9OyePHQYgxNXEuI6e9K4Ygm6skQBN00jJL+VQaiGH0wu5o5svRr1idVw2Sw5nEuBsR1c/J9p6OtDW869h8kHhTXOKmWi+Ovk48sKwULKLythxKp+tp/LYmpDLnd3LSxisPp7NjlN5nEwtoMgC7b0dGNvJq1Hqxjkay6cyXtnWnR8PZrBkzQq2/PErV1x9I9dOfrDei8B7BwQx891v+OnD1/n+3YUopbhv3qv1ek0hhGjJSkuKSYg5wqgJ1a8tm3Iyji0rf+Oadm6V66mra8epPD7cdZpLgszc9i+lfM44ml7Ikv1pHE4rwtmo49LWboxs695k8w0IcT71EpAlxB5l5i2j0Sno6O3IsG4+9A4yN3pWxMNphfx4MJ2DaeWjX1BeAHhUW3eCXExc18GT6yO9cDZJcg3RsFztDQwKd2VQuCsWq4Zepygqs/LOtmQKLBomYCDgnZjH00l5PDSgFV39GicdvNlOz4Qu3oxpW8Y30Wn8/uNnrPv1O0bcOImxt92Ho3P9pSTWGwxcO/khInv3x9M3AIDC/DxMDo7odHIDFkIIW0qIOYKlrLRGZVCWffkheqyMrmFmxdxiCws2niLE1cR/+wag1/37S/ttiXm8tSGR2RaNUcCpEgvP709j3olcnhwa3ORLIQlxtnoJyFx1Zdx0iR+9As2NUkm9qMzKobRCDqQWcDC1kHEdPeni50SxxUpsZjFd/Zwqa0YFu5oq/+jdm8DaNSHO/D4uP5rJQE3jYeD7in/JQD8rfLg5iVevisCqccGbVn1xczAwuacfY9t78PneNH7+6A3+/PELrpk0haHjb8VgrL8XMO269gLK0zG/9ui9WK0W7n36Fdy8LvxGVQghRPXEHY4GIKR9p2odn5eTxeqfvubyEBc8HY01upazSc+UPv6Eu9tjf4ERLotV473NSfxo0bisYls48LNFY0hOMavichgWUfPpkkI0lnp5feDqYGBwuFuDBWOapgGQWVjG9OVx3PTdEWavOsm30enklVgosZTv7+zjyLtjI5jaN4CRbdwJc7dvtIdZIS5kS2w2D1nhCuB1IAFYBbwMUGphV1I+E388xpvbkjmQWlD5d9DQ/Mx2TOsXwMLhoUSYivl0wRweGdyezSt+qfc+KaXoNWgkh3dv47EbhrNn4+p6vZ4QQrQkIW07cvWkB6qd3XbVj19RXFjA2HYe1b6Gpmkk5pQA0CfIGR+nCwdyR9IL8bL+FYydoQMesGhsicmq9vWFaAqa5ZBQXomF6NMF7K/418Hbkck9fHExldfyuqaDJ5HeDrT3djintpck3RDNSYlV4+xVi3rKpy0CuKIoLLPSzd+J1cez+f1YFn5mI1eEunJl24Z7GXK2cA975lzRil1J+Szancprj95L2669uGXqLFp3rp/i0kopBl17E2279OC1x+5j/v0TGHv7/zHunmkYjDV7OyuEEOJc4R2jCO8YVa1jLWVlrPjmYzr5OBDmXv11zstjsnlnezLPDQmhnZdDtc4psWi4nOeRzhUoqagFK0Rz0SwCslKLhlFf/pc3d81JdpzKRwOMOkU7L4fKWl96nWLOFa0asadC2E5Hfye+OZZF778NMsUAJzSNngFm+ge7UFhqZVNCLquOZ/PtgbTKmi8peSW42RsadHGzUoruAWa6+Dmx8ng2n8XE8eTEsfQbeTU3PvA4nn4B9XLdoIh2zP3kFz556SnW//o9oybcjbNbzdYvCCGE+IulrIyY6D2EtO2IyeHCgdLOdX+QlpTAnZcGVvsa8VnFvL8zhc6+TrTxrH4Q18bTnpesGqeAv99VvtFBhwBztdsSoilokgFZcZmVg2mF7E3OZ+/pArKLLLw7JhylFB28HGnj6UBnH0faeNo3iXT5QtSHUR08efR4Du3LrEwEjMAB4Hq94uqOnpWBloNRx6AwVwaFuZJTbMGlIinNa1uSOZ5ZxOWhLgyPcKvRG8u60usUwyLcuDTYwg8Hrfy0/Ce2r1rG6FvvZczEe6t1c68pk4MDd82aT05mBs5u7pSVlnJ073Y69Ohr82sJIcTFLvH4UZ66/Wrum/tqtWo/Lv/6Yzz9AugdWL1gqMRiZcHGUzgYdEzt41+jzNuORj2j2roz9kgmX1g02gLFwHvA93odL9ZgyqQQTUGTiGYsVg1rxVqTnw5lcNP3R5m96iSLD2WgV4qBoS6cGX0eF+nJDZ28iPRpGrXLhKgvPk5GZg8J5nV3E746RZhBxwCjjj6dvbg60rPKc1zOyhB6QydPegSY+SMmm4eWxTF9eRxbEnIbqvtA+U3zlihv3hgVTi8fIz+8u5CHrxvIpt9/rrf1ZS7u5TfiFd98zNy7xvP5wrmUlZbWy7WEEOJiFXdoPwBhHTpf8Nik+Fiit65nhE9Jtdfmf743jfjsYqb08cetFknVxnfxJrKjJ30NOsINCj+d4iNPe54aGoKHJGkTzUyj/MZqmkZibgl7kgvYk5LP/pQC5g0KJtzDnlA3E6PbutPZ15GOf1sDJkRLE+puz9MjwkgvKKWwzIqvk13l9N0L6ezrRGdfJ3KKLeXrzGKyOJ1fHpgUl1lJLywjwLlhSlH4OBl5pH8g0W0KeG9HCv97/P9Y8e0n3PbIXILbdqiXaw4edwvJJ47z66fvcmTPDh547g28/Ks/lUYIIVqy4wf3YXJwxD8k/ILHrvz+M/QKhoZXP7Oho7G87FDPWk4v1CnFdZ28GNvBg+S8UhyNuhpndhSiqWiwgEzTNJRSxGUVMW9NAqkFZUD5g1q/YGfsDOUPmV38nOjSSPWVhGiq6nKTcTHpGdvegzHt3KlIOMqa+Bze2JpMd38nRrVxp3uAU4MUao/0cWTB8FBWxGbx2Z7tPHHjMIaMv43/3PcwTs62LbxuZ7Ln9sefoUOPPrw39xGeuGkEU55/i06XXGrT6wghxMXo+MF9hLaLRKf/9xfjJcVFrPvmA/oEOdeofND1nbxsMlPCqNfRqiKXgBDNVb0FZGVWjcNphexKzmdXUj6XBJoZ38kLXyc7Wns6cF1HR7r6OeHfQG/ohWjplFJUvPegV4CZGzt7sexYFnPXJuBvNjK6nTsjW7vXeykIvU4xorU7/Vu58Pm+VJZ9+wlbFn/KDY+/wGWjx9m8wHOfYWMIad+Jt2Y+iNlV6tIIIcSFWC0W4g9Hc8U1N13w2G1/LiW3xMqw1tX7fP3uQDqhbiZ6Bpgl+7UQFVR9rONwNuk1ixUKy6zoFLT1dGB4hBuDwm37BlwIUTdlVo1NJ3NZciSTMqvGgmEhKKUoKLU02HTh2Iwi3t6RwuG0Qtp27cUdjz1TL9MYz4zSA/z+1Uf0Hnwl7t6+Nr9OS3RT91Y7NE3r2dj9aC7CO0Zpz3z+W2N3Q4jzslosHNmzHWc3DwLD2/zrsfMmX0/GoW28NTr8gjMtjqYX8siKeIZGuHFfLz9bdlmIJumqLw9V6/5YLyNkOqXoH+pMNz8nOvs6YraTdWBCNEUGneKyEBcuC3Ehr8SCUoqcYgt3L4mhu78T13TwpLVH/WZnDPew5/khwfx5PJuPd+/kiZtHMvyG2xl3zzQcnGyXuvhMMJZ6KoEvX3uWnz58nQeee12yMAohxN/o9Hrad7/kgselnIzjwPaN3BzldcFgzGLVeHNbMq72Bm7t4m2rrgpxUaiXNIXeTkbu6+VH31bOEowJ0Uyc/bc6LMKNnUn5TPs9jpkrT7DzVF69ZUWE8pc4Q8LdeHNUOEPDnFn2+fs8MqwzW1cutfl1vQOCmPvJEhyczDxzz438vOjNev3ehBCiudm1biW71v95wePWLPkWnYLBYReeAbX0WCaxmcXc1d1Hng2F+BvJGy+EOIeLSc/t3Xx4f2wEt3X15lRuCXPWJJCYW1Lv13Y26bmvlx/zh4bgYtLzyvTJvPTQHaSeSrDpdVq1bs+8z36l16CRfPXac7w16yGbti+EEM3Z4g/+x5KP3vjXY6wWC+t++Y6ufk4XTDyVXVTG53vT6OrnRL9WzrbsqhAXBSnUIISokpOdnms6eDK6rQf7TxcQ5FKexerj3afxN9txRZgLxnqqBdjOy4EFw0NZcjiDLzet5JGrVjJuygxG3DgJvcE2H1sOTmamPP8my7r0xM1Tps8IIQSApayM+CPRDL725n89Lnr7RtKTT3FH/4ALtuls0nNXd1/aeTlIIg8hqiABmRBNXHJeCevjcygstdLB25Fu/k71ngnxbEa9opt/eSmKMqvG/tMF/HAwg6/2p3FtRw+GhrthMtg+MNPrFFd38KRfsAvvbk/m84Xz2LB0MXfOnE94xyibXEMpxcibJlX+96rFX6E3GLh89DibtC+EEM1NYuxRSoqKCOvY5V+PW/vztzg6u9I78MJrfXVK1UtiN4tVY/upPA6nFWK203FZiCveTlKLTDQ/MmVRiCbs+32pPP7bcVz2pRF1MIMlGxN5/LfjZBeVNUp/DDrFC0NDeGpgK3zNRt7bcZrJS2LYm5Jfb9f0cTIy4/IgHukfQHbcQWbdOobPF86lqLDAptfRNI0tK37h7Sen8sGzj1NaUmzT9oUQojmIPbAHgIjI8wdkRQX5bF+1jMt8FHb/MlNC0zTmrU1gRUyWzfuZXlDKw7/EsmJTEl0PZmDal8bDv8Sy5GC6za8lRH2TgEyIJmpnUh5rDmYQbdF4XYNZwM4yjVF5Jby98VSj9Uup8hGz54aE8MzgYMLd7QmsqCeYkldCUZm1Xq7ZP9iF10eFMyzchV8/fZdHxw9l35Z1Nr3G9FcXMWbivaz87jPm3vkf0pMb7+cshBCNIf7oQRzMzvi2Cj3vMdv+XEZxUSEDw1z+ta1NCblsS8yjPvImvbE+kZsLStlaZmUm8I4V9lo1ftuXxoFU276wE6K+SUAmRBO14mAGT1o0zq7UooC5GhxILSStoLSxulapk48jswe2qlzQ/crmJCb/HMNPhzIorofAzGxXnvTj2cHBGHOSee7em/jguk7k5djm7aveYODGB5/goRffIfH4UWbcMspmbQshRHMwYdpsXvh2JTrd+R8R1//2A94Brejg5XDeY8qsGp/sTiXY1Y7BNp6ueCq3hBNZxcz8W6DXCnjEorH8YIZNrydEfZOATIgm6nReCd2r2O4IROgVqfmNH5D93YQu3oS4mfhw12nu+SWW345mUmqx/avRSB9HXh0ZyriOnqyKy+bREV3Z9udSm7Xfe/CVPP3JEq65cwpmFzebtSuEEE2dTqfD09f/vPuz0k4TvWUtA90L/jVBx4qYLJLySrm1i4/N1z2n5JXSUaewq2JfDyC1AbICC2FLEpAJ0UT5mu3YUcX2fCDGouFjw4XL+SUWCkotdW6no7cjcwcF88zgYPzMRt7ZnsLyelg7AGCn1zGhizcLhoXiZm9g4cOTee3Re8nJtM36gcCw1gy/4XYADu3cwuszHqCooP7WygkhRGNLiDnMu09PJyUh/rzHbF7xC1YNLg85/3TFUouVr/en0dHbgZ4BTjbvp5/ZSLRVo6qwazvg61JVqFY7FqtGTnEZZVapVynqj2RZFKKJGtbBk6fTChlp0TiTVFgDZijo5ONwwbov1bH/dAFf7UjhWHZ5Aot2biZu6OFLB2/HOrXbyceRZwcHszu5gI7e5VNatifmUWrV6BNktmna43APe14aHsqPB9P5auWvHFi7lFufepW+w8ba7Donjx1i0+8/c+LIAaYueB//4DCbtCuEEE3JwZ1bWL34K665c8p5j9m47CfC3EwEu5rOe4xRr2N6/0DsDbp6SXPv72xHqLs9c9ILmaeVT+cHiAde0CsebO9R52uUWjS+2ZvKimOZWK2AgkHhrtzY1adeMguLlk1+o4Roorr6OzEo0pNInWKyDp4EogyK5c523N3vwnVfLuRAagELVp9kRlYx2RpkazAts5j5q05yJL2wzu2fSf5x5sa19Fgmz69PZPqKeJtnZTToFP+J9GLhiFB8zUZef/x+Xpl+N9npqTZpf+j4iTz2xmdkpacy65bR7Fz7h03aFUKIpuT4gb04u3ng5R9U5f7TiSc4tm8xx4hsAAAgAElEQVQnl/3L6NgZkT6ORHjY27qLle6/NJBvnYx0NyhmA5N0ii46xZgo7zq/VAR4bX0CuUcz2VqmkWXV2G3R0MVmM3/VSbT6yFIiWjQJyIRowq6J9OLF0eGURHlzONKT6y4N5Nkrw3Ax1X1w+5udp3nVonE9YKz4dwvwgkXju12n69z+3z1xWRAP9PYjo7CMWX+eZM7qk8Rn2Ta1fLCriflDQpjYxZvdq5cx/T+D2bx8iU3a7nzJZTzz2a/4BAWzYOodHNyx2SbtCiFEUxETvZvwjlHnHdU683l6abDzedv48WA672xPxlLPU/zcHQzMHxXOmH6BHOroCV28WTgmnFE2GB07llHEsZQCfrRotK7YFgp8ZdHIySpi/2nJ4ihsS6YsCtHE+TgZubaDp03bLLVY2Z9ZxPgq9t0E3JtaiFXT0NlwqolepxgS4cZlIS78ejST7w+kcyK7mBC38097qe11ru3oSa9AM69uSeK1x+5j+xuPcuuidbi41+3n6B3Qiqc+/IGVP3xBu269bdRjIYRofEWFBSTEHqXnFSPOe8ym5Uto62mPr7nqNVoFpRa+P5BOey8HmyfyqIpep+gZaKZnNYpT18TupHzGWzX+fncyADeXaew6lUdnX9uvjRMtl4yQCdECnXn7WVWexhJAp/6ak29rJoOOazt48s6YCPpXvGX96VAGn+5JtUlikTNaVYyWTYjyZmtiHo+MG2yTTIx29g6MvGkSOp2O1FMJPH3nOE7Fxdigx0II0XgyUpLwDQqhdaduVe5Pio8l/nA0lwaff7rib0ezyC2xcn0nr/rqZoPQKapMGALl2/X1sC5OtGwSkAnRAhl0ip7ejnxYxb73gL7+TvWyEPtsZjt95QhcUl4J3x1I554lsSw9mmmzqS56nWJcpCcvDw/FizwWPjyZN2c+SH5utk3az0o7TWLsUWbdOkbWlQkhmrWA0AheXryWbpcNrnL/lhW/ANCvVdXTFYvLrPx8KINufk608Tx/fbLmoHeQma91ipy/bS8APtErep/nZyBEbUlAJkQLdUMPH2YbFC8Ap4Fk4FngeYOO8d18GrQv9/T046VhIQS52PH29hQeXHqcg6m2m6Mf4mbixWGh3NDJk41Lf+DR/wxh3+a1dW63TVR35n32K75BISyYegc/vv8qVqvtC2ILIURj27LyN9p52uN9npIry2OyyC628J9I206xbwxBLib6hbgwSK9YTXm5mQ3AML2iXYC52QecoumRgEyIFirUzZ6nh4WyLNBMhE7RVq/4M8jMvOGhBLnYdl1XdbTxdOCZwcE8dmkgZda/1q/ZKpuVQae4sbM3LwwNwbEog+fuu5mPnp9JcWHdMkp6BwQx+4Mf6Dfiar598yWWffGBTforhBAN6anbr+Hnj96ocl/KyTjiD0fT/1+mK3b3NzMhyptIn7pnOGwK7ujtR69uPkxyMuCp4BYHAx2ivPk/G2Q5FuLvJKmHEC1YsKuJhy6vOr1xY1BK0beVM70DzZULwt/dkYJeKW7o7IXZTl/na7TxdODl4aF8ujeVJd98TPTSL7nnje/Ou26iOkwODtw371Uie/Wjz7AxQHkgWd/TPoUQwhZyszI5smc73QcMrXL/lpW/AdD3X6bqBbrYMe4iGB07Q6cUI9u4M7KNe2N3RbQAMkImhGhyzgRjmqahAb8ezeSeJbEsO2ab9WUmg447u/syd1ArSixWnrr9Gr5/ZyGWsrJat6mUYuDVN2Dv6ERRYQHP3nMj+7esr3NfhRCivsVG7wYgomOXKvdvXfkb4R2j8KliuqKmaXy4M4XYjKJ67aMQFzMJyIQQTZZSint6+vHy8FBC3Ox4a1sK036PIy7LNjf+KF8nXh0ZxuWtnPj+nZd56o5rSDpxvM7t5udkk52RynP/dzNLP39fiogKIZq0mAN7UEoR1jHqH/vSk08RG72H/sakKs/dnVzAT4czibXR57IQLZEEZEKIJi/M3Z55g4KZ3j+AYouGo7HuUxfPMNvpmdo3gIf7BZByZC9P3DiCVT9+WacgytPXnzmLfqLHgKF8umAO7zw1jdIS2xbBFkIIW4nZvwf/kAgczf+ckrht1TIA+gZVPV3xp0MZuNvrGRBy/vVlQoh/JwGZEKJZUEpxabALb4wKw8fJiKZpzF+fyOKD6ZTZYBrjZSEuvDoyjHYu8N7cR3h52p3kZKbXuj0HJzMPvfgu106eytol3/LRczPq3EchhKgPEZ26cOmoa6vct33VMoIi2hLo8s9i0Ceyi9mVnM+Vbdwx6uWRUojakqQeQohm5Uz2xWKLRonFyke7U/kjNpu7e/rS2depTm17ORqZc0UrlhzO5JO1K3h8VA/uXvAxUX0H1K6vOh3j7vkvIW07ENIusk59E0KI+nLtXQ9VuT0nM4NDOzZxXUdPwPsf+385komdXjGijVs991CIi5u8zhBCNEv2Bh2zBrRixuWBFFs0Zv55kpc3niKn2FKndnVKcVV7DxYMC8Fsp+f5/7uFTxfMqdOUw16DRuITGIzVauXNmQ+y/rcf69RHIYSwlYLcHMpKS6rct2vdH1g1uCTQXOV+LwcDo9q442KS9/tC1IUEZEKIZq13oDOvXxnG+EhPDqYVYrDRp1qouz0LhodyZRs3ln7+Pk/eOpaE2CN1arO4sIC05ETenDmFL197DqulbsGjEELU1eIP/sfdg7tWmWV2x5rleDoaaO1hX+W54zt5cVs3n/ruohAXPQnIhKhne5PzeW1dAnN/j2PR9mSScqt+Eylqz2TQcXOUN2+OCsPRqKfMqrFg4ymOptet6LPJoOPunn7MvDyIrPjDzLj5Sv747tNaJ/xwcDLzxFtfMPi6W1iy6E0WTJ1EQV5unfoohBB1ERO9m4DQCPSGc0e5SooK2bdpLZcEmv9RU9GqaexKyq9zGZK8EguLD6Tz7Io4Xlh5glXHsym1SFZa0fJIQCZEPfp0Rwrvr03gqoQ85mYU0fpYFo8vPc6upPzG7tpF6cyi8qTcEval5PPIinje3Z5MQWndRqJ6BZp5ZWQYke4GPnz2CV55eDJ52Zm1astgtGPSjOe4/bF57Nm0mgVT75C0+EKIRmG1WIg9sJfwKuqP7duynuKiQi4J/Gd2xV1J+Ty1+iRbE/Nqfe3U/FKm/xpL/v40Hk8r4v9OF7B1WzLz/oinuMxa63aFaI5k0q8Q9eRgagGbY7LYY9HwqNg2QoPRFo1rNyTyzjVtMOrVv7YhaqeVq4k3RoXz+d5UfjuaxaaEPO7q4UPfIOd/vOmtLg8HA7MHBvHzoQw+XbOMx0f9wX2vfkmHHn1q1d7Q8RPxD4lAb9DXuk9CCFEXicePUlxYQOtO3f6xb+faFTgadUT6OP5j39KjmbjZ6+kZUPXasur4aGsSk4stzD7rfdT1Fo2rsotZciiDcZ28at22EM2NjJAJUU/WxmRx/1nB2BmXA2012J0so2T1yclOz+Sefrw4LARXk55v9qdT1+z4OqW4uoMn84eGYtIr5t19Pd+9taDKtRfV0emSS+nQoy8AP334On9892ndOiiEEDUQs383ABGdup6z3Wq1snPtH3Tzc/rHi8PU/FK2n8pnaIRbrV8q5pVY2H26kGl/+0zWAbMsGmuPZdWqXSGaKwnIhKgn+YUWQs6zL0TTyK1jNkBRPW08HVgwPJRZA4LQ6xR5JRZ+O5pZp7UPrT3seXl4GFeEmPnhvVeYN3k8aUmJtW7ParFwZPd2Pnz2CRbNn1XrAE8IIWqiTVQPbpzyBH7BYedsjz2wh+z0VHpXkV1xeUx5sDQ03LXW1y0oteKioKrxtRAgp47TzIVobiQgE6KehPo4sLSKt4elwCog4jxZq4Tt6XUKT0cjAKuOZ/PO9hQeXRFPXGZRrdt0MOp4sE8AU/v4E79/B4/fOJztq3+vVVs6vZ5pCz9g1ITJLP96EfMfuJW8HHlDLISoX4HhbRhz273odOc+Du5c+wdKp6N7FVMS9yTn093fCV/zPwtFV5eHg4FSneJAFfuWA63dTLVuW4jmSAIyIerJ4Ag3ftUpvgbOjMUUAf+ngxAPe0LkhtMoRrd15799/TmdX8p/f4/j0z2plFhqv4B8YJgrr4wIxc9QzMv/vZNF82dRUlzzQE+n13Pz1FlMnv0SB3ds5ulJ11FWWlrrfgkhxL8pKSpk1/o/KcjN+ce+XetW0sHThItJ/499zw0JYUof/zpd26BTjOngwUS9IuWs7fuAR/WKMZ3/WYRaiIuZBGRC1BNXewMzBgXziIOB9gYdIw06gvSKaG9HHrw8qLG712IppRgQ6srro8IZEOrCdwfSeX/n6Tq16e9sx/whwYxp587yrxfx9JWdSDpxvFZtDbzqema88xWjJtyNwWisU7+EEOJ8Yg/u48UpEzm4c8s529NTkog/HE2vKkbHrJqGXqdws697TrixHT0Jb+1GG51iiFFHX4OOAQbF+J6+dPFzqnP7QjQnkmVRiHoU4WHPa1dFcDi9iKyiMq52NeHvXPtpHsJ2XEx6HuwTwOUhrgS5lP8/ySwsw2RQOBr/+Vb4Qox6HXd29yXK15HXNicxY9xAJs15hf4jr6lxW+279aZ9t94AbFu1jKzUFIaOn1jjdoQQ4nxiossTerT+W0KP3etWAtDzb+vHMgrLeHh5HPf38qtyKmNN6ZTi5u6+jIn04lBaAQadYpqPI3Z6GSsQLY8EZELUM6UU7b0cGrsb4jy6+f/1Jva1LUmczC7mvt5+dPev3QNH70BnFo6wZ8HGU7wxYwoHtm/i1ofnYHKo3e/Apt9/ZvPyJSTEHuXWh5/6R/FWIYSojZh9u/DyC8TV89zpgbvWr8Q7oBWtXM59ebjqeDbpBWV1WjtWFReTnt5V1DoToiWR1xBCCFHh+k5emAw65qxO4NXNSeSV1C7Tl7eTkWcGBzOuoyerfvySJyeOIfH4sVq1df8z/2PUhLtZ8c3HvDBlIvm52bVqRwghznZs/24iOp9bf6ykuIj9G1bSyyn3nPqImqaxMjabjt4OBLrILA8hbE0CMiGEqNDey4GFI0IZ19GT1XHZ3P/bcWIyapeJUa9TTOjizeyBQeScPMas6wex7pfva9xOebKPmUx+8kUObN/E7IlXV7kIXwghqis7PZW0pAQiIrucs/3g9k2UWDR6/G1K4uH0IhJzSxgcVvtU90KI85O5L0IIcRY7vY4JXbzp18qZz/em4meuW2KN7v5mFo4IZcHGU7z15EMc3LmZidOfrvEUxoFX34BvqxD2bFyDg1mm9wghas/ZzYPnv16Os5vHOdt3b1iFnV7R2cfxnO1/xmZj0iv6BctnjxD1QUbIhBCiChEe9jw5sBVOdnpKLRqzV51kS0JurdrydDQyd1D5FMbVi7/iyYljOBUXU+N2OvToyw0PPIZSihNHD7J2ybe16o8QomXT6fUEt+mAu7fvOdt3r/+Tzr6OmAznPh5eHurCHd19apXwSAhxYRKQCSHEBWQVlZFdVMaz6xJZuOlUrdaWnT2FMSstlZm3jGLjssW17tPSz9/n7dn/5YtXnsFqrX0dNSFEy7P0iw/YufaPc7YlnThOSkI8PapIaNTJx5ERrd0bqntCtDgSkAkhxAV4Oxl5cVgo13fyZF18Dvf/dpztp/Jq1VZ3fzOvDHAnzNHK6088wAfPPl6rQtKTZjzPkP9M4JdP3uaV6ZMpKiyoVX+EEC2L1Wrl+7dfZldFevsz9mxYBUCPgHNrgK2IyeJEdnGD9U+IlkgCMiGEqAajXnFTZ29eHBaKs52Oz/akYrFqtWrLy9HIvMHBXN3eg5XffcZTt19LSkJ8jdowGI3c/tgz3Dp9DjvWrODpSdeRnZFWq/4IIVqOpPhYCvJyiPhb/bE9G1YR4GyH31lp7bOLynhzWzKrjkt2VyHqkwRkQghRAxEe9rw8PJQZlweh1ynySizsS8mvcTsGneL2bj48cVkgqTHRzLh5FDvWLK9RG0opRtx4B9NfXYSnrz9Ozi417ocQomWJ2b8LOLcgdElRIQd2bKKH/7mjY+tP5GLVYGCoZFcUoj5JQCaEEDVk1OvwdirPvvj9gXRm/nmS93akUFxW87VclwQ5s3BEKP6GIhZMncR3N3fBUlZWoza69r+CaQs/xGC0Iycz4x9TkYQQ4oxj+3fhYHYmIKxN5baDOzZTWlxM978FZGvisgl1MxHiZmrobgrRokhAJoQQdXBDJy9GtXHjlyOZTF0Wx9H0whq34Wu24/mhIYxo7cYPBzN45p4byExNqVV/fnh3IS89dDtLFr2FptVuSqUQ4uKVnpxEeIcodLq/HgH3bFyDnV4ReVa6+6TcEg6nFzEgREbehahvEpAJIUQdmAw6Jvf04+krWlFssfLIinhW12K9hZ1ex729/Hiojz+xe7byxE0jObhjc43buenBJ7hkyCi+fO1Z3pv7CGWlpTVuQwhx8Zr+6kdMf23ROdv2blpDpPe56e7jsoqxN+i4TAIyIeqdBGRCCGEDXfyceHVkGEMj3OjkW/6WuTYjVFeEufLSsFCcSrJ55p4bWPLx2zVqx87egfufe4Or75zC6sVfMf/+CeTlZNW4H0KIi5edyb7y69RTCZyKO/aP6Yp9Wznz2bWtK6dnCyHqjwRkQghhI2Y7Pff18sPL0YhV03huXSK/HsmscWAW4mZiwfAQ+gQ48uWrz/DKw5MpyM2p9vk6nY7x903nnqcXkpGaXOM1aUKIi9Pqn77mlel3U1ryVxr7vZvWAND9rHT3ZzLIGvXymChEQ5C/NCGEqAdFZVZKrRrv7khhzuoEMgprFhQ5GvU80j+AO7r5sGP1Mp4c3ZUTRw/WqI3LR49j/tcrcPXwoqy0lLjD0TU6XwhxcdmzcTWxB/ZitPsrScfeTWvwcjQQ6PxXuvsv9qXx39/jKKtlaQ8hRM1IQCaEEPXA0ajnyQFB3N3Tl+jUAqb8dpxNJ3Nr1IZSiqvaezBvUDCFpVZmT7yK9b/9WKM2DMby6UaL33+NJ28dy4alNTtfCHHxiNm365x095ayMqK3bqCrnxNKKaB8qvX6Ezk42+kx6FRjdVWIFkUCMiGEqCdKKa5s487CEaH4mI28v7N2qfEjfRxZOCKM1s7w5swpLJo/i7LSkhq1MeKmO2gT1Z03Zkzhh3dfkQyMQrQwWWmnSUtOJKJzt8ptMdG7KcjLodtZ68eOZRSRnFfKZcHOjdFNIVokCciEEKKeBbmYeGFoCHMHBWMy6Ci1aMRkFNWoDXcHA08PCuaqdu4s/3oR8yZfT8bppGqfb3Z15/E3P+ey0eP47u0FvDXroXPWkQghLm7HKgtC/xWQ7d24BqUUXXz/CsjWn8hFr8prJAohGoYEZEII0QAMOkVAxRqNxYfSeXh5HF/vT6tcPF/dNu7o7sv0/gGciN7BjJtHcXDHpuqfb7TjnjkvM+7eaexYs5yUhPgafx9CiOZJKUXrTt0Ia9+pctvezWuJiOyKs0kPlE9X3Hgyh67+TpXbhBD1TwIyIYRoYFe2cefSYBe+2JfGjJUnSMmr2fTDS4NdePFMavzJ4/ljUo9qT0FUSnHtXQ+x4Mc1BIW3BSAnM6PG34MQonnpMWAYT3/yM3b2DgDk52YTu38n3bUTlcdYNbixkzdj23k0VjeFaJEkIBNCiAbmZKdnWr8Apvb1Jz67mIeWxbE9Ma9GbQS7lqfG7xVo5sNdp/nf4/9HUWFBtc938/IBYOX3n/PwdQM5tGtrja4vhGg+rFbrP8pfRG/diFWDrn5/TVfU6xSDwl3P2SaEqH8SkAkhRCMZGOrKKyNCiXC3x8vRUOPzHY16Hrs0kAlR3mxdsYTZE8eSfOJ4jdqI7NUPZ1d3nr3nRsnAKMRFKjH2CHde3pE9G1ZVbtu3ZS32Bh3tvMpHzDRN4/djWaQXlDZWN4VosSQgE0KIRuRrtmPe4GBC3e0B+HxvKkfSC6t9vk4pxkV6MntAK7JOHGXmLaPZtW5ltc/3Cw5jzqLFtO7cjTdmTGHx+69JBkYhLjLH9u2iuKgQn6CQym37N6+js49DZWr72Mxi3tyWzM6k/MbqphAtlgRkQgjRROQUW1h1PJvHVsTz3YF0rDUIjLr6O7FgeCh+xmJeeuh2vn9nIVZr9VLsn8nAeOmV1/LNmy8SE727tt+CEKIJOrZ/F04urvgFhwFwOvEEKQnxdDlrauLGk7noFFwSZG6sbgrRYklAJoQQTYSLSc8rI8PoE+TMp3tSmb3qJBmFZRc+sYKv2Y7nh4QwIMSZ7995mYXT7qQgN6da5xrtTNw79xVmf/hDZVrs6gZ0Qoim7di+XbTu1K2y+PP+LeuBv9aPaZrGppO5dPJxxMVU8+nTQoi6kYBMCCGaELOdnun9A/i/3n4cTivkiT/ia5Qa32TQ8VAff+7q4cPuDauYdesYEmOPVutcpRTtuvYCIHrrBmbcNJLUUydr9X0IIZqGwvw8EmIOE3FW/bF9W9bh4WAgyKW8FMfJnBISc0voI7XHhGgUEpAJIUQTo5RiWIQbL48I5a4evuh1Ck3TKLVUP7X96LYezB0QQEFyPLNuHcO2P5fWrA86HalJicy6dWxlQVkhRPNjKSvl2slT6X75YKB85Dt62wa6+DpWjpgdSS9Ep6CPTFcUolFIQCaEEE1UkIuJHgHlD0i/Hc3i0RVxnMqtfs2ySB9HXh4RSisHCwsfnsw3b7yA1WKp1rkde/Zlzkc/YnJwZN7k8WxbtaxW34MQonGZXd257u6phHfsAkD84WjysjLPSW0/JNyNT69tg6ejsbG6KUSLJgGZEEI0A16OBlLyS5m6LI7Vx7NrcJ6RZwcHMyTclcUf/I+Xpt5Bfm71zg8Mb8PTixbTqnV7Xnl4Mkf2bK9t94UQjSTucPQ5f/Nn1o9F/a3WmNlO36D9EkL8RQIyIYRoBi4JcuaVEWGEu5tYuDmJVzcnUVRWvaQbdnod9/f2456evuzb8CezJowhIfZItc519fRm5jvfcOv0ObSJ6lGXb0EI0cA0TWP+/RP45MWnKrft37qeYFc7PBzKk3csPZrJ7FUnKK7m54kQwvYkIBNCiGbC28nIvEHBjI/0ZHVcNkdrUK9MKcXINu7MGxxMYcoJnrxhKPse7FOtc00ODgy/4XaUUqQkxPPOU9MoKiyo7bchhGggaUkJZKen0rpTVwBKS4o5vHsrUb5/jY6tP5FLRmEZJoM8EgrRWOSvTwghmhG9TnFzlDdvjw6nc8VDVWxGUbWLOXf0rlhX5mLHc+sS+e7tl2uU3v7o3p2s/eU75t31H7LSTtfqexBCNIyj+3YCVGZYPLp3JyVFRUT5OgLltQ8PpBbQO1CyKwrRmCQgE0KIZsjXXJ6uOiajiGnL43hp4ykKSquXsMPL0cizQ4IZFObCD+8uZOHDd1GQl1utcy+98hr+u+B9EmOP8uTEq6qdUl8I0fCO7d2F0WQiuE0HoHy6ok6vp5NPeUC2/VQeVk2yKwrR2CQgE0KIZizM3cTNnb3ZeDKXqcviiMkoqtZ5dnodUy7x587uPuxas5zZE68i6cTxap3bY8BQZr3/HWWlJcx/4Na6dF8IUY+O7d9FWIcoDMby7InRWzfQxs2IU0UCj60JuXg4GIjwsG/MbgrR4klAJoQQzZhOKcZFevLM4GBKrRqPrIhn6dHMap2rlGJMOw/mXNGKnMRYZt0ymj0bVlXr3PCOUcxZ9BP3Pv1yXbovhKhHk2Y8x00PPgFAQV4uMdG7z8muGOnjyNXtPdBV1CMTQjQOCciEEOIi0NHbkVdGhNLFz7Ha2RfPiPJ1YsHwEHwNxbw45VaWLHqrWmvSvAOC6NCjb227LISoZyFtO9K2S08ADu3cgtViIapiuiLAmHYeXNXeo7G6J4SoIAGZEEJcJFxMBmZeHlT5gLU7Kb/aUxh9zXY8PzSEvq2c+fK1Z3lj5hSKC6ufxVEI0bQc3LGJDUt/rEzaE71tA0Y7E+29HYDyZEDVXXcqhKhfEpAJIcRFRKcUOqWwahof7T7NIyvi+e1oZrVGvOwNOqb3C+CWKC82LV3MnEnXkZ58qgF6LYSwtZXff86Xrz6LTlf+qBe9dQNtu/TETq9D0zTmrU3gf1uSG7mXQgiQgEwIIS5KOqWYO6gVUb6OvLM9pdpZGJVS/CfSixmXB5FyLJqZ1/Tj8O5tDdBjIYQtHd23i9aduwOQk5nBiaMH6Vp8GIDYzGLSC8voFSDZFYVoCiQgE0KIi5SLycCsAUFMiCrPwjjt93hyiqs3RalXoJkXh4XgZKfjmTuvZdWPX9Zzb4UQtpKdkUZq4glady6vP3Zg+0YAulTULtyWmIcCugc4na8JIUQDkoBMCCEuYmeyMM4dFEzPACec7ar/sd/K1cSLw0Lp5OPEe3MfYdH8WZSVltZjb4UQtnBs3y4AWlcUhI7etgF7g64yvf22U3m09XLAzd7QaH0UQvxFAjIhhGgBOvk4Mqm7L0opTmYX89a2ZIqrkY3RbKfnyQFBXN3eg+VfL2L+/RPIzapeWn0hROM4efQgeoOBsA5RABzYtpFIHwcMOkVmYRnHMoroJaNjQjQZEpAJIUQLs/90Ab8fy2L68ngSc0oueLxep7i9mw8P9vHn8O5tzLp1NAkxhxugp0KI2rhq0gP877ctmBwcyExNJik+tjLdvZu9ntdGhjE43K2ReymEOEMCMiGEaGFGtnHnyQFBZBSWMe33ODacyKnWeYPCXHl2oD+laYk8eeMwdqxZXs89FULUhlIKNy8fAKK3lq8fi6pYP6aUIsTNhIeDTFcUoqmQgEwIIVqg7gFmFo4IJdjVjhc2nKp2UNbOy4EFw0MJcrHj5amT+O22bvXcUyFETSQeP8brT9xP4vFjQPn6MbOdjlB3E6UWK//bksSxatYnFEI0DAnIhBCihfJ2MvLM4BBu6+pNr8Dy9NfVqVfm6Wjk2cHBXBbiwp7kgvruphCiBg7v2srGZT+h0+sBOLBjM5E+joXczX4AAASySURBVOiUYv/pQv6IzSazsKyReymEOJsEZEII0YIZ9YprOnhip9eRV2Lh0RXx7E7Ov+B5JoOO//b1Z+aAoAbopRCiuo7t24nZzR2/VqGknjpJauIJOlesH9t+Kg87vSLK17GReymEOJsEZEIIIQDIL7FQUGrlqVUn+SY6DesFRsuUUtgb5DYiRFNydN8uWnfqhlKKA9s3AdC5IgDbcSqPzr6OmOTvVogmRf4ihRBCAOBrtuPFYaFcFuLC53vTeGZtAnkl1SskLYRofAW5OZw6fvSs+mMbcTHpCXE1cSq3hKS8Unr4mxu5l0KIv5OATAghRCUHY/lUxMk9fNmdnM+6+Ool+xBCNL7MtNMEt+lA2y49AdDpdPQMMKOUIq2gFF8nIz2l/pgQTY6qzgLuGjeqVCoQb/OGhRBCNEUhmqZ5N3Ynmgu5RwohRItRrftjvQRkQgghhBBCCCEuTKYsCiGEEEIIIUQjkYBMCCGEEEIIIRqJBGRCCCGEEEII0UgkIBMXLaXUFKXUQaXU50qpsUqpx2pwbqhS6qZ/2f+iUipaKfViLfrVVSl1ZU3PE0IIIWxB7o9CNC2S1ENctJRSh4CRmqYd/5djDJqmlVWxfSDwsKZpo89zXg7grWlacS36dRvQU9O0+2twjqL879Va0+sJIYQQZ5P7oxBNiwRk4qKklHobuAM4DHwIZFLxIa+UWgRkAN2AncDPwKsVp2rA5cAKoANwHPhY07SFZ7X9MzAK2Ac8B/wJvA0EVxzykKZpG5RSvYFXAAegELi9or1jFdsSK87vAORpmvZSRfv7gTM3uqXAKqAvcDXQDpgDmIAY4HZN0/Lq/AMTQgjRIsj9UYimRwIycdFSSsVRfpNJO/utW8UNxwu4StM0i1JqCfB8xU3CDBQBl/LvbwDzNE0zV3z9BfCmpmnrlVLBwO+apnVQSrkABZqmlSmlhgD3app23d/fACqlnuL8N5xYoJ+maZuVUl7AD5S/1cxXSj0KmDRNe9qmPzghhBAXNbk/CtG0GBq7A0I0km81TbNUfL0BeFkp9Tnwg6ZpCeUzIKptCNDxrHNclFLOgCvwsVKqDeVvFo216Ge8pmmbK77uA3QENlRcyw7YVIs2hRBCiPOR+6MQDUwCMtFS5Z/5QtO055VSvwJXApsr3tbVhA7oq2la4dkblVL/A1ZpmnaNUioUWH2e88s4N8GOfVX9BBSwQtO0G2vYPyGEEKK65P4oRAOTLIuixVNKRWiatk/TtPnAdqA9kAs4V7OJ5UDlAmSlVNeKL10pnwcPcNtZx/+97Tige8W53YGw81xnM9BfKdW64lhHpVTbavZRCCGEqBG5PwrRMCQgEwIeUkrtV0rtoXxx8VJgL1CmlNqjlJp6gfOnAD2VUnuVUgeAeyq2vwA8p5TaAOjPOn4V5VM4diulrge+BzyUUruBe4EjVV1E07RUym9cXyql9lJ+A2pfi+9XCCGEqA65PwrRACSphxBCCCGEEEI0EhkhE0IIIYQQQohGIgGZEEIIIYQQQjQSCciEEEIIIYQQopFIQCaEEEIIIYQQjUQCMiGEEEIIIYRoJBKQCSGEEEIIIUQjkYBMCCGEEEIIIRrJ/wPVwiKB6ziqMgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a0c869588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X, y = make_data()\n",
"\n",
"# we create an instance of SVM and fit out data. We do not scale our\n",
"# data since we want to plot the support vectors\n",
"C = 1.0 # SVM regularization parameter\n",
"models = (svm.SVC(kernel='linear', C=C),\n",
" svm.SVC(kernel='rbf', gamma=0.7, C=C),\n",
" svm.SVC(kernel='poly', degree=2, C=C),\n",
" svm.SVC(kernel='poly', degree=3, C=C))\n",
"models = (clf.fit(X, y) for clf in models)\n",
"\n",
"# title for the plots\n",
"titles = ('SVM with linear kernel',\n",
" 'SVM with RBF kernel',\n",
" 'SVM with polynomial (degree 2) kernel',\n",
" 'SVM with polynomial (degree 3) kernel')\n",
"\n",
"# Set-up 2x2 grid for plotting.\n",
"fig, sub = plt.subplots(2, 2, figsize=(15,15))\n",
"plt.subplots_adjust(wspace=0.2, hspace=0.2)\n",
"\n",
"X0, X1 = X[:, 0], X[:, 1]\n",
"xx, yy = make_meshgrid(X0, X1)\n",
"\n",
"for clf, title, ax in zip(models, titles, sub.flatten()):\n",
" plot_contours(ax, clf, xx, yy)\n",
" plot_data(ax, X0, X1, y, xx, yy, title)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"fig.savefig('svm_kernel.png', dpi=600)\n",
"fig.savefig('svm_kernel_lowres.png')"
]
},
{
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
BinPy/BinPy | BinPy/examples/notebook/ic/Series_7400/IC7440.ipynb | 5 | 9466 | {
"metadata": {
"name": "",
"signature": "sha256:3c782c6bcbb2318dc454ffa40838d60827668d71564deef73fcdb6eb2d96cc78"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Usage of IC 7400"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import print_function\n",
"from BinPy import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Usage of IC 7440:\n",
"\n",
"ic = IC_7440()\n",
"\n",
"print(ic.__doc__)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" This is a Dual 4-Input NAND Buffer\n",
" \n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The Pin configuration is:\n",
"\n",
"inp = {1: 1, 2: 0, 4: 0, 5: 0, 7: 0, 9: 1, 10: 1, 12: 1, 13: 1, 14: 1}\n",
"\n",
"# Pin initinalization\n",
"\n",
"# Powering up the IC - using -- ic.setIC({14: 1, 7: 0})\n",
"\n",
"ic.setIC({14: 1, 7: 0})\n",
"\n",
"# Setting the inputs of the ic\n",
"\n",
"ic.setIC(inp)\n",
"\n",
"# Draw the IC with the current configuration\\n\n",
"\n",
"ic.drawIC()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
" \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25e1\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
" \u2502 \u2502\n",
" [1] \u2500\u2500\u2524 1 14 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 2 7 13 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 3 4 12 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 4 4 11 \u251c\u2500\u2500 [0] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 5 0 10 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [Z] \u2500\u2500\u2524 6 9 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 7 8 \u251c\u2500\u2500 [Z] \n",
" \u2502 \u2502\n",
" \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Run the IC with the current configuration using -- print ic.run() -- \n",
"\n",
"# Note that the ic.run() returns a dict of pin configuration similar to \n",
"\n",
"print (ic.run())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{8: 0, 6: 1}\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Seting the outputs to the current IC configuration using -- ic.setIC(ic.run()) --\\n\n",
"\n",
"ic.setIC(ic.run())\n",
"\n",
"# Draw the final configuration\n",
"\n",
"ic.drawIC()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
" \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25e1\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
" \u2502 \u2502\n",
" [1] \u2500\u2500\u2524 1 14 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 2 7 13 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 3 4 12 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 4 4 11 \u251c\u2500\u2500 [0] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 5 0 10 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [1] \u2500\u2500\u2524 6 9 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 7 8 \u251c\u2500\u2500 [0] \n",
" \u2502 \u2502\n",
" \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Seting the outputs to the current IC configuration using -- ic.setIC(ic.run()) --\n",
"\n",
"ic.setIC(ic.run())\n",
"\n",
"# Draw the final configuration\n",
"\n",
"ic.drawIC()\n",
"\n",
"# Run the IC\n",
"\n",
"print (ic.run())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
" \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25e1\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n",
" \u2502 \u2502\n",
" [1] \u2500\u2500\u2524 1 14 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 2 7 13 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 3 4 12 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 4 4 11 \u251c\u2500\u2500 [0] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 5 0 10 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [1] \u2500\u2500\u2524 6 9 \u251c\u2500\u2500 [1] \n",
" \u2502 \u2502\n",
" \u2502 \u2502\n",
" [0] \u2500\u2500\u2524 7 8 \u251c\u2500\u2500 [0] \n",
" \u2502 \u2502\n",
" \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \n",
"{8: 0, 6: 1}\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Connector Outputs\n",
"c = Connector()\n",
"\n",
"# Set the output connector to a particular pin of the ic\n",
"ic.setOutput(8, c)\n",
"\n",
"print(c)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Connector; State: 0\n"
]
}
],
"prompt_number": 7
}
],
"metadata": {}
}
]
} | bsd-3-clause |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/lowess.ipynb | 4 | 148041 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LOWESS Smoother\n",
"\n",
"This notebook introduces the LOWESS smoother in the `nonparametric` package. LOWESS performs weighted local linear fits.\n",
"\n",
"We generated some non-linear data and perform a LOWESS fit, then compute a 95% confidence interval around the LOWESS fit by performing bootstrap resampling."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2022-02-08T18:15:17.128028Z",
"iopub.status.busy": "2022-02-08T18:15:17.127547Z",
"iopub.status.idle": "2022-02-08T18:15:19.693745Z",
"shell.execute_reply": "2022-02-08T18:15:19.692594Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pylab\n",
"import seaborn as sns\n",
"import statsmodels.api as sm\n",
"\n",
"sns.set_style(\"darkgrid\")\n",
"pylab.rc(\"figure\", figsize=(16, 8))\n",
"pylab.rc(\"font\", size=14)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2022-02-08T18:15:19.698056Z",
"iopub.status.busy": "2022-02-08T18:15:19.697582Z",
"iopub.status.idle": "2022-02-08T18:15:19.700378Z",
"shell.execute_reply": "2022-02-08T18:15:19.700734Z"
}
},
"outputs": [],
"source": [
"# Seed for consistency\n",
"np.random.seed(1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2022-02-08T18:15:19.713311Z",
"iopub.status.busy": "2022-02-08T18:15:19.703072Z",
"iopub.status.idle": "2022-02-08T18:15:19.739031Z",
"shell.execute_reply": "2022-02-08T18:15:19.739404Z"
}
},
"outputs": [],
"source": [
"# Generate data looking like cosine\n",
"x = np.random.uniform(0, 4 * np.pi, size=200)\n",
"y = np.cos(x) + np.random.random(size=len(x))\n",
"\n",
"# Compute a lowess smoothing of the data\n",
"smoothed = sm.nonparametric.lowess(exog=x, endog=y, frac=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2022-02-08T18:15:19.744206Z",
"iopub.status.busy": "2022-02-08T18:15:19.743747Z",
"iopub.status.idle": "2022-02-08T18:15:20.125336Z",
"shell.execute_reply": "2022-02-08T18:15:20.126078Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the fit line\n",
"fig, ax = pylab.subplots()\n",
"\n",
"ax.scatter(x, y)\n",
"ax.plot(smoothed[:, 0], smoothed[:, 1], c=\"k\")\n",
"pylab.autoscale(enable=True, axis=\"x\", tight=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confidence interval\n",
"\n",
"Now that we have performed a fit, we may want to know how precise it is. Bootstrap resampling gives one\n",
"way of estimating confidence intervals around a LOWESS fit by recomputing the LOWESS fit for a large number\n",
"of random resamplings from our data."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2022-02-08T18:15:20.129492Z",
"iopub.status.busy": "2022-02-08T18:15:20.128495Z",
"iopub.status.idle": "2022-02-08T18:15:22.079840Z",
"shell.execute_reply": "2022-02-08T18:15:22.080646Z"
}
},
"outputs": [],
"source": [
"# Now create a bootstrap confidence interval around the a LOWESS fit\n",
"\n",
"\n",
"def lowess_with_confidence_bounds(\n",
" x, y, eval_x, N=200, conf_interval=0.95, lowess_kw=None\n",
"):\n",
" \"\"\"\n",
" Perform Lowess regression and determine a confidence interval by bootstrap resampling\n",
" \"\"\"\n",
" # Lowess smoothing\n",
" smoothed = sm.nonparametric.lowess(exog=x, endog=y, xvals=eval_x, **lowess_kw)\n",
"\n",
" # Perform bootstrap resamplings of the data\n",
" # and evaluate the smoothing at a fixed set of points\n",
" smoothed_values = np.empty((N, len(eval_x)))\n",
" for i in range(N):\n",
" sample = np.random.choice(len(x), len(x), replace=True)\n",
" sampled_x = x[sample]\n",
" sampled_y = y[sample]\n",
"\n",
" smoothed_values[i] = sm.nonparametric.lowess(\n",
" exog=sampled_x, endog=sampled_y, xvals=eval_x, **lowess_kw\n",
" )\n",
"\n",
" # Get the confidence interval\n",
" sorted_values = np.sort(smoothed_values, axis=0)\n",
" bound = int(N * (1 - conf_interval) / 2)\n",
" bottom = sorted_values[bound - 1]\n",
" top = sorted_values[-bound]\n",
"\n",
" return smoothed, bottom, top\n",
"\n",
"\n",
"# Compute the 95% confidence interval\n",
"eval_x = np.linspace(0, 4 * np.pi, 31)\n",
"smoothed, bottom, top = lowess_with_confidence_bounds(\n",
" x, y, eval_x, lowess_kw={\"frac\": 0.1}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2022-02-08T18:15:22.085827Z",
"iopub.status.busy": "2022-02-08T18:15:22.085334Z",
"iopub.status.idle": "2022-02-08T18:15:22.394260Z",
"shell.execute_reply": "2022-02-08T18:15:22.393847Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the confidence interval and fit\n",
"fig, ax = pylab.subplots()\n",
"ax.scatter(x, y)\n",
"ax.plot(eval_x, smoothed, c=\"k\")\n",
"ax.fill_between(eval_x, bottom, top, alpha=0.5, color=\"b\")\n",
"pylab.autoscale(enable=True, axis=\"x\", tight=True)"
]
}
],
"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": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
mohanprasath/Course-Work | pyspark/Untitled1.ipynb | 1 | 3029 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2019-02-06T08:50:52.105424Z",
"start_time": "2019-02-06T08:50:32.081072Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Intitializing Scala interpreter ..."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Spark Web UI available at http://Kris:4041\n",
"SparkContext available as 'sc' (version = 2.3.1, master = local[*], app id = local-1549443043675)\n",
"SparkSession available as 'spark'\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"x: Int = 2\r\n",
"y: Int = 3\r\n",
"res0: Int = 5\n"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val x = 2\n",
"val y = 3\n",
"x+y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "spylon-kernel",
"language": "scala",
"name": "spylon-kernel"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"help_links": [
{
"text": "MetaKernel Magics",
"url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
}
],
"mimetype": "text/x-scala",
"name": "scala",
"pygments_lexer": "scala",
"version": "0.4.1"
},
"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": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
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| gpl-3.0 |
jasonding1354/pyDataScienceToolkits_Base | Scikit-learn/.ipynb_checkpoints/(3)linear_regression-checkpoint.ipynb | 2 | 145651 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 内容概要\n",
"- 如何使用pandas读入数据\n",
"- 如何使用seaborn进行数据的可视化\n",
"- scikit-learn的线性回归模型和使用方法\n",
"- 线性回归模型的评估测度\n",
"- 特征选择的方法"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"作为有监督学习，分类问题是预测类别结果，而回归问题是预测一个连续的结果。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. 使用pandas来读取数据\n",
"**Pandas**是一个用于数据探索、数据处理、数据分析的Python库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 230.1</td>\n",
" <td> 37.8</td>\n",
" <td> 69.2</td>\n",
" <td> 22.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 44.5</td>\n",
" <td> 39.3</td>\n",
" <td> 45.1</td>\n",
" <td> 10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 17.2</td>\n",
" <td> 45.9</td>\n",
" <td> 69.3</td>\n",
" <td> 9.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 151.5</td>\n",
" <td> 41.3</td>\n",
" <td> 58.5</td>\n",
" <td> 18.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 180.8</td>\n",
" <td> 10.8</td>\n",
" <td> 58.4</td>\n",
" <td> 12.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales\n",
"1 230.1 37.8 69.2 22.1\n",
"2 44.5 39.3 45.1 10.4\n",
"3 17.2 45.9 69.3 9.3\n",
"4 151.5 41.3 58.5 18.5\n",
"5 180.8 10.8 58.4 12.9"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read csv file directly from a URL and save the results\n",
"data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)\n",
"\n",
"# display the first 5 rows\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上面显示的结果类似一个电子表格，这个结构称为Pandas的数据帧(data frame)。\n",
"\n",
"pandas的两个主要数据结构：Series和DataFrame：\n",
"- Series类似于一维数组，它有一组数据以及一组与之相关的数据标签(即索引)组成。\n",
"- DataFrame是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型。DataFrame既有行索引也有列索引，它可以被看做由Series组成的字典。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>196</th>\n",
" <td> 38.2</td>\n",
" <td> 3.7</td>\n",
" <td> 13.8</td>\n",
" <td> 7.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td> 94.2</td>\n",
" <td> 4.9</td>\n",
" <td> 8.1</td>\n",
" <td> 9.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td> 177.0</td>\n",
" <td> 9.3</td>\n",
" <td> 6.4</td>\n",
" <td> 12.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td> 283.6</td>\n",
" <td> 42.0</td>\n",
" <td> 66.2</td>\n",
" <td> 25.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td> 232.1</td>\n",
" <td> 8.6</td>\n",
" <td> 8.7</td>\n",
" <td> 13.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales\n",
"196 38.2 3.7 13.8 7.6\n",
"197 94.2 4.9 8.1 9.7\n",
"198 177.0 9.3 6.4 12.8\n",
"199 283.6 42.0 66.2 25.5\n",
"200 232.1 8.6 8.7 13.4"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# display the last 5 rows\n",
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(200, 4)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the shape of the DataFrame(rows, colums)\n",
"data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"特征：\n",
"- TV：对于一个给定市场中单一产品，用于电视上的广告费用（以千为单位）\n",
"- Radio：在广播媒体上投资的广告费用\n",
"- Newspaper：用于报纸媒体的广告费用\n",
"\n",
"响应：\n",
"- Sales：对应产品的销量\n",
"\n",
"在这个案例中，我们通过不同的广告投入，预测产品销量。因为响应变量是一个连续的值，所以这个问题是一个回归问题。数据集一共有200个观测值，每一组观测对应一个市场的情况。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x82dd890>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
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AYUG9Azzmco2sz0r6X5o+56Kk2TiOn1r7s+OSbnf4+QA8lMebudmZyaPlPdHS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\nYwIQlnb9wKlq7ROh3nu4qosO662ze8gs+NzP+Bw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hpn76Ey++gzhGPv6zmFMqjMhCVjpO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\nqm14xm22yxo65wEC1ej+PzUyPHBNl3SnJP3Gcw876A0l6ZQ0j6NcvrD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"text/plain": [
"<matplotlib.figure.Figure at 0x82dd8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# visualize the relationship between the features and the response using scatterplots\n",
"sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=7, aspect=0.8)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"seaborn的pairplot函数绘制X的每一维度和对应Y的散点图。通过设置size和aspect参数来调节显示的大小和比例。可以从图中看出，TV特征和销量是有比较强的线性关系的，而Radio和Sales线性关系弱一些，Newspaper和Sales线性关系更弱。通过加入一个参数kind='reg'，seaborn可以添加一条最佳拟合直线和95%的置信带。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x83b76f0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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uxeRaPND/e1rGtfeAvvXWJtx6j8bqMxdHeO+yjOKWnNIaWSIiRfL8YCmmJ2PU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DMMwZrgLY/rfEp/OH/xqdz0vXvPd2xyG7IUJS5ySPjgJlfy9kjwG/9GcWy3vjShq72O2en4YeXo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\nkiRJkiRJUhb+P8+vLLOWg2xcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x83b7110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=7, aspect=0.8, kind='reg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. 线性回归模型\n",
"优点：快速；没有调节参数；可轻易解释；可理解\n",
"\n",
"缺点：相比其他复杂一些的模型，其预测准确率不是太高，因为它假设特征和响应之间存在确定的线性关系，这种假设对于非线性的关系，线性回归模型显然不能很好的对这种数据建模。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"线性模型表达式：\n",
"$y = \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + ... + \\beta_nx_n$\n",
"其中\n",
"- y是响应\n",
"- $\\beta_0是截距$\n",
"- $\\beta_1是x1的系数，以此类推$\n",
"\n",
"在这个案例中：\n",
"$y = \\beta_0 + \\beta_1*TV + \\beta_2*Radio + ... + \\beta_n*Newspaper$"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### （1）使用pandas来构建X和y\n",
"- scikit-learn要求X是一个特征矩阵，y是一个NumPy向量\n",
"- pandas构建在NumPy之上\n",
"- 因此，X可以是pandas的DataFrame，y可以是pandas的Series，scikit-learn可以理解这种结构"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 230.1</td>\n",
" <td> 37.8</td>\n",
" <td> 69.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 44.5</td>\n",
" <td> 39.3</td>\n",
" <td> 45.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 17.2</td>\n",
" <td> 45.9</td>\n",
" <td> 69.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 151.5</td>\n",
" <td> 41.3</td>\n",
" <td> 58.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 180.8</td>\n",
" <td> 10.8</td>\n",
" <td> 58.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper\n",
"1 230.1 37.8 69.2\n",
"2 44.5 39.3 45.1\n",
"3 17.2 45.9 69.3\n",
"4 151.5 41.3 58.5\n",
"5 180.8 10.8 58.4"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a python list of feature names\n",
"feature_cols = ['TV', 'Radio', 'Newspaper']\n",
"\n",
"# use the list to select a subset of the original DataFrame\n",
"X = data[feature_cols]\n",
"\n",
"# equivalent command to do this in one line\n",
"X = data[['TV', 'Radio', 'Newspaper']]\n",
"\n",
"# print the first 5 rows\n",
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"(200, 3)\n"
]
}
],
"source": [
"# check the type and shape of X\n",
"print type(X)\n",
"print X.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 22.1\n",
"2 10.4\n",
"3 9.3\n",
"4 18.5\n",
"5 12.9\n",
"Name: Sales, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select a Series from the DataFrame\n",
"y = data['Sales']\n",
"\n",
"# equivalent command that works if there are no spaces in the column name\n",
"y = data.Sales\n",
"\n",
"# print the first 5 values\n",
"y.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.series.Series'>\n",
"(200,)\n"
]
}
],
"source": [
"print type(y)\n",
"print y.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (2)构造训练集和测试集"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.cross_validation import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(150, 3)\n",
"(150,)\n",
"(50, 3)\n",
"(50,)\n"
]
}
],
"source": [
"# default split is 75% for training and 25% for testing\n",
"print X_train.shape\n",
"print y_train.shape\n",
"print X_test.shape\n",
"print y_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (3)Scikit-learn的线性回归"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"linreg = LinearRegression()\n",
"\n",
"linreg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.87696662232\n",
"[ 0.04656457 0.17915812 0.00345046]\n"
]
}
],
"source": [
"print linreg.intercept_\n",
"print linreg.coef_"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('TV', 0.046564567874150253),\n",
" ('Radio', 0.17915812245088836),\n",
" ('Newspaper', 0.0034504647111804482)]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pair the feature names with the coefficients\n",
"zip(feature_cols, linreg.coef_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$y = 2.88 + 0.0466 * TV + 0.179 * Radio + 0.00345 * Newspaper$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"如何解释各个特征对应的系数的意义？\n",
"- 对于给定了Radio和Newspaper的广告投入，如果在TV广告上每多投入1个单位，对应销量将增加0.0466个单位\n",
"- 更明确一点，加入其它两个媒体投入固定，在TV广告上没增加1000美元（因为单位是1000美元），销量将增加46.6（因为单位是1000）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (4)预测"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred = linreg.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##3. 回归问题的评价测度\n",
"对于分类问题，评价测度是准确率，但这种方法不适用于回归问题。我们使用针对连续数值的评价测度(evaluation metrics)。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"下面介绍三种常用的针对回归问题的评价测度"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# define true and predicted response values\n",
"true = [100, 50, 30, 20]\n",
"pred = [90, 50, 50, 30]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(1)平均绝对误差(Mean Absolute Error, MAE)\n",
"\n",
"$\\frac{1}{n}\\sum_{i=1}^{n}|y_i - \\hat{y_i}|$\n",
"\n",
"(2)均方误差(Mean Squared Error, MSE)\n",
"\n",
"$\\frac{1}{n}\\sum_{i=1}^{n}(y_i - \\hat{y_i})^2$\n",
"\n",
"(3)均方根误差(Root Mean Squared Error, RMSE)\n",
"\n",
"$\\sqrt{\\frac{1}{n}\\sum_{i=1}^{n}(y_i - \\hat{y_i})^2}$"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAE by hand: 10.0\n",
"MAE: 10.0\n",
"MSE by hand: 150.0\n",
"MSE: 150.0\n",
"RMSE by hand: 12.2474487139\n",
"RMSE: 12.2474487139\n"
]
}
],
"source": [
"from sklearn import metrics\n",
"import numpy as np\n",
"# calculate MAE by hand\n",
"print \"MAE by hand:\",(10 + 0 + 20 + 10)/4.\n",
"\n",
"# calculate MAE using scikit-learn\n",
"print \"MAE:\",metrics.mean_absolute_error(true, pred)\n",
"\n",
"# calculate MSE by hand\n",
"print \"MSE by hand:\",(10**2 + 0**2 + 20**2 + 10**2)/4.\n",
"\n",
"# calculate MSE using scikit-learn\n",
"print \"MSE:\",metrics.mean_squared_error(true, pred)\n",
"\n",
"\n",
"# calculate RMSE by hand\n",
"print \"RMSE by hand:\",np.sqrt((10**2 + 0**2 + 20**2 + 10**2)/4.)\n",
"\n",
"# calculate RMSE using scikit-learn\n",
"print \"RMSE:\",np.sqrt(metrics.mean_squared_error(true, pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 计算Sales预测的RMSE"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.40465142303\n"
]
}
],
"source": [
"print np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. 特征选择\n",
"在之前展示的数据中，我们看到Newspaper和销量之间的线性关系比较弱，现在我们移除这个特征，看看线性回归预测的结果的RMSE如何？"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.38790346994\n"
]
}
],
"source": [
"feature_cols = ['TV', 'Radio']\n",
"\n",
"X = data[feature_cols]\n",
"y = data.Sales\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
"\n",
"linreg.fit(X_train, y_train)\n",
"\n",
"y_pred = linreg.predict(X_test)\n",
"\n",
"print np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们将Newspaper这个特征移除之后，得到RMSE变小了，说明Newspaper特征不适合作为预测销量的特征，于是，我们得到了新的模型。我们还可以通过不同的特征组合得到新的模型，看看最终的误差是如何的。"
]
}
],
"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
}
| mit |
JeffAbrahamson/MLWeek | practicum/06_ANN/RBM.ipynb | 1 | 59497 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Restricted Boltzman Machines\n",
"\n",
"Un RBM est un algorithme non-linéaire et non-supervisé qui est basé sur un modèle probabiliste. Son but est d'apprendre des critères. Souvent, on suit avec un classifieur comme SVM ou perceptron. On l'utilise également pour initialiser les poids d'un réseau deep, ce qui s'appelle \"_unsupervised pre-training_\".\n",
"\n",
"Modèle probabiliste, il se base sur la distribution des données à l'entrée.\n",
"\n",
"Scikit-learn ne propose que `BernoulliRBM`, où les entrées sont binaires : 0 ou 1 : la probabilité que le critère est présent (est positif). L'utilité sur les petits ensembles est limité, mais il n'y a pas de règle facile pour définir \"petit\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reconnaissance de chiffres\n",
"\n",
"Un pixel d'une image en niveau de gris représente (peut représenter?) la probabilité que le pixel soit noir ou blanc.\n",
"\n",
"Pour avoir un ensemble de donner plus important, nous générons artificiellement plus de données étiquetées avec une perturbation d'un pixel dans chaque direction.\n",
"\n",
"Discussion :\n",
"* Le monde probabaliste\n",
"* Convolution\n",
"* Perturbation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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OHOltQqzD1jYrpk/Rz6l/c7uKDlvHx+QhiiJ0dnam6GN0jASDbX9szwCA/fbb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lzgC47D4XA4ckFhfDjvvvsuent7EyGzzIWzXA0AnHDCCeFY/QQswT5cH87ixYuDvDzz2GnS5Roe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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f192769d410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"==============================================================\n",
"Restricted Boltzmann Machine features for digit classification\n",
"==============================================================\n",
"\n",
"For greyscale image data where pixel values can be interpreted as degrees\n",
"of blackness on a white background, like handwritten digit recognition,\n",
"the Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM\n",
"<sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear\n",
"feature extraction.\n",
"\n",
"In order to learn good latent representations from a small dataset, we\n",
"artificially generate more labeled data by perturbing the training data\n",
"with linear shifts of 1 pixel in each direction.\n",
"\n",
"This example shows how to build a classification pipeline with a\n",
"BernoulliRBM feature extractor and a :class:`LogisticRegression\n",
"<sklearn.linear_model.LogisticRegression>` classifier. The hyperparameters\n",
"of the entire model (learning rate, hidden layer size, regularization)\n",
"were optimized by grid search, but the search is not reproduced here\n",
"because of runtime constraints.\n",
"\n",
"Logistic regression on raw pixel values is presented for comparison. The\n",
"example shows that the features extracted by the BernoulliRBM help improve\n",
"the classification accuracy.\n",
"\"\"\"\n",
"\n",
"from __future__ import print_function\n",
"\n",
"print(__doc__)\n",
"\n",
"# Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve\n",
"# License: BSD\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"from scipy.ndimage import convolve\n",
"from sklearn import linear_model, datasets, metrics\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.neural_network import BernoulliRBM\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"\n",
"###############################################################################\n",
"# Setting up\n",
"\n",
"def nudge_dataset(X, Y):\n",
" \"\"\"\n",
" This produces a dataset 5 times bigger than the original one,\n",
" by moving the 8x8 images in X around by 1px to left, right, down, up\n",
" \"\"\"\n",
" direction_vectors = [\n",
" [[0, 1, 0],\n",
" [0, 0, 0],\n",
" [0, 0, 0]],\n",
"\n",
" [[0, 0, 0],\n",
" [1, 0, 0],\n",
" [0, 0, 0]],\n",
"\n",
" [[0, 0, 0],\n",
" [0, 0, 1],\n",
" [0, 0, 0]],\n",
"\n",
" [[0, 0, 0],\n",
" [0, 0, 0],\n",
" [0, 1, 0]]]\n",
"\n",
" shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant',\n",
" weights=w).ravel()\n",
" X = np.concatenate([X] +\n",
" [np.apply_along_axis(shift, 1, X, vector)\n",
" for vector in direction_vectors])\n",
" Y = np.concatenate([Y for _ in range(5)], axis=0)\n",
" return X, Y\n",
"\n",
"# Load Data\n",
"digits = datasets.load_digits()\n",
"X = np.asarray(digits.data, 'float32')\n",
"X, Y = nudge_dataset(X, digits.target)\n",
"X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001) # 0-1 scaling\n",
"\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X, Y,\n",
" test_size=0.2,\n",
" random_state=0)\n",
"\n",
"# Models we will use\n",
"logistic = linear_model.LogisticRegression()\n",
"rbm = BernoulliRBM(random_state=0, verbose=True)\n",
"\n",
"classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])\n",
"\n",
"###############################################################################\n",
"# Training\n",
"\n",
"# Hyper-parameters. These were set by cross-validation,\n",
"# using a GridSearchCV. Here we are not performing cross-validation to\n",
"# save time.\n",
"rbm.learning_rate = 0.06\n",
"rbm.n_iter = 20\n",
"# More components tend to give better prediction performance, but larger\n",
"# fitting time\n",
"rbm.n_components = 100\n",
"logistic.C = 6000.0\n",
"\n",
"# Training RBM-Logistic Pipeline\n",
"classifier.fit(X_train, Y_train)\n",
"\n",
"# Training Logistic regression\n",
"logistic_classifier = linear_model.LogisticRegression(C=100.0)\n",
"logistic_classifier.fit(X_train, Y_train)\n",
"\n",
"###############################################################################\n",
"# Evaluation\n",
"\n",
"print()\n",
"print(\"Logistic regression using RBM features:\\n%s\\n\" % (\n",
" metrics.classification_report(\n",
" Y_test,\n",
" classifier.predict(X_test))))\n",
"\n",
"print(\"Logistic regression using raw pixel features:\\n%s\\n\" % (\n",
" metrics.classification_report(\n",
" Y_test,\n",
" logistic_classifier.predict(X_test))))\n",
"\n",
"###############################################################################\n",
"# Plotting\n",
"\n",
"plt.figure(figsize=(4.2, 4))\n",
"for i, comp in enumerate(rbm.components_):\n",
" plt.subplot(10, 10, i + 1)\n",
" plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r,\n",
" interpolation='nearest')\n",
" plt.xticks(())\n",
" plt.yticks(())\n",
"plt.suptitle('100 components extracted by RBM', fontsize=16)\n",
"plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)\n",
"\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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
atlury/deep-opencl | cs480/23 Linear Dimensionality Reduction.ipynb | 1 | 256372 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\newcommand{\\xv}{\\mathbf{x}}\n",
"\\newcommand{\\Xv}{\\mathbf{X}}\n",
"\\newcommand{\\yv}{\\mathbf{y}}\n",
"\\newcommand{\\Yv}{\\mathbf{Y}}\n",
"\\newcommand{\\zv}{\\mathbf{z}}\n",
"\\newcommand{\\av}{\\mathbf{a}}\n",
"\\newcommand{\\Wv}{\\mathbf{W}}\n",
"\\newcommand{\\wv}{\\mathbf{w}}\n",
"\\newcommand{\\betav}{\\mathbf{\\beta}}\n",
"\\newcommand{\\gv}{\\mathbf{g}}\n",
"\\newcommand{\\Hv}{\\mathbf{H}}\n",
"\\newcommand{\\dv}{\\mathbf{d}}\n",
"\\newcommand{\\Vv}{\\mathbf{V}}\n",
"\\newcommand{\\vv}{\\mathbf{v}}\n",
"\\newcommand{\\Uv}{\\mathbf{U}}\n",
"\\newcommand{\\uv}{\\mathbf{u}}\n",
"\\newcommand{\\tv}{\\mathbf{t}}\n",
"\\newcommand{\\Tv}{\\mathbf{T}}\n",
"\\newcommand{\\Sv}{\\mathbf{S}}\n",
"\\newcommand{\\Gv}{\\mathbf{G}}\n",
"\\newcommand{\\zv}{\\mathbf{z}}\n",
"\\newcommand{\\Zv}{\\mathbf{Z}}\n",
"\\newcommand{\\Norm}{\\mathcal{N}}\n",
"\\newcommand{\\muv}{\\boldsymbol{\\mu}}\n",
"\\newcommand{\\sigmav}{\\boldsymbol{\\sigma}}\n",
"\\newcommand{\\phiv}{\\boldsymbol{\\phi}}\n",
"\\newcommand{\\Phiv}{\\boldsymbol{\\Phi}}\n",
"\\newcommand{\\Sigmav}{\\boldsymbol{\\Sigma}}\n",
"\\newcommand{\\Lambdav}{\\boldsymbol{\\Lambda}}\n",
"\\newcommand{\\half}{\\frac{1}{2}}\n",
"\\newcommand{\\argmax}[1]{\\underset{#1}{\\operatorname{argmax}}}\n",
"\\newcommand{\\argmin}[1]{\\underset{#1}{\\operatorname{argmin}}}\n",
"\\newcommand{\\dimensionbar}[1]{\\underset{#1}{\\operatorname{|}}}\n",
"\\newcommand{\\grad}{\\mathbf{\\nabla}}\n",
"\\newcommand{\\ebx}[1]{e^{\\betav_{#1}^T \\xv_n}}\n",
"\\newcommand{\\eby}[1]{e^{y_{n,#1}}}\n",
"\\newcommand{\\Tiv}{\\mathbf{Ti}}\n",
"\\newcommand{\\Fv}{\\mathbf{F}}\n",
"\\newcommand{\\ones}[1]{\\mathbf{1}_{#1}}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Dimensionality Reduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Principal Components Analysis (PCA)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Principal Components Analysis (PCA) is a way to find and use directions in the data space along which data samples vary the most.\n",
"\n",
"Assume samples have $D$ attributes, meaning each sample is\n",
"$D$-dimensional. We want to project each sample to a smaller space,\n",
"of dimension $M$ such that the variance of the projected data has\n",
"maximum variance.\n",
"\n",
"Let's assume each sample $\\xv_n$ has zero mean. For $M=1$, we want\n",
"the direction vector (unit length) $\\uv_1$ that maximizes the variance\n",
"of each projected sample. This variance is\n",
"$$\n",
"\\frac{1}{N} \\sum_{n=1}^N (\\uv_1^T \\xv_n)^2 = \\uv_1^T \\Sv \\uv_1\n",
"$$\n",
"where\n",
"$$\n",
"\\Sv = \\frac{1}{N} \\sum_{n=1}^N \\xv_n \\xv_n^T\n",
"$$\n",
"To maximize $\\uv_1^T \\Sv \\uv_1$ in a non-trivial way, we constrain\n",
"$\\uv_1^T \\uv_1 = 1$. This constraint is added with a Lagrange\n",
"multipler so that we want $\\uv_1$ that maximizes\n",
"$$\n",
" \\uv_1^T \\Sv \\uv_1+ \\lambda_1(1-\\uv_1^T \\uv_1)\n",
"$$\n",
"Setting the derivative of this with respect to $\\uv_1$ to zero we find\n",
"that\n",
"$$\n",
"\\Sv \\uv_1 = \\lambda_1 \\uv_1\n",
"$$\n",
"so $\\uv_1$ is an eigenvector of $\\Sv$ and $\\lambda_1$ is an eigenvalue\n",
"that is the variance of the projected samples.\n",
"\n",
"Additional directions, all orthogonal to each other, are found by the\n",
"eigendecomposition of $\\Sv$, or, equivalently, the singular value\n",
"decomposition of data sample matrix $\\Xv$ with mean zero. \n",
"$$\n",
"\\Uv \\Sigmav \\Vv^T = \\Xv\n",
"$$\n",
"The columns of $\\Vv$ are the eigenvectors of $\\Sv$ and the elements of the\n",
"diagonal matrix $\\Sigmav$ are the square root of the eigenvalues.\n",
"\n",
" X = X - np.mean(X,axis=0)\n",
" U,s,V = np.linalg.svd(X)\n",
" V = V.T\n",
" \n",
"Then, to project onto the eigenvectors, just\n",
"\n",
" proj = np.dot(X,V)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's generate some two-dimensional samples from a Normal distribution\n",
"with mean [0,4] and covariance matrix \n",
"$\\Sigma=\\begin{bmatrix} 0.9 & 0.8\\\\ 0.8 & 0.9 \\end{bmatrix}$. Then we\n",
"will calculate the svd of the samples and project the samples to the\n",
"two eigenvectors."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(200, 2)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def mvNormalRand(n,mean,sigma): \n",
" mean = np.array(mean) # to allow entry of values as simple lists \n",
" sigma = np.array(sigma) \n",
" X = np.random.normal(0,1,n*len(mean)).reshape((n,len(mean))) \n",
" return np.dot(X, np.linalg.cholesky(sigma)) + mean \n",
"\n",
"N = 200\n",
"data = mvNormalRand(N,[0,4],[[0.9,0.8],[0.8,0.9]])\n",
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2, 2)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"means = np.mean(data,axis=0)\n",
"datan = data - means\n",
"\n",
"U,S,V = np.linalg.svd(datan)\n",
"V = V.T\n",
"V.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.9645509 , 0.26389686],\n",
" [-0.26389686, -0.9645509 ]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"V"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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u7PBEREQkB7QIOwBpvk489ETat2zPjn07mF4xnWpXTZEp95b6/exnP6NLly4s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\noeLyfaTHsNxxGVjeiPd3xx1w441Dx+gMF2dKtcdd+LxczI2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Z7nIXizpARHyKbExoOQk4\nP6X003r3UbZuWras3k1Kkvq1Q70UqdzNMqvdQMQKYH1K6YMl/rYQWLNmzRoWLlw49I9f+hJ88INZ\nF7VSZszIbtI50B3tjDNGFOdodOWVQ+9Fs3hx+3b7KqfUxAmtnsCgFiklTvuL03huz3NMHjeZFz/y\nIhPHTcw7LI1Sa9euZdGiRQCLUkpr846nFhExE5hZYbWNKaXjlUj/GJ2/qDTr2rB1kySpaVpdLzXi\nPjpjgNrP5M4/f2iSM24cXHHFYKvNwoUwdmwDwhu9uuGO0J12s7eIoGd+D19e92UOHDnA97d8n+vP\nvj7vsKSOk1LaCezMOw5JUueq9T46fwp8G3gGmAa8F7gG6BnudSVdcgksWgSXX54lNtdeC9Om1bwZ\nlddpSUK36JmXJTqQdV8z0ZGaKyJOB2YAZwJjI+KN/X9an1Lal19kkqQ81dqiMxv4CnAKsBv4EdCT\nUrq75j2PHw+PPlrzy6R2t2TekuP/v2vDXXx6yadzjEYaFT4J/ErB84HuENcB3219OJKkdlDrfXR+\nvVmBSN3ipBNOYuEpC1n7/FrWbVtH394+5kydk3dYUtdKKb0feH/ecUiS2otzMktN0DNvsDfnyo0r\nc4xEkiRpdDLRkZpgyDTTG3tzjESSJGl0MtGRmuCK069gyvgpQDYhwUincZckSVJtOirR6evL7g0z\nf3727/bteUcklTZx3ESuPetaALbt3cbj2x/PNyBJkqRRpqMSnZtuym6AuXFj9u+73pV3RFJ5heN0\nejfYfU2SJKmVOirRef754Z9L7cRxOpIkSfnpqETnlFOGfy61k/NmncdprzoNgO9u/i4HDh/IOSJJ\nkqTRo6MSnTvugMWLYd687N877sg7Iqm8iDjefe3lIy9z/zP35xyRJEnS6NFRic7s2XD//bBhQ/bv\n7Nl5RyQNb0j3NcfpSJIktUxHJTpSp1kybwlBAI7TkSRJaiUTHamJZk6ZyUWnXgTAj/p+xPN7nEFD\nkiSpFUx0pCYr7L62cuPKHCORJEkaPUx0pCZzmmlJkqTWM9GRmuyy0y5j6oSpAKzYsIJj6VjOEUmS\nJHU/Ex2pySaMncB1Z10HQN++Ph7reyzniCRJkrqfiY7UAk4zLUmS1FomOlILOE5HkiSptUx0pBY4\nd8a5nDn9TAC+t/l77D+8P+eIJEmSupuJjtQCEXG8Vefg0YN8b/P3co5IkiSpu5noSC3iOB1JkqTW\nMdGRWuT6s69nTGQ/OcfpSJIkNZeJjtQiMybP4OJTLwbg8e2P89ye53KOSJIkqXuZ6EgtVNh9bcWG\nFTlGIkmS1N1MdKQWcpppSZKk1jDRkVro0rmXMm3CNCCbkOBYOpZzRJIkSd3JREdqofFjx3P92dcD\n8ML+F1i3bV3OEUmSJHUnEx2pxZxmWpIkqflMdKQWM9GRJElqPhMdqcXmv3o+Z594NgD3P3M/+w7t\nyzkiSZKk7mOiI7VYRBxv1Tl87DD3bb4v54gkSZK6j4mOlAO7r0mSJDWXiY6Ug+vPvp4xkf38THQk\nSZIaz0RHysGJk07k0rmXAvDEC0+wZfeWnCOSJEnqLjUlOhHx0YhYHREvRURfRHwjIl7TrOCkblbY\nfW3FxhU5RiJ1rog4MyK+FBEbI2J/RDwVER+PiPF5xyZJyletLTpXAX8JXAosAcYDvRExudGBSd3O\ncTpSQ5wHBPAB4ALgFuBm4E/yDEqSlL9xtaycUvp3hc8j4leB7cAi4P7GhSV1v0vmXsL0idPZfXA3\nKzau4Oixo4wdMzbvsKSOklK6C7irYNHTEfFfyZKdW/OJSpLUDkY6RudEIAG7GhCLNKqMGzOOG+bd\nAMCuA7v4wbYf5ByR1DVOxHpJkka9uhOdiAjgvwH3p5T+rXEhSaNHzzy7r0mNFBHnAL8NfDHvWCRJ\n+aqp61qR28j6Qy+utOItt9zC9OnThyxbtmwZy5YtG8Hupc5XPE7n96/6/RyjUSdbvnw5y5cvH7Js\n9+7dOUUzchHxKeAjw6ySgPNTSj8teM1c4NvAP6aU/rbSPqybJKl52qFeipRS7S+K+B/A24CrUkrP\nDLPeQmDNmjVrWLhwYf1RSl3s3L88l/W71jN+zHh23rqTaROn5R2SusTatWtZtGgRwKKU0tq846lF\nRMwEZlZYbWNK6Uj/+qcC9wAPpJTeX2Hb1k2SlINW10s1t+j0Jzk/D1wzXJIjqTo983pYv2s9h48d\n5r7N93Hja27MOyQpdymlncDOatbtb8m5G3gE+LVmxiVJ6hy13kfnNuC9wC8D+yJiTv9jUlOik0YB\np5mW6tffknMvsJlslrXZA3VTroFJknJXa4vOzWT9ou8tWv5+4PZGBCSNNtedfR1jYyxH01ETHal2\nS4F5/Y8t/cuCrK5yvnZJGsVqatFJKY1JKY0t8TDJker0qomv4vLTLwfgyZ1Psvlnm3OOSOocKaWv\nlKiTxqSUTHIkaZQb6X10JDVA4TTTKzauyDESSZKk7mCiI7UBx+lIkiQ1lomO1AYuOvUiTpx0IgAr\nN67k6LGjOUckSZLU2Ux0pDYwdsxYlsxbAsCLL7/ImufX5ByRJElSZzPRkdpE4Tgdu69JkiSNjImO\n1CaWzl96/P93bbgrx0gkSZI6n4mO1CbOOvEsXjPzNQA8uOVBXjr4Us4RSZIkdS4THamNDHRfO5qO\ncs+me3KORpIkqXOZ6EhtxGmmJUmSGsNER2oj1551LePGjAOgd6OJjiRJUr1MdKQ2Mm3iNK44/QoA\n1u9az8YXN+YckSRJUmcy0ZHaTOE00ys2rMgxEkmSpM5loiO1mSHjdOy+JkmSVBcTHanNLDxlITMm\nzwBg1cZVHDl2JOeIJEmSOo+JjtRmxo4Zy5J5SwDYfXA3jzz7SM4RSZIkdR4THakNFY7TcZppSZKk\n2pnoSG3IcTqSJEkjY6IjtaHTp5/O+bPOB+DhrQ/zs5d/lnNEkiRJncVER2pTA606R9NR7tl0T87R\nSJIkdRYTHalNDem+5jgdSZKkmpjoSG3qmjOvYfyY8YDjdCRJkmploiO1qRMmnMCVZ1wJwMYXN7Jh\n14acI5IkSeocJjpSG7P7miRJUn1MdKQ25jTTkiRJ9THRkdrYgpMXMGvKLADu3nQ3h48ezjkiSZKk\nzmCiI7WxMTGGpfOWAvDSwZdY/ezqnCOSJEnqDCY6UptznI4kSVLtTHSkNjfQogOO05EkSaqWiY7U\n5ua+ai6vO+l1AKx+djUvHngx54gkSZLan4mO1AEGuq8dS8e4e9PdOUcjSZLU/kx0pA5QOE7nrg13\n5RiJJElSZzDRkTrA1WdezYSxE4BsQoKUUs4RSZIktTcTHakDTBk/havOuAqAzbs389Sup3KOSJIk\nqb3VnOhExFURcWdEPBsRxyLi7c0ITNJQTjMtlRYR34yIzRFxICKei4jbI+KUvOOSJOWrnhadE4B1\nwG8C9p+RWsRERyrrbuAXgdcA7wLmA/+Ua0SSpNyNq/UFKaXvAN8BiIhoeESSSrpwzoXMPmE22/dt\n556n7+HQ0UPHx+1Io1lK6fMFT7dExKeBb0TE2JTS0bzikiTlyzE6UocYE2OO3zx076G9PLT1oZwj\nktpPRMwA3gt83yRHkkY3Ex2pg9h9TSotIj4dEXuBF4DTgXfkHJIkKWc1d12rxy233ML06dOHLFu2\nbBnLli1rxe6lrjHQogNZovPH1/9xjtGonSxfvpzly5cPWbZ79+6cohm5iPgU8JFhVknA+Smln/Y/\n/wzwJeBM4GPA3wM3DrcP6yZJap52qJdiJPfjiIhjwDtSSneW+ftCYM2aNWtYuHBh3fuRNOjCv7qQ\nx7Y/RhDs+PAOZk6ZmXdIalNr165l0aJFAItSSmvzjqcWETETqPTl3phSOlLitXOBLcDlKaWHS/zd\nukmSctDqesmua1KHGei+lkis2rQq52ik5kgp7Uwp/bTC4xVJTr+x/f9ObFW8kqT2U899dE6IiDdG\nxIL+RfP6n5/e4NgkleA4HWlQRFwSEb/VXw+dERHXA/8APAU8mHN4kqQc1dOicxHwA2ANWR/pzwJr\ngU80MC5JZVx1xlVMGjcJyBKdkXQ/lbrAfrJ756wEfgL8Ddm93q5NKR3OMzBJUr7quY/OfdjlTcrN\n5PGTufrMq+nd0MuWl7bw5M4nOW/WeXmHJeUipfQ4cEPecUiS2o8Ji9SBeubZfU2SJGk4JjpSB3Kc\njiRJ0vBMdKQO9PrZr+fkqScDcM/T93DwyMGcI5IkSWovJjpSB4qI4606+w/v58GtTi4lSZJUyERH\n6lCO05EkSSrPREfqUEvmLTn+fxMdSZKkoUx0pA41Z+ocFpyc3bd37fNr2bFvR84RSZIktQ8THamD\nDXRfSyRWbVqVczSSJEntw0RH6mBOMy1JklSaiY7UwRafsZjJ4yYDWaKTUso5IkmSpPZgoiN1sEnj\nJnHNWdcA8OyeZ3nihSdyjkiSJKk9mOhIHc5ppiVJkl7JREfqcIXjdO7acFeOkUiSJLUPEx2pw11w\n0gWcOu1UAO57+j5ePvJyzhFJkiTlz0RH6nARcbxV58CRA3z/me/nHJEkSVL+THSkLuA4HUmSpKFM\ndKQusGTekuP/791ooiNJkmSiI3WBk044iYWnLARg3bZ19O3tyzkiSZKkfJnoSF2isPvayo0rc4xE\nkiQpfyY6UpconGba7muSJGm0M9GRusQVp1/BlPFTgGxCgpRSzhFJkiTlx0RH6hITx03k2rOuBWDb\n3m08vv3xfAOSJEnKkYmO1EWcZlqSJCljoiN1EcfpSJIkZUx0pC5y3qzzOP1VpwPw3c3f5cDhAzlH\nJEmSlA8THamLRMTxVp2Xj7zM/c/cn3NEkiRJ+TDRkbrMkO5rjtORJEmjlImO1GVuOPsGggAcpyNJ\nkkYvEx2py8ycMpOLTr0IgB/1/Yjn9zyfc0SSJEmtZ6IjdaHC7msrN67MMRJJkqR8mOhIXchppiVJ\n0mhnoiN1octOu4ypE6YCsGLDCo6lYzlHJEmS1FomOlIXmjB2AteddR0AJ089mR37duQckSRJUmvV\nlehExG9FxKaIOBARD0XExY0OTNLI/PnSP+f5332edTevY87UOXmHIzVdREyIiHURcSwiLsw7HklS\nvmpOdCLil4DPAh8D3gT8ELgrImY1ODZJI/DaWa/l5Kkn5x2G1EqfAbYCKe9AJEn5q6dF5xbgr1NK\nt6eUfgLcDOwHfq2hkUmSVKWI+DlgKfB70H8jKUnSqFZTohMR44FFwKqBZSmlBKwELm9saJIkVRYR\nc4D/Cfx74EDO4UiS2kStLTqzgLFAX9HyPsA+MpKkPPwdcFtK6Qd5ByJJah/jWrGTW265henTpw9Z\ntmzZMpYtW9aK3UtS11u+fDnLly8fsmz37t05RTNyEfEp4CPDrJKA84G3AFOBPxt4abX7sG6SpOZp\nh3opsp5nVa6cdV3bD9yUUrqzYPmXgekppXcWrb8QWLNmzRoWLlzYmIglSVVZu3YtixYtAliUUlqb\ndzy1iIiZwMwKq20CvgbcWLR8LHAE+GpK6f0ltm3dJEk5aHW9VFOLTkrpcESsAW4A7gSIiOh//t8b\nH54kaTRKKe0EdlZaLyJ+B/iDgkWnAncB7wZWNyc6SVInqKfr2ueAL/cnPKvJZmGbAny5gXFJklRR\nSmlr4fOI2EfWfW1jSum5fKKSJLWDmhOdlNLX+u+Z80lgDrAOeHNKyVuvS5LagffRkSTVNxlBSuk2\n4LYGxyJJ0oiklDaTjdGRJI1y9dwwVJIkSZLamomOJEmSpK5joiNJkiSp65joSJIkSeo6JjqSJEmS\nuo6JjiRJkqSuY6JTZPny5XmHUJNOixc6L2bjbS7jlSrrtO+d8TaX8TaX8XYPE50infZl6bR4ofNi\nNt7mMl6psk773hlvcxlvcxlv9zDRkSRJktR1THQkSZIkdR0THUmSJEldZ1yTtz8J4Iknnmjybhpn\n9+7drF27Nu8wqtZp8ULnxWy8zWW8zVNQ9k7KM442ZN3UZMbbXMbbXMbbPK2ulyKl1LyNR/wy8NWm\n7UCSVI33ppT+Ie8g2oV1kyTlriX1UrMTnZnAm4GngZebtiNJUimTgLOAu1JKO3OOpW1YN0lSblpa\nLzU10ZEkSZKkPDgZgSRJkqSuY6IjSZIkqeuY6EiSJEnqOiY6kiRJkrpOLolOREyIiHURcSwiLswj\nhmpExDcjYnNEHIiI5yLi9og4Je+4SomIMyPiSxGxMSL2R8RTEfHxiBifd2zlRMTvR8T3I2JfROzK\nO55iEfFbEbGp//N/KCIuzjumciLiqoi4MyKe7f9dvT3vmMqJiI9GxOqIeCki+iLiGxHxmrzjKici\nbo6IH0bE7v7HAxHxlrzjqlZE/Jf+78Tn8o6lnVkvNZ71UnN0St3USfUSWDe1WqvqprxadD4DbAXa\nfcq3u4FfBF4DvAuYD/xTrhGVdx4QwAeAC4BbgJuBP8kzqArGA18D/irvQIpFxC8BnwU+BrwJ+CFw\nV0TMyjWw8k4A1gG/Sfv/rq4C/hK4FFhC9j3ojYjJuUZV3hbgI8BCYBFZufDNiDg/16iq0H8C9J/I\nvr8anvVS41kvNViH1U2dVC+BdVPLtLRuSim19AH8HPBjsgLwGHBhq2MYQexvA44AY/OOpcp4fw9Y\nn3ccVcT5PmBX3nEUxfQQ8PmC50F2EnRr3rFVEfsx4O15x1FDvLP6Y74y71hqiHkn8P6846gQ41Tg\nSeB64B7gc3nH1K4P66WWxmu9NLK4OrJu6rR6qT9m66bmxNjSuqmlLToRMQf4n8C/Bw60ct8jFREz\ngPcC308pHc07niqdCLRl03s76+9WsQhYNbAsZb/OlcDlecXVxU4ku9rX9t/ViBgTEe8BpgAP5h1P\nBV8AvpVSujvvQNqZ9VLLWS/Vybqp5aybmqOldVOru679HXBbSukHLd5v3SLi0xGxF3gBOB14R84h\nVSUizgF+G/hi3rF0oFnAWKCvaHkfcHLrw+leERHAfwPuTyn9W97xlBMRr4+IPcBB4DbgnSmln+Qc\nVln9Fd4C4KN5x9IBrJdaxHppxKybWsS6qTnyqJtGnOhExKf6BxOVexyNiNdExIfImqv+bOClI913\nM+MteMlnyD6UpcBR4O/bPF4iYi7wbeAfU0p/2+7xalS7jazv/nvyDqSCnwBvBC4h67t/e0Scl29I\npUXEaWQV9HtTSofzjicP1kttF6/1kjqNdVOD5VU3RX9/ufo3EDETmFlhtU1kg/tuLFo+lqxv8VdT\nSu8fUSBVqjLejSmlIyVeO5ds8NflKaWHmxFfiX3WFG9EnErW5/GBVh3TQvUc34h4H/AXKaUZTQ2u\nSv3dA/YDN6WU7ixY/mVgekrpnXnFVo2IOAa8ozD2dhQR/4NsfMFVKaVn8o6nFhGxgmycwQfzjqVY\nRPw8cAfZCfDAiftYsi4YR4GJaaQFf5uzXmou66V8dHLd1Cn1Elg3NUteddO4kW4gpbSTbPDTsCLi\nd4A/KFh0KnAX8G5g9UjjqFa18ZYxtv/fiQ0Kp6Ja4u2v8O4GHgF+rZlxlTPC49sWUkqHI2INcANw\nJxxvxr4B+O95xtYt+iuSnweu6bSKpN8YWlgO1Ggl8IaiZV8GngA+3e1JDlgvNZv1Uj6sm5rPuqmp\ncqmbRpzoVCultLXweUTsI8voNqaUnmtVHNWKiEuAi4H7gReBc4BPAk/RhgO9+q+Y3Ut2lfJWYHZW\n/kFKqbg/b1uIiNOBGcCZwNiIeGP/n9anlPblFxkAnwO+3F+prCabFnUK2Y+y7UTECWTf0YGrJPP6\nj+eulNKW/CJ7pYi4DVgGvB3YF9lgcIDdKaWX84ustIj4U7IuN88A08gGf18D9OQZVzn9v50hfcr7\ny9udKaUn8omqPVkvNZf1UlN0TN3USfUSWDc1W151U8sSnTLa+crifrJ7FHycbC7458m+UH/Spv3e\nlwLz+h8DBUiQHeOx5V6Us08Cv1LwfG3/v9cB3219OINSSl+L7L4EnwTmkN0L4M0ppR15xjWMi8i6\nhqT+x2f7l3+FnK6iDuNmshjvLVr+fuD2lkdT2Wyy43gKsBv4EdDTYbOZtXNZ227a+VhZLzVf29ZL\n0HF1UyfVS2DdlIeml7cjHqMjSZIkSe2m1dNLS5IkSVLTmehIkiRJ6jomOpIkSZK6jomOJEmSpK5j\noiNJkiSp65joSJIkSeo6JjqSJEmSuo6JjiRJkqSuY6IjSZIkqeuY6EiSJLWhiPheRHwm7zikThUp\npbxjkCRJGpUi4u+A9wEJiP7FCTgX2AUcTintq3PbY4HDwI0ppX9tQLhSRxmXdwCSJEmj3LeBX2Uw\n0QHYkSpcjY6I8Smlw8Ot0oDYpI5l1zVJkqR8HUwp7UgpbS94pOKuaxGxJSI+GhF/HxG7gS9ExISI\n+KuIeC4iDkTExoj4vf6XbCJrHfrniDgWET/N481JebFFR5IkqXN8GPgE8EdkScwtwJuBm4CtwBnA\n3P51LwaeA94LrAKOtDpYKU8mOpIkSfl6W0TsKXj+rymlXyqzbm9K6fMDTyLiDOCnKaUH+xdtKVh3\nR/+/u1NK2xsXrtQZTHQkSZLydTdwM4NjaoabfGBN0fO/A3oj4ifAd4BvpZRWNT5EqfOY6EiSJOVr\nX0ppU7XrFj5JKT0aEWcCPwcsAb4eEd9OKS1rdJBSp3EyAkmSpA6WUtqTUvpaSuk/Ab8M/FJETE0p\nHQWOAmPzjVDKhy06kiRJHSoifpdsXM66/kW/CGxNKe3tf/4MsCQiVpPN7vazHMKUcmGLjiRJUnsq\nvo9Oqfvq7AU+CjwKPAycCry14O+3AG8hS3hWNyFGqW1FhXtRSZIkSVLHsUVHkiRJUtcx0ZEkSZLU\ndUx0JEmSJHUdEx1JkiRJXcdER5IkSVLXMdGRJEmS1HVMdCRJkiR1HRMdSZIkSV3HREeSJElS1zHR\nkSRJktR1THQkSZIkdZ3/A4BmS6CoEDr2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3ebccbcb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def drawline(v,means,len,color,label):\n",
" p1 = means - v*len/2\n",
" p2 = means + v*len/2\n",
" plt.plot([p1[0],p2[0]],[p1[1],p2[1]],label=label,color=color,linewidth=2)\n",
"\n",
"\n",
"def plotOriginalAndTransformed(data,V):\n",
" plt.figure(figsize=(10,5))\n",
" plt.subplot(1,2,1)\n",
" plt.plot(data[:,0],data[:,1],'.')\n",
" means = np.mean(data,axis=0)\n",
" drawline(V[:,0],means,8,\"red\",\"First\")\n",
" drawline(V[:,1],means,8,\"green\",\"Second\")\n",
" leg = plt.legend()\n",
" plt.axis('equal')\n",
" plt.gca().set_aspect('equal')\n",
"\n",
"\n",
" plt.subplot(1,2,2) \n",
" proj = np.dot(data - means, V)\n",
" plt.plot(proj[:,0],proj[:,1],'.')\n",
" plt.axis('equal')\n",
" plt.gca().set_aspect('equal')\n",
" plt.xlabel(\"First\")\n",
" plt.ylabel(\"Second\")\n",
" plt.title(\"Projected to First and Second Singular Vectors\");\n",
" \n",
"plotOriginalAndTransformed(data,V)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, if we have two classes of data, we might be able to classify the\n",
"data well with just the projection onto just one eigenvector. Could\n",
"be either eigenvector.\n",
"\n",
"First, with second class having mean [-5,3] and \n",
"$\\Sigma=\\begin{bmatrix} 0.9 & 0.8\\\\ -0.8 & 0.9 \\end{bmatrix}$."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LsXbt2i6fIyIioqBLypgiERkNYCiAl+1lSqm9IvIOgJPAoIioR6vbV4etTT2j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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3edd5a4630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 200\n",
"data1 = mvNormalRand(N,[0,4],[[0.9,0.8],[0.8,0.9]])\n",
"data2 = mvNormalRand(N,[-5,3],[[0.9,0.8],[-0.8,0.9]])\n",
"data = np.vstack((data1,data2))\n",
"\n",
"means = np.mean(data,axis=0)\n",
"\n",
"U,S,V = np.linalg.svd(data-means)\n",
"V = V.T\n",
"\n",
"plotOriginalAndTransformed(data,V)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And again, with first class \n",
"$\\Sigma=\\begin{bmatrix} 0.9 & 0.2\\\\ 0.2 & 20 \\end{bmatrix}$\n",
"and second class having\n",
"$\\Sigma=\\begin{bmatrix} 0.9 & 0.2\\\\ -0.2 & 20 \\end{bmatrix}$."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hn+JvUqI3ZM/hK14/SPRrSbGf1fgsaRPNrFNo/pfxg+tm49OY/YN/Gj7SQinag898MbDE\nOVffPlS1EvQ/ikq0+iVa+BJ9MuI+T5zd1LzHuoq6t0q8EGwzOvbQ5GxWNp+2usbnNLP++ArHu7Dn\n7/4acGmQ/TC6fPfQ28fwWQJPSbPrZ4A+ZjY+tI29gs+xCd+HKWvBjfkC4Pzw/0mQaW//TOubWUlc\nxcL8AMGJZBBxIXa19Ry+f9DY0D7a4cMjoxYBRwYZ6xLLnoGvwKWTaPmKfs8m03BjuFXgQ00vx1ee\nHgkqHmGJ/mvXRVc2LxHC+CY+w+R3wueUGJ/i05FHW9efwSc5uSQyfTK+pXw6GUT/B4LzaSLcNKvW\nfLU4iYiIVMv2xjbVcj/C94+aYWZ34W9uLsZn3vpeqo0555yZ/Tf+5uDfZnY//iajNz6EbCM+KQT4\n/ihfBl41P/7LQnyn67OAUc65TfhKz27gGvNj6mwHXnLOrTM/ZskDwBwz+zO+pawvPkztNXyfF/Dp\nhf8afJY/4DMNXo6/0YhNchExG7jEzH6I79uxxjn3Cj4TXBkw3czuxGf5Ox//hH1cFtttaDeY2Rfx\nT9KX4rMgXhb8nngy/h98X6LLzI//9CnwRhDyFuevwAVmtgXf9+YofCbF9THLpvou7ZnunKs0syn4\nivVTQVlH4L9rcduMOgn4HzN7En8DuwXfN+UCfGvBDaFlL8WnEF9gZr/Dh+b1xLfGFAf7BR8eNh6f\nFOAP+CyM3fDf0wuDCvDd+AFn/xRUlJfis6MdDlzuqtPH18a1+BaDGWb2R3w/pcvwrTOZbn77Buu9\nhO/ntzr4bN/Ap6W+1Tm3MbR8XSu6vw3K9IiZ3UF1OvLEg5Nwxeb3+DC46Wb2GP7v8g189sOUnHMb\nzOx14PtBpewj/N+5bz3KHd3HZjN7Gp9y3RHTB8w5lxjz7H/NbDDwNP7/YyC+/9jtwF3OuV1mdhm+\nwv2WmU3FhwsPAQY45xLnt9nBz98EoYI7guQij+KHAPiFme1PdTryscDN2bQW4cdha4vPHLkS3+/q\ncuDNmEQSKQ+KXnrppZdeerX4F9XpyIfXZzl8Nrpn8JWdzfjWgpGRZWqkIw+mHwo8gr/R2oq/eSqn\nZgrtPvgxlVYHy/0Hn8J6r9AyFwbTd0T3hQ/xeQZ/0/0pvkP5fURSnONv6BYE+5iPvym+n+zShhfj\nb3A3BPt/OTRvP/xYPB8H+3+DUOrmDNtdDDyZYZm4dOTLiKTzDqYfj08jvxzfL2IZvmIZTWV8enAs\ntgfbTpmaHB+S9wf8jeFG/M3kwGDb98SU89DI+jVSYQfT/xd/w7cl+F4dEN1mivLshx+H6vXgO7M9\n+PkEMDpm+f74tPcfBcdkabDs6ZHluuI73CeO3Yf4dNJdQsv0CL5biQQGb0WPXXBsdgNXRKa3CqZ/\nPzJ9PL6i9Bm+dfA04E/AexmOQyd8a1eikrwNHyr3D0IptlP9DYLlZsdst8a+g2P41+BvtRr4OXB2\nsM3o/9l3gmP4Kf6mfii+8vpczDEKpyPvja+IrMf/Lz2Ib6lKOmb4dOS7gc7Z/I9FynZasO6HGZY7\nOzg+m/Df+QX4SlP0/+jo4Lu7MXjNBv478jf/TfB92UUoNTn+gc0d+HDVbfiHRtHvTNugvLfElPFr\n+NbAVcF3ZzE+w2O3bI+HBRsSERGRPAnS4b4AHO2cSxfvLyLNhJl9F99S18s5t7bQ5ZHay2kfJzP7\nvpn908w2BSk+Hw+a16LL3WhmH5nZVjN7wcwGxW1PRESkmfh88HNdQUshIjlhZp+LvG+HD9tdqEpT\n05XrPk5H45twZwX7uhl43syGuCCu1cyuwccXTsA38f4YeC5YZkeOyyciIpI3ZtYe39fhSvyYJNkO\n2ikiTcuTZrYYH0q4D/7/fiA+XEyaqLyG6gXZWNbg46xfC6Z9hO+MNyV43xkf1zjRhUZmFhERaerM\nLDH46zzgMufc7AyriEgTZGZX4fuw9cP321mA73dTUdCCSb3ku+I0CH/BOMQ5906QBnMRMMw593Zo\nub8DbznnskqzKSIiIiIikkt5S0ceDDJ1B/Caqx6joRc+vWF09PLKYF7cdrrhc8l/iM+oISIi+fE5\nfHau55xzHxe4LI2Krk0iIgWTt2tTPsdxugs/aN6oem7nRHy6RRERKYxziBnPo4XTtUlEpLByfm3K\nS8XJzH4NnIxPu7oqNGs1fpCuniS3OvXE5/mP8yHAtGnTGDJkSMMXNguTJ09mypQpBdl3rjXnzwbN\n+/M1588GzfvzNZXPtnDhQs4991wIzsOS5EMo7LWpoTSV72MmzeVzQPP5LPocjU9z+Cz5vDblvOIU\nVJq+ChzrIiNsO+eWmNlq/CBjbwfLdwaOwA9+FWcbwJAhQxg+fHjOyp1Oly5dCrbvXGvOnw2a9+dr\nzp8Nmvfna4KfTaFoNRX82tRQmuD3MVZz+RzQfD6LPkfj05w+C3m4NuW04mRmdwFl+NG2PzWznsGs\njc65xIe7A/iRmX2ArynehB8R+Mlclk1ERERERCRbuW5xugSf/OHvkekXAA8AOOd+HoxrcQ+wN/AP\nYKzGcBIRERERkcYipxUn51xRlstdD1yfy7KIiIiIiIjUVVYVG0lWVlZW6CLkTHP+bNC8P19z/mzQ\nvD9fc/5s0vQ0l+9jc/kc0Hw+iz5H49OcPks+5HUA3IZgZsOB2bNnz25OndlEmoRly5axbt26QhdD\ncqh79+707ds3dt6cOXMoLS0FKHXOzclrwRo5XZtERAojn9emfI7jJCJN2LJlyxgyZAhbt24tdFEk\nh9q3b8/ChQtTVp5ERERaKlWcRCQr69atY+vWrc1inBqJlxgLY926dao4iYiIRKjiJCK10hzGqRER\nERGpLSWHEBERERERyUAVJxERERERkQxUcRIREREREclAFScREREREZEMVHESERERERHJQBUnEZEs\n9enTh4svvrjQxRAREZECUMVJRASYOnUqRUVFsa8f/OAHABQVFWFmDbbPBx98kF/96lcNtj0RERHJ\nHY3jJCISMDNuuukm9ttvv6TpBx98MACLFi2iVatWDba/adOmsWjRIq644ooG26aIiIjkhipOIiIh\nJ510UsoBflu3bp1x/a1bt9K+ffuGLpaIiIgUmEL1RESyFO3j9Pvf/56ioiJmzJjBJZdcQnFxMf37\n9wdg06ZNXHnlley333587nOfo2fPnpx44onMnz8fgKOPPprnnnuODz74YE9I4P7771+QzyUiIiKZ\nqcVJRCRk48aNfPzxx0nTunXrBlCjf1Pi/aRJk+jVqxfXX38927ZtA+Cb3/wmTz/9NFdccQUHHngg\n69at47XXXmPhwoUccsghXHfddXz3u99lzZo13HbbbTjn6NSpUx4+oYiIiNSFKk4ikhsjRsDq1bnd\nR69eMGtWg23OOceYMWOSppkZu3fvTrtez549efHFF5OmPfvss1xyySXccsste6ZdffXVe34/4YQT\nKCkpYevWrZSVlTVA6UVERCSXVHESkdxYvRpWrix0KWrFzLjrrrsYPHhwrdaJS1HepUsXZs6cyerV\nq+nVq1dDFlNEREQKQBUnEcmNfFQWcrCPww8/PGVyiFSiWfgAbr31Vi688EL69OnDiBEjOPnkk5kw\nYULssiIiItL4qeIkIrnRgCF0jV27du1qTPuv//ovjj32WB5//HFeeOEFbr31Vm655RaefPJJTjjh\nhAKUUkREROpDWfVERHKkpKSEyy67jMcff5zFixfTpUsXfvrTn+6Z35CD6YqIiEhuqeIkItLAdu/e\nzebNm5Om9ejRg5KSErZv375nWocOHdiwYUO+iyciIiJ1oFA9EZGAc65B1tmwYQP9+/fn7LPP5pBD\nDqFDhw48//zzzJ07lzvvvHPPcqWlpVRUVHD11VdTWlpK586dOfnkk+v1GSQzMzsauBooBUqAM5xz\nT4Xm3w9MjKw23TmnP46ISAumipOISCBT6JyZpRzLKaxTp05ceumlPP/88zz22GM45xg0aBD33nsv\nF1100Z7lLr/8cubPn88f/vAHbr/9dgYOHKiKU350AOYC9wEVKZZ5FjgfSPyBt6dYTkREWghVnERE\ngIkTJzJxYrSRIdmyZcuS3l900UVJFaGENm3acMsttySN4RSnY8eOPPjgg7UvrNSLc246MB3AUteW\ntzvn1uavVCIi0tipj5OIiEhNx5lZpZm9a2Z3mVnXQhdIREQKSy1OIiIiyZ4FHgOWAAOBm4FnzOxI\nV5eOcCIi0iyo4iQiIhLinHs49PbfZjYfWAQcB7ySbt3JkyfTpUuXpGllZWWUlZU1dDFFRFqc8vJy\nysvLk6Zt3Lgxb/tXxUmkliorYfx4WLUKSkqgogKKiwtdKhHJFefcEjNbBwwiQ8VpypQpDB8+PD8F\nExFpYeIeRM2ZM4fS0tK87F99nERqafx4mDEDFi/2P8eNK3SJRCSXzKwP0A1YVeiyiIhI4ajFSaSW\nVq1K/15EGjcz64BvPUpk1BtgZkOB9cHrOnwfp9XBcrcA7wPP5b+0IiLSWKjFSaSWSkrSvxeRRm8E\n8BYwG3DAbcAc4AZgN3Ao8CTwHvA74F/AMc65nQUprYiINApqcRKppYoKH54X7uMkIk2Hc+7/SP/g\n8KR8lUVERJoOVZxEaqm4GF57rdClEBEREZF8UqieiIiIiIhIBqo4iYiIiIiIZKCKk4hIM1dUVMSN\nN95Y6GKIiIg0aao4iYgA8+fP56yzzmK//fajXbt29OnTh6985Sv8+te/LnTRREREpBFQxUlEWrzX\nX3+dww8/nPnz53PxxRfzm9/8hm9+85u0atWKO++8s9DFExERkUZAWfVEpMX7yU9+wt57782sWbPo\n1KlT0rx169YVqFQiIiLSmKjFSURavMWLF3PQQQfVqDQBdO/ePen9tGnTGDFiBO3bt6dbt26UlZWx\nYsWKGuu9+eabnHzyyXTt2pWOHTsydOjQGq1XL7/8MkcffTQdO3Zkn3324YwzzuDdd99NWub666+n\nqKiIRYsWcf7557PPPvuw9957c+GFF7Jt27akZXfs2MHkyZMpLi6mc+fOnHHGGaxcubKuh0VERERC\nVHESkRavX79+zJ49m3//+99pl/vJT37CxIkTOeCAA5gyZQqTJ0/mpZde4thjj2XTpk17lnvhhRc4\n9thjeffdd7nqqqu4/fbbOf744/nb3/62Z5kXX3yRk046iXXr1nHDDTfwne98h9dff53Ro0ezbNmy\nPcuZGQBf+9rX+PTTT/nZz37G17/+daZOncoNN9yQVL6LLrqIO++8k5NOOolbbrmF1q1bc8opp+zZ\nhoiIiNSdQvVEJCdG3DuC1VtW53QfvTr2YtbFs+q9ne9+97ucfPLJDBs2jJEjR3L00UczZswYvvSl\nL7HXXv40uWzZMq6//np++tOfcs011+xZd9y4cQwbNoy77rqLa6+9lqqqKiZNmkTv3r2ZO3dubCsW\nwNVXX023bt2YOXMmXbp0AeCrX/0qhx12GNdddx33339/0vKlpaXce++9e96vW7eO++67j5tvvhmA\nt99+mwcffJDLL798T8vWpZdeyrnnnsv8+fPrfYxERERaOlWcRCQnVm9ZzcrNTSNM7IQTTuCNN97g\n5ptv5rnnnmPmzJn8/Oc/p0ePHtx3332ceuqpPPbYYzjnOPvss/n444/3rFtcXMzgwYN55ZVXuPba\na5kzZw4ffvghv/zlL1NWmlavXs28efO49tpr91SaAA455BC+/OUv88wzzyQtb2ZMmjQpadrRRx/N\nE088wZYtW+jYsSPPPPMMZsYVV1yRtNxVV13FQw89VN9DJCIi0uLltOJkZkcDVwOlQAlwhnPuqdD8\n+4GJkdWmO+dOzmW5RCT3enXs1aT2UVpayqOPPsquXbuYN28ejz/+OFOmTOGss85i7ty5fPDBB1RV\nVTFo0KAa65oZbdq0AXx/KTPjoIMOSrmvpUuXArD//vvXmDdkyBCef/55PvvsM9q1a7dnet++fZOW\n22effQD45JNP6NixI0uXLqWoqIiBAwcmLXfAAQdkeQREREQknVy3OHUA5gL3ARUplnkWOB9IBOFv\nz3GZRCQPGiKErhD22msvSktLKS0tZfDgwVx44YU88sgjVFVVUVRUxPTp0ykqqtk9tGPHjjktV6tW\nrWKnO+e+0RyPAAAgAElEQVRyul8RERHxclpxcs5NB6YDWOreydudc2tzWQ4RkboYMWIEzjlWrVrF\nwIEDcc6x3377xbY6JSSWW7BgAccff3zsMv369QPgvffeqzHv3XffpXv37kmtTdno168fVVVVLFq0\niMGDBydtT0REROqvMWTVO87MKs3sXTO7y8y6FrpAItKy/P3vf4+dnsiCd+CBBzJu3DiKiopqZLJL\nWL9+PQDDhw+nf//+3HHHHWzcuDF22V69ejFs2DCmTp2alI1vwYIFPP/885xyyim1/gxjx47FOVcj\n5fkdd9yhrHoiIiINoNDJIZ4FHgOWAAOBm4FnzOxIp/gTEcmTK664gq1bt3LmmWdy4IEHsmPHDmbM\nmMHDDz/MgAEDOP/88+ncuTM//vGP+cEPfsCSJUs444wz6NSpE4sXL+aJJ55g0qRJfPvb38bM+O1v\nf8vpp5/OsGHDuOCCCygpKeHdd9/lnXfe4dlnnwXg1ltv5eSTT+aLX/wiF110EVu3buXXv/41++yz\nD9ddd12tP8PQoUMpKyvjrrvuYsOGDRx11FG89NJLLFq0SOF8IiIiDaCgFSfn3MOht/82s/nAIuA4\n4JV0606ePDkpGxVAWVkZZWVlDV1MEWnmbrvtNh555BGeffZZfve737Fjxw769u3L5Zdfzg9/+EM6\nd+4MwDXXXLNnDKcbb7wRgH333ZeTTjqJ008/fc/2vvKVr/DKK69www03cPvtt1NVVcXAgQO5+OKL\n9ywzZswYpk+fznXXXcd1111H69atOe644/jZz362J5Svtu6//36Ki4t58MEHefLJJxkzZgx/+9vf\n2HfffWvd6lReXk55eXnStFQtaCIiIi2B5etJpJlVEcmql2K5NcAPnXO/SzF/ODB79uzZDB8+PAcl\nFZE4c+bMobS0FP3vNV+Z/saJ+UCpc25O3gvYiOnaJCJSGPm8NjWGPk57mFkfoBuwqtBlkaanshJG\nj4aBA/3PNWsKXSIRERERaS5yWnEysw5mNtTMhgWTBgTv9w3m/dzMjjCzfmY2BngCeB94LpflkqYv\nrpI0fjzMmAGLF/uf48YVupQiIiIi0lzkuo/TCHxfJRe8bgumTwUuAw4FJgB7Ax/hK0z/65zbmeNy\nSROXqCSBryiNGwerIu2U0fciIiIiInWV63Gc/o/0rVon5XL/0nzFVZJKSnwlKqGkJL9lEhEREZHm\nq1H1cRLJVrRSVFICFRUwahQMGOB/VlQUpmwiIiIi0vwUehwnkTqpqKgOz0tUmoqL4bXXCl0yERER\nEWmOVHGSJkmVJBERERHJJ4XqiYiIiIiIZKAWJxGplYULFxa6CJIj+tuKiIikpoqTiGSle/futG/f\nnnPPPbfQRZEcat++Pd27dy90MURERBodVZxEJCt9+/Zl4cKFrFu3rtBFkRzq3r07ffv2LXQxRERE\nGh1VnEQka3379tVNtYiIiLRISg4hIiIiIiKSgSpOIiIiIiIiGajiJCIiIiIikoEqTiIiIiIiIhmo\n4iQiIiIiIpKBKk4iIiIiIiIZqOIkIiIiIiKSgSpOIiIiIiIiGajiJCIiIiIikoEqTiIi0qKY2dFm\n9pSZrTSzKjM7PWaZG83sIzPbamYvmNmgQpRVREQaD1WcRESkpekAzAUuA1x0ppldA1wOXAyMBD4F\nnjOzNvkspIiINC57FboAIiIi+eScmw5MBzAzi1nkW8BNzrm/BstMACqBM4CH81VOERFpXNTiJCIi\nEjCz/kAv4KXENOfcJuBN4MhClUtERApPFScREZFqvfDhe5WR6ZXBPBERaaEUqiciItJAJk+eTJcu\nXZKmlZWVUVZWVqASiYg0H+Xl5ZSXlydN27hxY972r4qTSEhlJYwfD6tWQUkJVFRAcXGhSyUiebQa\nMKAnya1OPYG3Mq08ZcoUhg8fnqOiiYi0bHEPoubMmUNpaWle9q9QPZGQ8eNhxgxYvNj/HDeu0CUS\nkXxyzi3BV57GJKaZWWfgCOD1QpVLREQKTy1OIiGrVqV/LyJNn5l1AAbhW5YABpjZUGC9c245cAfw\nIzP7APgQuAlYATxZgOKKiEgjoYqTSEhJiW9tCr8XkWZnBPAKPgmEA24Lpk8FLnTO/dzM2gP3AHsD\n/wDGOud2FKKwIiLSOKjiJBJSUeHD88J9nESkeXHO/R8ZQtWdc9cD1+ejPCIi0jSo4iQSUlwMr71W\n6FKIiIiISGOj5BAiIiIiIiIZqOIkTUplJYweDQMH+p9r1hS6RCIiIiLSEqjiJE2K0oWLiIiISCGo\n4iRNSj7ShatVS0RERESiVHGSJiWaHjwX6cLVqiUiIiIiUao4SZNSUQGjRsGAAf5nbdOFZ9OapEFw\nRURERCRK6cilSalvuvBEaxL4FqVx42puT4PgioiIiEiUKk7SomTTmqRBcEVEREQkShUnaVGyaU3S\nILgiIiIiEqWKk7Qoak0SERERkbpQcghpklIleciU/CHRmrRokf9ZXJz/souIiIhI06OKkzRJqVKG\nK5W4iIiIiOSCKk7SJKVK8qBU4iIiIiKSC6o4SZOUaiDcfAyQKyIiIiItj5JDSJOUKsmDkj+IiIiI\nSC6o4iRNUqqU4dmmEq+s9P2hwhUsJYoQERERkVRyGqpnZkeb2VNmttLMqszs9JhlbjSzj8xsq5m9\nYGaDclkmEVASCRERERGpnVz3ceoAzAUuA1x0ppldA1wOXAyMBD4FnjOzNjkulzQimVKIN/R6oCQS\nIiIiIlI7OQ3Vc85NB6YDmJnFLPIt4Cbn3F+DZSYAlcAZwMO5LJs0HonWH/AtQOPGZRduV9f1wIfn\nLV6c/F5EREREJJWCZdUzs/5AL+ClxDTn3CbgTeDIQpVL8q+urT/ZrJeqVaqiAkaNggED/E8lkRAR\nERGRdAqZHKIXPnyvMjK9MpgnLURdW3+yWS9Vq1S2SSRERERERKAJZ9WbPHkyXbp0SZpWVlZGWVlZ\ngUokdVXXFOLZrKe+TCJ1U15eTnl5edK0jRs3Fqg0IiIihVfIitNqwICeJLc69QTeyrTylClTGD58\neI6KJvlU19afbNZTXyaRuol7EDVnzhxKS0sLVCIREZHCKlgfJ+fcEnzlaUximpl1Bo4AXi9UuaT5\nqKyEHTugbVv/GjlSfZlEREREpG5y2uJkZh2AQfiWJYABZjYUWO+cWw7cAfzIzD4APgRuAlYAT+ay\nXNIyjB8P//pX9fvWrTXIrYiIiIjUTa5D9UYAr+CTQDjgtmD6VOBC59zPzaw9cA+wN/APYKxzbkeO\nyyUtgPo3iYiIiEhDyfU4Tv9HhnBA59z1wPW5LIe0TOrfJCIiIiINpclm1RNJpbLSh+mtWAGdOkHX\nrtCnj/o3iYiIiEjdqeIkzUaiwjRrFmzfXj390EM1ZpOIiIiI1E/BsuqJ1EVlJYweDQMH+p9r1lTP\nSwx2G640gfo2iYiIiEj9qeIkTUqicrR4sf85blz1vFQVpLVr4ytaIiIiIiLZUsVJmpR0mfKiyR/a\ntvV9nDZvjq9oiYiIiIhkSxUnaVKilaPw+4oKGDUKBgzwP5ctgx49kpdX2J6IiIiI1IWSQ0iTUlHh\nW41WrfKVpnCmvOLimkkg0qUkTySTCG9LA+SKiIiISBxVnKRJiascpZOuopXoLwW+cjVunLLviYiI\niEg8VZykWUtX0UrXX0pEREREJEx9nKTFStdfSkREREQkTC1O0mKlC+MTEREREQlTi5M0e6kGzU2E\n8S1a5H8qMYSIAJjZdWZWFXm9U+hyiYhIYaniJE1SqspQnHSD5oqIpLAA6An0Cl6jC1scEREpNFWc\npEnKtjJUWQmzZiVPUxIIEcnCLufcWufcmuC1vtAFksavNg/1RKTpUcVJmqRsM+KNHw/btydPUxII\nEcnCYDNbaWaLzGyame1b6AJJ46cIB5HmTRUnaZKyzYgXrVC1baskECKS0UzgfOBE4BKgP/CqmXUo\nZKGk8dMwFyLNm7LqSZOUKiNeZaV/4peY3r27f/KXMGKEkkCISHrOuedCbxeY2T+BpcDXgPvTrTt5\n8mS6dOmSNK2srIyysrIGL6c0PiUlydccRThIcxK9x6qoyP89VXl5OeXl5UnTNm7cmLf9m3Mubztr\nCGY2HJg9e/Zshg8fXujiSI7U9Z9z9GgfHpHQsSPs3Ol/P/RQ+OtfVXESqas5c+ZQWloKUOqcm1Po\n8uRTUHl6wTn3wxTzdW0S1qyp+VCvuLhx3HCK1Ff0HmvUKJ+VuNDyeW1Si5M0Sok4cfBP78aNy+6f\nMxoWsWVL9e9t2uhCJSK1Z2YdgUHAA4UuizRuiWEuoup6TRNpTBSKqj5O0kjV9Z8zXVhEvv/BlV1J\npGkys1vN7Bgz62dmRwGPAzuB8gyrisTSDac0B9n2L2/OVHGSRqmu/5wVFb7peMAA6NQped7atfmt\nvCi7kkiT1Qd4CHgX+DOwFviic+7jgpZKGqVsHpLphlOag/A91qhRLTPZlkL1pFFKlfwhk3CYxJo1\nMGgQbN7s32/enN/wCD1hFGmanHPK5CBZqayEwYOrrzOpwvDqek0TaUxShaK2JKo4SaPUEP+cxcXQ\no0f1BQ3SV14auvOusiuJiDRv48cnX2PAD7o+cGDydUQ3nCLNg0L1pMlLFyZRm/CIuNC6+vRTUpO2\niEjzFvcwbvv2zCHa6gMr0jSpxUmavGi2okGD4IMP/BO+2oRHxIXW1ScTUp2fMK5ZA6++CqeeCp/7\nXB02ICIi+RCNLDCD8CgvqaIclGVPpGlSi5M0edELU6IvE1RXXhYt8j8T42nEPemLtkatXOlDLtLt\nKycefBDOPtsX6Mkn87BDERGpi2hkwYgRyfNTRTmoD6xI06QWJ2nyok/8IP1FKNWTvkTr1KxZPtRi\n+/b4feXcA8FQMRs2wIEH5mGHIiJSF9HIgrgBcKFmH9pu3dQHVqQpUouTNHkVFTVTj8ddhBItTW++\nmTw9UclKXAB7906e37ZtHvspvf02zJ3rfz/iCDjggBzvUESk5WrovkaJ68jrr/v3Rx7pt3v66cl9\naM3UB1akKVKLkzR5xcW+T1OmvkzhlqawuAQS4SeBI0bkMfY80doEMGFCnnYqItIy5aqvUXS7bdsm\nz1+3zoeQi0jTooqTNAupEjGEwyNWrkyet9devlGnoiJ5uW7dYORIf2HL63gbu3b5/k0ArVvDf/1X\nnnYsItIy5aqv0fLlye937Eh+v3Klb4mq77AXYQ09pIaI1KSKkzRrqVqZwFeaEpWt0aOTnw6OGlWA\np4EvvgirV/vfTzsNunbNcwFERFqWXI2398knye/DmfbA96FNpCt/7LGGqfAoU59I7qmPkzQr0Xj1\nFSuS56fqr9QoMhxNnVr9ex7C9DSOiIi0dA0x3l70XLpgQc0WplRWrIgfQ7AuGsV1TKSZU4uTNErR\n0Dmz5NC5VE/jok/cokkjwv2VEhe7Vatg7drk5fKe4WjjRnjiCf97t24wdmzWq9Y1PENPJ0Wkpavz\neHtUn3sTmVjBn0sPOST7baxfD61aJU+Lq/Bkc57PVeuZiFRTxUkapehNfUKmG/zoBadrVzj00Pik\nEdEwvk6doEeP6uXyGi/+6KOwbZv//RvfoPKTNlnvO1UFKFP59XRSRCR70XPqzp3wz3/Wb5tdumRX\n4cnmQVdtBnwXkbpRxUkapXQ38enmRS9AffrUvLgkLn4zZyZP79o1uV9TtN9TTltkImF6tWkNSlUB\nyrQNPZ0UEcle9JxqVv9tbtwIP/kJHH88VFVVT1uzpvYPuurTeiYi2VEfJ2mU0t3Ep5uXTbx64uK3\ne3fy9PXrk9/nrUVm8WL4xz/870OGQGlprfYdl049bp3o+4aI7RcRaQkqK31IXlg04UNdbN4Mxx1X\nXWkC30cq2s8pep5fu1b9UkUKQS1O0iiFQw7i+jjFiYZR3H13zbCF4uLUlZCuXau3sWJFzcQSOWuR\nmTat+veJE8GsVq1BqcIzundP3kb37snr6emkiEi8uLC8RD+m+igqSq4kpfLmm8npyisqYNAgX9EC\n/1P9UkXyTxUnaZTqclMfDaM46qjqi0w4VC1aKUno0yd1+vJOnXLUIuNc9aC3ZnDOOUD6WPW4vktx\nxyr6NLQhno6KiDRX4XPr2rXJ14/oALZ1le15eNeu6gx7r73mr4k9elSXCdQvVaQQFKonzUb0IvLZ\nZ/HzEyFq/fr5ClG/ftWhaqkuRD165CgxxOuvV3esGjPG197w+3rsMV8xWrXKXzwTYRnZpq79+OOa\n7+PS5ioluYhI8rk1XEFpSLV9gBW+JqUKyxaR/FGLkzQb0Zakdu2SL36Ji0y61qxUrVFr1/rKRYNn\n10u0NoEP0wtJldwhWrmbNcuXrXt3f1H++GNfzmioXklJ9q1ycTQqvYg0Z+lacL7wBfjgg9xVqFJJ\n9GVKhOspa55IYaniJM1G9KJyzz0waVLtLjKJbaxY4ZNFdO3qf27e7F8Nml1v2zb4y1/87x06wJln\nJs1OldwhWrnbvt2/j6ZtP/xw35IW/vxHHpm8zVStcnE07pOINGfRc2t4iIodO+pfaWrVqmZSokzC\nfZnUL1Wk8FRxkmYj7qISHuw2LlFENtsYODBHceVPPeXzzgKcdZavPIWkShARriCuWJF6hPqPP05O\nrx63zVStcnE07pOINAepWs8rKuDUU+Htt/1yQ4bA00/7ef361W+fZr6f1NattV+3rudaRQmINDz1\ncZJGJdoHp6H63GTbLyhOzuLKw2F6EybUmJ0qXXiicrdoUfoOyyUlNY/nPfdUb3PkSJ+lqW1b/zr8\n8PStcoqvF5HmINX1oLgY2rTxrfjbt/vBbU891Z87ly+v3z6dq1ulCWqea7O9Ttbnuici8dTiJI1K\nrsLB6tNakpO48spKmD7d/77vvn4gj9Cs8FPCN95I/ZSwa9ea4SNt2sCwYb6cp51WPbL94sVw4YU+\nzS34C2541Ps2bdI/jVR8vYg0B+muB9F5b7/dMGnIa8vMn5OHDq15rs32OqkoAZGGV/AWJzO7zsyq\nIq93Cl0uKYxcnejjWkuyfWoXbuFJxJnX20MPVQe7n3eeH9wDX6bBg9M/JQyXO5o5D3zo3rx5fr15\n85Ln/etf1Z+ztsc6J8dBRCTP0rWeZ9uSbtZw5Uno2LE6isA5X2Fr3br6XJs49ycefiWkOncrSkCk\n4RW84hRYAPQEegWv0YUtjhRKuhN9NhWdVMvEhb0VNIwhRZje+PE1W5CiF8Vwubdsid/89u1+mWj/\nJ+egb19/bKID4uqiKiItQaowaPADp3fqBHvt5X9+4Qs11zfb86wrVl0rVTNnQu/eydMSWVNHj/YR\nBDNm+DGewlKdu9N9ThGpm8YSqrfLObe20IWQwksXDnbaab7FBHyl4dRTk0PNIHUIQ9zYGQULY3j7\nbZg7F4Adw4/g+IsO2PN5V6youXi3bsnva1PO1q1rVp4Slaphw/yNwWef+SQR99xTy88hItIERZMA\nJR64RQe+3by5ugIVfqDlXPrseHHXm6IiH03w4YepQ/8OPrjmtHDW1DZtkue1agVf/GLqCpGy8Ik0\nvMbS4jTYzFaa2SIzm2Zm+xa6QFIY6cLBEpmOUr2HmpWK5cv9BbFfv5qtSwULY/jTn/b8+qsNE5LK\ntX59zcWjTy9rU86hQ/1FP87Chf5mYNcu/3PSpOy3KyLSXKQb+Pbjj/34TYlB0+vamuQcvPde9v2l\nEln4wnbuTH7fvn3N62SuEixJMh3nlqsxVJxmAucDJwKXAP2BV82sQ7qVROJEKxWffOIviNGL1apV\nBQpj2LULpk3zv7duzUO7v540Oy61+Jw58WGH6TLqtWnj4+XXrIEDD6z5pDJuX2++qQuAiLQ86Vrx\nu3evjoJYvz6+NSkbtV3PuZrn7ej7HTtqnq+VSS8/dJxbroKH6jnnngu9XWBm/wSWAl8D7k+13uTJ\nk+nSpUvStLKyMsrKynJSTsm9dGNOVFb6sLNwBWjo0Jrr79hRXaEYOtRPixu0cO1aPxhs9+4+FG7V\nKn/iy/k4Fy++CKtX+99PPZV2a7r5b3sg7mnkrl3+xHziifDWW9WtcgMHJo/JFLZjh39t2QJLl8Yv\nk2o/GthWAMrLyykvL0+atjEx7phIM5Jp4NtE+He+bd5csyyJcHXw14vo+VqZ9PJDx7nlKnjFKco5\nt9HM3gcGpVtuypQpDB8+PE+lknxIl2J1/PhEIgQHey+l0+79ePrpmuuHLyqtW0OfPskVh7Zt/VO7\nzZv9K3yxbMj05ymFk0JMnEjFkdVPM1euTB/GEc6QV1npK3/1keoJqC4AAvEPoubMmUNpaWmBSiSS\nG3F9axMP0AYOLGzZwpEB998PpaXJ14no+TrVwOnSsHScW67GEKqXxMw64itNun1rYVI9wams9JmF\nAOj9T7iqPzvOG82Ty3/Hhm0bUq4/a5ZPttCpk49NHzUKli3zT++yLUOD2rgRHn/c/96tG4wdm9Sn\na8SI9KsnKjqJlOVxLWkNQRcAEWlJ0vWtzff5MNqHKpEcYsYMHyURFS2fMuml11B9k3ScW66CV5zM\n7FYzO8bM+pnZUcDjwE6gPMOq0sykStYwfnzoCdtQ32KzvXgGF//1Ynr9ohdff/TrPPOfZ+j1+eQc\nrdu3+9amzZuro+Pi9pOuDA3q0Udh2zb/e1lZjYD1ior0/ZaKivzJPleVJjNdAESkZUo3lEW683JY\nuhTl2Uo8IGvbtuZ+N2+uvha2bRt/vtZ4e+ll2zcpUwVLx7nlKnjFCegDPAS8C/wZWAt80TkXM7Sn\nNGepnuAktQKtORhbWz2wxvbd23n43w9zykOn8J/T+vD5C75N79J5NS44iRTcp53mQx/iMiN16tQw\nlYaUJ9xQmN5Fr06sMb+4OH2rU1VVfNanhqKWJhFpqVLdUGc6Lye0bQuvvJI6i2ldxI0hldC7d9O9\nYU9XKcl1trps+yYp+YOkUvCKk3OuzDnXxznXzjnX1zn3DefckkKXS/Iv1ROcpBv6WZdy5NwFPD9+\nNsVLroSt1aO4rv2sko/6TWHlacNodflQOPI26Jh8Vpw3z/eDivbvadvWp5xtiItQ7Al3yRJ49VUA\nlrQbwh/eLo09IafKmJftE8/6+OgjXSBEpGVKFyq+c6cPEDDzP0eOhMMPT15+xAg45hjYtMmfw8M6\ndqx9ebZvh/ffTz0/3w+6GrJCk65SUpcKS23Klu0wJEr+IKk0uuQQIlE1O+4a48YNZ82M4VD0Cxg0\nHYZNxQ58Glfke9Ju7fQ2nPhd+PL3YNGJMG8CvPtVoF3sPuLSdddV7Ak3NHbTI+0mwGcWu3yi8rhm\nTfJn3rmz5mC/6bRt6wdu/PTT2pc/bhBekYZgZlm36TrnVIWXvEnV2X/8+ORz7+GHx5+jKypgwQLf\nD8knMvI6doSZM/04ebXNzhc9f7dt61uaooPD50O65E21la5SUpcKS23KFpcIJI6SP0gqqjhJoxc3\n+vmek2lVa3j/NHj/NPoeuJ5r//QwU+dNZeaKmX5+URUMfhYGP0urnZ3ptOprbH9xAiwbDVRXXjZv\nhlNPrV3lJJUaJ9xerjpMz4w3BpwL65OXj4p+5gULYNiw9KPVh+3eHT8mVDbiBuEVaSDhfOYGnBlM\nS6R/KQX2BtTTTvIq1Q11qhv5uOvSoEHJlSbw7ydN8ts79VSYO7fmQLbZatMG3nijMOF5DdkCk65S\n0q1b8rxu3TJvb/ny5PfpHv7F/d3iZFvBkpan4KF6InURV9no060rl4y4hDcueoP3Ln+PHx39I/p2\n6btn/u7Wm1jX9/dw4THwrYFw3PWwz6I9899+u35lSoQLRDP5PfWDN3z8IcCYMdzztz6xfbniwg0S\n00aMSF9pinZK3rWr7gM1du1at/VEMnHOXZB4AZXAw0B/59y4oIVpAL6v67pCllNanqxCxWPeJ6Qa\nMxD8zXdxsX8wN3Jk6jIUFcFhh/logTibN0PfvoUZqDzb45CNdBnpov2P4/ojR0Uf9i1fXv/jo+QP\nkooqTtIkVVT4kIlE5qGRI5NPvvt325+bjr+JJd9awsGzXoG3LoDtoUDzfZbAcTfAtwbBhaOh9F5c\n2w01d1QLiXCBRCa/Pn38CbfrU1OrF5owIeUJOS62+7TT/O/pxneCmoMBx8k241OfPqnn5brjrrQo\nFwK/cM7teSQQ/H57ME+k4NLd5IfPh4MHp95GuNUkXUtNVRX85z/QqlXqZRKJjvLdFzXb45DNdSFd\npWRd5JFJ9H2caHRFVVX64xMu7xFH+PuHTGXXtU8SFKonTVLi6V0mRVbE1neOg8XHwTO/ggOf8CnN\nB74AFjTJ9J0BfWewc/eVfP3RrzLh0AmcOOhE9iqq3b9HbCjDtm3wl7/4CR06wJlnJi1TWekrTIkB\ncMOWL68ZgpBKNuF1VVXp5++1l7+IpAtJOP306uO+eLGv2L35ZnZlFInYCzgQeC8y/UD0UE8aiXBo\nV2VlcvjWjh3Jg66nEm41iYapRYVD/cIDtkflO1lBuhC3aB+jvn19lER4IOFsZepbFL5mJkLo4lql\nZs3ylZu4/UfLm5Cuf1RD9vGSpk0XJ2mysn0CtOfEu7MDzD8Hpj3HiH8sZ/LBt1C0rjrfq2vlU5uf\nWn4q7X/Um0se/zZzV8/NujyxoQxPP+0HvgU466yk9EqJgWwTrUzRVqVPPsk+3G79+vqPIdKuXeYL\n3bx56d+L1ML9wH1m9m0zGx28vgP8Ppgn0qhEowKyDe8Ot5okWm5SheOF9e7ts73GaUzJCqKVuPq0\nimUaWDYuMuPQQ2tuZ/v21PtPV+lMNU9Z9iRBFScpuGwqQHHLRE+ggwbFb6OiwjfFJ8L6Dj8c/lbe\nm39O+R5Vv14A98yGmVfCp9WpzXe2XcM9b0/hsHsOY+jdQ7nt9dtYtTn9mTL2hD81OUwvbPz4mk8S\nzarLmSk8L6xrVxgypOb02owpsnmzP4bR4x8+9nVNOCES47vAz4HvAK8Gr28DtwJXF7BcIkDN605d\nM9hYep4AACAASURBVI6GKzmJlpsjjshuveLimufxVq0aV7KCbFN6h6W67mfqWxRXgfnrX2umh0+3\n/3SVzlTzGrKPlzRxzrkm9QKGA2727NlOmodRo5zzbSv+NWpUdssMGJA8LdM2omqsX7TDsf9Tjq+N\nd/yojeN6kl5FNxS5k6ad5Mrnl7utO7Zm3sHq1c61auU3vu++zu3enX7/9Xh16FBzWtu2zlVWOtep\nU+221batP36VlfHHPvw6/PAs/sDSbMyePdsBDhjuGva83hno3JDbbKBy/T9gCfAZMBM4PM2yujY1\nM9FzX/RcOnJkzWmJU354ncpKfzlIXLdGjXJuwQL/s18/v0y/fn57hx9evUziHLxggXMdO/rtmTk3\nbFj1vOh2E9PzqbLS77tt2+yvw9lc9+OMHFnzb1DbbSbKO2BA6mOebp1CHWdJLVfXpriX+jhJwWXT\nBB63TLpY8UxPusaPr9mnKJzanHbrGXD6wxR/uTq1eZWrYvoH05n+wXQ6t+3M2V84m4lDJzKq7yiK\nLKbxtry8OhXeeeclxdJVVsLatanLWFtx4zW1bu3T33bpkjrbU5xwmMVrr9U8loUcS0SaJ+fcpkKX\nIcrMvg7cBlwM/BOYDDxnZvs755T1rwWInvu6dvVhYdEU1Yl+T2vXJp9rO3WqHlh99OjkPjKTJmXf\nR+agg3wCoBkzfJVg7tzq83Nj6HuTavzBaAKJcN+kaOtdtqFv0VTu4ffZphDPNiV5fdeR5kkVJym4\nbAaai1vm7rvhqKPgs8/8xSScrjtdM3r4QhPVqpXPKtenT1cq7ryE4uJLKP3K+8zZ9SefVGLvZQBs\n2r6J+966j/veuo/+e/fnvEPPY2zv8/juhYP2nLRf2fQArRMbPu+8GmWoTWWmLrZsSe643L599fhO\nLou+U4kLWfTYjxihC4jUn5n1BH4BjAGKCQ+sBjjn0uQWy4vJwD3OuQcAzOwS4BR8xr+fF7Jgkh/R\nc18iU2pUYtrAgcnn9R49qkPN6ttHJtX6+ex7E5eYIRxKV5sEEtHww8Q1e8GC6ut6u3Z+3KqDDqpe\n7p13ktcLv1flRvJBfZyk4LJJcxodG6miAi65xF+kdu3yFYJWrXyH206d4J57Uu8v3YWlX7/qLHFH\nHun3XfnO/vDKTfDLJfDHV+jw/gV0bFOd5GHJhiXc+OqNHFk+mBkHjGbxPveyfvYMWs9/yy8wciQc\neGDaMrRt6zMR1TfBQzq7d/skf337xs+PZiZKXMgyddYFpWqVOvkjPrztJuAsYFzkVTBm1ho/GO9L\niWnOOQe8CBxZqHJJfmVz7gtL1w+mvn1kUq3fEH1v0o0hmK5fcW2SP8S13sUd26OOqr6ub97sr8Ph\ncqqfrRSaWpyk4LJ9SgU+TCKxbPREnGhx2rw5fRhEuhC/kpKaT8b2jKnhiuDD4yj6+DhW//HXPL7w\ncR54+wFeXPwiVS7I9R2kNv/P2FZ8/V2YMA++MuHc6panFGVo08ZfFDKlDO/QIT4sLxvO+QvgsmXx\n83v39gkAE0/6EpXPbJ7iNYZwEWlyRgNHO+eyT12ZP92BVvhBesMqgQPyXxwphNq2YKQLFcs2jKy2\n267vdiH+/A01p6Vr3crUGpVt691nn6V+P358zWiJuIx6IrmkipM0aulO1OkqQCtW+EpC3Em8osJn\nj4uGyhUV+dC26BOtaEtM167QvnV7zjn0HM459BxWblrJoec+yPp9p0KxjxvYtdduHj4YHj4Yij+9\niXOeW8KEoRMY2nMoZpZ0sYvGxadSVFT3ShP4z5UqRBF8pSlRjkyVzyilapU6WE4kPK85mDx5Ml26\ndEmaVlZWRllZWYFKJPmSrqJVlzCyTJWRum43Ktt+xt26JV9zwwP7Znp4FlfBi/t87dolXw/btate\nLjpmYNu2PqOetCzl5eWUl5cnTduYGPYlD1RxkkYtXf+ndJWP9eth6VL/++LFvqKU6KRbXOx/79s3\nOeV3VVV8GED0RN6nT/L83p17M2T995jx9NVQ8hZ9h97I1kOeZF0HP3/NZ2uZMnMKU2ZO4ZDiQ5gw\ndALnHHIOr73mP0w0Lj6OWXxrlFn2Yz2lM3KkvziFy1Gbyk82/dREIq4CfmZmk5xzHxa6MBHrgN1A\nz8j0nsDqdCtOmTKF4cOH56pc0oJEKyPh61hDSnX+jk6LJmYIP1TMVPmKq+CNHFndD3fxYjjxRP8Z\n587117UOHXwfp1T9kkeMaPhjIY1f3IOoOXPmUFpampf9q4+TNGqpYsyjI7i/8UbycpEHvmze7JdP\nxG0feaQPj0sn0V+qS5ea/aviy2kMaDec389qz0e3wVMPwQHLj4Td1Tuav2Y+V79wNX2m9GGfK8bS\nc0w5az5Jjk0wSxonF0hdORoxonZjNaXSunXNCmFtKj+17QsgAvwFOA5YZGabzWx9+FXIgjnndgKz\n8YkrADAzC96/XqhySdMX7Tu0YEHq/qHRykfiOlab7WfT3zTu/H333f7aEu43vC6SSzL8vrZ9rSor\nYdas5Gnz5sFbb1Vf74YN84khosdhr718Oe++W31rJf/U4iSNWqowhOiTuGhYWefONddZtarmk6tO\nnWCffXxq8nBWPqhuaUq0woT7V6Us56ZN0PNxqIKjF3dj8ft/h3Zb4KCH6TBqKp/uU53afEP36XDM\ndBjZGVt4Nm7uBFg2GueK+MIXfGVm1SpftrjBcDt29GEKa9b4iuCWLfFlC0sk0Ihub9UqX/lMFyuf\nLmxE2YykDq4qdAEyuB34o5nNpjodeXt8UguROoleuxLJEBLvwyFuceHomSIB6tLfNO78PW5czdDt\nuJapxHUhkcCpa1f/EC7Tw7O4/krR96kyux5xhC9vNMW7+tZKPqjiJE1SprCArl1rhr+VlNRcrkcP\nPz2cMMHMt+SsWVOH0LVHHvGp64Cn2pexc1Mb+KwrzLqEXfMvgY7vw6HJqc353CbcYffBYffBJ/1h\n3nnMffc8+rQfREmJjyMPpxU386nFu3XzF4qdO7OrNBUVwcsv+wR/0T5eiRHqH3usunI0blxy5UgJ\nIKQhOeemFroM6TjnHjaz7sCN+BC9ucCJzrkGHIFNWprodSSaDCE8P64/bqaWnPr2N03VnyjVw7Vx\n41IncKpNOeOEM7vGPdRT31opBIXqSZOUKSwgGnbWqZM/2catFz3Z9u8P//xnHUPXHnhgz6+v7jeh\n5vyPk1Ob89YFsD0Ul7fPEjjuRnZcMpjFx41ixrZ72d16A6NG+T5ZnTr5lqhPP/V9uGbM8OEN2aiq\ngh/8oLqPV1xoXbp0s7pISUMzs1ZmNt7MfhS8zjSzQo/ftIdz7i7n3H7OuXbOuSOdc7MyryWSWvQ6\n0q5d6vnpztXZbn/t2sxpxsMS14Bdu5Knr1njB1RPVF7uvttfH+IqWNlIdT2NG1Yk8VAvcb0eN86X\npyFSsYvUllqcpEnKlII1bn5xcfz0cePiO8bWOs3rkiXw6qv+9yFD+OnzI3g3FNq2c6evkAF7Upvb\n0uM4Yv2vOeP7j/PTvz3Apm4vQlEitfnr0Pd13tp9JWcdcjrrH5zIsqe/AlXJyc1rkxxi+fLkbINv\nvJHcubY2WQx1kZL6MLNBwDNAb+C9YPL3geVmdopzblHBCieSI9Hryj33+DC4VNeZ+qRETyRN2rzZ\nn7v79vV9e8OhgaedVh0WXlLiQ+7ihAdUX7w4dXh4Nn2bEqF9rVrVDJFPNaxIXMRDQ6RiF6ktVZyk\nScp0MUk1P256qpNvrfvtTJtW/fuECRT3tKT1/3979x4nR1nne/z7S0gChJg9kxAyMCFIQEDdBBLC\nzXhbXVw1QTdRYMQl4h4XXuue3ZMjF+GIN44HZZXgLiJxXUFQ5yXK4JoAhl28LSHkYiAEdQ8mA4GE\nyeSGQwgw5PKcP54uurqmuqq7p3uqL5/36zWvmaqu7npqBurJ73l+9Xu2b5emTCms3HfssdLKXx8u\n6SIt/dJFWvH4Vmn696UZ+dLmbuSAfvS7H0mn/Ug6cZK04SPS+oulbadKMo0aVfqigM8/n09LjEu3\nK7WKIZ0UquCfJG2SdJZzbrckmdkESd/Lvfb+DNsG1ERcv1LNlOfw50crtg4MDH6+df36/L6entKL\nDcUFTcGMWNzzsM75fWvXFrYhLngKhAfu4gb1yumjSyntDpSCwAktryqFDZzLp+mZSRddFHue2bML\n88HD6YC9vZL2HCOtuFJacYVGdjyqSX9+h1496Qfa9UrusYojtktn3ySdfZNs+59q6h8v1r51F2nv\nf6VP/xxxxOBnv6KdUVJwRAEIVNnbFQqaJMk5t8vMPi0pYcUxoDWV+4//pLUOi2lr888ppa0xGF0K\nY8yYfP8QV7RBii8p3tHhv+LOFx64i17Ljh1+MLLU4IdndFEtPOOEulVJWdVK3lMVK1f6ZHRJA3P+\nTHM6p8S2Ialsd2GKg+msqTP13HduUu/lW/XTC3+qecd/SHYwX9rcTdqgp99whbZe0CFd9F7pzV3S\nqJcKmjVuXP5cmzalP7cVBEebNvnvjMihhgYkxY1vHyGpxDlUoDX09Uknnlj4DOq8ecn9XdDfjBlT\n/HMPiQyfd3Tk+4Dw81VnnOEH/oL+5NRTC983Y0b+5+iA3Nq1xVMAOzryzy+1tRVf+qO7u3A2rFhp\n9mL/BuAZXVQLM06oW5WMEA3HqFLsqF+oKMQN2xZqxR/i25A0axPM9jz7rE+p27zZl1UfP36U+vvn\nqa1tnmYft1sf+txduqfnDq3cstK/0Q5KJ/5MOvFnGrFvnCZsO1+H/PZivX7kHN3TPaIg+CHdDnVk\nmaRvmdlfy5f7lqQzJd0q6aeZtQqoQwsWDJ79iabZRfu7oL/Zvt2/tmWLT9UOzxS98opPl5N8tdag\nIEP4/XGCz4zrS6KzQwMDflH6MDP/vNW+fdJ554We/5VPPY8O8k2a5Kvghn8Ha9fmZ52CfjmcChj+\nnfCMLqqFGSfUrUpGiIZjVClaee7CD74i/fCH/sWxY3XXvr+sqA1BJzVliu8ctmwp/L55s7T6V236\nt2sv08N//bCe/Lsn9Zm3fkZTx0997TMOjtqjHVP+Vb1/8XY99+Fpuvm3n9XG3RsHnYMZJdSBv5d/\nxmmlpFdyXyskbZT0Dxm2Cy0iswyFClTS/wWC+35Hx+BiQgcO5L+CggyBpN9PUl/S3T14lqutLT97\nNW6cb8fAgA+YopVhBwYGV3WVBgc7AwP5Y4J+OW6NwqBNLNKOaiBwQt2KKzWa1tGllWINK6XTjDsm\n2jm9cdNS6Y9/9BsLFmj8MUcUvF7uyFZaBxm8fuKEE3Xdn12nnn/o0S8X/lKXnHqJjhidP/fTf3xa\n1/36Op34zyfqLd95i5asXaLnX36+vMYANeKc+6Nz7gOS3iDpQ7mvk5xzf+mc68+2dWgFScsv1Jto\nP2Lmq+ElHRNVbvBVyu8nro+cNMmvhRgWTgE88sj0dkiD0/viArKgvcWuLfidMGiIaiFwQt2KGyFK\nu5GH3zNuXL4Ma9yxaZ8Vl1M+f/7gzqlzXz5NTwsXDnlkK63z27q1MNAbYSP09uPeru984Dvqu7xP\n35//fb2j41xf8jzn4Wcf1mX3Xqb2r7Xr/B+dr2VPLtO+A/vKaxhQA865jc65pbmvjenvAKqjkZ57\niT6v5JyvbBe37lGcvj4/kJgm3P+U8vsp1o/eeqtvU1zbon3c9On+2swK90fT++ICsuCzop85Zgwz\nS6gNAifUrbhF76IjUL29hSNewdoOcaNaaZ3AqlWFAUlcTnlvb2FwNnd2n8554X7/4pQp0jveMeSR\nreDzp071HU5Hh/8+OlcXolgaQ1+fdO47D9e1H/yIfrNouXTjM9K/f0Xa/sbXjhk44Eubz+uap47F\nHVr0s0V6tPdRuXIWgwKqwMzuNrMrYvZfaWY/yqJNaC2NtIBq0K8cc0zh/gMH/GK1e/ZIZ56ZvLBt\nuD8bEfnX3+jRgwONCRMKj4luS8X71Y9/3J8vaNsll+SPiQ4uLlvmr+3YYws/q61t8PmKDUxG9z/z\nDDNLqA2KQ6CuRYs9RNeYaG8vXhAi7WHQ6Ov79+cDkoceih9da2+PPDB7U5e0JrcIxUc/Org3qkCx\nB3KnTStsb7R94d+Dly9tfszpj+pD192hH2z4gXa85Icdt+/drptW3aSbVt2ky2Zdpm/O/eaQ2w6U\n4W2SPhuz/35JnxrmtqAFNUqxnHBBoqRZo717fR8RVygi2l9MmZIvA16stHl0Bii6LRXvZx9/vPC4\ntWt9HxacK66P6+jwz/KGt6PKWaMRqAVmnFDXojf78AOmwWhTsZmjJUuSU+aCEapoOdbgBh/toMaN\ni+lYQ9X0dPHFZV9fOdJGR4unmZiOGzNTN/3FTdr6v3xp8w+98UMaPTJf2vxtU99W1bYCJThC0v6Y\n/fskvW6Y24IWVIvnXmpRcCKcDrdnT36ZibhAJhDtD6L9RfiZo2LXvnNn4fb69YOvq9TUdOcGp/NF\nf1dpfTZQD5hxQl2LjmYFN/ukY4KZo0svTR6Bcs6XPY2uWj4wkP+8oGTq9Ok+naCgc9mwQXr0Uf/z\nGWdIJ59c9vWVI210NPp7GDfOpyuGjx01cpTmnTRP806ap90v79Zdv71LP/7dj/WBkz9Q07YDMTZI\nukDSFyP7L5T0u+FvDjB0tVgS49lnC7eDynGHH+5nmeJEA6VKZtfiyopHZ7SKzfTMmFFYYjwsCOqi\nv6tonx0EVqUu+AsMBwIn1LVSbvbBMatW+aApkPag74IF0po1hfuiq6EHJVNHj465YVd5tiltVfi0\nVIS431VSJ9N2WJsuO/0yXXb6ZUNuO1CB6yR1m9k0ST/P7XuXpE5JH86sVcAQ1KLgxPORYqivvloY\n0ARGjPBfhx02uFBEJals4T5l69bCUt9p17V0aXy/LOWDurTf1XCsywiUi1Q91LW0VIq+vvyN/bDD\nCl+rpDTr6NGD98Ueu3+/9L3v+Z9HjZIuvDD5ZCUYamlcyq2ikTjnlkr6oKQTJN0i6WuSOiS92zn3\nkyzbBlSqFgUn4ookxDl4MF+MIbweU1g5qYThPqVYNbu09555ZuH+cMp7uenn9Vz1EK2DGSc0tGhB\nhCC1bsaM9FSEiRMHj9rNmOHjoPDq41JMJ/Hgg9K2bf7nuXPjyw2ViU4CrcY5d6+ke7NuB1CpcKbA\nhAk+cAlKhpfSDxXzxBPSOedIL788eNHaUhTrPyqdxam0kEZSJkS56ef1XPUQrYPACQ0n3FFt3Vr4\nWpBaN2pU+oxLtDMaO9bv6+31zzSZ+YdjYzuJ7343/3OVikLQSaDVmNmfyC98e7ykrzrndpvZTEl9\nzrmtye8GshcNRMJK6Yf6+qR58/JV6GbM8Glu55wzeDmMYsykI44oPL5Y/xE3QBdNE7/1VumyywYH\nO5WkySW9r5L0cyBrBE5oOIPLbg9WymzNrl2F2/v355956unxVX02bYp54wsvSPfc43+eMEF63/vS\nT1YCOgm0EjObLuk/JPVLOk7StyXtljRf0rGSalumEkiR9typlNzXrF3rU+GSgqfos7arV0snnFB6\n0CT59Y9Wry6t/4gboIsGf+GgLWlWKvj9bNniF6tta/MFnLq7/SBk2u8uDSXGUY94xgkNp5SgqJTZ\nmkqegZIk/fjH0iuv+J87O4s/GFWmuGeUalHaFqgTN0q63Tl3oqRXQvvvk1/jCchUKc+dJvUjAwPx\nC5WH7+nRRd2l+KBpxIh8qe7oeoYdHfkF4ydO9AHbscf6Yq/hPqOvT9q3z6cSjhkjzZ4dv6THyy8X\nbq9a5Z9VOuOMwr4o+P1s3uzbvHlz/vc01Gd2gXpF4ISGk9RRHXJI6es/RNefmDEj+TxBh/fIJ4dv\n7aZKOh+CLTSI2ZKWxOzfKmnyMLcFGCQaUARr/BVbx2j27PzzTcU+I3pP3727tLYcPOgDmB07/NIY\ncesdnXeen3kaGPBfa9YU9hkLFhS+Pnq0nxmKrll46KGF2/v3+/etWVPYFxUbXOztjf/d0RehGZCq\nh4bT3e3rMTz+uC/LGn5W6cwzB68DUSxdIJoGsH17cqrDggXS1hVP6Sz9SpL09GEn67hoqaEqq6Rg\nBCVc0SAGFL/Q7Rsk7YjZDwyrStYxmjOnMJU8rVJcW5v/3FdfTW9PUDFv7lyfMR61fv3gfeHzxfUn\nCxYUznCNG+eDw8ceS25L0E/GlUUPrjn6u6MvQjNgxgkNZ9IkP1I2MJAPmsaMiZ9pio7uzZtXfDYm\nSHVob/edwvz5ha/39kof1fde277rsIXJS7dXQSWlbanOhwbxU0mfNbNRuW1nZsdK+oqku7NrFuCF\nZ5PiZpLiZvejmQxxleLCOjr8TFXYyJHJ7Yqm0iUJny+uP4n2D0ceGR+UxX1ucK1Tp/qAa+rU/DV3\nd6fPvgGNiBknNKToDfiYY+JnmlatKjxu/fp8mfG42Zik2Zr2yU4X9/g0vYMyPXL8RVW8onjVWO2d\n6nyoU5+S9GNJ2yUdJulXktolrZT0vzNsFyApP5i2YMHgCq5xRRXmzvWDekkFEYrd08P7lizx6zAF\n2489Ju3dm/+M6JqFgenTCwtNjB1b2GfEnXv+/Pj+Im4mafRoH+iVWmnv9NMHz76VUnADqGcETqg7\npdxY04KDUirvSfkArFigFQ7QfnrNSrXN3ShJWjf+z3TrvVNKvKLKDXW1d6rzoV455/ol/bmZzZE0\nXdIRkn7jnHsw25YBedG+ZMwYHxB0d0tnn1147Nq1+SyIYmnS4Xt6eAH39nZp5cp8UaCw++7zQdnL\nL/ugaeXKwe3s6/MJEMEsz/Tp/lmocN8Z158kBXLR9Qxnzy6vPyoWqJFKjkZG4IS6U8ozOmnBQXRG\n6pBD/PNPr75aOCIXBFzFAq2tW30KRne3NGlpvijE6f90sVSno2SUcEU9M7OzJU1wzi2TJOfcQ2Y2\nTdKVkg43s59I+h/OuYGkzwGGQ1J2Q3QAL7o24KpVof5j0uBBwXB/1NPjK+Gdfrr00kvSo4/m919+\neXr6XFD4ITB6dGkzOXGL6wZ9SNpzv2ni+iJSydHoCJxQd0q5saatpB7t0IKiEcU6gmI374EBH1C9\nador2vjSDzVe0sHDx2oEtVWBSn1W0i8lLZMkM/tTSf8i6buSfi/pCknPSfp8Ns0D8pKyG8IDeFu3\nFs7OSL6YQ1CB7qGHBg8KRp8BCvqb6KOzweK4UUmLwW/Z4oO2tJS4pIHKUgbhyk29I5UcjY7ACXWn\nlBtr2qxUsRmpuI6gr29wOdYxYwo7wXe8uFTj9UdJ0vKxC/TeI46o8OqAlneqpGtD2xdKWu2c+4Qk\nmdmzkr4gAifUgaTshnB/Eq2mFxYMzJU6u5I2MBhISknftcuvqyQlp8QNdQao3CqupJKj0dVF4GRm\nn5R0ufzaHevl0zTWJL8LzaqUG2vazb6cdLW4cqynnFKY9nCx8ml6PzjkYr23tI8GMNh/kxR+iuPt\nku4Pba+RVPsHCIESRPuSoJJeb680YYKfHdq50y88O3u2D1h27CjsU4LBv+ig4IwZ0qhRPl0vXI58\nxAi/blP4uDhJQU60vHmxY6Nt2rHDVwkstXBDuYEXqeRodJkHTmZ2gaSvSfobSaslLZK03Mze4Jzb\nmWnjkImkG2uQFhBX4ahSceVYly7NB2+ub7veu9f/u+4ZTdHTr39n5ScD0Cfp9ZKeNbPRkmZK+lzo\n9XGS9mXRMCBNdIYl0NPjS3Fv2lSYEj5hgrRvnw9GJkyQzjjDB1rhwOSMMwqfvQ0HTePG+f4ozsSJ\n8dXvpMHpfsX6yPBAZRDw7dlTeuEGUu/QajIPnOQDpSXOuTskycwuk/R+SR+XdEOWDUP9SapwVKm4\nG384eNvzf7p0yLUHJEm/7Pio7r6H5c+AIbhP0pfN7CpJH5T0kqT/DL0+XdKmLBoGpEmaUVm7dvBs\nTTiFLxxche3aVfwzjzwyvrBEd3dySl8wm5WUuRH9zAMHCmfKSknbI/UOrSbTwCm38OEsSf832Oec\nc2b2H5LOLvpGtKy09ZsqEXfjD3co9/V9Vyfljr34gb+q22p6QIO4VlK3/LpNL0pa6JwLJxZ9XNID\nWTQMza0aawhFB9rCBgb8a+HZmlJS2ZI+M5jBOe+8fPp4T49fzD0acI0Z4/vEUq8tOns2blz8uZOQ\neodWk/WM00RJI1WY767c9kmDD0erKyctIKmTDF7bskXavVtqa/ML+0VHCd+sDTpJvi7s+jGz9clP\nnMKCfcAQ5FKw32Zm4yW96Jw7EDnkw/IBFVBV5RYyiBMeaAs/4xStqhcESKX0WeHPnDjRzyTt2lU4\ng7N+feF71q/32RbRgKucgDAaxLW1+fWfmD0Ciss6cKrYokWLNH78+IJ9nZ2d6uzszKhFGA7lpAUk\ndZLRlL89e3wFougo4V/pzteO+dbAwoLSskAz6+rqUldXV8G+/v7+qn1+bgHcuP27q3YSIKQaawgV\nm2GJVtUL1gBcskS65JJ8SfF9+/wzUGkL05Yi6A+DhWqDcubz50t3313+QvIdHfRtQJqsA6edkg5I\nOiqy/yhJ25LeuHjxYs2cObNW7UKdCM8axY3EJY2qJXWSxTrMLVv89/Z2aXPPfn1U35MkvapR+qEu\nSHwv0EziBqLWrVunWbNmZdQiYGiGUsggLc2vWBBz6aV+MdpgNmr16soG36ZPLywgMX16PuCaNq3w\nunp7q7OQPIDBMn3K3Tm3T9JvJL0r2Gdmltt+OKt2oX4EN/+eHt/hrFnjfw5G1ZJEO8XwdrEOc3du\nrLu7W/r7Nz6oo+WjpHv1fu3SxMT3BmVqp03z37dvT7s6AMBw6e72xRmOP95/DwcKwf37uOOk173O\nf58zR3riCf996tR8XxTX/wRBzDHHFO7v7a3OTNeyZYVtX7Ys/1pcX1fKOYM2b9rkv5OCDqTLesZJ\nkm6UdLuZ/Ub5cuSHS7o9y0ahPiR1MNHXoiOCS5b40b640bRgpG3VKr+6e6CtzX+fNEm68dQ7OkPk\nkwAAHrdJREFUpN/57dUnL9TxryaPylUjfx4AWkk1CjaUKiklrlj69jnnFFaaC5S6LlIQ1FTybG6p\nWRZxM0fz51MmHKiFzAMn59xdZjZR0hflU/Qek/Qe59yObFuGelBKtaFANHA5+2xp48b4jiboQKN5\n6R0duR9eeEG65x7/c1ubrl//Pl0/Ormt1RhVBIBWUi8DTsXu1y+/HL+/vT0+6CuW/lZqSly0el6g\np0c69tj88htpz0iRhgfURuaBkyQ5526RdEvW7UC20jqhYtWGgvetWlX4eXv2SHPn+vzyYu8vVor8\nO3N+rKtzPeZLH+zU4aNToiaxECAAlKvcAadazVAVG6Q79FDpxVCNx/DagfPmDS4RvmpVfOBXajAY\nrZ4XFi7+kPZ5lAkHaqMuAidAKj7ymHbznzev8KHZsMcfzz+UGx29K/b5c+ZIL26cqQn6hC7QD/Wp\ntQv17RLazwgfAJSn3AGnocxQJQVdwf07ukTF3r3SY4/lP2P69Pz54kqEDweyGYDsZFocAgirNNUt\nKPM61POF96/XqbpU39JkbdMv9pz+2mtJBSB40BYABku6byYVbIgzlJTocLGhaIGH4P799NM+U/vp\np/32Cy8UfkZ00dlqmz69cHvsWD/LFZb2jBRFioDaIXBC3UiqglcOs3wnPGNG6eeL2/+KDlP70SbJ\nd0gnnphcWQlA4zOzp83sYOjrgJldmXW7GlUpAUupA05D6ScqCbqSzhcNcqLblYhWz+vpkZ55pvTg\nMul3DWDoSNVD3ag01W3GjHyeuSTNnp1/3mn79vRnpEptx4IFg6srkTIBNCUn6TOS/kWS5fbF1FZD\nKapZOKfY/TmahnfrrdJllxVu74iUnCol6Erql5YtK63PKue5rLhnk/r60tsZoEgRUFsETqgblT7M\nunRp8c5r0qTBK6ivXJk8qlmsHXEdEAUggKb1ItVdq6Oc55jSgoxi9+fos0/hMuLRbUkaOdIvWZEm\nqV8qtc8aauXAct5PkSKgtkjVQ8NLS/WoVupCtAMaN44CEEAT+7SZ7TSzdWZ2uZmNzLpBjaqc55gq\nvV9HB7aiZcSj2wcO+HX+iqnms0JbtiRvp52znFmkcp8ZA1AeZpzQkIqNSsbtL7XTShOXskEBCKAp\nfV3SOkm7JZ0j6cuSJku6PMtGNapysgkqTTWLzrQcdljhDFN0O+2zq7m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ETkCKBQukFSuk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\n/j8oecrTa7RRlwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3ebccbc828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 200\n",
"data1 = mvNormalRand(N,[0,4],[[0.9,0.2],[0.2,0.9]])\n",
"data2 = mvNormalRand(N,[-5,3],[[0.9,0.2],[-0.2,20]])\n",
"data = np.vstack((data1,data2))\n",
"\n",
"means = np.mean(data,axis=0)\n",
"\n",
"U,S,V = np.linalg.svd(data - means)\n",
"V = V.T\n",
"\n",
"plotOriginalAndTransformed(data,V)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sammon Mapping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Introductions to Sammon Mapping are found at\n",
" * [Sammon Mapping in Wikipedia](http://en.wikipedia.org/wiki/Sammon_mapping)\n",
" * [Sammon Mapping](http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf), by Paul Henderson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Sammon Mapping is one that maps each data sample $d_i$ to a location in two dimensions, $p_i$, such that distances between pairs of points are preserved. The objective defined by Sammon is to minimize the squared difference in distances between pairs of data points and their projections through the use of an objective function like\n",
"$$\n",
"\\sum_{i=1}^{N-1} \\sum_{j=i+1}^N \\left (\\frac{||d_i - d_j||}{s} - ||p_i - p_j|| \\right )^2\n",
"$$\n",
"The typical Sammon Mapping algorithm does a gradient descent on this function by adjusting all of the two-dimensional points $p_{ij}$. Each iteration requires computing all pairwise distances. \n",
"\n",
"One way to decrease this amount of work is to just work with a subset of points, perhaps picked randomly. To display all points, we just find an explicit mapping (function) that projects a data sample to a two-dimensional point. Let's call this $f$, so $f(d_i) = p_i$. For now, let's just use a linear function for $f$, so \n",
"$$\n",
"f(d_i) = d_i^T \\theta\n",
"$$\n",
"where $\\theta$ is a $D\\times 2$ matrix of coefficients. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To do this in python, let's start with calculating all pairwise distances. Let $X$ be our $N\\times D$ matrix of data samples, one per row. We can use a list comprehension to calculate the distance between each row in $X$ and each of the rows following that row."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1],\n",
" [ 4, 5],\n",
" [10, 20]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = np.array([ [0,1], [4,5], [10,20]])\n",
"X"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0, 1), (0, 2), (1, 2)]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N = X.shape[0] # number of rows\n",
"[(i,j) for i in range(N-1) for j in range(i+1,N)]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[array([-4, -4]), array([-10, -19]), array([ -6, -15])]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[X[i,:] - X[j,:] for i in range(N-1) for j in range(i+1,N)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ -4, -4],\n",
" [-10, -19],\n",
" [ -6, -15]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array([X[i,:] - X[j,:] for i in range(N-1) for j in range(i+1,N)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To convert these differences to distances, just"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5.65685425, 21.47091055, 16.15549442])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diffs = np.array([X[i,:] - X[j,:] for i in range(N-1) for j in range(i+1,N)])\n",
"np.sqrt(np.sum(diffs*diffs, axis=1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And to calculate the projection, a call to *np.dot* is all that is needed. Let's make a function to do the projection, and one to convert differences to distances."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def diffToDist(dX):\n",
" return np.sqrt(np.sum(dX*dX, axis=1))\n",
"\n",
"def proj(X,theta):\n",
" return np.dot(X,theta)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5.65685425, 21.47091055, 16.15549442])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diffToDist(diffs)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.3, 0.8],\n",
" [ 5.5, 4.8],\n",
" [ 16. , 18. ]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"proj(X,np.array([[1,0.2],[0.3,0.8]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, to follow the negative gradient of the objective function, we need its gradient, with respect to $\\theta$. With a little work, you can derive it to find\n",
"$$\n",
"\\begin{align*}\n",
"\\nabla_\\theta &= \\frac{1}{2} \\sum_{i=1}^{N-1} \\sum_{j=i+1}^N \\left (\\frac{||d_i - d_j||}{s} - ||p_i - p_j|| \\right )^2 \\\\\n",
" &= 2 \\frac{1}{2} \\sum_{i=1}^{N-1} \\sum_{j=i+1}^N \\left (\\frac{||d_i - d_j||}{s} - ||f(d_i;\\theta) - f(d_j;\\theta)|| \\right ) (-1) \\nabla_\\theta ||f(d_i;\\theta) - f(d_j;\\theta)||\\\\\n",
" &= - \\sum_{i=1}^{N-1} \\sum_{j=i+1}^N \\left (\\frac{||d_i - d_j||}{s} - ||f(d_i;\\theta) - f(d_j;\\theta)|| \\right ) \\frac{(d_i-d_j)^T (p_i - p_j)}{||p_i - p_j||} \n",
" \\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, we need to keep the differences around, in addition to the distances. First, let's write a function for the objective function, so we can monitor it, to make sure we are decrease it with each iteration. Let's multiply by $1/N$ so the values we get don't grow huge with large $N$."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def objective(X,proj,theta,s):\n",
" N = X.shape[0]\n",
" P = proj(X,theta)\n",
" dX = np.array([X[i,:] - X[j,:] for i in range(N-1) for j in range(i+1,N)])\n",
" dP = np.array([P[i,:] - P[j,:] for i in range(N-1) for j in range(i+1,N)])\n",
" return 1/N * np.sum( (diffToDist(dX)/s - diffToDist(dP))**2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now for the gradient\n",
"$$\n",
"\\begin{align*}\n",
"\\nabla_\\theta &= - \\sum_{i=1}^{N-1} \\sum_{j=i+1}^N \\left (\\frac{||d_i - d_j||}{s} - ||f(d_i;\\theta) - f(d_j;\\theta)|| \\right ) \\frac{(d_i-d_j)^T (p_i - p_j)}{||p_i - p_j||} \n",
" \\end{align*}\n",
"$$\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def gradient(X,proj,theta,s):\n",
" N = X.shape[0]\n",
" P = proj(X,theta)\n",
" dX = np.array([X[i,:] - X[j,:] for i in range(N-1) for j in range(i+1,N)])\n",
" dP = np.array([P[i,:] - P[j,:] for i in range(N-1) for j in range(i+1,N)])\n",
" distX = diffToDist(dX)\n",
" distP = diffToDist(dP)\n",
" return -1/N * np.dot((((distX/s - distP) / distP).reshape((-1,1)) * dX).T, dP)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This last line has the potential for dividing by zero! Let's avoid this, in a very ad-hoc manner, by replacing zeros in *distP* by its smallest nonzero value"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def gradient(X,proj,theta,s):\n",
" N = X.shape[0]\n",
" P = proj(X,theta)\n",
" dX = np.array([X[i,:] - X[j,:] for i in range(N-1) for j in range(i+1,N)])\n",
" dP = np.array([P[i,:] - P[j,:] for i in range(N-1) for j in range(i+1,N)])\n",
" distX = diffToDist(dX)\n",
" distP = diffToDist(dP)\n",
" minimumNonzero = np.min(distP[distP>0])\n",
" distP[distP==0] = minimumNonzero\n",
" return -1/N * np.dot((((distX/s - distP) / distP).reshape((-1,1)) * dX).T, dP)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"s 0.976151985787\n",
"theta\n",
" [[-0.21555675 0.97828829]\n",
" [-0.98868946 -0.21326493]]\n"
]
},
{
"data": {
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KFStmWtanT59APn8CAAAAwNnFxsYqNjY207JDhw75lMbDc+SyMLNPJW1zzg3MZh3PkQMA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\nODUaCSC8RERESNJ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q1KkyM917771+RwHCXmTxSDX7RTO90v0V/abNbzRr8yy9suIVv2Oh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\nMzOrzB+uGYeht5W7OgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3ebccb4128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = 8\n",
"X = np.random.multivariate_normal([2,3], 0.5*np.eye(2), n)\n",
"X = np.vstack((X,\n",
" np.random.multivariate_normal([1,-1], 0.2*np.eye(2), n)))\n",
"X = X - np.mean(X,axis=0)\n",
"s = 0.5 * np.sqrt(np.max(np.var(X,axis=0)))\n",
"print('s',s)\n",
"\n",
"# theta = np.random.uniform(-1,1,(2,2))\n",
"# theta = np.eye(2) + np.random.uniform(-0.1,0.1,(2,2))\n",
"u,svalues,v = np.linalg.svd(X)\n",
"v = v.T\n",
"theta = v[:,:2]\n",
"\n",
"nIterations = 10\n",
"vals = []\n",
"for i in range(nIterations):\n",
" theta -= 0.001 * gradient(X,proj,theta,s)\n",
" v = objective(X,proj,theta,s)\n",
" vals.append(v)\n",
"\n",
"# print('X\\n',X)\n",
"# print('P\\n',proj(X,theta))\n",
"print('theta\\n',theta)\n",
"plt.figure(figsize=(10,15))\n",
"plt.subplot(3,1,(1,2))\n",
"P = proj(X,theta)\n",
"mn = 1.1*np.min(X)\n",
"mx = 1.1*np.max(X)\n",
"plt.axis([mn,mx,mn,mx])\n",
"#strings = [chr(ord('a')+i) for i in range(X.shape[0])]\n",
"strings = [i for i in range(X.shape[0])]\n",
"for i in range(X.shape[0]):\n",
" plt.text(X[i,0],X[i,1],strings[i],color='black',size=15)\n",
"for i in range(P.shape[0]):\n",
" plt.text(P[i,0],P[i,1],strings[i],color='green',size=15)\n",
"plt.title('2D data, Originals in black')\n",
"\n",
"plt.subplot(3,1,3)\n",
"plt.plot(vals)\n",
"plt.ylabel('Objective Function');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's watch the mapping develop. One way to do this is to save the values of $\\theta$ after each iteration, then use *interact* to step through the interations."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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oAgAAIBQkmgAAAAgFiSYAAABCQaIJAACAUJBoAgAAIBQkmgAAAAgFiSYAAABC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\nJzNn+ufWrVMaBgCgciHRhK/lmj8//zQ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h8KvKZTHGGEJ4CnCcFeWGurQyduiQhMyZM+Ht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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3eb99baa58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.html.widgets import interact\n",
"\n",
"n = 10\n",
"X = np.random.multivariate_normal([2,3], 0.5*np.eye(2), n)\n",
"X = np.vstack((X,\n",
" np.random.multivariate_normal([1,-1], 0.2*np.eye(2), n)))\n",
"X = X - np.mean(X,axis=0)\n",
"s = 0.5 * np.sqrt(np.max(np.var(X,axis=0)))\n",
"print('s',s)\n",
"\n",
"u,svalues,v = np.linalg.svd(X)\n",
"V = v.T\n",
"theta = V[:,:2]\n",
"\n",
"theta = (np.random.uniform(size=((2,2)))-0.5) * 10\n",
"\n",
"thetas = [theta] # store all theta values\n",
"\n",
"nIterations = 200\n",
"vals = []\n",
"for i in range(nIterations):\n",
" theta = theta - 0.02 * gradient(X,proj,theta,s)\n",
" v = objective(X,proj,theta,s)\n",
" thetas.append(theta.copy())\n",
" vals.append(v)\n",
"\n",
"\n",
"mn = 1.5*np.min(X)\n",
"mx = 1.5*np.max(X)\n",
"\n",
"strings = [i for i in range(X.shape[0])]\n",
"\n",
"@interact(i=(0,nIterations-1,1))\n",
"def plotIteration(i):\n",
" #plt.cla()\n",
" plt.figure(figsize=(8,10))\n",
" theta = thetas[i]\n",
" val = vals[i]\n",
" P = proj(X,theta)\n",
" plt.axis([mn,mx,mn,mx])\n",
" for i in range(X.shape[0]):\n",
" plt.text(X[i,0],X[i,1],strings[i],color='black',size=15) \n",
" for i in range(P.shape[0]):\n",
" plt.text(P[i,0],P[i,1],strings[i],color='red',size=15) \n",
" plt.title('2D data, Originals in black. Objective = ' + str(val))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| lgpl-3.0 |
Sitin/neuromatriarchy | neuromatriarchy.ipynb | 1 | 1420602 | null | artistic-2.0 |
astarostin/ALTA-2016-Challenge | alta2016.ipynb | 1 | 27886 | {
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"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>AUrl</th>\n",
" <th>BUrl</th>\n",
" <th>Outcome</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>www.allmusic.com/artist/sufjan-stevens-mn00005...</td>\n",
" <td>www.rollingstone.com/music/artists/sufjan-stevens</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>www.imdb.com/name/nm0346360</td>\n",
" <td>https://www.fandor.com/filmmakers/director-jos...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>www.imdb.com/name/nm1017334</td>\n",
" <td>https://www.linkedin.com/in/juno-temple-32152375</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>https://www.linkedin.com/in/kathywolfe</td>\n",
" <td>https://twitter.com/RepWolfeMoore</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>https://www.linkedin.com/in/mipetersen</td>\n",
" <td>https://www.researchgate.net/profile/Mikael_Pe...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AUrl \\\n",
"Id \n",
"0 www.allmusic.com/artist/sufjan-stevens-mn00005... \n",
"1 www.imdb.com/name/nm0346360 \n",
"2 www.imdb.com/name/nm1017334 \n",
"3 https://www.linkedin.com/in/kathywolfe \n",
"4 https://www.linkedin.com/in/mipetersen \n",
"\n",
" BUrl Outcome \n",
"Id \n",
"0 www.rollingstone.com/music/artists/sufjan-stevens 1 \n",
"1 https://www.fandor.com/filmmakers/director-jos... 1 \n",
"2 https://www.linkedin.com/in/juno-temple-32152375 1 \n",
"3 https://twitter.com/RepWolfeMoore 0 \n",
"4 https://www.researchgate.net/profile/Mikael_Pe... 0 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"pairs_train = pd.read_csv('data/alta16_kbcoref_train_pairs.csv', sep=',', index_col='Id')\n",
"labels_train = pd.read_csv('data/alta16_kbcoref_train_labels.csv', sep=',', index_col='Id')\n",
"data_train = pairs_train.join(labels_train, how='inner')\n",
"pairs_test = pd.read_csv('data/alta16_kbcoref_test_pairs.csv', sep=',', index_col='Id')\n",
"\n",
"data_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>Outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.501255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Outcome\n",
"count 200.000000\n",
"mean 0.500000\n",
"std 0.501255\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 0.500000\n",
"75% 1.000000\n",
"max 1.000000"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f4724779850>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f47246c0bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_train.Outcome.hist()\n",
"plt.title(\"Training Label Distribution\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Посмотрим, разделяется ли выборка по косинусному расстоянию между tf ifd векторами url-в"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Z8G62TDjEccWKFQlG1Vyx9Am4+/eBK8849rUzXv8l8JdxnK+VhAtNnX0vkaUFqETSqqen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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4717dc8150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import tokenization as prep\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.metrics import pairwise\n",
"\n",
"vocabulary = set()\n",
"pairs_train.apply(lambda row: vocabulary.update(prep.get_url_tokens(row['AUrl'])), axis=1)\n",
"pairs_train.apply(lambda row: vocabulary.update(prep.get_url_tokens(row['BUrl'])), axis=1)\n",
"pairs_test.apply(lambda row: vocabulary.update(prep.get_url_tokens(row['AUrl'])), axis=1)\n",
"pairs_test.apply(lambda row: vocabulary.update(prep.get_url_tokens(row['BUrl'])), axis=1)\n",
"\n",
"# prepare train data\n",
"tfidf = TfidfVectorizer(tokenizer=prep.get_url_tokens, vocabulary=vocabulary)\n",
"td_matrix_a = tfidf.fit_transform(pairs_train['AUrl'])\n",
"td_matrix_b = tfidf.fit_transform(pairs_train['BUrl'])\n",
"\n",
"distances = []\n",
"for i in xrange(td_matrix_a.shape[0]):\n",
" dist = pairwise.cosine_similarity(td_matrix_a[i], td_matrix_b[i])\n",
" distances.append(dist[0][0])\n",
"\n",
"distances = np.array(distances)\n",
"class_0 = np.where(labels_train['Outcome'] == 0)[0]\n",
"class_1 = np.where(labels_train['Outcome'] == 1)[0]\n",
"\n",
"plt.scatter(np.zeros(class_0.shape[0]), distances[class_0], c='b', marker='o')\n",
"plt.scatter(np.ones(class_1.shape[0]), distances[class_1], c='r', marker='o')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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
}
| gpl-3.0 |
iurilarosa/thesis | codici/.ipynb_checkpoints/Verifiche HWI-checkpoint.ipynb | 1 | 578 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import scipi"
]
}
],
"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": 2
}
| gpl-3.0 |
dali-ml/dali-cython | notebooks/LSTM.ipynb | 2 | 22576 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from dali.core import LSTM, Mat, LSTMState, StackedLSTM\n",
"\n",
"import pickle\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"float32\n",
"[[ 1. 2. 3.]]\n",
"[[ 4. 2. 1.]]\n"
]
}
],
"source": [
"s = LSTMState(Mat([1,2,3]), Mat([4,2,1]))\n",
"print(s.dtype)\n",
"print(s.memory.w)\n",
"print(s.hidden.w)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l = LSTM(2, 5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" [ 1.000 2.000]\n",
"]\n",
"\n",
"[\n",
" [ 0.000 0.000 0.000 0.000 0.000]\n",
"]\n",
"\n",
"[\n",
" [ 0.000 0.000 0.000 0.000 0.000]\n",
"]\n",
"\n",
"[\n",
" [ 0.150 0.075 0.142 -0.004 -0.001]\n",
"]\n",
"\n",
"[\n",
" [ 0.212 0.312 0.254 -0.008 -0.002]\n",
"]\n",
"\n"
]
}
],
"source": [
"i = Mat([[1, 2]])\n",
"h = l.initial_states()\n",
"print(i.__repr__())\n",
"print(h.hidden.__repr__())\n",
"print(h.memory.__repr__())\n",
"h = l.activate(i, h)\n",
"print(h.hidden.__repr__())\n",
"print(h.memory.__repr__())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Activate sequence for 3 different inputs states and one hidden states\n",
"[[ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]\n",
" [ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]\n",
" [ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]]\n",
"Activate sequence for 3 different hidden states and three different inputs\n",
"[[ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]\n",
" [ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]\n",
" [ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]]\n",
"Activate sequence for 3 different hidden states and one input (!)\n",
"[[ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]\n",
" [ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]\n",
" [ 0.31556997 -0.05376546 -0.0430177 -0.00521236 -0.29317772]]\n"
]
}
],
"source": [
"print(\"Activate sequence for 3 different inputs states and one hidden states\")\n",
"r1 = l.activate_sequence([Mat(3, 2) for i in range(10)], l.initial_states()).memory\n",
"print(r1.w)\n",
"print(\"Activate sequence for 3 different hidden states and three different inputs\")\n",
"r2 = l.activate_sequence([Mat(3, 2) for i in range(10)], LSTMState(Mat(3,5), Mat(3,5))).memory\n",
"print(r2.w)\n",
"print(\"Activate sequence for 3 different hidden states and one input (!)\")\n",
"r3 = l.activate_sequence([Mat(1, 2) for i in range(10)], LSTMState(Mat(3,5), Mat(3,5))).memory\n",
"print(r3.w)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"INPUTS = [2,3]\n",
"HIDDEN_SIZE = 5\n",
"NUM_CHILDREN = 3"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = LSTM(INPUTS, HIDDEN_SIZE, NUM_CHILDREN, memory_feeds_gates=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StackedInputLayer in=[2, 3, 5, 5, 5], out=5>\n",
"[<StackedInputLayer in=[2, 3, 5, 5, 5], out=5>, <StackedInputLayer in=[2, 3, 5, 5, 5], out=5>, <StackedInputLayer in=[2, 3, 5, 5, 5], out=5>]\n",
"5\n",
"[2, 3]\n",
"<StackedInputLayer in=[2, 3, 5, 5, 5], out=5>\n",
"3\n",
"True\n",
"[[ 0.36870342 -0.03744119 -0.00326931 0.09401595 -0.29351875]]\n"
]
}
],
"source": [
"idx = random.randint(0, len(l.parameters()) - 1)\n",
"print(l.cell_layer)\n",
"print(l.forget_layers)\n",
"print(l.hidden_size)\n",
"print(l.input_sizes)\n",
"print(l.input_layer)\n",
"print(l.num_children)\n",
"print(l.memory_feeds_gates)\n",
"print(l.parameters()[3].w)\n",
"with open(\"/tmp/lstm_test_dali.dali\", \"wb\") as f:\n",
" pickle.dump(l,f)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named 'test_dali'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-780854079d07>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtest_dali\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLSTM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMat\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/tmp/lstm_test_dali.dali\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: No module named 'test_dali'"
]
}
],
"source": [
"import pickle\n",
"from test_dali import LSTM, Mat\n",
"\n",
"\n",
"with open(\"/tmp/lstm_test_dali.dali\", \"rb\") as f:\n",
" l_pickled = pickle.load(f)\n",
"print(\"==== PICKLED ====\")\n",
"print(l_pickled.cell_layer)\n",
"print(l_pickled.forget_layers)\n",
"print(l_pickled.hidden_size)\n",
"print(l_pickled.input_sizes)\n",
"print(l_pickled.input_layer)\n",
"print(l_pickled.num_children)\n",
"print(l_pickled.memory_feeds_gates)\n",
"print(l_pickled.parameters()[3].w)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" [ 0.028 -0.025 -0.020 -0.008 -0.049]\n",
"]\n",
"\n",
"[\n",
" [ 0.060 -0.047 -0.039 -0.014 -0.102]\n",
"]\n",
"\n"
]
}
],
"source": [
"hs = [l_pickled.initial_states() for _ in range(l_pickled.num_children)]\n",
"i = [Mat(1, ipt) for ipt in l_pickled.input_sizes]\n",
"\n",
"h_combined = l_pickled.activate(i, hs)\n",
"print(h_combined.hidden.__repr__())\n",
"print(h_combined.memory.__repr__())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'l' 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-7-90842db0b0b3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWcells_to_forgets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'l' is not defined"
]
}
],
"source": [
"l.Wcells_to_forgets[0]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l = StackedLSTM([1,2], [5,4], shortcut=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<dali.core.LSTMState at 0x7f564c946f80>,\n",
" <dali.core.LSTMState at 0x7f564c946da0>]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l.activate(\n",
" [\n",
" Mat(5,1),\n",
" Mat(5,2),\n",
" ], l.initial_states()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StackedInputLayer in=[1, 2, 5], out=5>\n",
"[<StackedInputLayer in=[1, 2, 5], out=5>]\n",
"5\n",
"[1, 2]\n",
"<StackedInputLayer in=[1, 2, 5], out=5>\n",
"1\n",
"False\n",
"[[-0.16443005 0.22159122 0.24860647 0.32551721 0.01914411]\n",
" [ 0.19727443 0.26258233 0.17914623 -0.17491819 0.1341583 ]]\n",
"True\n"
]
}
],
"source": [
"print(l.cells[0].cell_layer)\n",
"print(l.cells[0].forget_layers)\n",
"print(l.cells[0].hidden_size)\n",
"print(l.cells[0].input_sizes)\n",
"print(l.cells[0].input_layer)\n",
"print(l.cells[0].num_children)\n",
"print(l.cells[0].memory_feeds_gates)\n",
"print(l.cells[0].parameters()[5].w)\n",
"print(l.shortcut)\n",
"with open(\"/tmp/stacked_lstm_test_lol.dali\", \"wb\") as f:\n",
" pickle.dump(l,f)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==== PICKLED ====\n",
"<StackedInputLayer in=[1, 2, 5], out=5>\n",
"[<StackedInputLayer in=[1, 2, 5], out=5>]\n",
"5\n",
"[1, 2]\n",
"<StackedInputLayer in=[1, 2, 5], out=5>\n",
"1\n",
"False\n",
"[[-0.16443005 0.22159122 0.24860647 0.32551721 0.01914411]\n",
" [ 0.19727443 0.26258233 0.17914623 -0.17491819 0.1341583 ]]\n",
"True\n"
]
}
],
"source": [
"import pickle\n",
"from dali.core import StackedLSTM, Mat\n",
"\n",
"with open(\"/tmp/stacked_lstm_test_lol.dali\", \"rb\") as f:\n",
" l_pickled = pickle.load(f)\n",
"print(\"==== PICKLED ====\")\n",
"\n",
"print(l_pickled.cells[0].cell_layer)\n",
"print(l_pickled.cells[0].forget_layers)\n",
"print(l_pickled.cells[0].hidden_size)\n",
"print(l_pickled.cells[0].input_sizes)\n",
"print(l_pickled.cells[0].input_layer)\n",
"print(l_pickled.cells[0].num_children)\n",
"print(l_pickled.cells[0].memory_feeds_gates)\n",
"print(l_pickled.cells[0].parameters()[5].w)\n",
"print(l.shortcut)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'l_pickled' 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-10-f70ba0ce9ad1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml_pickled\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitial_states\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'l_pickled' is not defined"
]
}
],
"source": [
"l_pickled.initial_states()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<LSTM inputs=[1, 2], hidden_size=5>, <LSTM inputs=[5], hidden_size=4>]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l_pickled.cells"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<test_dali.LSTMState at 0x7f72e743a968>,\n",
" <test_dali.LSTMState at 0x7f72e743ad00>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l_pickled.activate([\n",
" Mat([[1]]),\n",
" Mat([[1, 2]])\n",
"], l_pickled.initial_states())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "Activating LSTM stack of size 2 with different number of states 0",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-e0b9f00b5030>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mMat\u001b[0m\u001b[1;33m(\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[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mMat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\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[1;32m----> 4\u001b[1;33m ], [])\n\u001b[0m",
"\u001b[1;32m/home/sidor/projects/less_important/dali-cython-stub/dali/layers/LSTM.pyx\u001b[0m in \u001b[0;36mtest_dali.StackedLSTM.activate (test_dali.cpp:68105)\u001b[1;34m()\u001b[0m\n\u001b[0;32m 808\u001b[0m \u001b[0minputs_vector_float\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmats_to_vec_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 809\u001b[0m return WrapLSTMStates_float(\n\u001b[1;32m--> 810\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[1;33m<\u001b[0m\u001b[0mCStackedLSTM\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfloat\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[0mStackedLSTM\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayerinternal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhiddens_vector_float\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs_vector_float\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m<\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m>\u001b[0m \u001b[0mdrop_prob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 811\u001b[0m )\n\u001b[0;32m 812\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mMat\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mRuntimeError\u001b[0m: Activating LSTM stack of size 2 with different number of states 0"
]
}
],
"source": [
"l_pickled.activate([\n",
" Mat([[1]]),\n",
" Mat([[1, 2]])\n",
"], [])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"h1 = l_pickled.cells[0].activate(\n",
" [Mat([[1]]),\n",
" Mat([[1, 2]])],\n",
" l_pickled.cells[0].initial_states()\n",
")\n",
"h2 = l_pickled.cells[1].activate(\n",
" h1.hidden,\n",
" l_pickled.cells[1].initial_states()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[\n",
" [ -0.007 -0.068 -0.095 0.145 -0.057]\n",
"]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h1.hidden"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"new_state = l_pickled.activate([\n",
" Mat([[1]]),\n",
" Mat([[1, 2]])\n",
"],\n",
"l_pickled.initial_states(),\n",
"0.5\n",
")\n",
"new_state[0].memory"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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"metadata": {},
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},
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}
],
"source": [
"plt.matshow(l_pickled.cells[0].forget_layers[0].matrices[0].w)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?l.activate_sequence"
]
},
{
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"execution_count": null,
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"source": []
}
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| mit |
mne-tools/mne-tools.github.io | 0.23/_downloads/afbdbca3a62dcdd6fc01894cd11b97b4/20_reading_eeg_data.ipynb | 1 | 9475 | {
"cells": [
{
"cell_type": "code",
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"metadata": {
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"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Importing data from EEG devices\n\nMNE includes various functions and utilities for reading EEG data and electrode\nlocations.\n\n\n## BrainVision (.vhdr, .vmrk, .eeg)\n\nThe BrainVision file format consists of three separate files:\n\n1. A text header file (``.vhdr``) containing meta data.\n2. A text marker file (``.vmrk``) containing information about events in the\n data.\n3. A binary data file (``.eeg``) containing the voltage values of the EEG.\n\nBoth text files are based on the `INI format <https://en.wikipedia.org/wiki/INI_file>`_\nconsisting of\n\n* sections marked as ``[square brackets]``,\n* comments marked as ``; comment``,\n* and key-value pairs marked as ``key=value``.\n\nBrain Products provides documentation for their core BrainVision file format.\nThe format specification is hosted on the\n`Brain Products website <https://www.brainproducts.com/productdetails.php?id=21&tab=5>`_.\n\nBrainVision EEG files can be read using :func:`mne.io.read_raw_brainvision`,\npassing the ``.vhdr`` header file as the argument.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Renaming BrainVision files can be problematic due to their\n multi-file structure. See this\n `example <https://mne.tools/mne-bids/stable/auto_examples/rename_brainvision_files.html#sphx-glr-auto-examples-rename-brainvision-files-py>`_\n for instructions.</p></div>\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>For *writing* BrainVision files, you can use the Python package\n `pybv <https://pypi.org/project/pybv/>`_.</p></div>\n\n\n\n## European data format (.edf)\n\n`EDF <http://www.edfplus.info/specs/edf.html>`_ and\n`EDF+ <http://www.edfplus.info/specs/edfplus.html>`_ files can be read using\n:func:`mne.io.read_raw_edf`. Both variants are 16-bit formats.\n\nEDF+ files may contain annotation channels which can be used to store trigger\nand event information. These annotations are available in ``raw.annotations``.\n\nWriting EDF files is not supported natively yet. `This gist\n<https://gist.github.com/skjerns/bc660ef59dca0dbd53f00ed38c42f6be>`__ or\n`MNELAB <https://github.com/cbrnr/mnelab>`_ (both of which use\n`pyedflib <https://github.com/holgern/pyedflib>`_ under the hood) can be used\nto export any :class:`mne.io.Raw` object to EDF/EDF+/BDF/BDF+.\n\n\n\n## BioSemi data format (.bdf)\n\nThe `BDF format <http://www.biosemi.com/faq/file_format.htm>`_ is a 24-bit\nvariant of the EDF format used by EEG systems manufactured by BioSemi. It can\nbe imported with :func:`mne.io.read_raw_bdf`.\n\nBioSemi amplifiers do not perform \"common mode noise rejection\" automatically.\nThe signals in the EEG file are the voltages between each electrode and the CMS\nactive electrode, which still contain some CM noise (50 Hz, ADC reference\nnoise, etc.). The `BioSemi FAQ <https://www.biosemi.com/faq/cms&drl.htm>`__\nprovides more details on this topic.\nTherefore, it is advisable to choose a reference (e.g., a single channel like Cz,\naverage of linked mastoids, average of all electrodes, etc.) after importing\nBioSemi data to avoid losing signal information. The data can be re-referenced\nlater after cleaning if desired.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Data samples in a BDF file are represented in a 3-byte\n (24-bit) format. Since 3-byte raw data buffers are not presently\n supported in the FIF format, these data will be changed to 4-byte\n integers in the conversion.</p></div>\n\n\n\n## General data format (.gdf)\n\nGDF files can be read using :func:`mne.io.read_raw_gdf`.\n\n`GDF (General Data Format) <https://arxiv.org/abs/cs/0608052>`_ is a flexible\nformat for biomedical signals that overcomes some of the limitations of the\nEDF format. The original specification (GDF v1) includes a binary header\nand uses an event table. An updated specification (GDF v2) was released in\n2011 and adds fields for additional subject-specific information (gender,\nage, etc.) and allows storing several physical units and other properties.\nBoth specifications are supported by MNE.\n\n\n\n## Neuroscan CNT (.cnt)\n\nCNT files can be read using :func:`mne.io.read_raw_cnt`.\nChannel locations can be read from a montage or the file header. If read\nfrom the header, the data channels (channels that are not assigned to EOG, ECG,\nEMG or MISC) are fit to a sphere and assigned a z-value accordingly. If a\nnon-data channel does not fit to the sphere, it is assigned a z-value of 0.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Reading channel locations from the file header may be dangerous, as the\n x_coord and y_coord in the ELECTLOC section of the header do not necessarily\n translate to absolute locations. Furthermore, EEG electrode locations that\n do not fit to a sphere will distort the layout when computing the z-values.\n If you are not sure about the channel locations in the header, using a\n montage is encouraged.</p></div>\n\n\n\n## EGI simple binary (.egi)\n\nEGI simple binary files can be read using :func:`mne.io.read_raw_egi`.\nEGI raw files are simple binary files with a header and can be exported by the\nEGI Netstation acquisition software.\n\n\n\n## EGI MFF (.mff)\n\nEGI MFF files can be read with :func:`mne.io.read_raw_egi`.\n\n\n\n## EEGLAB files (.set, .fdt)\n\nEEGLAB .set files (which sometimes come with a separate .fdt file) can be read\nusing :func:`mne.io.read_raw_eeglab` and :func:`mne.read_epochs_eeglab`.\n\n\n\n## Nicolet (.data)\n\nThese files can be read with :func:`mne.io.read_raw_nicolet`.\n\n\n\n## eXimia EEG data (.nxe)\n\nEEG data from the Nexstim eXimia system can be read with\n:func:`mne.io.read_raw_eximia`.\n\n\n\n## Persyst EEG data (.lay, .dat)\n\nEEG data from the Persyst system can be read with\n:func:`mne.io.read_raw_persyst`.\n\nNote that subject metadata may not be properly imported because Persyst\nsometimes changes its specification from version to version. Please let us know\nif you encounter a problem.\n\n\n## Nihon Kohden EEG data (.eeg, .21e, .pnt, .log)\n\nEEG data from the Nihon Kohden (NK) system can be read using the\n:func:`mne.io.read_raw_nihon` function.\n\nFiles with the following extensions will be read:\n\n- The ``.eeg`` file contains the actual raw EEG data.\n- The ``.pnt`` file contains metadata related to the recording such as the\n measurement date.\n- The ``.log`` file contains annotations for the recording.\n- The ``.21e`` file contains channel and electrode information.\n\nReading ``.11d``, ``.cmt``, ``.cn2``, and ``.edf`` files is currently not\nsupported.\n\nNote that not all subject metadata may be properly read because NK changes the\nspecification sometimes from version to version. Please let us know if you\nencounter a problem.\n\n\n## XDF data (.xdf, .xdfz)\n\nMNE-Python does not support loading\n`XDF <https://github.com/sccn/xdf/wiki/Specifications>`_ files out of the box,\nbecause the inherent flexibility of the XDF format makes it difficult to\nprovide a one-size-fits-all function. For example, XDF supports signals from\nvarious modalities recorded with different sampling rates. However, it is\nrelatively straightforward to import only a specific stream (such as EEG\nsignals) using the `pyxdf <https://github.com/xdf-modules/pyxdf>`_ package.\nSee `ex-read-xdf` for a simple example.\n\nA more sophisticated version, which supports selection of specific streams as\nwell as converting marker streams into annotations, is available in\n`MNELAB <https://github.com/cbrnr/mnelab>`_. If you want to use this\nfunctionality in a script, MNELAB records its history (View - History), which\ncontains all commands required to load an XDF file after successfully loading\nthat file with the graphical user interface.\n\n\n## Setting EEG references\n\nThe preferred method for applying an EEG reference in MNE is\n:func:`mne.set_eeg_reference`, or equivalent instance methods like\n:meth:`raw.set_eeg_reference() <mne.io.Raw.set_eeg_reference>`. By default,\nthe data are assumed to already be properly referenced. See\n`tut-set-eeg-ref` for more information.\n\n\n## Reading electrode locations and head shapes for EEG recordings\n\nSome EEG formats (e.g., EGI, EDF/EDF+, BDF) contain neither electrode locations\nnor head shape digitization information. Therefore, this information has to be\nprovided separately. For that purpose, all raw instances have a\n:meth:`mne.io.Raw.set_montage` method to set electrode locations.\n\nWhen using locations of fiducial points, the digitization data are converted to\nthe MEG head coordinate system employed in the MNE software, see\n`coordinate_systems`.\n"
]
}
],
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"version": 3
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"file_extension": ".py",
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} | bsd-3-clause |
fcollonval/coursera_data_visualization | WritingAboutData.ipynb | 1 | 5340 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regression Modeling in Practice\n",
"# Assignment: Writing about your data\n",
"\n",
"Here is my first assignment of the [Regression Modeling in Practice online course](https://www.coursera.org/learn/regression-modeling-practice).\n",
"\n",
"I decided to use [Jupyter Notebook](http://nbviewer.jupyter.org/github/ipython/ipython/blob/3.x/examples/Notebook/Index.ipynb) as it is a pretty way to write code and present results.\n",
"\n",
"## Research question\n",
"\n",
"Using the [Gapminder database](http://www.gapminder.org/), I would like to see if an increasing Internet usage results in an increasing suicide rate. A study shows that other factors like unemployment could have a great impact.\n",
"\n",
"So for this assignment, the three following variables will be analyzed:\n",
"\n",
"- Internet Usage Rate (per 100 people)\n",
"- Suicide Rate (per 100 000 people)\n",
"- Employment Rate (% of the population of age 15+)\n",
"\n",
"As the Gapminder database is an aggregation of data from different sources, I will described the data separately for the different variables."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## About my data\n",
"\n",
"### Sample\n",
"\n",
"The sample comes from the [Gapminder database](http://www.gapminder.org/) (http://www.gapminder.org/). This is a non-profit organization founded in Stockholm to promote sustainable global development in order to achieve the United Nations Millenium Development Goals.\n",
"\n",
"The data are gathered for 215 areas (192 UN members, Serbia and Montenegro being aggregated + 24 areas). But not all indicators are available for all countries.\n",
"\n",
"Although the database provides time series for all indicators on an yearly base, the sample used for this class uses data of a certain year depending on the indicator :\n",
"\n",
"- Internet Usage Rate (per 100 people): data used are for 2010\n",
"- Suicide per 100 000 people: data used are for 2008\n",
"- Employment rate for people of age 15+: data used are for 2007\n",
"\n",
"### Procedure\n",
"\n",
"The three indicators are collected by different organizations :\n",
"\n",
"- Internet Usage Rate (per 100 people):\n",
" - Data from the World Bank (http://databank.worldbank.org/data/home.aspx)\n",
" - The data are computed as the weighted average of different sources: International Telecommunication Union, World Telecommunication/ICT Development Report and database, and World Bank estimates.\n",
"- Suicide per 100 000 people: data used are for 2008\n",
" - Data from the World Health Organization (WHO) (http://www.who.int/violence_injury_prevention/surveillance/databases/mortality/en/)\n",
" - The database is built on annual report by the 120 Member States from their civil registration systems. \n",
"- Employment rate for people of age 15+: data used are for 2007\n",
" - Data from the International Labour Organization (ILO) (http://www.ilo.org/emppolicy/lang--en/index.htm)\n",
" - ILO publishes every two years since 1999, 18 key indicators of the labour market including the employment rate.\n",
" - The data were collected by different methods depending on the countries. The precise list is available there http://www.ilo.org/ilostat. Those methods are Population census, Official estimate, Administrative records, Population register, Household surveys, Labour force survey, Household income/expenditure survey or Other household survey.\n",
" \n",
"### Measures\n",
"\n",
"All three indicators are constructed mainly on report from the member states.\n",
"\n",
"- The explanatory variable Internet Usage Rate (per 100 people):\n",
" - Definition : Internet users are defined as individuals who used the Internet int he last 12 months.\n",
" - Scale : 0 to 100\n",
" - Management : Suppression of countries with no-data provided\n",
"- The response variable Suicide, age adjusted, per 100 000 people:\n",
" - Definition : Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted. Combination of data from WHO Violence and Injury Prevention (VIP) and from WHO Global Burden of Disease.\n",
" - Scale : 0 to 100 000\n",
" - Management : Suppression of countries with no-data provided\n",
"- Another explanatory variable Employment rate for people of age 15+:\n",
" - Definiton : Percentage of total population, age above 15, that has been employed during the given year.\n",
" - Scale : 0% to 100%\n",
" - Management : Suppression of countries with no-data provided\n"
]
}
],
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jdfekete/progressivis | notebooks/PsBoardLocalFiles.ipynb | 1 | 3610 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from progressivis_nb_widgets.nbwidgets import PsBoard, Scatterplot\n",
"import pandas as pd\n",
"from progressivis.core import Scheduler, Every\n",
"from progressivis.table import Table\n",
"from progressivis.vis import MCScatterPlot\n",
"from progressivis.io import CSVLoader\n",
"#from progressivis.datasets import get_dataset\n",
"from progressivis.table.constant import Constant\n",
"import asyncio as aio\n",
"import threading\n",
"import os\n",
"\n",
"def _quiet(x): pass\n",
"\n",
"def _filter(df):\n",
" pklon = df['pickup_longitude']\n",
" pklat = df['pickup_latitude']\n",
" dolon = df['dropoff_longitude']\n",
" dolat = df['dropoff_latitude']\n",
"\n",
"\n",
" return df[(pklon > -74.08) & (pklon < -73.5) & (pklat > 40.55) & (pklat < 41.00) &\n",
" (dolon > -74.08) & (dolon < -73.5) & (dolat > 40.55) & (dolat < 41.00)]\n",
"\n",
"try:\n",
" s = scheduler\n",
"except NameError:\n",
" s = Scheduler()\n",
"PREFIX = '../../nyc-taxi/'\n",
"\n",
"SUFFIX = '.bz2'\n",
"\n",
"URLS = [\n",
" PREFIX+'yellow_tripdata_2015-01.csv'+SUFFIX,\n",
" PREFIX+'yellow_tripdata_2015-02.csv'+SUFFIX,\n",
" PREFIX+'yellow_tripdata_2015-03.csv'+SUFFIX,\n",
" PREFIX+'yellow_tripdata_2015-04.csv'+SUFFIX,\n",
" PREFIX+'yellow_tripdata_2015-05.csv'+SUFFIX,\n",
" PREFIX+'yellow_tripdata_2015-06.csv'+SUFFIX,\n",
"]\n",
"\n",
"FILENAMES = pd.DataFrame({'filename': URLS})\n",
"CST = Constant(Table('filenames', data=FILENAMES), scheduler=s)\n",
"CSV = CSVLoader(index_col=False, skipinitialspace=True,\n",
" usecols=['pickup_longitude', 'pickup_latitude',\n",
" 'dropoff_longitude', 'dropoff_latitude'],\n",
" filter_=_filter, scheduler=s) # TODO: reimplement filter in read_csv.py\n",
"\n",
"CSV.input.filenames = CST.output[0]\n",
"PR = Every(scheduler=s, proc=_quiet)\n",
"PR.input[0] = CSV.output[0]\n",
"\n",
"\n",
"MULTICLASS = MCScatterPlot(scheduler=s, classes=[\n",
" ('pickup', 'pickup_longitude', 'pickup_latitude'),\n",
" ('dropoff', 'dropoff_longitude', 'dropoff_latitude')], approximate=True)\n",
"MULTICLASS.create_dependent_modules(CSV)\n",
"\n",
"# Create the dashboard object\n",
"psboard = PsBoard(s)\n",
"# Visualisations require registration :\n",
"# 1) When widget provides the link_module() method do :\n",
"psboard.register_visualisation(Scatterplot(disable=['init_centroids']), MULTICLASS)\n",
"# 2) When widget is generic or for overloading the link_module() method do:\n",
"# psboard.register_visualisation(FooWidget(), bar_module)\n",
"# Start application :\n",
"\n",
"s.task_start();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(psboard)"
]
}
],
"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": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-2-clause |
ES-DOC/esdoc-jupyterhub | notebooks/uhh/cmip6/models/sandbox-2/ocnbgchem.ipynb | 1 | 79366 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocnbgchem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: UHH \n",
"**Source ID**: SANDBOX-2 \n",
"**Topic**: Ocnbgchem \n",
"**Sub-Topics**: Tracers. \n",
"**Properties**: 65 (37 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocnbgchem?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 16:54:41"
],
"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', 'uhh', 'sandbox-2', 'ocnbgchem')"
],
"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 --> Time Stepping Framework --> Passive Tracers Transport](#2.-Key-Properties--->-Time-Stepping-Framework--->-Passive-Tracers-Transport) \n",
"[3. Key Properties --> Time Stepping Framework --> Biology Sources Sinks](#3.-Key-Properties--->-Time-Stepping-Framework--->-Biology-Sources-Sinks) \n",
"[4. Key Properties --> Transport Scheme](#4.-Key-Properties--->-Transport-Scheme) \n",
"[5. Key Properties --> Boundary Forcing](#5.-Key-Properties--->-Boundary-Forcing) \n",
"[6. Key Properties --> Gas Exchange](#6.-Key-Properties--->-Gas-Exchange) \n",
"[7. Key Properties --> Carbon Chemistry](#7.-Key-Properties--->-Carbon-Chemistry) \n",
"[8. Tracers](#8.-Tracers) \n",
"[9. Tracers --> Ecosystem](#9.-Tracers--->-Ecosystem) \n",
"[10. Tracers --> Ecosystem --> Phytoplankton](#10.-Tracers--->-Ecosystem--->-Phytoplankton) \n",
"[11. Tracers --> Ecosystem --> Zooplankton](#11.-Tracers--->-Ecosystem--->-Zooplankton) \n",
"[12. Tracers --> Disolved Organic Matter](#12.-Tracers--->-Disolved-Organic-Matter) \n",
"[13. Tracers --> Particules](#13.-Tracers--->-Particules) \n",
"[14. Tracers --> Dic Alkalinity](#14.-Tracers--->-Dic-Alkalinity) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Ocean Biogeochemistry key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.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 ocean biogeochemistry model code (PISCES 2.0,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.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. Model Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Geochemical\" \n",
"# \"NPZD\" \n",
"# \"PFT\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Elemental Stoichiometry\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe elemental stoichiometry (fixed, variable, mix of the two)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Fixed\" \n",
"# \"Variable\" \n",
"# \"Mix of both\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Elemental Stoichiometry Details\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe which elements have fixed/variable stoichiometry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry_details') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all prognostic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.prognostic_variables') \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": [
"### 1.7. Diagnostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all diagnotic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.diagnostic_variables') \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": [
"### 1.8. Damping\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any tracer damping used (such as artificial correction or relaxation to climatology,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.damping') \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. Key Properties --> Time Stepping Framework --> Passive Tracers Transport \n",
"*Time stepping method for passive tracers transport in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for passive tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for passive tracers (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.timestep_if_not_from_ocean') \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. Key Properties --> Time Stepping Framework --> Biology Sources Sinks \n",
"*Time stepping framework for biology sources and sinks in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for biology sources and sinks*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for biology sources and sinks (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.timestep_if_not_from_ocean') \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. Key Properties --> Transport Scheme \n",
"*Transport scheme in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of transport scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Offline\" \n",
"# \"Online\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Transport scheme used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Use that of ocean model\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.3. Use Different Scheme\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Decribe transport scheme if different than that of ocean model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.use_different_scheme') \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 --> Boundary Forcing \n",
"*Properties of biogeochemistry boundary forcing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Atmospheric Deposition\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how atmospheric deposition is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.atmospheric_deposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Atmospheric Chemistry model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. River Input\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how river input is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.river_input') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Land Surface model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Sediments From Boundary Conditions\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_boundary_conditions') \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. Sediments From Explicit Model\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from explicit sediment model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_explicit_model') \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. Key Properties --> Gas Exchange \n",
"*Properties of gas exchange in ocean biogeochemistry *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_present') \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": [
"### 6.2. CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe CO2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. O2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is O2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_present') \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": [
"### 6.4. O2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe O2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. DMS Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is DMS gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_present') \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": [
"### 6.6. DMS Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify DMS gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.7. N2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_present') \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": [
"### 6.8. N2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.9. N2O Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2O gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_present') \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": [
"### 6.10. N2O Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2O gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.11. CFC11 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC11 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_present') \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": [
"### 6.12. CFC11 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC11 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.13. CFC12 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC12 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_present') \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": [
"### 6.14. CFC12 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC12 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.15. SF6 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is SF6 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_present') \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": [
"### 6.16. SF6 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify SF6 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.17. 13CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 13CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_present') \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": [
"### 6.18. 13CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 13CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.19. 14CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 14CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_present') \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": [
"### 6.20. 14CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 14CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.21. Other Gases\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify any other gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.other_gases') \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. Key Properties --> Carbon Chemistry \n",
"*Properties of carbon chemistry biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how carbon chemistry is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other protocol\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. PH Scale\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, describe pH scale.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.pH_scale') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sea water\" \n",
"# \"Free\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Constants If Not OMIP\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, list carbon chemistry constants.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.constants_if_not_OMIP') \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. Tracers \n",
"*Ocean biogeochemistry tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of tracers in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.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. Sulfur Cycle Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is sulfur cycle modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.sulfur_cycle_present') \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": [
"### 8.3. Nutrients Present\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List nutrient species present in ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nutrients_present') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrogen (N)\" \n",
"# \"Phosphorous (P)\" \n",
"# \"Silicium (S)\" \n",
"# \"Iron (Fe)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Nitrous Species If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous species.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_species_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrates (NO3)\" \n",
"# \"Amonium (NH4)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Nitrous Processes If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous processes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_processes_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Dentrification\" \n",
"# \"N fixation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Tracers --> Ecosystem \n",
"*Ecosystem properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Upper Trophic Levels Definition\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Definition of upper trophic level (e.g. based on size) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_definition') \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. Upper Trophic Levels Treatment\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Define how upper trophic level are treated*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_treatment') \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. Tracers --> Ecosystem --> Phytoplankton \n",
"*Phytoplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of phytoplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"PFT including size based (specify both below)\" \n",
"# \"Size based only (specify below)\" \n",
"# \"PFT only (specify below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Pft\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton functional types (PFT) (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.pft') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diatoms\" \n",
"# \"Nfixers\" \n",
"# \"Calcifiers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microphytoplankton\" \n",
"# \"Nanophytoplankton\" \n",
"# \"Picophytoplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Tracers --> Ecosystem --> Zooplankton \n",
"*Zooplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of zooplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"Size based (specify below)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Zooplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microzooplankton\" \n",
"# \"Mesozooplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Tracers --> Disolved Organic Matter \n",
"*Disolved organic matter properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Bacteria Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there bacteria representation ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.bacteria_present') \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. Lability\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe treatment of lability in dissolved organic matter*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.lability') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Labile\" \n",
"# \"Semi-labile\" \n",
"# \"Refractory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Tracers --> Particules \n",
"*Particulate carbon properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is particulate carbon represented in ocean biogeochemistry?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diagnostic\" \n",
"# \"Diagnostic (Martin profile)\" \n",
"# \"Diagnostic (Balast)\" \n",
"# \"Prognostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Types If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, type(s) of particulate matter taken into account*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.types_if_prognostic') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"POC\" \n",
"# \"PIC (calcite)\" \n",
"# \"PIC (aragonite\" \n",
"# \"BSi\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Size If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, describe if a particule size spectrum is used to represent distribution of particules in water volume*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"No size spectrum used\" \n",
"# \"Full size spectrum\" \n",
"# \"Discrete size classes (specify which below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Size If Discrete\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If prognostic and discrete size, describe which size classes are used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_discrete') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Sinking Speed If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, method for calculation of sinking speed of particules*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.sinking_speed_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Function of particule size\" \n",
"# \"Function of particule type (balast)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Tracers --> Dic Alkalinity \n",
"*DIC and alkalinity properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Carbon Isotopes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Which carbon isotopes are modelled (C13, C14)?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.carbon_isotopes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"C13\" \n",
"# \"C14)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Abiotic Carbon\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is abiotic carbon modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.abiotic_carbon') \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": [
"### 14.3. Alkalinity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is alkalinity modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.alkalinity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Prognostic\" \n",
"# \"Diagnostic)\" \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 |
luwei0917/awsemmd_script | notebook/linear_regression_sep20_2.ipynb | 1 | 229586 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from datetime import datetime\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
" def __init__(self, attribute_names):\n",
" self.attribute_names = attribute_names\n",
" def fit(self, X, y=None):\n",
" return self\n",
" def transform(self, X):\n",
" return X[self.attribute_names].values\n",
" \n",
"plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_data(name):\n",
"# name=\"tr872\"\n",
" name_list = [\"Step\" , \"Chain\" , \"Shake\" , \"Chi\" , \"Rama\", \"Excluded\", \"DSSP\", \"P_AP\", \"Water\" ,\"Burial\", \"Helix\", \"AMH_Go\", \"Frag_Mem\", \"Vec_FM\", \"Membrane\", \"SSB\",\"VTotal\"]\n",
"\n",
"\n",
" location = f\"/Users/weilu/Research/server/sep_2018/03_week/02_week/{name}/\"\n",
" RMSD = pd.read_table(location+\"rmsd-angstrom.xvg\", names=[\"i\", \"RMSD\"], sep=\"\\s+\")\n",
" bias = pd.read_table(location+\"bias.log\", names=[\"i\", \"biasQ\", \"bias\"], sep=\"\\s+\").drop(\"i\", axis=1)\n",
" awsem = pd.read_table(location+\"awsem.log\", names=name_list)\n",
" rw = pd.read_table(location+\"rwplusScore.txt\", names=[\"i\", \"Rw\"], sep=\"\\s+\").drop(\"i\", axis=1)\n",
" location = f\"/Users/weilu/Research/server/sep_2018/03_week/{name}/\"\n",
" pc = pd.read_table(location+\"pcarmsd_scaled.txt\", names=[\"i\", \"pc\", \"pc2\"], sep=\"\\s+\").drop(\"i\", axis=1)\n",
" raw_data = pd.concat([RMSD, rw, bias, awsem, pc], axis=1)\n",
" return raw_data.assign(name=name).reset_index().rename(columns={\"index\":\"folder\"})\n",
"\n",
"def choose_top(data,col=\"RMSD\", n=5, ascending=True):\n",
" return data.assign(chosen=pd.DataFrame.rank(data[col], ascending=ascending, method='dense')<=n)\n",
"\n",
"\n",
"def read_data_2(name):\n",
"# name = \"tr894\"\n",
" location = f\"/Users/weilu/Research/server/sep_2018/03_week/{name}/\"\n",
" rw = pd.read_table(location+\"rc_rwplus\", names=[\"pc\",\"rw\"], sep=\"\\s+\")\n",
" rmsd = pd.read_table(location+\"rc_rmsdlowerBound\", names=[\"pc\", \"rmsd\"], sep=\"\\s+\")\n",
" awsem = pd.read_table(location+\"rc_awsemEne\", names=[\"pc\", \"awsem\"], sep=\"\\s+\")\n",
" freeE = pd.read_table(location+\"pmf3000\"\n",
" , names=[\"pc\", \"f\", \"remove1\", \"remove2\"], sep=\"\\s+\").drop([\"remove1\", \"remove2\"], axis=1)\n",
" raw_data = freeE.merge(rw, on=\"pc\").merge(awsem, on=\"pc\").merge(rmsd, on=\"pc\").assign(name=name)\n",
" return raw_data\n",
"\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.pipeline import FeatureUnion\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from sklearn.linear_model import LinearRegression\n",
"def my_transform(data, label, degree, FEATURES):\n",
"\n",
" # LABEL = \"Qw\"\n",
" LABEL = label\n",
" PolynomialDegree = degree\n",
"\n",
" num_attribs = FEATURES\n",
" cat_attribs = [LABEL]\n",
" num_pipeline = Pipeline([\n",
" ('selector', DataFrameSelector(num_attribs)),\n",
" ('std_scaler', StandardScaler()),\n",
" ('poly', PolynomialFeatures(degree=PolynomialDegree, include_bias=False))\n",
" ])\n",
" cat_pipeline = Pipeline([\n",
" ('selector', DataFrameSelector(cat_attribs))\n",
" ])\n",
"\n",
" full_pipeline = FeatureUnion(transformer_list=[\n",
" (\"num_pipeline\", num_pipeline),\n",
" (\"cat_pipeline\", cat_pipeline),\n",
" ])\n",
" return full_pipeline.fit_transform(data)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# folder_list = [\"tr894\", \"tr882\", \"tr594\", \"tr898\", \"tr862\", \"tr877\", \"tr872\", \"tr885\", \"tr866\", \"tr868\", \"tr884\", \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr922\", \"tr891\", \"tr948\"]\n",
"folder_list = [\"tr894\", \"tr882\", \"tr594\", \"tr869\", \"tr862\", \"tr877\", \"tr872\", \"tr885\", \"tr866\", \"tr868\", \"tr884\", \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr922\", \"tr891\", \"tr948\"]\n",
"# folder_list = [ \"tr862\", \"tr877\", \"tr872\", \"tr885\", \"tr866\", \"tr868\", \"tr884\", \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr922\", \"tr891\", \"tr948\"]\n",
"# folder_list = [\"tr862\", \"tr872\", \"tr885\", \"tr866\", \"tr868\" , \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr891\", \"tr948\"]\n",
"# \"tr877\",\"tr884\", \"tr922\"\n",
"# \"tr869\"\n",
"data_list = []\n",
"for name in folder_list:\n",
" tmp = read_data_2(name)\n",
" data_list.append(tmp)\n",
"raw_data_all = pd.concat(data_list)\n",
"n = 20\n",
"raw_data_all = raw_data_all.reset_index(drop=True).groupby(\"name\").apply(choose_top, n=n, col=\"rmsd\").reset_index(drop=True)\n",
"\n",
"\n",
"# train_name_list = [\"tr872\", \"tr885\", \"tr948\"]\n",
"# train_name_list = [\"tr862\", \"tr872\", \"tr885\", \"tr866\", \"tr868\" , \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr891\", \"tr948\"]\n",
"# train_name_list = [\"tr866\"]\n",
"# train_name_list = [\"tr948\"]\n",
"# train_name_list = [\"tr894\"]\n",
"# train_name_list = [\"tr885\"]\n",
"# train_name_list = [\"tr872\"]\n",
"# train_name_list = [\"tr594\"]\n",
"# train_name_list = [\"tr894\", \"tr922\", \"tr882\", \"tr872\"]\n",
"# train_name_list = [\"tr898\", \"tr896\", \"tr894\", \"tr895\"]\n",
"# train_name_list = [\"tr868\"]\n",
"train_name_list = [\"tr882\"]\n",
"# train_name_list = [\"tr866\", \"tr866\"]\n",
"# train_name_list = [\"tr870\"]\n",
"# train_name_list = [\"tr891\"]\n",
"raw_data = raw_data_all.reset_index(drop=True).query(f'name in {train_name_list}')"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"# FEATURES = [\"eigenvalues\", \"entropy\", \"pca\"]\n",
"# FEATURES = [\"eigenvalues\", \"entropy\", \"diffRMSD\"]\n",
"# FEATURES = [\"eigenvalues\", \"entropy\"]\n",
"FEATURES = [\"f\",\n",
" 'rw',\n",
" 'awsem',\n",
"# 'RMSD', # test\n",
"# 'Burial',\n",
"# 'Water',\n",
"# 'Rama',\n",
"# 'DSSP',\n",
"# 'P_AP',\n",
"# 'Helix',\n",
"# 'Frag_Mem'\n",
" ]\n",
"# FEATURES = [\"eigenvalues\"]\n",
"# LABEL = \"diffRMSD\"\n",
"# LABEL = \"RMSD\"\n",
"LABEL = \"rmsd\"\n",
"DEGREE = 1\n",
"\n",
"def pred_from_raw(a):\n",
" data = my_transform(a, label=LABEL, degree=DEGREE, FEATURES=FEATURES)\n",
" test_y = data[:,-1]\n",
" test_set = data[:,:-1]\n",
" prediceted_rmsd= clf.predict(test_set)\n",
" return a.assign(prediceted_rmsd=prediceted_rmsd)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# data = my_transform(raw_data, label=LABEL, degree=DEGREE, FEATURES=FEATURES)\n",
"# data = raw_data.groupby('name').apply(my_transform, label=LABEL, degree=DEGREE, FEATURES=FEATURES)[0]\n",
"data = np.concatenate(raw_data.groupby('name').apply(my_transform, \n",
" label=LABEL, degree=DEGREE, FEATURES=FEATURES).values)\n",
"train_y = data[:,-1]\n",
"train_set = data[:,:-1]\n",
"from sklearn import svm\n",
"# clf = svm.SVC(probability=True)\n",
"clf = LinearRegression()\n",
"clf.fit(train_set, train_y)\n",
"y_pred_svm = clf.predict(train_set)\n",
"\n",
"raw_data_all = raw_data_all.reset_index(drop=True).groupby(\"name\").apply(pred_from_raw).reset_index(drop=True)\n",
"\n",
"\n",
"picked_n = 1\n",
"best = raw_data_all.groupby(\"name\").apply(choose_top, col=\"rmsd\"\n",
" , n=picked_n, ascending=True).reset_index(drop=True).query(\"chosen==True\")\n",
"picked = raw_data_all.groupby(\"name\").apply(choose_top, col=\"prediceted_rmsd\"\n",
" , n=picked_n, ascending=True).reset_index(drop=True).query(\"chosen==True\")\n",
"# init = raw_data_all.query(\"i == 0.0\")\n",
"all_results = pd.concat([best.assign(result='best'), \n",
" picked.assign(result='picked')])\n",
"\n",
"picked_keep = picked.copy()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def choose_top(data,col=\"RMSD\", n=5, ascending=True):\n",
" return data.assign(chosen=pd.DataFrame.rank(data[col], ascending=ascending, method='first')<=n)\n",
"\n",
"\n",
"WIDTH = 0.1\n",
"# WIDTH = 1\n",
"def with_in_range(data, width=WIDTH):\n",
" return data.assign(inrange= (data[\"pc\"] < (data[\"pc_center\"]+width)) & (data[\"pc\"] > (data[\"pc_center\"]-width)))"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"folder_list = [\"tr894\", \"tr882\", \"tr594\", \"tr862\", \"tr877\", \"tr872\", \"tr885\", \"tr866\", \"tr868\", \"tr884\", \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr922\", \"tr891\", \"tr948\"]\n",
"# \"tr898\"\n",
"# folder_list = [\"tr894\", \"tr882\", \"tr594\", \"tr898\", \"tr862\", \"tr877\", \"tr872\", \"tr885\", \"tr866\", \"tr868\", \"tr884\", \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr922\", \"tr891\", \"tr948\"]\n",
"# folder_list = [\"tr894\", \"tr882\", \"tr594\", \"tr869\", \"tr862\", \"tr877\", \"tr872\", \"tr885\", \"tr866\", \"tr868\", \"tr884\", \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr922\", \"tr891\", \"tr948\"]\n",
"# folder_list = [ \"tr862\", \"tr877\", \"tr872\", \"tr885\", \"tr866\", \"tr868\", \"tr884\", \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr922\", \"tr891\", \"tr948\"]\n",
"# folder_list = [\"tr862\", \"tr872\", \"tr885\", \"tr866\", \"tr868\" , \"tr895\", \"tr896\", \"tr870\", \"tr921\", \"tr891\", \"tr948\"]\n",
"# \"tr877\",\"tr884\", \"tr922\"\n",
"# \"tr869\"\n",
"data_list = []\n",
"for name in folder_list:\n",
" tmp = read_data(name)\n",
" data_list.append(tmp)\n",
"raw_data_all_2 = pd.concat(data_list)\n",
"n = 20\n",
"raw_data_all_2 = raw_data_all_2.reset_index(drop=True).groupby(\"name\").apply(choose_top, n=n, col=\"RMSD\").reset_index(drop=True)\n",
"\n",
"\n",
"raw_data = raw_data_all_2.reset_index(drop=True).query(f'name in {train_name_list}')\n",
"a = raw_data_all_2.merge(picked_keep[[\"pc\", \"name\"]].rename(columns={\"pc\":\"pc_center\"}),on=\"name\")\n",
"filtered = a.groupby(\"name\").apply(with_in_range).query(\"inrange == True\").reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/weilu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:55: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x1a1d0fb160>"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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X26u3H+tvqVIsburWVmmn29GluKTbvtvHPTIAFBTCCwBH40u/JnUtsqZHa48kSZ+98dn0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\n6gu1MamGXJKCk3r2CudXa5NG3HnbCEBaTCCdXSZyq5rP3CmEObTTuSs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3m1+nzanmhZMs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b2uvkN6F6sOZGKnUXP78D4djVTQw5FlOUOWZUmW5fsuGLuveSyjjfN3NR9bfNF4cLN25MXnb5Fl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\n4oUkSZIkSSo0ixeSJEmSJKnQ/h9NBVMfk6P1BgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1076.88x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# FEATURES = [\"eigenvalues\", \"entropy\", \"pca\"]\n",
"# FEATURES = [\"eigenvalues\", \"entropy\", \"diffRMSD\"]\n",
"# FEATURES = [\"eigenvalues\", \"entropy\"]\n",
"FEATURES = [\n",
" \"biasQ\",\n",
" 'Rw',\n",
" 'VTotal',\n",
"# 'RMSD', # test\n",
"# 'Burial',\n",
"# 'Water',\n",
"# 'Rama',\n",
"# 'DSSP',\n",
"# 'P_AP',\n",
"# 'Helix',\n",
"# 'Frag_Mem'\n",
" ]\n",
"# FEATURES = [\"eigenvalues\"]\n",
"# LABEL = \"diffRMSD\"\n",
"# LABEL = \"RMSD\"\n",
"LABEL = \"chosen\"\n",
"DEGREE = 1\n",
"\n",
"def pred_from_raw(a):\n",
" data = my_transform(a, label=LABEL, degree=DEGREE, FEATURES=FEATURES)\n",
" test_y = data[:,-1]\n",
" test_set = data[:,:-1]\n",
" prob= clf.predict_proba(test_set)[:,1]\n",
" return a.assign(prob=prob)\n",
"\n",
"# data = my_transform(raw_data, label=LABEL, degree=DEGREE, FEATURES=FEATURES)\n",
"# data = raw_data.groupby('name').apply(my_transform, label=LABEL, degree=DEGREE, FEATURES=FEATURES)[0]\n",
"data = np.concatenate(raw_data.groupby('name').apply(my_transform, \n",
" label=LABEL, degree=DEGREE, FEATURES=FEATURES).values)\n",
"train_y = data[:,-1]\n",
"train_set = data[:,:-1]\n",
"from sklearn import svm\n",
"p = 0.5\n",
"# clf = svm.SVC(probability=True)\n",
"clf = LogisticRegression(random_state=27)\n",
"clf.fit(train_set, train_y)\n",
"\n",
"filtered = filtered.reset_index(drop=True).groupby(\"name\").apply(pred_from_raw).reset_index(drop=True)\n",
"\n",
"\n",
"picked_n = 1\n",
"best = raw_data_all_2.groupby(\"name\").apply(choose_top, col=\"RMSD\"\n",
" , n=1, ascending=True).reset_index(drop=True).query(\"chosen==True\")\n",
"picked = filtered.groupby(\"name\").apply(choose_top, col=\"prob\"\n",
" , n=1, ascending=False).reset_index(drop=True).query(\"chosen==True\")\n",
"worst = filtered.groupby(\"name\").apply(choose_top, col=\"RMSD\"\n",
" , n=1, ascending=False).reset_index(drop=True).query(\"chosen==True\")\n",
"init = raw_data_all_2.groupby(\"name\").apply(choose_top, col=\"i\"\n",
" , n=1, ascending=True).reset_index(drop=True).query(\"chosen==True\")\n",
"all_results = pd.concat([best.assign(result='best'), \n",
" picked.assign(result='picked'), init.assign(result='init')\n",
"# , worst.assign(result='worst')\n",
" ])\n",
"# all_results = pd.concat([best.assign(result='best'), \n",
"# picked.assign(result='picked')])\n",
"# picked.to_csv(\"/Users/weilu/Desktop/picked.csv\n",
"\n",
"# sns.set(rc={'figure.figsize':(20,30)})\n",
"# plt.figure(figsize=(15,8))\n",
"fg = sns.FacetGrid(data=all_results.reset_index(), hue='result', height=8, aspect=1.63)\n",
"fg.map(plt.plot, 'name', 'RMSD').add_legend(fontsize=20)\n",
"fg.set(ylim=(0, 10))"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"picked[\"init_RMSD\"] = init[\"RMSD\"].values"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"picked[\"diff_RMSD\"] = init[\"RMSD\"].values - picked[\"RMSD\"].values"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"out = picked[[\"name\", \"RMSD\", \"init_RMSD\", \"diff_RMSD\", \"folder\"]].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"out.to_csv(\"/Users/weilu/Desktop/picked_3.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.20157408, -0.69485223, 0.04456798]])"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.coef_"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x1a327cc828>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/nvG5FerKny68iKQFnKsupPveo6WdbTD0eGnnpt9q+i0I4Btd3zjo9JIk7a3v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\nULgTXtgAvd3jd+0JEOITwqrkVWwr2qbmhrupAksB/l7+xDWVgymFtu5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XQb5eJEUGasti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\nNAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1076.88x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fg = sns.FacetGrid(data=all_results.reset_index(), hue='result', height=8, aspect=1.63)\n",
"fg.map(plt.plot, 'name', 'RMSD').add_legend(fontsize=20)\n",
"fg.set(ylim=(0, 10))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(17,)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered[\"name\"].unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>RMSD</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>969</th>\n",
" <td>7.02078</td>\n",
" <td>tr594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1848</th>\n",
" <td>5.61065</td>\n",
" <td>tr862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3276</th>\n",
" <td>2.91322</td>\n",
" <td>tr866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4497</th>\n",
" <td>1.06823</td>\n",
" <td>tr868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6098</th>\n",
" <td>8.09616</td>\n",
" <td>tr870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6481</th>\n",
" <td>4.10102</td>\n",
" <td>tr872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8201</th>\n",
" <td>3.07719</td>\n",
" <td>tr877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9164</th>\n",
" <td>2.25775</td>\n",
" <td>tr882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10670</th>\n",
" <td>3.23479</td>\n",
" <td>tr884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11696</th>\n",
" <td>2.35937</td>\n",
" <td>tr885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15047</th>\n",
" <td>2.68814</td>\n",
" <td>tr891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16215</th>\n",
" <td>1.16419</td>\n",
" <td>tr894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17311</th>\n",
" <td>5.38768</td>\n",
" <td>tr895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18249</th>\n",
" <td>8.22359</td>\n",
" <td>tr896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22625</th>\n",
" <td>3.16510</td>\n",
" <td>tr921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23681</th>\n",
" <td>3.14700</td>\n",
" <td>tr922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26050</th>\n",
" <td>6.11488</td>\n",
" <td>tr948</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RMSD name\n",
"969 7.02078 tr594\n",
"1848 5.61065 tr862\n",
"3276 2.91322 tr866\n",
"4497 1.06823 tr868\n",
"6098 8.09616 tr870\n",
"6481 4.10102 tr872\n",
"8201 3.07719 tr877\n",
"9164 2.25775 tr882\n",
"10670 3.23479 tr884\n",
"11696 2.35937 tr885\n",
"15047 2.68814 tr891\n",
"16215 1.16419 tr894\n",
"17311 5.38768 tr895\n",
"18249 8.22359 tr896\n",
"22625 3.16510 tr921\n",
"23681 3.14700 tr922\n",
"26050 6.11488 tr948"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"picked[[\"RMSD\", \"name\"]]"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"picked.to_csv(\"/Users/weilu/Desktop/picked_2.csv\")"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ingmarschuster/rkhs_demo | RKHS_in_Machine_learning.ipynb | 1 | 316817 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "151ace28-1df8-4b52-baf0-4f792e91d5ff"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"$\\newcommand{\\Reals}{\\mathbb{R}}\n",
"\\newcommand{\\Nats}{\\mathbb{N}}\n",
"\\newcommand{\\PDK}{\\mathbf{k}}\n",
"\\newcommand{\\IS}{\\mathcal{X}} \n",
"\\newcommand{\\FM}{\\Phi} \n",
"\\newcommand{\\Gram}{K} \n",
"\\newcommand{\\RKHS}{\\mathcal{H}}\n",
"\\newcommand{\\prodDot}[2]{\\left\\langle#1,#2\\right\\rangle}\n",
"\\DeclareMathOperator*{\\argmin}{arg\\,min}\n",
"\\DeclareMathOperator*{\\argmax}{arg\\,max}$\n",
"# Reproducing Kernel Hilbert Spaces in Machine Learning"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"nbpresent": {
"id": "aa569eb6-ea77-4a63-89bf-c6e50dea77f7"
},
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ischuster/Documents/university/Promotion_NLP/software/python/distributions/distributions/linalg.py:35: UserWarning: warning: caught this exception:module 'numpy.core' has no attribute '_dotblas'\n",
" warnings.warn(\"warning: caught this exception:\" + str(e))\n"
]
}
],
"source": [
"from __future__ import division, print_function, absolute_import\n",
"from IPython.display import SVG, display, Image\n",
"\n",
"import numpy as np, scipy as sp, pylab as pl, matplotlib.pyplot as plt, scipy.stats as stats, sklearn, sklearn.datasets\n",
"from scipy.spatial.distance import squareform, pdist, cdist\n",
"\n",
"import distributions as dist #commit 480cf98 of https://github.com/ingmarschuster/distributions"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "facddd6a-d4b1-4885-bade-ce2aae8a72be"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Motivation: Feature engineering in Machine Learning\n",
"In ML, one classic way to handle nonlinear relations in data (non-numerical data) with linear methods is to map the data to so called features using a nonlinear function $\\FM$ (a function mapping from the data to a vector space)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"nbpresent": {
"id": "65cfcee8-427f-45f2-8825-5251c9ee0763"
},
"scrolled": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"image/jpeg": {
"width": 200
}
},
"output_type": "display_data"
}
],
"source": [
"display(Image(filename=\"monomials.jpg\", width=200))"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "39211b06-42c0-4c68-bbc0-eb5c00376df0"
},
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"In the Feature Space (the domain of $\\FM$), we can then use linear algebra, such as angles, norms and inner products, inducing nonlinear operations on the Input Space (codomain of $\\FM$). The central thing we need, apart from the feature space being a vector space are inner products, as they induce norms and a possibility to measure angles."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Simple classification algorithm using only inner products\n",
"Say we are given data points from the mixture of two distributions with densities $p_0,p_1$:\n",
"$$x_i \\sim w_0 p_0 + w_1 p_1$$ and labels $l_i = 0$ if $x_i$ was actually generated by $p_0$, $l_i = 1$ otherwise. A very simple classification algorithm would be to compute the mean in feature space $\\mu_c = \\frac{1}{N_c} \\sum_{l_i = c} \\FM(x_i)$ for $c \\in \\{0,1\\}$ and assign a test point to the class which is the most similar in terms of inner product. In other words, the decision function \n",
"$ f_d:\\IS\\to\\{0,1\\}$ is defined by\n",
"$$f_d(x) = \\argmax_{c\\in\\{0,1\\}} \\prodDot{\\FM(x)}{\\mu_c}$$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"nbpresent": {
"id": "9cf40433-1214-467e-a61d-1e15c8ca2f2a"
},
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = np.vstack([stats.multivariate_normal(np.array([-2,2]), np.eye(2)*1.5).rvs(100),\n",
" stats.multivariate_normal(np.ones(2)*2, np.eye(2)*1.5).rvs(100)])\n",
"distr_idx = np.r_[[0]*100, [1]*100]\n",
"\n",
"for (idx, c, marker) in [(0,'r', (0,3,0)), (1, \"b\", \"x\")]:\n",
" pl.scatter(*data[distr_idx==idx,:].T, c=c, alpha=0.4, marker=marker)\n",
" pl.arrow(0, 0, *data[distr_idx==idx,:].mean(0), head_width=0.2, head_length=0.2, fc=c, ec=c)\n",
"pl.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "e0cfa942-49a8-4265-87a0-c004b554b3fa"
},
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Remarkably, all positive definite functions are inner products in some feature space."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "80bb4b95-6e30-469a-b2cf-ffd1b507805d"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"**Theorem** Let $\\IS$ be a nonempty set and let $\\PDK:\\IS\\times\\IS \\to \\Reals$, called a *kernel*. The following two conditions are equivalent:\n",
"* $\\PDK$ is symmetric and *positive semi definite (psd)*, i.e. for all $x_1, \\dots, x_m \\in \\IS$ the matrix $\\Gram$ defined by with entries $\\Gram_{i,j} = \\PDK(x_i, x_j)$ is symmetric psd\n",
"$\\FM$ is called the *Feature Map* and $\\RKHS_\\FM$ the *feature space*.\n",
"* there exists a map $\\FM: \\IS \\to \\RKHS_\\FM$ to a hilbert space $\\RKHS_\\FM$ such that $$\\PDK(x_i, x_j) = \\prodDot{\\FM(x_i)}{\\FM(x_j)}_\\RKHS$$\n",
"\n",
"\n",
"In other words, $\\PDK$ computes the inner product in some $\\RKHS_\\FM$. We furthermore endow the space with the norm induced by the dot product $\\|\\cdot\\|_\\PDK$. From the second condition, it is easy to construct $\\PDK$ given $\\FM$. A general construction for $\\FM$ given $\\PDK$ is not as trivial but still elementary."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "df9eace4-d45b-4d9a-bf16-1eaa70581162"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Construction of the canonical feature map (Aronszajn map)\n",
"\n",
"We give the canonical construction of $\\FM$ from $\\PDK$, together with a definition of the inner product in the new space. In particular, the feature for each $x \\in \\IS$ will be a function from $\\IS$ to $\\Reals$.\n",
"$$\\FM:\\IS \\to \\Reals^\\IS\\\\\n",
"\\FM(x) = \\PDK(\\cdot, x)$$\n",
"Thus for the linear kernel $\\PDK(x,y)=\\prodDot{x}{y}$ we have $\\FM(x) = \\prodDot{\\cdot}{x}$ and for the gaussian kernel $\\PDK(x,y)=\\exp\\left(-0.5{\\|x-y\\|^2}/{\\sigma^2}\\right)$ we have $\\FM(x) = \\exp\\left(-0.5{\\|\\cdot -x \\|^2}/{\\sigma^2}\\right)$.\n",
"\n",
"Now $\\RKHS$ is the closure of $\\FM(\\IS)$ wrt. linear combinations of its elements:\n",
"$$\\RKHS = \\left\\{f: f(\\cdot)=\\sum_{i=1}^m a_i \\PDK(\\cdot, x_i) \\right\\} = span(\\FM(\\IS))$$\n",
"where $m \\in \\Nats, a_i \\in \\Reals, x \\in \\IS$. This makes $\\RKHS$ a vector space over $\\Reals$.\n",
"\n",
"For $f(\\cdot)=\\sum_{i=1}^m a_i \\PDK(\\cdot, x_i)$ and $g(\\cdot)=\\sum_{i=1}^m' b_j \\PDK(\\cdot, x'_j)$ we define the inner product in $\\RKHS$ as\n",
"$$\\prodDot{f}{g} = \\sum_{i=1}^m \\sum_{i=1}^m' b_j a_i \\PDK(x'_j, x_i)$$\n",
"In particular, for $f(\\cdot) = \\PDK(\\cdot,x), g(\\cdot) = \\PDK(\\cdot,x')$, we have $\\prodDot{f}{g} = \\prodDot{ \\PDK(\\cdot,x)}{ \\PDK(\\cdot,x')}=\\PDK(x,x')$. This is called the *reproducing property* of the kernel of this particular $\\RKHS$.\n",
"Obviously $\\RKHS$ with this inner product satisfies all conditions for a hilbert space: the inner product is\n",
"* positive definite\n",
"* linear in its first argument\n",
"* symmetric\n",
"\n",
"which is why $\\RKHS$ is called a *Reproducing Kernel Hilbert Space (RKHS)*."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "9d0c7200-6f4a-4d8d-9fd9-f5039b6d05dd"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Inner product classification algorithm is equivalent to a classification with KDEs\n",
"The naive classification algorithm we outlined earlier is actually equivalent to a simple classification algorithm using KDEs. For concreteness, let $\\PDK(x,x') = { {{(2\\pi )^{-N/2}\\left|\\Sigma \\right|^{-1/2}}}\\exp({-{ 0.5}(x-x' )^{\\top }\\Sigma ^{-1}(x-x' )}})$.\n",
"\n",
"Then the mean in feature space of data from distribution $c$ with the canonical feature map is \n",
"$$\\mu_c = \\frac{1}{N_c} \\sum_{l_i = c} \\FM(x_i) = \\frac{1}{N_c} \\sum_{l_i = c} \\PDK(x_i, \\cdot) = \\frac{1}{N_c} \\sum_{l_i = c} { {{(2\\pi )^{-N/2}\\left|\\Sigma \\right|^{-1/2}}}\\exp({-{ 0.5}(\\cdot-x_i )^{\\top }\\Sigma ^{-1}(\\cdot-x_i )}})$$\n",
"which is just a KDE of the density $p_c$ using gaussian kernels with parameter $\\Sigma$. For a test point $y$ that we want to classify, its feature is just $\\PDK(y,\\cdot) = { {{(2\\pi )^{-N/2}\\left|\\Sigma \\right|^{-1/2}}}\\exp({-{ 0.5}(y-\\cdot )^{\\top }\\Sigma ^{-1}(y-\\cdot )}})$. Its inner product with the class mean is just the evaluation of the KDE at $y$ (because of the reproducing property). Thus each point is classified as belonging to the class for which the KDE estimate assigns highest probability to $y$."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"nbpresent": {
"id": "1a23e96a-8352-4ecc-b47e-af3593998d40"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class Kernel(object):\n",
" def mean_emb(self, samps):\n",
" return lambda Y: self.k(samps, Y).sum()/len(samps)\n",
" \n",
" def mean_emb_len(self, samps):\n",
" return self.k(samps, samps).sum()/len(samps**2)\n",
" \n",
" def k(self, X, Y):\n",
" raise NotImplementedError()\n",
"\n",
"class FeatMapKernel(Kernel):\n",
" def __init__(self, feat_map):\n",
" self.features = feat_map\n",
" \n",
" def features_mean(self, samps):\n",
" return self.features(samps).mean(0)\n",
" \n",
" def mean_emb_len(self, samps):\n",
" featue_space_mean = self.features_mean(samps)\n",
" return featue_space_mean.dot(featue_space_mean)\n",
" \n",
" def mean_emb(self, samps):\n",
" featue_space_mean = self.features(samps).mean(0)\n",
" return lambda Y: self.features(Y).dot(featue_space_mean)\n",
" \n",
" def k(self, X, Y):\n",
" gram = self.features(X).dot(self.features(Y).T)\n",
" return gram\n",
"\n",
"class LinearKernel(FeatMapKernel):\n",
" def __init__(self):\n",
" FeatMapKernel.__init__(self, lambda x: x)\n",
"\n",
"class GaussianKernel(Kernel):\n",
" def __init__(self, sigma):\n",
" self.width = sigma\n",
" \n",
" def k(self, X, Y=None):\n",
" assert(len(np.shape(X))==2)\n",
" \n",
" # if X=Y, use more efficient pdist call which exploits symmetry\n",
" if Y is None:\n",
" sq_dists = squareform(pdist(X, 'sqeuclidean'))\n",
" else:\n",
" assert(len(np.shape(Y))==2)\n",
" assert(np.shape(X)[1]==np.shape(Y)[1])\n",
" sq_dists = cdist(X, Y, 'sqeuclidean')\n",
" \n",
" K = exp(-0.5 * (sq_dists) / self.width ** 2)\n",
" return K\n",
"\n",
"class StudentKernel(Kernel):\n",
" def __init__(self, s2, df):\n",
" self.dens = dist.mvt(0,s2,df)\n",
" \n",
" def k(self, X,Y=None):\n",
" if Y is None:\n",
" sq_dists = squareform(pdist(X, 'sqeuclidean'))\n",
" else:\n",
" assert(len(np.shape(Y))==2)\n",
" assert(np.shape(X)[1]==np.shape(Y)[1])\n",
" sq_dists = cdist(X, Y, 'sqeuclidean')\n",
" dists = np.sqrt(sq_dists)\n",
" return exp(self.dens.logpdf(dists.flatten())).reshape(dists.shape)\n",
"\n",
"\n",
"def kernel_mean_inner_prod_classification(samps1, samps2, kernel):\n",
" mean1 = kernel.mean_emb(samps1)\n",
" norm_mean1 = kernel.mean_emb_len(samps1)\n",
" mean2 = kernel.mean_emb(samps2)\n",
" norm_mean2 = kernel.mean_emb_len(samps2)\n",
" \n",
" def sim(test):\n",
" return (mean1(test) - mean2(test))\n",
" \n",
" def decision(test):\n",
" if sim(test) >= 0:\n",
" return 1\n",
" else:\n",
" return 0\n",
" \n",
" return sim, decision\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"nbpresent": {
"id": "1681f63f-4290-4335-bd75-0a261ec26735"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def apply_to_mg(func, *mg):\n",
" #apply a function to points on a meshgrid\n",
" x = np.vstack([e.flat for e in mg]).T\n",
" return np.array([func(i.reshape((1,2))) for i in x]).reshape(mg[0].shape)\n",
"\n",
"def plot_with_contour(samps, data_idx, cont_func, method_name, delta = 0.025, pl = pl):\n",
" x = np.arange(samps.T[0].min()-delta, samps.T[1].max()+delta, delta)\n",
" y = np.arange(samps.T[1].min()-delta, samps.T[1].max()+delta, delta)\n",
" X, Y = np.meshgrid(x, y)\n",
" Z = apply_to_mg(cont_func, X,Y)\n",
" Z = Z.reshape(X.shape)\n",
"\n",
"\n",
" # contour labels can be placed manually by providing list of positions\n",
" # (in data coordinate). See ginput_manual_clabel.py for interactive\n",
" # placement.\n",
" fig = pl.figure()\n",
" pl.pcolormesh(X, Y, Z > 0, cmap=pl.cm.Pastel2)\n",
" pl.contour(X, Y, Z, colors=['k', 'k', 'k'],\n",
" linestyles=['--', '-', '--'],\n",
" levels=[-.5, 0, .5])\n",
" pl.title('Decision for '+method_name)\n",
" #plt.clabel(CS, inline=1, fontsize=10)\n",
" for (idx, c, marker) in [(0,'r', (0,3,0)), (1, \"b\", \"x\")]:\n",
" pl.scatter(*data[distr_idx==idx,:].T, c=c, alpha=0.7, marker=marker)\n",
"\n",
" pl.show()\n",
" \n",
"for (kern_name, kern) in [(\"Linear\", LinearKernel()), \n",
" (\"Student-t\", StudentKernel(0.1,10)), \n",
" (\"Gauss\", GaussianKernel(0.1))\n",
" ]:\n",
" (sim, dec) = kernel_mean_inner_prod_classification(data[distr_idx==1,:], data[distr_idx==0,:], kern)\n",
" plot_with_contour(data, distr_idx, sim, 'Inner Product classif. '+kern_name, pl = plt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "22a5c812-ee34-42d5-b27b-5b3212e71336"
},
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Obviously, the linear kernel might be enough already for this simple dataset. Another interesting observation however is that the Student-t based kernel is more robust to outliers of the datasets and yields a lower variance classification algorithm as compared to using a Gaussian kernel. This is to be expected, given the fatter tails of the Student-t. Now lets look at a dataset that is not linearly separable."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"nbpresent": {
"id": "abdbdc86-9fe5-4658-adcc-b725c3b0eb9e"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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++wIKFt5M6s69ANSNHUHx3T+L6fyRxll/shVTRQWgDjah9kuuXxRzn6MHrOg2HJs2M2rZywgpUawWdD4fttKjuPJyyX99ZY8If80g3XV6ytXzCDA44vMgQIts0egWqgumUHznrUihQ9fkRjEaceXlIo36mNQ28VZnVBdMCRuHNxUuZ/sTD4bVS56MdI7M+w75f1iF/etd6jRJQNr23Uy879F2zx1tnBUKCARSyHD7HZ2NR6vAotvIf30lOn8AxWSKiFnQY66q7pGANc0gHR96aua/FrhdCPEX4BzAKaUsa+cYDY1m5K/4M8Nf/iPG2jp8aansv/XHlNx4TbN9ImeEdWNHgBAYGpuCapv2Z4eR6RwiBUs81RnRK4rJi+7F6KxDmoyqjQFQhMBUW9/uTLqlCGQA74B0Nq16Mm59jCRkE9D5fKBI9G43IqAAkobhp3X6nLHS2jX31KrjVCEuwl8I8X/A+UCGEOIIsAQwAkgpXwLeA64E9gEu4IZ4nFcjcejuZXj+ij8z5onnkUYDAZsFQ2MjY554HiA8AEQLbkOjC73L1SHB3RuCxXboKDqvD8VsCn8n9Xp0Xm+7M+mWYhziaUBuCdeQXITfT9LBw+g8XjVqGQkIbCWHKVh4M4aGpm5Tx/TGNZ+KxMvb54dSyhwppVFKOUhKuUJK+VJQ8BP08vm5lHK4lHKilPLLeJxXIzGIZRneVVXK8Jf/iDQaUMxm0OlQzGak0cDwl/8Y3iceLorx8JbpKK4huSgmIyIQCH8nAgEUg7FdG0V0fALE14DcEiXXX4006lH0eqROB0gQgqasTEx1DaQUH+hWdUxvXPOpiJbeQaPLtCd046GjNdbWoRiNzb5TjEaMtXXhz/EQ3L0lTH32VITXh/CpfzqvD29aSrs2iuj4hFhSTXR0EI4+BmDng3epg7DVjHeAg9qJ49D7/WpUtM8f0+Db2QlBR69Zo2U04a/RZdoTurHMyNsTBL60VFXHHIHO58OXlhr+HA/B3RuCpbpgCl8/sRjn6WNV7YmE2olj2P7Eg+2qTNozzkbSmUG4tWMAKqdPpW7saJwTx+JzpIWjoiNdbFsbfLsyIejINWu0jpbbp58Qi04+ep/qsyep+Wva0eO35xfeno42FiPr/lt/zJgnnkeHOuPX+XwIn5/9t/443GZ7LoqxEO2a2Vn//o5SXTCFTYXLO31sLP3rjD2jrWNKrr+a0+97jOR9B9B5feg8XqRO0DB8aPj41gbfrtpW4uGG29/RhH8/IBbhGr1P0oFDDPx4A435Q3DnZLXp9VJ99iRGLXsZnT+AP8mGZ4Aj6FapCt32BodYBEHIqNuWt0+8BPepKlg6Yyht7xiJDNl6CZhN6L1e9K4mfPbUZoNv9MQiZedeXEOHdKgvGvFFE/79gFiEa/Q+5uoapNGApfwY5uoa9E1uFIOBUU+9yKZVr4bbdmzazKC336UpNwdzVTX6RhdWj5fiO285IbjbmZHHKpRKbrzmJNfOaE5VwR0POhP929Yx+a+vxJuZiSsiWtlSWo6p1ok0GsKDL3DS5MNcXYNiMTcriKMZbXsWTeffh4nVYNaaTj5l197w8RkbPkd4veHt+iY3EoGxriEYKWtEKAHSvtrV7DyhQcOdl43z9HFUn3sW9WNG4vhiW3if9nS0mvdGz9AZe0Zbx7T0XLlzsvBkDmiW7bQlm09TbjbWo2Wa0bYX0YR/H6UjBrOWhKul7Bjmqprw8dKgJ7V4/4lqVVYL+qYmpF4XTo6GEARslmaG2lg9bCKjXEPlEUODVvXZkzTvjR6gM4bSto6JddBubZDwZjg0o20voql9+igdMZi1pHaxHi2jKTcnfFxj/hBSdu8lqeQwtWl2PI50TBVVqueGlOH8OA1DT2sm2DuqSmjJ/jDo7Xc5Mu87MRVH0eganVGLtXZMrAb21p6RujEj2fJS56OQI9Fy/XQcbebfR+mIT3tLszdvhgN3TlZ4H1+6nfrRIxCBAObKKhqHDaFuzAgUi1mNPjWZqB85HGlsHnjUUVVCa26fji+2xVQcRSNxiHUl0ZFnpLNxCFqun46jzfz7KB2dcbeUTyb6eGk0Ujl9ang2FjlLb21m11EPGy00/9QilpVErM9IZ/Mqabl+Oocm/PsoXfVpj+X4WF/ajqgS4pFvXqPvEfmMhFQ04x5Z1kxF01khrk0oOocm/HuBeOgnu+rT3h2CPRbiEYil0Xdpa3bfWSHuGpJL0oFDYZfkgNWCx5FO47DmcQSaXaA5QsrErJky8vRxctm7b/R2N+JOa6qURPR06K6X5eQKWf37JexPtKRuNNQ34slIB2h1W7RhOPLZJOAnZX8JitnULPp79323t5jxNdHfu67yvSFTNkspz2pvP83g28NEGzyF34+1tJyzbro7oeqgdqcRLdLtUzPu9i/aclSI1TAc/WxayyuQQqhpsH1+AlYrjflDmsWaaEXpT0YT/j1M5MNvrHGSsne/6kYplU4J2O4qoq29LBrdQVuxAbF6D0U/mzq/H8ViRjGZqJ56Js6JY3HnZDVTF/VGqu5ERxP+PUzkw287chSp16vBU1ZrhwVsd87OtZdFoztob3Yfy6ow+tkMZRENZRWFk50ItCjyk9GEfw8T+fDrXU3BACoFV14O0LaAjZ7lj3rqxW6bnWsvi0Z3EI90zNHPpisvB51PrSXQmrpIqwFwMprw72EiH36p0yF1eupHDMXnSANaF7AtzfLTvtrVLB8PxG92rr0sGt1FV20+0c+mNBppysqgftSwVgcUrQbAyWiunr1AyH0yJNCl0agK2DbcHlvygQ7YLCQdPELtgPTwfvGanfdWXnsNjfZo6dnc86uWg8aivdXilU7iVEBz9exlYnV7nH7ltaoPtE6EvzNW15K6qxjn6eNPefc1DY1YCL1Pqbv3Yqqspik3B3dOVr96N2J19dRm/r1MrEFULUXGSqOR2knj8TrStdm5Rr8n0pffUN+IkBJbaRkBqyWsVtVSPpxAE/59hNYiY/vDTEZDIxYiVaN6twfFZEIEAthKy3A60hBeLxkbPmf6lddqEb5owr9H6UrErKaD7x5yjqtqT0VRqKqqorS0FKPRyPjx4wF45JFHqKqqor6+HqfTSVNTExdeeCG/+tWvAJg1axZ+vx8hBHq9HqvVypw5c1i0aBGKonD//fdjt9tJS0vD4XCQkZHB6NGjGTx4cKt9KhsoWt2m0TqR6SECVgs6rxep16Nvcqsq0uL9BCzmmJPGnerpIDTh30PEmrGwrQdOK1HYdZoaXdQcryQ3WD/2scce46uvvuLo0aN4PB4Apk2bxrPPPgvA4cOHcbvdpKSkMHDgQKxWKyNHjgy3t3DhQgKBAIqi4Pf7cbvdDBmitu12uzlw4ABOpxOn00kgEADg5ptv5tZbb6WmpoYf/vCHZGdnk5OTQ25uLrm5ueRfPjXcP43YiVSNugblhgMoAxYLSSWHkah1K0Ju0dC6GqizGUb7Eprw7yFiyVjYHx64nmb/N7vZvvFL9m3fxf5vdlO6/yCDRw7jhX8WAuqMPz8/n+nTp5OXl0dubm6zWfny5cvbbP/nP/95q9tsNhurVq0Kn8fpdFJZWUlKSgoAgUCAadOmUV5ezu7du/nXv/6F3+/n9rTF5A4dwsE9+3j6zocYPGIoQ8eOYui4kQwdN5r0qORnGiqRqlGfPRVXXi7Wo+X4kpMwV9VQP3oEvnR7eP+23KL7Q5poTfj3ELFkLOwPD1x30lBbx47Pt1K2cQeLFi1CCMGLz/+J9957j6ysLMaMGcN3LrqMsWPHhtU9Dz30UI/0TafTkZ6eTnr6CbfcjIyMZucPBAJUVFSQlJREynGJ85iPrBQHuzdupWj1B+H9nnvuOc4991yOHDnC1tqDjJ48kZQ0O/2daNVo47Ah7HjkHqoLpoQTykXSllt0f0gTrQn/HiKWPPb94YGLNwf37OOfhWvZtuFzDu7eh5QSk8nE/PnzyczMZNGiRdxxxx0MGJD4s2W9Xk92dnb485gxY3juuecAcDqd7N27l+LiYsaOHQvAP//5T55//nkA8seMYPw5kxk/9UzOuWQWJou55y8gAehqyckQ/aHuhBbh20NERiUaq2pI27Id+9c7MFXXhHPxaCkV2qes5DBrX/s//JsPkXNc0rD5W959vZDM5DRuvfVWXnnlFf71r3+RmZkJQG5ubp8Q/O1ht9s566yzuOaaa0hLU90WFy5cyEsvvcTPfvYzstMy+Ljwrzxz5xKyjivkHJccXLuRo+9/SeZRX3il01/paIRvf4hw14K84kh73gGOTZsZ9dSLpG/9BqREGvQErFZ89lS+fmIxQL/JOR4rUkr2fb2LT9//iM/+XsThfd8CcN9997FgwQI8Hg+KomC1Wnu5p72P3+/n8OHDDB06FIAbbriB7du3k5KSwnnnncfE787krAvOw5aS3Ms97Rv01boTsQZ5acI/TsRaLKJg4c2kbf0GoSgIRSJ1AqnTUXvmBDYVLu+zD1w8URQFZ2U14xhAQ0MDF198MVJKpkyZwowZM5g+fXqbrpIaKg0NDXz++eesX7+e9evXU1tby4wZM1i2bBkA3ya7sdi0QTNeJIprqCb8e5i2KhRF5hO5bMx0NfWsXo8UAiElBAIErBY+3L2hN7qeMHy7ay+fvP0eRWs+IDMvmzdefg2ATZs2MXbsWOx2zajZWQKBANu3b8dgMDBhwgSOHz/OnLlzmXz+uZw/5wrOumg6Zoult7vZZ0mkSmFaeoceJlZjrc7rA51ACjWQRwqB0An1+37K+rV/p/D5FZTs3oder2fatGlcdtll4e0FBQW92LtTA71ez6RJk5p99/158/jHP/7Bpg8/ISkpiQsvvJCbb76Z3NxcLdCsg/RFTz3N4BsnYjXWKkYjSEDKiL/g9/0En8fLhr/9gwZnPQANdfWYrRbuvfdePvzwQ5YtW8bll1/ey708tRk4cCB333037777Lr/73e+48MILKSoqwmQyAbBv+66wfUWjffpi8SNt5h8nol3JLGXHsB4tw5zhoGDhzSAlhkY3itWM8HkRgrDOXzEYcJ4xtpevoPs5uGc/G19fw7vvvkttbS2LFy9mzpw53HDJXH566bze7l6/RK/XM3XqVKZOnYrP58MYnIQUPvYCGzdu5IwzzmD27NlccsklzYzq2sqgOX3RNVTT+ceRkMEnZddezFU1NOVmE7BaSNmzDwHUjRqO3u0h+UAJitmkzvhNxrC3T6IuD7uKu6mJX3//JvZ/sxuDwcCsWbOYM2cOU6dORa/X93b3NFqgsrKS9957jzVr1nDw4EGSkpL4yU9+wk9/+lPg1BP+XTXW9kWdf1yEvxDicuC3gB54VUr5X1Hbrwf+GygNfvW8lPLVttrsi8I/RKTx1759F7pgtS3FZMI5cSyW0nJMtU48mQNOWY+espLD7N6ynQvmXQnA8/c9xpDRw1kw7VIcDkcv904jVqSUfPXVV6xevZrx48ezYMECmpqa+L9173H+vCuxRqk6+iLxEtyRtQT0Ljd+i4X6cSN7/P3uMYOvEEIPvABcAhwBvhBCrJVS7ozadaWU8vaunq8vEGn81Te5UUzG8P8B3DlZSKOBDX10cGsNKSXb1n/G2tf+zOZ/fYrJYuaqSTNITk7mN3c/0Nvd0+gEQggmTZrUzFi8adMmfvfA4/zh8ee46qqrOH/RQnLy+67rbbyMtaF9J973KDqPF0tDI6aNdaTsKmb7Ew8m3AQvHgbfqcA+KeUBKaUX+AswOw7t9lkijb8BqwURCKjZBa2qK12i6wI7w77tu7jruz/moWt/zv7tu/nBHTfzctFqkpO1gKJTjQsuuID//d//Zdq0aaxcuZJbZs7hkRt+iauhsbe71iniaawd9dSLWI9VIqSCYjYhpIL1WCWjnnoxXt2NG/Ew+OYBhyM+HwHOaWG/7wshZgLFwJ1SysPROwghbgFuAcjMy47enBDEohuMNP668nLCOv+G/CERYeIt5xTpSwT8fupqnIyTDlDsBBrcLF68mCuvvDLsNaJxajJx4kQmTpxIRUUFb7/9Njt37mRYoxXhkmxuPExO/mCE6Bt2gXgaa1N37UMxGpAGVbRKgwFFSlJ37Ytbf+NFPGb+Lf3C0YaEvwL5UsrTgX+K4Et6AAAgAElEQVQCv2+pISnlK1LKs6SUZ9kd6S3t0quEdIPmyppmKZdDuXlCROYR0fn9OE8fS+3EMegCgXZzivQVtq3/jF9cfg3P3LUEgJycHAoLC5kzZ44m+PsRmZmZ3Hrrrfz2t79FCEFtbS2/uPyH3Pf9G9nx2dbe7l5MxCuPj2PTZgyuJgx1DRjq6hEJHrsTj5n/ESBS4TcIaLZeklJWRXxcDjwRh/P2OB3RDZ6qhVfKD5XyxgNPUVRURF5eHj/4ztzwtr4y09PoPmw2G3fe8UteffVVfr3gJqZNm8Ztt93GmDFjgMT0EopHlbzQxNBvsWBwNSICCoZGF4rXiM7nJWAxM3nRvQnl3NFlbx8hhAFVlXMRqjfPF8A1UsodEfvkSCnLgv+fC9wnpWwzbDMRvX2mX3mtasjVRTzAisRcWXXKGW9b4qsNn/PIT3+JXui48cYbueaaa/rsLF9KiByroj9rdA23201hYSGvv/46DQ0NvPPOO+Tl5SWk8I8HIQ8/4fORunsvwudH+P0IIGA2Uzd2JNJkauZF1F25gHrM20dK6RdC3A58iOrq+ZqUcocQ4hHgSynlWuAXQoirAD9QDVzf1fP2BNE/jj/JgqHR1acCOeJB9jEFIQSpeeP4zhVXcuONNzbLO9/X+OCDdJqa9MyZU4kQquBfvToDqzXA5ZfXtN+ARrtYLBauu+465s2bx8aNG8nLywOg/MMtnH766eH4jlNlMAh7+OkEdWNGYistw1RVg5SSunGj8DnSwvvmv74SoNer9sUlvYOU8j0p5Sgp5XAp5W+C3z0UFPxIKe+XUo6XUp4hpbxASrk7HuftTlrS71uOV2GqqDilc3xH8/k/1nHzzTfj8XhISkrigQce6NOCX0poatKzbp2d1aszwoJ/3To7TU16EjTmsc+SnJzMJZdcAsD+/fu55ZZbuPHGGykpKendjsWZSA8/nyMN58SxKGYT3gHpzQR/yIuomQo5WFM4YLWFB4aeQMvt0wot/TiezAw8WZkxF4ToywT8fv7wxPM8euOduN1u6uvre7tLcUEImDOnkpkznaxbZ+euu4azbp2dmTOd4ZVArEQPFNrA0TbDhg3jkUce4fDhw/zoRz+isLCQRM0w0FFaMhorBj2eAc0DGkOagkTIBaQJ/1Zo7ccxNDax5aUn2fDuG2x56clTUvA7q2t46NrbWfXC/3LZNfNYsWIFGRkZvd2tuBEaACLpqOD/4IP08MoBTqiOPvigc15q/WEgEUJwxRVX8Je//IXJkyfz5JNP8tiNd50SA0BLlcKK77wVncdN2pbtOD7bQtqW7ZgqKii5/uqEqNqnJXZrhb6YqClePHfPo+za/BV3PLWEixdchfkUKwEYEtSRrF6dEfMAEKk6AnXgCKmOZs50dth43N9sEJmZmTz77LO89dZb1CbLU8ZLLNrDz7FpMwJxwhleoH6mtUSQ5Zgc6T3mFaQJ/1boaMHnUwEp1Rfx5od/hWFvFRMmTIBTVPBHqnpCnyG2FUDkymHdOnv42M6qjuI5kPQVhBDMnz9f/XBc8sknn3DkyBEuvPPaU2IwcGzazJm/WIyhrp5AchL1+UPwpdsx1DeS//pKtrz0ZNi9NHX3XkyV1TTl5uDOyeox46+m9mmFjhZ87ut8/ObfeOqOxQQCAbIG56qC/xRECLBaA80EdcgGYLUGYha08VAdRbYTDxtEX6aoqIhnnnmGZ+95BL8vsYOj2iPkLGKsq0exmNF5vaTs3Y+xxtlMr19dMEW1FbjcCH8Ac3UNRmddjxl/tZl/G7QWqJUotTrjxepX/8SKR57m7LPPZsARzylfDP3yy2uazahDArgtQRs9A1cUWLOmZdVRqM3Wjo0mdP7Q7B86N5D0ZR566CGys7NZvnw5DYeO88QTT1A3tG/mhQo7iyQnofN6w6kebEeO0mgwhFXHkYNEIGKQqB85HJ89tduNv9rMv4NEuoAqBj0DNm5m6nX/QcHCm09K89AXWPncClY88jQXXnghv/3tb09Zwd+WTbGlgK9I3n+/uXE3EICHH87nzTczmDHDydNP7w/P3B97bAjvvNMxQ3BrNohTwA4aM0IIbr31Vh588EG++OILFi1aRFOUQbSvEHIWceXlIAIKwu9H6nToG5q7hkcOEkJRkAYDUq/HduRoj9gXtZl/Bwn9YMLnI2V/CVKvQzGbSCk+0ONBGl3lrRd/zxv//TsumPcdHr/voVO2sEpbBlUpwe0+sU1R1G02m2psff/9dD791E59vXpvZs+uZOnSfLZuTWLgQF9YQM+eXYmU8O67DnbvVr3E5sypZM2aE/p7RQFd1HQr1JeiIjuzZp2wQRQVxW6DOJWYPXs2DoeDL7/8kqENFkTjiRGwrwSEhZxFfI406kcMxVZahqHBhS81pZl8CAWGufJySNn3LRA9SHSvfVET/h0k9IPZd+xG6nXqkk5KdF5fWE/XV4T/mCmnc8W13+fWR+9DX3VqLgIVpblBdfbsEwLZ4fDh9eqor1eDu+bMqWT+/HHU1hr5+c9Lw8d++WUKVmuAoiI7K1dmUl1t5Jxz6jjjjAY2bLCzc6eN8eNdSAkpKQFyc72sWJHNihXZjBzZxKxZzvB5oz14hIDiYgupqQFmz1YF/ezZlWzdmkxxsaVfCf4QM2bMYMaMGQCUlpaSmppKSkpKL/cqdiKdRXxpdhqNxhaLw8Q6SHQXmvDvIKEfLLJISyhXf6IXbA5RfayC8SKDnPxJXPzLSVDV/jGJTGt5ekIz/tmzVT18SHjr9ZLvf/+Et059vZ7CwkxWrsykvNyMlJK1awdgtQbYujWZxkZ1YNyxIwkhwOHwsXRpCUKoM/nCwkzWr0/D4fCxcGEFikJw5i4oLzcxe7Y6m1+//oQHTySjRrkpKlIjjufOVQeJujo9Z57ZcMp6+8SC1+tl0aJFZGZm8sILLwB9p2qYP8lG6s69ANSNHdGiMI91kOgu+rXwjzbcVp89CccX22LK1a8YDAi/H4RABAK4BuX2iTiAb3ft5d65N3DP3b9i9uy+X3OnNZWOxRLA7W4+4w/N2h0OXzN1ysqVmRw6ZMFoVBg82I3FolBcbOPxx4dgsUhmznSyb5+Vqip1sM/O9rJmjWrcnTv3hKG2utrIJ5/YOX7cRE6OFyGgqsrI9743gZSUAAsXVoQNwo89NgSAxYsPMWeOqjIqLMzkrbcyGDjQF1YB9VfBD2AymfjlL3/J/fffz3333ce9byxDb0hskRVZErJmyhkYGl0YXC3bLuKRTbQrnJpr/RhoZrg1GshYt4lxjzxN5vpNKEZDu7n660cNRefxIXU66kcMQxoMCZ/nx+N2898/vx9rko3zzjuvt7vTZdrK0+N2qzP+mTOdFBXZueaasVRXGzEaFeCEgXX27EqcTj1CSHw+HdXVRjweHULIsA0gJMSNRklmppfUVFUF9M47GbzzjtpOdrYXj0fw9ddJVFQYufrqCv70p10YDJKaGgNHj5qRUu3zO+9ksGNHEjt2JIWPB3XwCAREWAXVnwV/iIsuuoj777+fTz/9lD8++bve7k67dDRnT3XBlF7LGBCXAu7dQXendA6nYPX7Sdm7X/W1DQQAkAY9DcNOw5eWhicjnS0vPdliG+GVQ3jUTmyXz5cfepK/vb6SpW88z3fGtJlRu88QKfBDRPrIKwphwe9w+FiwoIJVqzIBWLCggrVrB1BcbMNu9xEI6DAYJLW1BkAipUCnk5jNkiFD3KSl+bHbA9TV6UlJCWA0KuzZY2PYMDdnnNHAihXZwUFC4ZJLaggEdGzalIrBINHp1PcsKUm1Ccyc6QTU2b5eLwkEVEmfna2uGPqbn397PP7447z11ls888wzTJ8+HUhMA3AipH3vsZTOfZXU3Xsx1DdiqqkFSVjwAyAh+cBBaicmtanD70sFW4q/2sHfXl/J9274AZNnnnvKRO5G+siH9OMhNYqUqi++Xi9JT/eFBWtoAHjttWwaGvSMGuXiqquqWLVKVf/4/YLcXC/XXXeMF17Io75ex8GDFr73vVLmzVP19zt22DAaFYYNc7N3r5W9e60UFNSxeXMKFRVG/v73AYAkI8PHLbccxevV88wzg6ipMdDQoOfpp/ezenVGeEXh9Qpuu+0oc+fG5u3TmXoEfbmGwd13343dbm9WSD4R6UtpYfql2sexaTOmymr0brcq+JUIwS8EUqcDRZJ08HBC/mid4eiBgwwZMoS7f7KInFNE8MOJmf/x40bKy01ICYsWjeShh/JZskT1xZ83r5KRI5toatKxfr0dISAry0tamp+sLC9XXVXFhg12Fi6sYPz4BnJzPQwd6gZg4sQGkpIU3G4d27adCDqqqjLichkoKHAyfrxauHzXriRcLj1paQHS0nzodOBy6fnsMzuBACQl+QFBQ4OeH/xgLIWFmQwY4EOvVzAalbAh+KqrKklNDYS9fUIDWYgPPkg/KZbgnXeaxxJE7i9l80R0ob+uJKLraUwmE7fddhvJycn4fD4URentLrVIvEpC9gT9cuaf//pKmnJzsJWWIYVARL4oOqEGXOh06F3uhPzROsP5c69kYcGlp4Qvf6RgW706g08+sTNsmJv6elUds3u3le3bk7FYFM47z8nWrcl8/nkq55xTx6RJDezYYQv72+t0sHOnjRkznGFjrNkcYPPmFN57z0FKSoAzzmiguNjGxx+nsWXLBEaNaiI1VVX/eL16HnjgEHfdNRwpwenU43LpkVJgsSgMGuRhzx4rRUV2kpMD/OpXh/nLXzIpLzcBgjvuOIIQqvpn1apMdu604fXqwt4+fj88/vgJ47CiQGOjnlWrMtmxw8bixYd47LEh7NiRxMKFFeF7ExnH0NSk/ubr19vD23fssFFdbexz+YNqa2u57bbbuPrqq5l685ze7s5JtGbEBVXVnEhZAfql8LcdOoo7J4uA1ULStwcx1dYBatV5qdMhAgoBq5W68SN7/QeKBzs+28rYs8/ok4I/WjC9/346X36ZwvjxLubOrcRiCZCaGsBgUBgwQKGqykhaWgCvV4/fDxs3pqLTQUFBHUuWlLBmTQbV1UZmzDihc9+xI4lx41xhd8zp0534fDoOH7ZQUFDHww+XsGRJPh9+6KC21sA33yQxYUJjM/99gGPHTPj9qrHYaJQYjQppaX4OHkxCSkFqqiqMc3O9NDaqv0VkjqCVKzPZtCkVn09HQUEdu3ZZWLNmNG63OlK99VYG77/voKbGgNEo2bEjiTvuGM7evdZmwWMhG4jD4cPj0dHQoGfmTCczZjhZvjwHl0tHTo437H3UVwQ/gN1ux2Kx8MILLzD+6otISk08//+Wsnv2dtWuluiXwj8yuKLWkYb1UCnJ+0tACHxpdjwDHEijnuK7f9ZmO30hx0/xVzv49YKbuP2JxVx/UeLNlNoi2o1TUeDTT+1s2ZLMjh0ndKrFxdawLn/9ejvZ2V6ys73N9nn44RJ0OrDZTiR1C1FYmMnbb6sulqFtNpua5K2uTs9ddw2nrMzEgAFeXC49Oh2Ul5u46qoTAWOzZjkxmwMUFg7kyBEzRqOCz6fjq6+SURTIy/MgJaxalcnChRVhnX9IDWWxqOcbMaKJY8dMOJ161q+343LpGDvWxXe/W8Wrr2ZTU2NEr5dceGEt9fX68IBTUFDHunV21q9XbQUzZjjZscPGvn1WRo5sYt06O0ePmqiqMpCcrJCd7WXu3L4l+EFNA3HPPfdw3XXX8ebvXucnv/6P3u5SuzTzAILwv9EBoT0tT/qlzj9aL+dLT8N12iCqzzodd/ZAGocNaXdUbqnMY0uuob3Nu78vxJpkY8Z3L+ntrnSI1tw46+v1TJ7cAMCLL+by29/mUV9v4Pvfr2DnThvl5SbKykx89VUyHo/A7dbh8QiWLMlHUdSkbpHJ1+bOrSQ728vAgWomydBAc8UVNTz8cAmgCvrqaiNTpjRw5pkNOBw+qquNLF2aj9msDiazZ1fi8eix2/1Mm1bH7beXYjJJmpp0JCUp/OlPu5gwoTF8faFzz5ypDhqbNtmprjZy7JiJrCwve/faMBjUFcS+fVZefjmXmhojZnOAlBQ/W7cms3NnEqWlZkwmhUmTGsL3LXQd48er/uV791r55pskjhwxA4KBA70AzWwA0fc+kRk7diwXX3wx7/9+Fcn768g5LhPajhVL1a7ekCf9Uvi3lK756ycWs2nVqzH72yZCDc72qK91suGv/+D8eVdiS+lbGRKjUx1fe+0YCgszmTnTydKlJWRlefF4dPh8gtpaPX/4QxbffJPEsWNGDh0y09Cgw+fTkZvrwWCQfPRRWngAAFXwhRK2Rc5+IwXimjXqNr3+hBCeOdPJn/+8i4KCOvbvt+DxqPEEOp2aKvr8850sWVKC16snPd2Hw+EnPd3H2rUZPPDAIRYurAivKkKpHDwePXV1egoK6sjK8lJcbKWmxoDBIMnL8+DzqUZiRQGfT4/TaQivAISQZGV5WbUqk717rWGj95o1GcyerRq6Dx40U1+vx+fTMWCAl/nzK7qUiC4RuP7662lsbGT16tW93ZV2iazaZaxxYt++C8dnmzFXVIWFe2/Ik36p9oGuu2mGcvxEkmjpHf797kd4PR4u/UHfUveE9PyhAeCTT+wEAoLqamPYs6W42IrPJzAYJIoC5eVmkpL8mM2S+noBCMxmPzZbgAEDoLTUzK5dqnoo5EoZMtpGJlQLuYwC4XQMc+ZUho2qoPbr4YdLwrl6Qvr2yy+vCad6XrfOzvz5lVx1VSVr1zYvFhOpnw8NGrNmObnqqkp+9KOx+P060tJ83H57KatWDURRBCFPZEVRC+74fIK6OkPw+tXAsOxsLykpgXAQ2pYtyaxfb8fvV+MXzOYAkyc38OabmYwd2xhWDYVUT32pkMzo0aN5/vnnmTx5cm93pV1CWQEMdfXYjpQSygviTbOHdf+9IU8SXvgnql69L/jzbin6lEEj8hk+YUxvdyVmIvX8oAr6Y8dM6HSqr/7y5dm4XHpAcPHFNUya1EBhYSYlJRZcLj1uN8FKUGrEbmpqgEGDVDWHTgd33jkcIWDWLGc4BURkURdQhTE0D7RavPgQ77yT0WzW3pKxNCTMo4vFhNqNzuoJzQcNvV4yYICPgQO9rFql2g/y8jx4veoAUF1tBARGo4LRqKae2L/fwqhRTSxZUsJf/6oObOXlJnbv1uPzgU4nSUvzk5rqZ8uWZKQUHDpk4eyz61m8+FBY4HelIllvUFDQNwIVQ5qGM3+xGKGAP9mKKy8HnyMtXNmrN+RJQgv/RLWSQ98o83jfi09QefRYnymLF6nnD82+CwvVaNz589XArWefzUOvlwwc6GXp0hIAtm1Lpq5OT3W1EUURQeEs8fkEW7cmc+aZDVx9tRrYVVFhZODAE7l92irqEr0t2kDa2m3taLGYkJomtFoIeRC99lo2gwZ5+M53qvj3v+0oCnz5pR6/X7UjCAFnnKHaIYqLLfz1r6qqZ906OwMHejl2LIm0tACZmW5KS83U1elpbDTgcPhITg7g8ageR3PmVFJUZA/3L7pCWSLz5ptv8u2333LPPff0dlfapLpgCp6MAdSNHdUs+jc0u9/54J09Lk8SWuffWT2YY9NmJi+6l+lXXsvkRfd2i9GkL5R5HFSlY5I5J6ENYpHGxZAOfMYMJ+vX23nrLdWFMtIlMSfHG/5bvTqD1aszcDr1DBzow2yW4TZD6iC3W8eePVa2bUtuljsnpNuPFshtCfeOjKEdOTa6tKROpwrgG24oZ9iwJj79VFXDZGT4MJnAYlFXBw6HjxUrsvn6a1s4M+jDD+cjJRw/bkJRBJmZPpYv38OAAT78fh0gsdlU20RkkrmQrQAI5yxKdL0/qCmf33zzTWpra3u7K+0SqfsPEZrd94Y8SeiZf2f0YNGrhaQDhzjrprvwZjioGzMyrmqjRE7v8Pyvf8OoAXn85Cc/6e2utEpLGTl/8xs1oElKwsVSFEX9vqrKGB4I1qxR1Rvp6T7q6tTEaSNHusjO9vLvf6fidutJTg7Q1KSnqsrIRx+lc9FFNSxdWhKeZUPiJFBrabUwd24lNpt6j666qpJ33x2FogiGDWti4cLjbNxop6gojcOHLSxefIht25LZtCkVh0NNZTFqVBO1tXoefTSfrCwv1dVGTCbVRhBKc/322xlhW0ZkviEgHDSWCPenNS677DL++Mc/sm7dOs4eldhZatvTFvS0PEnomX9bI2VrRK4WjLVObKVlCKmGWSeqO2a8aayr56NVaxN6NtSSK2co2+Wnn6ZSVqbORPfutbJiRTaHD5tJSQmEVSJms2rYtFgUxoxp5Nxz61i+vJjzznNy3nl1pKf7GD26iYKCOlJSFEAyaVJDpwu29wQtrRZCrql6PcyfX8GsWbXY7X7+8Q8H9fV6Zs2qZf78CgwGOPdcZ1jwCwFLlpSQlhZg3z4LdnuAzEwvJpOkqsrIww+rnk87d9oYN66RhQvVGInQoDh+fGNYzZXIrp+jR48mIyODjRs39nZXYsKfZCNl917SN3+FFDKm2X13aTISeubfGb165GrBVlqmVtvS69G7Pa0GV5xqfPHxBvw+PxdccEFvd6VVIg2hRUX2sJ5/5Mgm9uyxhoWOokBDgwGv10d9vZ6lS/Opq9OHPXXOPLMhnMhNp4Mrr1SrZJ15ZgM6neqxM3FiA+XlJnbutDFvXmwF2xOJUD+vuKKGyy6r4e67h4e/X7pUDV4LlaMMCX6AtWszOOccdTZfV6dn+HA3TqcPp9PA/v0Wli7Nx+k84e20fr2digq1ZsEDDxw6qeRlZAWyREEIQUFBAevXr0dKmbD2rY7k+W/tuHjbPRN65t8ZPVjkakHf5Ebq9eFKWxB/96mesC90lI3v/wvHwAwmTJjQ211pk1Bka0jfLIQ6ex09ugmdTqLTwahRTZx/fi1er1oVa9OmVMrLTc2EVsj7BlRVktutRwhV8M+Y4WTWLCcTJjRSXW1sVdffFwgZhiNZsyYjXHc4FGkcWVDe49FTUKDeg4cfLmHWLCd2u59AQLBlSxJ2+4nVlJRQU2OkrMwUXhmE2m1q0ifsCmDatGlMmjSJpobG9nfuJWKxX7YkS7rT/z+hZ/7QcT1Y5GohYDGrmTsB1yBVVRRP96lE9EbyuN1s/uTfXLTge+ha8itMIBQFNm60B90XVT/1jRvt4XQNodn8GWc0hAuoV1cbEUJy7JgpXPM2unrX+vVqXptQ/p7QIAAknKonViLrFoQMw5F1DCyWk91Lv/nGFk7+FkllpRFQawjU1urDKwCn04Dd7mPgQMnGjalcc81YsrO9CV9V7NJLL+XSSy+lLCVBO8jJ9ktjjRPb4VLsX+9k8qJ7qT57EoPefvckWWJodNF42pBmbcVrApvY0qETRK4WfMlJSKHDlZeLz54a9/SqiRjl66prYNoVFzE9wdM5hGaxocjW7Gwv5eUmiorSqKvTs2BBBcuW7Q97tBw9aqK83ISiQFmZmsIhFLEbOTsNVe+qrjaGc93MnOlk7ly15GIiqi5iIdojKNp2ccUVNScJ6PHjXc1WO++8k0FhYSY1NQacTgM+n466OgN791r46qskvv3WwsiRbl59dU8w1bTsUyqyRE3zDCdH+abs3Y/e7cafkoS5soZRy15G+AInyRK9y91hu2esJPzMvzNErhZOrrYVvxqZiRjlmz4wg7ueeUT9kKDunaAKs+JiCykpAZYsKeFXvxpOVpaX8nIjgwd7mDdP1eN7vToaGw2YzZLTTlPr65aWmpFS4ZNP7Pzwh2PJyTlRGevvf08PF3eJ9FvvC8KrPdqLH4iOTwgZbN98M4OVKzPD26ZMaWDvXitut+TIETMul5o6Ij3dx4MPqoFikbaD1aszEv4e3nTTTaQOyeKe5/+zt7vSIpEaCdvh0uC3AtegXPwpSej8AcxV1bjzssPH+JNs+K0W9E2u8Od4+v+fksI/ko6ojToaTZxoUb5SSo7sK2HQiPyENXyFCAn24mJr2DddndmLsC++TgfTpqlC/cABC8eOmcjO9jJ4sAeDQeGjjxw0NJxIUx1S70TWxYW+Ibxipb34gWj32auuqmT58hwaG3UkJwf46U/LAdWLyucTYXWa2awwYkQTN988Gq9XMH9+ZbiqWKK5xbaE2Wym/NCR3u5Gq0Tm+bd/vRN/ShKuQbn4HGmAKtj1Lczw68eq7undMYE95dQ+naUzWfUSrWrPgR17uO2i+Wz46z965fwdZfx4F/X1BoqK7JSVmRACkpMVGhv1YVXFFVeovvkDB55wYXz44RImTnSRm+vB59Oxc2cShYWZzXT8M2c2N3yG2jsViUzK1tSkp6hIvV5FgaVL82ls1KHTgcejY9WqTNatszNsWBNOpyEcEZ2T46WuzsCBAxaMRomU8OGH6QnrFhtNZmYmzrLKhA5oDBVrP37heTQOPS0s+AE1jbzB0KIs6a4i75rwD9IZ/X2iRfluKVJ9nSecm/hurCG1xLnnOgE1aVtVlZGbbipj4cKKcH6dSA+XkHF36dJ81q1Tyy6OG9cYTrEMbevFE1l4dZbI8oyhCOnU1ACrVmVwzTVj2bgxlaFD3WRne/D7VWPv7t1WtmxJxucTmM0KJpPk8GELBw5YGDrUzbBhTaxfbw9XAJszp5LLLktsW4nD4aC6urq3uxETLU0apVFP8Z23aBG+naUrSeA6q79PpCjfr//9BfljRpAedR2JzPjxLv75z3QaG/UYDGod21AE72OPqV4OoXKDc+ZU8s47Gbz1VgbDhqleXEKoXkJAM8+WWPPq9GUiA+XgxH2rq9OjKAKdTk0D8core7jlltF4PHo8HgmoKwGHw8eNN5azalUmR4+aCQR0eL2C4mIbw4e7T/KmSlRffwCbzYbP58Pn82E0Gnu7O23SVqlHxxfb1JKCPUBcZv5CiMuFEHuEEPuEEL9uYbtZCLEyuP0zIUR+PM4bSVeLIXQmmjiR8Hl97PxyGxPPPZlo5vEAACAASURBVKu3u9IuikIz7xOrVcFgUPD7dbz6ajZLluRTWKiWNCwpsTB9ujMcyCUljB7twmRSWLdO1fEvW7afhQsrwp4t0ZyKgh9Ornlw113DefPNDGprDWRlecnK8pGV5eWWW0bjdgvS0nyYTBK/X6AohIviqLmQFISQSKnaXOrq9GHf/77g6z9x4kTmzZtHIJT7uq8QvJ+pO/a0Kb+6I56oy8JfCKEHXgCuAMYBPxRCjIva7UagRko5AlgGPNHV80bTVbfLRNPfd5S9X+3A6/YwfcyZCa33fOKJQeEAoh071OpG2dleRo92Ybf7qKoy8fHHqi508uQGUlJUdc0HH6Rz882jWLkyk/HjXUyZUk9KSoDt2218+GF6uCrWqareaY3ISOlQkFZpqRm7PcD//M9+7PYA336rprseNqwJRVEN6YoiKCszsWpVJvX1eiwWSWqqn6oqddackqLWBLjrruHNYgsS9d6ec845/L//9/+wWCy93ZV2aWmi2pqrZ/7rK7utylc81D5TgX1SygMAQoi/ALOBnRH7zAYeDv7/TeB5IYSQMn7ziK66Xba2FEsUlU575I8ZwZNPPsnZZ5/d211pFUWBxkYDmzalsnRpPgUFTvbvt1JaaqagoA6LRaGx0YAQkuxsbzgJWyg//aFDFkymQDiVwZ49Vurr1Zq6l112sp97fyA0Mwd1IEhP92E0SvbutfCjH41l4EAvQ4e6cbsFjY0GBg/2UF2tVgLbuTMJk0mSlBTAbvezYEEFQqiDclWVkfJyU9jInuj31u/3o9PpEj6wEVqu6duaq2fq7r2c+YvFGOrqCSQHPYTS7eF2uiKf4nGn8oDDEZ+PBL9rcR8ppR9wAnFVTHdFbRNaUo17ZBkAOx+8M65W9Z7AlpLMhRdeSEpKSm93pVV0OtVTp6Cgjk2bUnnmmcFhwX/GGQ1UVJjCBsjychOrV2eE9c5ZWV7sdh+KouPZZ/N48cVc6uv12O2BcK3aRBZO3UF01O/TT+9n/vxK0tL81NYaqKoycvy4ifnzj2OxSEpLzYwY4eZvf/sGm00hEFBvWHq6Lyz4rdYADzxwiNTUQDjIC0h4b6k33niDqVOn4g5G9CcyrdX0jXb1tJQdw1RZjbGuHsViRuf1krJ3P8YaZ1ziieIh/Ft65aIfk1j2QQhxixDiSyHEl87qjhmWOqu26SuF2Nvj/T+qRS0SlZDgCA0Aoc9er6p/fvNNNQjpZz87SkFBHfX1egoLM1myJD/s8z9qVBNJSX58Ph0NDWpRk4ULK04qstJfaCnqN+Ttk57uZ8AA1QvqjTey8XoF555bx5Il6mrKYlHVY36/qgLati05rNdfvVo1Gs+fX9ln3GVDQt9sNvdyT9qnpYlqS66e1qPlNOXm4E9OQigK0mBA6vXYjhyNiz0yHsL/CDA44vMgIHpICu8jhDAAduAkvywp5StSyrOklGfZHR0rJNFZt8tETNHQUXweL7974HE++uij3u5Ki0S6IyoKLFmSj8cj8HgEUgo+/TQVmy3AwoUVANTXq9k6k5ICHDhgYdYsJ089tZ/U1AANDYbwjNXj0YWNwJF0REB15dhEIJTyOeSVE/L2WbCgkj//eRfp6b5whbOHHiph9eoMVq3KxG4PcNll1Qwe7OHoUTWtRkpKgDlzKrHZAs3y+fQFd9mGhgaSk5MTPrgRYnf19DjScedk4crLQQQUhN+P1OnQN8THHhkPnf8XwEghxFCgFPgBcE3UPmuBnwAbgfnAx/HU94doz+2yJVfQREzR0FGqjqlCMzMzs5d7cjLRpRm3bk2mqMhOcrLCTTeVsWVLMuvWpVFWZuKTT+zo9Wp93dmzK/nwQzVD5+zZaqRpcbEVvV4hM9NPSkqAo0fNPP98Llu3JvPIIydSG8fqlthSMZlEd2lsiUi31lAx+NmzK8OFcdLT1Tw9a9dmsGOHDbvdH7aR3Hmnmh66oUHPtGnOcA2BjpShTAScTid2u723u/H/23vz+KjKs///fc+ZfZJMMtkISwhLAgIq4Aatiq19RKgVcKvb000fS/u0tcWW+qu1Upfvt7VV+3R5tFqtrbZVseJS61L1K0g1iiAqIBKWGCAL2TOZmcxy5v79cXKGSZgkk3VmyHm/XnklM3Ny5p4z51znvq/lcyVFf/HF6muOms6Fq9dia2ol7MnFO3MazsN1mDv9hHOyR6QGYNjGX0oZEUJ8C3gJUICHpJQ7hRC3Au9KKZ8FHgQeEULsRZvxXz7c9x0sfSlwRrI0vYx0kWgYCoHuJWRWVlaKR3Is8dkomza52bvXETP8q1Y1sXJlE7fcUsb27a5Yc3Pd0OgGWDdqWVnazFPv5nXLLWX8+99u3nsvi6efLughR3D22e39SjcnypFP9n/TGb0Z/NNPF1BZmUM0CtdeW48Q8PjjhXR0KCxe3BG7OcQHdQMBhaeeKsDlUhPeANL5mLS1tWWM8Yfk6oPi9YDCuW58FgtKwD9ixV8jUuQlpfwn8M9ez/0k7u8u4NKReK+hkijCDoCUKIGA9lyaNmIfCLNF+xojkUiKR5IY/QawaZObmTMDSNmzGfr8+Z3s32+PGSI90PvMM9osfOnSVpYt024E+kzdZNIamTz9tDabfeMNTcETSCotsfdNSb8JpHtK40Do8RGHQ2XhQk3Abf36QoqLQ3R0aJlR8+b5e9wcLr20HZOp581Bz6jKlFXRueeeS4tDpa4oQ7+4BIx2BuJxVeHbH325d2xNzRmd4gng6M4c8Hq9KR5JYuLTEXXiDfymTW5mzOiKdejauFGbzXd0KOTnh/H7lZgcs74vp1O7KWirB2LdrSB5F0X8TWmw/5uOxLuxli1r5bzzWlm3TpPCaGmxIKUWUF+1qokXX8xj4cJOtm3L4sEHJ1BeHqCjQ2uCEwqZePfd7JhkRiasilauXJm2hj9Z5YG+thstWzRujH9/CpzpJNEwFDzFhdzz/KOc5i5L9VCOoXc6ot2udjce1wyuzabidqssWtROMKjEcvobGyUXX6zNzN94w83OnU7mzvWzY4eTXbtcXHppIxs2aFkrlZVujhyxUFgYRoijKp5wrLtCjzTpzx8vCqC93Vj6jXXPHgdSiu4Wh0e3j785vPJKHtu3K1itksWLtUwrvfo3E1ZFUkoaGxuJ5Hkwp5m0Q7INn1LRGGrcGP+h9APOFEwmEzNPPAF7Glb1xqcj6gZJn+Hrnbc6OpSYi2HTJndMq2fVqqbYfp54opBNm3Ixm6Pk5Khs356F16vQ1mbm8GEbpaVdnHWWFrDUg8sAVVV2Kiq6WLlSCyD7/ZpYmcOhEgho6aRz5/r48Y9rMka+OBHxbqwnnyzgsce04H9Hh8LEiUGAWPosaMfWZNJcbhs3ugmFTITDmtSzHlPRW2HqpOsx8Xq9LF++nGt+soaV116V6uH0oC93c+8CrWS3G0nSvxxuhBhqKmg69uhNxOH9n/Czn/2Mtra2VA/lGPR0RJNJMyBLlrTT0aHw8sseNm1yk58fPsY1VF9vjQm7rVrVxIQJIfLzw0QiJoSAt9/WevmGQoJFizpYvryFzZs1Q3XWWe3s3Olk0yY3Pp8mGb1hQwF+v2b8nniikEBAYccOzV2mF4llQkpjf+h5/qqqqaQeOWLB7VYpLw8wYUIoNpvfudMZW/WsX19IVpaK1RrFbJbU1tqIRrXXbrutZ/vADRvSM8+/qUm76aWjoGFfBV29swmT3W4kGTczfxi8Amc69ujti4DPz5NPPsns2bNZuXJlqodzDL3TBvUZtpQwZ46f9eu1Game6//EE4Xs3OmKuWV09U7dDy2lVvWrB371Ju5vvOGOuXn0lEc9rhBvuPROX72LxNJ1dpsM0aiW56+vnOrqrN29eRU+85n2WMqs06nGZByiUSgqChMOmzCbtTqKRx4pjom/XXttPRdd1BQT4dOVU9PpGKWz8U+24ZO/dCKu/TXYWlpRAl2oDjtBTx6+6aW9dzlijCvjP1hSsRQbKjPmzWbKzGk88Y8NnHHdKoC0FHfrPcPXjfbcuT527nT1MMqgGaiWFkusUUt9vZXaWhuRCOzZ46CiIsAzz2jBYynhyBELRUVhQJsF66sNfb+6YdSNV+/q4HQyaoPhhRfyeOstN/v22Zkxo4tf/vIjvvCFefh8ClVVDs4/vwWTqefnPfVUL8GgCa9XobQ0SEeHwu7dTlpbzXg8Eez2aEYcj8ZG7VzJn1CU4pEcG7TVG7ND/+7mltPmU/TaZqTFTNRiQQkEcFV7OfjFC0dtrOPW+CcTgc+kAjAhBOdffTEPrPslVe/vovzk3sKqqad38Dc+i+Sss9pparL0MMoATudRP/3GjW7KywPdaYgmfD6F9nYzr7/u5h//8FBbayUnR43N8NetK4sFkvX3r6+3AsRy2zM1wBuPnpZZVWXHZpO0tytce+0sQiETJpPE71eorHRz/vmtxOueCaG1b1y4sJOVK5tYs2YGubkRFEXhuutq6epSeqTQpquURroY/0SegslPPc+hiz6PZ8v2frMJPVu24ysrjZv5OwhOysOzZXuPwq+RZFwaf/1LEmFNSS9rfzUFm99mz/e+3uNAp1uP3oE495IL+Ns99/PoL/+Xnz7y21QP5xgSadHoOv07dzp7GCbdKC9d2hqThygqCrNoUTvz53ciJTz6aDFNTWb8fhONjRZU1cSpp3by059Ws25dGW+9lUNNjY3c3Eiswbse8NQfZ0KAt3dqZXzGkv7bZlMpL++itVVhzx4Hra0WHI4oEycGmTmzK6bPH5+3HwgoMTfa008XUF9vpaXFgscTjgXg4wO+6Wj4AU499VS+/e1vY3c6UjqOvjwFni3b2Xbfnf3+r7Omlq6S4h6qnkSl4fMfacoefhwRVnEerkMqJlSHHVMoRMU999Mxd1bsrpxpGUKunGyuumE1DQcPo0YiaAXX6UXvylGd+G5d+mpgxw4nc+Zowdj2djPt7drpajZH+egjF3Z7FNB8/4oimTmzC69X4YYbZiAlLF7cgcMRYfbsrphcRLx65dKlrbG/h2vUEhnokTCUiSQo9EC47nuPRom5fKJRQW5uhPZ2C2azJBoV3HJLNc8+W9Djc8bfeJ94ojBm9Fev1ozNpk1utm/P6vE50nWVNG/ePObNm0ddiscxHE9BKiaa4ybbJx5nTS225hakYuoWVupE8Xdh9vmouPu+2Hbp1qM3GS74yhe55uY1KOb0va/39rE7nceuBnQf/1NPFbB9exaXXtpIe7vCK6/k8dJL+Xi9SiyLpanJgpQCszlKfb015t5xOCIEAmZ277Zzxx2l+P1aEZPdrqV53nFHKXZ78hWrfYnAxQvX6c8//XQBL744OHHCRO+n5+7r+9+wQavMfestdyz75umnC6iqcuD3KzQ1WTh82B7rzlVcHOLZZ7WYSO/PqfdRVhSJxxNmwoRQTHIjJ0dl3z5NVC/dlT137NhBR0dHqocxLFn5VDSTEqOgrzYilJ80R97z/KOjsu+Fq9dS9PqbRBUFsz/QbY0kEpBmM+/8+TdpbeCT4YM3t9C+bT+XXXZZqoeSFIlmzlJqfvvKyhzM5iher4KqakqeubkRcnIizJvnY+5cP489VsiRIxaiURMul0o4LHA6NSM/aVKQhgYLXV0KxcUhyssD7NnjwOs1s3hx+zF9fxONpy8ROL1WIVEcYySKouLjJPrj7GyVqirNxTFhQoj6ei2rJztbxes1E4kILBZJebkft1ulo0PpodKZaN/659XH/OKLeTHXj44u76CvmNKBYDDIWWedxTXXXMOFN6d2RR7v84/3FCSaMCaKOQK9lAaS70EezxdKT9kqpRywn2v6Tg9HkeqvfJGCzW+j+ANHOw1IiNptRK3WtMzmGSyvPPEcm555kfnz51NRUZHq4QxIb2OiZwGtW1fN5ZefwMGDNsJhLYBpt0uCQRMdHQpz5vhZsaKJ997Lor7ehqJEu41fFL9fYfLkIG53hIYGKz6fiZoaO01NFqxWrW3h3Ln+HoZOd6PoukK6UFp/InArVmgGcuPGo9WwZ53VHmtEAwO7hfpyEyWSoNA1je69dyLNzVqQ/NOfbiMUMlFdbUdVBRMmhMjJOVo53du11V/wXf+MAC+9dPSmpz+XTho/+/fvJxqNMn369FQPJWktnv5SyAeKDYwk49L4tyw6hT3f+zpzbtM6d0lFELXZwGTCN3VyWmbzDJZrb1nDe5ve4ub/cyt3PfsnFLM5LVM/+0M3UEKAqgqEALMZSkqC1NVZsdm050wm+NSn2qmqclBcHOKjj1xUVARoaLBy2WVH+Ne/PMya5WfbtmxCIYHPpyCEGqtk1esAgJj8hD5b1m8Edrsac30kkjuw29WYMF1jo+UYDaJ4gzkYKen41Ni9ex2oqmDDhgJeeikPn88UE3ILhUxs3ZrF1KlB7r23KmbIg0EllvIaT1/BdzgaA8kE5dP3338fgJNOOoloaocCJFdLlC4p5OPS+ANUX3MlE158jew9BzBFIqgOOyF3Dq6aQ5gCQc49fRmq007H7PIhL79SSU5eLqtvv5GfrV7LE795iCu+d12qhzQo4memWVkqhYVhVBXa2ix0dJgpKQkxb54Pp1MFtFTHoqIQDQ2az7+hwUphYYjKSjcNDRZaW48WhwH4fAoffuhk1aqjRu/11900NGgZL4sWdfQoENNn+IlE4PSAa3wB2h/+MAG3W7vBbNhQwBtvaPsYaBXRW4coPhXW5dJE737zm0mEw4JQyARIbLYor76ahxBQWNgZ+4z5+WEcDvUYw68zkG5/Jiifbt26lZKSEoqLi1Me8E2WdEkhH5cBX509N3yDwOQJdJxQgX9SCY7DdSidfgQSs8+H7UgTrv01GdnWEeDTy8/lnFXLeex/HuDArj2pHs6g0GfTOTkq2dkqX/taPf/1X/V4PBH8fk3iYc4cP+ed19rdnaqAjg6t+fukSUEKC0N88ok9ZtDb2hRUFWw2id0eJRyGl1/28Pe/a7PqFSuaaGiwYjJpwc+ODi1rKN7wP/PM0eI03TDrK4P2doUzzuiI1Q/4fGba25WYK0g3mHrRmb6KWLNmRp/xgfjZucOhsmBBJ0uWtBMImAgGtTiVokA0aiISEbhcEdat0zJ7Nm1yM3eun6VL+3fNJHK39X4c7/eH9EmLDQQCvPnmm5x55pmpHsqgGE5geCQZ18Y/Ppsna181UZsN1WknarMStdmQZjO2ltaMa+sYzzfuuJFv3PH/MXX2zFQPZdAsW9bK4sXtnH22Jti2ebOba6+t4/rrDzN3ro/Nm90880wBNpvKzJldNDWZ8fkUsrMjeL3a31KCqkosFomqgskUxeWKMGFCiEhE8Ne/FhGNaoFlvd1hcXEoljUEHLMCOO+8lpj0tP7+nZ0KFkuUxkZt9j9xYhAhoLHRQn29FbtdjX2u+DRLnb4M6tKlraxY0RQruJo/vxObLYrFIjGZtJuZxRLFZJIUFYX5wQ9msHGjm5wcdcRSWBPJcadDnojdbuehhx7iyitHpwhqtEhFZk8ixrXxB+0GsO2+O/GXTqZt/jxMahSpdFeEKgpKoCttq3qTwZnl4vyrLsJkMrFbaaO2kLTVPU/EsmWaZr+eDrpqVROrVjXx4x/XxGbEy5e3csst1Sxc6CMQUNi718GhQzZUVfOFl5WF+OxntepWVRVkZUX50pca8HjCBIMmrrrqBCorc1i0qINHH/0It1ulpcUSuwHoPn89iKsrkcYrk3Z2Krz1Vg4tLRaamy00NFjp7FRoaTHT3GzhyScLeeqpo32MN2zQiqq0DKWjqwg46pp64YW8WMzDbldxuVR++9uJBIMmVFUgJYRCAhCYTFBXZ4uluepKqcMx0r2DwumW8imEYNasWUyZMmXgjVNMvEBk2cOPc+iiz6c8hXzc+vx7oxdZ6AVf0mxGqCqqw37MkizZ5gzpxMG9B1i76mt89abrOe/y9BN+6w8hEheHxc+WFUXLDLryyhNobrbg8ylkZak4HGGCQUFlZQ6KIhFCU7z84x9LmDy5C6/XHMtz/8lPqrn11jL27rUzZUoXZWVdzJ3rj/ncAV5+Oa9Hds/LL3uIRqGiIkBVlYOODoVQSNDVZUJRIDc3hM0Wpq7Oyvr1heza5SQUMrFnjwMhYNYsP/v3O3jwwWK2bcuKuW1sNi3wvH+/PXbD2Lw5B5/PjNMZQQjw+81IKTnzzDZ27HBRV2fjnXeycbmiVFQEemQbDfW4DxQUThU7duzgscce4/rrr0/L3tXx9CX7kGwKqCHpPMroS7GgJw8RUTEFg4hIhKAnr8eSTP8ibU2tPdK00j0mMGn6VKbPncXvf3Inn3y8L9XDGRL9+af1GXpxcYhgUDutg0ETM2YEqK+30d5uweVSWbjQi8UiaWkxU19vpbnZEkuN1A3/jBldLF/eEgvg6hLRb7zhZsuW7FgXMj0jpqHBissV4bLLGsnOVpFSYDJJSku1iuPc3AinnNLJnDmaeN3GjVrzmRkzAoTDJg4ftmEywdatWVx3XQUbN7r5+98L2bPHwfTpXWzapOnrBIMKQkgsFohETEyYEOTcc1tpbrYwfXoXLleEvLww+fnhmJxDdJgpMLocd+8gcCrTPKPRKHfffTdvv/02Tqdz4H9IMT2ye0yCSLYroSt5rG2LMfPvJj5H1xQMYg50oTrt+KaX9rj7pkua1mAxmUx8/9e3851lV/Lzb/yQv/7xzzgcqdVCGQ69s2I2bChg40Y37e1mbLYoWVmScFhQU2MnHNYEzPx+hW3bsrFaozgcUaJRrReA16tQX29l1y4ty+eWW6oxmbQUyvgCKH32Hy99oOvh+HxmolGt6KqjQ6GtzcLhwzbs9ihut8ott2iy01dfPRuXS5NOfuedHPLywkyaFKSx0UJnp5mqKjMlJVrzFSFgwYJOXn/dTVWVtlLweMLMmhXgyBErRUUhTjmlE4dD62ZWUhJCSi1mkZOjYrOpPeoVhspAQeGx5h//+AcffPAB1991Cx3Tskh9bW//JJvdM9a2xTD+cSSTo5suaVpDIa+ogO//z+3cfNU3ufvuu7nppptSPaQh0TtPHmDHDiednQrhsGDx4g5+8pNqrr12FgcO2Jk5s4uysgCvveYBJPn5YebO9bFrl4v58zt5442j3cMWL26nO+TTQwpabzovJWzfnsVbb+WQn69JIpSXay6fqioH5eUBZs0K8P77JiIRE2ZzNDYL37Yti/Z2c3dDGonZLKmvt2K1SkIhExMnBmlutlBSoo3l7LPbefDBCXi9WhaTfiM5ckTLSsrN1YK6waCm2V9efrQPcnu7puSp1yukQ07+SFBdXc1dd93F/Pnz+ezFF6R6OEmRrG7PWNsWw+0zSNIlTWuonHzm6Vzyza/QSpBDHpW6IpFRAeBEejdPP11Aa6uFsrIuLr64iXXrqjGb4bLLjrBkSTuXXXYERdEycEpKQpjNknnz/Fx6aSO7djlj1cQTJoRiQdL4LJcjR7Tg74YNWvB10aJ2zGYtw0YIreJ27lwfc+f6WLy4nexslaKicGy/2dkqzz/v4Z13cliwoJNFizoAQVubmUBAIRLRViagba8Hmrdty+qWa9BWIQUFIbxe7c5ktUqqquwEgwp2u8o557Szbl11rEtaQ4M1ps2TLqmZI8Fdd92F1Wrl9ttvx9RXAUOakWx2z1jblnGp7TMcBqPfka5Eo9FjLpxMqv7trXcDRwuPoOcMV1WJ5b2fdVZ7rKJ340Z3txbOUd2b+IIq0LqC6X/rUtCXXdbIe+9lsXGjm6ysKBUVfpYsaefCC7X97tzppLnZEivYWr++kOZmC1ZrlAULOlm4sJNNm7Qm9W1tZlpbzbFsnuLiMBUVAbxeTadn69YspNQ+TEuLGYdDE2rLylLJytIa3y9e3M6yZUeD4VLCmjUzYo/vvnvfcWP4Adra2qitrWXOnDkZNWmJBXL70e0ZKdtiaPuMAvoXqHj92Bpb4iqAj9XviN8+3bKCdMN/sOoAT/z2Ib5z582AJbWDGgSJ9G76mt0qSt8ZK3v22GONTHpnskDPGwpoN4AHH5xAKGRi+vQu3O5ILN//vfey6OhQyM8Px1JS4WgDdEWR3HprNS+9lEd2tkp9vZZ2WlAQprAwjMUSxes1M39+J0Jo/Q1yclTmzvXxox/VcMEF82hrs1BXZ2XBgk7OOae9h2xDvEyE/hjSV4Z5MIRCIR5//HHO+c4VmIvyyKvIy5hq3t42YNfN3+vTBiSrDTRSGMZ/APQvL2d3FdamFgITS/BPK43T9k9s0DOh/2/tgRpe3/BPstzZrPvWD1I9nKTpq/CoLyPXn4xBf/IG8a+tWqXdbBRFyw568MGPefZZbQVRX2+lsVFyySVNPW4Weo5+fNcwAK9X6XYTaUv8jRvdMb+8y6Vy3nmtOJ15zJ3rj61UKioCvPeeJknd0GA9Rq8nGaG2TLwBdHZ2cuONN1JZWUneKTM55ZxPpXpISTNYGzDWk8XMcJqliPjUK7PXh5AS5+E6LG3tfaZr6SSb3pVKzjhvCSuuvYp/PPw46995Jeb/T+fl9FALj/rKWOkvk6W3xo4QWrPzCROOauTrxr2oKNzDwMaP8Z57tDG+8YabnTudnH22JiO9alUTDofmvnE4VC66SOtcpstIxLuo3G5N3qGkRGtiv25dWY80zr5y8vVCuEw0/Fv9h7j62q+w5d0tfOcXP8koww+DswGpSCE3Zv79EP/lKV1BolYrQtU6gLV7cvuNxGdKVtCXb/w2u7e+z6/X3kbZCeVMnlGW6iH1y1AKj4bTZauvGbXu6umr01V/Y9T18PXeu3o1rr4P/b3i9Y06OpSYq2fdujL27bP3aMsIAwu1ZRKVlZXceNOPEEJw21/+lxMXD+jCTjsGYwNSkUJuGP9+cNbUErWYcX/4EUoggKmrC9VuRwl0Af1H4jOl/6/FamHt//6MNRf8J4//+kFu+J/bUj2kARmMkRuMfHIiEhlyvX/Avn32Nf6/YgAAHwFJREFUmKunt4slmTEmo5q5bJk2xvh+A+vWVcfy95MRZssk9FVndGY+k2eWccOvbmPC1MkpHtXgOOq+OYSjtg5fWSnhPO277csGpGKyaBj/fohkOXB/8BHSaiHidGLu9GHu9BF2Z8elayXuHpRJ/X+LJpVwx2O/pySDLrJkjNxI6dH3NuQmk1YPsGBB4mBxMi6l+OcGCl7HZ/Po75+pM/q+kFLy6quv8s6BXXz1puuZNqeCO596CJFhHzLez985fSo5e/aRvbsK76yZSIsFJeCn5bTPsHD12h6+faOHb7oh5dFGXxYzqsMOJhOmYHhAMaZM6/87ddYMrHYbne1eNtz/COmaAjwY4v3eA8knJ7OveJYtGxnZg2RVMzN9Rt8fH3/8MV//+te58cYb2bbpLfydPoCMM/zQ030Tzs+jY9ZMojYbWfuqCRbkceiizzP5qeeP8e23nDZ/zJU+jZl/P5h9XXRUzMBZW48S6EJ1OfGWT8ekqkm1W0umYjjd2Pj0Czx0+69ob27lyzd+GyFERtUA9GYwaaFD2Xd/jwfieM3QSZa2tjZ+8eDvePmxp8nKzeEbd9zI0itWoZgz1yz1dt+EPbm05bqxNTWz7b47Wbh6bULfvmfL9jFN8wTD+PeLvhRrP/GE2HNmrw9/QV4KRzW6LP/SpXzy8V7+fu+fCPj8XPfTH5DJC8TBpoWOJemsmjmaSCljs/p///NVvvC1K7ji+v8iKzcnxSMbPv7Sibj212BradUmjA47QU8evumlQP++/bGeLBrGvx8yyW8/UgghWH37jThcLp76/Z9paWjklzffjt1uT/XQBk0mzKyPpwydgWhra+Pevz/Cnu07uOOx3yOK8njwzedwZmelemgjRstp8yl6bTPSYiZqsaAEAriqvRz84oVAeiWCZO6UbgwYjt8+vnnDwtVr017yOR6TycRXb7qe/1r3ffbt2E1XV1eqhzQkMiX3/Xj25wPU1dXxi1/8ggsuuICn7vsznuJCuvwBgOPK8ANMeOn/ETWbUQJdWDq8RBUzvrJSPFu2A+nTxQsMbZ9RoezBv1Jxz+8xRVQiLifBfA/SoqR1wLcv/N5OnNlZRMJhKl96nU9//nNMbEz1qAbHcPL8DYbHli1b+O9vfQshYMnKZVy8+suUVkxP9bBGBU/lVk6/6psICUJKpEkgzQodsyswRSJs7rZnyej8DAdD2ydFeCq3UnHP/Qgpta5g4TDOw3X4J5WkveZ/IvSZ2Wt/f57frL2NhUsWc+N/f4/p0zPnAj7eZ9bphJSSt99+m66uLs455xxOPvlkLvnmVzj/qosonDgh1cMbEfqSYai4615MUW0yLYUACaZQmKyq/TSdvSj2/+mSCDIs4y+E8ACPA2VANXCZlPKYXDchhAp82P2wRkp54XDeN52puPs+zD4fIDAFQ6h2O1IxYWtuQVoy9177ucsuJBjo4i933cflV1zBsqsv5so1XycnLzejs4EMRoZQKMSLL77IX/7yF/bt28eshScy67LPADb+8wffTPXwRoz+9HpyPtqLarehdAURHL0BmP2BlLh1BmK41uhG4FUp5c+EEDd2P/5hgu0CUsr5w3yvtCVe/M1ZfQiJRJgEIiox+/xEnA7MoUDaVfcOBpPJxBe+ejlnr1jK3+65nxce/Tt11Qf56SO/TfXQDFLMiy++yD333ENzczMzZ87ku3et4+wLl6Z6WKNCfzIMANKsEHE5Ubq6EGoUKQRRmzUtZvq9Ga7xXwGc0/33n4DXSWz8j1viZwJmrw9pEghVghrV9IQFKIEAqtORlnf/weL25LH6th+y7OpLiHYrizU2NvLoo49y5ZVXUlxcnOIRGowF+/fvJy8vj7y8PJxOJxUVFVx11VWcccYZ1Bcfv3kk/aVqdswpP6oIkJ2FUFVEKEz7SSf0sbfUMtxvqVhKWQfQ/buoj+3sQoh3hRCVQoiVfe1MCHFd93bvtrekrkH0YOgt/qY6nZpT2SSQAohKRFSy53tfT8u7/1CZOmsG004oB+DVvdv422OPsWLlCn7605+ye/fuFI/OYDSIRqO8+eabfOc73+Gyyy7jD889Rl2RoPySJfzo8d8y9cLFx7Xhh/67be1Zs5pgcSHSZMIUDCFNJoLFhexZk56p4QPO/IUQrwCJIjWDaQBbKqWsFUJMB14TQnwopdzXeyMp5f3A/aBl+wxi/ykjfiagOuyYQiGtJiAQIGq3EzWb8VZMo/qaK1M80tFjyYrzmb3wJJ5+4FFe/tvTPPfcc5x00kncf//9mDO4WtPgKE899RR/+ctf+OSTT8jPz+fq73+D86+6ONXDGnP6q/1pWXQKH/z8x6OayTOSDHhlSik/19drQogGIUSJlLJOCFECHOljH7Xdv/cLIV4HFgDHGP9MJL5owz95ItlV2scK5eXimzYVJeBnzw3fSNuuXiNF8ZSJfP3WtVx1wzd4fcM/aaptoHGi1h3smT/8lXmLFjJ97qyMSxMdz9TX1zNhgjbve/29t7HmZnHDmtv49Of/A4s1czq/jSQDddtKl0yeZBhWnr8Q4hdAc1zA1yOlXNtrmzzAL6UMCiEKgLeAFVLKXf3tO1Py/Hv33bTXNeCorSeYn4f3hPKYnz/T+/4OlbamFq5ZfAGhYJCJ00o5/zOfY+nSpcyYMSPVQzNIQCQSYfPmzaxfv5533nmH9evXU1ZWxoGsLmwOe0aKrY0G6TyZSzbPf7jGPx94AigFaoBLpZQtQohTgdVSymuFEJ8Cfg9E0WIMv5JSPjjQvjPF+MPARRsLV689tqTb6yNYkJeUQFym09HaxlsvvMYbz/2LD996l2g0yg3/cxvnrFreQ+fFSBlNHZ2dnTzw3GO89NcNNNU1UFBSzPlXX8zy/7yE7Fz3wDvIcAZjzAfbaH2sbxRjYvxHk0wy/gNx5vKrtbiAKW7WFJXYmppjVX/jhdbGZt7856ucecF/4M7P44VHnuS5Pz7GqeeeyfkLz+Tkk0824gRjhKqqNDc3U1RUREdHB+cvW8bcMxaw/EuXctpnz8xodc3BMFhjPpjJ3GD3PRIYFb5pRDqJOaWavMJ8Pv/ly2KPc4vyyZ9QxHMP/Y0Nv38ER5aL+Weezg/v/TmKosS2M1YFI8fevXt56aWX+MfLL5BXmM9dz/4Zitw8VPk87vzjV7E2nvjZuK2pmVCum0hRci0U0709Y7IYxn8MaDltfkKtn+NZHTRZFi/9DIuXfga/t5Ptm99h28a36GhpjRn+u66/mXAoxOkVJ3LiiSdSUVGRkQqj6cArr7zCAw88wL59+1AUhZPPPJ3zLl8Zc72NJ8MfX6Xr2l+NudOH6rAT9uQC/bdQHMxkLp17eRvGf5TxVG5l8lPPE5hYgq25BcXnxxEMsed716X8zp9OOLOz+NSyz/KpZZ/t8bzVZmXXu9v59/OvAGBSFJZesZJv/p8fAbBry3YmTZ/aw3AZqwSNw4cP849tm1iychnu/Dxqoh2Ycxysvv2HfHr558gt8KR6iCmh92xczXKhBAI4D9fR3m38+1uZD0bqPZ1X/YbxH2ViJ1qRi65JWtqc2evDs2X7cZ37P1J8+86bAWhpaKTq/V1UfbCTiWVaY4yAz88PL74GgNwCDxOnlTJp+lRWnPUfLFq0iGg0SjgcxmazpWz8Y0kwGGTLli1s3ryZyspKDh06BIC7wMOSFedz/lUXsezq8Zeb35ves3H/5Ilk79mLudOvySwP0LdjoHTPeNK5J4hh/EeZdF72ZRKe4kLOOG8JZ5y3JPac2WLh1kd/xyd79lGzZx+1+2vY8uobnDylnEWLFnHw4EEuueQSiouLKS0tZfLkyZSWlnL22WdTWlqawk8zMrS2trJr1y6ysrI4+eST8Xq9fPe738XhcHDaaadx+eWXM/OCTzFp+lQgM3vijga9Z+PhPDf+yZOwtrVja2pOqoVisvn8g7lRjDWG8R9lBrPsS+fc4XTEYrWw4OxFLIiTywVNhqDOJPBGXVzx3euora6hrvogH732Kt62dhwnTMJy6lQ+eHML96y5hZKpkymeMokJpZOY45lCWVkZpaWlaRNbiE+Hffjhh9my50P27dhNU20DAIvP/ww/+o/5UFTI/13/ABUnz8VqHx+rnd4MdA15KrdibW7B/cFHqE6txaKtuRUlEKBt/lz2rBl5w5yuhV9GqucQSdZQJ5vqlYqUsPGIt60di82K3eFg7wcf8cyDf6Wh5jANBw/TcqSpx7bFxcVMnTqVKVOm8LWvfY3i4mI6OzuJRqNkZ2eP2Ew63ri/++677N27l4MHD3Lw4EFqamooKiri/vvvB+DLX/4ybV2dzJg3m5knnsCMebMpP3kuDpdzRMaSyQx0DcW/LkIhsvYewOzzo2Y58c6cjrRYjotrzsjzH0WGXOTRj97HeC8ESwe6AgHqDhzk0L5qag/UcGjfJ9Qe+IS6Tw7x6xf/RkFJMet/+xB/vvN32J0OCiYW4/bkkZWbw5pf3YYzy8W7r21m93sfYrFYQAiiEZVoNMpVN2g+3hceeZL33qjE19FJR0srbc2tmC1m/lj5TwBuv2YNb/9rIw6Xk5JpU5hYVsr0ubO49L+/CmirGpPp+BZPGyoDXUO9X3d/+JGmuOtw0H7iCcdsn6kYef6jSO9sAREO4zhUz6nX3kDTmacfY9yTWfb1jg1YWttxHjyM+4NdLFy91nABjQF2h4NpcyqYNqeiz20WnL0Ys9VCU20DTXVH8La10XCwFqvNCsD7/36Hpx/4S4//MSkKV675OkIIGusaqD1QgzMri+LSScxacCJ5RQWxbVff/kO+9fMf487PS7iyOJ4N/3DdngPF13q/rgS6upusdyXc/njHmPkPgfiKXUtLG9l7D3Tr+EfpmDOrz1VAfyd3/KzE0toeE4hT7faYQFymL0fHC1JKoqpKNCpRzMpxbbBHipFwexozf41kZ/7GWTkE4jW9nYfrkIoJhEB1Oohku1AdzlhnHx395LY1tfZo/+ap3ApoKWFKwI/Z68N58HD3fwn8kycm3KencisLV6/lzOVXs3D12th+DFKPEALFbMZitRiGP0l6rKZNos/rqD/iryGiErPX151W+UUt0NvSivuDneRu+xBLcytBTx4iHCHoyTtm+/GAcWYOgfiTTPEHQEqEquKfrGXwJFo6DnRy6ylhwYI8zF4fqt2Od+Y0QJuh5Oz6mILN7+Cp3DrgjcTAINNw1tQS6RW0HsgF03sCBMSuIVtTM8GCPHbdvAbQVHVFFDpOqAABObv3EnZnsfuH38I3vbTH9uNldW34/IdAfO6uNJmQikLnjGmE8zT1w0SpnMnk++uxAX15KsJhzaWkmLp/FObcdjcRpyNt9UIMDIbCYCth+2uk3ttls3D12h7XS5snF7PXRyg/j+prrhy3xZbGzH+ItCw6hW333cm7f7iLwKQJSLO536Vjf+3feqOvLFzVB5HdSqAiGsVXVorqcJLz0d5Bz5IMDMaKobgk+3PZJGIwbqKhrCrGA4bxHybx7pr+lo6DObn1fQpVRahRolYr3vIZhPPcsZM42RtJXxgxA4PRYKguyWSvI53+DHrvczvisg/7ejkeMbJ9xpBk8v3j6St7QZq0kzdRZgQwYLqcUVBmMFqMVb1Kn9eGkJj9gR7ntq2xCYkkVFg4Ls53I88/DRlsmXdfolA9jHycXgjQpx80/n3TWWPcILMZKy2rvq6NiMuZ8NyWJgh58tJOXyeVGMY/jUmmWXQ8C1evRYRVXA01KIEuVIemXdLbqA/3AjU0iAz6YqwkjPu6Nubcek/Cc9vW1EzlEw+M6BgyHcP4pzm9Vwu6PzOR4c3ZXYXtSBPSbCZqtWAKhXAeOowSCvbY53Au0P6yLIwbgMFYShgnWkmns35+umEEfNOY3oGrsgf/2m8wTfF3gRBa5lHcb8Xf1WO/g82siCdRloUIqyz4zo+N4LHBoAO3I02ic9vW2IS1ucU4P3thBHzTlERB2ezdewhMLIk1hYGewbQlZ6/C3tSMNCtIRdGyhSIqXYX5bNy44Zj9Dyb4rNO7Gb2ltZ3sPXsRUWg+Y+FxH0wzSA/6cz3Gn9sRlwNbQ+O4CfaCEfDNeBIFZU0RFVtzSw/jH++r984pJ7rfhq2l9ajPf0IevunHNi4ZqsZ472W181AtCEEkyxFbCejjH82Ly4g7HH8MRSY9kesx/txeuHotIoqR3JAAw+2TpvSVx6z0k69c/ZUvIi0KvrJSWk6dj6+stLtR/MhplfReViudPpDgn1QCgKWlDdeBTyh67d+jtsQ25C2OPwbznRoFXiODYfzTlEQVwcF8D9Js7tNXP1b+1ojLSfbuKvK2vk/UZsU/eSJhT25M4VTp6iKS7Ro1ozwSImAGyTFWxYCjZdAHU1k/3jCMf5qSKHAlLQp7vnddv8Zdl53Y/PyjbLvvzhE1/PrsTESh9ZST8c4uJ5zrxhTs0tRID9UCWgzJP2XSqBllYzY3NozlCmu0DPpwkhuOdwyff5rSX45/qoSo+ioO0wto3B/sIpLtwj9lUkzkbjSMsr90Iq79NT1jG57EsQ2DoZNMMeBIxV4Gk6I5mHTSdG6gnmoM45/GpFvj576Kw/QCmoQl96NR4HPafIpe24y0mLs7MQVwVXs5+MULR/R9MpGRDIQPVAw4UjUf8Vr7qsOBb+pkpNU6YgY93a6jdMEw/gZJM9DsbDQLfOKNmq2pma7CAszBYPfM30FwUh6eLdvHrTwvjHwB3kDf90jIhMSPueOEClyfHCRn917aTj6h33EbBn34GD5/g6QZyH86WgHn3r5nc4cXW2sb/kkltJy+gPYTT6CrpHjc+/xHOhA+0Pc9ErGX+DGHPbm0LTiR9pPmEMr3GMZ9lDFm/gZJoxv3irvuJW/r+wB0zCk/ZpuRvmh7zzDVLBdKIIDzcB3tnlzAyOCAkRdV6+1eibgcRJwO5tx6D/7SiUSyHJh9/mG5+cZKCM7gWAzjbzBozP4A3tnlMdfOUF0Lyfqns3dVYfH5ULqCqA47IXcODr8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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
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],
"source": [
"data, distr_idx = sklearn.datasets.make_circles(n_samples=400, factor=.3, noise=.05)\n",
"\n",
"for (kern_name, kern) in [(\"Linear\", LinearKernel()), \n",
" (\"Stud\", StudentKernel(0.1,10)), \n",
" (\"Gauss1\", GaussianKernel(0.1)),\n",
" ]:\n",
" (sim, dec) = kernel_mean_inner_prod_classification(data[distr_idx==1,:], data[distr_idx==0,:], kern)\n",
" plot_with_contour(data, distr_idx, sim, 'Inner Product classif. '+kern_name, pl = plt)"
]
},
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"In this dataset, the Linear kernel is not a good choice, simply because the classes are not separable linearly in input space. Gaussian and Student-t work, and Student-t shows slightly better robustness properties."
]
},
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"metadata": {
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"source": [
"## The kernel mean map\n",
"One of the objects we looked at, the kernel mean map is particularly interesting. In fact, for so called *characteristic* kernels, the integral\n",
"$$\\mu_\\PDK = \\int \\PDK(x, \\cdot) \\mathrm{d}p(x)$$\n",
"preserves all information about the distribution $p(x)$, like e.g. characteristic functions, while not even assuming that $x$ is numerical (remember that the only restriction on the codomain of $\\PDK$ is that it be nonempty).\n",
"\n",
"When we have an estimator $\\widehat{\\mu}_\\PDK$ and a function $f \\in \\RKHS$, we have\n",
"$$\\int f(x) \\mathrm{d}p(x) \\approx \\prodDot{f}{\\widehat{\\mu}_\\PDK}$$\n",
"because of the reproducing property. In other words, integration for functions in the RKHS $\\RKHS$ is just a dot product in $\\RKHS$. The unfortunate restriction being that functions in the RKHS are real valued (or complex valued in the more general case).\n",
"\n",
"**Lemma** If a kernel is *strictly* positive definite, it is characteristic.\n",
"\n",
"Thus the Gaussian and Student-t kernel and many other kernels induced by densities will be characteristic. The embedding gives us a distance measure between distributions for free.\n",
"\n",
"**Definition** The *Maximum Mean Discrepancy* of two distributions given their kernel mean embeddings $\\mu_\\PDK, \\nu_\\PDK$ is defined by\n",
"$$\\mathrm{MMD}(\\mu_\\PDK, \\nu_\\PDK) = \\|\\mu_\\PDK - \\nu_\\PDK\\|^2_\\PDK$$\n",
"\n",
"Furthermore, marginalization, the chain rule and Bayes rule can all be represented as operations in an RKHS. Also, independence tests for random variables operating in a RKHS have been proposed."
]
},
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}
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| gpl-3.0 |
charlesll/Examples | PySolExExample.ipynb | 1 | 96528 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Charles Le Losq\n",
"Friday, 22 May 2015\n",
"Modified the 16 June 2015.\n",
"\n",
"Geophysical Laboratory,\n",
"Carnegie Institution for Science\n",
"\n",
"Example of use of pysolex, the library using the software SolEx developped by Fred Witham, University of Bristol.\n",
"\n",
"From the header of the solex.cpp:\n",
"\n",
"\"An object that calculates the solubility relationships for basalt given basalt compositional parameters and PT conditions.\"\n",
"\n",
"Difference with the SolEx GUI software:\n",
"- C++ code modified to perform only equilibrium calculation at given P, T, X. \n",
"- The core library is written in C++, only functional modifications have been made, the calculation stays as it was written by Fred Witham.\n",
"- The C++ library SolEx is not directly wrapped to keep it running without modification and for simplifying the wrapping process. Rather than a direct wrapping, I created a function pysolex.cpp that is wrapped using SWIG. Then, in the setup.py file, the distutils function calls the wrapped version of pysolex.cpp with knowing that it has to use libsolex.a! This allows to keep a clean C++ code for the SolEx core library that can be used independently of Python.\n",
"- Results are in a SWIG array... For now, I only found a way to have them: directly reading the memory block were they are stored with ctypes... It's working but such low level in Python seems not ideal (=nice) to me?\n",
"\n",
"Let's start and call the useful libraries:\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np #for handling the numbers/arrays\n",
"import scipy #for a lot of scientific stuffs, optimisation, interpolation functions, etc.\n",
"import pysolex #we import the pysolex library!\n",
"import ctypes #for reading the SWIG array output\n",
"import matplotlib #for doing nice graphs\n",
"import matplotlib.pyplot as plt # For doing the plots\n",
"from pylab import * #for doing nice graphs\n",
"# We need this in this case because we use IPython notebooks, but not needed in a .py code\n",
"%matplotlib inline "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, now we start by defining a starting chemical composition of interest (warning to put dots so that Python interpretes the numbers as float and not int!):"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"CO2 = 4890.\n",
"H2O = 3.67\n",
"PPMS0 = 3560.\n",
"PPMCl0 = 1572.\n",
"Si = 52.12\n",
"Al = 16.38\n",
"Fe = 5.82\n",
"Ca = 10.72\n",
"Mg = 6.71\n",
"Na = 2.47\n",
"K = 1.89"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we define the value of pi to use, which is the parameterisation described in Dixon(1997). This value is not used unless piswitch is set to 1. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pi = -0.05341"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is the wt% of SiO2 that was used for the SiO2 only parameterisation. From Fred: \"I think (I should check but dont have the code available now) that this is no-longer used as the SiO2 wt% is calculated from Si mol%.\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"SiO2 = 52.12 #should match the good value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the pisol switch: if 1 solubility is based on the value of pi, if 0 it is used on SiO2 wt% only."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pisol = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now a second switch to determine if pisol is given or should be calculated: if 1 then the value pi is used for solubility calculations, and if 0 pi is calculated from the composition of the melt."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"piswitch = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's fix the other parameters of our system: temperature, pressure, and oxygen fugacity! We will start with a fixed oxygen fugacity, pressure and temperature:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"T = 1153.; #in K\n",
"P = 100.; #in bars\n",
"NNO = 1.8;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, for this single calculation, SolEx has a flag for terminal output, but it is not working in the Notebook. So let's put the flag to 0:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"flagout = bool(0) #has to be a bool value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And the function to call is pysolex.pyex:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Swig Object of type 'double *' at 0x104ec6de0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output = pysolex.pyex(H2O,CO2,PPMS0,PPMCl0,Si,Al,Fe,Ca,Mg,Na,K,pi,SiO2,pisol,piswitch,flagout,T,P,NNO)\n",
"output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oh... Here is the result contained in output => a SWIG Object containing double number... To read it, I only found one way online: reading directly the memory block allocated to this Ojbect using ctypes:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wt% H2O = 0.98987741142\n",
"PPM CO2 = 2.74547200339\n",
"PPM S = 0.0290543523075\n",
"PPM Cl = 1397.16576496\n",
"Vol% Exsolve = 85.0234203264\n",
"XV H2O (mass) = 0.756599096781\n",
"XV CO2 (mass) = 0.137967284682\n",
"XV S (mass) = 0.100498044885\n",
"XV Cl (mass) = 0.00493557365189\n",
"molV H2O (mass) = 7.23188901801\n",
"molV CO2 (mass) = 0.0292865664245\n",
"molV S (mass) = 0.0240400681552\n",
"molV Cl (mass) = 0.0\n"
]
}
],
"source": [
"rawPointer = output.__long__() # we're going to read the \"address\"\n",
"pC = ctypes.cast(rawPointer, ctypes.POINTER( ctypes.c_double )) # and we read the array stored at this address\n",
"print((\"wt% H2O = \"+str(pC[0])))\n",
"print((\"PPM CO2 = \"+str(pC[1])))\n",
"print((\"PPM S = \"+str(pC[2])))\n",
"print((\"PPM Cl = \"+str(pC[3])))\n",
"print((\"Vol% Exsolve = \"+str(pC[4])))\n",
"print((\"XV H2O (mass) = \"+str(pC[5])))\n",
"print((\"XV CO2 (mass) = \"+str(pC[6])))\n",
"print((\"XV S (mass) = \"+str(pC[7])))\n",
"print((\"XV Cl (mass) = \"+str(pC[8])))\n",
"print((\"molV H2O (mass) = \"+str(pC[9])))\n",
"print((\"molV CO2 (mass) = \"+str(pC[10])))\n",
"print((\"molV S (mass) = \"+str(pC[11])))\n",
"print((\"molV Cl (mass) = \"+str(pC[12])))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, it's working. Now let's complicate the case. Let's imagine that we have a closed-system degassing, going from P = 4000 to 100 bar, as you can do in SolEx. You will write something like that to reproduce the calculation in Python:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Pint = np.arange(100,4000,100) #start, stop, step\n",
"rev_Pint = Pint[::-1] # To have the first values being the highest ones\n",
"results = np.zeros((len(Pint),13)) # For storing the results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create a loop in which we will call pysolex for doing the calculation:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(len(rev_Pint)):\n",
" output = pysolex.pyex(H2O,CO2,PPMS0,PPMCl0,Si,Al,Fe,Ca,Mg,Na,K,pi,SiO2,pisol,piswitch,flagout,T,rev_Pint[i],NNO)\n",
" rawPointer = output.__long__()\n",
" pC = ctypes.cast(rawPointer, ctypes.POINTER( ctypes.c_double ))\n",
" \n",
" results[i,0] = pC[0] #wt% water\n",
" results[i,1] = pC[1] #co2 ppm\n",
" results[i,2] = pC[2] #S ppm\n",
" results[i,3] = pC[3] #Cl ppm\n",
" results[i,4] = pC[4] #EXSOLVE\n",
" results[i,5] = pC[5] #XV H2O\n",
" results[i,6] = pC[6] #XV CO2\n",
" results[i,7] = pC[7] #XV S\n",
" results[i,8] = pC[8] #XV Cl\n",
" results[i,9] = pC[9] #molV H2O\n",
" results[i,10] = pC[10] #molV CO2\n",
" results[i,11] = pC[11] #molV S\n",
" results[i,12] = pC[12] #molV Cl\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Done! Let's do a nice graph for those results:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10502aa10>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
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DfBGvMJfAB+pn4JXSGR3EX0uG42hdbGEjvGutpmwZ/gc34JmPV9774vdtkeM2s1/j1rzj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\nP6hRkQN4NfchAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x104ee3e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(rev_Pint[:],results[:,0])\n",
"plt.xlabel(\"Pressure, bars\", fontsize = 14)\n",
"plt.ylabel(\"Water content in melt, wt%\", fontsize = 14)\n",
"plt.title(\"Fig. 1: Water concentration vs pressure, closed system\",fontsize = 14,fontweight = \"bold\")\n",
"plt.text(2000,2,(\"T =\"+str(T)+\"\\nNNO =\"+str(NNO)),fontsize = 14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's do the same thing for the CO2 now:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1052dee10>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SZraqma0Wb6ua2Wr1CNA1NBFm5j4a2E/Sd6psfyOhJqDiP0SxqnNz4LxEkgPA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x104fd2350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(rev_Pint[:],results[:,1])\n",
"plt.xlabel(\"Pressure, bars\", fontsize = 14)\n",
"plt.ylabel(\"CO$_2$ content in melt, ppm\", fontsize = 14)\n",
"plt.title(\"Fig. 2: CO$_2$ concentration vs pressure, closed system\",fontsize = 14,fontweight = \"bold\")\n",
"plt.text(1000,800,(\"T =\"+str(T)+\"\\nNNO =\"+str(NNO)),fontsize = 14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's make the case of an open system. Easy, we will just take the H2O, CO2, S and Cl values from the past output to input them in the next..."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(len(rev_Pint)):\n",
" if i == 0:\n",
" output = pysolex.pyex(H2O,CO2,PPMS0,PPMCl0,Si,Al,Fe,Ca,Mg,Na,K,pi,SiO2,pisol,piswitch,flagout,T,rev_Pint[i],NNO)\n",
" else:\n",
" H2O = results[i-1,0]\n",
" CO2 = results[i-1,1]\n",
" PPMS = results[i-1,2]\n",
" PPMCl = results[i-1,3]\n",
" output = pysolex.pyex(H2O,CO2,PPMS,PPMCl,Si,Al,Fe,Ca,Mg,Na,K,pi,SiO2,pisol,piswitch,flagout,T,rev_Pint[i],NNO)\n",
" \n",
" rawPointer = output.__long__()\n",
" pC = ctypes.cast(rawPointer, ctypes.POINTER( ctypes.c_double ))\n",
" \n",
" results[i,0] = pC[0] #wt% water\n",
" results[i,1] = pC[1] #co2 ppm\n",
" results[i,2] = pC[2] #S ppm\n",
" results[i,3] = pC[3] #Cl ppm\n",
" results[i,4] = pC[4] #EXSOLVE\n",
" results[i,5] = pC[5] #XV H2O\n",
" results[i,6] = pC[6] #XV CO2\n",
" results[i,7] = pC[7] #XV S\n",
" results[i,8] = pC[8] #XV Cl\n",
" results[i,9] = pC[9] #molV H2O\n",
" results[i,10] = pC[10] #molV CO2\n",
" results[i,11] = pC[11] #molV S\n",
" results[i,12] = pC[12] #molV Cl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now plot the results as we did for the closed system case:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x105904fd0>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/MVXHYmbWCWV7W32f1AD3C9KNgsXiSu8VXVrvHaQ2oGuqDsTMrBPKtnk8Beyf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10508ffd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(rev_Pint[:],results[:,0])\n",
"plt.xlabel(\"Pressure, bars\", fontsize = 14)\n",
"plt.ylabel(\"Water content in melt, wt%\", fontsize = 14)\n",
"plt.title(\"Fig. 3: Open system \",fontsize = 14,fontweight = \"bold\")\n",
"plt.text(2000,2,(\"T =\"+str(T)+\"\\nNNO =\"+str(NNO)))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1059fed10>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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k7Q2cBJxecmxmVjKJDwAXAbtHcFHV8djQ0miV2kmk0QamAIsB1wMvAydHxPdL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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1050a1610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(rev_Pint[:],results[:,1])\n",
"plt.xlabel(\"Pressure, bars\", fontsize = 14)\n",
"plt.ylabel(\"CO$_2$ content in melt, ppm\", fontsize = 14)\n",
"plt.title(\"Fig. 4: Open system \",fontsize = 14,fontweight = \"bold\")\n",
"#plt.text(1000,800,(\"T =\"+str(T)+\"\\nNNO =\"+str(NNO)))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.5889504867610182"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results[0,0]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3900"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rev_Pint[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
sunilmallya/dl-twitch-series | E3_finetuning_randall_not_randall.ipynb | 1 | 148805 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a model to detect if Randall is in the image or not!\n",
"\n",
"Randall or Not"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset\n",
" \n",
"- Randall : s3://ranman-selfies\n",
"- Not Randall: http://vis-www.cs.umass.edu/lfw/lfw.tgz (celeb faces)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### most of the code is borrowed from\n",
"https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/finetune-CNN-catsvsdogs.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# helper functions\n",
"import mxnet as mx\n",
"import os, urllib\n",
"\n",
"def download(url):\n",
" filename = url.split(\"/\")[-1]\n",
" if not os.path.exists(filename):\n",
" urllib.urlretrieve(url, filename)\n",
" \n",
"def get_model(prefix, epoch):\n",
" download(prefix+'-symbol.json')\n",
" download(prefix+'-%04d.params' % (epoch,))\n",
"\n",
"get_model('http://data.mxnet.io/models/imagenet/resnet/152-layers/resnet-152', 0)\n",
"sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-152', 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download the .rec files from\n",
"https://s3.amazonaws.com/smallya-test/randallnotrandall/rnr_train.lst.rec\n",
"\n",
"https://s3.amazonaws.com/smallya-test/randallnotrandall/rnr_valid.lst.rec"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"download('https://s3.amazonaws.com/smallya-test/randallnotrandall/rnr_train.lst.rec')\n",
"download('https://s3.amazonaws.com/smallya-test/randallnotrandall/rnr_valid.lst.rec')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Data Iterators for cats vs dogs dataset\n",
"\n",
"import mxnet as mx\n",
"\n",
"def get_iterators(batch_size, data_shape=(3, 224, 224)):\n",
" train = mx.io.ImageRecordIter(\n",
" path_imgrec = './rnr_train.lst.rec', \n",
" data_name = 'data',\n",
" label_name = 'softmax_label',\n",
" batch_size = batch_size,\n",
" data_shape = data_shape,\n",
" shuffle = True,\n",
" rand_crop = True,\n",
" rand_mirror = True)\n",
" val = mx.io.ImageRecordIter(\n",
" path_imgrec = './rnr_valid.lst.rec',\n",
" data_name = 'data',\n",
" label_name = 'softmax_label',\n",
" batch_size = batch_size,\n",
" data_shape = data_shape,\n",
" rand_crop = False,\n",
" rand_mirror = False)\n",
" return (train, val)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_fine_tune_model(symbol, arg_params, num_classes, layer_name='flatten0'):\n",
" \"\"\"\n",
" symbol: the pre-trained network symbol\n",
" arg_params: the argument parameters of the pre-trained model\n",
" num_classes: the number of classes for the fine-tune datasets\n",
" layer_name: the layer name before the last fully-connected layer\n",
" \"\"\"\n",
" all_layers = sym.get_internals()\n",
" net = all_layers[layer_name + '_output']\n",
" net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes, name='fc1')\n",
" net = mx.symbol.SoftmaxOutput(data=net, name='softmax')\n",
" new_args = dict({k:arg_params[k] for k in arg_params if 'fc1' not in k})\n",
" return (net, new_args)\n",
"\n",
"num_classes = 2 # RANDALL OR NOT\n",
"(new_sym, new_args) = get_fine_tune_model(sym, arg_params, num_classes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import logging\n",
"head = '%(asctime)-15s %(message)s'\n",
"logging.basicConfig(level=logging.DEBUG, format=head)\n",
"\n",
"def fit(symbol, arg_params, aux_params, train, val, batch_size, num_gpus=1, num_epoch=1):\n",
" devs = [mx.gpu(i) for i in range(num_gpus)] # replace mx.gpu by mx.cpu for CPU training\n",
" mod = mx.mod.Module(symbol=new_sym, context=devs)\n",
" mod.bind(data_shapes=train.provide_data, label_shapes=train.provide_label)\n",
" mod.init_params(initializer=mx.init.Xavier(rnd_type='gaussian', factor_type=\"in\", magnitude=2))\n",
" mod.set_params(new_args, aux_params, allow_missing=True)\n",
" mod.fit(train, val, \n",
" num_epoch=num_epoch,\n",
" batch_end_callback = mx.callback.Speedometer(batch_size, 10), \n",
" kvstore='device',\n",
" optimizer='sgd',\n",
" optimizer_params={'learning_rate':0.009},\n",
" eval_metric='acc')\n",
" \n",
" return mod"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-09-07 20:48:08,544 Already bound, ignoring bind()\n",
"/home/ubuntu/mxnet/python/mxnet/module/base_module.py:449: UserWarning: Parameters already initialized and force_init=False. init_params call ignored.\n",
" allow_missing=allow_missing, force_init=force_init)\n",
"2017-09-07 20:48:22,861 Epoch[0] Batch [10]\tSpeed: 76.39 samples/sec\tTrain-accuracy=0.850852\n",
"2017-09-07 20:48:31,228 Epoch[0] Batch [20]\tSpeed: 76.50 samples/sec\tTrain-accuracy=0.967187\n",
"2017-09-07 20:48:39,613 Epoch[0] Batch [30]\tSpeed: 76.34 samples/sec\tTrain-accuracy=0.981250\n",
"2017-09-07 20:48:48,034 Epoch[0] Batch [40]\tSpeed: 76.02 samples/sec\tTrain-accuracy=0.992188\n",
"2017-09-07 20:48:56,484 Epoch[0] Batch [50]\tSpeed: 75.74 samples/sec\tTrain-accuracy=0.985938\n",
"2017-09-07 20:49:04,923 Epoch[0] Batch [60]\tSpeed: 75.85 samples/sec\tTrain-accuracy=0.987500\n",
"2017-09-07 20:49:13,375 Epoch[0] Batch [70]\tSpeed: 75.73 samples/sec\tTrain-accuracy=0.990625\n",
"2017-09-07 20:49:21,834 Epoch[0] Batch [80]\tSpeed: 75.67 samples/sec\tTrain-accuracy=0.993750\n",
"2017-09-07 20:49:30,282 Epoch[0] Batch [90]\tSpeed: 75.76 samples/sec\tTrain-accuracy=0.993750\n",
"2017-09-07 20:49:38,685 Epoch[0] Batch [100]\tSpeed: 76.17 samples/sec\tTrain-accuracy=0.989062\n",
"2017-09-07 20:49:47,069 Epoch[0] Batch [110]\tSpeed: 76.35 samples/sec\tTrain-accuracy=0.985938\n",
"2017-09-07 20:49:55,431 Epoch[0] Batch [120]\tSpeed: 76.55 samples/sec\tTrain-accuracy=0.996875\n",
"2017-09-07 20:50:03,786 Epoch[0] Batch [130]\tSpeed: 76.62 samples/sec\tTrain-accuracy=0.995313\n",
"2017-09-07 20:50:12,169 Epoch[0] Batch [140]\tSpeed: 76.35 samples/sec\tTrain-accuracy=0.992188\n",
"2017-09-07 20:50:20,541 Epoch[0] Batch [150]\tSpeed: 76.46 samples/sec\tTrain-accuracy=0.996875\n",
"2017-09-07 20:50:28,904 Epoch[0] Batch [160]\tSpeed: 76.54 samples/sec\tTrain-accuracy=0.990625\n",
"2017-09-07 20:50:37,276 Epoch[0] Batch [170]\tSpeed: 76.46 samples/sec\tTrain-accuracy=0.996875\n",
"2017-09-07 20:50:45,669 Epoch[0] Batch [180]\tSpeed: 76.26 samples/sec\tTrain-accuracy=0.992188\n",
"2017-09-07 20:50:54,092 Epoch[0] Batch [190]\tSpeed: 76.00 samples/sec\tTrain-accuracy=1.000000\n",
"2017-09-07 20:51:02,531 Epoch[0] Batch [200]\tSpeed: 75.85 samples/sec\tTrain-accuracy=1.000000\n",
"2017-09-07 20:51:11,007 Epoch[0] Batch [210]\tSpeed: 75.51 samples/sec\tTrain-accuracy=0.995313\n",
"2017-09-07 20:51:19,451 Epoch[0] Batch [220]\tSpeed: 75.80 samples/sec\tTrain-accuracy=0.995313\n",
"2017-09-07 20:51:27,877 Epoch[0] Batch [230]\tSpeed: 75.97 samples/sec\tTrain-accuracy=0.995313\n",
"2017-09-07 20:51:36,259 Epoch[0] Batch [240]\tSpeed: 76.36 samples/sec\tTrain-accuracy=0.992188\n",
"2017-09-07 20:51:44,632 Epoch[0] Batch [250]\tSpeed: 76.45 samples/sec\tTrain-accuracy=1.000000\n",
"2017-09-07 20:51:52,980 Epoch[0] Batch [260]\tSpeed: 76.67 samples/sec\tTrain-accuracy=0.998437\n",
"2017-09-07 20:51:58,840 Epoch[0] Train-accuracy=1.000000\n",
"2017-09-07 20:51:58,841 Epoch[0] Time cost=230.046\n",
"2017-09-07 20:52:01,370 Epoch[0] Validation-accuracy=1.000000\n"
]
}
],
"source": [
"num_classes = 2 # This is binary classification (Randall vs not Randall)\n",
"batch_per_gpu = 16\n",
"num_gpus = 4\n",
"(new_sym, new_args) = get_fine_tune_model(sym, arg_params, num_classes)\n",
"\n",
"batch_size = batch_per_gpu * num_gpus\n",
"(train, val) = get_iterators(batch_size)\n",
"mod = fit(new_sym, new_args, aux_params, train, val, batch_size, num_gpus)\n",
"\n",
"#metric = mx.metric.Accuracy()\n",
"#mod_score = mod.score(val, metric)\n",
"#print mod_score"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-09-07 20:56:10,875 Saved checkpoint to \"resnet-mxnet-rnr-0001.params\"\n"
]
}
],
"source": [
"prefix = 'resnet-mxnet-rnr'\n",
"epoch = 1\n",
"mc = mod.save_checkpoint(prefix, epoch)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/mxnet/python/mxnet/module/base_module.py:64: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])\n",
" warnings.warn(msg)\n"
]
}
],
"source": [
"# load the model, make sure you have executed previous cells to train\n",
"import cv2\n",
"dshape = [('data', (1,3,224,224))]\n",
"\n",
"def load_model(s_fname, p_fname):\n",
" \"\"\"\n",
" Load model checkpoint from file.\n",
" :return: (arg_params, aux_params)\n",
" arg_params : dict of str to NDArray\n",
" Model parameter, dict of name to NDArray of net's weights.\n",
" aux_params : dict of str to NDArray\n",
" Model parameter, dict of name to NDArray of net's auxiliary states.\n",
" \"\"\"\n",
" symbol = mx.symbol.load(s_fname)\n",
" save_dict = mx.nd.load(p_fname)\n",
" arg_params = {}\n",
" aux_params = {}\n",
" for k, v in save_dict.items():\n",
" tp, name = k.split(':', 1)\n",
" if tp == 'arg':\n",
" arg_params[name] = v\n",
" if tp == 'aux':\n",
" aux_params[name] = v\n",
" return symbol, arg_params, aux_params\n",
"\n",
"model_symbol = \"resnet-mxnet-rnr-symbol.json\"\n",
"model_params = \"resnet-mxnet-rnr-0001.params\"\n",
"sym, arg_params, aux_params = load_model(model_symbol, model_params)\n",
"mod = mx.mod.Module(symbol=sym)\n",
"\n",
"# bind the model and set training == False; Define the data shape\n",
"mod.bind(for_training=False, data_shapes=dshape)\n",
"mod.set_params(arg_params, aux_params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets see if we can predict if that's a Randall image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://d0.awsstatic.com/Developer%20Marketing/evangelists/evangelist-bio-randall-hunt.png\"/>\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Randall 0.999914\n"
]
}
],
"source": [
"import urllib2\n",
"import numpy as np\n",
"\n",
"from collections import namedtuple\n",
"Batch = namedtuple('Batch', ['data'])\n",
"\n",
"def preprocess_image(img, show_img=False):\n",
" '''\n",
" convert the image to a numpy array\n",
" '''\n",
" img = cv2.resize(img, (224, 224))\n",
" img = np.swapaxes(img, 0, 2)\n",
" img = np.swapaxes(img, 1, 2) \n",
" img = img[np.newaxis, :] \n",
" return img\n",
"\n",
"url = 'https://d0.awsstatic.com/Developer%20Marketing/evangelists/evangelist-bio-randall-hunt.png'\n",
"req = urllib2.urlopen(url)\n",
"\n",
"image = np.asarray(bytearray(req.read()), dtype=\"uint8\")\n",
"image = cv2.imdecode(image, cv2.IMREAD_COLOR)\n",
"img = preprocess_image(image)\n",
"\n",
"mod.forward(Batch([mx.nd.array(img)]))\n",
"\n",
"# predict\n",
"prob = mod.get_outputs()[0].asnumpy()\n",
"labels = [\"Randall\", \"Not Randall\"]\n",
"print labels[prob.argmax()], max(prob[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"yay! that's Randall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lets visualize the filters"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['bn1_moving_var', 'bn1_output', 'relu1_output', 'pool1_output', 'flatten0_output', 'fc1_weight', 'fc1_bias', 'fc1_output', 'softmax_label', 'softmax_output']\n"
]
}
],
"source": [
"## Feature extraction\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"import numpy as np\n",
"# define a simple data batch\n",
"from collections import namedtuple\n",
"Batch = namedtuple('Batch', ['data'])\n",
"\n",
"def get_image(url, show=False):\n",
" # download and show the image\n",
" fname = mx.test_utils.download(url)\n",
" img = cv2.cvtColor(cv2.imread(fname), cv2.COLOR_BGR2RGB)\n",
" if img is None:\n",
" return None\n",
" if show:\n",
" plt.imshow(img)\n",
" plt.axis('off')\n",
" # convert into format (batch, RGB, width, height)\n",
" img = cv2.resize(img, (224, 224))\n",
" img = np.swapaxes(img, 0, 2)\n",
" img = np.swapaxes(img, 1, 2)\n",
" img = img[np.newaxis, :]\n",
" return img\n",
"\n",
"# list the last 10 layers\n",
"all_layers = sym.get_internals()\n",
"print all_layers.list_outputs()[-10:]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-09-07 20:59:01,715 Starting new HTTPS connection (1): d0.awsstatic.com\n",
"/usr/local/lib/python2.7/dist-packages/requests/packages/urllib3/util/ssl_.py:334: SNIMissingWarning: An HTTPS request has been made, but the SNI (Subject Name Indication) extension to TLS is not available on this platform. This may cause the server to present an incorrect TLS certificate, which can cause validation failures. You can upgrade to a newer version of Python to solve this. For more information, see https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" SNIMissingWarning\n",
"/usr/local/lib/python2.7/dist-packages/requests/packages/urllib3/util/ssl_.py:132: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. You can upgrade to a newer version of Python to solve this. For more information, see https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecurePlatformWarning\n",
"2017-09-07 20:59:01,827 https://d0.awsstatic.com:443 \"GET /Developer%20Marketing/evangelists/evangelist-bio-randall-hunt.png HTTP/1.1\" 200 102795\n",
"2017-09-07 20:59:01,844 downloaded https://d0.awsstatic.com/Developer%20Marketing/evangelists/evangelist-bio-randall-hunt.png into evangelist-bio-randall-hunt.png successfully\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 64, 112, 112)\n"
]
}
],
"source": [
"#fe_sym = all_layers['flatten0_output']\n",
"fe_sym = all_layers['conv0_output']\n",
"fe_mod = mx.mod.Module(symbol=fe_sym, context=mx.cpu(), label_names=None)\n",
"fe_mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])\n",
"fe_mod.set_params(arg_params, aux_params)\n",
"\n",
"url = 'https://d0.awsstatic.com/Developer%20Marketing/evangelists/evangelist-bio-randall-hunt.png'\n",
"img = get_image(url)\n",
"fe_mod.forward(Batch([mx.nd.array(img)]))\n",
"features = fe_mod.get_outputs()[0].asnumpy()\n",
"print features.shape "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-09-07 21:41:53,716 evangelist-bio-randall-hunt.png exists, skip to downloada\n"
]
},
{
"data": {
"image/png": 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10WQysRqkLEtzQg8TPP7HMIFmjUZDs9lMjUZDnU5Hx8fHCsNQb775pr785S9v\nYHnpYiMkZAD1HYviyQxJBiGBNNLFBlDgFBkC44aY8KpzD5E8zMXIuU7P5nnn8zWWd2IOXSU6E5WZ\nT2lzcy2wCYX+3t6e1dCQG9zHZDKx70VhX1WV7ZSGokdmxz369aS2RTRAlvYqm06ns9Ebpcamdvav\nZa14PTUcGc33LT37TTDjc7EJtgdxmC6OBylGS4HAS9/vquNaOFwcxzo+PrYohd4NR/DNb2APkwjW\nX61WarVaGgwGOjw8VBzH+hf/4l+Y8Pm3f/u39f73v1/SZh/nMlSVtHFW/+VNlywkRs1iYzwwW5Is\nALDthKYqLGmSJJYVfI8LJwvD0M4emc/nyrJMo9HIvg8EQJRvt9uq1WoajUY2NwQqHBMi4+bNm6a0\nIKsTrUEH6Bh9q4T79DsRjo6ObMc2w2dW9Kq7u7sbdD+sdLfb1cHBwYYcjuuVLoKiJ6n8GkraCEZk\nSoIZMJAA4PcqIi6gxYEEDBULaMafUwpR9/3kfj/Q1v/z3eMffxB5iDh+kcgmfjIxNhTkzWZTu7u7\nunXrll599VUdHh7qzp076vf7+lf/6l/p53/+59VoNPTgwQNJF9uBcDJvTD6jEf24LkmmQGCxuVbv\nBGEY2t4rsqCHyTgzdZ4nNoBO3O/jx49NtEuQYY6AnxgTW1aQYXFil3Rx9B5E0Gw2s9qKwDMcDi0L\n45xA1Z2dHauBeY3PyjCXQD6o+K2trQ3GmDqQ+8PQ/Zkzl9s81OpZlllvj3nwTXPqQi+58ptc+T3f\n4VlGvpNDhXAqyDqv18Q+GF7A/oPGtXA4ohGKkTAMrdkqyaKij9RMwGc/+1klSaKf/dmf1dtvv61b\nt27p9ddf11/91V/p7bffVqPR0Oc//3mLVL5f5qEj13EZq0OkEB3ZBOubryweGctHXwyBY8dHo5Gp\nzZGDJUmimzdvWlOY5j9nbUBRQ25gwDjM4eGhGTSnj2HAQDhob67F17mQM14ozr4/nOzJkydm7KwB\nBivJNJnUtDB7wGbmlyyI7A3jZm08QYbyhAOe6vW6ut2u9UcJQHF8fvb/ZDLZ2LfmCSpQBw7P/GNT\nrC8wW5KVOQQG+o6IoKfTqc3rVce1cDjpggUDUuzt7dkhLqPRSNvb2/pn/+yf6d/9u39nsqmf+Zmf\n0c/93M/p937v9/T5z39+oylalqXu379vxTfRiYL4+0U+zyZ6qNJqtbS9va04jk3F8aUvfWmjJ7a7\nu6udnR3VLB8eAAAgAElEQVQzQl//efiyvb29wXrt7Oyo1+uZphJDZe8VCgmo/nq9rtFotNFjk87Z\nUg7DZfc0Cptut2v1FzviIXRo0mPoBBjmA4hP9qeVwByTrREtY+gQLz4g+RO0CToQKZAavV5Pp6en\nCoJAvV5vQ1bFvbMW2AqQGN0qdaZnqz3ZRuCA3m82m7ap1JcJ6D/JvDTLsZder2do4qrjWjgcNCxF\nKYfMDAYDbW9v6zd/8zc1m8307W9/W2VZ6uDgwLLFX/zFX9j2eU8KeOYvSRKDb0RyMk6n07FF9YoO\nzz4ymOydnR396q/+qp4+fWpPp2FzKKoNf3QCjCbFNpmFHehk9SdPnljtQNTv9/tmzPw8SRJ1Oh07\n2h2anNOu0KVChCBO9m0TDw19xqJukc6dmE2g9K8wUv8eggQ9Mt8ohzwCehNUmHsa5ARG6t1Wq7VR\nu3nIB6yjX0YvjzUjCOH0zDv3RDYjmJHtgKq+7ypdtKCki32NIAeO3rjquBbbc37lV36l+pf/8l/q\nr//6r3V0dKTFYqHj42P92I/9mD760Y/qj//4j60wZXF9/wrHiqJI9+7ds2PM2E3s9YZMJs7EAaQ4\n/HK5NNExtQ4GUa/X9RM/8RMGeQ8ODoxB43FItBmo5SRtRP7hcKhGo6GTkxMzGL/NZLVaaXt7W91u\nV2dnZ3Y4Ku2SD37wg1qv17p//77BI7IhRuXJBLL1ZRmWr79wbuZwZ2dH/X7f4NLZ2ZnVi2RC2gT0\n17hfftfpdDacnHlhe9P29vbGGSU4JvW8b71AchAAvL4RpEAwpU/q783LysjU6CQhSfhdv9+3zMbw\n6+fbCF7C9m/+zb/50dme81u/9VtqNpv69Kc/rXfffVd3797daNRyEi9RHjzui2sMged4+U2tkjbO\n7mfiiKRem+dPeGo0Gtra2rJj+TAsFv6dd97RJz/5Sb366qsGaSBQpIvzQYAdKNfv379vDk6GpZZ7\n//vfb8+nS5LEjLLdbmtra0uPHj2yuYGdpXHs2yY4Ak5JloH95BqZYyJ3mqZ64YUXNJvNDH5C2JA9\ngJJkfDYN83PqLJyQ+eT6qur8SAnm3hNPfqMpKhjQAtmaz4EV5vqazaY9Voo6mODCuhFYWBPYWMoX\nT9pByvh6VJKpS/yRflcd1yLD/et//a+rwWBghIFffH9ctj9AB0hEz0u6OPWKHb/r9cUReZ6JWiwW\n+tjHPqbBYKCjoyN9+MMf1ptvvqmqOt+aQxYCpm5tbVn2QXfH4pFdvNgYOEIkpJfE9R0dHZn0iZ3D\n+/v7evr0qer1ukEtnIR2iM9oSI+I+BASZEPmx6vZcQgvZ/OnoIVhqJ2dHdveglEPBgP1ej01Gg0d\nHx/rIx/5iN5++21T2vvacHd3V5Js642X64VhaAfzEGC4HmAd7/OSPUkbsBTFiCeoIG1AIkmSWNvD\nb5bF3lkznBVJl69vsSmCBn/8tieC/7/9t//2R+cZ3ycnJ8ZKeoqZKMXEShebVcuytGhIb8lDS+oi\niAPEuv1+X7u7u7p3754ePnxoxnl4eKiXXnrJnC6OY4OTyJOqqtLh4aFliqIoTPbFYkkXzXiul4wk\nSfv7+zo4OLBMilM/efLEaljkVjSgyQ40WTE8f4IyEIxsdZnu9rAN44bdI4tAz+NAyKVefvll6y/S\nV6T+pBYqivPHfbFmkjYCBk5y//59ez3XtV6v1ev1TM4laSOg0Lzm58wJByixHjTZz87OdHJyon6/\nb4e3AhOZI1/r+TM7YTQhxAigtVrN6j3p4pkVBKWrjmsBKdmOAvRgRzE3i2pbunjIOczdcDi0MxyB\nP/5ELRQRGBTaxCiK9Pzzz2symeiNN95QWZb66Ec/quPjY4vSN2/etE2inP708OFDHR4e6v79+zo4\nOLCj1shKKE34TqItp2E9fPhQp6enVssgOMYJ/ZYV32saDAaSLs7MT9PUjuWG5PCtFZrnPngAo/b3\n93V2dqblcqnxeKwXXnhho+lOXbK9va3Dw0Pb6MsWJWrjR48eWaYmqGHMftMpWcizw1wbMBKCBbrd\nN+5v3ryp09NTq3PpvaKSkTblXxA9QFK0ufQIL9dhfnOxF27DYBMw2KoDI4yszYsmftC4FhkOQ/Vn\n//uNgp6EABYtl0sdHx8bDGWCWFSyX7fbNUh4cHAgSVbjUXvU63VtbW3p7//+7+3kXX8suM+6LBB7\n6IA9XtMJ83eZrKG+4pHH3Fscx3Z+CLCHrFYUhREz1CxlWdpmUKhpiBf2n5Gler2e3TtPhaH2i+PY\nZFl+5wEETFmWunfvnmVLMg4O7ql27pvXxXFs/SovxfL6SN7n+3V+hwDMIXvUqHtxGEgRMhR1POQL\n/TOeOUAjHCgpyTK/33KEOoX6mxqfBzjS0PfzdtVxLTIc/ROMZjgcbhSwGCKLBJw5OTkxKEm9cHp6\nqjiONx7mQGQ8Pj62SaLgD4LAjg/wJ/myEZPsdPPmTSXJ+XMNgL88xI8IydNZPJyDUeN+MBIgc1EU\n9kTOk5MTi8aj0WhDXU8g4Oh3MieOwQG6WZap3++bwXNqFrUnbOr29rZdF2yvdF5vEtiopfg3tdbp\n6amtGduf/OFAl0XdYXjxDAbmHodhfZgn6iSMnmMkPFFDwCWT8hn8nuY1woAsyyyrcw1esXTZthCI\ncy2tVsvaOogVCGbc21XHtXA4ei3sqJa0ETnJfCyuf6Iljkbzkl3MMIJgdaIV7B0Rc2dnxxw2jmN7\nlhqqeT6XvVZkL97vBcJeDgabSuaFWcOYMBgcH+fFCTBWjqMDMvld58AntpT48ynDMLQdzn7PHqod\n5oGMgRH69oiHhD4DIF2DfCED+34mZEkQXBzvEMexNf5BA7u7u6ZPZJsW8J/amLq03+/brmt/hAUZ\ni+vHcelXnp6eGu3v2zVkUYg3nBwY6ZED6wk3EAQXAm7KnauMa+FwRH4EwT6y4jQsLnQsRsCpWv4I\nBJzUi3upZWgvYASTycSIi+l0aoseBOcP+1uv1/YkTBZTkl2bh7kEAV+/wLKxSIPBQB/60IfMceP4\n/KwQv0OAurXf72tnZ0ftdlv37983AbB00dy/d++eORTGh3HAXnLUHHPj95hxP5AuNHQJfMA7dnQQ\n/Gg5YGyoabh3yBnOe2k2mxtPLvIKjn6/b/1I7AEtKHVnGJ4/OpjrpfSgniJresUQDsu5OF5FI2kD\n8vN/gpZHG6w7gZT58rrPq45r4XAY7Wg02oiQpHVqBrIBnX1YJBwwiiIjErwmj6YoUZ3X8jeQhQxT\nVecnhPFUTaj6xWJhxoHezl8nMIVMSiFORrxx44be9773qdvtqt/v67XXXtNisTAYBzxJksRqrvv3\n72swGFjNwH3jOAQk4JPP5tK5VAoqfTKZWG3LXPrMhaN56AbJMZvNbJ44/oDM6FliWGV6ZjTAb926\npdPTU8soXDdBFMYVR8VRPCJg/YDrvr/Wbrc1HA713HPPWaOevZBhGBq76SE+dhUEgQ4ODjQcDu0o\nB69FxelQ7rAL3zPkV7b1fxyX+ccZl4Wkkv5BphuNRganqJHYNXByciJJxnh5oax08cAOFhws7uli\n+l9kWYwY9jGKLh5Ty/ANddoD/KGuwunSNNV3vvMdewiFh16SrAbsdDqSZJ8HRPJbf6DgyfY+S9FU\np2bk7EuYQHYoAKvyPLdjD2h14IgcGw4MJjDB0vmfXWZICYhoJdmWQwb2pAkNaWo6kAP78vzWICRY\nQGGYTY5J4Ll1wEJPhEjaCK4ogHzw9A9+YX7H47EFJumit/gjt1uAKOQV4ziGdEErs/P6cq2E0TB5\nvV5P4/HYojRZCtGtp6nJCP7nND6BFKhBMD50kCw6bCaOy30A57jHwWCgx48fKwzPn2XgjyTgPdSE\ng8HAniRDwMEgmBcIEH4OnOPf4/FYy+XSHM/384ji3W7XMjctEK+o4XroJXqIV1Xnm0/Pzs7s2mFN\nIUV6vZ7NK9/d7XatBOCEsMlkYhpQ2kT00MbjsV0XGbQsSyODcAiyJXWrz3LYFcoYWGpaAZx5WlWV\ntSmYB7+LxStmOp2O7R656rhWGU7SBlwoimIjwhMN2exIhKOpCkHiCRcPL312ky6UKb4v1O/3VZal\nRUco9CRJrKbzMh9gpCd7qBnINgQUj/mpX5BAeXU9hTrtCGAg8JQWCnMlyTI6hAJOwKZSsh/ZYWdn\nR9vb25pOp3bGvw9kkgwFeAUG1wRRgVFjuGRsjBNEwhoC7yAnIFq8xhVn8AiATELmp4WAsJt55Hhz\n6WIHCtmVDOqDKu+VLuptz5CDdDyxxmCOf+QyHBnGd/+9RhJYSZ1DE5gFhhrHsWjAwiL5zwP3o8bg\nO8HxLLB3+pOTE3MCf66HJ29wLq7ncrPekyhkLIYnB1DGJ0liZI7XJNLCkC727wEPyQRkJ1+j4BQQ\nGJPJxE7XArL5fidkRK1WMwUIzobhca1AeObNE0f1et1OTuZIcIwUA0eWRf2GtpHHkNF28Hv0gLH+\ngNeyLO30N+Anc09G4rFZzAtkDfeBDI9/Iw/ETgnmBAYc8qrjWmQ4jIqB0QADgGospv+3dLFNw58b\n6ZuqngWVZDUMD+rodrt67rnnTJvJIThkUl8rstUDo5zP5zo7OzOpFUZBL0zaDCT+Onw2xGiAXBg+\ntQwkBMECuREPgwdOQ1x4pYrvh/mjwf3xg/SjfPCguQ/RgZNxH6wNsM037b0CxNdP6FE9KQNR5kkK\nnNmzkcBWjx74bKA/2Vy6OEQYlnpra8tIIggt1pX5g1zjddSk1JY4MXaFM191XIsMRzbxhTo3RVq/\nrAfs9XobB7hKF3CURSQK0mz1+Nw/riiOz89Ukc4N5JVXXrGHPbBpFFqa+mt/f3+DyDg7OzP2D3KH\nWguG0LN+GA5Qk2CB/Gx7e3uj54cQmZ/RFJZkxkhddnBwoKq62DIE3MQBuXfmg2DnG+G0ODAmNm12\nu10dHh5qMpmY9AkGECWGbylw/5w3I13spGYDLOvFfeG83B+6ToycXiB1VlWdy9/G47EdggSDyPpA\nBEF4gBToS8IQd7tdbW9v6+zszOaWTO0F59T3qJyuOq6Fw2G4XlnOgrNoOBZR2B+eyvs9rJQuICe9\nJX6HI7HAQAgyga+FKMBhDr2eUDoXQ/sj3vx3e0U8UZv9dtLmU22I2nwOB+pQJ9G0hsEjs3ndJQ4C\nDCfDIhhoNBpGzRPY+ENEh2Dh+/18kJ29VMsbvc+w1IyIxj3p4+E3ARVk4LMmZYGH1J7R9IQR6woM\n5fdFUdgBs2EYmhOyIx4Ewfxub29vNOC5DtQnZFLe53uuVxnXwuGkCxW4z2hEysvqAP/ESYyWm8YI\ngUg4LBpEjJINif47gZNHR0dmkNLFiV55nmt3d9ec0+84pneIEZFZgB3UdBgBPR3uE+gH3OEaeagH\nyobDw0ODTijlMTbpQtwNrCRQsPsAlg2WD0QAOeOP0yuKQltbWyZr4mRm5pWARTChJpIuzpVEHYNc\nzh90RIDkZ8Ph0FQwfD/rxzrgYGQ5amt4AOniaT7cG3bC+1ETwUQnSaLnn39e8/lc9+7dU7/ft/Vh\nVwdZzTe/+Vzs5CrjWjgcNRaORQT1Kg4in2cYiTielfTyKC8BkmRGTBT2taFfvOl0alGM7yCTcg30\ny6hb2u22LRKKFSCQpI1axd8T98+18xxrincUF0FwvkuaA1Z5CqxvifAdkiwI+B3SzBOQ7bJqhkxN\n5Ia8wLFhFglQ/E3W9VIxn4kITL6WJoDivOgcWSdPwnhGmPf0+30LmO122xQtfk3J9GxpogblCbc4\nJq0RenhVVVmgYCcKLRJJGzWt5x6uMq6Fw/mDX6SLaMTiU2/4grXVatl+Md8IJ0Ly+GB6d9IFK0Wt\nQ6YBNmC41InUfXxHr9ezJjA7GXBE4BAqDHR3/Ix7IYP4o/aIoqgYoNF9q4O+VJZl6vV66vV6Gg6H\nJmLm9QiW6Wlh9P6RWt65eT/OxhYXrpHjw6mn0aoSdJjrqjpX0MO4tlotexKO153C4Pp68fT01Jxx\nsVhoZ2dHo9HI6jvfAsGZPakCk9ntdjUcDg050EZAZ7pYLEzLybWzgRZ9Lozoer22QEN9x9xIF/Iz\nv3viKuNaOBzDF9ng58uSHCYZKIejYFBEQdT8RDacFkobGIZzY7RVVZniwmc+jIboKckkTMAMMqBn\nSqWL3cWereMe2WPFLnIMCKodw6Hohz2koe3rRuYQmRSBg6AC63lZeUN24rqpZblOrpk52tvbM2fi\nWW/MMXPPunFNXDv3iAjdG7B00b/zkNU7GiQFayHJ1CQI1pkPfwQF8wYKYc0RQPPZq9XKdhYQWPyp\nb9wD100mvOq4Fm0B6bxHQ2FPX0i66CGRIYA/EAsIkFlsaitPhft/M1EYoK8ZPdwh+8GMesPAseh1\nccQax92B6TF2PsfDHh9cMFYv20K32Ov1bPcC5MJ0OjVVCJ8r6R8c3EPW5HtwYuAwjWVqRmCYV7N4\nQgDIT/+P7SneyD2RgONwf/zfB0rmn1LA119cFzBRumhHgA78d3CP/rMIPuxYp/5jLobD4UYrgOcx\n+HofGR6DA424R67tKuNaZDhgF5mECI4xUg/4Wo2o5nt4ZD+cx9dG1IAeSkoyap7IBZ1MFI3j2Bq/\nOAUGxhYW6HEc22cj/9A+yAs+h2wDpPQ9JBQiNHYRUvN5QEKuC6YxDENtb28rCAJ7zhsCAO7VP7oZ\neAfBwjz5YISTQMYAzd59910tFgvNZjPt7+9vQF/EAtRGRVFob2/PRMhs/qXHiaAAgomg6QkwCAqC\nH9nQy+gGg4HN/d7envL84tHP+/v7+u53v6uDgwONx2MdHR1ZnYo4fTwe2+5yn/G73a7BVgbr+p8z\nroXDeaYRRThNV1Tqlzv8GIw3DrIRhAX7utikSKZqt9v2NE9eHwQXOjvp4lnUGBuGSKQ7ODjYMAwI\nES8tIluQhYCNGCl1kSdRgIjUYNwji+17c762BR2gNyTI+LrYq2ykzePcvU6TIEWmDsPQnAw4SN8N\nwiEMzwXeSK+o4/gORAFeVoUuElhdr9ct+3G9sJ2sqYf0QFzq+SAI9NJLL+nJkye2VQsVztnZmX0v\njWyepIu2E3INUol52NnZ0fve9z7TTXLyM8fr/chJu1BzYxC1Wk3b29smYsXgEcXidMAH6G5Jph4h\n0+3u7pozpWmqD33oQ7b/DIzuD4nd2dmx2gUDoT7D6Nh5DaSiVuIa6LNxrj5H3aGX3N7e1mg0Ur/f\n13g8NngDq4YBk52I/kBSnBtDoe5DjIuDQyx42IcDQCDxnT5L+l4mcA7yAsULPcUkScyxJBnkxRFx\nojw/P0GbI/T8Nh5ILL/Zk54jlDxIQrrYDcJ9U1LMZjO99NJLqtfrevr0qba3t/Xee+8pjs83r5K1\nOCkgTVMTCdCS8MGdbVmoc+AS4vj8eHmeZ/cjV8NRUyCpIppDXviIxyIDEyRZg5r6DRjgVev0UabT\nqX1Gr9eTdIHJ6W3xEHb2xPmez40bN7S1tWWwjYgL3AvD8+0ox8fHthPbb+Evy/N9f5J0dHS0oRgh\nkPiaDtjM8ebj8dgcbzweWx2GfpQjBXEkaHv+cPam3/7C96HcJ3NKF1kQR+OhJDjparXSjRs3FEWR\nOQxOihMTaIDpvm4m6/B5OCcBmM/hOjyMpMYjQO7u7urhw4d2Gtu7775rG0Wpw5gvz37zPdyjrze5\nZkoJ2gQ02D0vcJVxLTIcEQyaP89z3b17d0PTBjRkAX0tQ6QkAgELoyiy58Oxxeb09FTdblfHx8e2\nsJ6U8L0mDC2KItvtPBgMVFWV9YGofzhifLVa6eTkRLu7u9YwLorCpFq+Vm00GhoMBsqyzUcLQ8lz\nfdyTnwf6c9Da1H4cruQ1p2Thg4MDPX782NodOPdoNLJ+G2wgNVYURXYE+82bN62WOTw81MOHD+1n\noIbhcGhz7yEcDsX9wBbyfPAg2NwF4Ykbrp96jwCZJImdO+NZRq/G4WyYer2u09NTjUYjdTodVVWl\n/f19zedzTadTbW1tqdFomDyNXQwgEJ67VxTFxmHDyNyuOq6Fw7Gw7HNiwXE+qG7/HDjO+lgulxqN\nRhZtPGPkIyTZA71cGIZGTLRarY1eEHveMBiazEALoiYLgwFRvLPFx9Phg8HA3sdCAseCIDDtZhiG\nJpzmRGaOJ2ATKXCPh7vjyLCSsIhkSdQcJycnG3Prj0XguqDhISokGavXaDTssKLlcqnt7W1tbW3Z\ncQTMuX+6qv8M1gG0ghP5x5VdPvb9ckZDr8pmWu6b3/mT2E5PTzfmi2zuFSReZMG/IbZYVw+vYUPL\nsrRNrqhrrjKuhcPBQDJ5l5vZKEMuN6ypUfxJU5zQTJZqtVrK89walzs7OxvKinq9vtFzAaJQQNdq\nNd2/f3+jxoHQoDBHmOs3bHJwDQuEgoVID2xZrVbWMIYe5/1FUdg2Fe7Z75T255SgaqeeZE4hKabT\nqTXHvdJd2tytAaGAAYdhaCeGIcjGMQgCBBqyOUyvdEGI8URRvmO1WtkDODjGMI5jOy2Z+ZUuSgYk\nYhxL4cmXer1uKh2CNA6B8BjngW2ljULgBj1hi2Rp6UILChO7vb2tR48eqdFoaGdn58q2fi0czjeM\n6XMxKRAErVbLBKREGt9z8rDF1zC+bweZQS3FAjDBXlEhXZydSKT2/SaeVAPckc5JDQTOfpMmekju\nkWeHs98M2h5D9KweRpMk5w+aABKTraD5pYt9hbCJRHsiP9fDnHBffI7vYXlRMveyXC7VarW0tbVl\nrCTkCY+W8g5Lb5J7JusjmPbqHOmiV4jD+V4c6wLCoCfpH/bhEQcCda8a8rsMQESQc/T/eC/XgR2Q\n8bHHBw8eqN1uazqdam9v78q2fi0cDlUCk8xCkP7Z8ezpV6CJ1/XRRqDuY3HIdERYjMlvPMRYyQ6Q\nH/yOTAbR4Y9V8BsiEQ1zH8BTqG+MnLoPB6OGwdmWy6VOT0+t3uO1RVFYJMcAKOzpXUJ8oKMkoPkm\nM++D1PGDnhavhxBigyxP/jk8PLSaEngOVY9Y+t69e8a8eiLCCwlgGQlcfB91NQHQ1+mtVsvkV6w3\nOsmTk5MNPSWfywMbCchFcb6NajKZqN/vm11IFzDbHzLrW1bYADzBVce1cDgfcZkgagBgjT+4hQjk\nj7wDptGfo5byfTXfMPX1omfWiIo8PQZ6OkkSq7OQCUHbo94nOhI4yGQ8L8BnCnpKnPkBHIQJBDpS\nP6ELvazmp671jXoMH0iJztETQpfVNCAEYDBG7mGYr5EJLuv1+XMBQAdHR0d2/gt/CEbMvbS5o561\nIItQS5P9QCTUh56qJxhBsIASCKY4+OWHuhA8yLqQNayvZzEJ9tRqvJZWCSKKq4xr4XDcJOpsJpgs\nAXXue1BEHKQ6LJI/7AXDBjJS6HoFiJcd8T2eLc3z3CAHbYVOp2P7tGq1mo6OjtRut20h/C5hAgYN\nbe4J4fFwOLRtH7ym2+0a5U996h+lK8kORMWQfaOW6B1FkSbjoV7/9msK9D3IWWSKg0CL+VSLxVLD\n2UJBINXTVGEgVVWuMAgVxYmitKHdg+f04R//hJ32hQP3ej17yk6WZXrw4IEZZFWdK/DZIMya4bCg\nDzIr68DBPwREoKZ3wIODAwvIfD7rVK/X7dFikFJoJam9qL/9s9qpB8mWvuQg2MKM9no9EzcAW/1R\nHj/Q1v8R/ea/eGAcRGpglVdG4GReHUGWIzv6/WWXhcZMtJd9ebGw34gImSHJ6hKP+9kWApEAXKTo\nx9GAJz5KAxGBsl6mxfWSqXzm9s9SI5hwahX34zN/rVbTX/3ln2lycqSqOjfseq0mFbmSiI25Uj0K\nVKpSGlYqg0BZVqoKpCJbK1xneryYajg41ovv+4A+8KEfU1mWtk1oOBwawsCZCJhkNeaM675MTkn6\nB5mFIMM6km14PYQM80OwIYiCNNgWBCKgbLmsBvLCczI7dgliIIhjkzS8UQVddVwLh2MrDkbJInll\nAdnBU7weMvBvSWbowBU+i/oF4wRCegU9zsvnkMWI5Nvb28bM4XheggWDiNQIRg2WDKEs9RMkBc15\noCQGyc9gYLkm7hkxMt83m0713t23de/eOyrnU8WBlNZSlUWlehwqqwJVZakokIKqUiONVUla57lK\nVYrCSEEQqgqkoCgVS1pMR/r2N76iLC/06c/8lNUtBAsIEFoYq9VqA37jbL5V4vfn0Uz38wAS8YEJ\n+/DKfdaYgOiPjCf48Xjp1Wpl9RwOCYOcJOensrF+BA3swguv+R7aS6z/Vca1cDgumqjOBEsXUYzF\n8c1Q73T+pskuFPKoxBmeuSQjYtBlWW6cEEXGA5bQAKWO8wZwmRiA+ZJkdHUcx3ZsOE5LgPAFOjUi\nf2NsRF3UKZJ09523NTh+qqfvfleqpLAqlYaByuB7cq7192q2rFKsSqUqZfn5/NbSQGmSaL4KVUpK\nkIFVlRSEkkoVZaFmHOvdb35Fb7/2VTW29/SBD35YL778PssIGCrBTLrYsQ2MJFtAkhEsaPZDnvR6\nPUMYwEuYWtaRE8rYBTAej62mpEeIE0fR+bEIvp+J83D9MNrYDkGBn/P4ZbIpT8all3jVcW0cDqMH\nGvqoCKtFIxcDZTJYhMukiKSNRaIuAKr4+odo6QkFyAQyITB0Mpno8PDQnJ0mcr1eN0Pge2DhfKai\nJYAzE6XREBLN0YT64yAwklqtpnvv/L2ePn2iN7/1mhQEaiahkjhSu1lXUeRarUrVasn3gkqooqi0\nzksFClQEgVQVOpuuFIQrqZQUBOo2UsVsl4kjhaE0ni21XmeK40T1ONLq7EivfuGp8vVK7/vgR0yQ\nDZxk3oGKXi4GAUSQZK5g/Gj8g2ZAIp1Ox+pkf24nTgBKwglpbtNawSnoRXIEHuvHnAPt+Z10ng0h\nrCBJ2HRLf/Cq41o4nO/JMJFkEd+kRQXicTUL6Z2SLAK0kC6ecIKKAww/m82sj+IXnoYzMMfvF7tx\n4+jAO7YAACAASURBVIYV4ShQPMlBBuW+gKRsFSmKQrdv39447xFRLxnXK2+qqlKjXteDB/f0lb/5\nCxVZJlWlwiBQURZq17+nvQwjFWWh6eTcaOpxqCgMNMsrTedrVWWpIAoV6XubcYNIzUQqylK5ShVl\npdFipSSOFSiQAqmqSkVRrDSta7leq8gyRZWURKHufev/1t1vflmZIn3oEz+pm7deVBhdHBnvFRyS\nNsgJjBQ4t7OzY09WJXgBqf0TVQmgOzs7G/Xh3t6eFouF6VeZN9Y6CAJNJhNbU3+UIJ+BxhY9KsIG\nbIrAgQ3y2T9yGc5HO+9g3IhvRLKxEJ3l5TrOF8hAMHB6WZZ68cUX1Wq19I1vfMMg5GAw2FBawIwS\nUaXzrLmzs2Oaxq2tLbsuDIOMRRHOs82g94mGZVnq0aNH9jjkfr+/EeF9JiTQ/N1f/qlOHr+npFor\nDCsVRaUqqBSoUlV9r94MCiVRoFoSK4hCKZCWeaXFKlNelgorqd9MFQShwjBQnhXKilxpHCkJpbIK\nVQbf23kdhaoUaLbIVFaFwjCTVKkKAiWBVFWFirJQFARKwkrf/Lu/0bvffUM/97lf2QiIGDtC7CdP\nnljfjLWlHcI+wlarpfl8rl6vp7OzM8to1GowtAjSEZsjTKY1BHqAiGEdOfyWIApDTI3tn+lOKQCf\n4PdDnpycqNvt2gbgq4xr4XAwPxgcPR0chajvISbSLE+YUOvxPDdJ5qRQwQcHBybJuQwZMQKv0PdP\n8SyKQrdu3dLTp0+tLnz69Km63a6Jh4FWVVXp6OjIYK7XGmZZZk9jxUAODw9tJzdHGuRZpr/5T/+r\nstl51A7CQI0oUBZUyqpSWXHuNIEC7XeaaqahlkWp6XypYp0rL6V1XqgKwnMipCo1mq8UqNJOq6ZW\nLM2qQFUVKAwDhaoUJbESSVUYabRYqd9INVpmWuaZkrSmNAyU54Xmea5VXiqJQwUK1EgjZdOB/vyP\n/xdF9ab+ya/+D1YnEcx4VgIDPSONZPStCATIksBKglCj0bAd7+xz4w99V2orHNHvkCeQ8SQkWhb8\njNaAr+GomUkEHCwL7L3quBYOB4Qk+kva0AFGDqbgVEyeb2biVL42wmk5Ccsryvkdn++Zzjg+P0eE\nrUBsVsXBOIzm8PBw4xxGFo/oCG3tNY4cYS7JjIbeEgZ597vf0Tuvf1PTwZFqodSIzpnDsqgUFqVC\nlVotC4WB9KGXbyipAj05GWiZZUrjRFleKCtzKQxUlYHKqlAUSFEYqh4H2us2tF6uFcWx1nmprJDC\nQMrz80zdSWKFQarpaq29Tk1ZUdOT8UJVFKgeJ4rjUKEqKQgUBaGmq5VqRaJesyZVS33xz/4Pvfzh\nT+jG7ZeslqUWQrTtd6lT21Gz+rMhp9PphpqFEqKqKh0eHlpNRV3sTzEjGyVJsrFvEXLl+7WbvGTQ\nE1d8t2dEffa8kq3/YzrOf+lAmEyWAjNToDLB0PoeerKx1C8CUA+4AuSTpJOTk42mqH+t1/BJF2zn\nbDYz6AArxkMwWDBfvHtpGc3Rsrx40AQGyDVnq6XuvfOWxsOB3v363yqS1Gqk6pWZ6s1EaRxrna2V\nFYVqSaxZdg4DP/rBA63XuY5PjrXKSkVhqGYYarpYKk0TNeqJFutSZ6tMtTRVpEoHzVhpEmmxWKnV\nrGk9WageBoqqUFWRKY1iRUGlssi12zqvO5Ow0mGnpn4j1YPJUvP5UkkYqFlLlMbROd+iVFEYajSZ\nK4lDbQdTPfjaX+vtr31Rtz/8Cd268wEjnQisKIjq9bparZadDo2AwCtnVquVOZ4/l2WxWOjs7Mzq\nd79ZGREETozWlTXisdTeURuNhjXtYUv5bBhKf3YMtflVx7VwOLKZr+Wkiy30vobzPTbo5stiYXos\nvAanAKtDaAAvqDE8LJUu2gdlWVp/B3iDttIfgIr0CCPx/TkWiyIcsfR0ONDDN7+m8fFjhaqUxpGW\ni5WmRa5KpVq1muLvkSHNRl2L8US9Zk2d9jljtlxlSqNIVVlKwTnETKNQcRAoLwqti1ytNFa9nqgZ\nB6qFpeIoUKNWU5zEStJItTBQXkrZqlSlQGFw3lrotZpqN5v61ruP1EgT1dNEz/dqelBWWmW5irJU\nWQaK4kiVQgVlqThJpKrQYp0pCqV6GurxG1/VwXM31ej0bGsPc4hq37cIut2u7t69a/UfZAqkk19T\nSdb7Y48d/UDswAuT+R7PjEOksXWLDNztdm3/IIGYa/YBGlH8Vca1cDgin6/jWBQfXTBwL0yF/oX6\n9WoHaGPgalEUlqGAgDiDV5SjjfRYnt4Z2QtlCYschqEdmoNG0SvfgZPNZlPTyViDe28oO3lPYRSq\nmC6UhKWKSspXK2VZLsWx2o2a0jRWkOfa6TQUR6F6ybbm85miqlQSR2pGqSZVoVra1Ml0qZ1uS1m2\nVl6WqsJUB/2W0jRREoVK4lDdVlNxFJ//vqzUiqV1Vpwznq268rxQmecKJD0eDNVt1PTK4Y5mpVRm\nuZ7f3VUjGWmxynT/dKJVLoVFoTROJAWqp5EiBYrCc4Z0vV6rrKTvfvFPtc5LvfgT/0Q7+8+ZM7Dr\nHnlYv9/Xw4cPFUWRZTrqY44hPzk5MSMHCRFE/dNkEVG32207OAlJGe8DxkoXxwaidDo9PTUbnc1m\nG08zooWDiPmq41o4nG9mAyN8bwRjJQOBxXkfzsYkShfHu5HVKLZpNHNKEzQw76EhzkDmRR2YZZlB\nyd3dXQsWREwOOuUzgS/8XVWVHn77SyrnJ2o32irKQmVZKFClZlrTslyoWY+13WlJgaQ8V5Fn6m11\nlSaJHj46UhgGitNE3UZD8+lUjUZN61I6jFqqx4FW36vhntvbURolajRT1ZJUYVApbTTOnasola1X\n6rebWue55tOZMgUajcYqwkphkqoZp1oUpXpJpXi90CgP9eT4WHt726qvMp0uMq3yc2dVed4oDyWp\nOr/0NIrP5WJlpTIvlIaB3vv63+po56b2b72sRrtrcwpyofYFhqPaYeMphNTlXSKgEl8mcFRHFEXa\n399XEJyfZIaT0zKi8b61tWWbh9FuEoQlme6VoO6b5lcd18bhfNaitqH4pTGNEQM1eD3aRT9gl1hI\n9odRTPtDQ31hDVHCgqG35PQnPpvPuHPnjgaDwcaTXojKXtO5Xq8VR6Gmw4Hy6UCNWk3r1ULrLJeK\nQoGkqsyUpolq9ZrqUag8L7VYr1RLEoUKtFoupTBUFQQqy0JpHGulQLU0UbFYaauRKFClNE7USFv6\n4O3npEra3jk/2i0IQ0VpTZUkFZVm85nCJNZkOtdwmCovpbAstFqvlOWlet22FutcYV6oWRaaFbnm\nq7Xm86VqtUT7/baOhnOVRa4gkPKq0HItpXGkMC+UB4HSOJKCSlUphaGkfKWzh+/o8bt/r0/+t/9U\npUMmXoZHq8YH1tlsZk3/IAiMTW42m4Z+gPYcVETJQd0/n8/VaDTU7/dN0E5Gq9Vq9hQkJHiwy71e\nzzbHgn4kWSvhquPaOBw3ATwg25F1/O5b4J+HbiyOP0wVmEekQnDL59JfgZoHurD/jonk55AiOzs7\n9vmnp6emZFksFjo+PrZaD1ib57mSoNDdv/0PyrO1lGVaZJmy1VL1Rk1bnYYUhJrOl9rd6qqR1nR6\ndqpssVAzjqSq0mQ81dnivAY56LbVTGNV65kO9rZUrtdaprFe3t5Su93UIq90Y6+vnf1dhVGoJE0V\nxIGiKFTYqEtVoHyVK4xClXGo2Wiu2WSm+WKhFw62tFzlmixWev2tt7W3vaf3Hj1Wu9VWfT1RVoV6\n9/GJanGiG7tt7d3a0sl4oZPhRJ04kuJEqyxX8j2Usi5KJXGsfJ1pvQ6UJoH69UTrWPra//V/al4E\n+u//p//Zdt4TRDluDzECTiRdPAWHHRfL5dLOlwzD0HSOfqcJSh16fIjQaW7TjyXwD4fDDb0l69np\ndOwMzuFwaGesXHVcG4djb5dnKiUZrIA69o1xoJwfZDZwPO/HKTytTOHOYvrDg6jR5vO5HTFAY5WN\nof5zIAOki9OZueayyPX0rdc0nc5UlIXSOFUcSM00UFJraLVYaLJYKY4ipXGiSlJYlmrW6+eMYJrq\neDLX2WKtn775nMIy13Ix0wt721IUaTHK1G+0dbC3rSgM1ShKbfU6atZihXGiMA4VRqHCKFIUJgrC\nUEGtrrI6V5eEnaZCVWo260rCUMOzsco81+FWX4oDRWGsyXSqViPRbJ2pkSRarTOt14XiYKV+s67p\nfK4oCBWq1KKqlJVSkOdqpjWVkuIo0Hyda52VatYrNRupugoUrwv9p//9j/Wpn/oZPX/rtgU5UA2U\nvxe4n52dWdAbjUbKskz7+/sqy9KeKYFd+S02BGgchDYR2Y9nR7AToF6v69GjR8YtgLIePHhgxyyQ\nVa86roXDeUWFV9DDXALh/G5cnMg/YoloNZvNzCHIeMAQIB8OiIzIQxSchXoRrSCPbup0OvZwQT6f\nutPT3kTGr//lf1AcZGq3O0rCQFV1vqN5nQc6G5+pkBTHkRppTctsrbA8r4EWq0JJ0lAYxxpOxjro\nN3Rzq6mdfle9VktnR08VBqHCxuH52Rr9pparTM/12mq2GwrSUCpL1eNUiqQoiRU1UgWVFNUaClQq\nLyolSaEgz8+h6zpTGgYKq1zTelNvPnioF/a2NJwudTQa6+ZuX8dnI6nW0GK2VKCaUgVqRLFmq5W6\nzbpe6jX1YDCV0rpm65W6jbqSelP1eqHjyVx5Jc0Xax0c7Or0dKxiMdBX/vw/6O8PXtCPf+q/UVpv\nGOtI1qH2Bv5xPszu7q7ti4NlBPHQgqGuQ2ROzxP5VhzHOjg4sDoRuj9NU925c0dZlun4+NjKC+yB\nHRM/cmeaeGKEzMTw53DU6/WNbSkwm9RlRCJ6dWRJqF5PXHj1OvWb10wyuUi92GbT7XYN+uCcFM9A\nTmrMOI5VFrlm46Fa9Vhlmmpdllqul1qtMi0WSxVFpXozVVDmKoq1QjVUZOc/n8wWatVSHZ+Otdet\n687Btp7f21G7UVMcRco7nfPdAWGgrV5HYRQoDkI1ui2FaawojM9FyaoUJ6nCpKYwTqRACkz+VymK\nA8VJqKiWqCwytWuJlq22xtN7+vgrL0hVpeE00yov1S0rJVWlMKqUBZXKslKe5ed1YSWt81JpVmh/\nZ0sno4lUlN+TZSWqpYnC8fy89RBFWizmarUbGs2XyoNAw6OH+vyf/yd99r/7H60VA1rxTXGQA4f+\nSDIGGGfzD0LxtTT1vj+/hq08HD2IvI7dB7yX+hGb4hHUl4+o+H8b18Lh/JYcvw+Jf/uJI1JJFw/6\noH5Dk4f+zavvwfCQMMBHsmIUbW6AlS4U7zBn0My+p8bnsfmRLIny/O3X/k6NNFGZlxqPp0qSWKvV\nWqoqNVpN1eJYQVUoCs8/L8nXqseR3j0aKk0ivX00US0K9Zu/8U/Vb9UVBaXiMFGz3dLwbKLp2VDd\ndk2Nel1ZnqkdRkrrdSXpeYApslxhEqmqzmn9IDoPDlUoKY4UR5ECFYrjmuI4UVqr6b133tOqKPQz\nH/+gApX661e/rdHZifppXfefTnRr+xzqdpqpRpOFaq2mFouVVmWppCyVrTOVpbTTquvxaaZ5Viie\nTjUtS7VqiaaLldZ5qKrMpUqqpZHyZaGyyJQvC/1vf/j7uvXS+/W+j3zM0A+ZRZKdA0P9zjmT9DZ9\n/5ad2V5L6Tf2cvgRTDafzQFMl3ejYHPYgd+CdZVxLU5eli7kXRg/mYYGNbgb2p4sCMykn0bWogbg\n0FgmBrgobe6pI4rRo+P7Pd3sH7RIQMDJuG7pQoxdFpnmZ8eaLpZaZoXiNFVRnjeXkzRVmeWqpYmq\nSoqTVLX/h7o3+bFtTdO7fl+3ut1Gd/p778m8WVVZmVXlchkXBpcK2TWwACExQCABQowMQ/4CxvwD\nDJkgMbCAAbJHFpKx7MJyWtiFK13l7G7e/t7TRcSO3a3maxh8+11nnesSPomNdHJJoRMnYseO2Ht9\nb/e8z/u8haMpSvZdICYwxrIsHX/1z/8qD6/Omc9q6maeB0ojhDjgqtwjtM6hbUEzX1DVM1xRk1Ao\na0kxYJzDFBXaGLRzuabTCrRGnWojYmS/3eFD4Mm9+4SQI/jd3Q4VM51sbjxVWVEWjsVsgTWGw+5A\nU5fEkBjCa6Gi3nvOV0uO3UDdNGNvzlmTqWchEVIi+MySCSkSfaTQ8OLTn1G4183lq6sr5vM5l5eX\n444GAdrknkyl5+UMTVWmBQldLpcjArndbkcEWto3UspIJIQc0aQPJ4JO8thfBDR5JwxuSnUSaFgW\n5knfQw64PF6MRSKSGIekeoI0Tvl0whCQ3ydGIgYkBfW0DhOvKix24UrCa4MVytc3hVP/+B/8HT7/\n7DPOVguigufXG3aHIyEoPvn6lvOLNT4MGKNoCsuTq3PujgM/+fQ5yUeG/ZH/9q//h/xH/97v0Swb\nqmVDPa/QFrruSKng7PKcarnAzRuas3OWV1fYskA7gysbimZOc3mfan1OMW9wZYUrSpwrcbaAENEx\nMZsvCD4f+PvrJWbocES6fmB5tuT3fvNXGPqB337/HuvCcL5ouJhXPH18xbxQFCpxf15xt++5PfSk\nmJhXFYfjjsIZPru+w7mcwq0XDZeLGe0Q6EOiT3C96yGZzF5Jihg9f/dv/g2++vmPuHfv3ohWSlNc\nnJycB4H4hWmS0muhJ0EYBUST8qSqKs7OzkbjqqqKe/fujd8DxnMhJIvpGZPWwy9C7XonDA4YUwFg\nLFwlV4fXDeRpqilgiqBM8FrIVCKcRK4pvUve/CkjXIxcim7py0jxPI2QktdPqWZTUrL8LZuXX/P0\n/gXPbrd0MVE7w6OrCz57dYcrCnRS9L2nKkuePHgIEZ5d33H0kcoZfvd73+bqfE3TzNDq1D4JMPQn\nRa3ZjNlyibYGW9cYq7FFCSRUys3xDI6AShGjLUrpXL9Ju8U6tMvzb7PVCn88ooInBc/y8oxqMWfd\n1JzN58yrzH45bwrO6oLGGprCsZ410HcYdVoS0g/0wXN7t+Nw9KAUvYdDHwg+0LctTVUQg89tA2Ow\n1uBDzI3y0yRCGDxffvIztnebMWsQSXYxMEGZpZa/vLx8IxJNATBplk+1SaQEkVEp2f8t5YXU5fCa\nGAGv99T9WUj5/9v1TtRwYgDTNE4ii7zY6TDmNEqJt5kakcDHU+Uo8YJjX+yEgg7DwMXFxVj/hRDG\nTSuiDykFu6S9AjULj3IqySfP+8VPf8i9VcPz6w1og9ERW9T804+fcbGasawtd4c9RsNvfOdbvHx+\nzcubO17ujhRa8d/8Z3+NR+89YDavAY9xFe2h43D9Cm0LiqqkrBxFaSjqNSFljRJTGBwLiJHClWhT\nnCYGEhgDKFIY4JRKW2NBQTAe3/bU6zXbzz/j7ME9llcX1GcrPv3oE+7ubnn/YsmqsFw8fkRIib4f\nuNm1NCmgY2BzaFnVBbfHnkul8EXBLEa63jPEyNd3Ld97kgdNt4eWyhXs+4GXbW4y709iS9ZZYgj4\npLjbbvmnP/j7/M5f/qvjohfR/xfSsczNwWvpBTFEcbbCLhLe6zed5rQNsVqtxjN4d3cHvB4hm55B\niZSSeb3N9U4YHLyeW5OwL+F7RPviazluYORFSoiXXH8qvDOVGp+iSXJDhO4VYxwJsjIOIjdL/i/R\nVm6QeEyp+QQ0AegOO/Zf/YybzRaF4tC2PLlc8tMvbvINN54jCds4VosKQuTjz7/g/OIC5Qf+wq88\n4uJsSdVUEHq0LegOLbtXt5RNQQiJ2XIBIVI1K5RxJO8p6hJNxNjT3ryiRGmL1oaUImhHigNROZQP\noE/sHjTWFSQF89WazZefEwFtDfhAVRYcUqJwDusKZoslrnD0+wNlVXHsDvTDDK0U27sWq6DzMISB\nISQimXGyO3SgNNYYdm1HU9cMIbDtPUM7oEx2ug5LXVW0XcswJJ5//eXogKdKXWJI0oSWtFCQTXm8\nOOv9fs/Z2dnYWpCzJfdOWj8vXrx4Q+hVesRSL4rDlbTzl9LgxFtMmR9T6TTJy8UopzNs8Hq1sBx6\niWhTwVcZ0ZDUUlJXAWbEG0430og3g9cN7Wn0FGaDFNyvPv8pf/SHf5vN7oDv8wzaB/fOGPo8MT2E\niA8QY2I5m3P//Ix/8Ed/yn5/5N6V4r/4q7/Dr374Hqt7ayCSoqbdHwgpcPbggpgczXKGQlE1a4xS\n9G1HWc3y3xkjdtkQ/YDJuWOWVtAaEqAMxlmSBUJEmdNqZ61x1vH+975HIPHqox9zzgc06zWlMRx8\n4Gox4+JsxXJRY8sSfTZjeRyoy4I/aX/C1XJB//GX6BDZbHPaHE8cy81uT0yw7ROXV/fpvv6KzbFn\n0dQcQkvrT4say4JjN9D1A7OqoFSghpbnn39Es743ZiLTKezNZsNqtXrDSU+JDNO6XBBmKWHEkGUP\nuOAIH3/88fjc4lTFkUvmIxnVL10NJ4dddBenRe4USJEoJgYjL1jaAhIdp4AIvF5pLMYm35Nh1GkO\nPm1PyIfI4k1nomTZxrQ3U1UVP/mj/5O73T732TpP7z2KyGZ/JKWIVlAXDq0Us7IkdJ7bwxGnFZXR\nvP/oiropMMZhXIH3Az566rM1br6gmje4soSUDQcFSelsANphqgZrDTpl+0IXaO1QygIxfzGePlQi\nhR7ikGs6EorI/W99QH11j9gP6D732KwrKJzDWEtZFDSzmqqpqZcNy9WCurCYBFerBYvCEL0nxMSh\n7TEqO0ofIs+vb/A+UjjHsR9o+x5FxMjBTYmYICaVpftC/ps//8kPcdaM8ubb7XZkhMgwcFmW42ov\nYQtNx7GkXIDXOwmnUVL4sV999dV4Flar1bgARgy867rRGU/phG911v/VTOVfzyWRbNqw/rNSuOVy\nOaaAEuoFBpYG+ZR3OW1i13X9hh6FGI8YWAhh3H4pkUzAF9EmkUanoKTTZrsAK8++ek7feUKIeQQm\nwXbfMUTQSmO1xhmN0jCva3aHAySoy4KZMyyWDbZwpFPdZayhahqKsiK2PWVZoJRDlw1JKZQpUM5h\ni4qkNFoXxGEAY7MBuhKsRRmH0g5SyqCJUplQjCbFREqRFHpSCtSl5eHTD/FdSwwDpqhQxpBiIPnc\nYtBOFKsLiqrgvfffwyjF04szPrx/xtJqGPqTs+uonaN0lmM3cLvbYYuSpDT7LuD9SXNSKWKIaHNa\nlJggndK1m+df88mPfzjOJZZlOS5ZlDEZcdjSHBegS5rg0l+bspjE+QoBWiY9RCVMMhgpN6byG/Ic\n01LnX3rW/3+yoV/oEiRS6DaSwk0HOaUXJ2+eGJvUdhLqp0ySuq5H7ydpRYxxJLNOr6n4qDDFJXoJ\n+0CeZzqTB6+35vz8T/4x28ORkBQxQVUY1ouK622uEZrKUhhNoQPrWcX17Q2vNnu+dW/Odx5c8qsf\nvEdRlVSrGbYoiSFgtMMWNSZBvVximzP69khZFeikISnKuiaFgZRAkXDFkjh0GZHEoq0loUjaA+kU\nNRRKFSidsKo4CQWVkDwxwHK1or3ZcP3Zl1zdu+J2c0dhS/pjhzuxNFIyxOgpXcHjDx5xcbHm66+e\n8/DijOWs4uV2xx99ecfN0ePDgUprora8vN0xq7KzapPiGCIO0EqdqGaGqAIpQYiJ0mliCHz2z/+I\nh9/67uj0ZFmJlCBN07DZbN4QgBJ+pAwod13H5eXlCIYJpU/0Rh8/foy19o3JAJHbEMGi2Wz2Bifz\nF+FSvhMRTmhXMqULr+WvJWqJgU037QhaKZJqwEjBmrJUZFRejGzKJhHPJz8vzy81oygaS8QVg5c0\nVB6/393x6Z/+4xx5YyLEiNGKwQdQCmssKilKo3FOM6trDseewlkulgseXJ6zXM5xhcGZgpQCCk3V\nzHF1Tb1c4WbrLI9nLYoEaSCd0kGFwiiHxqJizM1sY1FGweDBB1IIEE8ruJTJyl7GkLQmoUkhczgV\nufa7fPyIy6ffZnW24vGTx8QYcsTxgTSEzONMp/W8KIq6YrmcM2sazudz7p8t+dZ5w1nlMEZhSMxL\ng9aJPmYxotwyyE1wZ0zOeGNCG5OzhBBoe0/r8+dTQoSsDLu+vuZwOPDll1+O91kyI7mPMi0u9+/h\nw4djuim1/nSXOrzm7Ermtd1uR8qZ7OwTWuDbXu+EwUlol+LTubwLTQYRp9IG0veQf6cAiuhCipDo\ntKktYV+inKSU8Ka8+cXFxZhOSJ/lmyRpeX6Zkwsh8IO//Tf46rOPQWuGGDBWUbgs0NMPkW6I9BGc\nM9xbr4kpYkvHvYs13/u1b/Hk6WMWFwvK1Rm2btDa0MwXmKLAlQ5br7C2BBTWFZh6ScShixJtG2yx\nQMeEP25Q1mGMIw2e1A1E30EYUKehNGVLUsq0KpIiDT4bsrHo5DC2QGPQSjFfL3j83e/y53//LxOU\n5dmrG159+Yx+36KiQp1AmOgHVMzpe7PM4MqD9ZrfeHLFX/n1J3ywrFkWBlJAG8P+0DLExO3+CMqw\n7zIsXxaW4+DxQ8QDQ4Rj72mHyK4NpNCP5YNQq4R2J5oxsr55Pp+PkU2QRjGwjz/+eJzcF7HY+Xw+\nzjFK+yHGOE6ZC6VLol45RvpfMmqXGI68kZIXSw0lb4A8VnopU1a4ABxTrqUYnHwOvPHc0zEguXli\n+FVVjWnEVMNQfoegqPI3b29e0vaeEKFwjrq0DDGxbz29TwwBZrVl1pR0/cDNdk9VlVyerVku5lmi\nzhqsKwCFLWuMNpiiRLsKpQyc5O6Mq8B7bFFiqzkahYqQ0pCh/qEjhQQxEmPIRhVCbg3EkBvbKaIU\naFthygqUInPMcmTEaLCOpIRup/ngvccs64rPP/mM3e0dQ3skntTPbFGQSEQ/kEiUdUXhHFVZUheW\nR2cLruYVygeGrqewmt5HYkpoEkorfMo6m0ophhDRWhFTIqIxRlMVmhfPno1kBtEGlZk3YHSK8B1t\nzQAAIABJREFU03GeKflZ0j8BwySKQd4f9+zZs5EnKdERGBdLThvdkl39Itc7UcMJ53GKRn6TNDod\nv5E0bjrbJhFMkCqJlrK/W3pysthhCshIXSZF8nTYUQx0sViMHlFuhERKUqTvPcchYQtDqTTBB/ad\np+3zaMiqcZzPLCkFbnY9dVXy3W89ZVk5ZouGoiozkKAVytisfqw1pp6TYsjRKZGNLXqUMtiiAjQx\nDfi2w5ZVPvTBQ/IkNMQh8ylDQKVEIuKHSIo+G4vvUdaiEsQ4gO/zNEEIqBgw2hC6/Hp/4/f+MlcP\n7vPsiy/48rPPuLvbsFjOcWUBSuclIUXB/tTTtEqhjKbp8zKRZWnZtQNfHzvQnkDmlQ4+k7djzBt8\n1rOGY9ujrSJG8MNAigmN4vrLn/Od736fvu/HMSwZ4xHjEu2S/X5PSnl11u3tLev1mmEYuLu7G8/a\ndrt9Y+2VtBKkBSAjP69evWK9XrPdbkfQ7O7ubjT+t73eCYOTvsg0Gk1HX6aagPJ48WDSfJboE0IY\n10PJGyPRSoxwyoUU8GOxWLwB0ghBWoxTDBMY046RuTJ0DEOum+zJW2qlOfY91liuljWLxtJ3LaYu\n0dZweXGO1WALk2XFYzqJ96gcbdD5EIcAMYCzkCIpDChtMvqYAil5kvcZ4k8R0ChiNlIsOah1uQWn\nNSYZklEEn5WbjS1RypBUyPUgBlLu2ykF2hTE7gghYQrFvQ9/he64pV5UdLuO/tAR2oFyMSNxkpy3\nlth32MJS+DK3QOqOMPScNwUHH7juPKU2HENE6ZM0YYIUIyl4rFXEFIkhAadIlyB0RzY31ySlR7lC\ncc4Cpsh9kTMlSLPUbFIuTGXz9vv9iHbK/RYU8sWLF6P8gmRT0seVAPC21zthcFKbiZFM+2DTye0p\n7D+lcElUlDdIQJAp97EsyzEtECLytK8nzyO9OngtlS3MAqkF5LHy/93mJoMi3mMLS9sNHPtAXRas\nasfD+2cMvecQchvi6XuPWVZlFvLpBhRgC4stCrTOTgJdoY1FuQKVVAYrkgaj0CbXTDrCae8USllU\n1Gg0sffoomQ47kmhPUHrCZTF2yLbsmtQ1nASMgFjMPbkuKJGG0dIAzF5jC0JOjJs7lBWc/+Dp+yv\nX7EvW6q6wfuO4/6IVg58R11nNA/vmdUlQ2E5ixEDXO0OKAXqrudl70laZd2UPKxASImYPNZoCmMp\nbb6fh67j0A9s9i3uT37I01/73hv0LXhdi0+NQO71lNywXq9HCQfpqb3//vu8evVqpIyJSKyM98jv\nEIAupTSCcL90ysvyAqbNbUkRp1+f9uTEowlcP20RiHKWzKlJv01SB0kn5GYppd4grE5RTPlcpobF\nyGTB/DAMdIc93ZCZJBrN4DOjZFk5FrOCmCLH7oj3ieWygpg4tkd8XZCizbNr1mCsy7UV6jQgqlHh\npL6TEiqSmSHBk0LMjepUoHWFdi6jhzGhy5ow9MQQULZGayAFlHLE00iOVlmICJXyUGr0Y40YVcpo\npTaYpIjEjK+cFjpqpSlmMw67AyEMFHVNfzgSYo+KHqVdnip3JSlFgg6neqfEOUtTO2Zt4PmxQys9\nqb81IQV8zKlvSgmjFOrUvxRnut3eviF7P92f9+zZs1GeXh4jLSFhEUkvT+pzaQsIvWu6L1D4l0IB\nk3bR9fX1G0Hiba93wuAkJZTwD68HBaeGJ8b3Z41bTNf9CgAylTWXN0wMTDyeQP11XY9NUYF6p4Iz\noncIjDdEtpm++vozdIJFUzKEgdoolrOCJw/P2B17rm93dG3P00fnlMYQw0DCsl4sWVY2B5+YXbw2\nNsPtOoMXMWRtEOWyhn/yp9qSCLYm+oB1eabOFFUGR/yAqWp0VZJCPrxonefiskA5oT/C0AGGlDyq\nkGhRghoAhfbxhGgOGBToQFL5GVyqcXVFu9kQjpbybMHx9o4YFVpHysLk+nDW4LuQ+33OUlcl+7Zn\nVRnmB8um95SFISWFDxHnDH2MRFSeITSastCUyp5m6TS1eY0qi/zCFNCaqiaLAxaF5/1+P1K8pqyk\nhw8fjrsgBIBZrVY8f/4c59yoqyIZ05TQ/ks38S35srxp8LoPJ2jiVPdEDEKinHwuPEsJ91MZ6uk4\nxjfZ5FMNEvldxpixBycG7r0f01KZ0+u6ltgf0RoKrdEaSqMxCro+8Px2l3tcKTd0g4+EoNBOYXSW\nGtAmQ/DKGBQa1GkgNJyihZLJiIDSjpQCKIc2DnSuIZUyJ6QyM0kkBVUG0BaUQuOIQwAN2pUZ0fRh\nlM3LqWtEu9xSiEM8IZ+5zyeCk8o6VEzYssBUJcEHXIw44zhykqgkoqxCkfmRccj3uCyKLEw7BAqn\nKJLBn5gvO59wGLQKgBpRysF7XFlkNJZA37djdJOMR5ztcrkcHejLly/HBrUoKwuqeHt7O5YpgnhL\nxiKNcYliglzv9/vx3kszfdrOepvrnTA4OfBS3Ir3gdf58XQ+bpqzCylV0gXxXlNQZfqmSFSczsHN\nZrNxu4pER5m7m5Ki5fdM2SxlWeKDpywshc2pz9B1rJZLvn51x/6YH79oSrq+xWiLDxGlSkjxdJDA\nOId1DqV0pjyGgZDsydgiKiS0seRD71DGMnQHiOQGd4woPGiDci7321JW/c8vXKGsg9jm38GAUjnq\nZWEUlY3O5nm54CPaVTn6xQGMyQ4hJvA9CoW2Fm0NqijptwcwClNb0Jqw1xirc7M8kRvZXZ8VyZzB\nOkNpDLVVHEIkKU1K4FOicBZ/amcordAnJDMqcnN8IpMokUYOv5wdoWNJw1vEn2RPhLSWhHsp93W5\nXPLTn/6Up0+fcv/+/THVlPaPACri+H/Rie93wuCkx/XNYleMRkK/GJ3w5CS1kzdD3kiBhUUOW1JJ\n6aHM53MWi8WYFm42Gy4vL0ejF0RTagvRWJFWwNSzKaUpmwXzOtd2VinOr86JQfHx8y1l5bi/KDmf\nlyjMScOjYFE3WKNQYaCuKuqixLq8uy0jcgqdFOmkbJzQJOvwfY/vW4bjAa0NWI02JaaoKKslKkV0\nOUfphFZZXkHFQBwGwr5FV0U2YG0zysmAKk2uDU8bekJ7wNT1iVniSH2epbNqhu9b2AWUVRT1nOgD\n3W5HtJZIHGUksIaYMh263+6p5nN23UDXB5SylGXBvOrQJqEDhBDprObYD5RNRWUMw9CjT6M9hVMY\npdAGtHmNVsscW0p5k47s45su3JAsSURg5TwJB1ci1uFwGFsGP/rRj0bw7XA4vCE+JCRoURSbrn/+\nl13vhMFNdUgkhROgYgqaTFsEYgRC3xFWiNRwbdvy5MmTcVOOcCilGS49mq+++mpsDUihvNvtxrw8\nxjhqZQgpVnJ3SX8vHzzm+Ud/jDOGqrQ0VcPLm7vcK1OKeV3StkdmzYLj4cCj9RKjc5KWDdhhqwqt\nTmluceprqUxzSinimoroB159/lGG+mMkaZvFgFyJtQWLZULFyNLVmMKBUyRyahk1YDUpRHSRU8sU\nPKgsLqS1BW1OLQgHRQF9l3t3Kc/MQURpg25q0AoTsxqYP6V7XXsk+Ay+xJjTQh/zwsgYwml5ZAZg\nlMoCtwYwKlLXjmPv6Xxg13bMS4fRBk5Nca1zCg6JfvBjL3aqqrXdbseVwVOJ+qlGqMy1yRkQ8rsA\na865N8R95exJm0GAE0E3JRt667P+r8dk/tUuQSWnzA15cyRXFzgeGNkkU/kF2SsgHmq9XnN+fg68\nlsie1nPT+TqJpGJUYtjTJrjUdiJIJJy8GCO/9bu/z//2v/4NPric8/DqnL4PtMNAJDJvGjSBoplz\nc3PDoilxOk9eawXNfE69WGC0RqWEVoY0DCRdEcIBbUsUiR/94d9je7fh6ANoS+ha5hcXFGXN9asb\nXFGiUsIVlqur+zSLM87v3aOcLaibOteCVhMOBxIJWzYMKGJ3yAOsaJLS+HZHSH0Gb2xB7DtMUZ9G\n6RRoQwjDqaaLGFtQNgvaoSd1PcejByIhZPm+0HUoa2i3B7yPGRyJkZBy5M7yD4kUPFfLgqqwfL09\nsu86LhYz+mE4tQpg8IGqrrg5hrFlI70wmRaQ+ymrh4V6J/dqNpuN5UJKaRweln3q2+0WGWhdLpcj\n2i1gTErpja1Jv5QyeeJlpqmlGKAYyXRzphiAGMV0h/OURSJeT9IOrfVY7Eo6OR1kFI8ohbU812Kx\nGEdzpFA/Ho9vKPxGFHWV09tju0VpTeEsTWFoZjPutoesdFVV9N6znje512QN1rqM/A8D6QTTh5jZ\nFcPxyIuvv+InP/t5PqBlBWqgPR4ZTMXZRUF59oD+sKOqS3a31/jwFYtDhysdQ/BUsznEQPSREAN4\nUNqfAJPceA6+P6GinnDcEYs6o55KodAobQgpgBpQ2p4e74gMxJijkNIG7wPeD6/rU5OpVz7lcaV4\nKinTqbZM6QR+hMj5vCbpQHHo8OGkN2oNXTfg6oIYT/SzxLi9VA6+lAshhBGRnNbhomkp91xk8/q+\n5/Lyks1mM06Mt23Lo0ePxkxKQDZjzHgOhMFyPB5/+XYLTAU5xfNAnsIVnUCl8n4A0bEARlBE0k2J\njgKI3NzcjHWY5O+CZsnXBLUUFV65idJWEHFQib6Hw2FsLci6JWMMf/67T4nHG56/fEFRzZhXFd95\ntGTZlAxd4Ovn1/zGtx4wLytKo2kPex7e+zYP33tESgPJp5zWoYk+EDV0vefv/e9/hxQTF/ceZkfk\nPavLS2KELkT2d1sWZyX2tG11df8x+27gRz/+MaYqqLd7Xnz2CbPlivnZGc46qmZJionoO6IP9O0r\n5hfv0+9v6ds96rTr2xoNVhN9FhUKYSCmyBA8bXsgxMDR9xzu7miHjrvrazbPXtH7AVM4qqpksV5g\nhYAwZDJXBPyJaiasFm3y7oP37s94dntAGc3L7ZGLeU1Tl2gysdkYTVPqUUlb6nmRmZfaXMqSpmnG\nHW8y1d00DWdnZ+MOAuHFCmIt+idyPu7u7kanLYYnlDLn3JhJvdVZ/9dtPP9fLhm7EWObEoqnObgY\n0lR/UsAMefPhzRZA0zQcDoc39gZ8k4gszyEGLJ5L2CjiOaXGFIcg0VcpxeW9B3z5sxckY9F+wBhY\nNrnpfL05UBSGWVURQ6DvehbLGfPZHOtO0LxSKKtz30zl3dyfffY5TTNntlzy4L0nuZe42YPR1PMz\nyuWKOAzcXL+kWq3Y32zwfcuDRw8wWvHFzz/l27/2a4SQaIeBw1dfcv/BQ/p2B7bKQ65aND3zFhxi\nynN4pjlFMYPf7wg+cDzuCX6gO24ZgufVq1dsN3e0x4GQPNvtjq+e3+AKgzYH3M4QNVhjaHt/muIO\nWQz6lJTmciKhtUIbjQGq0uH7gA+B7bFjNSszGYCENQqjXqtly32Wfpu0k6ZrsKaGJJo3d3d39H3P\nq1evWK1WY8Yk50FKk7ZtR4GoYRhGQ40xcn19/YbI7Ntc74TBSS0mBakc6qmMgtR5gkJKKij1nERC\nKYAFjZyKzQBvLE/0J6b7dPG7aE+KRsZUT14EhYS1IF606zoefvt7vPjsp6QY6IaO2WxBJDJr5nz+\n1Uc8fXCPrmtpihKrFFfrBcvzdeY2pkiKkSEcwJSkZPnZP/sRh92BD7/3fep59tplUfDer/xWpoBZ\nhy0coe950H5Ad9zhLy6JRA53G+zlGen8DD9keYboBxSaoe0o6ppIIDLg2yOx7xhubzHFjBQV5flD\nUhiIJhI7jydyc3vDyy++pOtaPv7JRxz7jpbEtuvZHVtu7+7yrrrVmv2LPQwD1kD9+VfM5wvOFguO\nx45gFKEb8EOPPgFHISYwGj/02PWSp48v+OFHz0hJ0Q4ec9ScNTXOqozyGlguF2y3uzeMSc6S7AGU\n0kDQanksMHIpZSi1qqpROEjOxatXr8aRHzHG3W43PsdqtXqjJHmb650wuGkdBozghMC3Us8BI0F1\nSteZIpuyqEEiYNu2ozFCTl8Ph8O/wLmE7Oklv5c3Hl4zYaYpjERJKdTLep7pV9rQtR1NnYleIUQW\nswajYRg8URtcVTGrS1TKdVXmTupxlEbpPAI6W82ZLWYUxlDNZ2hlKYqsdWKKjIyZugQiipI2dMQU\nKAuFXhQMXSBpm9WdTaZyDV2LP+5wixUqKrS2earAgy4NbnWBq0tUHE576Aa2d5ts0Cmwb1uisXT0\nbA57IurUD7XMLq/YDx2kgCkKhuDRQ8DfbdEmtwmG3ufB01NdpHU60dRO20yPRxazhsKZ8TwAHLsB\nhT297oD3YRQCur29HaPMlJ4nGc90N8ButxsdvGQ9dV1zc3NDWZbM53OePXv2BvIo20+FiTRdCCPO\n+W2vd8LgZKOopAXb7Xb8nhjbVLIM3ly4KNEvxjgikkpleWz5+nTc5uLigvl8/obK11QDQ27QtDk+\nlcGTiCnOIMaIdXk98KEL3Ny1VGVL54fM3vCew2FPYR3b1PHrTx/x/tMnWdzVGJIBYy0qWvqo6I8H\n6tLRrNaomDBNQV3N0KfFHYpA7GNeV+wj0bfgOwwt0bdo3+I02CplrUqdKVzRaMqyIUQPvscow/Le\nE2Lf0e3v6LfPSHXNcDiiYuToOw6HPYfNlhfPX3E87Glbz9EP7NsjFsVitaKsS0pjidHnSXJbsL/b\n4EPg+cuXvNpt+PTZM7Q1zOeLLPceIjEECIG+DzBA9eCCYTjw8Oqcbz+65HrzCVYbOu859iprnfiI\nPfW+5F6fnZ2NTlBqbaF1Oef48MMPRwctzld4k4I6yrjNq1evxvbSYrEYS522bUclASk1ZrPZGwrQ\nb3O9EwYnqaL3efeazJ9JFJoqecnjJSpJ8SzwryzqEIRTIpbk48CoZTEd6Zm2GaTvImNDArIAI5gi\nhFk4LZcIfV5q7yM+Bg7HI8aJmNGAMxV9P6Ct5uxsTXXqZcXQYuzsNIajCAFs4Th7/JA0BGbzBUVV\nn9S5DCF5QtdDguj3RO9pD3eEYSC2O0LKSlcJSMZlBeZoTpt1FM5kEQVlHcZZiqpmSFkOom03KA3h\nxDxpt7d0fUvfHVF4Ysj0ssJZ1vM5F5fn1PMZzlmKssQ4R+Es2jiOd1t2mzsMWUl5e+joek8Uklg6\nzRKqLJY0kGiPHWWV4f71sspULqUIKYKS+jyBZcxm5NxIJjPVI12v12O7R0jnsurKOcdmsxmR6+12\n+4bQVF3Xo9RC0zSjY5fzJGgm5L7t217vhMFdXFyMI+zSaJzSdaQwnnqvqcCrGIX04yQd/Oijj3jy\n5AkppbEBLm+utADkZyRFEDRL6oLpCJC0Hpxz7Pf7sQCPMWK1JsTEEAaauiJPtCkOx5aiKHlxu6Mp\nC84WMy4uFuiUl8Bpm7mRQ9R56rlvWV6cs1w3xBCwpgCViHiOt8959cdfoesZdr6iuXqMMorNYaA/\nBvpnR3w4cry7JQ4tD56c05wtia5CqdMUtPJoZ+F4JLUaVS1wTU30c5qHH9Dub+kOd3S7Ay++/hyq\niru7Hd1uj9OGcuG4PH+Iq0tmiwXFrMFaR9Escjp8Qlnb1ZLu/iVV6Vg2FYe+48Vmhw+JpG1ue5DQ\nKgMm0Udu7vac6YahH6gLnR1WSDijObQ9latofeQ3/+JfGg+5DAVPa3cBuT755JPRaU4BtsvLy/EM\nSatAyhrhXsq9lV3wUstLnbfb7d5oH73t9U4YnFwCZABvcNYENJGaTdLPaR33zd5dVVXc3t6yWCzG\niW+5JDIBY/Nz2kqQm2GtZbPZvDGaIY8To5ZGa653oO0HnLFvePC28/RDYFZD4QzG2iyFFyPJZRKy\ncxoClE2NK8qsfRFypElac/3zH3P3/JrZ0+9QzObE4PnRD/4uL5695NMvP2HfJZ6en7NazXh4/5IY\nSm5e3uF94PL988xc0QZdnjb9UKBiJMUeXVRAInRHkrL4fsfu5StuX1xT3rvguNkydB2z1RJjNOWs\nxFUF9aqhKCpcUWGMzTVi20MIaBUxIWCNoS5LFmXFXdHStgeMzZtZ6TNxWkuvLHh8SIQQgZRVvFKm\ngkUSSSl0UXLv0fvjmI0AJLLsQ0oDcZSr1WrceiSpobR/ZJWZ1Giy9x0Y9Uzk+eT+i1KYlDTT6YK3\nud4Jg5PRGmA0GOmlCAIp1JppUSxGNgU+BPqX3tkXX3wxPv+UsTKF9CWtlOeW+brpgkVpoksqIQ5A\nxvx7HwkK+sHTVBV+yM1Row2bY0fjHE1Vs1otsc5h64oYepQuUfbUfyMxW84piprU9QSdUFoT2z3P\nP/mU1bc/pN3d8umf/gkff/Qxf/MPf8Ddpuff+c//a/7n/+V/4sm6ZhY8/+Vf+YucrWcUVUF/8Pih\nRVmLQaFjQhcGTR4yTaEDn9WyUBrf7emOLbfPn9MOHnU8st1sKFyBKSymsLiqwBiFSgGlA9pEUuoh\nRcAT+5449ATfoVSgqQuaqmLRVPRtB0phbCYri0qYNTnNjSmhksa6PKYU0qkNY3JLYXF5xb2H742A\nmKyGFpKyEMoFeZR7JcKt5+fn49SAZFESLafAmzj0zWYz1mhCF5NzJFnW7e3tW5/1d8LgZGBUEERp\nYMvErXgZeG0gkkbKTNqUSTJV5ZoOCAoLYTolLBQtYCympckpb+iUcbDb7cbdzpLOaq0JGGazOYvq\nDgX4pPMQZUj4qFAaHl6c0ViDKwusMUBBUJq6XpIUKG1prMUVFccQSP2e2+c/4+arF/yj/+uP+fxv\n/R3s+orCOq7uPeDf+rf/gFCVfPzZR9y/vOB3f/c3UcPAxXd/i89/+jPST37EvQfnfPDbv40tZiiV\niL7HdgrbVKA0w7Gl3x9obzccNje0MbC9vuXjT79gGwban3/K1YNLqvUKdOJ4e8OLL16yfvAEs35A\n12v87kh7d8vQtbSbGxha6tKijCZEj3KJ1XLBMQxsvc9yd+g8wcBJvwiF94nNvmW9aFnUc1SW3WTw\nkctVbtf8zu/+HuVkEy7kGmqxWIwkhen9kmxmPp+PkU4IzPIcsrxlytVVSo2ppPweYZhMkXI5d297\nvRMGJ9D6VJtCIogQi6fDguJ5pJaSr8kbCbzRLJ8aoAAjwJh2fLM1IG+yjGyIJ5SZqe12O6ayAi1X\ndUM1O8forwnek0IgBkXnA1rBvKlQGsqixFmDRsFJfVhbk8VjY0+7ucFri65XKO3w/YBSiYeP7/Od\nP/d9quUD5ssLZhcPsnJyUfL1559ifv8v0N/dolOi9oHH6zV/+rPE7R9/xAff+4h7v/r9LKswHEgq\n7xn3/YHheCS0Ld12R7u9o237rLgcE4d2wLc91XzO0Hfsb685HDq+8zu/S9U0/OSf/CFffv4x1zfX\n3Hv8a1ycnbNYn+FRfPbPf0xVKM7vXxJiwGqFM1mXJaOzGuMsevAYY0m9J5HwIeIHT9/2WcErZRBn\nXjr2KvLw0Xv0fT9mG1JvyeGXbGUa3aRftl6vR4fsvWe5XDIMA7e3t2/0bKUFJfOUQgsTDRRgnLGT\nn3nb650wuM1mMyofT3tm0y04YjDivcTTyOcSFaW2EtRR6rqptLU8h6SpYoRTcAQY+yzTglyAG601\ny+VylNter9esHn/Il5/8mL5rscaybwcOXeDR5ZxZddrYWRWUVeZO2maRRXy0hWHAlRWzRV415Q9H\nbPAsz85Zri948r3voXWBLpco47BlQ9IlfgicNQXDfk/o9wztnv3zr1mta/7gP/kPKOc19bxA64iz\nAzE5knZ0xy1x8PhdT1IJ5SN1sebu2Uf0uz2zuuKLzYak4dWzZ7z37af8/f/jB9x/eI97XvPZBv7u\nM89/+tf/O37wj/4p/8P/+N+zWMxRLz7n/VXFb334HhQzfvLJpzilWSxnHI+Wusg76rQr+fL2LgsF\n+UBUmpiysGzvAzNOKZsaCCFRWIU2Fc0JwhdwQ8ap2rZ9Qzrh+vqa9Xo91t3Pnz8fVbukDpeB1fV6\nzc9//vOxNgfG7EqoXHIWZVhVxIoky3nb650wOIls0rEXCFbybvmYtgOkxpIoJjWYeDqJhvK96Ryb\nGJU8Bl6nqvKzAvtLpAPGdESapdJErarqhF6WGJ11KUNQ9G1On7Q6ySiQKK1FiwiSzcsxUoz5a3iS\nz193zQJbr1HIAC6EIRD6PbpwGKtez7MVCZVbWdB5yqYgeU/qjvjoaWMNqzXaZcXlFAMxeWKKYB0K\ni0o9hHwYrbEUrsiLNVBc3Lui39xgF2ecP3zM4/tXPFAOM7xP99M/wn/+J3x4saSuZ7TpHoUa+Cc/\n/gnzsuI7T+5x7AfWl2tm8xp343DO5hEkMsFLaw1B42PWzUwxv17nNHEyQ+vDa/R6mslIrS173YTf\nKHW81PmbzYbFYsFutxtl8eW5yrJksViMUwZS5ghaKQ522sMLIS/7kB1yb3O9EwY3VeoSHRMBS6aM\nkCkMPxUaEiOTS94UIUNLmiGGLEYCjN5K/gb52jSNBP6FqCnDh3I1TYPmAmssfZ/lx4OPLOoKZ7Pk\nwvuPH1AVYJTG1gusKyFCVAqtE04bdJGbq6asMLYgdmn8na4osVVDHAb8bkO32+GHAX84EP3A0LeE\nzhPQqLIhmjnRVhns0AUxWoyyRN8SOyBqFAEi2NISQ0dMmu54pPdZFImUuHn1kqff/VX+4+//JtV8\nyfz8jBQU7//+v0tIil9//B7//m/9Gm17YH+4wwfPz//5j/nyxXP+4Y8/o7aGsrLMZzOqlGjKknrW\n8HlVMvQ9pk+EfkApRyAw+JMOZcyD6PZUa93/1vfHMyIsHzE2yUr2+z2bzYb5fD6m/vJ4GXCWiGSM\n4fr6epyTvLm5GeckD4fDuNxDzogQMqTckX6vpJlvc70TBjedP5seYgFFBLiYap/Am/C+pAnAmHJM\nf1Ya6GKMAs6IN5NJc2GzyGNl9m0acYVtAK9pQ23bokwB9ZJuu6UwFmvViWyrWS8XVM5mfUebVa0w\nJjfMtcMQMCoPXKIUxri8tVTrrGwVPDF4Yj9kiB+IOk+CU88wJ3JvQmF1mRvluz31xYJvRrrbAAAg\nAElEQVS6ysrJ2hZZ2StlkEIZS+r6cTeBJkf/pBQhJEJIxDhwc7NDffQ5RblEFRG9fYlB06wvAE1K\nHlsYiqAJVuGV4t7FjNJe8uXzr2mHXAft9vs8eBs87d2Gspkx9F1WXkad5gFVTreNRSmwSlG6fEwX\nF1cjKCLpv9yPKdFdsiXhQU6zGXG44kwlg5nWYvJ46e9NxayE0RJCGKcPfumYJtJkljF3QQjlEnkE\nedHTem76guVz6dUJdefPYvdLHSceU35e/hUvKM81TV2qqhob59LHkdfwX/3+H3DWHvjBT3/C3/rh\n/43WisV8wbee3OfV8xc8ulxgsChbQh+IPmG1x2oDJq+X0tYRo8JoS/KRFBNh8Cg/kPxAGgbwnnLw\nmYQcssS5tSWoSCITg5v3HmLL6mTYAykMDPG0MVZFUuryrFuE5AM2aqxKOCDLK2iicVTWEo5Hnn32\nOf2hz6mh1Qy7O4rZDF1UVLXCFSVFsWAYWtpthYoDf/CXfpsQBp692NB1LX/hN3+Fsp7x8uaOT/7Z\njymbCnfcU2pOUwqK0jlIEaMShdM8OatoI3z72x9ydnb2BmgFjNzYqf7MNxlEcs+lThNhV6kBpZyR\nOl9Uvo0xo2EBbzhviXi/dBFONN+/SbESupbWbwq0TlNGueQNn26mnNZ9kk4KjCt5uRiTgCeS84vX\nk+YoZMcgqOhU3XlsJfiBpyuNXV3x8OaG0hhU4ThfNdSlZbcJeQJcn3ayxZC1/BUnpWOdI1MCfVoV\njDVZBisFolKYEEjGoPLoKGoYSENuNit6lFW4ZoXWBmuyiGwMAyF0uZGeAinmRfcpZEmGFFIWhS00\nOoFxRa718psNKeTXEjzd/o7lxVlu6Pe516ZNJhUnH7IKNImyabCuYHGa7r56cI+u7dhu74jBU1rF\nerni5W3Aao1ReXWPNZnwnVJe0ugUNIXmeCJJHw6HsYV0PB5HJFnoWvCaDD9lEcnZEFpWURTc3t6O\nhjgViJIypG3bMVoKYCbnaSrT90vXFhDloykcO9WqmEqQT/mU8v3pfJvUatPZNfmYssjFWKZgzXS2\nTjzadGuq9GakMJf8vq5rXr18yb+pXvLyRSBWNeuy4t/44D3+9PaaR5dn3N3eoVNiXjfQQ6ogpkiI\nnlKVpJgnBPJyi6zhkQBbNzBkyBxtcEMAk3Uog+9JVY2tazhtryGSdxSQ8OEIURF9ThvzGFDWHEl+\nyPxNTJaz6z1KK1xRUZUV87rBGkNIGp/yzNrZrKaoC8J+g1ssmV1cZcegoNveEYcBt1rjSodrVqQh\niwTZuqFv9+y2Nxy7jlc3G/oQUSqxXK/ZbQ4UfSCpSO3ykKn3EX/apzCrS84ff5+qnr2RsaxWq9Ho\nJCJJJiSZyVTnVOQzhFhxcXExzsVJvTZtQ61Wq5FXKcBIWZbjCJfQvn7pQJOpAUhKIC8ceMN4pv00\nebwY4XRmbgq2yOOnBinGPf26vNHT9EN6ctLrEY8mN3QEcoLH+IH1t75NrGZs7jasZzWzQ3FSPVa5\nF3WqR0gqRzKV5b3zSoG8ry1rkSu00sS+Jw191vePieSzbqR2lhiGXN/ZRFQavAcVswz6iTWhkjql\nmQGIKOfQPpCSJvUDWhUnMaEsk67QGG0orcNqQ/CnVkxMeSOrUhQkVHuEbo+rZ5SzJfVslvfLaY3W\nCbtyQOK439FubyAqynLOvGnYtx272y1FWdJ3HUZnYnXUCqcUPnhCsLkiPQ2wzpbno5J2VVXcnRaG\nAGMtLhmJpPriQKd6lDItIronVVWhlGI+n2OtHZWbtdajdIIYmZwzoXMJE+oXSSnfiXVVAsPLgZ4a\nlff+DfUloV1NEUhgfIOlnpsijlPVpmlbQYxpmhJMB17lc6H5CJtcNFjk+yK3du/BE+bLc2bzJbPl\nguV8TuMMdVVAjBQ6S5enE0iQx1kklXx94LU2ZFUrhT/uiH4AH/LugrLJ4IfKs2zZYiPq1MPStswC\nQMbm78eA1glTWGxdjuBE6hMkkczLupHGFmjlRoNz5vQe67xUJAwDpEyBckaj/ABhIA4tzmiqylFX\nBXXZoFNOVwvnKOo5+7tbhjZvZS1dkVdtaU3hHNroPPGtNUZlwSAfAlZbnMnqXrPZ7I2eqmhNTjOg\nYRjYbDbjdLawlUY20GSi+/r6+g2qn4gQXV1dvcG7lVRzqhYn50t+5y/lxPd0JEbeqGlKKPNNssBc\naq5pLj0FUqYRTIpc4VAKoim8uOkIh0Qz7/0bctbTBrmgnFPUy2jFr/+577M2DUMCU2geP3nKZ9sN\nu7stQ+eJVtEPA8l3xNihrT1JwZE3zkRIPSQdiaZnOHa4k2oXJ2NINkuTxxjRTU089ugYiNqgywLt\nHNbZDKsXCuUaUuyIYWDYtaSYAyFklr5SoFV5Igp7rLKUtmTmHCtb0PcdbddTGMPhcKSsa1buPDPt\nXXY+yg+YeFqRpQpC8HnFVYz0/YEw9KAtNy+eUy6XuP2RUme+5KzM24SsVgxAUZpMjfOBqjCYqE8a\nM+UIUkgEa5qG58+fj/dPPqTmEnBlStlKKbHZbMZsCRh1TEWtWZrnApQB4y4J4WlO0c4XL1689Vl/\nZyLclPMoaOC0TfBNpFEMZzrDJjXdtC8j0Wr6/ylbQB47TWHFkKdzcBLNxHCF9jNqWhKpmwblDLaq\ncWXBrC45W8woqtxbG06pGTrXVSnmpRzGuUwaTCmPuKREOPSkPhBDJPQdyhVgbVbHgqx4fJprw7gs\ns6dVruWUwRYlxua/2bcd/a6lbwfa7R39fnt6rad2QFQkfVpT7DSmdNhZReUshXbsu45D19P3Hd1x\nnyNVypMQSp9oaiqnwcl7iIFhu2c4Hoh9JPiUo2NU3G02uLrE2NdjT3Bac6xyDds0M2KE0ipKZ4gJ\nyqoao5wAIK9evaKua66ursY0L8YshTefz0dJPLnXVVWxXq9HByxk5c1mQ4zxjdaQLPUQJy4pqfTn\n5HFyPt72eicinPDh5JpGLqmp5HMhpEpqIRFQDFG+/83JcElDp9sypUiWiLZYLP4FFoqkHRIRp/sO\nxGCdczgGqnhCS61Cp57+sOdXnjzlH/70h3Rty2q9wvtETIHge0x0aGdAWU4C+xns8D0MEWsrlPcY\nW2CKKhuXK/MeNz+Qhh1aR2KTDc26bHzD/ha/vYblHKUM3fYWfMpk/m2PcQa3btBKUzqH1o6+j6TQ\n49YHUqloVGDWVFS+R3d5I9C2bamHmu5ugyFQL+YYTkJMs2VmpvQDoe9x9RzfHQl+z7Dd4tselOWL\nTz7BFg5lCyptOKRAVZWg2xypfaReNOy7A/O65M57/CmNFXQSGEuQae9N0jyZfTwcDqOqmmQukiXJ\nzwjYIhnO9fX1CMIVRTECJEopdrsdZ2dno8OdyjK87fVORLhvgiYygDpNB6e9FEkppjD+dCIb3tw5\nJ8YpqcYUuZJLnls+4HUdKDdlmvbWdT2O5iulqMoqz671LXHoiSlw9eARTz54mqeuT5qH2miSzkx5\nVFYSzgYeiSEvV4xdnxd66PzBaU8chQOb9U+wBbosSEahtEUXNdFr/N0d+6++pBsiJMtwOOaIO1tQ\nzBcUqzXFao0uClRhscsV7vIher5AFQW6rvK/RYkpHdooZlXD+WJB23X03YBrSpRRxKHLzXk0Shm0\nLpCoCRGVAtoWWb9y8BnkMY7DqT86DC1WZbjdKE4QviIEjztRy1LMkVea3CmlcWpbzojMs93c3IzA\nl0D8Mg0gUnhCWhZRKEHIJas6OzsbRaUEpJESQkobKW/Ozs5YLpe/0Fl/JyKcFL6S6kmqIdC7eCUZ\n1ZmOz0xBD+mdyBsuPZgpr1KMbTq4Kn/DlKkCjOmEoGDy3IJSbjabkTR9THlCIPQdh77n9m7L09/4\nTV7tbk66Ioo+BLRVp0OUpwS0LWDwKFMQ8YRDizYKjSbFHsoaZR3JhFyP6SzKmvqe5CMkQ2j3DLsd\n+2dfMvgD5aMHlNWcw7OXuIsZxeL8dX+vGUjJE/qEcQY1b2BZk25fgE4k50iLGYmWbX/gdr8lpERQ\niuVyxddfP+fx/fs0zYzkPX63ycsUuy5rS1oHQ5udRVmS9tus2Kw0afAYozn6RHu7wbeeOBwwRYEx\nms576lmDM5amLDnc7agL+/9Q9yaxmm1Zftdv7326r7ttNC9evDa7qpQSZVJUJiXKBcIuytgwsMBC\nBiTEBDFBAjGyxAAxAQuEhDxgYIkJSCAGlkBYMo0K4UYpU5SpcuGsykq/em28FxG3v19zut0x2N86\nd98oRIapQoo8odCNuPdr7nf2Xmuv9V//9V/oejUN3FgsFtze3k7Mf9EXkXkCUme7vb2dmk9zruXZ\n2dlU2onxTkVZiBLS5iM5n0Q1cNfKlYNo4sRf93ojTjgJ6XLpOfmrlJqKz3lxOgdCJFmWx+dhIdwV\nykWPHrg3Wlael5+cUugUxDSfkCJGKIilc46oDRGD61q2m2tOHj5CFRVVWSSphKLAhoC1Po2QYq8+\nrEzKf8J+XLDSaPZSgdalpk4fwZHKC1IwDw5vHdYN9Ltb+m7DbnfN4AYSPSzivCOWiqgiIUa81Kes\nxweb/m9bhosXjP0G60e88kQDPnpu11t2o8WTUEMfAkUz4+OffMzt5RVqv9H80BHGET8MhNEmR+Ac\nygXiOOzBmZTvlVpjtKb3HqU11jpMVWKMIviAUiKlnhxoYQymaKaQTxj+IggszaOy/tfX11OY2bbt\nJE0vgzrk5BNubt5FIqdl3vcmeTvcRUGy914tS73O9UaccCL8M47jRNMRNserCJPkYZKLSewuJ14e\neorBSa4lhVBJgnPEUp4v/xZDzA07J0/Lz6fFKCt+74vnPKgdDx4/YfngEVY5KlNz0MzQZcm27Qll\nkgsQx2GMxo9AcJiqSHJ2MRWBdZO4lyE6/HZE931S6RpHnB0YNzeMfc/m+TOGzZZue83RN77F/OQR\ntCPaVLRX1/SbDeN2x/lHH/Mbf/O3+c6v/IBv/aPfQdmW4Hpct9di9IHW9/S7NZdfveDTiyusgnlR\nsB1H7BZMjHz5/EtiWTA/OmRxcEgdwbZbimZO9B3RgxtafD8CiuAsse/x40AcRuamoLOWwTqcgtqU\nFBqCDxT7cV7BB8oA3hhOnn4wGUeMcZLFk2GZEhZK+nBxcTGVk6y1nJ+fc3BwMAlUCRtJukfgLuKR\n/EwoXdI5vtlspueJ85cO8J+5Opx0esvpkjcCvmo8Eh7KsS5TTuW0yttt8qEfktPlhOa8jykvZuc1\nu/z3kfeXr/nvtusHfufFGcvjx9SLI4r5wV6nUvHg8ZOkwkxEa4P3KaQkjchIf7RJ+ZCKRDyKsB8w\n75Mac/C4IXneQOI6jpsNw/U17e0Nt2cvmH/wdY4/+CZFUJigKMsZ7rZnd3HF9cef88Vv/R5Xlxf8\nr3/1f2B7e8M4jAx9y+AtfbejH3uGfsd2s+bq6obBJYZLP/R0w8AwDvRjz+W65ZPPP+f24pJ+s8V1\nQ5Iw3zfe+jDg+h5PQJcVyOBJrQneo0mNuDEm7ZIQ06QdiGhjqKoaNEkbxlTMD0+mvH65XE7qx5J2\nSNgnJSNppTHGTD1xEpVIiSen70n9Ttq7cjGiXIBIQBeJyK6urrDWcnl5+fp7/Y9mKn881/n5+fRh\nXuW0CVXn1S6BnKIlbIGcMS7GILU2MR7xWHmja94TlSOfWuvJy+Ws85yFIOFqCIHf95qmbGhmC3TU\nKN1gKsMvfv8X+eyjn7Bdb2i3W4jgvSNEUgjmFdpFlE4ScEoXYADrcQqUSfzH6MDaDh88427HzfNn\nrM8vuH7xnHd/+Zf58NvfQUfAB2KpKY3m8OAxn/y93+L25QsePH2Xf+1P//Popubq068YDnuoPWEY\nGUnqx1cvX/Ly+Rkfff6cHz275uWm57Qp+ODJEe+9/YDN7YaLTU9RGj777Av60fLgoUdXDb6sMLMa\nPzpGN6ShJB7cPvdWgFeRrusJhaGzlqquUzVEK4qywpQ6AUfe08xmqNP3ePjw8UShE5lykSeXORAy\nwENEfHPgTZgit7e3U2Tx1ltvTfMmZOqSUPZub2+Zz+d0XXfv5BRATrpGRO3rZ47aJcYgJ0tOt8rF\nX+F+Xe3V7+fHvQArrxa+5f85iyRv6RDDz0+5/PF5mQDuhkkKCuZtxO96/OhRRaRsZiyWS8qm4fTg\nkDY6fHB7epXCDwn211ETQpqtHYl4Z1Heo4qKGDx+z30MdsQOA8NmzfbqivWLr3jyC7/Akw+/hnKk\nWtiYHqtUEgx6/K1vszx+gCpLmrJGz2YcPX0LGxxXty9SLx2p2Hx+dsWzFxd8+uUZ42hZVZqjZWLS\nrG82nJ/d7Iv1iZo29D1d3xKUgv30VCUakgQiCm8dfhyJYc+wIXUGeB/puxY9X6S2okKnPE5roovE\naDh59PSeqFSOOuZTax8/fnwv7xJVLqnZySkoTlymKkln99tvvz2FooJuSo4uJ57gCbKf2rb92UQp\nJXeS8E++Su0rZxKIkQlML7G41ObktMnbe8Rz5Y8RQ5GFlLxRfi5GCNxLiuX3eZUuJsDKpu8xY0sw\nUB4cYWJNUzZ84xvfoL08p9JpvHA1WyRuZJlqaLEo0C7iiYTgccoQ3Zg6uwO4riUET3dzhe16utsb\nrl5+xeqDd3nn6z+PGjyjHyB4xps1isguOoqqglpz8ME7afM7z6AUZjZjuL5i3LS0ux0Xl9eMzvLZ\nly/5/OyStht4erxAxYCLcHN1y06ne7Q6rDlZzSkoUFFhh57dzSV+tqA5PEztQkUywOHmBjcOeOdh\n37HddT1d8FBoum3HYrnCB4XeU6yU3UtmHLzD0/c+vKeUJZFP0zT3FAGurq4mipV0e0u719HR0XQK\nSmg4DAMnJydT98fZ2dlELRTUU6iCEq5KXij7RiKdfK/91L3+x2Ewf9QrP9VyAENOMrlBcqNfHZ4g\n4qxS+ZfHiQRCXt8Tw5UbKsYtXk0gZGMMBwcHExydy/dBQjmF8JoToqNzONfjgoW6QTcmEZaLA44f\nPeTF+cuU93mXaFaDo6hLVKWTXJ4d8BGIFlRqd0mSCB7rBvwwMmw2bM8vWL7zNg+/+Q3wgaAU3vUE\n63FFkgS3BOzQYYgU+GRwPmLxzJfH1GVBdX3Nrl/Tbndsup7r2zXBOeqqRJUmkaU1VFpTGlAhctTU\nLOdzTCSFwj4hotYNVHZEVRUqVNihg9IQhySSO45pvrj3nt6OFPUMTEfwDutSiI2CcdtTLQz10YOp\n+TPvZZMhinVdc3t7O0UqcqLlo6VkbznnOD09nRymyCWIIUr+J8YmhwAwdZYLUCLpy2q14uXLl1Nq\n8jrXG2FwubaE5Em5yKqcRrlcee5lDg8P2Ww2040XAxWgREJFQbNe7XvKc0IJHaQhNoQw5QUSkspJ\nmfdKTShnCWZxiN/d4nyPiU2qTemK04dPuVzfpn42lcb/huBQ8yQqRGmIDigVwVp0TNoj3g447wne\n0283bC8vGfqWJ9/9R2iWhwSTZoHbccD7PesmRExdMrY9uqkJSjEOiUtJY/DGoWYVJ1//BuiSs/NL\ndq5luVzRzBcEBTvn6boeZaAxmhg8lTHUZU1VFOBsIk6PHucdxu9Z+kVBbBPS6LzD9z1UJVEXEBWj\nT6OFzSyRpn1I0uzee4Jz2G2XivzVbMqzpN9N8m8xItGY0VpzdXUFMDH7xUCMSYMUxTghRS23t7cT\nsVmimpwrKYYrIaSMsMrRaanRvu71RhicnB7yIfLCo8TRxhjW6/U9YSAJ/2SaCdyFf4JAysAHMZxX\ngZKc9iXGlr+OGG7esCqPF4qXnMzWWoJrcVWBOX6A1SPDbo3vYX50xMmjB5yuH1OYEl3WxOBQuiBq\nBUVCKSMQ3IjRBdZ5lHdEP+JdoL+65ur2nOrxMY++821mhw9QRMaQkD5rHXYcAIX3ioKAbsqU+5UG\nPWswZYFpKsbRoYKjejDjpHnKdx4tGNoNF89f0Pct613P519eEA4X9MMIIWBiGg9c1hW+LOnQ0A8E\nrYlXN4TjA+pmRlDgncUPI8EmSfPxZs049LT9QOc9FoW3lqqpUUpTVCXWOUZrcesefXLEbLGcnJw4\nNTE8STOEZaK15ujoiK7r6LqOo6MjQgg8ePBgUlQWMEWM6ujoiLfeeouXL1+y2Wzu7bntdsvh4eHU\n2pNr3sggzpubm3u0wtfa639ka/ljuCS8y8EO2fAyTFEaVMUYBd4XsR/JAYXlvdls7niO+74okUaX\nsFCMTUJJCSPkxgrhVX6WhymbzWYaOCIOIcZI1azYbG4pjmDZzDBlifMj2AFtNFVd7wvGMdGhVCIr\npzlpfupjw+wHNbqkS6kLxdBtKA5XrN5+Qlk3RB9QRUE3jkQ/okOq0dUHK0p0kt4rS2KhCT4J8xR1\niUERywI/jtixZYwDqlZoXVEfzqEEGxWr5QwVIxud6E3eptne0RgskSFCGQJDP9IMFrVp4cFDIKn/\n6LpCyey7YcBZS+8cvUshZGU0ui5x3mJDSI2nY0ABdWGm3DsX/hG58bxl5vb2FrhrFhYtE3GIgjBK\n+CgOE+DZs2f0fT8ZkLT+SLohYaxc0piag3Q5ePfTrjfC4CQ8k+kneeuNSJe92kwoRUoxUEmgb25u\npjg7HzOcn1RiNOIlhSspoSMwnZxwJ1AkybuEqKvVajppJ72TRU21fMyoFbppsMOG6LqkNGwKVosl\nXWjxzlPXJrEwigJUQul8UEStMKWhKJf0V1eYcsawPuP2q895/1f/NHjDuB0IyiVt/hJQBlNo6sUJ\n0UeKqsKo2X4+d5IoV6agrOf44HFjz6gMbX9DrUrMsqJwDXWdqGbDdsfJwYJ+2/LV2RXt4On6Huc9\n7TjQmIJrN9Arz4GBcujxOrJdr6mbOrUIxUC3WeNiZNCONoysx55+tIn5YgfKpqSpZ6ioUEEz3LQc\nVgV11UzjoSQVkJNNhJ9kD4geqayfOGEBOoQWKLVbQZWlTCRdBkJmflWmY7FYTLnjNLxlX8/LZfpf\na6///2A//9BXThKWsE1umvQ0iXEIeVSUdKUJUSg8xpjppufMEAkJ5WSU8FK8VN56kzep5tolAowI\nUNK27eTdJu6ddxRlg4p+375S4uyaEDzzk7eAvQ59jKg9fzk6B2UB2qCUQ6kEr+tCU1YFKkIYRpyL\n+C7lZ2hFUdQEozAqibmWxmC0QZUmqRorRfQeUzcoFVGloZw3FApqO6PYtri+T/MLisgYS8yixg8d\ntS4IncMExeG8pa4D4BlGTT+MBMD7gDOBYDSt9wy7jhPAx0CpNZpAv1mj6hofI8NoE0nbJTkHUCiV\n6F3eOdQ4oq1ncbzE752prH3O7JF7fXR0NDFA5MSTrgJrLYvFYgLPiqJgt9tNaKdEVVLXy3mSUpvL\nEWgxMjkccsnFnzlqF9yNApZ+JYFgJTmWU01CS0mEBcXMDUEQR4nVBS6WRZHTU0LFnPolBijeTG6m\nPDcvqgrLRMJZYww//Owr/omvv4+JFudaUI7m5ARtFM3xCY+V4ZPPfjfRt2IKK6Mp8CGigaJuUHog\nxIDSimI+J46W5elbHD44I6qI8wNVsyBEGNo15aJIuVA/oAqDLhTEAYoGVVboQqFMCUFhO4vWYOqS\naj5jNSzw0eO9Yzav8XaHKyNFNPSzljg4Tk+OUxNtY9A4hqrAOQ9G4WJgax0UnpOjQ14+f8nqwQGP\nigJVJGaJvV1TFCXbfuRyfUvwjqbcQ/rOUSwWLL1NvX5aUVYl26Dwu909cSm4Ew1WSk25u6y1c47N\nZjPJGgo9MCdOiHPtum5afzGu+Xw+/VveJ28Dklpr3/dUVcX19fW9OXGvc70RBidquEqpacSU3DAZ\nR+R9Em+dwIn9DZHEWUKLHDWSkFJyADn6V6vVvfYdCTXy0FMMWUKYV0mu4inldJbXaYeO0XYsyoqi\nnIEf0SpidJp2WhQxgRg66eYnFCxpgZjCpMbOuM/xInsKGFTNnPnBinq1oBgcCk13fYsfWpqTx0S7\nPzkqQxgjEYfyGhU0jqRlqdAY1aNNiV5W6ABUDdqP+BhQpSbYBHg42yc9FRWTxr9WmKLAFJ7CeELw\naUai0vgQ9mgjFNbhB0cwUJUNRV3jfEAXhm6wuBAoqxJTGIJK6mTBOWqtWB0fsb24wVlPp0rqfVgv\nTKKc6yiUvr7vWa1WrNfrKYcXIEvmsQNTKUD4lTJBNZfHy1OGfL2Xy+U9KUVJbQQX+JnL4USuQDxO\nLoUmre/W2ilelvgZ7sI+uJMiz7sJpI9JXk86dyXvkxYPCUskpzs9PeXZs2fTYyVfzEWE8p+JA/gb\nPznjk2eX/Evf+3kesm8GWCxBGTaXF2zPXxKi2Ssz+9QfFzwqVImSFRRal4QY0KZE6QI3OurlA44+\n/BBCoFzO8drQ4KlszfDiK6z3+M7je4vdpHqcjol9Tz+iiwKtTFJ8LgymWaCVBp90LUMNrusZ2x0W\nT7/dsrnd0I89OxyDs1xvNwSjidFTVyVoTdVUNGVFoSKDH5nNK8bdBhtOWC0OqKqaftfR9h1n19cs\nm4a6qijKknW7Azx4RQUcnhxRNw27wTN/+2v3SMIS8byqvl0UxUTFEl6krD8w1etyzdPFYsHV1dXE\nJslJEVJuEg6lAGKyb+S0yzV3ZFLq61xvhMHlFCwhIEuuJYmvnE653omEjHIKiWGtVqvJgMQAcw8m\nY4hEI2O73d7r/A0h3BukPpvNpsGMeTlA8kS4Iz/HGPnEG/6r3/wR/9af/AG6VKjGEQbP//x//j6z\n2xc8+uABZWESWqkUKiY6E1phlEkn1b4BtTAFOIfWhnq2ZBw7isWS4DwxWtyw4fd/+Jtsd33q+q40\nT771dXY3G9753jvo+oCv/se/S7UyLJ6ecPP8itBH6uNTimaB6ZJA7DD0+OgTgXkYaLuO276jsyNd\ncNgQ6P2YRlDF5CiKooQAMXi0UoS+IzRpvHFV1VTzOdE6VPBc39wyOMvhasmiadcnpcgAACAASURB\nVBKx2SjQ4KzDhYAxSePFz2dU9XyiXkkeLWi1XHLqxRjZbDYTQp2H/OKQpalU9ohEJzIFR8AVqf/K\niSmGKDl/3qCcv8brXm+EwYlhCdSaD+QQjyVIlPy773seP37M5eXl5O3EC4oRyuMFZZQbVJblJLOQ\nM0wEmTo8PJzEafNcD5hAGvGgOVFavCHAiyHyF//qr/NO4RlXx7zYpBBEK8W/8e4JMyIxOIJNcuaM\nI9Q1qigpygZnN0QNqizQsxm+3VHOj+h2t/vis6G/Glh//iV/+b/5n7gpa/7Sf/IXOTg55Z333qKo\nG2ozYPAc1COzr/8AypLCNFz81t/kwQ9+lbGzfPzDX2f71XMuf/IZzZO3WPcXuCqysxs+/+IrDt9/\nwBc//oKAZtDQtiNNU7OYNxyvZtANjETUieX49JCDx6fM5g0Pnzyh0Ibt1RVlveDZly+Z1Q1PTw8p\nm5rzmzU6KlRUdNsdXWf56qNPePTWY+KD96ZBiHnjsKQG4iC1ThNrRQZBQkzhS8YYJ+ead5KIceVE\n+eVyiTFpfoBMRBIqmHQZSJiakyEgCRa/7vVGGJxoCeZAhAAhMpRBThPxTmVZ8vHHH0+PF8/T9z3z\n+XwyNDHe+Xw+DVGXUDWnjElhU1p0xDAF0RIwJp8lJyGqJO+5UdZ1jZkt+MI53M3ujmIWI7tdz8Gs\nmkRzYlQEn4rcXlmMBkIg7MM9SMhfURh0jISxw1QVZVUxPz3hdHWIJ4Ka069vKcv3GduO+viYECy2\nbljODtJpWlQc/9w3WR6fEo/gre9+l+6996gOG57+0p9ic/4pwxi5+eoLjt+9ZPXWAf3VGrM65MXZ\nBS8u1vyDi5a6LPn+u0ccLRrq5ZyH7zzh5PSYg8MDlqsldTPHWcvQdhx9+A1CgNmyoSpNQihjRClN\nWVaM/SUK6Dctu/kW83aFMnfjo+V+yvAMCeel1ppPrhXDyDm3knPLHpEQVXJ14UOKKKxgARKKWmtp\n23bKD2ez2QTO5MDa61xvRD+cxMJCrQImj5UrKnVdx263m9BKIZbK80TCOpdNEM8lncFSIpD3zDsD\nxLicc7RtyziOUy0ub/+RArgQlpumuYeiSXE+V32SBBug63tCBB/2fXEqEjCEYPbGFzDSH+cCWiuC\nJpUCmgUET9hriZi65lf/6V/iw6ND/v2//F/wH/3n/yVf/u7f46//lb+SNla74T/4z/4aFy+eUWpP\naC/ZbjuUAttvKLWnmhWEB085enjC8vQhy+PHLE/f4v3v/4DF8SmP33/Cu9/6gIOjBQdHc3yE7ei5\nvO3BBh4uVzx8fMzqYMnq8IiD00eYqk5OpCxZPDwBY6irIimJcYcaps/oKQoDIeCVubcH8g6NvMNa\nCtAhBFarJMEgLTp1XTOfz++xhyS/k/XMDbIoCs7OzqZ9JDU7Ac6EWAHcS3X+YY0N3pATDu7Y/7lU\ntWzcqqqmf8tNEdZAvigSFkrPm9y4HPbPNUmE/yg3N+dRSqE1D2vlOeIM8tcRD5oTWeX18mKrMYa/\n8aOXvPfoFDdadNTYCJEkN6fqMrWnKIMfB4JLOv+mMISxxyyWuLGD2GGKmmZ5wJ/5C3+Wf+5f+Rf4\n0V/7W/z4sy/51/+d/5TLTct/+9/9KUZX8d7pgn/3L/zbfLrrWfcjp8uav/Qf/5vUleaoUeyuL/lf\nfv0z3n9vRdydgSspledqPVDZLScfvE998pTFx39AVUKjFXVdUVcNH3zwNb793Q/RhwcYFXnw9D1m\nyyX99TXnf/AJ737/F1DaMcTI44MVTV1ztdnRdkn3xHkPo6WpG9zjd+nf+zn6PRldjEzy8hcvXrBY\nLCbO44sXLyjLku12y3a7nQrl2+12Gj8sJ6BENLKeedng4OAApdT0PkJ6F2fZ9/29CUoiLiv75GcO\npcwRQwkPxXCA6RiX8E/qKMI2EHAkp2yJEcmNlMWQwnbeTCqPzcnTsuCSl+UdCrmRSjIuNz0HeOQ5\nUnKQnOAqOnbdkOa9FQXepoJ3KAGlUx9ZiMnwdOo7Ez+awJKAiqknTWtFVTeYesHP/8ov8vVvfZ3v\nfu19fv/Llzy/PMcEi3EjwXu+r+DL9ZaX12v+vf/wv6aZN/z8vGYMnq++uMD3W568c0htKi6u1vzw\n43O+8/YJVdfz3T9xytglbctCRQ4JfP8bH/Ctb71Pc7hi9I56mfQ4CYF+veHm5UuOv/1NtucXiXSw\nmKG0wo6WGMHjMcHjnMcUivjWBwyjndZCjKOqqun0EsBCIpxcAVsAEbnvuV6NOFKppUIKSY+OjiaO\nrlKKy8vLKdwUYau8XCT4gTjRvEb4OtcbYXACdOThg5x24pnkNAMm6pbkc8Igl5Ajr9FJyCnfFwpY\nfpNyDqUYWS70mssuCJq52WzunXDyXDlFc0Z5DmFrrRnNjP/t7z/j1/6xD9Bas5wVqEIRtSeQ2nYw\nBj+2SSTWjpgI3ieBIK0LgtZoHFW9ROski37w+Anu+Jjm8dt8vesw0aN1UtQKmy328pqLy2t248Dl\nzQ3boeOTdUfb33JSNPS/+Sk//lGkd5HBes53A//973zK2WhZ/e3f5XuLhgUF/+L3vsaTJ094+/0T\nqBTokvlqTjOfo4m4oefq5Reogxm76ys+/+gz3nn6kMePTmnXa6z1oDRDGCmBi1hRPP46K13sB4ww\nbfZcI/L4+HgCM2QdrLXT7G2ZblSW5RQ6np+f3+txlAJ3Xde8/fbbk2iQoJESJckJJxGK935KaYST\n++o6v871RhicJLt5IRm4pyMpbTh5/iVhnnzwPIkVgEXAF0G7BOkSkqp4U1kQ4dblSKe8n1DI8uZV\nQVPhTv05h50l5xADF2cyujSWyQVHAKyzmFAmFSsNSlephhY9yug96x5QER8iwXqKskIXNcrtZ/QW\noGNJcWyIpUH1Fk2gPj4hnDjKowe8/aTFti2PNlu8iXyrKhiVpsfT3m558ZOfsLu6oe92fG+0NEXB\nTQxsR8uirDg8OuTdb35IM6/pbMeyOCC174mWp8PZkRgDs8MD+nbg7PKKg6ODdP+dZ7RpzRSKwY3s\njp/w+OBoqqdK6UZAMwnrBPQSZyrrc3R0NDk/Ab+01hOoIimKOGA5BUMIE+ooOXaOI+RlBwlvpWwg\nWIAY+eteb4TBiXFIHWU2m90bEwVpowocLIms5HlSsBTjkBsD3GvdkRDVOTehn7IIAj3nhXQJZ8QJ\nyEmWU3lk0WVRd7vd9PvIiZ2HQmKIv3dh+Ke2PUPbo5VmNp8Td5G5KZJIq/cYU2K7Nom+GoUqDb4b\nU8gZA94BPlCXc4qixI4DUWnKEjiooCwomgYzRuLCUK6O8T4JEx3iCTHQ3V4StEJHgzYR/7WvYduO\nYC3b7RW73ZbWWsYA7ehQGka/ZXtzzeO3nxJDZNhuKZsVJQbvHd5bmgenVBT83t/9W/z4o4/51X/2\nT6IU7DYdo4+M0aPLii/7huXB4dRGdXR0NBnUdrudHBukEFCGJ0oYKca2yfI+cYA5ET1X5RJS/Kef\nfnqvq1sceB6iivFK4RuYiuxidLJHX+d6IwxOPpDA986lQRpy44EpTMhbciakK4PppQAuNzevxcnr\nSvgo9C55H+ngltfMcz5ZiPw58vN8MaUmI8CKeFHJQ6WA65yj60dmdUHXdoCCucZbSwyBsqlRyjD0\nOyg9ZVUmOF0rgh1Jk3EM2qSxUDFGFEnfMtX7QM8MykCIaSKPLiq00vjgif0IfkA3C6K16LJCmYia\nR5QPab7AuKOwFj+kBlgbEim8H1uKsqQqS1w/ELxCqYNUvA9AAGUqfNfzyRdfoY2mWTQMbYcN7HUu\nA4PW9KpOc+j2kYAUsPOeyOvr6ylKkJxqtVpxfn5O0zQTQcF7z+np6RQOyhpJSJ93dUMKNx8/fsxi\nsZgQaNlfwJSji2CUvP/x8fE9jufPHHlZaFdyAolB5cjeq1w24J7Wv9RInj59+oc4ckIPk+eLl8t1\nKPMQMG9Ylc4DCTtyVkzOOsi7zcVIZdMYY5jP55Osn/z+f/23P+XP/eDrtN1eLdoHUJF6tiASKIwh\nushme81qtUgz4fbSeoMNlHFARUvRHEKMGKVhr0ESlYayIAYNM0NQCh0D9YNDoosM11tCC6YYiKMl\nWAsu4PdUTtuNDN4yhBGnLG2/43q7waJROvLo8IDoHWOwFKpi8JEqeGZ+xIUE0tycX/Lsqy/5pV/5\nx7HOcnV9zXYY2HRtAmp6g68Kwj4nOjw8vFcnlVNOwkU5bZbLJS9evKBtW548ecLNzc1Ur3v//fc5\nPz+fukwkBZF1yWuvDx8+RPrp5ITLUWwhW8geEuQ6P2Hh/qz5n3a9EQaXQ/0ya01OJ2kYlPhcQBKR\nOjs/P5+MTcLHzWZz76QSaFgpNcne5RqG8jNpy5BFl+/naKkk12KU8j25hJ0ujAcposqCCrm6rmtu\nnGIcLOz1Ku2wZb6YMw5jok0pjamrZAzaEJVi6EesG1HKoE2D0SbNJDB3U3XoR6LReOswKo0Cxhi8\nHwm3V0Qb08kUAtEAtSaMlmAHfAh4FQhFxKs7BWcXI6MLWA1zaYMaenwMGF3joyNGD1ETPfhh5OVX\nX/LOh+9xdLRkd7tmve1Zty2DtSitGdVsT2NLG1cEffK6m0Qj4hhPTk4mSXKhVV1eXk4NwdJuI3tB\njA2YZsDl75Hr1OQloFxeAe6MSpxsvr/yUtBPu94Ig8tnvglLQCaXCFBxenoKMIUOghgppSZY13t/\nL/8C7oWHUgAX1FNQLkmCJQTMk29gMv5XjUtyNglv5XvCipAwMy+AA3ceHHh5u+PJgxXDOKJ8ZL1Z\nU9cN1qe8Y1mVMKvxJp2aox0SyDJ0lMsKFwNht4bZnLKoIURKrfFuwJuC0faUiyNi9ESjGLY94LHt\nNokKDXulZD8Qo8f2Lc5ZnB3YhpHee25HR+ccVhf4oWO+mhO8px0GiqYiRI9WmnI2Zxw7nHf89t/5\nDX74G7/Dr/wzv4ypa15+9pKz2x2X6zXRGJwqgVTslrRBNvLx8fG01hI1yP3cbreTAJBoR9Z1zfHx\nMc45Pv7443t5Vc6rFARaEM+Li4sJQMuRcQFK4A7kElxAKGbL5XJa9zy/+2nXG2FwclJIHC+bVpjg\nuZyChJFyiuSkZ/GUkvzKWFjxWnlOJvnYq0TXnHEuXlC8ZW5w4hyExydFVDHOPMfMideSX5RliSkK\numDo+o7SGEKMdF1S7QohKTgOyiPzCLxP87utTTO7nfeUyhCLNKa3NPuRX1qhVXJimAo3dEmnUpc4\nH4nR4X2C7/04EGJkHDtQinEY8dbiXLrXgw+4EPE+oGJgzzcmhL0jMwaHwkVPNIbRWZwd+ejHn+zD\nT03Xduz6kdvtloACXXAb0hzyPP+ReW7AxO4QRa1XO73FMT558oQQApvNhtlsxtnZWboF+k5SMT/l\n5P+Xl5fTY6T7W9ZRopC8LCSvJamEoNUy5/t1rzfC4OSSRLQsSy4vL6cwbL1eTzO/BDKWcVFS+Ja4\nOgdaBBaWcFWMNcY4wc15vC71wBjvdAklMZbcTUIbQUGFxQJMeaLklIK+StgqryE9fmo+53df7vjm\nWw+4vLqmahq6bqRZzCgeKoYRvO2pTMH2tkWXJU01o2kOMNqy69es5jAOAacchdPoSmN0RfQxjYpS\nCu19EmoNEeUd/a6nvTqjdyM+QLfdEULqBHCjJXhL3+4Y3Jg6sSPEGKgI6LpOMwj6HlM0uOsrKDRP\nfv6bjENPt77l+T/4hI+u13zn299gfX3Lrh345OyMTddDWXFhK9ryIM0Q358Q4lCFcOy9nxxmjHHi\nOx4eHk7NwUdHR1xeXk5tN6KULC1e0gUg7VdSmpGUQtYjr6PlNEOJinJupjxG0Ovtdjvthde53giD\nEyheDEVyqFy4R2Jna+1kLLLBJTbPu7nn8zlHR0dT6Pjo0SPOzs6m3Gu3202njzAU4K50kHcJyM8k\n6QYmjyvGJuGQnLRixDJWSVju8npijJcucnyw5PpqTfAeRo8vSrxzjH1HtVww2pGqKvcngsHZEcoa\nv0v3Q5GKz147glPoskjTeGJMOV0IaDQejxoD8foGTyAojQ0Wl7TX0dFjNGhToHaeInWYplqeKXFl\noKorok4Ml9GNxADzgwPsOOD6lvXLC37vxx9z+uCEuqoYrWPTdty0bRJ71YqWGXHffPtqN0YeEeSM\nIIko1us1fd9PSOGjR4/o+55nz54hvYyv7hnp8JDUpWkaHj58yHq9nlQDBKCTtcmNTE416SzI6Xp5\nZPU61xthcLJxc8a/NJhKi7vE2vI4qctJDC9xtBiU3Gg51Z4/f36vcTH/m9fNxKvl/EkJaYXSk4e/\n8jzgXh1Q9DPm8zmr1YrdbjdNfJH33G636MowqwoenJ5wcXFJWTbYvmUcF5RhD85EjzE1ow8EXTAO\nPUO7pmrm9MPAoq5A7YXFdUnwjpQfOZQvUCGiTIEqa4abM3ADpikodaTb3FJqhW5q8CMxJqn1xhhG\nBWocaYxGG40xJaowFGUBRcl6u6aqK+arBW4cGLuBF8/P+OirM04eneKCp91ZXq63DD6gdMHLVuGb\nErXfxBIhSG6V30+JQOAuHBdRKGnNOT8/5+HDh9O6S9QjTP+ceCA1u7Ztp67vPNUQJFrWTYriee4n\nDlr2lSCZr3u9EQaXd09LEVIYIxKv5/UrCT3yUFAgWzG2HEnK0UYx3Dzfy2UVZGHzEoDUaHLWi7xG\nXk6QzZKTrPOBEDn6JoZXVSXKaJ6cHqD9yPVuwCpFt9uhV8sUYi+WDM7T9wNKrTGmompKQohQFMSg\ncH5kVCVFtHhdY1QkEojBoqqC6G3Styw85VtHNF1H2OyY1zXFrMagMLMSu77BjyPz5WO67Y6hNrDT\nFLpHVTNUU7LetXRDy9m65fTxnNEHzs8u6duBX/8//i+u2pYHPOR22/Hpy5ds90RlFAzVETG7P0Je\nkLAy15iR/EnI6wLNz2azqRtf8qzlcjmVYiTHl5FmklJImUFyvocPH04RTb5ms9mM9XrN8fExL168\nmKIYydmByThlD77u9UYYXI7owV3oJqBGzmUU4xEUSRApMSzxkvIzKXgKjxK411qRx+ZiyBKayO+U\nsxdepQDJ8+V3yuHknJomzxeKWf68sioBxcHRkutNR1RgvWXsOrqqQlUVOEeIiWFiiIyDxSuFcx7t\newrToMOAqkoowduRQs+IzqayQQyYsqJoGmy3AyJVU2KmUwWM1nhjoCyJwWOKIskxaE25XBBwqKrC\n3q7p+p7ZIpGV1+st6nDFbtdytdlQN2nc1GazZdu2qSYYofMFLrp76yeb+dVCtVw53U/ar8Zx5PT0\n9A+Fg5L7ibGKTEI+Q150TcRQc4U3eS/Jy168eHEPQa3renpP2QtCD3zd640wOFFBkk0o+VwOSOT0\nGYmfZXHkZ4IuybGfI4XiwXKoP/+a5xN5eJH/XH4P+bm8JtzRh+SUFfKzfLbcCYhXDSGgo3S6G5az\nOUYlwZ3drqNtBx6gUHgKU+KJbDY71npEVxWlLinLYg/F75gdHtN3WzQjyptEBFYFpmyIUeFdwDQN\nuizQZUEMYLuWfuyJbsC2I9Y7FAFNpCAQCsPR4ZJoDG2wtF3Hy23Hrht4+s4T+q7lsu0Zup4f/f7H\nVE3Dg+NDvrq45HbXoqoG7zyXocbVp5j9/ZUTK5cuEKRXDGWxWEwIpYTzIiokebUYwnq95uDggKIo\nJnpdzjKR0C+ncc1mM+bz+bQekq4cHh5O6LSEteK0c+MUhyslq9e53ogG1Fe1KPKT7VXBFmAyRrgr\nAQATUVnAFClCi/HkZOLci+a9bFL3EWN/1fjyBFq+5ieh1Ixk4SXc7ft++n3yz6A1iXalNaYsWC0a\nCIHZfJ5OHOfpNh1FafBjmpvm3IgdHVVRMI4O1zv6fmDbdfTO46LCx5hY+Xs6GPtBUcYUaFPgR08Y\nB8rFnKKqibqkKGuawyOKxWo/BDISieiqIBaGECKj9QQUSsH17Zrrmw1utFyeX3B+eUVT16Bg3bV3\nY4q1xizfmk4QQRQlKlmtVhweHnJwcMDBwcGUQ+XiPoJgG2PuTRzVWnNzk7Q+N5vNlL8BEwFa6F8i\nrZETKN599917TcvW2kliIQfAJLLJ2UKyt87Pz197r78RJ9yrTA5BinLO4ziOzOfzNKdsvZ4+bF7r\nkkURxoLkgrJQOQKWG5OcSPm4IskHchqPvBbcJfGLxWKqwwhYIqdsrqGRJ+857Kxg38EdIESODlbo\nQnPTjQQi682W2aJmMTaUVUHbe1ShwA/0tkYZxeLgkKqc0dqOop6x7SzL+ZzKFGgNptpLBPqIHTpi\n9Jimxo4QiRRVgfMF2ircdg0RDIohpomkITpGP3J5ecHWB2JMlMlt27Ppdzx69Jj//Td/m6LU+OC4\nurklqhLTVOgQuewDzPRUu5LIQxo/r66upum00nYjJZpcj+S9997j888/v8c26rqOxWIxGUQedQhv\n1ns/EZ/HcZzafM7OziZCvEQkcFcakFC1LEtubm4mEaOcHpazll7neiMMLm8UBO7NGACm00NyuLw3\nTeplUlrIT8f8yqFi8U45ypgbpoSBAnbIgkuSLzmgPDZ/XckRRPUJ7pBW+Qxi6FOIG0NqxyFSzxoq\n7zC9TZJ0PoElw2CZzQx26DGqwVlPWc6wztMsZ/gShq3FLBfUdYE2BheS8jJ1GhqijMHFSHTpcypt\nGNoOrSNDP6QRv7ok2hFdlgRnGLsWF0Y2zmF9pOs9aE1rHbuhxYfAy7MrPIpS3ZEDoi72o4Q161BT\nZWwPqYkJICXKanl0IKfTycnJVE758ssvJyPNwSxgapXKnajojkgZSYrdV1dXGGMmI7PWcnh4eK/+\nKijnMAxTnU/agzabzbQ3c8f6OtcbYXDStSveSUATqYdIzC1y5nKTBSESjymGIzc9P6EkBMxHYAH3\nkmVh+8uGkMfIAuccvBw8EbEiCUck5pcyhJymsjjy+VJumWS/YxgwVcFcLVAmedDBOawucdZy+eKS\n1XJBuVoxjC1aF6xvrzg6PuKmHYibjsLUdMNIfXBAMIaqbiiLIjFXRk+0HQpPwDOEwGBHNrsNZVlg\nCoOPlqLQUCzox4F2t0vTVqsk9rodfeJCOs9t27HpE7r75fklVVlijKbr+jTXOwR0CDSrQ55/9oyH\nRUL3csaO8GTFwQqYIfcm1+6XHrarq6spdei6boL0JYrIowpI9LDdbje9Rz41Ka/1Xlxc3GMIiY6l\nrFs++VScvbRliV7O61xvhMHlLRDyoWVDy+nyKmNEGCDW2mkxcthdqTRF5eLiYsoFpGCZw/s5+VQW\nVriPcgJKbimxvtx4GWO02+2mBdQ6DXTPW/3ldMs5nXelBU2UMb0xgneUFMyaOfQ9brAT5WqnDQdN\n4kt2Q0c9W9DudvhQYEzBbF5QmTJl5hp0WeC8T/PpVKJ/ua7Fe4dXEa/TZ47Opmk9GLzRjP2Ormu5\n3mxxw4A5XKKLkjGkEcIX6zWbPQWtHTpcCBQxEDwp31MaHX2q1wHz5fIeP1HWQtZJHFPu5IDpMRLZ\nSG0VmEoFOXNIOvEl+shFgPNeSLhDP/PcXhx4TvcD7hEgZC9JbThHQF/neiMMTgAF8SZCMM6RSil0\n50P2pKYCd2wAqcEsl0tubm7uiXuKgWmtOTk5QanUfyUIoxiwzAUT6F9yQnkvOS3lxBWgZr1eMwzD\nlETLxpLH51N/5Lne+zR2ymh0WaLLgtoUOOMpN4ZhvEUZRWdg2+7o2p7ZvKKeV9xcXqBMwer4AePY\nYYxm1kQW+1Ny1+2I3lGVNVobnLOptKDUXjnME+sCpyvGvif0AypGtpst3a5lfrzExQVjUNyu11yu\ntwyj5aMXF+leec/oPKXWFFoRtUYVFd4OzOqKuizodMGjt55yc7O+x2PNSerGmKknTfJ1pRTX19cT\n0CTlHemTXO6NOCdGKKWm4Zy5lmSMcToRJTeXnF94lMD0b+FH5toleceBgHwSVf3M5XB5TiPJqHjE\nuq6nGyWJtSCD8/n83vPEKGS8UE61gjtpNO89V1dXrFar6bnSLi8tQWL8ecE6bxE6ODiYCrEA19fX\nU/j4KgslH7skDkRI0iFoYkhdbtF7dFWgiFRFjS36CYk0KKKCbhgIeEypUNbR7lq0qTDKsAkRO4ws\nmxKllvjRoj1Yk06xaBNf0o7pq1GaYQDb9+jRo2JkHFo8EbOcYVSkCAHbDQzdwO2u5WK92Q8vjgRU\nAkH3JOSiLHG2R8XArG5QwWFMyaxpuI6300kiayahvpxygi7KBpdOEfmedAiEkHrYJNKZz+ccHx9z\ndHTE7e3tFEKKY5PnSm+lhIzyWvJekgaIUQpDSE5aSV/E4GR/5ajpT7veCIMDJhEYqa+Id8obA6+v\nr6fRwhISCktBbkheJJWTSTb6q0VqCVmbpplyxrK8G2sr7yMk6Pw9xCjFG8vfHJyR3z1nM8BdO5KE\np6asCK4nRoVSDfhIVZbo04cMdmS0Jaw3BFXhY8/NruXqdsPjB8fUheH8y89RZc3q+BHWeraLmpur\nS4qipiwqKp+g+gJwbU8g0I0jSmuGdkiz5KqICRGnaoqyIfYDPox03ZaL55d89MUL/uDsgsF6tCkT\nVdNZKqOo65J6NsPbEWUtzayhNhqHAlMQ3d3wQoky4C4qkU0vIbjWenKY8jjZ4EKZE2NbrVbEGHn5\n8iXPnz+f3uPm5ubee0ruH0KYuvIlqhLATPJwYFp/AWck/IW7WRh55PW61xthcJIf5WhlPjFHTjcZ\nnCfhwYMHDyaVJrmZcnoA09QV8aZwN/BD0Cgx6qqqpqKrLJTkXjl/bxzHKVzNPbV41FfrNK+irXk4\nqZTCR4UiNXrGEBOjXxu0glIpirJOHMngiQRKpVjNGra7ltubNaZM88G73RbvFX1To0MaWTxbzNEh\n7uuSmrpIM7att3TdLsnw+YR8RhUxStH3I9ZD8J7Nesvm+obPzi+56lpGm9dtgQAAIABJREFU64lK\nYbTCDkkhuig0ZVmklp5+oKwLyqJkHHqUSaK2OQKdOyf5mzPzX6135vXSV8kIwHTCSW4uoZ441DxP\nFEeYNwcLF1f2huyhXDzo1d89zzPzUtPrXG+EwcnoIanBdF03dXELpCtgSR6WXF5eTkmxnD7ClRQx\nWFFslteWBDovH0iCXZYlu92Og4ODybMtl8sprBGjlskrAmXnXQ6r1YrNZjO9p2yAvDguzqUoCtrR\n44JPxW9TQggoTRINKgpWhwds1pGqrsBasJ6y0Cznc9quw/UDzayhXJZcXl3T7UoMEa00B66nLCqi\ndwRvGcqGsU+eO6hEaC6KGj8OtNZS1TOG0dL3qbH24vqKru/46OKaXZv65cqyZOxaVIB6XlEUBq0N\nfugI0VEVM9Lo4JHaVHgVGcc7UCQn+ko0kIMmcBeBSCSQG6AYgDiscRynQrU4R0EPc6aRvLY41zzU\nFP2ZHCmXNc2Bs7z0IIb8/1SC+n+73giDkxhYgJA8JFuv75JtWTBJYE9OTri4uJhqI1VVcXNzM8XW\n0o0toIl4QfGc4sVkMeFOCj0XIJVFyBdGQqOcdSIeMacpyabKW07y0NmFiBstVV1DBKUM0QdMXYGO\nLFZLyrLGjgOmbenaEescynuassBZx/Z2gzaat09WjD7w+cefEqLi4GBFaRRRQ13WuDHgY2A+rzFK\nYYqSzXaHLkqaotprmgSub2653mzpRsez80tudltCBAOMuy1Nk1jz87rC2hG7S71+VVkmMrZPcxF0\nUbBsanYv19N9yrmtuREInC/RihSV81Ad7sjKrz5ejFZKC/I9qfcJ2UDCS3HsudHI73FwcHBPMUzS\nF3HUeQ3v5ORkQthf53ojDE7aJaRZUBZE6yQYc319PTUV5lSvFy9eTAiWnEiSXwlrQbT/X+0QEAMB\n7hmdGKQspDxW/i6Xy+l15T3lNQRVFcMWDy0Aj8T+4omlqbIfHWVVEkMawYvelyoCFMqAVsyXK4L3\nzJuSbnCMMRLCviFWKbrOUhcDhTGcHs7Ztj0X5+fESKJlRfDOT6WLqtCEEIkkGfX5no3SDgPr7Y7B\nec6vr9n1HVopVIw4O1AUJp1qaq8wljF9jCkIziaHYTRq/yfnr8opJU4zDyVz8aAcBZTHiXFIvmWM\nmQgG8n2408iRE1B+nr+O9MjBHR+z73uWy+UEuslz8v0i4abk/3ne9zrXG2FwEkJKLeXg4IDFYjFp\nWiyXS5bLJR9//PF0rMuiSBiYh6OyMJLfSRggiKecNgIdizJUbnCyMaWgKgbknJs6zmXRc85dXtjN\nWSWSO+avKSGptSkkCSEQYkj1qxhRRYXyEVMaTo6PmS8XBCL1rmPsHZ0f8SHgQhJVvb7dEr1nWdcc\nlSXloWK0gc16S3BJUrzF4/e5IkqBKkArvEsOpx16rPOEGHHOgvcUCoxRxLJGo4jjCEph985OK0VV\nGkqjCEOPipHCzDEhUBZpU+cGIA5TJBUWi8X0MwkVZe3hjnggRpmzhOQUbJpmgvXz3Ct3pHmHiDhU\n+SppibWWly9f3pPOE0Ob1mjv1I0xU+nida83wuCEmS052tnZGU3TTM2EwleUcETyOUhKTwKsSH4g\no4bysbJyw8WTiXFKDQeYEnjxXHfF6buOgbxPT3q48uRevKuEIMDUhSyIpqBksjm+vO45WjWYqoEY\n0vScokgjh9X+pNCaoiqYL1ZsXaDRCtcHTIzM5xHnPHVRYFXkthsoUn84WimWi8SIN9YxoyQQsG7f\nz+UjLngGawnBJxWtGCliIEZPYRTaaLRWOO8TsLO/dLppQAo3cQGjFCidhi0Cxvzh7m25P+KIiqKY\nKHH5vcwL33lPmkQkOWCRn0Z5CUbucU6ElnWUtRajy1FkITMIDiBrl9P4ZC/9zBW+5SZJkiunkkDA\nm82GYRiYz+eTUKh4OeHh5Z3Zwr0THRFZXPn+q8m3oKG5WJCEh+Idc2QM7oCevMaXG5Uk5xICC11M\nKTWVK44OlvzCd77B04cV3lkKY1D1IYqkGBSVRddzCq9AjeANx6cnzJcztje3qO0OHyJNU3O0mtH2\nI+t1y2azxTqH7Ua88wyjBQWVMbi9WpgxGhcCMYCOgFE4pdFlklUIwWNM6reLwRNcTLXCGFH7+lvc\nNyIopdDOYbRGyfcC2evcdennbTNw118oaYOcTvKc3JHlKGXuBIEpd5M6bc7oydlFwrGU18l1Z+Q1\npegtU3JFHUzea5kxZ/L887X2+v8nC/ljviTHEcMRhElYBOJNJAnOSwOyiDHeCc1IMi6eS0K/Bw8e\ncHV1dY9xAql8sF6vJza5IGSS/+XUsbyvKudayldJpqVmJAub98TJ4v75P/tPcrSsCe0Xie8Y92WB\n0hBDABdgrlFFgbYBQyoVRBWpmj4hjkSiSw2es7mmMAUqRvp+YGNDCh+VxntHCI5A2rjOOyLgXWrB\nSSyUiHeOQCT4gPVJtkGTWGeKmHRStEYDSqeTjxiTFoomK4SD8p7B+slAXs2lBJgSAxTjyskDeX7+\nqtPLN7qghpIPiiOW54lx5SCahIyyltIxIvqWubqAOHClkq7KRDznrq76OtcbYXBCxxEajwAkEkbK\nqSf/h7uJN3koKTkU3Hk+eT1p+Tg6OpoWcLVacXl5OdXf5ETMeXMyB1w2S178FgZJDidLiCqJuLy/\nOIaiKPiX/9yv8bV3HnJ99oKrZ18wqztWZU1wHm97iJ56eUoMkeBSQm6KCmNqXLumUIrVvknSj5bF\nUhPcnG60OGupZyXRR25fXNCOlsubDTEExmFk01tGZ7E+eftxsPgQsCHiJVQDokraJum8S8bmI5Ta\nUBiN0lCik5GlUeVJpsGY9BwVMVpjN9dsPv+It54+xVdLNnAvRBTWjoSXcrLJ6SQAlQzizGt1YqSy\n+SUkzIEZiXpkTwhtUK582KI8TtBlcaCSOojDzZkr8hle93ojDE4GOciHkZAgP4nyTtscZs/ZHXlP\n26sMg7xvTi7RMJSu31wzRW5uDorkBGqBnuVEFqOShRYkUrz1BDmvFvzcB08Yhx6327J++ZzloeHR\nYZU0SCJJcSs4dFnjR0vc/1GFoahKbDtSGE0zn+HqCrfbUijDcrXAjhZTGGLwxOMlZdsn8CNEhlIz\n+ogCbGdR2lAUKf8SrUvlI0rvi9KoZG1WPr9Gq4gmFckrU6AKIAYM+9xVxX1uZ9CFYXu9Y7SeUjse\nLhU2JiWvPCqQ9RPWSC4vnqO/stHF2PK8MC/d5GuRgx7GmMmxyokr5STJ44+Ojqb0Q14jZ8DIa0g3\nuJSBXvd6IwxOELw8qc1zLdnkEirmRWS5+WIgArdLoixG8WqYIiFInlOJoUmdRkRI8xg/n+ctJ2pu\ncOIkpO4nIIlzjn/1z/8aP/fhu9xeXdPdnPO7P/zb/MGzL/kT3/sWX3vnlMropB+pC/zQY7ynaOZE\nBcF2hHGgKCvMqsDagfk8eeVeG7xLBtQ0NYtFmq99sFjg+5FHb3UM1uKd5/2uJ7jA9vKWoGDbj/jg\n2bTpdL/c7PA+haK7YSA4TzAFSivMHpEstKYoDXVZpAbaGCHs57iVKQ8f7YhVcHm9prWRqmooy4L/\nu70z+ZXsSs7770x3yOFNNbI4VZNSU61ujZa6LUtyy9YEwQMMr7ww4IX/I6+88dYw4IUXBrwQIMgL\nT5LVklrqSeqJapLNqmLVq3pTZt57zznhRZyTL6shGSUYLhSBDIJkN99QmTdPnIj44osv/u5P3OHB\n2cD3Pz6lbbst2lv7btWJ6mdV2y+7rJTqhLsg1+7FDOocVfauppd1600tHWq2klLi/Pwc0OdZh03r\npVk/18rBrI5YCRafuhquNolrfVQfZmWD11ppdyC0Qsk1tQO2aFK1mhrUv6sz11Sk/vxugV6jWOXc\n7ebnu9Gz/jn1Q685/26TttK9APqu5b133yYOA9P6kvWTU3744BHrMWFyUgHWNmBy1sFNWyTEJeGs\nBxeU6Z8TRsDbhmgGrIWubxknA6JyDWMeCG1DRvDG0sxbBMjTxPrqijRlOuuIaWIpmWEz0DSezUaR\nymGIbGKCLEw5EoNKNTgM3upl1fhKFdPnKeiGHlM2tlprSHEiTZFUXpezDuct7967QYyRj0+vaJrD\n52qsXanBH00Z+75/bmxqt0VUD32t/eE6Xa2oaAVNdontwDbVr9Gx7i2A69Gcyjay1m6nVupFUNHo\nF7FXwuF2d7DVh19Thl2GwG5RvbuPufZvdrmYlea1WzRXQGNXhKhGv2pbGfLSNthli9dotxvNdtOJ\nXYSsOrYiqS3/9Ld+VVftpkxebzh/+ID1NGFDz9wqYz/NOzAgGHKKZRUV5Bix3mOtYdxcqXqWsVjj\n1CmdozUN42bEOksIHcYoCCOhNvgFGgVUYsw4a4kpMW7WeGsYp6Rro2LGmok8jJANPhnWOWOdU9DG\ne1rvCE7VvJIILmeQMnKTteltxRCnpH8Xh2vbTpXAnOFzn3kNie9jWZNSxDiHMdpUr5fU3wRK1QsU\neG7WsdouiX21WnF8fPzcWaptmtpb3R2zqp9pzWrq51gzoB8tZ3ZxgxexV8LhdsV1ao+kHurdm2Y3\nj6+bJ2sfrkafevPUuq0+yBr+ga3z5Jw5ODh4rhkL10V3/d7daeJaC+6qTdXXW2/cWsPN+o4v/cJP\n8Ztf/qKihJs1cRz5+Jtf4y++9S0uh4h1mds3jthcrugXMySjIqkpY2wipwkwuKi9ONe0pFFl8KyU\ni8JajG+x4smAC0IeJ9rDI5VvEEFiBDF0fk5Miav2EkmRywsdPG1WIxbt2TUh4oGrNDC2ls52WGcx\nOBrv6RuPNQbnLZJ0x8EUE84YomSmGBmmDSKJTRJGA0kM88WCWdcTgqcJDYuZ5+r8nPt3b+JCIIte\nAsvlkq7vWa0mPnx0xsXqWoJ+F46v9VV1uvq1WiLU2m23j1adbFfWoZ4vER1kXS6XW/ZTjXxPnz7d\ntgPgetnj7rLNF7FXwuHgWkOyhvcfrblqbVVvs8ViwdnZ2ZbdX51ttVpt55Wqg9U5p91eS4V2qwjN\nLi1oF76vufvuTVfTk/rB7+b5R0dHBals+Nf/8p9x9/YNrLdkScQUGTcrHj97xhgjV+s1x40leM9m\nWJHiiBGdDLChbn/JWBuQFBFROT28RySh20yzghwVipfSeG70mSGl7jFO1wL7gjlWzmcIiFwhWSH8\nKV1PNzRtwAVLihPWWRrfYKxl3rW60ANhSto/DNYgGSQLUV84IhnV+AIdaBfapsEHPej37t7je6vv\nEaeJ4APLxYJEYr0Z8CHw2p0Tbt+5ydnVwDf/8gdM8foi3Z3OrqnnrpZo7fft1nS7kWkXma6f6Wq1\n4vT0dHt2qrZJVWneXS28O9P4t7FXwuEqf221Wm3z612ovz7YClg453j8+PE2pB8eHm7JysaY56Bf\nEdmOY9T+3nw+36JQ9c/aZZEAz6WMu+2A3YbpLgBzeLjk3t1b/MaXv8jhwQLvLSAgkKcMEUjC1cNH\nXFxc8Oj0lHYa+KXPfYbQNqxXVwyrgTRtMK7HiYr9EIyyO5w2k01F3MKSQa7IBlJO2xTUIeRpwlhD\njpM6YHEyleNyGGvp50umcWQYnuF8QKzDesHlgDWRxXKmm3ySJTmDs45F32vdRhkhcgY765GSgqWY\nyIMhGoOz2hvMovzQ0DSEJjCfL8CAdZbbt07w3vDowUMdR3KOeTPjeC7ElBk3a7p+xjv3bvOFz71D\nzoIzTnUznQeBp6enPHl2xh/+6V9wfnmNHO5OaPxoxlKZPrWhXYWMKoey1otd1z1Hhq8TLDVq7iKk\nL2qvhMPVCYGajsHzTU247rHtNrV3IWJgW6N1XUfbttvFjLXXsss42O3T1WJ6NyWs0bR+b324P8pC\nQRI/dv8tfue3foW+KE6ZstJJjAWDLjtMiRQjq6sLxBjON8K9RcfhYgFWsN6rQjK20KdUcgEodV1p\nGDuHSIaccDaQbSZPSUGRlLZSDVsHK75mABt0IiHHEZzBWEMqP9vXGsZNDBvBxEzfNYzjRDTQeE/w\nDgP4Rp+Ps0KKiXGYcI0ni2CtIpmmLAKRcmF1vsE7W9Sci6Kz85wcC48ePAR0zs55R9s2NDmRcmKa\n1lxcgms93gdNY0sUd85xeHTAYj6jC4ZvfOcjvvuDhwDbtH+XjL5bWtR2Ts1e6hbUGjErnXB3p8Uu\nYLPL1fzUoZSVub3rPDXC7EL4u9y7SmquKaAxZktgrmhXJbTWlKMyD+B6FqtGqV0ouubpu9EMtBgP\nznDzZMk//u0vc+PkmK7vSSnifCDnBOV3iTWq5w/EnJAUWT1+yNnFJaSJ20dL7i8tJycHhK7BXmTy\nlMkSgYacJ4xYhMKlTJlMiZSijA8TPMEGxBjitCHnhOSMFUGs0eazU4VlYx2SgZQwMSObyLQZmDYj\n5LJTPOrr74InxcSzp+cYAWcdbdNwcrTUaYGgu+zSMDEBxveYYcLkzDgYfAWNRLQ/l0eMEaRQ6FyR\nT8eAMTNObt5kvVoRgqdtA918oaCMt+Ssn8OwWjOwxh8dY71GSmsNk3cYDP2s5+T4hI8/OWMYr1dE\n1yhXqXi7WUu9aCuQ4pzb9oRrpAO20ybAlrwObH+mRswXsVfC4Xah9F1S6i46BWwRqpoa1nSzTnDv\nzkhVlLLCtvWW221U7/bddqPXrjN675n3DbdvHHK4aPnxd97i9p1b3Lp1R1O5nEsEKeRCIyUalXoL\nwGRMKA3+LGBV1aptGkLjMM5hS4SnkPgxdruwUFLU7TemDD2iq6gka3TyzuDsnM2wAsmknDWVBGKc\n8NZjnEGKQljGkEXKul+LSB26zDhriIny+4WcIvPlksWsp22aEr10RbIJShuTGLEmUhWkbYnCgmAN\ntGW9VdO0ZQuPjvd4HxCB46MDyOm5z8B6ZaxYa8Dq6E9CX083m2OtRlHvlabdhQUWy+c/+wbf+PaH\nDOO1lOJu7V3PQC0RKhpZZx13GUNwjYjX79mdmwOe49O+iL0SDrfLMKmoT03rarSprO3FYrFNQesN\ntVtvee+J08A//NWfZ9Y1eGu4vDzn+x9+gBHDs4uWs8uR9WZ8DgZ+jo3QeF67fcyv//LPE7wW0w8f\nPuTs/Ix33vkMTduRRFkhTduUmS/BOUPKE9ZYzeSMQaaJmCbiek1MMKxWLPo5P3j6jP6NQ5quxXmL\nD4E8RtI0kUMCn2tSiHUOsgIPOSeNVjaDEYxAjhkxiSb0GlFTJI4bco7gDBJH5V8JyKSRJ2VdCDKt\n1+RyQRjnSauBOAwkERaLGYdHC7om0HhP0wXEFg5lVrqXZKFxFimSeD5MjDlrSilKkj5oPQeLGcuS\nkXStIrwuBLq2J04TzjrOzs+xzursXxbavscUupjCQrpvfLi6IMeRrp9B0s+/Xyxoj0/49S9/mV/8\nuWd89OEH/M8/+QsurnTcKHjPVFDMXQW3ruu2tdx8Pt9S+Sr3tQaC6mi7bata3nzq2gK1j7aLQu6O\n21dnA9WPr3m3c24Lz1dU8wvvvcUv/OxP0LYeEd3uOW6uuDy/ICWFjo9nPcG3jFMuY2Eeayy3bhxw\n6+SAz332PofLOZIz0zSyWW+wxnLv9TfwTQMWcsqknPApYJTLpMOiDiBjjCOnRM5Sop/uBKBSzDDq\nSAiUGbgoqoSVyUpkTgmbszKCncWgCl85J03RcgJRCpa22lRNq+l6nA/EzRUpR1IaQTKSQJJVYnIc\nmeJEnCLijK6TyoItlDEw5BiV7W90l4BxRqOO95B0EYkXQUZwORNTLjWjRvosGW+gs4bD5QGhbXDO\n40NDShPWGKZxxHlPCA0xlVo3qQxF2B567eMhmdY1ej0YWF+e0/eL7fiSC57gPcYdgwi/MK2xNoA1\nhNDgrWeKiQ8/fszlesPZxQpjLFOMzGc9MSbm8xkp6k6EGh3hGr2u2VCNjrUUeVF7JRyu3ji7Bepu\n+K4FcG1Q7qabzjlmfcs7b9/lC+/dZzbrWBwsaULLOOqIRdP1+G9+i3EauHXzJkfHR7zz9jucHJ/Q\n9o0e8CzEcUNoerpO4WWXHetxhXWB2/deYz5blMOuSIQzlpwT3joyQhRl5DsfdOdNLLXENHDx7JTN\n5QWkyKOPP2Y9rAm20NemEdc4rA/kPJFyVw6tjreQEq6bkZgw3mNEEVAFJkSFXGXaUo1lXGmjOXSI\ntcS0IY4jcdowrC+Zhg0Xj54wTpHu4AAbAr7xIJlxtWFab1Qaz0R82+CDJVhHaFqs9zrlDXjJWNdi\nQyRfCnHU1BcTkSTkbJh5cC0sZi3z+YIUE8Z6Gu+JOdF4Txsnhs0GbwutD4PJCe+8Nv+N7izQDMSS\nsrJgXGg5O3vKNFxxp38H47S2dM5xeHLCW+k+z5495ujwRNN8q5nHa6/dxkgZYoUippTwPnB2fkoI\nKraUM6w3I1NMjDGSUgaB//Wn3+Ly6nor0qcuwtUGdI1edcK76onUYrf26GoU7LqOWd/x5V/6Ardu\nHuGMZbNZk9Kc5CdlR4RAEjg+PMbaS+7ff4tbN29ycnILY+22Dksp0rZ9UcxyWKMgRUyJfrak72c4\nDxhH3UQD2ueSDFkycF171r6TBcZcSLQIs77nyeMnxJh03AW2Iy/iigNxncoAYA1C0mBoFAkk1+/R\n6Edy+vus0zowa0mpfTyrl4B1xM2E2EC3WNB55Uha71R1a8rkGLdCRr4JYCjO1uBqq8NZjBEMAWJE\nsoWsiSnlNWUyCWiDhQxtGcPRPl/CmIB1miIv5gfEKWJ4WJ4fRS0anNdtP178zqSAJUkkxpG+n+OM\nZX11gXE6niQiWGOYzZfkaWQcNyyWh3TzRck4BGv9NlsQY3SsCctULjjjVB90tliUD0FlJpz3LOYt\nHz085St//t2/1fZTeEUc7vDwkNVq9ZzK1eXlJXWOrIIqVRLhp3/yHe7dOeHO7RMODpc0vmGcRozR\naOkyxCnRdT2z+ZKTY8/f++KX+N73v0/fNgTnSHHAGFsmqRv6foFIpp91NL7DiOHJ00eE0NB0Dc7r\nTZhTJDQdmkVIOQQZ74ICDegh169rhBpWa6YxcXVxgZXE+ZNzOmdxJU201uP7tsirAllTOZvqkJkH\n0ZTKyrXzqbMnbUNYR0wJpqQ3thVy1uiX0CZ0zhEbLF4C/XKOGEOeSiN5vSFjcM5g+haCw00TUi42\nGxxGNLq7tujzj1HT2QRYj3GRPE3EcUSDgTCfN/SdgiZI1jQyZpxXx2yalmxGjo4P+fiHOqIUmrBt\nQcRpA5MjtAqwSAYRQz9fkDOkYYX3ltVmw+PvfZs33363XMraIjk4ucWzx4948OFf8dobb9HOlgpS\nGVWKdoXdIjlhnHC4PNBJB1eoakVuQilnCjrd/8yct++/wy/94s+RkvDv/9PvvvBZfyUcrvbYKmhS\nU8naXK4o0xv3bvH5z97nM2/f3RbRknWY0liLFehmPZKEzbgm+ECOE6MI3azjM/fv88GH7zNsNrqK\n1unN772mqV0326awYxwYp4HgA13bYkuDGezzObup4jhRI5X3hQuZsUZvzHG9ZrNeY61jc3GBEVW4\nct6TUlQJA+9qOYcYi6kOJdpLE+V8IZK3YzNZMqbA66SENY5E1HS09g5xGJPIKQParjDeYlKRKjCl\n/eE9ptSLJpXd2s5hvMM50fpNLLZEHmsh15TMWa0RRfQQO0cu4ELfBAxF6Wq+0MNNxoZAKEifpIy1\nZer+qipgFcTSWXUA4xS9RcryE11SafsZjBuWQQnLTx4/4Oj4Jk3b4b0jxcji4IhxHFhdXYA1NF2p\nUUubUrLgGo8W4Nr/w6INGWvwqOCSNUn7f1ZTdxM8YPjtX/vSC5/1V8LhKsq427iGazrObNbz1uu3\n+Y0v/x2CD4XXZ4lxLIigIUsiY5l1VWbhgtFvWB7oInUXGhaHnvCgjM2UxYRN0wKC92WLSxzxNiA2\n4Jyna2fkFEkipDL3ZazFel/AEAGrlKqkbWvVIgEq02QcNmTJLJYLLj95gLOGtvU4b7He6qH2AcmZ\nZLTpnY3BGtFBUGO15qBENN8AVUDWgJYW6gjeIZKKA5RGedJIlFPS1+wcJgRsKvvTAdd4bM6krPWX\nNQbTNgqSNI1eCgYMFuMtOSXEZqSkmaJfRMxWdwEXtCdKjMzmC6WnAdO4wcgS5z0xRfQqsBwcHhGn\nWMRjtWmOdRgLKY04yiBvaMi5sEgk40NLGtYcLJe8/8H38S4wW2bapsUEPR8nN25x+vgB3gVCaEqL\nRLVXnK9Ur5nS5HLWdNNYBIMz7OAGtS2j5HCDcPfOi29AfSUcrgImTdNsZ5UWizlv3L3BFz53nzu3\nTohTpC2S5mTR0f/SWzHGKDs9RTDaZlgsliUiRFK2zBczUsy89/mf5tmTx1w8fcri4IDgHMuDE4TE\nan1J8IqWxRSZ9XOctdjCIZwFyzSWWqpsB3XOKVLohJQy0zQoc77UWEYyq2nEYBiGDb5pMNYwW3aE\nwoDxXYdvWmX3x6TR0lqyUfRPxpEcGqz12NAiUQ+uaXQtVK1rrEUPorXkBPoPjbzO+RIxUdCjMeRo\nMaJjM9dKYg3ZKHpapRRc0NEb50vGEcfSkN8eP430ZXAVoz3I46UqXzsjNG3L/PCY9WYN1pIyEGPh\nWVpM03Drzj0OlkseP/oEkUw3myGSkAhickFRHWlSufbIoBG+XD4pZm4e3eLBxx/Bgx/yxtufoWkC\nadJ0cb48YnN1QTef40JDaBsMaOotA0+ffsLB4THBt0ht79Tfv8M0MsbpWJKkMgsof+25/uvslXC4\nivTs6pn82i//DPfu3KLvgub5O8wTnN1Sp6zzSE6kJJAycdIZOt8E4jiSIxivGo65oAn9Ys6TTx7o\nety+Zxw3GAvDMJJDpu/mxLQhuCp4k7BAHEvDWUpvjNIXM4WOJYKzAWe0hojDhhQnnA0kBoKzTBuV\nAHfOYpxRKTwD1hgwHmMTRlyBvi1ilcpk1FM0htoyjS1ZmS2lk0ZjR2QgAAAUEUlEQVTWdgQ2Y0XT\nOnLCyjUbR2sXh8sa/bBZ6y8RBQWsAep2Vikbi/XgKUna6oXi9OCJKQRzASv6+7NA17U4a/De0gRP\n0+t+OGXtwzCsmbcH2gAPtiC1LTZNzA8OGIcNiOCbhhwzxrbkNCmaHBM5brA20PRzbVRPAylFnp09\n47vv/xUXZ5e88ebbGBxZRnJKNG2Dd4dIynryRcuB0HiMW9K2s1LrJij1tDWVh3pNwCj/B21KCvZv\n4UWvhMPpyteWd+/f4zf/wRfpuh6yjnl475EspNLgbvuO0PQM65WyHHIkFRa9lLom50zTBkyB6m3U\nHNxYw6xb0LcLns4f8/HHHxFT4viGQYwhZXC24fLyHG8VCQO0VpBMjKn8OaakNNrTMr4cOOcJTney\nxaSRKo4jcbPm/Nkpi6NbhOYBoQxv+qbBtQ2u6THO4XHkrIs3UorK+ndeaWLl8y0vEoMKsRoxuEaj\nXxo2iFMUNduMSSgULuo0WYd3yCmC8Tiftbenma+CCVYvmJyiOldhtXhntVZLGeMdJiWtIxPkKWK8\nQyZDihHvPAfzbjsZ3h8cMU0DMUf6rmczaFO+7zpSFoZhXXiSgbadEZoZeRy4OH9KevqEtutYHBzR\ndzOGGHFB9V1ERjbjhmkcef/9H/DgwSP+y+/9PiYm3vvxd3Fdj+86pjzgXYuMA818hkQh28J0yZlp\nHLGhoesaBbyyTmFsiagFDP6bTNszL2avhMO9+cbr/Pqv/Aw3jg80hXN6s3elKJ1GrdXEaNOYnPBN\nwzSoAKfBYBykSU+OiCBJnYiEMu3r7W50sfyNO3dYrS559uSU+cGR0jgQpmmNs/YabRS95YzVAVBM\nFRbVZMpaWyhMumI3F/BAayshjkor81aXPeKC9pesLSlSuT2dx1iHM8ryt86RcyRnrbeA8t6UtpWJ\nGFxRadYvmqbV1KpA88Zqb806RRK1XWGxeDCZOsYludBYKI1rEf2+OmDgO42GRp2dHNX5zXUKCYA1\n2OAJYvAoXzK0njCf44zDNx1ZMmlKjJtBmR9A2/Sq5JwmvSTjQEwTQ9QVWqvVGkmZsVvjWq3RU4xM\nceLp+TlPnz3jf/zB/+avfvARRixvvHmPd959F+M8OQuN75Hy2ZAyoWkwPkCJZlJYOFiLTIWP6sp/\n0wP219o1JfFTRu36V//itwhB08bTTx4jRchGScPKSDfoDZyzUpMQ1VQ0Xoti8Brai8MZ6wCr9MaC\n2uWsWh05RxYHC37svS/w4V+9r6t7j25o2tq2NEEPP6LKVBn9OWMykqQcTq8EkBDIomBE49sCbEyI\nV5WrPEVWl+cYa/ng/e9gkxTiiCEngWTBNRgXtAFtI+I9KWVcaEukM1q7TRkTNIXGuG2zTciFHFze\nd67pnupuGRmVBuYcSCJbRRWNsRgfsFZrTWJFQBU4sKFwH41TXqf1Gn1zhqSOblQXr7RCMv1iRowZ\nHxzBW0UEb76JKTIYYjyhbbSFkcpW2qQKbT/86BEf/PADvvGX3yZPI3du3WJ5cEjf9xwsZjhjsUZb\nAD989IjTJ6c8+OA7rFYbsp9ztDjgp3768/z4u+/y2huv0zctohky3nVsBqcZgfc6nZF3epQOpjix\npeSVulj/DX+TUxljteZ+QXslHG4cJ/rZEgLMlnN8gZ7TlLEWyIlsBC/K4NCxGBQdpJCEjc5gZbbA\nYekRJbItUnZRqVKqJGxxfeDWnbt8//vfxFvPzTt3UUKCOpykSUm1KIUq5ajNdAPWat3pjUOswZbD\nqPzOqGgiwjgOTONA2wTGYcLn8uIK2lko2wVgKRQiY0hJkBxLBC2XhjPX0VOy/pn152ujRLvwiNG6\nLudc6kN1Cqk8x9pgzwLOkKcJGSPiCjfQWSyibQJnCrsFJRDnVC6+GiIVjHHeY7MQGqsLP4yhn89Z\nW1fqZK/gUM4qTpQjm2HN2dmas7Nn/NFXv8Yff/XrfPv997EGlos5x4dLjg7nvPnaPfq+I8eJi8sr\nnpw+4+rqihvLOX27ZHZ4zGJ5wDufuc/t27fpipxDlkTOqhodXFki4oI6ouglZABrPK5cHFlMgYSu\ng/f/1V48wL0aDndx+hRnFfnpZwucL/uZyYhxYLSIr1qJ+kGXuk1KL4i87V8p/65ssBk22BgJbYuz\npX4RAy4gOdItekLoiZJLTp+wCDlNpHzNpaucvjSNZGdpSg9qHAc0FSt8RSyb9YYcJzbnp2xWVwq7\nZ9ikxOsHJ3yUhTHFbV1mrS+cSAUmNIIqWJLzhDeWOE34tsGhs27GQhqH4mhgcZruYTEeJCZ1QwM5\nFX0Vq60LZeYbJEedGhjLJeE0izCF5eK7Xi8bYxQZNVaXdWRDHEeQsivbGJq+J6/XdEE1S0KnKl20\nC3Lo6fs5pvQd265jmia++73vsYnCv/m3/47L1bo8S0vbNLg6IRBhvRZOzy4JVxu6pqFpeu7e7cHA\nvdsnLNqGo+ObzA8PuXnnNvPlkr6bkVLp5zaBKY44GxinNYmkg6xG2yyRTCgTDLnoadZeprA7wVFa\nMVxPtwB4+ymTOrfWc3b2lJyFW00AsmozGvNc85cyByap0pwsxuiNpIeuDKSa2iMpJKyqme99ueUV\nGfPO4vGc3LrN0eGBEnUL3SmPEe1+ai1TH7z6etGJNNoKSGnatihSGQIlFtaIMfh+Rt91bJWtsvL0\n6iuUXNI9q18XBGs1dTPWFoZIS44jLgRN4RBsCKRx3EZfR1D43GqqyLABhJQrAGILiglSdglITkXv\nBH1aBYm05ZkrxanwOseJSqA2FMDIaFN7zIr0Nr4hNJ521uOtZWw7sI1G7TjhmxmNSeSY+Moff5Vv\n/+BDrtZrQLDGISiy6b1nPp9z4/CAk+ND7t68gQ2OedfRWEfT65TB7Vs3OJjPWRyp+tfh4Qld2+jn\nE7ReHjeq0E2ZrXNepy9y6W1aY/Vyr/B++byNkW0dJzuNkBrRrFHHVFTzxeyVcDjnLSlPNLYhJ3Wg\nJGhq5MoBkrSNZhp19L9hjRbxJeLoLBYkmTBG6VNYvYmTRJrQYZ3HGUvMkZyFe3de08luWylQDtfo\na9E+V1F6dg5EG7Pe6+CnMYL1Oq2cRZgmjRo2tORpYpgi6/MzXE5crAeG5aTpmnMglpwMJoM1DQav\nfR/JWKfARaxS4RhM6BGnabIxFos6s+pWTmRnVYBoigUM8OTNWr9OwogtCORYkDhBpqJ0Zj2uXFjG\nFva90yMmOWk0dp6YR3JG61arwq9pGLDe0TYz2tkMVybCQ9Nwlm/TtT2Lw5ucPviQZ48+wvvAMEV+\n/7//odZy5QT7oOJE8/mcWdvxMz/5Hndu3+Tk6IjlwRxvA+28x1vD4dGJEtzHFbPZAud9qdczYtTx\nxRZVbmOxVpTU0LQ4G4g5lvoX5aqmOqREZclCIadfp+B524NTP9SLx3zaQBPfNGyuNrjOYGxQ8qox\nJEoNgjqgLQ1ntUqWrTQmjRha4kiJHpUh4EjDQBojyEjbKInWiMUXfYpZPyNLYooTYoRp3JCy4K1X\nOqPVCJpRReFc0g0jGecdOcFUWBNkQRjJtiGL4eZrbzGtzji/vCTePCl1lb6LlKKO5MQJ1zRaqKPv\nAVAqVcpQbn9jg3Irc0JiwkjVhNTeH1nrLn0uGeMCSNQ6eBr0Qku5MFjM9fvwugjSOl9qXB3DqQOw\nGYFho/SvpBQnQ1KJu5KCWeex3uF9oOlasC3TaAkIvvGItSwPj8k58+j0I6YxFlDGKLOmMIuWsxkn\nR0vu3rnJjZMbHB4dcnJ0ouk2VRjY0oSWyYJvPAZXVhyXZnrt1ZbxqJgFZzVVzdtem5YhWkMXblBJ\nFaUQxHUsB7YtgvI/JeskvMaFT1mEy5JxRgmlBb/WQ4amhElqT6QU/qKIyTV0K7rM0GpzuAylaerj\n3PaGIgspTcRslX3uPTlNeK8iq5tRx0TGadzqizgjtN2MJpdUJKkSlUUl6YZhU/pumu5675QHmau+\nSMN8NuP88hkXlytWm822eZ6Ve6W6H5RIYs01HI1oCmd0EsFIJqdJkUWn835iCsJoHSbl4meKsBkp\nF48YjZ7ekPKoy0FQypgNupZKCorpvCsrp0yhiQF4SBFx6pQU3ZLsHd4bxosLpvWG7mCJbYJOazvH\nZXRcXTxlbk/KuxHa2Ywnp6e8/8GHWqqasuIK2erPnBwtee3OTW7fuc3R4RHzxZzZQpXRcqoDqdA0\nqi+ZYtJJjmyVQ1nqsc1Q1grnhDWojor3xFgk1Mv7UVKFI0nEiN1mFBnRyXkdyNO2UvmrApeKdH7K\nNE0onD9jLKmMsaQy9i+1xmHnkAoYSppTf94KIraMo1DqgSKsY2tLIMOojVnTO5wzZAm8/71vc3W1\n4s++/g0+fviI82eXrDcrVoPuSrt584CTwwOOTw54/bU32Ewjp09Puby8YBojxni+9933Ob+64vhw\nzj/5nd/hp37qZxHXkTH4xuGbBk/matDBz1SWzefCkkk54wyY8v6db/Q9m6QTQZL0/RVWhxjlOGIM\nadzoYGvWZ2ML4dtYj/UGnCfHBDmqLF5WOpZ1Ws/VtMnVJnmhcgHkOBDLBVGZLGHW6+ckiXHMTMkw\nPznS4VLjMS7w8XDI1WrFOAyE1Yrh8hwrwrf+8js8/uQxP3z4lGCtPh8vtM5zeLjg4GDJz3/hC7z5\nxuu8ef+twpWtI0lKvPZW20PDUHRcpIr6aA9ynLTWdNaCdZpBJFUy89Yx5VFFbLNGev0TtIY0ZTwo\n5+t5QwXoSr1daXSu4gT6sy9qr4TDjdOIJN3MKcZsxzA0iheo21AIvGX2rIxKbPsm6Neh/KzXiJhS\nwlsK0VWVs0Kjfb2UioxBgk8ePeTBgyd88vicj3/4gIurFa4oMD87PycYi/HCrP9z0hRZDwMpT/Sz\nOcvFEud7+t7x8JNn/O7v/VeEzM/93JeIWVhfrREMh12LE2Xep1wqBlF8SIW4lMtI0QMx5O1IkKlr\nbChaJ1K8DsEWWXRT6e/GbtsJYg1is15I6KVmkkDlXJYGtaptqZqz0gf1EMv28Bl81+s4z3rQ52Yd\nNhj6Y0+OEbGGSEuyhzx9ek4ILU2rw7TDMNAfHHP29W/x8MEDHj95uq2ZrHU0TWAxn3Hj5AZ3797h\n5MbJ9mvOGMRqHZ0qmuvK67WWaRyIk471OK+6JcYb8piLMrUyhaZhAHT2Td+TkrQr2KWm6aXW5IVU\nYcCJuZ6OkOpk+jXkU+Zw0zjhnUWIOm4TVeNCSsguACFiRAv2nPDeFoYE2gQut00uEa4OQsY4gZGt\nzHaOSklab9b0vSodv/n2W7x27zV+8Uu/hGTLn/3Z1/j6N77JJ6ePeePubU7PLzl7do4Plq7rtwq/\niKjeR9dwuDggC1xcXjDGxDe++V1++vM/w8GtW8z7Dnt8izuH/41F4zgVQ0yZzaCKzzENRNH1wtYJ\n3io9C+MLlw+qbANpgmRL6mkL20MjgOk67TuWFca1lskxYkXI1mFzKhJ5ocx5KZfTlF6d3R4kQ2g6\nEMGGNTklVufnJDKu7bA4zkbLGB1jyoxGgSvfHIAYjl6/qZqfUUGmjx59wne++23+w3/8zxgEX1BZ\nEZjPe5bLBT/53me5fecWd+/dYTnXXepZhDglhvVK2SreE1F+KM4xTpFp3GyFo7z1Sm7PmeADzjrW\n6ystOZzFYol51IsHtqwVigaLtZpqVqggF2ha69QyjyhVNFj7v3H6lDW+tXkdsMYVDqJGMCfXuoYK\nhihZlIIL1XSyzn7lWhvlgihRJfBUA9E6S3RJR0uyAhbKgneE0DCMG6z1vPcTnyV0LV/72ld5443X\n6U6f4qzRyYLlkilpSphTxju9Uft5h3WOWaeaGzFr66LrejYxs1gu+fv/6J/z8Xe+xvt/+ieFvyjE\naSopT9Yl9W1HEsFS1LREawdcURajoJj1oysisfrGo06se0eOIKRSJ1bScVLgxVuIScVUy3Q3KWKN\nByuFeaHoWyr0qlzQRGsMk+u4SD0XpjTYPbjZjNpG6fqeZ5cXyNUV7XzBMI585St/xB/80R/jvVN4\nvbZujKXrWg4WS26cHHHz6IjgHDnrBh79bH1Jo6U81wSF4C0p6ohO2+rYjWg50qB91owykgSBDDFd\nS+ZXkrIrU+IlXQIU1dSzdM2+qSNPtqajXEfIF7VXwuFSTjRGCL4pAkFFGQkp1JmpOBuYLIo6IrhS\nuWbJWKxC31kh7Rjj1oFFCrukjNFripaRpAWxL4Ou1uihbILlnbffJJCZHSx1iuBm5sbRITdv3SDl\nxLBRpxungbbpMEb3QYe2UVaGwMePHiKiqJl3N/Anx7z7K7/F5fmax3/5NaYYSRg2w6B8SeeIo27M\naWYBhyVfrhR+X/SFeYIij+V2rWdB5eoUtTTWYoOyQ7JkktFpcRNzQWe1R4Xo/BzG4tu29Ai1hsSg\nN3/UNofkzEY6LkbhLLVkY/CzI65WF3zy+Ak/+OCbKkcYE49OT3ny5BldP+fJ4wecPjlntdrQdYG+\n8Qq3o2lb17T8xDvvcPPmMa/dvsV8Pkdyaa9k0eZ1IWSLXGc7rqaBxtF3c5azAwVLfN1hPuglLVJU\nqBWQijkpD1bKVE0W3XGn95iWGtOEDW7L/smSsVsp/MJ0yqKgkjb0Xvism+e0M/a2t739f7UXj4V7\n29ve/p9t73B729tLtL3D7W1vL9H2Dre3vb1E2zvc3vb2Em3vcHvb20u0vcPtbW8v0fYOt7e9vUTb\nO9ze9vYSbe9we9vbS7S9w+1tby/R9g63t729RNs73N729hJt73B729tLtL3D7W1vL9H2Dre3vb1E\n2zvc3vb2Em3vcHvb20u0vcPtbW8v0fYOt7e9vUTbO9ze9vYSbe9we9vbS7S9w+1tby/R/g9wPoEN\nxT++eAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9104274690>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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/A2sizw3zB1oeAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9118150f50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Dc9AbvVJW5IzlEflQqDrfrSd1rT9/v3jc2Mb94jFZ8GFngkljYcEJIHqUzX7U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\nTAghhBBCSMT/B/zcjNHQ+3W/AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f912c04f490>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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5VMm2WtJfDSlcZ1WcmHkzWirkEUkGF8FIE5/ZQ3Wa3L6yMrc+kLBdhKoRSqnQ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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9258046490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"%matplotlib inline\n",
"\n",
"w, h = 112, 112\n",
"\n",
"# Plot helpers\n",
"def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):\n",
" \n",
"\n",
" if type(ims[0]) is np.ndarray:\n",
" ims = np.array(ims).astype(np.uint8)\n",
" #print ims.shape\n",
" #if (ims.shape[-1] != 3):\n",
" # ims = ims.transpose((0,2,3,1))\n",
" f = plt.figure(figsize=figsize)\n",
" for i in range(len(ims)):\n",
" sp = f.add_subplot(rows, len(ims)//rows, i+1)\n",
" sp.axis('Off')\n",
" if titles is not None:\n",
" sp.set_title(titles[i], fontsize=16)\n",
" plt.imshow(ims[i], interpolation=None if interp else 'none')\n",
" \n",
"def plots_idx(idx, titles=None):\n",
" plots([features[0][i] for i in idx])\n",
"\n",
" \n",
"fname = mx.test_utils.download(url)\n",
"img = cv2.cvtColor(cv2.imread(fname), cv2.COLOR_BGR2RGB)\n",
"plt.axis('off')\n",
"plt.imshow(img)\n",
"\n",
"plots_idx(range(0,5))\n",
"plots_idx(range(5,10))\n",
"plots_idx(range(10,15))\n",
"\n",
"\n",
"#data = np.zeros((h, w, 3), dtype=np.uint8)\n",
"#img = Image.fromarray(features[0][:2028], 'RGB')\n",
"#img.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://d0.awsstatic.com/Developer%20Marketing/evangelists/evangelist-bio-randall-hunt.png\"/>"
]
},
{
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
idekerlab/graph-services | notebooks/DEMO.ipynb | 1 | 10163 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# cxMate Service DEMO\n",
"By Ayato Shimada, Mitsuhiro Eto\n",
"\n",
"This DEMO shows\n",
"1. detect communities using an __igraph's community detection algorithm__\n",
"2. __paint communities (nodes and edges)__ in different colors\n",
"3. perform layout using __graph-tool's sfdp algorithm__\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python 3.5.4 :: Continuum Analytics, Inc.\r\n"
]
}
],
"source": [
"# Tested on:\n",
"!python --version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Send CX to service using requests module\n",
"\n",
"### Services are built on __a server__\n",
"You don't have to construct graph libraries in your local environment. \n",
"It is very easy to use __python-igraph__ and __graph-tools__.\n",
"\n",
"### In order to send CX\n",
"- requests : to send CX file to service in Python. (curl also can be used.)\n",
"- json : to convert *object* to a CX formatted string.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url_community = 'http://localhost:80' # igraph's community detection service URL\n",
"url_layout = 'http://localhost:3000' # graph-tool's layout service URL\n",
"headers = {'Content-type': 'application/json'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Network used for DEMO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This DEMO uses `yeastHQSubnet.cx` as original network.\n",
"- 2924 nodes\n",
"- 6827 edges\n",
"<img src=\"example1.png\" alt=\"Drawing\" style=\"width: 500px;\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. igraph community detection and color generator service\n",
"In order to detect communities, __igraph's community detection service__ can be used. \n",
"\n",
"### How to use the service on Jupyter Notebook\n",
"1. open the CX file using __*`open()`*__\n",
"2. set parameters in dictionary format. (About parameters, see the document of service.)\n",
"3. post the CX data to URL of service using __*`requests.post()`*__"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = open('./yeastHQSubnet.cx') # 1.\n",
"parameter = {'type': 'leading_eigenvector', 'clusters': 5, 'palette': 'husl'} # 2.\n",
"r = requests.post(url=url_community, headers=headers, data=data, params=parameter) # 3."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What happened?\n",
"\n",
"### Output contains\n",
"__graph with community membership__ + __color assignment for each group__.\n",
"- node1 : group 1, red\n",
"- node2 : group 1, red\n",
"- node3 : group 2, green\n",
"...\n",
"\n",
"## You don't have to create your own color palette manually.\n",
"\n",
"To save and look the output data, you can use __*`r.json()['data']`*__\n",
"\n",
"__Note__ \n",
"- When you use this output as input of next service, you must use __*`json.dumps(r.json()['data'])`*__\n",
"- You must replace single quotation to double quotation in output file."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re\n",
"with open('output1.cx', 'w') as f:\n",
" # single quotation -> double quotation\n",
" output = re.sub(string=str(r.json()['data']), pattern=\"'\", repl='\"')\n",
" f.write(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. graph-tool layout service\n",
"In order to perform layout algorithm, __graph-tool's layout algorithm service__ can be used. \n",
"\n",
"## C++ optimized parallel, community-structure-aware layout algorithms\n",
"\n",
"You can use the community structure as a parameter for layout, and __result reflects its structure.__\n",
"\n",
"\n",
"You can use graph-tool's service __in the same way as igraph's service__. \n",
"Both input and output of cxMate service are __CX__, NOT __igraph's object__, __graph-tool's object__ and so on. \n",
"So, you __don't have to convert__ igraph object to graph-tools object.\n",
"\n",
"<img src=\"service.png\" alt=\"Drawing\" style=\"width: 750px;\"/>\n",
"\n",
"\n",
"### How to use the service on Jupyter Notebook\n",
"1. open the CX file using __*`json.dumps(r.json()['data'])`*__\n",
"2. set parameters in dictionary format. (About parameters, see the document of service.)\n",
"3. post the CX data to URL of service using __*`requests.post()`*__"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"data2 = json.dumps(r.json()['data']) # 1.\n",
"parameter = {'only-layout': False, 'groups': 'community'} # 2. \n",
"r2 = requests.post(url=url_layout, headers=headers, data=data2, params=parameter) # 3."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save .cx file\n",
"To save and look the output data, you can use __*`r.json()['data']`*__\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re\n",
"with open('output2.cx', 'w') as f:\n",
" # single quotation -> double quotation\n",
" output = re.sub(string=str(r2.json()['data']), pattern=\"'\", repl='\"')\n",
" f.write(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Color Palette\n",
"If you want to change color of communities, you can do it __easily__. \n",
"__Many color palettes of seaborn__ can be used. (See http://seaborn.pydata.org/tutorial/color_palettes.html)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import seaborn as sns, numpy as np\n",
"from ipywidgets import interact, FloatSlider"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Default Palette\n",
"Without setting parameter 'palette', 'husl' is used as color palette."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"palette: husl\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b55ad65924cc4fe3a8d3a9b2618593ec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"A Jupyter Widget"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def show_husl(n):\n",
" sns.palplot(sns.color_palette('husl', n))\n",
"print('palette: husl')\n",
"interact(show_husl, n=10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other palettes"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef95c35844e84cd2811995072fcfc2eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"A Jupyter Widget"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def show_pal0(palette):\n",
" sns.palplot(sns.color_palette(palette, 24))\n",
"interact(show_pal0, palette='deep muted pastel bright dark colorblind'.split());"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ac6bbc3e20cd4dc5adb6f83db25b0d88",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"A Jupyter Widget"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.choose_colorbrewer_palette('qualitative');"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "421dbb1e626c4084aef8b2c7d87cb4ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"A Jupyter Widget"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.choose_colorbrewer_palette('sequential');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"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": 2
}
| mit |
indranilsinharoy/PyZDDE | Examples/IPNotebooks/01 Notes on ipzCaptureWindow functions.ipynb | 2 | 1048183 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using `ipzCaptureWindow` and `ipzCaptureWindow2` for embedding graphic analysis windows into notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/indranilsinharoy/PyZDDE/master/Doc/Images/articleBanner_01_ipzcapturewindow.png\" height=\"230\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Please feel free to [e-mail](mailto:[email protected]) any corrections, comments and suggestions to the author ([Indranil Sinharoy](http://indranilsinharoy.com/))* \n",
"\n",
"Last updated: 12/27/2015\n",
"\n",
"License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<font color='red'>NOTE:</font>**\n",
"\n",
"The current version of OpticStudio doesn't support function the function `ipzCaptureWindow()`. This is because the dataitem `GetMetaFile` has been deprecated. However, the function `ipzCaptureWindowLQ()` works fine as it uses ZPL macro to grap a snapshot of an open graphics window.\n",
"\n",
"Both functions works just fine in Zemax version 13.2 and earlier.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Why are there two functions for doing the same thing?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we explore the different use cases of the functions `ipzCaptureWindow()` and `ipzCaptureWindowLQ()`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<font color='red'>ipzCaptureWindowLQ()</font>**\n",
"\n",
"`ipzCaptureWindowLQ()`, which takes as input a window number, uses ZPL macros to retrieve a \"screenshot\" from Zemax main application (note the window open in the main application is retrieved and not in the DDE server). The number is assigned by Zemax when an analysis window is opened in Zemax, and is not tied to any specific analysis. The quality of the rendered image is dependent on the quality of the JPEG image provided by Zemax. Frankly, I haven't found the quality to be any qood. The \"LQ\" in the name of the function indicates low-quality. For using `ipzCaptureWindowLQ()` we will also need to specify the ZPL macro path to PyZDDE. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<font color='red'>ipzCaptureWindow()</font>**\n",
"\n",
"\n",
"`ipzCaptureWindow()` generally produces better quality graphis. It takes as input the 3-letter string code for the type of analysis. It uses `zGetMetaFile()` to request ZEMAX to output a windows metafile (standard or enhanced), resizes and converts into a PNG image using ImageMagick and embeds into a notebook cell. Note that since it uses `zGetMetaFile()`, the analysis window from the DDE server is retrieved unlike in the case of `ipzCaptureWindowLQ()`. We can also ask `ipzCaptureWindow()` not to render the PNG image; instead to return the pixel array as a Numpy ndarray. We can then use any graphic rendering tool such as matplotlib to manipulate, render and annotate the graphic. This notebook mainly focuses on `ipzCaptureWindow()`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`ipzCaptureWindowLQ()` is much quicker than `ipzCaptureWindow()` as it doesn't have to do any intermediate image conversions. It is meant for quick interactive use (provided the ZPL macro path is provided to PyZDDE as explained later). `ipzCaptureWindow()` provides more flexibility and better quality images, though it is a little slower than `ipzCaptureWindowLQ()`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that it is assumed that PyZDDE is in the Python search path."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import PyZDDE and create a pyzdde communication object"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import matplotlib.pyplot as plt\n",
"import pyzdde.zdde as pyz\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l = pyz.createLink() # create a DDE link object for communication"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load a lens file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zfile = os.path.join(l.zGetPath()[1], 'Sequential', 'Objectives', 'Cooke 40 degree field.zmx')\n",
"l.zLoadFile(zfile)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform a quick-focus"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l.zQuickFocus()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Example of a Layout plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using `ipzCaptureWindow` to directly embed a Layout plot into the notebook."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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QadEiRYp11mnSJEmSu9xlkskPbl7LRUIfOY3mY6TfF0Ms1b1iT69cpEgHSBFm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\nswtYDgdst212OhLJUkPeVB2qmhK8F/YhfZYFlYZJ3WjS8pU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JquB3fwU+D02apHjLGmu0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yEuLi7QarUoLS1FYGDg7z6OSqGCDjocwRHUoe5CldNQW6GxXxMCOHPGNhOnZ0+gVSu5L8Pfhkaj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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindow('Lay', percent=15, gamma=0.4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. ** Why do we need to set gamma? **\n",
"\n",
"2. ** Is there one gamma value good for all analysis window rendering?** \n",
"\n",
"\n",
"Upto Zemax13 there was no way to control othe thickness of the lines produced by ZEMAX for the metafiles. Generally the lines produced were very thin and the rescaled version would be too light to be visible. One way in which this problem was addressed is to lowpass filter the original image, rescale and then use a gamma value less than one during the conversion from metafile to PNG. This is probably not the optimal solution. One obvious side effect is that the black text becomes very thick and ugly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of embedding the figure directly, we can also get a pixel array using PyZDDE. Plotting the returned array using matplotlib may allow more control and annotation options as shown below:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arr = l.ipzCaptureWindow('Lay', percent=15, gamma=0.08, retArr=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the pixel array, we can either use the convenience function provided in PyZDDE to make a quick plot, or make our own figure and plot as we want it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first see how we can use the convenience function, `imshow()`, provided by PyZDDE to make a cropped plot. The functions takes as input the pixel array, a tuple indicating the number of pixels to crop from the left, right, top, bottom sides of the pixel array, a tuple indicating the matplotlib figure size (optional), and a title string (optional). "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mjON7e0Ug/f738MEH8rP160UgZWX5X4tWWl0U40VkzKLG/D0DN26GGGKAATx4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JOFLCSY+htrnyZBzqRN2vj2vkxLB+jo6ODmm28mlFJWvVSd8G525kTT0kdnDNtaP6Nt8WIFhPr7\n04BoKCGJRmHLtvn89H8kPn8PefkQ7zPIBDIkuDSICWhKQF1PC13J1+gMv0xPcyX97WGSkVoytG66\nL9vAmnVPMTe/mOJQAfm5kJkDoXyJ36+Ajq6lyhOlHm63Wr/cbnB7wNDtHObUXfUzj8EjP4POHnjw\nj+GqjVBUah85oEpImtLEJQ2wDDDg+bZdvNS9F1PfzQ720JmIkGFkMIUprOMalrOCaUzFg5tMMgkS\nIsPODhrV70mRxMBgxqzpJM0EdbW16nPNrU7weBL51JOqVEVVFUSjiNmzEJ/5LMbqVRihEN6CwlGj\nkfKQlQGLBFKq8YjF0l4uWwDTNNXnkYgK7zU2qlBfR4cCsW+8kQZGoPrV61WhzYwM8PstsoOSYEhS\nUGowda7BkgBMOfE07H6OSHsNBz0RDt9jce5DFh2eZnoyq9DJYjpTuZcPsMxaQUBm45N+sgnhFw6i\nmEhlnotUurOtifNmi/FoPZyaGuXN2btXxW6vuQa++lUVi83JGfk9B8ixUoJ5o4uIVlHF8zzPIQ7R\nQANRouSQw1rWspGNlFKKHz955F3gjbJ1n+CdEX7TZU4tDIzhtnro4QmeYDe7qaWWGDEWspC/5q9Z\nwAKyySaf/BFt2fs0CXImbdImZu87Do+mafT399Pb28u8efPweDyXFPX9rhGkbRdTZrYB0PAzgJBo\nQuL1qocyHRtYXD53Pcd3l/DhzTHu+Tgc6KvkROY5Kr3VnDNraTvaSsdzYWKHWnHXN5AfjbI+G4rW\nFPLsaTd33Ptl7v7M/QSMEjI0MLzg9loYQi19b24SLOVKkejIA4fg+99Xro4N18JX71HEJkNT2UDS\nwhISQxjKvyI0mkQTj/Io+9jHef087Voba8VavsgXyRbZZIpMQoTIJhsfFwosOr0DqS5DSNAsCTos\nXb2a/NJSwqnq8uKxxxA7d0J1NSISUboC994Da9ZAdjaytHTk4JgWUpDqj/QCa9NUMjJGRmnGs1gs\nDVjt52QyBZQSks4uSbhD0lBvEWnRifZp1HeC2QjWjmNE23+CPnACvauatsFmBgoE0eJCki/NxFe3\nhCneNcwPTWd+vocFZTksnFJIWZHByMiOxClvLEgT0C1LhfqG+9F5riIRMu2yEqB4OY89BocPq89v\nu03FWadPVzE82yzVf1JT9cVsLRs75DTEENvZzl72Uk01DTQgkcxiFvdyL4tYRC65FFAwXHB2uOlR\nGjnvhABsAxyJHAY5GhoDDPBrfs0udlFNNb30MpvZfJpPs5zl5JFHEUVj7tek+vGkTdrvv73ngAfg\n5MmTxONxiotTOi/vEUj5XZhEIoUcfgbHZCltxnFqYwuqrXMcTx6lKdTEmaIz/If8Hs97nyc80M7g\nM024jjZT1BTmqjaoGMqitHgp3nvvIWPpakrKgnQPxNh+35+y6eZbmFlcohoFnIv66OrzF3S9mUTo\nqsQAHRHEd7+DeOUVlcb2ve+okEZWlvIiWAnQBLow0FELwpP8mt3s5iQn6Zf9XM/1XDt4LY/FH+MK\n1xV8gA8w2uxFaWS4BYRFmktiGCAUaLaAYl2nDGj8wQ9g+3aM6mrklCmI225T4ZbcXEW2EY7squEa\nTQJ0bVzoNxqkjv7cSWvxep2ZeLZHzP6iQRpg6vQDlWdep/7nW3h++xFeaj5EtKwRY7OHouWrWJC4\nl6nhldBQTGe9h8i+XOKxYprjUGfCczrgAcNr4cuU+LwCf6Yg4BcEg4L8fBVSKyxU4bSCAuVZujAB\n0q5aamAjJ/nwo5gv7UQeParimJ/5PGLxYpg7B5GhwlbCtNRIpaKGCkCka5Gd5hQvsZPjHKeWWiJE\nCBJkJSv5JJ+kgAIKKaSMsnHH/1Jo04wGOTYI66KLZ3iGfezjHOeIEGEOc7iP+5jPfAoppJzyUT01\nKQw4aZP2h2jvKeCxgc2RI0cwDGO4bMKlytB6L02O+gOG73jHcne3085xcYwqWUWjrKPL6qLDFSXM\nEH1uk0yS9Ax0IX9UzdyXj3JHK2T05JGVt4q8pevIv2EhoZJctaJNmQ6ZaiKuOX6apAXTpk3DtCyE\nXVvIsQvjZv+nPCW4DPX6xynexuAg3H8/rF+PLC9Xy7k5hKF50DW1EL7BG2xl67DIXznl3Mu9zGc+\nK1nJ4e7DPBV7iqzMLEDd/dt32yOUbm3+iMPrkO5kqVLgXn8d6+xZtJYWCt94g4NDQ1Rv3MjMP/1T\nZFERYvbsC4/LRicTVD8eJhJfLIyJxExp3VhScWNc6GDpw5in3xNnH4c5wSGa9pyhYfdZfMePsbZz\nkPlXFjD9qg+xfM61hGZmE9ALKKKUQCKA2QvRfujqg/4Y9MdMBmKQiAvig4JIh0YkosJqLS3Q1KhU\nxmMx1X2GobxTNrcoEACfT+JzW4RCkFuik1OokT8UpqzqMfLrXiervgp90QL44ZehYj4EHCUjSDHX\ndQMhDbTU8fXQz052cohDVIsa6qglLgaZznRuEjczT8ylgAJmMOMCL46zVtSlKKVggxwLCxeu4fZ6\n6WUrW9nHPk5ykjBhpjGND/JBFrGIcsqZxrQL9u1Sga9Jm7RJU/a+5PBUVVURDAaHazy9Gx1gF8y8\n1G2PBjZ26up4wKabbiqp5CxnaaaZDjqIWGHarVY6iWAZboL6dMr1GazoLqJ0xykyj9cxsyrOvx+I\n09QT4q7FdzLnj66BghCUFcPM6eBNCfihfAkGFkODQxx+ZReZmV5yc3Mv2JdxzbLSqyTAnj3wk58o\nQsqGDXDbbViLFil/gGmiazroHjro4AVe4BCHOMIRJJJruGYY6Mxk5vBPdCY66TP70A21CA2HPmzv\njZUCW4YxEpScPatCLOfOqUdjo9p+5ky46y7yZ8wgsn8/1TfeyMz169MBsGQyHZu6RCUenPyP4WOw\nVYYFYEG71cx+4yBVNNBCN+dramjZfgLz0BFKT8RZnDWHhRs/y7ylK8mqKCRUOp8AqbhZEuWEc0nI\nMfHmCAqxQ5BjpJCnMgG7uhTFxq4TaofVBgcVOb67S9LRatLeqVGn6/Q2wOALx5FtT2O0HcTb30Ym\n6/CX3kUwYyneF0tx7wS/SBAMWORme5hS6KawAIKl0BA6wVH9VZo5RyPVNNOEHz+XsYw/4nr8FDGN\nGZQyPe1IAoZSAEepMyuIY0dYJW8vfX8sT46OTg89bGUrxznOSU7SSCNllHEDN1BBBdOZPrIGGCNB\nzmQdq0mbtD98+70APE1NTRQXF+MdT5HvHZgNcOy2x6wUPgF7qx6bAQY4xzlqqKGJJjrpJEKEJppo\np52YNYDP8lBCKdOMBazUbiZENjnhLor2VFJ6ppP8IxGoH4LcRXDd1awMHubU/ldouvcm5lx7HUOk\nAyTCVFBHF5oKs+gaQ/EEJ09Xkp09vtLsyIOUChi4XAocnDmjSKr79sG0achvfgNzzWUITya6lQqI\n6To72ck+9g2TTyuo4GN8jPnMZzWrhxcLCyuVeeXB0A10oafHw3QAHGeZgZ4e9funTilRo8pKtaIX\nF6sw1aZNUF6OmDMHSkooKytDvvEGTSmRRhmPq+MZnTL3NsxeSO3xd/I/bDsrqzhqHqZWq6dN6+Wc\n1k5rsgF9Sy3TjtSx8rUByiMzKVzxANM/u4SZyxbComXOH8GUSaQGwlCtCykAg1SW+jCIc4bTbCwX\nCKjHeEdgh61iGHRGoOfx7XQ/vYdoyz76yy0G77uWWN5K+jxr6er3Ea6H1mMmslfH7HcxFIdeq4uE\n+zVM/QxiXjWtoSrqh3qxvFPI913GbP9dTPVPpSy4iGn5GfhywJcLZiHoejIl2KjhuQiIkDKtbeU0\np6dtWHNKKJgzOlzVQw+72c1pTvMGb1BLLYUUcgVXcC/3soAFzGHOiPaTJC+Zh2nSJm3SLm7vO9Iy\nQGdnJ6tXrx4Oab0bnVBSUgKoMgsXs9GgZvTiNtqSJKmllhpqaKaZLrropJNGGmmmmR560NHJJZdy\nq5QV1hKyRS65ehnF2ix159schX1nofosHDoKtSchNxNWLSF567VYixbhXrSMwt3TSZ44Qt+gCifo\ng4PoLneKf5KenKVlIYDBwUFqamooKyu7YL8vPJBkujJqJAJPPQVPP63Ixx+7C26+GT2/SJ0wJpzT\nz/IyeznCEQ5zGB8+1rOeO7mT9ayngIIRfWTzlIzUKTfQ14+VTJJpj7nHUZ/p6FGl3Fhfr55ratS+\nzZ0LN96oyCi2bHXK7JGZkZ1NMDOTjo4O9YGNBN6ijeW1Gx3KMDE5zgkq5WlarAaaaKJSr6PDSODD\nw9Sj3Vx+sJWiQ32UH/FTkX8bRdesg/mzYf06CKS8ctJSgFXTEJqGPvqynEg4zaHrM0LjR8p0UVaP\nSrXWeqJ4X95J6f69lB54DQry4I7NsGwFLBlVYyUGkW6dPd3H2BM5wZGeBmr6jjHUf56iXg8LvBWs\njW4mWTOdvuZFDPVPpb8PKgfhkADpTuLNUJyi/FxBMMsgEFBhNTtLLRRSj2BQRWQLClRW4MUdcTY3\nCoSVChsC3fTxMi9TKSp5gzeoFucIksXlYg23iltZxjLmMtfRisTEHAY5xu/HlDhpk/a+sPdlSKu3\nt5e5c+cMA553wwKBAEKIYY+C0/Vtv7+YxwaUpH0DDbTQQjfddNBBE0000EA77VhYBAlSTDFllLFI\nLiRbhiizSpiuzWK6NhvdXjPr++DoQajeBfv2kKypUuzS5csQm+5ErFqDmFuBQZpmPDsnlwxNoz8a\nVR8YBukGL7R4PE5HRwdXXnnl+B3jzJWOx1X46pGHserrsNZdgXH3x9FTwCKaiPCacYCT+ile5mXa\naGMRi7ibu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Jv36VhjMvozOwJph7QWLLh4m/39CvzU1SmPUXOz8iL19CiQVFubFn60i81q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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa8459e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyz.imshow(arr, cropBorderPixels=(5, 5, 1, 90), figsize=(10,10), title='Layout Plot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we will create a figure and direct PyZDDE to render the Layout plot in the provided figure and axes. We can then annotate the figure as we like."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But first we will get some first-order properties of the lens"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Paraxial magnification : 0.0\n",
"Real working F/# : 4.978219667\n",
"Effective focal length : 49.9999995\n",
"Paraxial working F/# : 4.99999995\n",
"Paraxial image height : 18.19851153\n"
]
}
],
"source": [
"l.ipzGetFirst()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Pf/lL+XmuuALuuENrt7TAz38ucbRoEXzmMyovnzhR+7Cnrc5B/5yLFSN4DAaDwTA+ea8B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\n9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mJKS0uRhw7RlBBplNGoOp6RKXPvwuyF1B7/VP6HbWdkFUfMg9Rq9bRpvZzV2mmNN6Bv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xH3OYwzShCWMYQx3qOC7no49QliyGRx+Dt97SepPsidMiX9l7eCpWrEh8fLzT9oLYvxHMmzcP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\nTwIeIYQQQpR7/w+6/4yxpjgz9wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xaa00cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10,10))\n",
"ax = fig.add_subplot(111)\n",
"\n",
"# Render the array\n",
"pyz.imshow(arr, cropBorderPixels=(5, 5, 1, 90), fig=fig, faxes=ax)\n",
"\n",
"ax.set_title('Layout plot', fontsize=16)\n",
"# Annotate Lens numbers\n",
"ax.text(41, 70, \"L1\", fontsize=12)\n",
"ax.text(98, 105, \"L2\", fontsize=12)\n",
"ax.text(149, 89, \"L3\", fontsize=12) \n",
"\n",
"# Annotate the lens with radius of curvature information\n",
"col = (0.08,0.08,0.08)\n",
"s1_r = 1.0/l.zGetSurfaceData(1,2)\n",
"ax.annotate(\"{:0.2f}\".format(s1_r), (37, 232), (8, 265), fontsize=12, \n",
" arrowprops=dict(arrowstyle=\"->\", linewidth=0.45, color=col, relpos=(0.5,0.5)))\n",
"s2_r = 1.0/l.zGetSurfaceData(2,2)\n",
"ax.annotate(\"{:0.2f}\".format(s2_r), (47, 232), (50, 265), fontsize=12, \n",
" arrowprops=dict(arrowstyle=\"->\", linewidth=0.45, color=col, relpos=(0.5,0.5)))\n",
"s6_r = 1.0/l.zGetSurfaceData(6,2)\n",
"ax.annotate(\"{:0.2f}\".format(s6_r), (156, 218), (160, 251), fontsize=12, \n",
" arrowprops=dict(arrowstyle=\"->\", linewidth=0.45, color=col, relpos=(0.5,0.5)))\n",
"ax.text(5, 310, \"Cooke Triplet, EFL = {} mm, F# = {}, Total track length = {} mm\"\n",
" .format(50, 5, 60.177), fontsize=14) \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Example of Ray Fan plot"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9fpRSPO95z+PGG2+cCeZO9LWbb+abt95KUpKP22+/nVe84qo5X9fU1MRnP/MZ6g8dwjAM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\nMjI6NTM6MDUtMDU6MDCo509RAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindow('Ray', percent=17, gamma=0.55)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rarr = l.ipzCaptureWindow('Ray', percent=25, gamma=0.15, retArr=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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7QSAQYNCgQa6F37p1a7p16+ZqHr799lvXwo5EzI2W3VZh+f1+11SVDm6G70zR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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa845860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10,10))\n",
"ax = fig.add_subplot(111)\n",
"pyz.imshow(rarr, cropBorderPixels=(5, 5, 48, 170), fig=fig, faxes=ax)\n",
"ax.set_title('Transverse Ray Fan Plot for OBJ: 20.00 (deg)', fontsize=14)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Example of Spot diagram"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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QYRSj8ODBj9XcpFAsYAEBAkSI4MBxwn3k58M551hLpfl8VmtRUZFVl5SUwT5D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\nFOKVpa+wZPESrr/+Bvbt+yZf+tKXmDB+Att3bKeuvp6CwiLA6ij9z3/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1LHv9dbKysrj8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\nNTM6MTktMDU6MDCj7SW7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindow('Spt', percent=16, gamma=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sptd = l.ipzCaptureWindow('Spt', percent=25, gamma=0.15, retArr=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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58MQT24OmEKL5SMAUcXa2kl2yxQbL+++HG2+En37a/l3U4sUQCMBpZ2B16IAv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\nBEwhhBAiCv8PQAUIqbr2bPQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xae08da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(8,8))\n",
"ax = fig.add_subplot(111)\n",
"pyz.imshow(sptd, cropBorderPixels=(150, 150, 30, 180), fig=fig, faxes=ax)\n",
"ax.set_title('Spot diagram for OBJ: 20.00 (deg)', fontsize=14)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Examples of using `ipzCaptureWindowLQ()` function in Zemax 13.2 or earlier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`ipzCaptureWindowLQ()` is useful for quickly capturing a graphic window, and embedding into an IPython notebook or QtConsole.\n",
"\n",
"In order to use this function, please copy the ZPL macros from \"PyZDDE\\ZPLMacros\" to the macro directory where Zemax is expecting the ZPL macros to be (i.e. the folder set in Zemax->Preference->Folders->ZPL).\n",
"\n",
"\n",
"For this particular example, the macro folder path is set to \"C:\\PROGRAMSANDEXPERIMENTS\\ZEMAX\\Macros\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l.zSetMacroPath(r\"C:\\PROGRAMSANDEXPERIMENTS\\ZEMAX\\Macros\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Timeout reached before image file was ready.\n",
"The specified graphic window may not be open in ZEMAX!\n"
]
}
],
"source": [
"l.ipzCaptureWindowLQ(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the above command didn't work, because we need to push the lens from the DDE server to the Zemax main window first. Then we also need to open each window."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l.zPushLens()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now open the layout analysis window in Zemax. Assuming that this is the first analysis window that has been open, Zemax would have assigned the number `1` to it."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": 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dPGyc3dU1OSaX1n/EOc2wfvcS5twvwdTj7uIp5/nuGq5zga7+DC5hwfw/HPeO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/Nv8A4bz8C+OP+JhpPxV+K+teCrn9/o/jD9hb9ib9q/8AbF+HOrXVr/oN1b+Gf2uvBX7Nvxg/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\nHjMJWqYbFYTFYapGth8ThsRRlCtQxFCtCFW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f+CWX/SNP9gD/AMQ3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\nF5s8sknlwRRwpu2xxogVQUUUAf/Z\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindowLQ(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open the MTF analysis window in Zemax now."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": 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fx71/4E/HT46ftB/tJ/FD4t6xriHTfD3j7Qf+Cdfj6x8F6T4H8KXnjD4BeE9L1TxlYax4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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindowLQ(2)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pyz.closeLink()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### Examples of using `ipzCaptureWindowLQ()` function in Zemax 14 or later (OpticStudio)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to do this experiment, a new instance of Zemax 15 was opened, and new link created."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = pyz.createLink()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zfile = os.path.join(l.zGetPath()[1], 'Sequential', 'Objectives', 'Cooke 40 degree field.zmx')\n",
"l.zLoadFile(zfile)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l.zPushLens()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Set the macro path\n",
"l.zSetMacroPath(r\"C:\\PROGRAMSANDEXPERIMENTS\\ZEMAX\\Macros\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now open the layout analysis window in OpticStudio as before. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": 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/bzdQkI/8gqh/77H1pp+H4vudY8Qa3q4/55/a/scY9RtthHuHs5alzt/DH9P+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d8JrP4S23xOmuNLTwPcxQzRar/ZrfMssixRjyvL8wMXYLtK7gc5Awar/wDC2fhQvjG4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\nguLi70i7k1KyAmZFj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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindowLQ(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open FFT MTF analysis window"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": 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Jb/43WlRQBzmrzakZtN32tspF0Nu24Y5Ox+D8nA61o+fq3/Pnaf8AgS3/AMbpmt/8fGk/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RRRQAUUUUAeY/DfwHYat8O/C19eaj4iuLu50q1mmmfxJqOXdoVLMf3/AFJJNdH/AMKz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\nFABXy/8AtQf8j9p//YMj/wDRs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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindowLQ(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, the FFT PSF analysis window was opened"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": 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PZ2kq21tpGm61b37sNt1BeNchCE+8uDatndg/MOPXsaACiiigAooooAKKKKAC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\nDeXHBFGikk4jgggj3E5coXwu7aLjOVN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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindowLQ(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A few others .... just for show"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": 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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.ipzCaptureWindowLQ(4) # Shaded Model"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l.close()"
]
},
{
"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
}
| mit |
mohanprasath/Course-Work | machine_learning/Machine Learning Concepts - Code and Implementations.ipynb | 1 | 1180208 | null | gpl-3.0 |
goedman/RobGoedmansNotebooks.jl | notebooks/SheehanOlver/12.ipynb | 1 | 8563 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture 12: PLU Decomposition\n",
"\n",
"For MATH3976 students: in the assignment, we will be using a bitstype. The following creates a new type of precisely 128 bits, that is a subtype of `AbstractFloat`:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bitstype 128 Float128 <: AbstractFloat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create a `Float128`, we need to reinterpret another 128-bit type. The easiest case is `UInt128`:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Float128"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u_int=rand(UInt128)\n",
"f=reinterpret(Float128,u_int);\n",
"\n",
"typeof(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can manipulate `f` by reinterpreting it back to a `UInt128`:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0x4488af451cad267a956f4096d9ef0c36"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reinterpret(UInt128,f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that it has exactly 128 bits:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"01000100100010001010111101000101000111001010110100100110011110101001010101101111010000001001011011011001111011110000110000110110\""
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bits(u_int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will need to access subsequences of the bits. In the following example, we decompose a 32-bit unsigned integer into two 16-bit unsigned integers."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"01011101000011011101100100011001\""
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x=rand(UInt32)\n",
"\n",
"bits(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The syntax `x % UInt16` drops the first 16 bits, and returns the last 16 bits as a `UInt16`:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"1101100100011001\""
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_16 = x % UInt16 # drops the first 15 bits, and keep the last 16 bits\n",
"\n",
"bits(x_16) # same as the last 16 bits of x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get at the first 16 bits, we will need to shift the bits right. This is equivalent to dividing by two and dropping the extra bits:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"\"00101110100001101110110010001100\""
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bits(UInt32(div(x,2)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But it is more convenient to use `x >> k`, which shifts the bits right by `k`:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"00101110100001101110110010001100\""
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_shift = x >> 1 # shifts the bits of x by 1, dropping the rightmost bit\n",
"bits(x_shift)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"00010111010000110111011001000110\""
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_shift = x >> 2 # shifts the bits of x by 2, dropping the rightmost two bits\n",
"bits(x_shift)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We thus get the first and last 16 bits as follows:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"01011101000011011101100100011001\""
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_shift = x >> 16 \n",
"x_first = x_shift % UInt16\n",
"x_last = x % UInt16\n",
"\n",
"bits(x)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"01011101000011011101100100011001\""
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bits(x_first) * bits(x_last)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(\"0101110100001101\",\"1101100100011001\")"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bits(x_first),bits(x_last)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Matrix norms\n",
"\n",
"Just like vectors, matrices have norms that measure their \"length\". The simplest example is the Fr\\\"obenius norm, defined for an $n \\times m$ real matrix $A$ as\n",
"\n",
"$$\\|A\\|_F = \\|{\\rm vec}(A)\\|_2 = \\sqrt{\\sum_{k=1}^n \\sum_{j=1}^m A_{kj}^2}$$\n",
"\n",
"This is using Julia's `vec` notation, which converts a matrix to a vector:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9-element Array{Int64,1}:\n",
" 1\n",
" 4\n",
" 7\n",
" 2\n",
" 5\n",
" 8\n",
" 3\n",
" 6\n",
" 9"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vec([1 2 3; 4 5 6; 7 8 9])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While this is the simplest norm, it is not the most useful. In a lecture we will describe which norm is used in Julia. \n",
"\n",
"The important thing for us is that if $\\|A\\| = 0$ then $A = 0$, so it can be used to test if two matrices are equal: if $\\|A-B\\| \\approx 0$, then $A \\approx B$:"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.507977739769666e-16"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A=rand(5,5)\n",
"\n",
"Q,R=qr(A)\n",
"\n",
"norm(Q*R-A)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.3",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
sgkang/AGU2014MovingDimensionsinEM | examples/Hydro1Dinv_1_realistic.ipynb | 1 | 582413 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Vendor: Continuum Analytics, Inc.\n",
"Package: mkl\n",
"Message: trial mode expires in 10 days\n"
]
}
],
"source": [
"from SimPEG import *\n",
"import simpegEM as EM\n",
"from simpegem1d import Utils1D\n",
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib\n",
"from IPython.display import set_matplotlib_formats\n",
"set_matplotlib_formats('png')\n",
"matplotlib.rcParams['savefig.dpi'] = 100"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mesh3D = Utils.meshutils.readUBCTensorMesh('mesh.msh')\n",
"sigma3D = Utils.meshutils.readUBCTensorModel('sigma_realistic.con', mesh3D)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/sgkang/Projects/AGU2014MovingDimensionsinEM/examples\r\n"
]
}
],
"source": [
"!pwd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x1 = np.arange(30)*10 - 300.\n",
"y1 = np.arange(30)*10 - 150.\n",
"xyz1 = Utils.ndgrid(x1, y1, np.r_[0.])\n",
"xc1 = -150\n",
"yc1 = 0.\n",
"r1 = np.sqrt((xyz1[:,0]-xc1)**2+(xyz1[:,1]-yc1)**2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x2 = np.arange(30)*10 + 10.\n",
"y2 = np.arange(30)*10 - 150.\n",
"xyz2 = Utils.ndgrid(x2, y2, np.r_[0.])\n",
"xc2 = 150\n",
"yc2 = 0.\n",
"r2 = np.sqrt((xyz2[:,0]-xc2)**2+(xyz2[:,1]-yc2)**2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dobs = np.load('bzobs_realistic.npy')\n",
"Dobs = dobs.reshape((900, 31, 2), order='F')\n",
"Dobs1 = Dobs[:,:,0]\n",
"Dobs2 = Dobs[:,:,1]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.contour.QuadContourSet instance at 0x10e45f290>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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kyWXXDBkWHTFRuK2JZlAxeAAAibBecJtKpSBXEUuZjiaXtoV8i55RuEuKelgx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\nktQCG25JkiRJklrw/xx6dVH/aPxSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e13cc10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
"ax[0].contourf(Dobs1)\n",
"ax[1].contourf(Dobs2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"meshType = 'CYL'\n",
"cs, ncx, ncz, npad = 20., 25, 30, 12\n",
"hx = [(cs,ncx), (cs,npad,1.3)]\n",
"hz = [(cs,npad,-1.4), (cs,ncz), (cs,npad,1.4)]\n",
"mesh = Mesh.CylMesh([hx,1,hz], '00C')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"active = mesh.vectorCCz<0.\n",
"layer1 = (mesh.vectorCCz<0.) & (mesh.vectorCCz>=-60.)\n",
"layer2 = (mesh.vectorCCz<-60) & (mesh.vectorCCz>=-100.)\n",
"layer3 = (mesh.vectorCCz<-100) & (mesh.vectorCCz>=-200.)\n",
"actMap = Maps.ActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)\n",
"mapping = Maps.ExpMap(mesh) * Maps.Vertical1DMap(mesh) * actMap"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig_half = 1e-3\n",
"sig_air = 1e-8\n",
"sig_layer1 = 1./300\n",
"sig_layer2 = 1./100\n",
"sig_layer3 = 1./10\n",
"sigma = np.ones(mesh.nCz)*sig_air\n",
"sigma[active] = sig_half\n",
"sigma[layer1] = sig_layer1\n",
"sigma[layer2] = sig_layer2\n",
"sigma[layer3] = sig_layer3\n",
"mtrue = np.log(sigma[active])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xc = -100\n",
"yc = 100.\n",
"dind = np.argmin(abs( xyz1[:,0]-xc)+abs( xyz1[:,1]-yc))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def circfun(xc, yc, r, npoint):\n",
" theta = np.linspace(np.pi, -np.pi, npoint)\n",
" x = r*np.cos(theta)\n",
" y = r*np.sin(theta)\n",
" return x+xc, y+yc"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xcirc1, ycirc1 = circfun(-150., 0., 250., 60)\n",
"xcirc2, ycirc2 = circfun(150., 0., 250., 60)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ind = np.argwhere(xyz1[:,1]==0.)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-130.0\n"
]
},
{
"data": {
"text/plain": [
"(-300, 300)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Tdig5EdWaLsWlGrgoWI2fyCAixUE1cGnEuwbOMZc2PMBF3MSNYQeTM1Gr6VJc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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1047803d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize=(7,3))\n",
"indz = 20\n",
"print mesh.vectorCCz[indz]\n",
"mesh3D.plotSlice(np.log10(sigma3D), ind = indz, ax = ax, clim=(-3, -0.5))\n",
"ax.plot(xyz1[:,0], xyz1[:,1], 'r.')\n",
"ax.plot(xyz1[ind,0], xyz1[ind,1], 'k.', ms=10)\n",
"# ax.plot(xyz2[:,0], xyz2[:,1], 'b.')\n",
"ax.plot(xcirc1, ycirc1, 'r-')\n",
"# ax.plot(xcirc2, ycirc2, 'b-')\n",
"ax.set_xlim(-500, 500)\n",
"ax.set_ylim(-300, 300)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig_test = (sigma[active])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# plt.pcolor(X, Z, np.log10(Sig_test))\n",
"# plt.ylim()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-300.0, 0.0)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Gh+uXT02loZ52rcfYWP1yr8ccr8ecZtdjamqKqaqFbd3a5M5aRAzsAKwCbgT+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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e667710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize = (3, 6))\n",
"Utils1D.plotLayer(sig_test, mesh.vectorCCz[active], showlayers=True, ax = ax)\n",
"ax.set_ylim(-300., 0.)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pymatsolver import MumpsSolver"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prb = EM.TDEM.ProblemTDEM_b(mesh, mapping=mapping, verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prb.Solver = MumpsSolver\n",
"prb.timeSteps = [(1e-4/10, 15), (1e-3/10, 15), (1e-2/10, 15), (1e-1/10, 15)]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Mopt = []\n",
"# Dest=[]\n",
"# # for i in range(Dobs1[ind,:].shape[0]):\n",
"# for i in range(1):\n",
"# rxoffset=r1[ind][i]\n",
"# time = np.logspace(-4, -2, 31)\n",
"# rx = EM.TDEM.RxTDEM(np.array([[rxoffset, 0., 0.]]), time, 'bz')\n",
"# tx = EM.TDEM.SrcTDEM_CircularLoop_MVP([rx], np.array([0., 0., 0.]), radius = 250.)\n",
"# survey = EM.TDEM.SurveyTDEM([tx])\n",
"# if prb.ispaired:\n",
"# prb.unpair()\n",
"# if survey.ispaired:\n",
"# survey.unpair()\n",
"# prb.pair(survey)\n",
"# std = 0.2\n",
"# survey.dobs = Utils.mkvc(Dobs1[ind,:][i,:])\n",
"# survey.std = survey.dobs*0 + std\n",
"# dmisfit = DataMisfit.l2_DataMisfit(survey)\n",
"# dmisfit.Wd = 1/(abs(survey.dobs)*std)\n",
"# regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])\n",
"# reg = Regularization.Tikhonov(regMesh)\n",
"# opt = Optimization.InexactGaussNewton(maxIter = 5)\n",
"# invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)\n",
"# # Create an inversion object\n",
"# beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
"# betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)\n",
"# inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest])\n",
"# m0 = np.log(np.ones(mtrue.size)*2e-3)\n",
"# reg.alpha_s = 1e-2\n",
"# reg.alpha_x = 1.\n",
"# prb.counter = opt.counter = Utils.Counter()\n",
"# opt.LSshorten = 0.5\n",
"# opt.remember('xc')\n",
"# mopt = inv.run(m0)\n",
"# Mopt.append(mopt)\n",
"# Dest.append(invProb.dpred)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# fig, ax = plt.subplots(1,1, figsize = (3, 6))\n",
"# Utils1D.plotLayer(np.exp(mopt), mesh.vectorCCz[active], showlayers=True, ax = ax)\n",
"# ax.set_ylim(-500., 0.)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# np.save('Mopt1_realistic', Mopt)\n",
"# np.save('Dest1_realistic', Dest)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Mopt1 = np.load('Mopt1_realistic.npy')\n",
"Dest1 = np.load('Dest1_realistic.npy')\n",
"Mopt2 = np.load('Mopt2.npy')\n",
"Dest2 = np.load('Dest2.npy')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Mopt1_FD = np.load('Mopt1_realistic_FD.npy')\n",
"Dest1_FD = np.load('Dest1_realistic_FD.npy')\n",
"Mopt2_FD = np.load('Mopt2_realistic_FD.npy')\n",
"Dest2_FD = np.load('Dest2_realistic_FD.npy')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sigma2D = np.load('./inv2D_realistic_line/invTEM2D.npy')\n",
"sigma2DFD = np.load('./inv2D_FD_realistic_line/invTEM2D.npy')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Sig_test = (sig_test.reshape([1,-1])).repeat(10, axis=0)\n",
"x = np.r_[xyz1[ind,0], xyz2[ind,0]]\n",
"z = mesh.vectorCCz[active]\n",
"Z, X = np.meshgrid(z, x)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"time = np.logspace(-4, -2, 31)\n",
"z = mesh.vectorCCz[active]\n",
"Time, Xtime = np.meshgrid(time, x)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"SigMat = np.exp(np.vstack([Mopt1, Mopt2]))\n",
"DpreMat = np.vstack([Dest1, Dest2])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"SigMat_FD = np.exp(np.vstack([Mopt1_FD, Mopt2_FD]))\n",
"DpreMat_FD = np.vstack([Dest1_FD, Dest2_FD])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"DobsMat = Utils.mkvc(np.vstack((Dobs1[ind, :], Dobs2[ind, :]))).reshape((60, 31), order='F')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"DobsMatFD = np.load('./inv2D_FD_realistic_line/bzobs_FD_realistic_line.npy')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sigest3D = Utils.meshutils.readUBCTensorModel('sigest3D_realistic.con',mesh3D)\n",
"# sigest3D = np.load('inv3D_realistic/model_15.npy')\n",
"sigest3DFD = Utils.meshutils.readUBCTensorModel('sigest3D_realisticFD.con',mesh3D)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BrYCrgCOBt/SOm5RkM+AQ4BHARcDBVfXeuY5ZkqSlbKQkQVX9Ksm2wKeTPBP4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\nAuD/A7o4Q0p8qVSbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111ad4510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
"vmin = np.log10(Utils.mkvc(SigMat).min())\n",
"vmax = np.log10(Utils.mkvc(SigMat).max())\n",
"indy = 21\n",
"indz = 21\n",
"dat = mesh3D.plotSlice(np.log10(sigma3D), ind = indz, normal='Z', ax = ax[0], clim=(-3, -0.5))\n",
"dat = mesh3D.plotSlice(np.log10(sigma3D), ind = indy, normal='Y', ax = ax[1], clim=(-3, -0.5))\n",
"for i in range(2):\n",
" if i==0:\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Northing (m)', fontsize = 16) \n",
" ax[i].set_ylim(-150., 150.)\n",
" ax[i].set_xlim(-300., 300.) \n",
" ax[i].set_title(('Depth at %5.2f m')%(mesh3D.vectorCCz[indz]), fontsize = 16)\n",
" elif i==1:\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Depth (m)', fontsize = 16) \n",
" ax[i].set_ylim(-600., -10.)\n",
" ax[i].set_xlim(-300., 300.) \n",
" ax[i].set_title(('Northing at %5.2f m')%(mesh3D.vectorCCy[indy]), fontsize = 16)\n",
" cb = plt.colorbar(dat[0], ax=ax[i], orientation = 'horizontal', ticks = [np.arange(6)*0.5-3])\n",
" cb.set_label('Log10 conductivity (S/m)', fontsize = 14)\n",
"fig.savefig('./figures/sigtrue.png', dpi = 200) "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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6xPWHnsYu9szjFM3MbADcWt3qPTEQBemcEynzCkGZW+Av1f3+lBUL1kp6FfAN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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ea8ae90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
"# vmin = np.log10(Utils.mkvc(SigMat).min())\n",
"# vmax = np.log10(Utils.mkvc(SigMat).max())\n",
"dat = mesh3D.plotSlice(np.log10(sigest3D), ind = indz, normal='Z', ax = ax[0], clim=(-3, -0.5))\n",
"dat = mesh3D.plotSlice(np.log10(sigest3D), ind = indy, normal='Y', ax = ax[1], clim=(-3, -0.5))\n",
"for i in range(2):\n",
" if i==0:\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Northing (m)', fontsize = 16) \n",
" ax[i].set_ylim(-150., 150.)\n",
" ax[i].set_xlim(-300., 300.) \n",
" ax[i].set_title(('Depth at %5.2f m')%(mesh3D.vectorCCz[indz]), fontsize = 16)\n",
" elif i==1:\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Depth (m)', fontsize = 16) \n",
" ax[i].set_ylim(-600., -10.)\n",
" ax[i].set_xlim(-300., 300.) \n",
" ax[i].set_title(('Northing at %5.2f m')%(mesh3D.vectorCCy[indy]), fontsize = 16)\n",
" cb = plt.colorbar(dat[0], ax=ax[i], orientation = 'horizontal', ticks = [np.arange(6)*0.5-3])\n",
" cb.set_label('Log10 conductivity (S/m)', fontsize = 14)\n",
"fig.savefig('./figures/sigestTD.png', dpi = 200) "
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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fqIHhU1ak4qZPmpOKeyWXpuKO4qRU3PidV6bijr8tFdZ2Lf4HPeu1L+pBpa9I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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1119a3cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
"# vmin = np.log10(Utils.mkvc(SigMat).min())\n",
"# vmax = np.log10(Utils.mkvc(SigMat).max())\n",
"dat = mesh3D.plotSlice(np.log10(sigest3DFD), ind = indz, normal='Z', ax = ax[0], clim=(-3, -0.5))\n",
"dat = mesh3D.plotSlice(np.log10(sigest3DFD), ind = indy, normal='Y', ax = ax[1], clim=(-3, -0.5))\n",
"for i in range(2):\n",
" if i==0:\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Northing (m)', fontsize = 16) \n",
" ax[i].set_ylim(-150., 150.)\n",
" ax[i].set_xlim(-300., 300.) \n",
" ax[i].set_title(('Depth at %5.2f m')%(mesh3D.vectorCCz[indz]), fontsize = 16)\n",
" elif i==1:\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Depth (m)', fontsize = 16) \n",
" ax[i].set_ylim(-600., -10.)\n",
" ax[i].set_xlim(-300., 300.) \n",
" ax[i].set_title(('Northing at %5.2f m')%(mesh3D.vectorCCy[indy]), fontsize = 16)\n",
" cb = plt.colorbar(dat[0], ax=ax[i], orientation = 'horizontal', ticks = [np.arange(6)*0.5-3])\n",
" cb.set_label('Log10 conductivity (S/m)', fontsize = 14)\n",
"fig.savefig('./figures/sigestFD.png', dpi = 200) "
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ncIK7Fgtrh9c0vE/Dz8ABYHi+j7Hnzx8T/3yFdQ/cALvs+T4AMwA2Nev7uo06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\nuFSRwX8CuwHk1nJdIuB+b4d7vRvu92XhX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DVVsbVRLO/9ecJk6k7unKadusMVdYfGxfpU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\nzMwA+H+bhBicrbTJEgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111aca910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
"vmin = np.log10(Utils.mkvc(SigMat).min())\n",
"vmax = np.log10(Utils.mkvc(SigMat).max())\n",
"ax[0].contourf(X, Z, np.log10(SigMat), 100, vmin = -3, vmax = -0.5)\n",
"dat = mesh3D.plotSlice(np.log10(sigma2D), ind = 21, normal='Y', ax = ax[1], clim=(-3, -0.5))\n",
"for i in range(2):\n",
" ax[i].set_ylim(-600., -10.)\n",
" ax[i].set_xlim(-300., 300.)\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Depth (m)', fontsize = 16)\n",
" if i==0:\n",
" ax[i].set_title('1D stitched inversion model (TEM)', fontsize = 16)\n",
" elif i==1:\n",
" ax[i].set_title(('2D inversion model (TEM)'), fontsize = 16)\n",
" cb = plt.colorbar(dat[0], ax=ax[i], orientation = 'horizontal', ticks = [np.arange(6)*0.5-3])\n",
" cb.set_label('Log10 conductivity (S/m)', fontsize = 14)\n",
"fig.savefig('./figures/1DinvTD.png', dpi = 200) "
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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B4F8A/E1+1ZVHlnV5v7Jq+HlhPf1dKuBrAE4uqNf8/Urj2WXVHrlXWb0nlFXD\nWgAfPg14+Xvz6t1V2B8A7FlYL/frXnHUvX5aVK972hl46nsfnV1vsrq/yOX7eFRRvUvmH1RU75Bl\n1xbVA4BrT/sw7v3el2fV+cW1DyvrbHlZNQA49F7/W1RvT9ydXWf2iuux8QVvB4ZwrRdhoIzqTm0S\nxb8euy0HQN8ZP72+eduIFgLmrG3mvmnCecB6Nihnm7s/Z/uEZWPPQednnaqe97XKmZv1Ncw2VZXf\nE/0fkjmgZ19wdfZYDexxvK4XOhZzjkxb3kwgs74dFiP9Nkw/Rss6EM1zb49lCtron6oeG62xue/T\nBLRT0zH6eY/jb8AY9sQkFA5HFx3MAwA6nqv0qtV7YN3xB6DTUFTG9X6sBQB0MYouRjGCSWzC/uig\niw5GsAE3ooP5qp+9vP0YOtiBDgjujZgZZ9fZ3q16Mq/rz6OYwgQmAKzHXujgbpi7kQ6WY32l+Oix\ncwrYODog6xzNAdgTY1iO0b234f7Hm2OZwyYQuliOTdgLq6sxTlVvQn88Y+hgBp1qTP3zAnRwNzrV\np18fPxrnwBzrJPbCJuyHKUxU20y7XUxgqiqlz3WHuZr22++3az+7r1Mw5cdWr8D64/erHXsXY9jb\naVN/nvZh2xrBDFahi9XVsRi2YQPuQQcrsD+WoYNV6OCe6mj3sMcyrftZBmB2qgPMrQC2A9gC/bVb\nBgC/g15FaQ30XfGIfjkJ/d0y36+Rqp5hDRyxYBbA1qot8wgibu/tUJ3H9QAOyaxamiA7tx/D/Qvr\nRT9LHvaAVrgKjPxlx5d1mToZ0BpfL6y3F8ruDfN+D/scV1ZtYqSs3sFl1bAfgLHVwGGZ7/8gv2aF\n2kfpMY7dt0xom1u9Egccv2+8oMP+tZvqdC4v/J1ZMX9UUb2xZeUW94rVoxg7/rC8SnsV/sYMYBWP\n3LfsN3/lQMpX+9d6EQaEFjkAwEMAfKW5axb1G1+ba6ANy0mEBYIpa5t908yVcff56tr4tsf2xTBj\nuA39Y3T3ueWnqrL2ORtBmhbgwl37fQKK3X+sjk/EmZxjDHyw21y66CSV02Za31DN6adTmZE5mPKh\n8fnajRm/OeNZj03YhPXROnXjvV/WfY7RQbdnfIfqpb5vbp1cOujW6k1gqje+DmbQtSyH2Hi6jpVh\n2jYiA4Dq09VpjNWIAjWivxEj9e+i+7vlft/N/qzv/OqcwoIgCIIgCD1EGBBa4gAA9wHgU/VmgamR\nxqbei9kLhKwJAAAgAElEQVStwM0Haq8CWyCYQtPw9Bn/3P9Tzjb3/xiDCAIuMZe2kEHOjiOmFFjn\n2/UWSMV37m1RgHsNsAZpCq6R6TMgO9VM/IQz48uX7bKvff3H2nDHmGLclxjCvnFw/RnDdqJ6kzhR\nINd4B9Brz/f/QmMfjzmn7utUXHGg7oHAv1+sKBBlbf1fThzkxAFBEARBEIQFQoQBoQWMKPBQaGPU\n4zLUsGFt99hr9PPNR/fFACMQxOwQd+aaM/45gSBVHGiTlHCIEMleAyO1pyK4c8+FEJjn6vXIRJkB\nahObmY9tz61r95tCigdByv5S74UOuliPTb32uXGHvAUGFSkGfX8NJca8S8hrIG0MY9Zr7S3Q7fmi\nWGEPKYIA+1tlxRW5nlF2HZ+nEMsalLuAC/nsQL671pp4EZbxwnp7xIuwHDxAf6vK6pd4vgHARVxu\njRRK80eWuWkvOCsKL/SFuQL2/dMbiuqtxyZc+4Hbce8n/jir3u0D3KzdU2jmbCjMEfIUnFdU70Lc\nhOfh89n1Drnm5qL+nnCf7xTVu2TZA4vqrUVZ4vy7sCdOx1V4O96SVe8n9/vvov6mBvisHVwY7r+8\nIOzsemzFO4p6iyPCgDAgligwMqJf3ngKsGMEmHWv/vb/5kfiNgD/A+Am9MSB2YcCs9WFbgvis9zG\nyE/xDnBDEAY11GPYYx8/paz+FtSPjxUHmBsDsynFW4AL4eBEgSn4xQHndY63wImnHNjYluKe7noL\nxAQBYyhz8e4pxESAnO0p5+UZp/A3++nhFrzXRsgjow3hwLRrCyjcM1c2BfN58XkN5I91rPdsRAF7\nbD5mp6p9WxIupe73kfPGEXFA2KV41s4ewCKlLA57KbD3KSft7CEsSp52yiCzOLs3jztFkrwvJCIM\nCAPAiAIHAzj4FJ2o7pqRyoD1CQJGFDD7ZwF8r9r+ZODmkaa96/MesI1f1xvAl4/ArZ/jIR0ry/2O\n7WsJAzERImSY14hcTHw5DNxHjJAo4GzrjOfP/j6cEQaAsFFmu7Snzsqb+eCcfrg+uJnuHMEhxQie\nQQcnebSkkABijy0WRsAZ73b73DYfucb9IOLAQ045pFZyUK8B3f9YLYQg5C3QEwRyMb8LvvAn7+/K\nCOrhCEYQEHFA2Jn80c4ewCJFhAEfe5/yxJ09hEXJ00UY8PJ4EQYWFBEGhEKMKHCfvihwLJqzx9dA\n5xaoeQ+Y0IHLPW1fXpU5GZg90ON26GycHemvLjABv6cB5zHgm8Ergetz0Ha52UNfSIHv2jLI76or\nSHDnrCfMaNdLY3wPOx49lOSQM4rH0EVnR7e/fKNFzsx+Sk6DWHuDuuP7RAl7m9uXLYrMOKJATAwY\nxMVuGJhj5rwGOr2VINIEAlsMCHkLZAkCte/nSFNMc8VL93/7Oxt0vRZxQBAEQRCEwRFhQBiQahbL\ndUG3XWJ7tqHJJ2BCB0LcBOAb0Ksc2AkN7Tgl+2Z4DTBbjcUWCXzY4kHJDDoi7dv9pGyz97kxx779\nLq4o4IYQpB6Xz8XZ3e8xdPrG6EyvSooRHJplz/EIcJ85o7izo4vuyrgLfU5SQ0PItb2t2HwArOeD\n2xd37Hb9mcA557BDN1wvBC4Zov26zeSLQF/M4DwN+u/BjLVtjC3jvm7sq7wFoqLAlPOcAye+ZbVj\n4tklc6EgCIIgCGWIMCAMyFYAB/bXurdvjqeg71N7s13GUyAmChhM3gFz02sLAbdZz2uc12v7OQo4\nbM8CQyxEIQXu5r6kLU4ciHkN5IgCnBDCiSK+Gc3As0k8WJIJv9RVPSQKGIxh3NlRrx8yjkOGbogU\nI9gdo89I9fVpjickDvjCJgwzTj8piQBLRAF3G+edUOqVkYIrEoS8CHKFkiCcV0/o+2VvH66jjSAI\ngiAIQgMRBoRCTI4A6JvfLdD5BdxZs5q3wID9NJ7d1zbO8mC25cy55fqM9qnIfh+looBdPiRW2PtS\nRYEST/BMccDOL9Dm7LjdHrdUoK+/toz5UDuu8d3WzHgIV+Doruw0xhHzKAi276kbS2To2+eKHT5B\nIDWpooFb2tINf2iKEGk5CIb2Poa8kkQUEARBEARhJyHCgDAgtwFYq/MI2De0NW+BWfQN/NxYWONl\n4G7jXrtl7OWiXKEA2qsgNGsHz76U7TalodlcWAbnNcD1FRIFBhU5uHYnAEzO9Wap3ZnlNkQC15i0\njcCQgZljIPtc06P1jKG+suniPogRzDGGLkan53v/bxtfVhcKmPwJKaSGPqQKMVwogXt+c7wUQvti\n7xknEPjw7R+Z6KblGGh8X5jfHlOudU+B1YM2IAiCIAjCEkWEAaE9bCPW/I9Z1FciyMWICobbnH1m\nmysEpHCgXwxwQw1yKK0XazNmROSIAr7wgVD/QNBrYGTCNgRnWk88yM0M57izu7PsKf1w/9uYZIbG\nUO/uk+d9MOYckwtn8LrH4RMJ3BwKubjHkXWuPaJBTCwJ1ef2hdqx+zKfRVsgsOsPHV9kUyicaQKZ\nKQPWANicU0EQBEEQBKGHCAPCABiDfKtePWAL+gZrI7cAFwaQ04+BiwOYdcq5IgGXtXuNrmdyEdgZ\nwF2BgzOcuZt4TkxwjfJcW9mtE1vf3CcKxIz/lHH62qxWIuiM894BbXkMAP5lAl1CSffaoiYKVNpX\nZ7xbm7HPWekgtU8A3sicUfRFAow3EywaUmPpQ276XBn3f84zIGUFhFgfOeOz99vLGqbSGe/2EhAm\new1wuIlOQ9heQkBkVQIgXxQV8liJ6NKwDdYV9nWfwnoHl1XbGauk3VlYT80VVjy4sN4ehfWOLKs2\n8vCyen9XVu3YV/+4qN5L8bGieqWTBVfh8KJ6ALCx8L3fE3cV1VuHW4rqjWB7Ub3SKN0DNpVVPGD0\n/LIOS6OJd5RVe+y+PyqrOIjT3bYB6mZy6ZXAO4bUtggDQiHu8oNWOMEWMN4CptygfcXKpfRhCwUH\n6ifb2HaN/FLvAd9MvWkzpx0upMAVL1JEgZBQ4As34LwqHFGg7i3QrT3aJMd4jGbtr1YmMAkIU13p\nTds1UeAOAKur2fvxLmY8BrnrLWCHIADNJIRc/6PT87o/G3Mxs75uo9BjAdK9B3LDCULbuVCC1HZS\n+wDSwhE47wE3z8BM9YldMDgvIIP7++BbnhRAXRQYH3xcgiAIgiAsSUQYEAbEmp03SQjN695+22Mg\n1cgvxfUecLFFgYDXADCYOBCL6S8RCEJ95YgC9v8hD4fQNkcQMN4CJTP0bYsHw4YVBdB/HsU8xvbx\nJyTMOV5bHOgJCZzy7goF6I8FQNB7IJWcVQJSQglyhJjQPk4o4HIPNMMYZnrbdxohMaCBO7XrekUB\n6Su+CIIgCIIg1BFhQGgJa9lCALy3QKnHQAkp3gNm/1oAI00X/VLPgZgowPWR0qbPa8DtyzX4Y8Z/\nrjcEIwoAtqeAzi8wTM+BGK5AkZpfoFaHGXNMFAAArK36W+mfza55CzCYs+a63vfyCXiEAB/GeyBF\nHPAZ7Kl5E3w5FGbQFAVyxAbuf5eUcIKUJIShfQOFEwDpeT1Y1oIPlTJI8kFBEARBEMoQYUAYAHOD\nap6t2ffe/oUWBGyM98BN4GfbLGHAdmRwhQBXHCghVD8kDnBjcbE9HWI5DlLKcUz2Yzs5QWAhGGa+\nABefKMByB4Bp9L24t/Zfu4n23HZ7hr6Vm8AXTtATElJFAVOuwFZMTfAXqsctn8iJA6E2QttC/fu8\nBsz/pqxv2cJG4kUrzwDgEQfY79Da+v6cXB5teBMJgiAIgiAkIsKAMCC2OGBugt2VCMxj2GEEIWah\nxQFXzAD0WCuvAYD3DEjxFhhkJj63rJ2YzK2f6jEQKzfZTPTkEwVMGMHO8A5IwcyU27P0ZpudiC86\nM+6b5WdCuzs7dK4Bt82Yt4CP7soORnGHNvRTxIGWJo9jM/BumZCAw4kDg/SfQug97WAmsM9JnGjy\nNViJCAFogWByDti4wpPDY8T//ea2Tzn7GslG3XAow1oAN3g6EgRBEARBCCPCgDAAxl3f3KQaA3ur\ns31niwKGkDhQ7c4RBEpm9FJDB3z13Ge3TOr/ISEhUxDgyMkWX0psWb4ZdBpGqluWy86f5S1gjG8n\nlGB0eh7bxpdhzDKAS3MwDIPScbjnKzfhoynjS/YYarsE39KIbs6HlHGEBIKBQgtiTMLzm+GKAgCw\n1/DGIQiCIAjCbo0IA8KA2LH89vNi8RSIYecZQH8mvtRrwJCTZyClnVAoAWfkszOXnrI2EVEgFEtu\n8gtwDCoOlOYMsA1Zt43UJfu8GA3Mhpmlb7il73DyBQTwnrNUr4GWaUvYsMWBhaDxHqBbW7awU40n\n1gbgFwjYxJ9t/fS12ZYgCIIgCAKDCANCC8SEgcWE6zVgqJInAtp113bT94kEpeQkHOTquHkPzLZU\nDwG3vHlOFAXsLPk2ZrtvreKcWPUczLKDMUqFgGRjOOC635a3wLbxZRjdGhcUSsgJbxh0hQOgf14H\nFmgicGELZtnCnFUR3M+vKxDMYu/wcqCDUluykPMW2BkL0guCIAiCsLsgwoDQAtwSgYvZW8AnDpjt\nFTkeAjZtGAWhmOSY90Cqx0CoHzRFAU4QiGWN5zwFBhUI3Bn3bePLitppjZAgUK1OYIzfHOPbe37W\nQnsLcGEMGWOr9cWMy+fRsG18WaP8IEJBTDAZhnBQT4DIJyDk6gB+gYD/LhUY63Y7je+4r71q+0oA\nO/K7FGKMgU0kEuRehX0dWVZtQ2F3BxfWW1lYDwDuKaw3tSZehuPOJ5fVK72WH1tY79Fl1R7wvJ8W\n1fskXlhU7+izry6qVxrptOX/pP1Gc5yHpxTVuxnri+rtWfgDvLWxXnYa+x1U6DpYmo5mU2G90v5u\nLKy3rbAe7/SaxnRhvZKxlvaVgAgDQku4ngGLVRRwcZJ4mTwDrteAoa1M4SkrEdivQ/kFJjwPX5tg\nyjjeAjFRgBMEfGEEPkrCC3xu+KleA7m06TqfSspyerVQgoywAu48JYU2mJCJtfVyRpSJ5XsIjcfg\nq2vCDlyC58ftx/EaqIcV6ASEvn7cMbACwXjlMQDkL2nqbne/243fCTuGZa27UxAEQRAEoQgRBoSW\nsHMN7Aq4ooW1qoLtKeAa5DEyZuSj9blQAi6kIHUsvm0ZooAvx0CIkIdAG+EFJtGfjc/Ac+PMQ5Tm\nNfBh1/cZ4SHjNEhhzgGvKODmTnC3V18V12sjVaDhziW3bRhiT68/J88Ah/0ZcEMf2KSFNbENwM3Q\nk/mT8It2dl2fF1BjZQKgEUIwAmA4USaCIAiCICwBdrIPrrB7Ya9SsCt5Cxgca8jcjLflJeCSs2qA\nr67PWyCUkNDXJvyJBu2lCN0lCTuYqR5db34BAL2auft6XgquEbsVtbcsZri77efMOHsxttlqDLw8\nYLbw4PaX2b/3fNpfgzs8D6fs6PR8r53QcXR2dGv9ug/fGBurSxS8d5yo5fu8jjk5IVyBzPac6e2z\nBbZBBEGOmJepiTAYbroGQRAEQRB2Y0QYEFpmFruGKGBwEyRWYzf2gj1TFxMIBvUW4Opxr1PbTREI\nLGPGiAI2JmmeXwxohg/EZuJjAkESjoYTcoN3+7P/942lzWUFB/U0COIKEgPkFKhhBIDQ/wbGuyDW\nvu/94kQCnzhQCvf55Ix+sz20EkdjTO73KyXFgE/4C/6eMAkHh5HwUBAEQRCEJYMIA4IAoCYOLKSu\nETL2Q6JAyFPATT7IGRmTcz1RYGSiWxMFXG8BG04McI2nVHxGue2XAHjCApzQai4BYUgQMP9zr719\n+vqzx7K2uT/VHb4R+++c0xl00F3Z0W2vdfp1xQH7f6uc6WOYLvopxBJGtp1QkvusuatsuAKAvT30\nvxHOasJaKGQgVMYn5hlGen9QCyEQBEEQBEEYEMkxIAg9rDwDAJ9rIJXSsr5ZwlDywZR2maUIAd5L\nwOB6C9iCACcETGCqsd3nAeAuHRfa10v2trKDzo6uXrJver4hDhhj1zXqQ14IbkI6u++ZKiGd276Z\nwTbGqzsWs901vu36tboWbvy6b2yNc7AVdTHArFpgCRXceBrn08z+5+QsiIgh9nk0fXL4xAD3fe06\nz4bUzx633SQgdNsJrbrhvied8S5mJ/b2G/spQoHve23/DgBoiAK2EMjmIxAEQRAEQQgjwoCwhOFc\nA7YCODC/qYT4/eL2zP9c8kHzzD08YgDgCRsYj8/6+wQBjpBhGxMD3O3GuOQMS9ugjBmPueN0xQGg\nbvTaRj63n+3POgafiOAbJwBgJSMu2AIBEBUF3LGw4oCNKxQEQhZcUcA8u+JAildAqijg25YLJwq4\nHjJmecPg6hE+McAnEHDfa8OUU878bLmigCAIgiAIwgCIMCAscW5Db6nCHrPA7Ej/xtteujDXcyCH\nkBHBiQKh2cVI7gADJwb4wwj62zkxILbqQKoYYO+z69vGpTFkOcM6RxSw6+SIA716lhdAjns+J3Ck\nrEZgiwOmX5/nAhAWBdyx9MQBQ4oY4HgLcKKAOZ+pngNuO419kfc3J6zFXpnA9dLQ22Zqber9faGg\ni7FaiMEtk3PARMJl1RUSQ99r11tgBLymOenZLgiCIAiCkIAIA4LAigOFtOUtEPIYgPOayy2AfEGg\ntt8TRuDzDkhJOOiWSxEKbEGBCykw/wN1w3rQ2WNOHAD8CfBKYvZjddjl8NA/NuNBAfCeCznjqokD\nmPcvV5iILQr4xJ3UsbreAu7rRjvMZy3EBKY8YQQzzDZXsGom36warQQ66//4QJpeA663gNnmegvE\nVi0QBEEQBEGIIMKAIADwigMpHgJtehD4Eg6megxU3gL2soOpxAwpVxRINbzc2XhfG9w+ri4XUsAZ\norlw4Q5cAkAfg2TNT/EW8NWxBQKgnwMhV6yonVMTUsDlGnA8B3wCDZfs0RYHUsbHhRCkegzkCAT9\nOiZMoC8KuJ/7KUx4RayRiSrPAEfq70SKQOD7XZLlCofEKuSf3IKQNADwfHyiHF5Y76jCeoWHBwDV\n12zh6u1XWO8+/nC8EIcedFVRvaNweVG9v8QHi+od/eCri+q9/2dF1fCQsmp46BUeATaBJ7ztO0X1\nLsIji+rtwJ5F9TZh/6J6K/ffUVRvw+rflfV3Q1G1cmvznsJ61xbWmy6sBwC/Kat2d8E5nSs9LwmI\nMCAIPczKBJV/9CzqGb9jIsEgAoEvR4ErDtivOY+BihSPgBi2twCXuZ0rn5LwrSTXgL3PZ1hyM8q5\nqyWkiAM+QrPhbePOwgOouekXt7uyA4x3g8s/9mBWO3DH546VCyvwEUoimeMx4Pvs+bA/70YUsNsy\n2+wQBKBancBOQOjLJWCYnAO2rOhvD4l/rkBQa8faVn7/LAiCIAjCEkeEAUFosBXa6rFUgWHmGZhw\nns3rUJIyXyyy7S1QaKSaMAJue+h/sy02Yz9IrgGuXzeEYFCBwGdYpma9B9K9B0o8Bbgx2AJBTv/u\nWGo5HExIQWCFAjd8YcaZ0Xdn97k4frsuh/t+puSQ8PUT+2yGRAH7tS0QBPF9tw22OODWc0OHzHZ7\n1QEJIRAEQRAEoSXaXSx6J0BEpxPRxUS0nYhu95Q5iIjOI6JtRLSZiM4kouVOmaOJ6CIimiWiG4jo\ntQtzBMLOJSFbV+j+n7vxT8WXS8BXhnt2vAVGJvSsZcwQTjWUjaFkG0nmkdN2t1azORMc2+dus7Pd\nc+V8AkEOqePkjNWYwT9jHUOI1HPp/m/aTx2HfT5rs/9rPRUZbwGfQMMJBW7fsXHmvp8p4gGHTxRw\nP/Pcd6C3bXIu/t3O9TziPAQmPft3U+RaLwiCIAjDZXfwGNgDwOcBXAzgJe7O6qbgPACbAJwAYD2A\ncwDcDeD0qsw4gPOrx8sAHA3gLCKaUkp9dAGOQdipBJIPuuEEbZFrNLieAq7HQOUtYMiJq/bty83u\nDvDGNEeq1wDngWCHFLjlfXHt7msfOaEE3LGmuMunJElM6dPuz67nejhwngScIV47V76QAiu/gG+Z\nSM6Ad9+H0DFyde1tuR4Dsf7c7Zwo4JZN9kTxiXk2xmuA+04DfDiBTxQojcHeNZBrvSAIgiAMkV1e\nGFBKvRUAiOhUT5EnAjgSwOOUUrcCuIyI/h7Au4joLUqpOQDPhz4XL67+v4KIjgXw1wDkZmFJ4Fm2\ncBiqQGqG8l7Gccdw4HIMVKR4C8TIdUNP7W/KOXDbFZsz8jh3cJ8R3IYg4L72GYE+8cM1RjlxIHXl\nBG6fz+D2jZUzXn2CgPu6EVJgs7aecJATBVwPBnc8qTP5vvZSPAbcz4+9jfNE4QiJCZxg1YGVgNAV\nAZzvtAn5mZ3q+L/jAC8QuOFMXJLC3Qy51guCIAjCcNnlhYEETgBwWXWjYDgfwIcA3B/AL6sy369u\nFOwyryei1UopT4StsHthkg+uQT/PQMvEXIi52UTz7DMcqv328oSlMeY+fK7TqUxhomGgu0IBkCYW\nuPs4g9S85mLMQzkN3LK+PrkxmLKcOOBruwROFOAEk9jsdtS7YyWaqxSsba5CYOqGRBp3nDHRxx1X\nyCuEw+cxEKpnf/Z8n3X3PHPjbiQgNN9XT9jRyETXLw4AvECQGpawtJBrvSAIgiAMwFIQBvYDsNnZ\nttna98vq+beBMnKzsKQIhBbEEg/a+7jkgLE6zP9Rw8H2GLAMeJ/h4sMWE3Q79fwC9j4fMeMrFEIA\n+L0KuHqugcyJAr6Z45TxpXgMhDwNYn1wfaVijjUkCsSEDe4YbGbQAVaiH1LAiAJ2skF7XNxre3zu\n69hxuq9d7M/NBKa877vtMWDDnS9fKIHvM9zBDC/IMaKAyQUCAN3pjv87btUJrlYgAHKtFwRBEISB\nWJTCABG9E8DrIsWOUEqlLsJKkf0qsR2HbwDYy9l2FMoXBhZ2aWKJBFPq2NstL4Cg4VCVM4bGmCUO\nAGnx6jYxwz+0j2vTeAt0MQazPnxoDJxXATez7G7jhAK3TM65GEQQiAkSueKAz4U+ZvxzM/Mp7dba\nrPIN9OowokBoVj935t43Trs9ztvEMIWJoDiQQsp3IPT9uWVyDphYwc/sT9bXRu+Md/3igD6gen0j\nCt70WeDqz9Y7v3vXsmt3nWv9B9FM4PA4AI8va04QBEEQEvjcXcDn765vmy68kqWwKIUBAO8GcFak\nzHWJbd0M4MHOtnXV8++t5/0iZRhOhs5vJCwJ7CUL24YLI3DwGg4Vrrt/zE2bM35KXNxTZru71U11\nl8mOFhILOug2jEDXMPMZ3L44++bY4kY+169vDKYsN6MfqhMy4N1ytqEc8xqIeQ+ExAGg7kkSEwXc\nsdlGfCyUwB2Pe7y+fT5yxQFuLDkCgTnym+uDCHr39Or6xAG7jusxcMAp+mEzdSlw0QO9Y16E7CLX\n+tcAODxxGAaP11kMd3SpbCisd0RhvQcU1gOw5xHTRfU2rLmxqN59cVVRvfvh10X1jsLlRfUehEuK\n6t3/n1wnmDTe9rOiasV8o7DeYf9Y3ucBj95aVO/gx20sqvdbHFpU75bez1Aed2FlUb3bV5W5m607\n4paieuvXl9VbUWpildb7TWE9AFgeL8Kxx1x4/59UD5tLZ4EHbizrL8aiFAaUUltQX615EH4E4HQi\n2seKPTwJ2mXw11aZdxDRCiv28CQAV0rMoQAgHkJQgm/1ActbwHY3BhhxAHW3ZCAtH0Bb3gI5s9w+\nIz0kFnCz/nbfIbd1nxu5rz3f61SPgZTZet858ZW3sY3ukJjBiQI+o9g349/AuveIiQJ220YUCIkD\nIbjjNB4oKfVccSAFXwiBOx53n/1/LwGhHkTt2f6+RsWBKesSzSUYdH9D7kk6xEWDXOsFQRAEYfGw\nKIWBHIjoIGhp/iAAy4noGGh3wt8opbZBJxb6NYBPEdHrAOwP4AwAH1RKGeeMzwB4C4CPE9GZ0Fr4\nqwH81YIejLAImQVmR8KLE5QIBpnJw4zRADjigF0G3d7sbnMWfqxRtr+vadDa+QVShAZ7u92eMQh9\nRrV/TPy6az4j3TW0bXdybryhmWefMe8eW6hOqJ2Qp0NMvPCN2Rf77hNE3HFwYoqNWV0hRRQwr+33\n3vb8yA0lsNu2RQHfZ0T3McOKAzFi4leK10HPY8BNPgg0vrOmjlccSBEDlghyrRcEQRCE4bLLCwMA\n3g7gT6vXCsDPq+fHQmcfnieip0JnJv4RgG0AzgbwZtOAUmqaiJ4IHUj4MwC3AnibUupjC3UQws5k\nFs2Eg2aFAs/KBIN4EIQ8BSpvAZueYceJA0BtiULbiK/PzvaFghyRgCPFwLLphxE0Z+Hr5QJx7h6j\n195nG4/m2U1emOran+oxEBI8Uupx4/KJA+6DOz7fMfo8BzhRxfc+5IgC3PFy4kDss8SJTHq7XxQw\n+81n3s05kEKsHPfeTmAKm7C/3j/exSx4jwFTn/PwMHV74sAWp43deDnCBORaLwiCIAhDZJcXBpRS\npwI4NVLmBgB/EClzOYBHtTYwYRfkNut1YXxojMQEhcbd2DXyXXHAYHsLmP/t57rhaNz0ORf+NIPI\ntz3F6Ob+97XPCxtNLwBXFOAMUXt7bHz8GHiPAdOH7aHAHRNngHMz+q6hGMLNxu/WD7nu2yJD7BzY\n+OqEZvbNDH7JMbreB5zQ5KLbHuu9dj1IzPa2MSsTdGAlIAQa4UHm+zrjea96+MIJgCUnEsi1XhAE\nQRCGyy4vDAjComDQcIIEbHEAALrTnZp4YMq4YQR23fo2XiDwLVMYGheHmzRwpuqfXdKtwj/r3h+j\nO0vOiQLm2TXKY6KAr39fObdP7pyleBpwBrO9jfMWcI/JFSh84oDdpu94OHzCiNuWKwrY4o59nlKF\niJxwFHu/TxwIHV8OPpFBe1c08wy4Yh8ArzhgC4BebwHxIhAEQRAEoUVEGBCENgndrGfmFTD4DDs7\n4aC7RKHuYqpm6Pnd8sNhBvaDG1sMt78Zj1HHCQacQc8tYdgUBZqu5Nx4Ysama/xxru0xb4hUjwG7\nj1LKytoAACAASURBVBwjlRMIUjPyc8cT68f1GLBfu+7+7jG74kDuMdptcsdj8HlhxMSBNumMW8LA\n5FxPFOj0jqL/HtvfCVscMOFCAEQgEARBEARhqIgwIAhtUWj4u/kF7JlFN5TAYBs+rhFpz/abZ1cg\n8BtUTW8DHznGq08MiJUZQ9OgdY1cThSwn31eAykz5j5BwdenW8fXT8xjwC3LeQv0j2+m0aYZR4rH\ngOuBECMmCthjax5TP6wgFU4USPUYcPt0PUBKwwm4/vvv0Fh1xqtwAlTeAo4owPXttjsy0cUsOvHV\nCcz2u4oORxAEQRAEAct29gAEYckxgOeA67Ls7vMZ9q6xGvMCiM2qphpUroHIPULMOGWMWeUug9cU\nBert1mey093opzDRe9j/c33adbjj4zwG3HFxr2Poc9LMF+FzwQ/1E0/qx4sC9fbH2DKx9yjc31hj\nm80MOrWHr08g7hnhG0Oof44OdDiBvTxhSBQwdeznHr6EpYIgCIIgCC0hHgOCkEtbKxIktmHikA3u\nDLAvZt+deea8B+xydlmzvTR8IIeQO7hbJhY+UP+f9zLg2uUMdPc4uTJ1g3WMrWfDjS2WIJFrox67\n38zDYLa7KxdwHgOuB0LKbLx7HK7Hgf3aLHVojzfHc8A3rhQvFFPPzTdQSsp4TY6OGfTDfXjPnqaI\nF0xG6Fu6UEIIdi/2KqwX1vT8TBbWO3hHYUXgvmuuKqp3HH5RVO/Ywnon4uKieg+7taw/fKGs2hVv\nLKu3q/Ch5iqvyZz+vbJ6Bz/uuqJ6N2JDUb2tvhWwIuRMIthsKeyv9Pgmx7cW1Vt/zKaievsfUVZv\n9Mr5onoAUHhq9AK8uWwGsLGwvwgiDAjCIsU2+s3zDHijVW/rJw10vQnsi4ebe8DtM7Qt3VOgOUvt\nXsBibdnlfSEFIVGAEwC40IDUWXv3PLp9cqJF6LiMMR0LdbDPH9cu17+bdI8bvz2G9Ez/YVHAdfc3\nn1cjDpjttjgQwif2+EQB07YtRnD5BoC8HAd2vVTsPAJuCIH5rnLURMBKWOgtW6gHrpmy/hdxQBAE\nQRCEARFhQBCizAKzI/1/S2dYfN4CnvwCHK5AEHJNdj0LXHEA4A1YzmAqmWVNmXn24fbHCSIpngL9\n428udwj4vQA4gzUkCrivffHjITHCFS3iXgP+nAmc94H72n3vc96vFFGAOxbXUI+FLthtuaKAb7wh\ncQBofhZSPtupooAv6SMnCviSg7J9Ts4BWOH3FhBxQBAEQRCEARFhQBBq3MZsK3O56uHa3qVJCi3c\n8AIbWyhwxQGgbnDYxmjISCmlbwZ1essrptaz4Y3fmagoEPIaCIUGcAar3R/Xpy8kw8VN0mevnuAe\nr92eO7vP9dX0PggvEWja4zw6fPhEAXe/mx/CZ6hzx+u2lSoK2PU4ccBt13wWSrxiYpj+Q9TFoGYo\nSOM4XQHAFQduKR2tIAiCIAhLHREGBKHHbQDWtNtkC6KAz+D0zUJy9VOMPi5BYej/Enprs3vwCQdm\n/GO1c+F3f3dd14H+TLE/dCCWeM8vCrivfR4HfELAZuy7r117m32Mblk77wC3RKAtMrjGuw/7c8SJ\nAra44LaTaqhzxEQBrq49Y+/mOND701ffyMH13ulaAp37PY19n+x8AzVCoQRl4aaCIAiCIAgiDAjC\n0BhAFLDncd3thtQZXlPPF1YQq7eQuMJBZ9xvRDZj/usGpC/8gHfz94sMPhdvbjacK9fsqznL7pb3\nhyL4Dfkx7znivRLctmOz23wYRPO8uQa861rPGeqhY/a1GRsr12esvN12zmff54Vje+50MIMNuNH6\nP+172BnvYnaKKSehBIIgCIIgtIgIA4IAAJgFMBItVUxIFEjML+AyiPtzyCgZhqcAgJ5xMzKRYXBZ\n4Qeu10BIFPCLCc1cA7HEe74Ze4MrRBjcWemYQe2Wt43glCX2ZpzzY+r6cgy4IoM7Hhc7Jt4OaQh5\nT8TEgZS+U8MHAETDVVI/83bZku9W//UY6yXAhfqEGJno1hMQGlxx4OasoQqCIAiCIPQQYUAQhoHP\njgt5ERRgZiJ1U2H35JS47lJjiKOWh93yBGBnPy044YAz9N0ZXs6ADCUtNGN0t8XwzY77zlnqqgdu\n+VDcvR2/zyVqdN317ZACzgPCzQcQGldIAJjh9lXGeu4svo+GmDHd/H+Y/aXgijE+UcB+jvY9OQdM\nMZds8RYQBEEQBKEFRBgQhLbxGf8tiwI+csSBUN1Sj4TUGW4ftnAwMtH1eg2E3NfN65DXQGxlgRip\nMe+ugOFm708VbLgZfrc/U9d117dDCkJtceO32+bCIDhhxhYFzLPPWE+B9UoI5Kvw9beQmHABVxRw\nvQVi3ju1fTERQHIMCIIgCIJQiAgDgjBMOFGgwGb2GTWh9dBzcgnkjCPUZtv9zU51eh4EPuOOMy7t\nsq7XAGfUuq9j+IQIAJFYdj6BoXtctrCSI7LYQoCbgNEnOPRWjXDOk6/tlLwCbP1CYz1XFAj1B4SP\nr23xwM4p4HvmvF9sWHEA8K9OIAyBOQB3Z9bJLV9x5x5l9ebKqpUOc9ny0g6BVZgtqlf6/VyHzUX1\nzPc3m5+XVcN3y6p9u7C7XYXyTxqAK8qq7XfzHUX1Jva/vajeLdi3qF7pPdeWwtW2VuKuonq3YF1R\nvU1YX1Rv3cqy7/z+x2wqqgcA9znk+qJ6Kw4rqHQlgP8o6i6KCAOC0CacADAkUaDeLW8VcIZ8qddA\nan2O2akOsCXx52ayeRvg8xpgXdedbbEQBPe1L6Gfuz/Uj7s/d3tKGEFKeXtsJSJIvY/48pC158p4\nt8WdUPs2TS8LvyjAhaaE8liEzltsPClt+ura3gLudtOGTyDwJiC0mQDwu3ARQRAEQRAEHyIMCMJC\nwYkClRFsJx7sO3p3vQaJcYcvdf33GfehuOdS4yhb0bYFhMk5r9fAIKKAb3yu94HP1d1nZIeNzrHG\nOGcC5d1Ef6meGlxcvU+4cEWG7nQHGO+XCS0PyR0DJwqYZzssJDY77z2/ibkqfP110YFv9YYQpbMy\nbugA15f9frn5Jexz20hAyHkJSCiBIAiCIAiFLNvZAxCE3RLXWyAgCuSgTbm8NdhDoQbuI7V+qgt4\nagy5F0sksI3C2Oy3W9Y1XjkhITfePWkcPaPeH5PfrBMWAXrlPO70McPdK+CEYvZdbwCn7ZTxGkO+\nl3fA+fSF+nXHF509t9uY9p8Prp9BsI9oPTZ5RQH3O2ee3QSi0WSJQ8pTIgiCIAjC0kM8BgShbWI3\n6wWCANBczq7f3S4SXJwyTM+547wGXGPOna22y3L/+0SBEjd7d5m82GoI7nhCngN2WXaG39Qbbx6n\ne7z2ay4koWdwW14DZkwxUcDnLYAtKxqfeW5ZwaBHREwUcMNUXE8Tp7++p0CeyBYiFCbAeQu4yQfr\neRBm+PPBrUzA5RwQBEEQBEHIRDwGBKHBbWXVYqsRTM4ViwIcOaEDg7Yfw2ek90jNLzDlPJy6sVny\n2jaP14BrDLchCtjbfQKA/Rzrsx5Mku5B4Ds/sZn97nQnaRa+SBSwnu0+UhIIuuVqY9yyov9wYfqz\nx9n/fwwhmj41eZ4FnEdOKLyg1DsHgHgPCIIgCIIwECIMCAJLoTjgIyAIhBKlxXC9BWIx9CkMKjI0\njK9EA5DFOryaUekxrLm+Qsffqijg6dttN2SklxifjTameaPd7jcWNtGd7puznGeBO+YkPOKA++CO\nxa2XLDSBD2Hgxp8qAsTKcOECsXAdu575TnuFv5AA0E5EhCAIgiAISxARBgTBS8vigMXIRLf3ANBw\nq44R8xbgYujbpMSIHgjGa8A1zDgj0jU0bYPYJwrw5lzTEAy5t3PGNydm2M8hY9010M0Mv3k0ynuM\nYLdN01aPLSuingM+YcPrLcC4uIf64ESCIlEgUM491zlLQqaSIgaYcr5tte/5uCMOmIcgCIIgCEIL\nSI4BQQhyG1C43iuA2o17zDMgPJvYjIWO5RYoMcpLvAW8s/du/6U5Bqb6231L38Vi0Pux290kgz2G\nrz+T1d+XW8CX+M4977ljMmMILQvIHXutfsSQtpeHTA4hMO+5eQ+tfAOhZQy5Y6u16+J+tsznqOrP\nXaXAjD31826EA+47x7VjB4GEsHNRuHkGDGPMthoiDgiCIAiC0AIiDAhCjdsArHG2bQVwYF4z9s36\n5FxyuECKoTKMfAJuu633kZoYzWfgATWjsmbgeYz0FMOzTVGA688Y1FyfXH9c0sIcbIGAW6rPTj4Y\nOl43CWEnQRRIxhEHbNzzFxQFQp8pS0xqiANwEzI2RTefB4G73RfK00EX+2MTOjssgWhl8zxxRn8H\netlCLgFhY8nCZoPCUJhGvgdZocfZrevK6v2+rBo2l1Wbv2m0sENg87p9i+ptKRTpS+vdgrJxHrBu\na1G9wu5wfFk1fLuw3i7FtsJ6t5RV6+xfltB2T9xVVG8HVhbVi+XXaZuthd/BEcwW1dtc+GXahPVF\n9QDglvGy3+4ND70xu85Ne9wNYEtRfzEklEAQkij7cRo2bRjwKe7OQNxwrhvZ/YtOztJyDaacZ/gT\n2PmMyJRlDgcVBRrlGMObC13whUXUynjCCFJc6mMhBY2kg1PIit+3iYYQ2P97+rDDI4pFgQS4z+oU\nJnqPVLiyHVRLFe7oYnR6HqPT83r7jm5NKODqGdw8A8MSAwVBEARBEAziMSAIqcwiz213AjVvgdQ8\nAiH3YzdB2aC0nWhw6DheAwafkZ6zzGHv/4QZ8EZ/Hm8GIGEFgsBSh9HzaxvMVoJLb8iFr70tK7zG\ndpcZTzSEwMaevbdfp1IqCjBeAzYzDc+Bdozv9diEMWhRANXE4ejWeWAtsG18WU8cMB4E9WUKE97z\nEAv8dRQEQRAEYfdBPAYEYQHITS6Y1OYAhkyqlwCHL5u+wZ5FrZXlliJMIZK8jjXS3fElLnNYtIKC\n1V9oebwUF/yBjEJn+b5QNv5G0kGmrdmpDuttkRRC4Hp6TDH77PHmLGcZ2ud+thwvhdjyhS4pSSjN\n573nKYDKM2ArgDusx1Y0PAgMXBiPdylDd4UTSUQoCIIgCEILiDAgCDnEDNrCm/Oc3AJteQssKNyQ\nXaGAe0TaCIoC3PJ4gdn7VFHA6+LuGLe5Rj7nyWCHEQyDRkhCRLThxpGyCkESrlDgCgbc5yFFaHL2\npbzPsRwMqWV6ooB5bTwIksUBHa/a8CISMUAQBEEQhJYRYUAQdiOG7dofi8uPZeDPhjP4GIPf3u6r\nF4znd8ISQg+2P09fbn+cC747Mw/4Vy+o5RcIGcQBrwH2MxISA6b9SzVGQwh8/+eKBwkiUc5+nydH\nkrGfwOj0vBYCppmdVl4yNyGh7cnDCYDD8DwSBEEQBEEARBgQhPbxzOIN4r4PZCTIa3mGOSfpILut\nMJldDc4l3cbXR0aCPiAjUWIg7t3noeDiW1Yx+73LDc2AZ9bcbYNxvbfrsufK5xkSex0iVxQI9WUn\npWQSEHLoRQfrjxCN2f1p9L0GErHDB5ISEE7O6cfEnL+MIAiCIAhCABEGBGEYWIkHO+N1QcCOVLZZ\nTJnHBxYD2iTgNdB47Snvzpwb2hYFfKshAPHYfM5rIMvrwnWr93gNGIIrG+QIDSneAj5i4QClogBX\nvnptv8+h8xsSAepCgacNO7+Avc2D+7vgWz60l1TSiAFuzgFBEARBEIQCRBgQhB6JSxK2uHRozItg\nEGN7IbwGXBf5xuuSZH4+UozCyCyxTZEokNJfoD2fC34saWEtjKAAnzjgjttnoPdCEUIhBKFEg6me\nApk5Jrx1I/iWsNT/x70Csol4DfhEwglM9fIM1MqJGCAIgiAIQsvIcoWC0BYlS7ENmUGWYXPr+pb7\nc+t4KUlKx51Pc54zl7EzS/ixM+c+QqEIiceTtOSgZehF37PA0oK1sZlz5BiRSWEEVl+z4M9btD5n\n2E8wrwfBN+7MvlI9ZHzvS2P/VtTFgNXh/m3Md6y5jGF8KcOVne3Ykd6VIAiCIAhCDxEGBGGBGHiN\n8kVAaPm9UNniTPXGqIsZdz4jNGAgAx5RICUnQsjotfrqTnd6CeNSEvbZ5bPGEDo31XiMMNIbQ0xY\n8ZzzrISDIXLEgTZCCsznYVJvShHNuJUiDEmC2x3QosAdAMbTh+vmGDAeDB3o0CTzHvTCCirm07sQ\nsrgFwMrMOhvLutqyrqzeNWXVsLaw3gDC3vUThxfVW3XvRK8+t16qN2BL9dYeszVeiOGgp9xSVO/h\nvymqhtu+U1bv0rJqxTxtkMrr2xpFGisLpdnluKeo3j1YXlTvruzfM812rCqqVzrO0vOytfCHbau5\nQShgS2Gfm5H/m38tpgBcVNRfDAklEIQFgEsmtlAsVDhCbMWCVigVGCpsIaBYFAiREE7gw01aWHvE\nQjJKXO9DZTzbW084mOL+30byQft/TwJCm5zlCt2wg86ObnOZQiApCaHvt8EsWmkYmeg2RAFBEARB\nEIRBKLoLJqLlAB4M4PEAjgOwDsDeAG4HsBnAJQC+C+CnSimZxBCWNNzNvus9wJWxZzS7GKvFGseI\nuT4PSsxboMaAxry3TdtVPLO/gUSBlP4cr4FGv25fHi+DxphjYzRjcWfzLK8Bbx37f7t+FU7QaC/W\nTmiMvhCRlLo5BLwezDmeSQyRKfou2R4Dq61tkYkFW0icwFTjt2KhPI/kWi8IgiAIS4csjwEiWkdE\nfw/gegAXAzgDwLMAPBzA/arnZwF4B4AfAbieiN5ERPu2Our+eA4moo8T0bVEtJ2IriGitxLRHk65\ng4joPCLaRkSbiejM6obHLnM0EV1ERLNEdAMRvXYYYxaWFiG38GF5D6SsGFDqCZDSvj2DWpzUz4ZL\nYGf+Dxnpbj1Poj97n7f/lIR4JQLIsOsEcgew5VJFj1C9WPLB0LhCDBKOEiElb4btwZGE6zFgewsw\nnsa+ZQlt3xF3KcTOeLf2WDW2LW1sEeRaLwiCIAhLj6QpMiLaC8DrALwewAiAe6DDjH4A4Nfop1oa\nh54LeQD0jcPRAN4O4O+I6F0AzlRKtZkb6XAABOBl0BF2RwH4KIBRAK+txr4cwHkANgE4ATri6BwA\ndwM4vSozDuD86vGyatxnEdGUUuqjLY5X2F1oMdFgSpxzWwkES0ldnnAqdlJyjbtQ8sFB2wYGn/n2\nba/G14jr5/r0lB8okSN3jtw8CyVt5Iwht71BcMfi68caQyyfQ4pY0PbsvZ14kGu/gy5mqjwDw0Cu\n9YIgCIKwdEkNJbgSwEEALgdwFoBPK6Wii7YR0T4AXgDgRQDeBuDFAA4pG2oTpdQ3AXzT2rSRiN4N\n4C9Q3SwAeCKAIwE8Til1K4DLqpmQdxHRW5RScwCeD30uXlz9fwURHQvgr6FvPgRhwZhBpzEzaDOF\nCUx4rLIcd+hhigbs9tJZ4hRjMmakc0kI2xQFUgzxWBuBpIXREICYAZ97DplwAkzOxc9ZjgfFIMkH\nY+27bXv64r4Dud40HXR5QWy182wTCCWwxQHzMOEEXIhQy2EFcq0XBEEQhCVKaijBnQCeo5Q6Rin1\nvpQbBQBQSt2qlHovgGMAPK9qZ9hMoO6oeQKAy6obBcP50DMe97fKfL+6UbDLHE5EGQtNCUK7tHHT\nX7y8YEKbyQkHB80z4LqkpxidIYbhKeDZNzvVCWfyZ8qzpIQ7RMaCLSv4UIAQTNK+1tz6Q2Eaob5K\n8hF4PjtuqADHTJX+LwRbdzXz2trma9M2/J00lM1wAmvfKLYHx5iAXOsFQRAEYYmSKgzcXyn15dJO\nlOaL0G6HQ4OI7gPglQD+3dq8H3SSJJvN1r7UMoKwYPgMhhwjP+rWn9Bmanl3W8yIapXU+HXOwI3V\nKek/pb1coSOn3ZQY/1CbpTkMSj0H3PZiYsGgIhPQ+Az4Pq+uIBATCLrooLsyIhCYslw5CzcHyQSm\neqsT2I+WkWu9IAiCICxRkkIJlFJlC0kWtkNE74SOcwxxhFLqaqvOAQC+AeALSqmPu03GhpYyribf\nALCXs+2o6iEI7ZKzMoFPFBg0X0Hof+/2NrwFUnMKcO7wbcTKp8xml4QTuPU8qxkkjSWG3U+OEOEe\nU8p5z20zlRbzGtgrE7jEhC2zf8yXY8A395w4J228AmwBoJ7XoIsbP/sj3P7Zb/XqzGEF5u8YTCiQ\na72Pc6FTLtg8sHoIgiAIwnD4wWdvwg8/e1Nt2/Y77h5afwMu2j003g0d3xjiOvOCiNYDuADAD5RS\nL3PK3Qy93JLNuur599azO1vglmE4GTq/kSBUBAyRMXSzZtLtXAMxg942TmxRwOQjSKnv2x9fhSAQ\nTpC6BGAKMUO/NIdBzvbUutz4zLlIaJtNWpgzPlcEiJ2vKaZsTBTIOUeJMf9JbeUSOCYuAWHJdzQo\nDjBiwLZxv7PeGLro7Oj2PApcDwEtEHax4ZQTsOGUE2rj3X7pVbj6gacmj38RsItc6x8P/lp/m78K\nrgnsC7GmrNqvDiurt7KsGvaIF/EyF9NveK6487iievfcb3m8EMNd2LOsP5T195Sn/XdRvYNW3FJU\n72mFt69P+2FZveIAo2ML6w1St3B9lR2FX6jSz0xpve1YVVTPXnUqh7uKf2jKSPWYdRkkfLe0zyns\nHdy/5pT74Wmn1LdtuvT3uOKBnyzqL8bAd+1EtAHA/mhOnfdQSn0/p80qrjEptrGaPbgAwE+hEx+5\n/AjA6US0jxV7eBL04lG/tsq8g4hWWLGHJwG4Uil1BwSBo8C4yRUHAL/R3lbyQF97KR4BC7We+sBw\nosIwRAGuz9Q2QuXbElhCfcTCDVLqteHmH+ujtL5PHKg+97EQASC8gkjUc2C8eg4kHjSiAAB0dnQx\ns7LuKWAnITReBCW/KSXItV4QBEEQdm+K7zaJ6LkA/gHAoQi77ymgUNKKj+EAABcC2AidmXgdkR6K\nUsqo/+dD3xR8ioheB31jcwaADyqljC/GZwC8BcDHiehM6PjIVwP4q2GMW1h62AnDcm7kYysUAH5v\nATtjuu01kOsd4Nsfy2ngdYV3CRl9qcJLzuz9IIJAyji4cIKcPnzhB6FcAD6PAM4YTjHkubql3gJc\nm/b+2Htc0lfK+Zjs7wqJb9z/oe+P/m47NibjNeD+BtiiwOj0vN62Tz2UoO85wM/aDJx6kEGu9YIg\nCIKwNCgSBqobhc9V/94OfbH2WS+FMX1JnAR9s3JvAL9z+lwOAEqpeSJ6KoAPQc8WbANwNoA39wor\nNU1ETwTwQQA/A3ArgLcppT42xLELS5hBZ/lsY9/eZhjGTH6WKMD1X+rqPxF49rXrEwRKZstTyua6\nyid4C2SFEMREglD53PfFV7fUi4KrF/PqSNkXOw9bVnjL5AhkLD6vgEogcBMPNkSBrboN4zXQzDMw\n0wspsAW/VdgWHlcmcq0XBEEQhKVDqcfAG6vnv4JW41tJWJSLUups6At/rNwNAP4gUuZyAI9qZWCC\nMCRMfFeqS7PrNeBuT+szbAQNTRRIwWeku//nGOm55VIN1FJBwQ0jiI035CmQKhrk1uHaGISUY0xp\nIzJ2LgFhiajGxl2a0IFAroEgW4FRzNe8BjroYgNuxI3Y0OvXFgfSUpNmIdd6QRAEQVgipC5X6HIE\ngIuVUv+6s24UBGFRkGgAud4BJd4CIYOlZGnC0LrtoT59Xgp6X1limiLaMh5j+6cC5Upm3kMhAYZc\nMaCEUg+O2OtYP8MWDTIxIS+h70Pqd8U8eskFjRhg5Rcw+1LaBLTXgGl5PTb1cg10qsAFoC8Urmo/\nmECu9YIgCIKwRCgVBqYAXN/mQARhqZArCswkGhCAa/inGek5M6RhUaDl5fV87ZS6l7v7Y+2UCg/u\nOH1tDWIwlx5n6phT68X6bptBwh4C9VMFgVRjHoAWBcwDqIUR+H4DTG4B3FE9tupt++/Y1BMFzIMT\nB0ZbDiWAXOsFQRAEYclQKgxcCKBs3RhBWMIMI3s45y1giwLGkAl5FcQ8A9z/F1QUGNTV390X8wDY\n2YatKTMsr4E2DP5cb4E2vAZKj7+w75AAYAsE5vUUJvpm+8pO3Vugeh1aprDHVn7zWM0noSkOdIYR\nSCDXekEQBEFYMpQKA2cA2EBEb2hzMIKwW+AxPNoUBYwh4m5Lrevbbhs7vjpukkNfe2zyvGFREv+/\nUGVT6vvaG2SZwlwBYKG9BXb2+Q54Dwz6Xeph5xmoEhLOWKZ9FMtrgAspsMUBAFiF2aRxZyDXekEQ\nBEFYIhTddSql/peITgbwOSJ6OoCvA7gBwLyn/DnlQxSERU5pgrYC7KzkuuuYt8BYlcG8nmwwZ8nC\n3JUPckIfiik556E6bRmppg/3Oadtt16KwV5yLkoZJLeAO05TJjT+Nj0l3H6slQmCHgLTzSSFbDlr\n/h5r76itWrhtfFljNQIWE0YA9L0OrESE67EJm7C+91w7nHjrWci1fhCuKaxXKAaWrglxyWFl9XYU\n9geg2LmlsN7VU0cX1Zs6sezCXnr9245VRfUe++QLiuodd8QVRfVwSVk1dxXXZNYU1gOAo8qqXbf/\n/kX1bsG+RfVKPzOl9WYxssD9lX225wpXwF2BspQ4qfm6OFITerfR59bitQPiDNLyIwHsDeAAAA8N\nlFMA5GZBWJKkGhTe+pYBn2PM+8rbqxPkrExg6ob6C23P6KSObRz7DG1f/H7qb20bokBKf4OOM6Vt\n7ty45zBlXFz91DGUEHrfh4zxbPF9T3vJCaf95TgPgG3jyzCK+Zq3QBF3oCcQdHZ0gZX9GxD3+zuE\nHAOAXOsFQRAEYUlQJAwQ0csA/GP172XQ0rhPyx3m2saCsDgYgteA69Y/lmDEG28BNwmhL/6474EQ\nFhzSRIGMFQly8gYMOssfMo7bzl9Q0lauqJAylpTPYo7IYQszOeNIaTunfmqZFr6LRgjwbfcKCZVI\nsD9Qyy1gkg7aWQIA57tn8gtMWw06XgMY72Jmpa7T9BoojQ7kkWu9IAiCICwdSj0GXg3gHgDPxqGT\n/gAAIABJREFUVEp9rcXxCIKQiJnxz4mHDgkA7r5UUSBZEFiA2d+sfmLlSuLyuTCCXAEjt15qu+Z1\nSb1Y3UHaLSHlvfN5RyT07RMF3DKd8W5fBIAjyK3sYHRtge8sV8XyGgCA/XdsAlZqQcAWB9bhzvz+\nwsi1XhAEQRCWCKXTC4cC+J7cKAjCcIivmd40xnlvgWY7vnimWL1m7oGxnSMKpBqoodexNgadsU6p\nM4inQRuixrDKD1sAGlT4Cby/3elOkihgl++9ZlYCSWojVHYa/ZwDViLC0en53ioFAHo+CENArvWC\nIPx/9u49zpKqvvf+5zfdMMBMDyOgg6AEvHAJgkSihhOvGNSoMfEYY3xMvJ1oLhof9RHN0aOi5qLI\n0USDORo1qOfI0XjPgwIaMeiBxMgYMRFUEARBkEZ6pnumuUz3On9U7e7a1XVddd27vu/Xa792d9Va\nVatW792r1q9WrRKRgfANDNwB3FZnQUQmXpVOX0FJz1UP3rM76PH1WcGBpMkG47cmlAoI1Hnlu8r6\nOvYxqbI6yXnpqta7zzZ995WUvmCAICsgkPeEjaXI6JrRd2WJOfZs25R6G0HhJxPsZjxA8EPg9nC+\nAcaDAgfVP8eA2noREZGB8A0MfAH4JTOr94ZGkSlUdkK+5I7/XKHHHcafrZ62PfCbCTU/ABF2U3bP\nVXvUXlSdk9q1nbZshzz+nvWz776LbKfLEQFN7Dcr7/xsZsd/tG55Ya7QIzg3BO9SggKpDk5Zvhu4\nkbUAQdKogS3szS1fSWrrRUREBsK3sX8jcADwHjPbv8byiEiMz20FRdMtsD01QJA0WiCtfGWe/V6L\nIle68/KUzV9mO3VeJa9LEyMu8v4OddVHB6NB0gIB8eVrTy2IBeAWmct8PGE0fZGg35rR6IFw1MDW\nyDewgREDautFREQGwveS3osJriT8IfAUM7uE7Gcbv8VzPyJToeyjAYtsDxh72kDaCIGkSQWzHmM4\n+n3jPjfOYdALVTuNbXdQ49vznaU/uixtG1Un+iszAaHv9iG7jB0EVIqMDFhemOPA7euPEr0vG0fq\nzMVG8CQF0Ma+j4dS7FnfYXBgy+7xpxRQ5skgxaitFxERGQjfwMCbIj//HPCCjLQO0MmCDEO0IzU/\nW6lTlv5YwPWORPHRAuPBgLTgADAWIIjPYdB5UMCnoxqfld+3o5s3UiCrc15kO6NtRN+LlqNIBzu6\nj2iesnWYVo4ivxeto3gZW1IkIBBPHw0OwE8iP7P2czwoMB48KBEwHM0zELndYMsDVtl67+DpCA3c\nSqC2vnVXe+bb55fNLfvl+87JfvmgvTlGRub9sv104SivfF9+lF87ecc2vxOGm9eei1LO9465wivf\nccd8zyvfoWvPYy1nhRmvfADzHOqV70bu32o+37/h7Z7HN89hXvl8zwGXOdAr311s9srna8b3/yh+\nt+cCHOjRbi9xj9e+ivANDJRp/PVsY5EajP4hb03oSCSNFlhibkPaRbaujTJI65T4/nNrVJGOaV76\npDRF9+2zvsxogLqqPClAUDYYklaeNjvpPR0lkGeRrSywnSO4OfX2mrSgwOLmObaMhgtsIxgVELUr\n9joqTHc7zIWjBg7ybtJTqa0XEREZCK+zCOfcWTWXQ2QqLS8EzzqHYlcHi4wSKGKJjbcTFL2tIF6W\nTkYLFL0CnpXXd33ZdEn5ilwZL7v9tjvLdYyCKLPNNqSWcxYOS7lSEJ1EMy1NTNIIgazvz2JCEG9M\nPCgwWnYw6xMR3nsRuFeh8hWltl5ERGQ4ar+8ICLpinbw1zoU4cRmo+BC0raSRgtkb3t91EA0X9HA\nQ9Z+lnI6QKnKXHX36VyWyeN7e0HdV/6rDKdvqixl8jQtax91HXv8yRqj3yMBguWFORa3zaUOBE27\nhSC6fuy7F3b218SDArtiyyOjBra0POxSREREpoceQSRSt5xOUR1X3rOCAdHRAmmznSfNTZD8mMSt\nsd+LlX15YS79GfILCa+6VLkiXzZP0Xvzi6Qvc79/mXR56cvut8my1LmNqvuYn81+3GbC+qRHEuaN\nEoiv37Mt1iSnBQWi6yITEfrcqygiIiICBQMDZvbKqo8qMrPNZvaqKtsQmUSjq/61bIu5xM5+kUcK\nxtOlBQeybmfwUlcQoGpnsM7OZJv7qiNfndtpulNedf/RdD5lyQoIlEybN1ogqvAjC+NzD8Da4wu3\n3F3tcYVq60VERIar6IiB/w5838z+wMxK9Q7MbLuZvQz4AfCOsgUUmUZFOtnxgEKRPPG5BeLLNpYj\n/akGvXkSQRFlZvCvkq5N0Y5tHaMfyo5w8NlHH/MVDRDkjRIoIAgEBBMQJo/ASb6VYOw7dijB7QTb\nwvfIEwgSRW4tOHBP5fn/1NaLiIgMVNHAwDOAFeC9wC1m9r/M7IVmdryZWTShBX7ezP6Lmf1v4Gbg\n3cDd4XZEpl+sI1IoEJBxRTFp1EFm+pzZ0MeXF3/2+VI4YqHw1c2mNHX7QdX0WbdOFN1fH4IU8YBE\n0QBF2vHXoep2Ghwlsbg7/QkEeUGBsbSbU75XaQGCUVBgNIqgel2rrRcRERmoQpdHnHOfM7MLgT8O\nX88JXw5wZrZAcGqyjWDaJwtfADcA7wHe45y7u97ii0yuohMRRp+VPjJ6FGH88YS++944IWH+3ALR\n/WXOqF5V3uR3ZW8BKPPovir7Krq97Qk/N7H90e9VtlWnpiaE9CkH5D+BIa2887Mblo8CAqPvWPx7\nNHokaHR95gSEWSJPJ6hKbb2IiMhwFR436Zy7CzjHzN4FPB34DeDxwP2AQ8LXyI+BrwCfBT7vnFut\nrcQik2IBOGz917KPHITkZ6vnbSdpxvNoICGtMzIKBkQDBFFZgYeljABCo4oOj096skHd+ymy36rp\nkt4pmL/M9srkT/u9yDaLHENTQYG8gEDSvnPyLK0FBILvUNLogIVY5uj3cIk59mxbZMvtq0FnPz6f\nwGjZbsZHEIyeTpD81S1Fbf2kusYz37JnvgrB4BtP8Mu3cEh+miTzftl88y3f4vfY0K8/6gyvfD85\n9givfN/nOK98D+Rar3w7uNUr3/74xxnvxm/KlPj/6aLmoyd9JdzOoZ7788vnO+rT9xxvLwd55Vth\nxi/fql++mU0rXvkAZvDLe5DHpMF3NXi1pPQNlc65FeAz4QszOwzYQXCqtADc6pzz/Tcs0mPLsHxg\nuU5YwtXEqLROftqEhWnpq/yTj2/P90kEa/lrnGxxg/gVdt9t1J2+bOe+zPbKzA2QVIYmRiKUVaQM\nbd1C4RMQSFqfMEpgPegWzDMQHzGQFRQY+y4eynpnv+hIgN3UEhgYUVsvIiIyLNVmWgLCEwOdHMiU\nux1Gkdm0TkNKR2N5YY65bSlDhktY3L2+naikCQfH8mWMGihbptH2c9NXnMStsjo6w2U68T77bqIj\nXKTTW+d+Jk2dAYqUuk66hSBIvn0sTZB1YWOezXNsiUYD0m4TGN1C0BK19SIiItOt6OSDIgP2s2LJ\nCnYoNk7+t3GCsrikWwryRgnkPUO9SNq0JxvkPaMd6Ffnsau5BLqew8Bnuz63GBS5jaBPn4ckC3iX\nMe2Wn2Cz2xODAkm/j60r0unfFXtVe1qhiIiIDJgCAyIt8Blen9TZWNteQoc9tYMRkTW6IC9/0WWN\n8OnY+u7Hd19l7qcvs85HlbJU3faApU0yGF8/khQwCOYZCJvmUXCgxZEBIiIiMkwKDIjUyXPUwNi6\neBAhNiw/r4Oe9fz0vHIkjQSoMjKh0eHyeevKXrFu8+p+0avwaVfvuxrRUHQbadud1KBCgYDR6Hu7\nPhHn+mNA80bYJH5PR3NKbQvfD478vo31RxgqaCAiIiI1UGBApG4pHQivSfniQYEKE/tl3RaQlT6+\nrMzyStrsRDZ5xb7KKIQ6tTliYZJk/X3i60o+eSHpe7HI1rXXKE30HYJ5BvZs2zTe+U97jeyi1skH\nRUREZFgUGBBpQoGOVtWOdGKHouBw/6LBgbT1ZYIJpTvFvvd6t/WUgir7alOdZaxzpELfRzvkrcsJ\nBETfx9dtHRtFkJR+QyAhbdRAGgUGRERExJMCAyJNSejgZl3xj3YIkuYXyJrgLHF7CfsqG4zIe+JB\nkqy5ERJlBQOK3sNftBNXx4R4VTukXQ6zr3sOhZJX0GsTDVRkvYpso+y6pPWRkT3jnfytiQGBPEvM\nFRs1ICIiIlITBQZEmpbx6L7CHfWy8wzszr/CnzVqoMhEhpVGPFQZGTDKX1UTQYEykxHWtc8m8jbd\nua9SLt8RKGXndSiZNhoQKzMvx2gCwtTRBkmjBhQUEBERkZp1/LBxkWFYXpjjwO3hc8p3zzG3bTEx\nXZE5BJLyl+mkR5+ZvsQcW1kcWzeS1LmJ7zu6rTFJV+a7uHe+jvRV82VtYwFiE9enL2/7yQU++ZsM\nKNR9a0TD9Rv9bqQF8ubCsf+jtNF3ADbDFnatBwJ2h++76i2rDNlNnvkKPsa3zryLx/vlu/zBfvlu\n88vWdr5rf+FEr3zXPfRor3z333GjV74d/NQrX+I5RkEzrHjlu5v9vfLt5SCvfL4XWnzzLa8e6JVv\n75Lf8S175mPfjF++Lsz6fdb2P+Cu0nlWd93uta8iNGJApK9GowRKdliiwYWxnws8wjB1myVHI7Sm\n6FXgNvbT1rb7VJa29tXEfAkNSPpuZD+NYPzJBdH3kT3bNgWjBg5m/GkESTTHgIiIiHjyHjFgZtuA\nPwKeABwJHJCW1jn3AN/9iEyLtFEDZTrWY9uIX10sIX6lPz5yIJouMX+RkQN9HY7e1/2M9pU0gqBK\n2rx0Xd7yUOQY+jDRY1Y5Y+tSR9GkWGSO7eFBJn2nx7Z1MMGogVFgIDpyoKFRBGrrRUREhsFrxICZ\nHQF8G/hzgpOF44GjM16NMbPPm9mPzGzZzG42s4+Y2X1jaY4yswvMbI+Z3WpmZ5vZTCzNyWb2tXA7\nN5jZmU2WWyTN2OR90U5RwlwFG54WkDThYMqogXje6MiBtNsIMn9vauRAE/ftF91v1XkIsspeZL6C\nomnaHkVQ59X+rEkD27p9oshEhCXKsshcMIFgxmt8F9vH8o5tazQJYXTUACSPHKh5xIDaehERkeHw\nvZXgz4GfA/4N+G3gFOABGa8mfQV4FnAs8EzggcCnRyvDk4ILCEZHnAY8H3gB8JZImm3AxcB1wMOA\nM4GzzOzFDZddBiba6S8yn0CavABAZt6STxrIe1qCTxla1/Rw/T6MjsgKELR1y0Vdmg52xPdVtAxV\n6jcm6xGg0QDChiBdXnCgXmrrRUREBsL3VoInAz8FTnfOdXo66Zz7y8ivN5rZ24HPmNmMc24FeCJw\nAkFZbwOuNLM3AG83szc55/YBzyWoixeFv19lZqcArwL+ttUDksEa61THRwdEhitHbyfIUnjCw9hk\nhNHlRcqctt21cnehzJD8pLxNpk/bhm95fbZVNFCQNjFi0e31KdhQRpERBPG6mZ9lmZzvQ0x0AsL1\n38dvMZpjkcXNc7BtkS2sBgl3EQQHdtM0tfUiIiID4Tti4F7AZV2fKMSZ2SEEDf8l4YkCBFcOrgxP\nFEYuJjitOjGS5tLwRCGa5jgz04OhBsNvltZE8aHRGY8srEP8Sv3oyn7aCIWyw/6jefNGDTR9rN66\numpe5spzlXV91tdyV7lNIiXd4u71WwWWkq74x9OzNXG0QPT3JeY23lLQDrX1IiIiA+EbGLixQt7a\nmdnbzWwJmAeOAZ4dWX04cGssy62RdUXTiOQrMAS60u0EkU73KG9aUKCM1EkGC44aaFWRe9Z9ttnk\nfAJtK9rh7bqcfeLzGchIH3/0Z/y1cVPbU28n2BAcSOvCbk1Z7k9tvYiIyED4Xtr7e+D3zWyrc672\nBySZ2duA1+QkO9459/3w57MJhgEeDbwJ+KyZPcY550abzNmWy1mf4kI2TtB8UviSwckbwj0/C4ft\ny0iQMvFgxnaLdMqznoZQZvb0aNmK3s5QmzIz8FfJU0aXneqmJxuM19toWZXbCOq8XaIOHZdnKXKr\nQNTYLQQJAYRR+vP/Bc7/AnAPsA9Yhl131l5MtfWA2noREenCyqc+yeqnPzm+cFdDjyHCPzDwVuAp\nwCfM7IXOuXgEvqpzgA/lpLlu9INz7nbgduAaM7uK4CrHacBlwC3Aw2N5d4Tvt0Te41cL4mkSPBk4\nIqeYIhvFO+tV8seXF9lfUnAg6f7mouXbMNdAX69ExzuDXZQzqyO9PeXnOvfdp865jH3Xtkf+2GmP\nI926eY4th+7iOU+E5zySYJ6BXcFr59Vwar13yqutB9TWJ1mukHenZ74fe+a7yS/bNSd47m5Hfpok\n1/pl4z/8sq1+c4tXvh898Hi/fPf3y1epzdqafTGmN+707I75BoN9v74r+UkS1R+0zub7Z69yJ+ys\nX+a7ZzdnJzjxRcEr6ns74fJTvfaXp9BRmNnfsTHSfi3wG8APzOybwA0wmhlpnHPuRUnL0zjn5gmG\nCvqYib1fDrzOzO4duffwDIJTqe9G0vyZmc1G7j08A7jaOddcWEamR50drug9+iWugPvcQhCXO4og\nMuohHpxIDSB0Pbt8XenrytuWMpMHFt3etKh7ssc1wQSEwPqTA9g4KiB9U9vH0icF7paYY8+2RbYc\nvBq0YjVSWy8iIjJcRcMbz89YtxV4XE7+UicLRZnZI4BHAF8H7iB4fNFbgR8QnAAAXERwUvBRM3sN\ncN8wzbnOuXvCNB8jGJb4QTM7G3gI8HLgFU2UWwYso2Pts42RIkGBorcUjK5Qpj6msMAtEa0rchU+\nL23VfRXZdxv7rXPbecdQ9mkEfR2xUOQ4K5Q7+l0qEiRIfTpB+PNo1MBaYKC+Lq3aehERkYEqGhio\n0th73tNXyF7gGcBZwBbgJ8AXgbeOrgY451bN7GnA3xCcQOwBzgPeuFZA53ab2ROBc4FvArcBb3bO\nfaDBsstQpD3erOw2auhQ1TXfQNL2Rr+3YpI7mG3qaz1NiqQJJSvWZ7Sjn5cu6daeIG9s1MDB1BUc\nUFsvIiIyUIUCA8658xouhxfn3L8DTyiQ7gbgqTlpvgM8pqaiyZBkdb6S1qVccU+ceDDDqFOe2Bkf\n3Y5Q8cr+2O0BZR9D2NdbCPq0r6Q5D5qYWyBt303kL/LUiC6CFdEJFNPqPJ4+azsFZAXc8p74MRoh\nkDjvB3PjowZ2FytPHrX1IiIiw+X1GCIzOyp8jnBeukPM7CiffYgMQekr7LHOSm7+hM582uMS4x2P\notttbJRA0qz2dWzHdxs+2ymSp825EaZFmb9H2b9dkfQ565cX5tYfJxo+brCspFt64o8vXHt04TbS\nH2FYgdp6ERGR4fB9PvH1BLMJ53k7kRmFRQYlq2ObdvU9aeLBpE5I1tX7+Lqc4ECesiMZarkSXXQ/\nbXSS69pHVx36pgIaVdQ18WPeZ6DugECRbad8N30CBEnBgZZdj9p6ERGRQfANDBRl5D9XWGT6lOhg\n1HrFvUjAISZt1ID3vnwldcy6vjreZKe6jk5oG5L27XsbQVVZ+/X57NR5O0XBwELadywIHGxlkTkW\n2L72hIJocGDDa/McHEowWuBggukBu6G2XkREZMI1HRiYA+5ueB8ikyOl41DoqnxepyOvo16wI7/W\nESkxv0BucCPvqm4fr1B3HZRoy6QcZ5lRJG0HLlK2m/YIz2hnfyljJEE0ONCD0QNZ1NaLiIhMuEYC\nA2a2ycxOBh5P8MxjkelU5mpqXJGOetHtF716nzI3QFoHpnCZfLR1K4CPOstV1xD1qmXom67rpCV5\nwYE8C5GZDuOjBzbMM9DyiAG19SIiItOjcGDAzFbNbMXMVsJFLxj9Hn8B9wD/BhwGfLqBcotMliY6\nNqNOft1D+iPKTG6YmLbuK7eTNKdA0/usElip6x7/qtvMu8Lvu92i+25qO/Oz3oG30e0E0cBB/NaC\nJkcNqK0XEREZprIjBuL3EVrKa4X1SYveVLmUIpOs6L3PSRMPlslTlO+ogSIdqdG2y9wL3pY27jkv\nqs4OfZnJGutI05SFlNekKXlbAbB2O0G8wx//PT56YMM8A/WNGFBbLyIiMjCFAwPOuU2jV7jow9Fl\nsdf+zrkHOOde45y7q6Gyi/RP2QnQynbs6xp+nbPfWoIEdeRpU51X0eu+FaFoui72Ow3yjrWmCSWT\nvld5V/+TRg80OWpAbb2IiMgw+Y5BfgvwrToLIjL1FiBywS89TR3bWoi9Z6RdXpjjwO2L6QniIxny\njqFubeyziaH1VVQ55ibqa7TNrp5G0JX4UwdK1mvSd2tx9xxz24Jli8wxx8bv3iJbmWMptiwIBETT\nB/nn2LNtkS0Hr8KWcuUrQG299MhPW873Db9syw/yy/etE9rNdy+/bBztme9+nvkO88wHsN2zm3OA\n5/588/lq7k7SZPta3p+vtusF/P/2PmW90XNfBXhNPuicO8s597m6CyPST4fUv8lohyPt6n3dw80L\niF7RLPwYxQbnOPBWtAPbxXD1LjvXXdzL30e+t1V41F/S9yhrQsL1CQa3rr2S0jQ91wCorRcRERmS\nymf0ZnYa8DjgSIJ7Dn8MfNU5d3nVbYv02jzZkeykK4xpVx2zOtfRq7VVRhzE88/PwmEbw7+JnZa0\nTlLbowfq3G8dHeG+dqaz6ih+FbzMNusuSx+kfU/LpE8S+X7ljRyAYJ6BrQmjB4Cx4EDSSIKtm+fY\ncuiuRp9KoLZeRERkunkHBszsQcBHgF9KWO3M7BvA85xzP/Ddh8hUS+tgFLnfuUxHpsA+c28nKLCN\n0mVpSl33i5fdT7xe6u4Q+3bom9anslRV5608sfVZ37G0WwqS025lge3Msbg2amCOOaCZwIDaehER\nkWHwCgyY2RHApcDhwF7gQoKZiSG4++jJwCOBS83sF51zN1UuqUjf5V2lbfu+77x8kDpqYE3R2wSS\nttNFh7HiPeGNa+MKfZ9M0jFU+R4V/KwljhyIBQXybg9ICyDs2bYJWM0vRAlq60VERIajyuSDhwOf\nAv7IOXdbdKWZ3Rs4F/jNMO1/qVJIkYnjEyTwmRugxuHduaMG6pzzYNJNwjHVERip87aNvgVp6lDm\ndqFQ1vcs63aCkWCUwNKGuQa2bp6D7btKFb8AtfUiIiID4TX5IPAU4CfA78RPFADCZb8bpvlV/+KJ\nTIG+T/iWMCogd+LBhdh7ynZa1VZdNrmfPnwehjSqoenyp3wnRt+v6HweTU8k6EltvYiIyED4BgYO\nAb6W9dzicN3X8X8gish0qdLhinfEG+rQJAYE+nBve1p9pP2et7wtXe+/S3099rrLlfb9GP3sETCL\njwbY+ArmGYguW2KO5S1W5UiSqK0XEREZCN/AwI8p9sTkA4GbPfchMr3a6Mh63oawFhzwGQHQ185g\nXarc2uFzq0hVfRqtMu2fjSw5o3KiowWW+jVyQG29iIjIQPgGBj4BPN7M7peWwMyOBE4HPum5D5HJ\nkXfVMC9vU1cxF2KvNHlBgD51MLvaf5fH2WUAoc48Q1bxVpulcFTAUsZIgj37F+nDl6K2XkREZCB8\nAwNvBb4D/KOZ/Vp8pZk9DfhKmOYs79KJTJum79/OCwDE11fp3PUhWFA08NLGfuradluPW2zSJJQx\nqu7PSNr2EoIDefMMjIIBHVFbLyIiMhC+lzC+QPBcpAcDnzWzBcYfYTS61/By4AKz8fsenXOne+5X\nZHqUnam9yLPT65Y2/L0PM8z3KfjQZP6q+vL3gm7LEt930bKM/n4tljsvEBCMEFhkge3hT0H6ZQ4C\nluositp6ERGRgfANDDw28rMRnBwkTTx0muf2RTpySH2b8njO+VratO1Ef4++VynbyPwsHLbPY2M5\n+xmash3Ovkt7JN8QxEcBlA0O5HzPoo8uXGSOuZxHFY7SJT3SMAgO3F6ygLnU1ouIiAyEb2CgylUA\nVyGvyORL62D0/T7vrjuDVa42++bt+pgnUZFbIdoeNVDXd6tI2YuMTIgF4RZ3zzG3rVhQIP57w485\nVFsvIiIyEF6BAefcV2suh8gw9WGYd1rHpeo2Jl1fggJ13f8e76yWST8tinbsy26jxvpKGjkQ7/yP\nJiCcC28bWJt8kIPqKURIbb2IiMhw+E4+KDJsaZ2HtKuMvttvu3OaFBAoO8lgXzrUVbR9DGUnIOxS\nn8uWpMxkjkWPreykl0npw+9a2mMLs5aJiIiI1K1SYMDMNpnZU83sT83sfWb2osi6+5jZsWZW7RlN\nIn3n05nw7VzVGTCo+4kEfe8w1tnpq6rq37/tvHXr44SOZbdZ9jtQcvtptwmMlgXrt7LA9rW0e6n9\ncYWA2noREZEh8A4MmNkpwFXAPwCvA14MPCqS5Ixw/VOqFFBkIpXpBCyQ3+FvulMX3f5o1MAkdv6r\nquv4Jqme6ixrG5/TMsGxNr83FeU9tjBreZPU1ouIiAyDV4TfzO4HfAk4lOBxRv8EvD2W7LPAPuDp\nwOcrlFGkBQd2XYDpNgkd5UkoY9e6rCPfyQDTttXkHApZE4x67jcaFIg+mWA0WiAYMVDvHANq62XY\nlj3zfaflfJ7uOLLdfN/yzMf9PPOB95OmLD9Joq2e+TZ75vM9dfXd336e+XzHk01KOaHdsu7y3FcB\nviMGXkdwovBK59zTnHPviCdwzu0Bvg08vEL5RCZb1r3jXdzHHr3iWqYMbd4uUUc5yuSP10nTio4K\naaJcXdzCUMc284boT9LoloTROT7zDCw1/0QCUFsvIiIyGL6BgScD33PO/VVOuuuB+3ruQ2TY6ppH\noEoHs8vh2vMNbTeqr53HItru5JeZxK9OfZobImu/DQUnNjyicPfGYMBonoEG5hhQWy8iIjIQvoGB\nI4ArC6RzwDbPfYhMj7qujkbXp11hLhIMqKMT02RHrMmgQNVgSZn9NK3JY2m7/GmvstuLvhdN36QS\n37V4pz8tKDB+W4Hv2NVC1NaLiIgMhG9gYC9w7wLpjgHu8NyHyHRKG27vc0W2qY5hjbOOubhdAAAg\nAElEQVSrlzLPeFCgjVEDdejDkwzaviWir7qug5pGCRQZKRBPs6fmOQZQWy8iIjIYvoGBK4FTzeyw\ntARm9nPAycAVnvsQkTb04epqPCAwrfoQQJiWffZdXtBvtCxhnoG4vKBA2qMNa6C2XkREZCB8AwP/\nE5gDPmhmG25qNLPNwHuB/cO0ItOv7DDmstutW5MTtpXZzqQHBCbpcX9d76+vehSwSQoCRMUDCA1P\nQKi2XkREZCB8AwPnETy26NeAq83s/eHyh5rZu4HvA78K/CPw8aqFFJlI8UCB7y0EWdtuQ1MTEPqO\nEuhyQsSu9lNWX8s1qTqsz2igYBQUGC1bigQFFtjexOSD56G2XkREZBC8AgPOudEzi88HjgR+L1z1\nC8DLgPsDnwKe4ZxzNZRTZLplBRHa3H/ZMvimaXpywS7kjcCY1s76tB4XjM/bUNdTQjJsGA2QEBRI\n0tQEhGrrRUREhsN3xADOuUXn3HOBE4Ezgb8B3ge8HniYc+5ZzrmleoqZz8w2m9m/mdmqmZ0cW3eU\nmV1gZnvM7FYzO9vMZmJpTjazr5nZspndYGZntlV2kVRJTx7oA9/bEKZpLoEu/xaT/IjIrlWZUyMt\nb5nAXlKwKJxnYGRx91zuLQWjWwii7w1MPqi2XkREZCBm85Nkc85dBVxVQ1mqOhu4iWASpDXhScEF\nwM3AaQSPX/oIcA/BiQ1mtg24OHy9JNzGh8xswTn3t20dgEygeSB1Wq4EC8D2hJ/bVKUMRdKndYra\n6HTGO11N1e8kzAVQx/F39RntUpGnPxStk5rqb2y0wPxsJ38TtfUiIiLTzXvEQJ+Y2a8CvwK8OmH1\nE4ETgN9xzl3pnLsQeAPwUjMbBUaeSxAkeZFz7irn3MeBdwOvar70IgXUNYw564pmV48ozFM0oDAt\n8wr0ZVRIEZNU1rik0ThNPKGj4iSfG4ICkWXxUQNLzU5E2Dm19SIiIs3xGjFgZg8DzgB+HjgUcMDP\ngO8AFzvnrqythPll2QG8H/h1YDkhyWnAlc652yLLLiYYDnki8O0wzaXh/ZTRNK81s4Odc7saKbxI\nFWWvXPruQ6pRHU6nukdTzM/CYftYXpjjwO2LQHJQIG6JOeZYZJGt1YcAxqitFxERGY5S5xFmdjTw\nAeD0jGRvN7MvAS92zt3oX7RC5TGCWZP/xjm3Myxf3OHArbFlt0bWfTt8vzYjjU4WpJq04fvxn5va\nd976Kh2cLoabx/eZde93nWWbtE5+1vHHj2UI8wskqfIZKZo3Kd1omef+F3fPMbdtsXzGAtTWi4iI\nDE/hwICZHQNcBuwIF90BXAHcTnBLwmEEMxVvJxjSd7mZ/bJz7kdlC2VmbwNek5PsBOBJwFbgbfFN\n5Pwe5zmb8oXAAbFlJ4UvGbQqJ/1lOvLRtG13gNM6O2W3M02amLOhr4b2t+2ZfZ/8FD/57P/iNvZx\nA/s4gDtZ3VV9DkC19UnU1ouISAduPh9+cv74snuai2GXGTHwIYIThR8Ar3DOfTGeIIzqPwV4F/Cg\nMM8TPMp1Tpg3y3XA4wmGBt4V7HrNN83sfzrnXgjcAjw8lnd0wnNL5P3wnDQJnkwwv5FIA8p2vJKC\nBHnzBkxqpzRq0ucWmJa/Q5qyE3ROurSgYMm/c+ptBAsAs8z+5jPZ+qIncgQ3s5VFjuBm7tp5FZ87\n9e0VCg+orU+gtl5ERDpwxHOCV9SunXDZqY3srlBgwMweDjyW4EThEWn34YXPMb7AzL4OfAN4nJmd\n6py7okyhnHOFHmpmZi8nnG04dCRwEfBbwL+Eyy4DXmdm947ce3gGwZDB74a/Xw78mZnNRu49PAO4\nWvccSiEVhwXXrsqcAkWeOtCHYyyqrtn525R1i4mu1PdTjd+LsaBAQYvMsX/F/aqtFxERGa6iTyX4\nrfD9FUUazzDNKwiG9T3bs2y5nHM3Oue+O3oRnMwAXOucuzn8+WKCk4KPhs8vfhLwVuBc59w9YZqP\nAXcDHzSzE83s2cDLgXc2VXYZoPij9KI/Fx2636Wu9x9XZhb5IVMdNavKEwriTwlJmWAwSfzJBDVR\nWy8iIjJQRQMDvwjckTSkMMOFBPcm/mLpUlUzdg+hc24VeBqwQnC14KPAh4E3RtLsJrhX8hjgm8A7\ngDc75z7QUplFupUWtEj6vQtdTozX1aSQXYnXdZXH7Q11QsM6bbiNIN1eDqy6N7X1IiIiA1X08sSD\ngJ1lNuycc2a2Ezi+dKk8OeeuB2YSlt8APDUn73eAxzRTMpksh3S36zLPUW96OH/VjmufOr5t3v4w\nabdatGFo8wxkqfHzMXoyweiRhTVQWy8iIjJQRUcMHIzftZ/5MK/IcGU9Sq9IniY72HVtu09BgDpN\ny3FNy3FI09TWi4iIDFTRwMAWYNlj+3eFeUWGp8oQ7EkxbccT1eaxtf1ZqbJt3UZQXJGgYNY8A2m3\nEUR+Hs0xUNNcA2rrRUREBqpoYCDv2cBN5RWRqEnpiE9KObtS5pYRn3xltdGJ7zpQEN1/vDNel6a2\nmyRhosI91fvmautFREQGqvgUyPAgM3teifQGPJDYBEEiUkITHYy0e5zL3PuclLbvwYCyxzdtNPdB\n+lwHTdZN3dtO2N4ic2ytZ44BUFsvIiIySGUCA48KX2XpZEEmRE2TDuZ1BOLPqE97Xn0Tolc0i3RW\n2rwCOk3UCR8Wn793Vp74aICU79/yQjD5YM3U1ouIiAxQ0cDADRX2oZMFkTLafjJBE1c0m9DGzPYK\ngGQb6vwCVb8jZQJxJfcTfTLBQR5Fi1FbLzIYN7Wcb4K4MtdNIxa3eebzvTDluT/vOWl8H4nrW07f\n/bWdr0re/Srss36FPvnOuaMbLofIdGurY5G3jappJ/1KeNZtFF3rc93WVbauHltYJVBRdpRN1nbS\n8pcdwZNib8XQgNp6ERGR4So6+aCI+GhqjoAmtz8kC7Rfh13+zYoe77Rd8ffRxWcjSYEy1PREAhER\nERkwBQZECjm03s3FO/dVHldXJF2d28rK04eOVJ4F+tPpmyRDuo2gyGMGfbZR11Mm9PkVERGRmikw\nIFLpnqKeyOoo1NHJkfKaehTepGs7cFB0f0UDRlXX+0rabmSSQo0aEBERkSoUGBCpqkzHo440TeT3\nueo/LR3VoZuGK/xtq/LZzwvUlfwuLi8oICAiIiLVKTAgkqjADLF97RjXNbS/r8c3ybq62iz1q+u2\ngKr7JXgyAcBetrRUCBEREZk2CgyI1KVsh7xsR6LM9uu8B1mdVSmq6OiDoY9SaPp/hYiIiEhJCgyI\nNG1aTuqThjq3begdSimnyOelyVE1LU4uqDkGREREpAoFBkTKWq6Qt2/372tUQbNUL/Waj7ymkW41\nERERkY4oMCDSpT496q/J/U9rR66MaZ/vocm/cVIwoOr+mqzTIt/rBoKEezmoeGIRERGRCAUGRMTf\nJHRYxU/TwZwi288bHdCHgFMd3wGf+UbqnEdEREREBm82P4mI1GoB2N5B3ug2qGE7aduVaor+jaOd\n4sMaKkvTmh5l0Bd1fm/j5mdZDucXOLTiLkRERGS4NGJAZFK1NRS6T9uSZNN8332eaTvuPt1eJCIi\nIoOhwIBIU8rcQ5yVtulOelv7nmZZndM2h3yXDRDU1anuunPexTEnKftd8vlcZOTZozkGRERExJMC\nAyJtKNMBaCptndvr8ukKXXdC4/pWnqGaT/m5T6o8vlBERESkQQoMiNSpi451G510dUy6o7ovrq8B\ngSLif2f93UVERKRFCgyITIM6hyTX1SHpsmPTVgexqf1Urbs2O8h964z3pTxVvl9NfzdFREREYhQY\nEJk0Nd+XXHk7dd07XZeuggJ96ZBK+3oyGmfv0pb6NiYiIiKDosCASFua7iz3pHOiq5otUBCiXm1/\nZuuYR2Qh8pqfZXlhrnKxREREZLgUGBDpSpkr7ZPS2e66nG1NQDcJHfM6yjgJxzlEXX/PREREZOoo\nMCAyTfo2rF+mmwIHG/Vl5I6IiIhICQoMiDRpkjsJefvvunxxkz7hoPRXE5/1stvs2/dNREREpooC\nAyJjDqm+iawTeJ3c12MInfPR/eNNGUId5ikS/Jrk4J6IiIhIQQoMiNTBt5Pl23EoGnyo46p/V52b\nMvtNq/+6O7952+tbZ7tv5Zkk8c9fk9+DSZ9bRERERCaeAgMifdF1J6Dr/UctxN6n1SR33Ce57Fmy\nRgl0+ajQprYnIiIiggIDIhlquK2gTmUfcdZmB6LLxx1O+twCZbc7rR3yrvXltgF1/EVERKQDCgyI\nABMdBGhLG1fxm9i2OtJSpy5GDRTMf9fiQRV3JCIiIkOlwIBI09q8V3mS+YwUmPTRAm2Y5LL3VV9G\nByxEXvOz1csjIiIig6XAgEjXFChQHfRZUmBh0oMNTZc/6fOsz7iIiIj02MQHBszsejNbjb1eE0tz\nlJldYGZ7zOxWMzvbzGZiaU42s6+Z2bKZ3WBmZ7Z7JDJV2nhGeZNXLYtuu8wTECZVU3MATHrneuia\nnlSwjieKTBG19SIiIs2ahrGHDngD8LeRZUujH8KTgguAm4HTgCOAjwD3AK8P02wDLg5fLwFOBj5k\nZgvOueh2Zeoc2HUB2jfqUGzvtBTr2ujgzAOHtbAfGZYFmvke6fGFSdTWi4iINGjiRwyElpxzP428\n9kbWPRE4Afgd59yVzrkLCU4uXmpmo8DIcwmCJC9yzl3lnPs48G7gVW0ehEwg3xP1rHy+6+rQ9nwI\nfe/oTMscBvHta7RCN/oyN8HkUlsvIiLSkGkJDPyJmc2b2U4ze3Vs6OBpwJXOudsiyy4GtgEnRtJc\n6pzbF0tznJkd3GjJRYZquJ0bf33o0PehDNNEtwyUobZeRESkIdMQGHg38GzgccD7gNcBZ0fWHw7c\nGstza2Rd0TQi/dPEXAZtdES66Ow0NVeAyEhfvl/TSW29iIhIg3o5x4CZvQ14TU6y451z33fOvSuy\n7N/N7C7g/Wb2J865e0abzNmW8yvphcABsWUnhS+RiKbuRZ5EPsGMaau7LoIOReZZmMa6zlP1mOus\nszLb2nk+/Pv5sB/rrzsnK+qgtl5E+mlffpJEP2s5nyTz7d5WmXdsW0P7vAy4PLZsb1LCWvQyMACc\nA3woJ811Kcv/leC4jgZ+ANwCPDyWZkf4fkvkPX61IJ4mwZMJ5jcSSdBWJ6uO/eRtI2nuAZ99Tkq/\npWrHXZMdShaf70/8u/Ow58Dpzwm2M3pdtRN+59Q6StgWtfUiIiKp/lP4irqOYAqd+vUyMOCcm8f/\n1PwUYBX4afj7ZcDrzOzekXsPzwB2Ad8Nf78c+DMzm43ce3gGcLVzbpdnOUSyJXUOmuw4d90p73r/\nUp1uryiuzcDghFJbLyIi0h8TPceAmf2Smb3CzB5qZg8ws+cC7wQ+GmnkLyY4Kfho+PziJwFvBc6N\nDD/8GHA38EEzO9HMng28PNyWSLeKPCnAp3MwxBnSi3RBuur89rnT3eeyydRTWy8iItK8Xo4YKOEu\ngsmI3gRsBn5I0MCvNfLOuVUzexrwNwRXC/YA5wFvjKTZbWZPBM4FvgncBrzZOfeBdg5DBqGO4cNt\n6jLY0LY2O75ddrJ1i0N/RP8fpP1vmNTvU/3U1ouIiDRsogMDzrlvETx+KC/dDcBTc9J8B3hMTUUT\n6Zc2Oxh978wM/ep32vHn/d2GXm99NJDJItXWi4iING+ibyUQmXh1dqL73iGXatQxH4a877G+5yIi\nItIABQZE6lK14zZNJ/zTdCy+1JHvnzY+l03sQ98nERERaZgCAyJNKjJxYJP7ayuvyJClfXem+Skj\nIiIiMlUUGBDpq4WUn5N+L7qdMut89tWUPpShDnmjCDTKoDnxz1Af6jrrOy4iIiLSIgUGRMSP7oUW\nyef7PSibbwFY9NyXiIiIDJ4CAyJdq7sD3UWHXEEAkXQ+nfwm04uIiIjEKDAg0gdNndi3dbVSpof+\n9v2lv42IiIg0RIEBkQ0OaXbzkzozepPbnVZ13cfeh/vhfU3CZ6bP38mkfJNQpyIiIjJRFBgQaZrP\nSXwdJ/5d7VeSTXLnXkRERESmmgIDInVqs/PXt068rmzWY5oCCH35+7ddjjKPL6z6ZIK+1LGIiIhM\nNAUGRNY0fAtBVbo9QJrS9GdAn7F1qgsRERHpIQUGRLrQ585Bn8sm0lf63oiIiMgEU2BApG/KdDC6\nvtLbZmdIHS9p20LkVfd228gjIiIiUtBs1wUQ6V7PbyGQyTZNcwaUMckd2bS5ALb3oBzx9dEyLTVY\nFhEREZlqGjEgUtWkdYCauAIqzZmWwELfP3NFRgfUeQxpkw7W+VhDERERkYIUGBBpQ5Mz9rfVIVDH\noz3TEgzogz7cBiAiIiLScwoMyIAdWCCNx20GTXbq+vBkgrJlUCd3eNR5FhEREZkoCgyI9EnXkwmK\nQD8CUH1W5DhUhyIiIjJBFBgQmQbqLIi0q80gnr7fIiIi0jAFBkS6pBN+ke5Uvc2ly1EBChyIiIhI\njRQYEGlL1yfvXe9fhqHvn7Mmy1d22+rci4iISE8oMCAyLap0SqpsT52bZszTzsSN+puJiIiIDJ4C\nAyJ94dMRH5ohH/u0aPNvqM+LiIiISCEKDIg0YRIf0adOlIiIiIjIICkwIDIEVTv9ChpIEUP8nGQd\nc931kbe9xZr3JyIiIoOhwICIj+Uat1Vn56GtjtkQO4AiXUj6rmleDxEREamZAgMibUo7iZ+mk/tp\nOhZpxpA+I0M6VhEREZlYCgyIiIiIiIiIDJgCAyJ91+T8ALqaKWn6+tnoa7myFP0OTuKxiYiIyFRQ\nYECkz7q69aDs9vPSq8Mz/Zr6Gy/EXk3uqw59LpuIiIhICgUGRBIdUn0TXT2ysErHRJ0a6YN4IGAa\nNHEs01Q/IiIi0ikFBkTappN5kfTZ9vX98KN6ExERkQoUGBCZZnV0Fny20WQnRR2gQFd/2yb4BAQm\n8fYVjeYRERGRnlJgQKSvuuoIFN1vPJ06Lu2b9Cvsk17+pqluREREpCUKDEgDvtN1AdpX5AR+4fzG\ni7G+r/Z2VYt9LdbNJFkuWC9d/73b3n+dn5c65gIpc/xd/61EajPAtr4Q1Us61U0y1Us61U2bpiIw\nYGZPNbN/MbO9ZvYzM/tMbP1RZnaBme0xs1vN7Gwzm4mlOdnMvmZmy2Z2g5md2e5RTBN9iRNVCQyk\n3Y9dNk+VdE1amfDAQFN1WDQwMCpDH/6WbRh9Xib1eCe13B1TW983auuTqV7SqW6SqV7SqW7aNNt1\nAaoys2cC7wf+K/AVgmN6SGT9DHABcDNwGnAE8BHgHuD1YZptwMXh6yXAycCHzGzBOfe3rR2M9EAN\nTyMQ6dICsL3rQkgp0b+Zz99vAH9ztfUiIiLNmujAgJnNAn8FvNo593eRVVdHfn4icAJwunPuNuBK\nM3sD8HYze5Nzbh/wXIK6eFH4+1VmdgrwKkAnC+JvHjgsYfkATuQHQ1d/u6Xv0tRTWy8iItK8Sb+V\n4GEEVwWcmX3LzG42sy+Y2YmRNKcBV4YnCiMXA9uAEyNpLg1PFKJpjjOzgxssv/RGz0YKqLMpk0Kf\nVT9V621Y9a62XkREpGETPWIAeED4fhbwSuBHwP8HfNXMjnXO3QEcDtwayzf6/XDg2+H7tRlpdsXW\nHRC81TFr1TS6k2A0Z98dQHDOuMj6Wfbobzo6R9wW+/2A9eyrsc3dk7Kb5fB9ZRcs71xfnvXtW05Y\n1uS3dSVl+aJHvkVgKfx5maBe4nW1wS5Y3ZmXaF1aXbctWj9NlMntgntS6iWrTn8GzBXYft7fpch2\ncv+2TYh9XpLqPqtc8fRJ37eyot/PJWBrgTzR78/dCeWJfr6Svot3R9IuAUtXjdYckJB6kqmt76VJ\naevbpnpJp7pJpnpJ51M3vifMmz3zAWxpcZ9r9VF7W9/LwICZvQ14TU6y41kf8fCnzrnPhHlfCPwY\n+E3WhwZazrZcySIeHbx9umS2IXl/1wVo3l05v49EzymvObWhwkyBu0rUTVpdd6mpMs17fmbqKk8f\n6xrGPy9lyxhPP339vqOBy7ouRB619dNgAG29F9VLOtVNMtVLOtVNiqOpua3vZWAAOAf4UE6a64Aj\nw5+/O1ronLvbzH4IHBUuugV4eCzvjsi60fvhOWmiLiK4V/F6glCWiIhI1w4gOFG4qONyFKW2XkRE\npJzG2vpeBgacc/MUuIZjZlcQXPs5njBiYmb7AccQDDUEuBx4nZndO3Lv4RkEQwa/G0nzZ2Y2G7n3\n8AzgaudcfGghzrnbgY/5HJuIiEiDej9SYERtvYiIiJdG2vqJnnzQObcb+B/Am83sDDM7DvgbgrtL\n/z5MdhHBScFHw+cXPwl4K3Cuc250p+nHCO7W/KCZnWhmzwZeDryzxcMRERGRGLX1IiIizTPnyt5y\n1y/hY4z+Avhd4EDgn4FXOOeuiqQ5iuAk4nHAHuA84E+cc6uRNCcB5xIMRbwNeI9z7h3tHIWIiIik\nUVsvIiLSrIkPDIiIiIiIiIiIv4m+lUBEREREREREqlFgQERERERERGTAFBjIYGafN7Mfmdmymd1s\nZh8xs/vG0hxlZheY2R4zu9XMzjazmViak83sa+F2bjCzM9s9knqZ2dFm9kEz+6GZ7TWza8zsrHCW\n6Gi6IdbN683ssrBe7khJM7h6SWNmLzWz68Pj/Gcziz9ubKqY2WPM7B/M7CYzWzWzX09I85bw/81e\nM/uSmT0otv4AMzvXzObNbNHMPmlm92nvKOpnZv/VzP7VzHaH34nPmNmxCemGWDd/aGbfNrNd4esy\nM3tyLM3g6qVuau83UlufTe19cWrr1daD2vosfWnrFRjI9hXgWcCxwDOBBwKfHq0M/7lfQPDYx9OA\n5wMvAN4SSbMNuJjgWcwPA84EzjKzF7dyBM04DjDgJcDPA68E/gD481GCAdfNfsDHgfcmrRxwvWxg\nwYzg/x14E/ALwLeBi8zs3p0WrFkHAd8CXhr+PjbJi5m9Fvhj4PeBRxJMoHaRmW2OJHsX8DTgN4HH\nAkcQ+b80oR4DvIfgmM8g+B5dbGYHjRIMuG5uBF5L8L/gVIJ26fNmdiIMul7qpvZ+I7X12dTeF6C2\nHlBbP6K2Pl0/2nrnnF4FX8DTgRVgJvz9V4F9wL0jaX4fWABmw9//kOA5zbORNH8BXNX18dRcN68G\nro38Pui6IWj870hYPuh6idXFvwDvjvxuwI+B13ZdtpaOfxV4euz4fwK8KrJsG7AMPDv8/WCC57n/\n50ia48JtPbLrY6qxbg4Lj+lRqpvE+rkdeKHqpdE6VnufXC9q6zfWidr77PpRW6+2Pq1u1NZn10/r\nbb1GDBRkZocAzwUucc6thItPA650zt0WSXoxwR/rxEiaS51z+2JpjjOzgxsudpu2E3yAR1Q3yVQv\ngJntTxAV/fJomQv+i32Z4PiH6BhgB+N1spvgpGpUJ6cSRNijab4H3MB01dv28P1n4bvqhuAKpJn9\nNrAZ+Bqql0aovc+ktr64wdeN2vpE+r+9Tm19gi7begUGcpjZ281siSCiewzw7Mjqw4FbY1lujawr\nmmaihfe4vAx4X2Sx6iaZ6iVwGDDDxuP8KdNzjGWNjjvpb78jkubusEFISzPRzGwT8JfA151z3w0X\nD7puzOyksB26E3g/8FvOuWsYeL3UTe19NrX1palu1NYn0f9t1NYn6UNbP7jAgJm9LZwIJOsVnQjj\nbOAU4IkEQzQ+a2YW3WTOLl3O+t7wqBvM7EjgQuATzrkPxjeZs8uJqBufesnbZM76iagXaU3e52Xa\nnEtwP/NvF0g7lLq5GjgZeATw18D/NrOHZaQfSr1kUnufTG19OrX30qGh/d9WW79R5239bN0bnADn\nAB/KSXPd6Afn3O0Ew+auMbOrCCaHOA24DLgFiM+sOorK3BJ5j0dF42n6olTdmNkRwCUE0b6XxNL9\nhOmpm1L1kmOa6qWKeYL7d+NRzB0EdTREo7/tDsajwjuAnZE0+5vZtlhUeAdT8Nkws78GngI8xjl3\nc2TVoOvGOXcP8MPw129ZMKP3H7I+Cdwg66UAtffJ1NanU3tfL7X1Gw26PQO19Wn60NYPbsSAc27e\nOff9nNc9KdlnYu+XAyfFZlY9A9gFfDeS5jFmNhtLc7VzbldNh1WLMnUTXj34KvCvBBNjxE1N3VT8\nzMRNTb1U4Zy7G7gC+JXRsnBY2RMIjn+IriP45x2tk20EkeNRnVwB3BNLcxxwFBNcbxb4a+DXgdOd\ncz+KJRls3aSYATY551QvGdTeJ1Nbn07tfb3U1ica7P9ttfWltd/Wux7MutjHV1jZLyMYVvhzwOnA\n/wG+x/psspuAKwmG150MPIkgkvOnke1sI4iKfphgsplnA0vA73V9jBXq5kjgB8CXCB6FcfjoFUkz\n1Lo5KvzMvBHYDTw0/H3LkOslpa5+i2BG1ecBJxDct3o7kRmcp+0FbAk/D6cQzBT7ivDn+4frX0Mw\nCc+vAScBnwWuAfaPbOO9wPXA4wgmm7mM4Epe58dXoV7eC9xB8CijwyOvAyJphlo3fwE8Gjg6PO6/\nIJjp/PQh10vNdaz2Prle1NZn14/a+2L1pLZebX30mNTWJ9dNL9r6ziuiry/gIcA/EgyDWiYY2nEu\nkQYxTHcUwXNq9xBMpnI2QXQnmuYk4NJwOzcAZ3Z9fBXr5gXhP7qV8H30WlHdcF60PiLvjxlyvWTU\n10vDf2J3EkQ0H951mRo+3sclfD5WgQ9F0ryZ4ERxmWB26gfFtrGZ4N6z2wlOID8J3KfrY6tYL0n/\nT1aB58XSDbFuPkBwFeVOgk7FxcAThl4vNdex2vvkenlByndz8G19eEznJfw/V3ufXFdq69XWk/L/\nRG29609bb+GGRERERERERGSABjfHgIiIiIiIiIisU2BAREREREREZMAUGBAREREREREZMAUGRERE\nRERERAZMgQERERERERGRAVNgQERERERERGTAFBgQERERERERGTAFBkRERERERDSJyuQAACAASURB\nVEQGTIEBERERERERkQFTYECkR8zsejNbLfB6ftdlTRIp/1Fdl6UIM3uXma2Y2cNa2NcxZna3mX28\n6X2JiEi/qb1vl9p7kXyzXRdARBJ9HbgmY/0P2irIiJmdBzwPeKFz7sMpyVz46j0zOwF4GfBJ59zO\npvfnnLvOzN4HvNTMznXOXdr0PkVEpPfU3jdM7b1IMQoMiPTTB5xzH+m6ECmyTgROB/YDbm6pLFW8\ng2DU1Fkt7vNPgZcA7wJObXG/IiLST2rvm6f2XqQA3UogImVZ2grn3HXOue875/a1WaCyzOxY4CnA\nPzvnrmprv865W4EvAL9gZo9ua78iIiIe1N57Unsvk0iBAZEpYGaPMLOzzewbZnZLeG/brWb2eTN7\nQka+Z5nZl83s9jDPvJl918zeb2YnhWmONrNVgmGFAH8Xu//xTZHtJd5zaGZfDZc/1sxOMbNPh/u6\ny8z+w8xelVHGLWb2VjP7QZj+JjP7oJkdYWZnxctQ0EvD9/NS9hkt7y+Z2QVhHS2a2T+Z2WMjaZ9q\nZpeY2YKZLZnZxWb2Cxn7Hu3zpRlpRERENlB7r/ZepCkKDIhMhz8HXgXsD/wr8GngRuBpwJfM7OXx\nDGb2RuDjwKOBK4FPAJcD+4AXAY8Pky4CHwauDX//OkFjN3p9K7bprKGHTwL+GTgWuAj4P+HP55jZ\nuxLKuAW4BHg9cB/gQuBrwJOBncDohKTsfY6/Eeb5ck66pwKXAjvC8n6foL4uNrNHm9krgM8T3Jb1\nRYI6/xXgn8zsgSnb/Eq476eYmW7nEhGRMtTel6P2XqQo55xeeunVkxdwPbAKPL9kvicDOxKW/xKw\nANwFHBFZvhnYC+wCHpyQ7/7AcbFl54Vle15O+VeAo2LLvxrmXQVeHFv3+DDPPcCRsXXvDPN8J3p8\nYfk/EdnmG0vU1QPCPLdkpBmVdwX4f2LrzgnXXUNwEvX4yLpNwN+H69+fsf1vh2l+uevPnF566aWX\nXu2/1N6rvddLr769NGJApJ/iw/fir23RxM65C11wPxux5f8MvJdggqBfj6zaBhwA/NA5t2HGY+fc\njc6579V7SAB8yjn3t7F9XUIQnZ9h/aoFZnYg8GKCaPsro8fnnLsL+COCk52yRo8qKnKv4d875z4W\nW/Zn4fsDgHPD8o/KtUpwNQeCiZnS/Ef4njUEUUREpp/ae7X3Ir2gYS0i/ZT3+KJ74gvM7FCCoXAP\nAe5FcHIA8ODw/dhRWufcbWZ2PfBQMzsH+KBrZ1Kef0hZfjXBVZAjIstOBbYAtznnNgwBdM7Nm9mX\nGD8BKmJH+H57gbRfSNjvHWb2M4I63rCe9b/bEQnrRkb73pGRRkREpp/ae7X3Ir2gwIBIP5V6fJGZ\nvZjgkTgHxVY51mcV3hZb9zzgkwT3Kr4qbPy+AVwMfNQ5V6QhLeuGlOW7w/cDIsvuF75fn7G9H3mU\nYXtsn1nSyrtEcKKwYb1zbtHMIBj+mGa073sVKIOIiEwvtfdq70V6QbcSiEw4MzsVeB/BFYPXACcA\nW5xzm5xzM8Dvj5JG8znnvg4cDTwLeA9Bg/xEgvv8fmhmWUPjfK165MmaaKjsJEQQ3IMJG0+ckuSV\n1+d4ovu+wzO/iIgMjNr70tTei5SgEQMik+9Z4ft7nHPnJKw/NmEZAM65O4FPhS/M7DDgT4GXAB8i\nOJHoyo/D96wyZK1Lc0v4fqhH3rqM9r3hPlEREZEUau/LUXsvUoJGDIhMvkPC9w3D3MzsAOCZRTfk\nnJsnuAoBcH8zOziy+u7wva2A4hXAMnCfpGczhyc1Z3hsd2f4fkKFslX1kPD9ig7LICIik0XtfTlq\n70VKUGBApJ8sP8ma74bvzzezrWsbCE4S3ktClN3MjjKz3zOzuYTtPT18v4Px+/JuDN8fQgucc8vA\naEbjd5nZfUbrzGwz8NdsvMeyyHavIziWe2c8e7gx4cnXiQSPPvpG2/sXEZFeUXuv9l6kF3QrgUg/\n/Z6ZPT5j/UXOufPDn/8O+H8JHoVznZl9neB5vI8mmBDnr8L1UYcA7wfONbN/Y33CnwcDpxDcS3em\ncy56T99ngTcBLzezkwga21Xgc8656OzDZU5y8rwe+GWCGYuvMbNLgDuBRxH8//ow8HzWr24U9Rng\n5QRXIK71LJvvcZ4e5v2Cc27FcxsiIjId1N4H1N6LdEyBAZF+ceHrPxE0kPF1Fr7/DDgfwDm3y8x+\nEXgzwWRCTyJ4PM6F4bJHJ+znGuAVwGOAk4BfDZffRND4vts5962xnTv3HTN7JvBq4BGsP7f3BtYf\nSzQqf9px5R33+ELn9pjZ44D/Cvx2eHw/A74E/Lfw+ADmM7ad5Fzgj4EXAP+jrvIW9IJIGUREZJjU\n3o/vU+29SMdsPEAoIjIZzGw/4N+BBwGnOuf+rWT+fyB4DvTJzrl/b6CISfs8nODE6krn3C+2sU8R\nEZFJpvZepB2aY0BEes3MTjWzTbFlWwnuOXwwQaNb6iQh9BpgH8Fwyba8gWCk1qta3KeIiEjvqb0X\n6ZZGDIhIr5nZ9cCBBFcLfgrch+C+yHsRDKH8Fefctz23/U6C+zEf7pzbmZe+CjN7AHAV8Bnn3G83\nuS8REZFJo/ZepFsKDIhIr5nZHwPPAI4nODlYAX4EXAyc45y7qcPiiYiISA3U3ot0S4EBERERERER\nkQHTHAMiIiIiIiIiA6bAgIiIiIiIiMiAKTAgIiIiIiIiMmAKDIiIiIiIiIgMmAIDIiIiIiIiIgOm\nwICIiIiIiIjIgCkwICIiIiIiIjJgCgyIiIiIiIiIDNhs1wWYRGZ2EHB81+UQERFJcLVzbm/XhZh0\nautFRKTHam/rFRjwczxwRdeFEBERSXAqsLPrQkwBtfUiItJXtbf1CgxU8pvAfVivxpnYz4S/R38e\nvc/GlsXzJm0nL088XVbeFPGizkSWJe1uhvUbUqK7GK2Provn3ZRS1Pj2ipQhr9xZebO2l/RnTNtv\nkWOeAWZc+PNKmG4V2xT8PDMbvs+ssmkmYdkoHavhrlbZxL5wd6tsWls+SreylnYmXBbkWRlbNjO2\nbH0bM4WXrZcnWLcvZf3Gcm0qvO2V8DhXEo4z75iSjzN5fby+kso1Xu8b6zXjmFZWmFkJl604wj/p\n2sfBViHcNGHW4H01/HlfwrKVyLrRz6sZy1Yi24kui24nvu2V2M9tLEtan1QPRY45rx4i6/aFP+8L\n1+1bhZXRspXxaopvOrosvumkdCuxPEXzJu1vHvg0Ur//DBxOsfY92qgktfk+5wl56bLypijSZia1\ne9F2O5q36HlAfL/x84Ciba9vu12mLc87x8g65rV3N9bWA9imlbF2HWDTTMKyTePtZHabs97ORNv6\n4L1Y2zpTYFleWz/aR1bbmr2/9byziecsycfkf04Tra+s85N9Y/VeqA5jbX2wLNbWw/g/9GgbldA2\nJTYgeecGeW19fNtdtu9F6iHvmEucI+W19aOs8TY6rd3Oat+j6/Ly5p0nNNnWKzBQyX2AI4D9wt9n\nYz8T/h79OZ4uLW/Z7UTXFcmbwMIXrDd48QY4vuloQxhdF2+094vlqbK9snny8uadw6WVP563cJ4w\nMDC7svZuM8FXftN+YQM1GzlJmA1PBmZXmFkLDEQbodHJwsrY8uB931rjOp5nfX35vOt5Nm4vOW+x\n/eVtu/68Pseynm4mIe+mWLqE89cVx8y+oLWaXXGEf/q1dyva4pTpjZZdVmZ/dZQhrfVr45g3RX4O\n190zWhR+Ve8hOGFY+5n1n9OW7Yv9XCRdmbxJ25GmHMbGth782+Uq24mny8uboEhbH990W+cBVcrg\n024nld8nT2o6N9bWA9jMvrG2HmBmdmWsrQeY2VSmvWqqbU1ry+s/T6iSt9o5TZG8Mwl5N4219UHe\n9LYegnZ+rK2HYm1h2rIi7aRP21t3GZLK0sb+4ss2jW87r60fZfVpo7PS+bTvbbX1mnxQRERERERE\nZMAUGBAREREREREZMAUGRERERERERAZMgQERERERERGRAVNgQERERERERGTAFBgQERERERERGTAF\nBkREREREREQGTIEBERERERERkQFTYEBERERERERkwBQYEBERERERERkwBQZEREREREREBkyBARER\nEREREZEBU2BAREREREREZMAUGJD+uOn8rkswVXadf2HXRZg6Xzx/d9dFmCrnX9t1CUSkdWrra7dw\n/kVdF2GqqK2vn9r7yaDAgPSHThZqtVuBgdp98fzFroswVXSiIDJAautrt+v8i7suwlRRW18/tfeT\nQYEBERERERERkQFTYEBERERERERkwBQYEBERERERERmw2a4LMNl+Gr6PqnEm9vNo3Uws3Wzs56S8\nSdvJyxNPl5U3gQtfAKvh+0psPQnrR+GlfZFdjHa9KbIsXqxNsWX37IKFneN5RutmIj/HtxNdn5Yn\nK2/W9pL+jGn7LXLMM8BMWJEzYeXOruI2BT+vzgbvNrMaWR++z6yyOkoX/gFWWGVTWPHBz6thEVZY\n2bXE8s6rmQmXzYR/zE2ssin8eWbtPbpsfRszhZetrm07WLcvZf36fuN58re9ElbryliejeUvuixt\nfby+1su1tGuVq3beGdbhvrA868dRqA5XVplZCT4DMyuwafTnDd9tlfXv0krkffSd25ewbCWybvTz\nasaylch2osui24lveyX2cw3Ldt0NO+cz0iVtJ6keihxzXj1E1u0Lf157X4XwTza2mX1kL4tvOind\nSixP0bxJ+5tHmjFPetua1PjE29t43rLnCXnpsvIm8G3rIWjfom39qHjxdjutTYy29aN1We12Wtvr\n226XacvzzjGyjnnt3Y219QBu08p4Ww9BmkhbD7C6aSW1rYfxNifa3kfb+uC9WNs6U2BZXls/2kdW\n25q9v/W8s4nnLMnH5H9OE62v9W1E2/pg3b6xtr5QHcba+mBZrK2H8X/o0TYqoW1KbEDyzg3y2vr4\nthtq83fdDTtvL7kdn/OhEudIeW39KGu8jU5rt7Pa9+i6vLx55wlNtvXmnMtPJWPM7GHAFV2XQ0RE\nJMGpzrmdXRdi0qmtFxGRHqu9rVdgwIOZHQQc33U5REREElztnNvbdSEmndp6ERHpsdrbegUGRERE\nRERERAZMkw+KiIiIiIiIDJgCAyIiIiIiIiIDpsCAiIiIiIiIyIApMCAiIiIiIiIyYAoMFGRmnzez\nH5nZspndbGYfMbP7Fsj3ljD9XjP7kpk9qI3y9pmZHW1mHzSzH4b1co2ZnWVm++XkO8/MVmOvL7RV\n7j7zrdMwrz6jCczs9WZ2WVgvdxTMo89oBp86DfPpMyqtUFtfL7X39VJb3wy19/VSWz+5FBgo7ivA\ns4BjgWcCDwQ+nZXBzF4L/DHw+8AjgT3ARWa2udmi9t5xgAEvAX4eeCXwB8Cf5+RzwBeBwyOv5zRX\nzIniVaf6jGbaD/g48N4SefQZzVa6TvUZlZapra+X2vt6qa1vhtr7eqmtn1B6XKEnM3s68Blgf+fc\nSsJ6A24G3uGce2e4bBtwK/AC59zH2yxv35nZq4E/dM49MCPNecDBzrlntFawCZZXp/qMFmNmLwDe\n5Zy7V4G056HPaK6idarPqHRNbX391N7XS219fdTe10tt/eTRiAEPZnYI8FzgkqQThdAxwA7gy6MF\nzrndwL8ApzVeyMmzHbg9J40DHmdmt5rZ1Wb23vBvIcny6lSf0frpM1ovfUalM2rrG6P2vl5q67uh\nz2h99BntCQUGSjCzt5vZEjBP8CF+dkbyw8P3W2PLb42sEyC8h+hlwPtykl4I/C5wOvBa4LHAF81M\nn+OYgnWqz2j99Bmtlz6j0jq19c1Re18vtfWd0me0PvqM9sSgP7xm9raEiUPir2MjWc4GTgGeCNwF\nfDYc/lJqtwRRxqnjUZ+Y2ZEE/1w/4Zz7YNb2nXMfd879/865/3DOfQ54GvBw4HFNHVPXmq7TtN2i\nz6gXfUbrr9O03TKln1Gpn9r6+qm9r5fa+vqpva+X2vphmO26AB07B/hQTprrRj84524nGK51jZld\nBdxIMMTlsoR8t4TvOxiPgO0AdvoWuOdK1aeZHQFcAnzdOfeSsjtzzl1nZvMEk0N9pWz+CdFkneoz\nmuy6nPWF6TO6xrdOh/gZlfqpra+f2vt6qa2vn9r7eqmtH4BBBwacc/MEQwV9zMTe464j+KD/CnAl\nrE2k8QjgXM999lqZ+gwj3ZcA/wq80Gd/ZnY/4FDgJz75J0HDdarPaMP0Ga1scJ9RqZ/a+vqpva+X\n2vr6qb2vl9r6YRj0rQRFmdkjzOxlZnaKmf2cmZ0OnA/8ALg8ku5qM/sNABc87uEvgf9mZr9mZicB\nHwFuAj7b/lH0R9iofRX4EXAmsMPMDjezw2Pp1urTzLaY2TvM7JEWPMf3CcDnCP4GF7V7BP3jU6f6\njGYzs6PM7BTgKGDGzB4a/g/YEkmjz2gJZetUn1Fpk9r6+qm9r5fa+maova+X2vrJNegRAyXsBZ4B\nnAVsIYgGfhF4q3NuXyTdscC20S/OubPDL8H7CWaN/RrwZOfc3S2Vu6/OIBhq9QDgx5HljvGrMtH6\nXAFOAp5HUJc3E/zzfYNz7p6mCzwBfOpUn9FsbyH4vEFQj98K3x8PXBou12e0nLJ1qs+otEltff3U\n3tdLbX0z1N7XS239hLIgSCMiIiIiIiIiQ6RbCUREREREREQGTIEBERERERERkQFTYEBERERERERk\nwBQYEBERERERERkwBQZEREREREREBkyBAREREREREZH/2969R+tR1Wcc/z5cBRvAGAIYLtEoSEQC\nymWJXFIuJYoUqJWihSgQtWBBXQtpREtAYAkVF2BFQbmEohSQVkUhlDYJhLAQLZKCF0qCSbkYEiAk\ngCZEkl//2PvlTObMvOc957znnOS8z2etWZPM3rNnv5fz/vbas2fvDuaOATMzMzMzM7MO5o4BMzMz\nMzMzsw7mjgEzMzMzMzOzDuaOATNrK0kTJa2VNG2o6wLtqc/68Jok7SDpFUnnDFUdWiVppKSXJX11\nqOtiZmbttz7ExSLH+sHnWD/8uGPA1nuSxuYf6hlDXRcASWdIul7SI5Jey3U7pIdzdpB0raTFklZK\nekzSOZI2Gax6D4EYrAvlz2B2D9naUZ91ypC0SNLCNpTbivOAVcA3SnWQpBMlzZL0gqRXJT0r6ZeS\nrpR0cF2Bkr4pabWkrdpZ0YhYBnwTOFPSW9pZtpkNT471GyzH+vY6D8d6GyLD+YfKhp9BCz49uIJU\nl8XAUmA7mtRN0vbAg8AY4N+B+cBE4EJgP+DYga1ux6j7DB4E3gk834+ym5Ux4N9LSTsDpwJfi4hX\nSsnXAR8HlgE/AZ4BtgD2yueMAObUFP2XwD0R8dIAVPty4CxgKnDmAJRvZsOTY70141jvWG8DxB0D\nZr13FPBQRCyVdBXwqR7yXwLsCPxdRHyncVDSTcAJkk6IiJsHrrqdLSJWAo8PdRn9dCpphNf3iwcl\nHURqKDwMHFJuSEjaGhhfVaCk95C+l5cMRIUjYomkWcCJks6OiFUDcR0zswHiWL8Bcax3rLf+86ME\nNuxI2iUP5XsmD7V6StI1knaqyb+npDvzc1LLJd0haQ9J0/OwtZ2L+SNiRkQsbbEuI4C/AZ4oNhSy\nqXn/yV6+vmMk3Z2Hkq2UtFDSv0h6VynfKEmX5/RVkpZIuqWcL+dtvNaxks7Mwx9X5eFz50pSxTlb\nSLo4v78rJT0qaUpNnRtDRK+vSa8cHihphKRpeSjnH/Ln80tJX5G0ifLzgDl749nAxvbxXEa3ZwYl\nLZD0kqQtaupzez7n7VVlNF4PsDMwtnTdaZIOy/++sqb8cTn9rqr0Cp8AHouIX5WOvy/vb6i4u0BE\nrIiIB2rKPCbvby/Ua62k2ZLGSLpJ0vP5ffqppLfmPOMl/UjSspz2A0mja65xK7ANvlNmZm3mWP96\nPsd6HOsd660dPGLAhhVJuwJzgVGkH8FfA+8GTgGOlnRgRMwv5J8A3EcajtUY+rdvPvY/bajS+4DN\ngP8sJ0TEk5IeBw6QpIjocZiapK8DnwdeyPVdSgpYhwH/TXq9SNoWeAB4GzAbuCn/+6+BoyQdGRH3\nV1zia8AhpGFqM4DjSM+7bQZ8uVCPjUjv72HAI8D3SO/5ZcA9TV5Cs9dYfqZvNHAvsBupl/xbpM7M\n3YGzgUuBhcD5wDRgETC9UMTDTcq/MZ9zLPCvpeuOAiYBP4uIBTVlvJiv+7n8/8sKee6JiDmSFgAf\nk3RWvgtR1GhUlRuQ3Uh6B7ATUNXQagx33K2nciocCzwcEU+Vjr+J9P1fnK+5G/AhYLyk40hDFR8E\nrgH2AT4MjCR9F8oaDZXDAN8pM7O2cKx3rC8U4VjfnGO9tS4ivHlbrzdgLLAWuLOFvLNy3iml46fl\n4/9VOn5fPn5C6fj5+fgaYOcm17sq5zu4Jv0zOf3zNek/yeljW3htH8p55wFvKqVtBIwu/P+6nPfC\nUr4P5OOPAyocn56PLwC2Kxx/M+l5thXApoXjn8j57yiVswdp0py1wLkVn+F1Na9tLTCrdOy2fPyC\nivzbAhs3O7+QNrGiPuMa9a/I//c57bRmZeTji4Df1Vz3rHzO5NLxTYDfk4LxxlXnlvKfnMs5vSJt\nDLA8f0+/Rwrctd/Xis/jHys+h7XApaXjV+bjK4AzSmk/zWl7V1xnI+Bl4Dc91cmbN2+dveFY38jr\nWN+V5lgfjvXeBm/zowQ2bORhgBOBX0fENaXkq4D/BQ6VtGPOvwvwfmBedH/u7xLSj3B/bZ33K2rS\nXyrla+b0vP9sRLxYTIiItZGHPEraDPgoqYf5wlK+GaQ7Gm8nvfayCyJiSSH/C6S7BSOAXQv5Juf9\nlyJHhZz/V6Qe+n5RmsTpr0iNl/PK6RHxXESs6Wv5EfEEqYf7iHzHpegkYDVwS1/Lz6bncspDLo8C\nticNCWzlNbw1758pJ0TEM6QGwlPAx4AfAIskLZV0s6Q/rymzMbTwxxVpL1O4Y5Q17rQsiYh/LqU1\n/nb2rKjfWuBZUuPEzKzfHOsd61vlWO9Yb73jjgEbTvbK+3vLCTmgNWZrnVDadxtmFxF/JPXWd3ve\nbgjtB6yKiG6vr+SdwObAz6N6Eph78n5CRdpDFceezvttCscmAK9ExLyK/HN7qF8r9sn72f1pFPTg\nRlKP/kcbB/JQvn2BuyItw9NnEfE88G/AgZKKw/+mkIYplhu0dUbl/YtViRExk3RX5AjgK6Q7O5sC\nxwMzJV1UcdoxwP9FxCMVafMrvjfP5n1V/kZa3VJFLwCbS3pjTbqZWW841ieO9a1xrHestxa5Y8CG\nk8b6rEtq0heX8jX2dZML1ZXTG427B3V3CbYq5Wtma7p+mJvp7ftQVLWUzWt5v3GpLs/VlN+O963x\nfnXrOW+jW0i9/CcWjp2U9/2+E5JdnfdTAJTW+f0AcG90f6axzyJiTUTMjIjzIuJoUgPjk6TP7ouS\n9m7klTQSOIjqOwjQ/DvQLG3TmvLWpwa3mW34HOvXLdOxvjnH+mqO9daNOwZsOGn8kG1Xk759KV9j\nXzfLal05vdFY9mbXmvR3AK8CT7ZQ1nK6XkMzvX0f+mIF6dm/KlXXbcwm3G3CU6VldsoaQzvH9L5q\nrclDNO8E9sl3DyA1HJaTngdtxzXmAI8BkyVtSnqGcCPgu70opjHp0MheXHdNRFxLmogKoDjM8ChS\nw+9HvahDf4wEXo2IPwzS9cxseHOsTxzrW+BY71hvrXPHgA0njZlpDy4nSFI+HqRhgxT2B1Tk35I0\nhK7H2YN78DNST/XhFdfYhdSIuD8/n9WTB4E3SJrYQ77fkhog+9Ys0dM4v2poYKvmAX9W7J0uOKji\nWLPgX1XGL0gNjEMltbJ6SrDuXY5WNe4WnCTp/aTn426LiNUtnr+mheteTWpYHUuaMXsZadhhq36X\n931pODUCdPF7fEyuw5zu2dsrz2i9A2niJjOzdnCsTxzrW+dYP4Ac64cPdwzYsBFpKZbZwB6STikl\nf4r0PN6sPIkLEfEk6ZnDvSUdX8r/BdJSLq2qHEIVES+TJmwZJ+nTr2dOjZev5v+22qPcWCf3Cknr\n1E1pnd/R+Zp/IvUebwt8sZRvEvAXpGfLqpYwalUjyF6UA0Kj/HfTNUTvdRHxEmlCqIMkjSvkH0HX\n+1DMv5QUUMeRlhpah6TRkopBehmwYx9exx2k5/n+lq5JlnoztHAZsK2kzZvkuYE0e/NlpMmFbuxF\nYwS6nuPcr5wgaZLSWtfdGixK6zJ/hNRQmJuPbQ4cSZr1u5UGan/tDmxJe55FNTNzrHesd6xfN82x\n3tqmld45s/XFnpKm16T9NiIuIS1VNBf4rqSjST3q7wKOJj1feFrpvDNIvanfl/Rh4AngPcD++fjB\ndA2NA0DSVFLDA9LaxQBTJZ2c//3DiCg+0zWVNLzrW5IOz9c4JF/j9ohoaUbciJgh6VLS0jjzJf2Q\n9OzfGOBQ0rrE38jZ/yFf48uSDgB+Tuoh/wipZ/lk+ucG0sy4k4CHJd1FGkZ2AvAfpOWWyr5OWsv3\nAUm3kTomJ+W67V+R/3TSkkhfkvRBUkNQpDsvR5CGhTaGSM4Ejs/vyTxS7/6PI+LRZi8iIlZLuhX4\nNGlZpkURcV8rb0Dhuu8FZkiaS7pjdG+xjIhYnq8xmRS4ezO0kIhYIOlJKu52kdYdvgx4XtIc0h0H\nkWai/iDpN/7bEfGLnP9w4I3UP3PYbo2/j5mDdD0z2/A51jvWO9avy7HeBkesB2smevPWbKNrHdY1\ndK29WtzWUFjXFtgZuJY0mc1q0ky71wA71ZQ/AZhBCjwrSGu1jqdr3eGtSvlnF65b3BrHzq24xva5\nDotJPcqPAecAm/Th/TiO9OP7IrCS1PiYDuxeyvdm4HJgIWm44RLSJDzjK8q8npp1nEm9+Gsord8M\nbAFcTFo+ZyXwKHAqqZHSbR3gfM5ppLsJr+Z6TSMFtMq1iUlLJ50P/CZfRiwZ7QAAAgxJREFUYxlp\nNuVpxfeO9KzjzaQG4Wu5vpNz2sS6+uT0Awqf3YU1eSrLIAXeq/N37U9NPv9D8/n39/FvYFo+f6/S\n8VH5Pb+V1DBekd/bp0kNguNK+b8D/BHYsuY6dZ/DWGrWpm72/gJ3k2cqHujfCW/evG3YG4715bIc\n6x3rG8cd670Nyqb8gZpZQR6u9QTpR26Hoa6PbdgknU1qWJ0SEdP7cP6OpDsEV0TEF/pYBwG/Bx6K\niKq7PG2V16d+GrgyIj470NczM+stx3prJ8d6x/oNnecYsI4maWNJoyqSppLuRgzWbK42TOVJoT5D\nugNyc1/KiIinSXfGpkiqWnqqFfuT7rYM1tDCz5HumF08SNczM6vkWG8DzbHesX448IgB62iStiEN\nu7sbmE9an3V/YB9Sj+t7I6Ida/Vah5F0IGmo5ZHAgcDUiPinfpS3HbAAuDgiLmpPLQdGXj95IekO\nwjlDXR8z62yO9TZQHOsd64cTdwxYR8vrzV5Oei7sLcAbSI2EGcAFEbF4CKtnGzBJ00jPCz5Hmv34\n7Bic2YHNzKzAsd4GimO9DSfuGDAzMzMzMzPrYJ5jwMzMzMzMzKyDuWPAzMzMzMzMrIO5Y8DMzMzM\nzMysg7ljwMzMzMzMzKyDuWPAzMzMzMzMrIO5Y8DMzMzMzMysg7ljwMzMzMzMzKyDuWPAzMzMzMzM\nrIP9P0fAWeKUzQBmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f3f0450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
"vmin = np.log10(Utils.mkvc(SigMat_FD).min())\n",
"vmax = np.log10(Utils.mkvc(SigMat_FD).max())\n",
"ax[0].contourf(X, Z, np.log10(SigMat_FD), 100, vmin = -3, vmax = -0.5)\n",
"dat = mesh3D.plotSlice(np.log10(sigma2DFD), ind = 21, normal='Y', ax = ax[1], clim=(-3, -0.5))\n",
"for i in range(2):\n",
" ax[i].set_ylim(-600., -10.)\n",
" ax[i].set_xlim(-300., 300.)\n",
" ax[i].set_xlabel('Easting (m)', fontsize = 16)\n",
" ax[i].set_ylabel('Depth (m)', fontsize = 16)\n",
" if i==0:\n",
" ax[i].set_title('1D stitched inversion for FEM', fontsize = 16)\n",
" elif i==1:\n",
" ax[i].set_title(('2.5D inversion model (FEM)'), fontsize = 16)\n",
" cb = plt.colorbar(dat[0], ax=ax[i], orientation = 'horizontal', ticks = [np.arange(5)*0.5-3])\n",
" cb.set_label('Log10 conductivity (S/m)', fontsize = 14)\n",
"fig.savefig('./figures/1DinvFD.png', dpi = 200) "
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
"# vmin = np.log10(Utils.mkvc(DpreMat).min())\n",
"# vmax = np.log10(Utils.mkvc(DpreMat).max())\n",
"# ax[1].contourf(Xtime, np.log10(Time), np.log10(DpreMat), 31, vmin = vmin, vmax = vmax)\n",
"# ax[0].contourf(Xtime, np.log10(Time), np.log10(DobsMat), 31, vmin = vmin, vmax = vmax)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Itime = [0, 5, 10, 15, 20, 25, 30]\n",
"Itime = [0, 15, 30]\n",
"color = ['k', 'b', 'r', 'g', 'c', 'm', 'y']\n",
"legendobs1 = [('Obs %3.1f ms')%(time[itime]*1e3) for itime in Itime ]\n",
"legendobs2 = [('Pred1D %3.1f ms')%(time[itime]*1e3) for itime in Itime ]\n",
"legendobs3 = [('Pred2D %3.1f ms')%(time[itime]*1e3) for itime in Itime ]\n",
"legendobs = np.r_[legendobs1, legendobs2, legendobs3]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dpredline = np.load('./inv2D_realistic_line/dpred_13.npy')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dpredline = dpredline.reshape((30, 31, 2), order='F')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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aEACqV68eDR8+nNavX0+RkZG0YcMG+vzzz0lHR4e6detGGRkZGl9LTSfq0LRD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4EHYPVqKCz06RPr15d/vfVWv553OF98AUOG+K/T06FRox23rVsjB9p/+YsPprOy/NawYfmvmzaN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\nQIG2iIiIiEgcKNAWEREREYmD/w/gbd4u1Mii9QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f32eb10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
"for icount, itime in enumerate(Itime):\n",
" plt.semilogy(np.r_[xyz1[ind,0], xyz2[ind,0]], Utils.mkvc(DpreMat[:,itime]), color[icount])\n",
"for icount, itime in enumerate(Itime):\n",
" plt.semilogy(np.r_[xyz1[ind,0], xyz2[ind,0]], Utils.mkvc(DobsMat[:,itime]), color[icount]+'o', ms = 3)\n",
"for icount, itime in enumerate(Itime): \n",
" plt.semilogy(np.r_[xyz1[ind,0], xyz2[ind,0]], Utils.mkvc(dpredline[:,itime,:]), color[icount]+'--', ms = 3)\n",
"ax.legend(legendobs,bbox_to_anchor=(1.4, 1.00), fontsize = 10)\n",
"ax.set_xlabel('Easting (m)', fontsize = 14)\n",
"ax.set_ylabel('bz (T)', fontsize = 14)\n",
"fig.savefig('./figures/1dinvobspred_TD.png', dpi = 200)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"frequency = np.r_[1, 10., 100.]\n",
"dobs = np.load('bzobs_FD_realistic_line.npy')\n",
"dest = np.load('inv2D_FD_realistic_line/dpred_14.npy')\n",
"Dpred = abs(dobs.reshape((30, 2, frequency.size, 2), order='F'))\n",
"Dest = abs(dest.reshape((30, 2, frequency.size, 2), order='F'))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"DpreMat_FD.shape\n",
"legendobs1 = [('Obs %3.0f Hz')%(freq) for freq in frequency ]\n",
"legendobs2 = [('Pred1D %3.0f Hz')%(freq) for freq in frequency ]\n",
"legendobs3 = [('Pred2.5D %3.0f Hz')%(freq) for freq in frequency ]\n",
"legendobs = np.r_[legendobs1, legendobs2, legendobs3]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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S7xYttOcsMFB/3nFH+SxjZigYtm/XxnZAQKG+yNntdqwRERATQ8hf/4p3Oi9SBj3w8tIx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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ef04890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ind = xyz1[:,1] == 0.\n",
"absFD = lambda x, y: np.sqrt(x**2+y**2)\n",
"mradFD = lambda x, y: np.angle(x+1j*y)*1e3\n",
"realind = [0, 2, 4]\n",
"imagind = [1, 3, 5]\n",
"fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
"for itime in range(3):\n",
" abs1 = absFD(Utils.mkvc(Dpred[:,0,itime,0]), Utils.mkvc(Dpred[:,1,itime,1]))\n",
" abs2 = absFD(Utils.mkvc(Dpred[:,0,itime,0]), Utils.mkvc(Dpred[:,1,itime,1]))\n",
" ax.semilogy(np.r_[xyz1[ind,0], xyz2[ind,0]], np.r_[abs1, abs2] , color[itime])\n",
"for itime in range(3): \n",
" abs3 = absFD(Utils.mkvc(DpreMat_FD[:,realind[itime]]), Utils.mkvc(DpreMat_FD[:,imagind[itime]]) ) \n",
" ax.semilogy(np.r_[xyz1[ind,0], xyz2[ind,0]], abs3, color[itime]+'--', ms = 3) \n",
"for itime in range(3):\n",
" abs1 = absFD(Utils.mkvc(Dest[:,0,itime,0]), Utils.mkvc(Dest[:,1,itime,1]))\n",
" abs2 = absFD(Utils.mkvc(Dest[:,0,itime,0]), Utils.mkvc(Dest[:,1,itime,1])) \n",
" ax.semilogy(np.r_[xyz1[ind,0], xyz2[ind,0]], np.r_[abs1, abs2], color[itime]+'o', ms = 3)\n",
"ax.legend(legendobs,bbox_to_anchor=(1.4, 1.00), fontsize = 10) \n",
"ax.set_xlabel('Easting (m)', fontsize = 14)\n",
"ax.set_ylabel('Amplitude of Bz (T)', fontsize = 14) \n",
"fig.savefig('./figures/1dinvobspred_amp_FD.png', dpi = 200)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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io0V69NBtNm4sMmNGnvo7nfreCQsT8fQUsVpFhgwR+eKL4n0ddrtd5s2LlHnz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\nfnxBMxsQ5X5eEY3BrovGUP8yK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"text/plain": [
"<matplotlib.figure.Figure at 0x10f30ee10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ind = xyz1[:,1] == 0.\n",
"absFD = lambda x, y: np.sqrt(x**2+y**2)\n",
"mradFD = lambda x, y: np.angle(x+1j*y)*1e3\n",
"\n",
"fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
"for itime in range(3):\n",
" phase1 = mradFD(Utils.mkvc(Dpred[:,0,itime,0]), Utils.mkvc(Dpred[:,1,itime,1]))\n",
" phase2 = mradFD(Utils.mkvc(Dpred[:,0,itime,0]), Utils.mkvc(Dpred[:,1,itime,1]))\n",
" ax.plot(np.r_[xyz1[ind,0], xyz2[ind,0]], np.r_[phase1, phase2], color[itime])\n",
"for itime in range(3): \n",
" phase3 = mradFD(Utils.mkvc(DpreMat_FD[:,realind[itime]]), Utils.mkvc(DpreMat_FD[:,imagind[itime]]) ) \n",
" ax.plot(np.r_[xyz1[ind,0], xyz2[ind,0]], phase3+np.pi*1e3, color[itime]+'--', ms = 3) \n",
"for itime in range(3):\n",
" phase1 = mradFD(Utils.mkvc(Dest[:,0,itime,0]), Utils.mkvc(Dest[:,1,itime,1]))\n",
" phase2 = mradFD(Utils.mkvc(Dest[:,0,itime,0]), Utils.mkvc(Dest[:,1,itime,1])) \n",
" ax.plot(np.r_[xyz1[ind,0], xyz2[ind,0]], np.r_[phase1, phase2], color[itime]+'o', ms = 3)\n",
"ax.legend(legendobs,bbox_to_anchor=(1.4, 1.00), fontsize = 10) \n",
"ax.set_xlabel('Easting (m)', fontsize = 14)\n",
"ax.set_ylabel('Phase of Bz (mrad)', fontsize = 14) \n",
"fig.savefig('./figures/1dinvobspred_pahse_FD.png', dpi = 200)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
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| mit |
ivannz/study_notes | year_14_15/spring_2015/netwrok_analysis/notebooks/labs/struct_sim.ipynb | 1 | 8473 | {
"metadata": {
"name": "",
"signature": "sha256:79e4643379428e41b4ae254d74267decd5a93e7c64fb2f846ab3b8f3465b771a"
},
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Structural Similarity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"During this seminar we will:\n",
"1. Consider some node similarity measures, particularly *Euclidean Distance*, *Correlation Coefficient* and *Cosine Distance*\n",
"2. Take a look at *[Cuthill-McKee](http://en.wikipedia.org/wiki/Cuthill%E2%80%93McKee_algorithm)* node reordering procedure\n",
"3. Calculate *Assortative mixing coefficient* for some **[Game Of Thrones](http://cdn.meme.am/instances/55597956.jpg)** network"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd\n",
"import scipy.spatial as spt\n",
"import matplotlib.pyplot as plt\n",
"# plt.xkcd()\n",
"import networkx as nx\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Task 1 - Similarities Calculation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Calculate *Euclidean Distance*, *Correlation Coefficient* and *Cosine Distance* for some toy-network (Zachary again?) and for [Les Miserables](http://www-personal.umich.edu/~mejn/netdata/lesmis.zip) dataset\n",
"2. Visualize them\n",
"\n",
"**HINT:**\n",
"For correlation coeficient you can use *np.corrcoef()*, for the distances you may implement them on your own or use *scipy.spatial.distance.pdist()*"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def pair_plot( G, order = None ) :\n",
"## Get the adjacnecy matrix \n",
" A = nx.to_numpy_matrix( G )\n",
" if order is not None :\n",
" A = A[ np.ix_( order, order ) ]\n",
"# plt.figure( figsize = (12,12) )\n",
" plt.subplot( 221 )\n",
" plt.imshow( A, cmap='Greys' )\n",
" plt.title( 'Adjacency matrix' )\n",
"## Correlation between \n",
" plt.subplot( 222 )\n",
" plt.imshow( np.corrcoef( A ), cmap = \"Greys\" )\n",
" plt.title( 'Correlation metric' )\n",
"## Euclidean mteric\n",
" plt.subplot( 223 )\n",
" D = spt.distance.squareform( spt.distance.pdist( A, metric = 'Euclidean' ) )\n",
" plt.imshow( D, cmap=\"Greys\" )\n",
" plt.title( 'Euclidean distance' )\n",
"## Cosine of the angle between djacency columns\n",
" plt.subplot( 224 )\n",
" D = spt.distance.squareform( spt.distance.pdist( A, metric = 'Cosine' ) )\n",
" plt.imshow( D, cmap=\"Greys\" )\n",
" plt.title( 'Cosine metric' )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## Load Zachary's karate club and the coappearacnes of les Miserables characters.\n",
"G_0 = nx.karate_club_graph()\n",
"pair_plot( G_0 )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"G_1 = nx.read_gml( './data/lesmis/lesmis.gml' )\n",
"pair_plot( G_1 )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Task 2 - Node Reordering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Without special preprocess procidures graph adjacency matrix can look very noisy and hide network's structure (just look at the matrices above). Offcourse usually you don't know the structure itself (communities, groups of closelly connected nodes, etc.) unless it is given, however there are some procedures of node reordering that provides a better view of the network's adjacency matrix.\n",
"\n",
"*[Recerse Cuthill-McKee](http://en.wikipedia.org/wiki/Cuthill%E2%80%93McKee_algorithm)* finds permutation of the nodes that minimizes the **bandwidth** of the matrix, which is calculated as:\n",
"$$ \\theta = \\max_{a_{ij} > 0}|i-j|$$\n",
"Unformally, this algorithm puts some *mass* on the diagonal of adjacency matrix.\n",
"\n",
"Run this reordering with *nx.utils.reverse_cuthill_mckee_ordering(G)* and compare with the results above"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"order = [ i for i in nx.utils.reverse_cuthill_mckee_ordering( G_0 ) ]\n",
"pair_plot( G_0, order )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"order = [ i for i in nx.utils.reverse_cuthill_mckee_ordering( G_1 ) ]\n",
"pair_plot( G_1, order )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Task 3 - Assortative Mixing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this task you should download some data, convert it to network and calculate assortative mixing coefficient. Particularly, download [*characters*](https://www.dropbox.com/s/cmenu2mo20ow9ep/characters.csv?dl=0) and [*events*](https://www.dropbox.com/s/rgooboda819cvuk/events.csv?dl=0) datasets.\n",
"\n",
"The first dataset provides information on characters of the Game Of Thrones universe. The second one -- describes some events that have occured with them during the story. We are interested about **kill** events since they can be considered as binary relations and consequently -- graphs. \n",
"The attribute wrt which we are going to compute assortative mixing is called \"Team\".\n",
"\n",
"We will explore datasets with *pandas* module. The list of usefull functions:\n",
"* read_csv()\n",
"* characters.head()\n",
"* dropna\n",
"* set_index('characterID')['Team'].to_dict()\n",
"* events[events['event'] == 'killed']"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Put your code here\n",
"#\n",
"#\n",
"events = pd.read_csv( './data/events.csv' )\n",
"\n",
"characters = pd.read_csv( './data/characters.csv' )\n",
"characters.set_index('characterID')\n",
"\n",
"## Select the events with specific attribute: one kills another\n",
"kills = pd.DataFrame( events[ events['event'] == 'killed' ], columns = ['characterID', 'withID'])\n",
"kills = kills.dropna()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## Create a graph of killer-victim relations\n",
"G = nx.DiGraph( )\n",
"G.add_edges_from( ( u, v ) for u,v in kills.itertuples( index = False ) )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## Deduce the colouring of the graph\n",
"allegiance = characters['Team'].to_dict()\n",
"for k in allegiance.keys():\n",
" if k not in G :\n",
" del allegiance[ k ]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"G.nodes()\n",
"# .set_attribute()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx.draw( G, node_size = 2 )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
google/jax-md | notebooks/customizing_potentials_cookbook.ipynb | 1 | 68516 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Custom Potentials.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/google/jax-md/blob/main/notebooks/customizing_potentials_cookbook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vTSgycb_WCAx"
},
"source": [
"# Customizing Potentials in JAX MD\n",
"\n",
"This cookbook was contributed by Carl Goodrich."
]
},
{
"cell_type": "code",
"metadata": {
"id": "y9jcXj44Bvcj",
"cellView": "form"
},
"source": [
"#@title Imports & Utils\n",
"!pip install -q git+https://www.github.com/google/jax-md\n",
"\n",
"\n",
"import numpy as onp\n",
"\n",
"import jax.numpy as np\n",
"from jax import random\n",
"from jax import jit, grad, vmap, value_and_grad\n",
"from jax import lax\n",
"from jax import ops\n",
"\n",
"from jax.config import config\n",
"config.update(\"jax_enable_x64\", True)\n",
"\n",
"from jax_md import space, smap, energy, minimize, quantity, simulate, partition\n",
"\n",
"from functools import partial\n",
"import time\n",
"\n",
"f32 = np.float32\n",
"f64 = np.float64\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams.update({'font.size': 16})\n",
"#import seaborn as sns \n",
"#sns.set_style(style='white')\n",
"\n",
"def format_plot(x, y): \n",
" plt.grid(True)\n",
" plt.xlabel(x, fontsize=20)\n",
" plt.ylabel(y, fontsize=20)\n",
" \n",
"def finalize_plot(shape=(1, 0.7)):\n",
" plt.gcf().set_size_inches(\n",
" shape[0] * 1.5 * plt.gcf().get_size_inches()[1], \n",
" shape[1] * 1.5 * plt.gcf().get_size_inches()[1])\n",
" plt.tight_layout()\n",
"\n",
"def calculate_bond_data(displacement_or_metric, R, dr_cutoff, species=None):\n",
" if( not(species is None)):\n",
" assert(False)\n",
" \n",
" metric = space.map_product(space.canonicalize_displacement_or_metric(displacement))\n",
" dr = metric(R,R)\n",
"\n",
" dr_include = np.triu(np.where(dr<dr_cutoff, 1, 0)) - np.eye(R.shape[0],dtype=np.int32)\n",
" index_list=np.dstack(np.meshgrid(np.arange(N), np.arange(N), indexing='ij'))\n",
"\n",
" i_s = np.where(dr_include==1, index_list[:,:,0], -1).flatten()\n",
" j_s = np.where(dr_include==1, index_list[:,:,1], -1).flatten()\n",
" ij_s = np.transpose(np.array([i_s,j_s]))\n",
"\n",
" bonds = ij_s[(ij_s!=np.array([-1,-1]))[:,1]]\n",
" lengths = dr.flatten()[(ij_s!=np.array([-1,-1]))[:,1]]\n",
"\n",
" return bonds, lengths\n",
"\n",
"def plot_system(R,box_size,species=None,ms=20):\n",
" R_plt = onp.array(R)\n",
"\n",
" if(species is None):\n",
" plt.plot(R_plt[:, 0], R_plt[:, 1], 'o', markersize=ms)\n",
" else:\n",
" for ii in range(np.amax(species)+1):\n",
" Rtemp = R_plt[species==ii]\n",
" plt.plot(Rtemp[:, 0], Rtemp[:, 1], 'o', markersize=ms)\n",
"\n",
" plt.xlim([0, box_size])\n",
" plt.ylim([0, box_size])\n",
" plt.xticks([], [])\n",
" plt.yticks([], [])\n",
"\n",
" finalize_plot((1,1))\n",
" \n",
"key = random.PRNGKey(0)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lHhvM9_nZ9TT"
},
"source": [
"##Prerequisites\n",
"\n",
"This cookbook assumes a working knowledge of Python and Numpy. The concept of broadcasting is particularly important both in this cookbook and in JAX MD. \n",
"\n",
"We also assume a basic knowlege of [JAX](https://github.com/google/jax/), which JAX MD is built on top of. Here we briefly review a few JAX basics that are important for us:\n",
"\n",
"\n",
"* ```jax.vmap``` allows for automatic vectorization of a function. What this means is that if you have a function that takes an input ```x``` and returns an output ```y```, i.e. ```y = f(x)```, then ```vmap``` will transform this function to act on an array of ```x```'s and return an array of ```y```'s, i.e. ```Y = vmap(f)(X)```, where ```X=np.array([x1,x2,...,xn])``` and ```Y=np.array([y1,y2,...,yn])```. \n",
"\n",
"* ```jax.grad``` employs automatic differentiation to transform a function into a new function that calculates its gradient, for example: ```dydx = grad(f)(x)```. \n",
"\n",
"* ```jax.lax.scan``` allows for efficient for-loops that can be compiled and differentiated over. See [here](https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.scan.html#jax.lax.scan) for more details.\n",
"\n",
"* [Random numbers are different in JAX.](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#%F0%9F%94%AA-Random-Numbers) The details aren't necessary for this cookbook, but if things look a bit different, this is why.\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uUb4pEymgfpW"
},
"source": [
"##The basics of user-defined potentials"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AGQNsI7Q00_3"
},
"source": [
"###Create a user defined potential function to use throughout this cookbook\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RLo12Gsf1G9k"
},
"source": [
"Here we create a custom potential that has a short-ranged, non-diverging repulsive interaction and a medium-ranged Morse-like attractive interaction. It takes the following form:\n",
"\\begin{equation}\n",
"V(r) =\n",
"\\begin{cases}\n",
" \\frac{1}{2} k (r-r_0)^2 - D_0,& r < r_0\\\\\n",
" D_0\\left( e^{-2\\alpha (r-r_0)} -2 e^{-\\alpha(r-r_0)}\\right), & r \\geq r_0\n",
"\\end{cases}\n",
"\\end{equation}\n",
"and has 4 parameters: $D_0$, $\\alpha$, $r_0$, and $k$.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hZysrOXi0xdp"
},
"source": [
"def harmonic_morse(dr, D0=5.0, alpha=5.0, r0=1.0, k=50.0, **kwargs):\n",
" U = np.where(dr < r0, \n",
" 0.5 * k * (dr - r0)**2 - D0,\n",
" D0 * (np.exp(-2. * alpha * (dr - r0)) - 2. * np.exp(-alpha * (dr - r0)))\n",
" )\n",
" return np.array(U, dtype=dr.dtype)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3yVG0w4oBV1q"
},
"source": [
"plot $V(r)$."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TgIbQa_D1kIM"
},
"source": [
"drs = np.arange(0,3,0.01)\n",
"U = harmonic_morse(drs)\n",
"plt.plot(drs,U)\n",
"format_plot(r'$r$', r'$V(r)$')\n",
"finalize_plot()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "C1lMC0LeCSe9"
},
"source": [
"###Calculate the energy of a system of interacting particles"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kyuIoCz3maxs"
},
"source": [
"We now want to calculate the energy of a system of $N$ spheres in $d$ dimensions, where each particle interacts with every other particle via our user-defined function $V(r)$. The total energy is\n",
"\\begin{equation}\n",
"E_\\text{total} = \\sum_{i<j}V(r_{ij}),\n",
"\\end{equation}\n",
"where $r_{ij}$ is the distance between particles $i$ and $j$. \n",
"\n",
"Our first task is to set up the system by specifying the $N$, $d$, and the size of the simulation box. We then use JAX's internal random number generator to pick positions for each particle. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "q77Cepyy4M14"
},
"source": [
"N = 50\n",
"dimension = 2\n",
"box_size = 6.8\n",
"\n",
"key, split = random.split(key)\n",
"R = random.uniform(split, (N,dimension), minval=0.0, maxval=box_size, dtype=f64) \n",
"\n",
"plot_system(R,box_size)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lB5ISBfB4txR"
},
"source": [
"At this point, we could manually loop over all particle pairs and calculate the energy, keeping track of boundary conditions, etc. Fortunately, JAX MD has machinery to automate this. \n",
"\n",
"First, we must define two functions, ```displacement``` and ```shift```, which contain all the information of the simulation box, boundary conditions, and underlying metric. ```displacement``` is used to calculate the vector displacement between particles, and ```shift``` is used to move particles. For most cases, it is recommended to use JAX MD's built in functions, which can be called using:\n",
"* ``` displacement, shift = space.free()```\n",
"* ``` displacement, shift = space.periodic(box_size)```\n",
"* ``` displacement, shift = space.periodic_general(T)```\n",
"\n",
"For demonstration purposes, we will define these manually for a square periodic box, though without proper error handling, etc. The following should have the same functionality as ```displacement, shift = space.periodic(box_size)```."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Z93nLpSk4Krm"
},
"source": [
"def setup_periodic_box(box_size):\n",
" def displacement_fn(Ra, Rb, **unused_kwargs):\n",
" dR = Ra - Rb\n",
" return np.mod(dR + box_size * f32(0.5), box_size) - f32(0.5) * box_size\n",
"\n",
" def shift_fn(R, dR, **unused_kwargs):\n",
" return np.mod(R + dR, box_size)\n",
"\n",
" return displacement_fn, shift_fn\n",
" \n",
"displacement, shift = setup_periodic_box(box_size)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q_vlEs5DEq-b"
},
"source": [
"We now set up a function to calculate the total energy of the system. The JAX MD function ```smap.pair``` takes a given potential and promotes it to act on all particle pairs in a system. ```smap.pair``` does not actually return an energy, rather it returns a function that can be used to calculate the energy. \n",
"\n",
"For convenience and readability, we wrap ```smap.pair``` in a new function called ```harmonic_morse_pair```. For now, ignore the species keyword, we will return to this later."
]
},
{
"cell_type": "code",
"metadata": {
"id": "y2KDd1x6AJPw"
},
"source": [
"def harmonic_morse_pair(\n",
" displacement_or_metric, species=None, D0=5.0, alpha=10.0, r0=1.0, k=50.0): \n",
" D0 = np.array(D0, dtype=f32)\n",
" alpha = np.array(alpha, dtype=f32)\n",
" r0 = np.array(r0, dtype=f32)\n",
" k = np.array(k, dtype=f32)\n",
" return smap.pair(\n",
" harmonic_morse,\n",
" space.canonicalize_displacement_or_metric(displacement_or_metric),\n",
" species=species,\n",
" D0=D0,\n",
" alpha=alpha,\n",
" r0=r0,\n",
" k=k)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "QYoPcRjXEfx-"
},
"source": [
"Our helper function can be used to construct a function to compute the energy of the entire system as follows.\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "nT_ftIE8CQuA"
},
"source": [
"# Create a function to calculate the total energy with specified parameters\n",
"energy_fn = harmonic_morse_pair(displacement,D0=5.0,alpha=10.0,r0=1.0,k=500.0)\n",
"\n",
"# Use this to calculate the total energy\n",
"print(energy_fn(R))\n",
"\n",
"# Use grad to calculate the net force\n",
"force = -grad(energy_fn)(R)\n",
"print(force[:5])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-gYTfzbXijOq"
},
"source": [
"We are now in a position to use our energy function to manipulate the system. As an example, we perform energy minimization using JAX MD's implementation of the FIRE algorithm. \n",
"\n",
"We start by defining a function that takes an energy function, a set of initial positions, and a shift function and runs a specified number of steps of the minimization algorithm. The function returns the final set of positions and the maximum absolute value component of the force. We will use this function throughout this cookbook. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "XX-Jdq_7maw_"
},
"source": [
"def run_minimization(energy_fn, R_init, shift, num_steps=5000):\n",
" dt_start = 0.001\n",
" dt_max = 0.004\n",
" init,apply=minimize.fire_descent(jit(energy_fn),shift,dt_start=dt_start,dt_max=dt_max)\n",
" apply = jit(apply)\n",
"\n",
" @jit\n",
" def scan_fn(state, i):\n",
" return apply(state), 0.\n",
"\n",
" state = init(R_init)\n",
" state, _ = lax.scan(scan_fn,state,np.arange(num_steps))\n",
"\n",
" return state.position, np.amax(np.abs(-grad(energy_fn)(state.position)))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "48YgfOdVnf5m"
},
"source": [
"Now run the minimization with our custom energy function."
]
},
{
"cell_type": "code",
"metadata": {
"id": "nY_hzbgIaZNI"
},
"source": [
"Rfinal, max_force_component = run_minimization(energy_fn, R, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"plot_system( Rfinal, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "NKL3FcddI7GM"
},
"source": [
"###Create a truncated potential"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tWsu6nvTJCsz"
},
"source": [
"It is often desirable to have a potential that is strictly zero beyond a well-defined cutoff distance. In addition, MD simulations require the energy and force (i.e. first derivative) to be continuous. To easily modify an existing potential $V(r)$ to have this property, JAX MD follows the approach [taken by HOOMD Blue](https://hoomd-blue.readthedocs.io/en/stable/module-md-pair.html#hoomd.md.pair.pair). \n",
"\n",
"Consider the function \n",
"\\begin{equation}\n",
"S(r) =\n",
"\\begin{cases}\n",
" 1,& r<r_\\mathrm{on} \\\\\n",
" \\frac{(r_\\mathrm{cut}^2-r^2)^2 (r_\\mathrm{cut}^2 + 2r^2 - 3 r_\\mathrm{on}^2)}{(r_\\mathrm{cut}^2-r_\\mathrm{on}^2)^3},& r_\\mathrm{on} \\leq r < r_\\mathrm{cut}\\\\\n",
" 0,& r \\geq r_\\mathrm{cut}\n",
"\\end{cases}\n",
"\\end{equation}\n",
"\n",
"Here we plot both $S(r)$ and $\\frac{dS(r)}{dr}$, both of which are smooth and strictly zero above $r_\\mathrm{cut}$.\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bPs51LI_t684"
},
"source": [
"dr = np.arange(0,3,0.01)\n",
"S = energy.multiplicative_isotropic_cutoff(lambda dr: 1, r_onset=1.5, r_cutoff=2.0)(dr)\n",
"ngradS = vmap(grad(energy.multiplicative_isotropic_cutoff(lambda dr: 1, r_onset=1.5, r_cutoff=2.0)))(dr)\n",
"plt.plot(dr,S,label=r'$S(r)$')\n",
"plt.plot(dr,ngradS,label=r'$\\frac{dS(r)}{dr}$')\n",
"plt.legend()\n",
"format_plot(r'$r$','')\n",
"finalize_plot()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5Zmyipr-xaDj"
},
"source": [
"We then use $S(r)$ to create a new function \n",
"\\begin{equation}\\tilde V(r) = V(r) S(r),\n",
"\\end{equation} \n",
"which is exactly $V(r)$ below $r_\\mathrm{on}$, strictly zero above $r_\\mathrm{cut}$ and is continuous in its first derivative.\n",
"\n",
"This is implemented in JAX MD through ```energy.multiplicative_isotropic_cutoff```, which takes in a potential function $V(r)$ (e.g. our ```harmonic_morse``` function) and returns a new function $\\tilde V(r)$."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TCv8qIyeGA-k"
},
"source": [
"harmonic_morse_cutoff = energy.multiplicative_isotropic_cutoff(\n",
" harmonic_morse, r_onset=1.5, r_cutoff=2.0)\n",
"\n",
"dr = np.arange(0,3,0.01)\n",
"V = harmonic_morse(dr)\n",
"V_cutoff = harmonic_morse_cutoff(dr)\n",
"F = -vmap(grad(harmonic_morse))(dr)\n",
"F_cutoff = -vmap(grad(harmonic_morse_cutoff))(dr)\n",
"plt.plot(dr,V, label=r'$V(r)$')\n",
"plt.plot(dr,V_cutoff, label=r'$\\tilde V(r)$')\n",
"plt.plot(dr,F, label=r'$-\\frac{d}{dr} V(r)$')\n",
"plt.plot(dr,F_cutoff, label=r'$-\\frac{d}{dr} \\tilde V(r)$')\n",
"plt.legend()\n",
"format_plot('$r$', '')\n",
"plt.ylim(-13,5)\n",
"finalize_plot()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "AyHHLfQ1O6w0"
},
"source": [
"As before, we can use ```smap.pair``` to promote this to act on an entire system."
]
},
{
"cell_type": "code",
"metadata": {
"id": "abP6O5atO6F-"
},
"source": [
"def harmonic_morse_cutoff_pair(\n",
" displacement_or_metric, D0=5.0, alpha=5.0, r0=1.0, k=50.0,\n",
" r_onset=1.5, r_cutoff=2.0): \n",
" D0 = np.array(D0, dtype=f32)\n",
" alpha = np.array(alpha, dtype=f32)\n",
" r0 = np.array(r0, dtype=f32)\n",
" k = np.array(k, dtype=f32)\n",
" return smap.pair(\n",
" energy.multiplicative_isotropic_cutoff(\n",
" harmonic_morse, r_onset=r_onset, r_cutoff=r_cutoff),\n",
" space.canonicalize_displacement_or_metric(displacement_or_metric),\n",
" D0=D0,\n",
" alpha=alpha,\n",
" r0=r0,\n",
" k=k)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "UgvzLddhBIhe"
},
"source": [
"This is implemented as before"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CW_fOcqJKVlJ"
},
"source": [
"# Create a function to calculate the total energy\n",
"energy_fn = harmonic_morse_cutoff_pair(displacement, D0=5.0, alpha=10.0, r0=1.0, \n",
" k=500.0, r_onset=1.5, r_cutoff=2.0)\n",
"\n",
"# Use this to calculate the total energy\n",
"print(energy_fn(R))\n",
"\n",
"# Use grad to calculate the net force\n",
"force = -grad(energy_fn)(R)\n",
"print(force[:5])\n",
"\n",
"# Minimize the energy using the FIRE algorithm\n",
"Rfinal, max_force_component = run_minimization(energy_fn, R, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"plot_system( Rfinal, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "PAjtb9XZfLGp"
},
"source": [
"##Specifying parameters"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HxXmuqQoXACk"
},
"source": [
"###Dynamic parameters"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "korJBavMISgx"
},
"source": [
"In the above examples, the strategy is to create a function ```energy_fn``` that takes a set of positions and calculates the energy of the system with all the parameters (e.g. ```D0```, ```alpha```, etc.) baked in. However, JAX MD allows you to override these baked-in values dynamically, i.e. when ```energy_fn``` is called. \n",
"\n",
"For example, we can print out the minimized energy and force of the above system with the truncated potential:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "osQ-l1cMQ7Xf"
},
"source": [
"print(energy_fn(Rfinal))\n",
"print(-grad(energy_fn)(Rfinal)[:5])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5JduIzmbRuXP"
},
"source": [
"This uses the baked-in values of the 4 parameters: ```D0=5.0,alpha=10.0,r0=1.0,k=500.0```. If, for example, we want to dynamically turn off the attractive part of the potential, we simply pass ```D0=0``` to ```energy_fn```:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JlNgP8D0Q49c"
},
"source": [
"print(energy_fn(Rfinal, D0=0))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SC0GqWcoCXoE"
},
"source": [
"Since changing the potential moves the minimum, the force will not be zero:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7Fb3FDnuCW9M"
},
"source": [
"print(-grad(energy_fn)(Rfinal, D0=0)[:5])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "iDl-nqTV8VNR"
},
"source": [
"This ability to dynamically pass parameters is very powerful. For example, if you want to shrink particles each step during a simulation, you can simply specify a different ```r0``` each step. \n",
"\n",
"This is demonstrated below, where we run a Brownian dynamics simulation at zero temperature with continuously decreasing ```r0```. The details of ```simulate.brownian``` are beyond the scope of this cookbook, but the idea is that we pass a new value of ```r0``` to the function ```apply``` each time it is called. The function ```apply``` takes a step of the simulation, and internally it passes any extra parameters like ```r0``` to ```energy_fn```."
]
},
{
"cell_type": "code",
"metadata": {
"id": "2gxiNvCp7JHD"
},
"source": [
"def run_brownian(energy_fn, R_init, shift, key, num_steps):\n",
" init, apply = simulate.brownian(energy_fn, shift, \n",
" dt=0.00001, kT=0.0, gamma=0.1)\n",
" apply = jit(apply)\n",
"\n",
" # Define how r0 changes for each step\n",
" r0_initial = 1.0\n",
" r0_final = .5\n",
" def get_r0(t):\n",
" return r0_final + (r0_initial-r0_final)*(num_steps-t)/num_steps\n",
"\n",
" @jit\n",
" def scan_fn(state, t):\n",
" # Dynamically pass r0 to apply, which passes it on to energy_fn\n",
" return apply(state, r0=get_r0(t)), 0\n",
"\n",
" key, split = random.split(key)\n",
" state = init(split, R_init)\n",
"\n",
" state, _ = lax.scan(scan_fn,state,np.arange(num_steps))\n",
" return state.position, np.amax(np.abs(-grad(energy_fn)(state.position)))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zc9_8W0YMkvt"
},
"source": [
"If we use the previous result as the starting point for the Brownian Dynamics simulation, we find exactly what we would expect, the system contracts into a finite cluster, held together by the attractive part of the potential."
]
},
{
"cell_type": "code",
"metadata": {
"id": "-nC5cDZ-7OJD"
},
"source": [
"key, split = random.split(key)\n",
"Rfinal2, max_force_component = run_brownian(energy_fn, Rfinal, shift, split, \n",
" num_steps=6000)\n",
"plot_system( Rfinal2, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fZ9aBlNTgoa7"
},
"source": [
"###Particle-specific parameters"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AHDic52sg6iR"
},
"source": [
"Our example potential has 4 parameters: ```D0```, ```alpha```, ```r0```, and ```k```. The usual way to pass these parameters is as a scalar (e.g. ```D0=5.0```), in which case that parameter is fixed for every particle pair. However, Python broadcasting allows for these parameters to be specified separately for every different particle pair by passing an $(N,N)$ array rather than a scalar. \n",
"\n",
"As an example, let's do this for the parameter ```r0```, which is an effective way of generating a system with continuous polydispersity in particle size. Note that the polydispersity disrupts the crystalline order after minimization."
]
},
{
"cell_type": "code",
"metadata": {
"id": "iio81Poz1zfj"
},
"source": [
"# Draw the radii from a uniform distribution\n",
"key, split = random.split(key)\n",
"radii = random.uniform(split, (N,), minval=1.0, maxval=2.0, dtype=f64)\n",
"\n",
"# Rescale to match the initial volume fraction\n",
"radii = np.array([radii * np.sqrt(N/(4.*np.dot(radii,radii)))])\n",
"\n",
"# Turn this into a matrix of sums\n",
"r0_matrix = radii+radii.transpose()\n",
"\n",
"# Create the energy function using r0_matrix\n",
"energy_fn = harmonic_morse_pair(displacement, D0=5.0, alpha=10.0, r0=r0_matrix, \n",
" k=500.0)\n",
"\n",
"# Minimize the energy using the FIRE algorithm\n",
"Rfinal, max_force_component = run_minimization(energy_fn, R, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"plot_system( Rfinal, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7Ftp_f710xLl"
},
"source": [
"In addition to standard Python broadcasting, JAX MD allows for the special case of additive parameters. If a parameter is passed as a (N,) array ```p_vector```, JAX MD will convert this into a (N,N) array ```p_matrix``` where ```p_matrix[i,j] = 0.5 (p_vector[i] + p_vector[j])```. This is a JAX MD specific ability and not a feature of Python broadcasting.\n",
"\n",
"As it turns out, our above polydisperse example falls into this category. Therefore, we could achieve the same result by passing ```r0=2.0*radii```."
]
},
{
"cell_type": "code",
"metadata": {
"id": "4HnQK3fK1Wyp"
},
"source": [
"# Create the energy function the radii array\n",
"energy_fn = harmonic_morse_pair(displacement, D0=5.0, alpha=10.0, r0=2.*radii, \n",
" k=500.0)\n",
"\n",
"# Minimize the energy using the FIRE algorithm\n",
"Rfinal, max_force_component = run_minimization(energy_fn, R, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"plot_system( Rfinal, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "cN-rwzbARInR"
},
"source": [
"### Species"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fed3OOzMJmzu"
},
"source": [
"It is often important to specify parameters differently for different particle pairs, but doing so with full ($N$,$N$) matrices is both inefficient and obnoxious. JAX MD allows users to create species, i.e. $N_s$ groups of particles that are identical to each other, so that parameters can be passed as much smaller ($N_s$,$N_s$) matrices.\n",
"\n",
"First, create an array that specifies which particles belong in which species. We will divide our system into two species."
]
},
{
"cell_type": "code",
"metadata": {
"id": "zODRlLMhNQ2t"
},
"source": [
"N_0 = N // 2 # Half the particles in species 0\n",
"N_1 = N - N_0 # The rest in species 1\n",
"species = np.array([0] * N_0 + [1] * N_1, dtype=np.int32)\n",
"print(species)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "aVFp3U1LBVRF"
},
"source": [
"Next, create the $(2,2)$ matrix of ```r0```'s, which are set so that the overall volume fraction matches our monodisperse case. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "FPylQCNNA1P5"
},
"source": [
"rsmall=0.41099747 # Match the total volume fraction\n",
"rlarge=1.4*rsmall\n",
"r0_species_matrix = np.array([[2*rsmall, rsmall+rlarge],\n",
" [rsmall+rlarge, 2*rlarge]])\n",
"print(r0_species_matrix)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Mec9jBIqRZy3"
},
"source": [
"energy_fn = harmonic_morse_pair(displacement, species=species, D0=5.0, \n",
" alpha=10.0, r0=r0_species_matrix, k=500.0)\n",
"\n",
"Rfinal, max_force_component = run_minimization(energy_fn, R, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"\n",
"plot_system(Rfinal, box_size, species=species )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "vvjaCB0cN8rA"
},
"source": [
"###Dynamic Species"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jyYqW7dCDi0y"
},
"source": [
"Just like standard parameters, the species list can be passed dynamically as well. However, unlike standard parameters, you have to tell `smap.pair` that the species will be specified dynamically. To do this, set `species=2` be the total number of types of particles when creating your energy function.\n",
"\n",
"The following sets up an energy function where the attractive part of the interaction only exists between members of the first species, but where the species will be defined dynamically."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Q30CEsxaQGc7"
},
"source": [
"D0_species_matrix = np.array([[ 5.0, 0.0],\n",
" [0.0, 0.0]])\n",
"\n",
"energy_fn = harmonic_morse_pair(displacement, \n",
" species=2, \n",
" D0=D0_species_matrix, \n",
" alpha=10.0,\n",
" r0=0.5, \n",
" k=500.0)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "a2_Er0ttWcmu"
},
"source": [
"Now we set up a finite temperature Brownian Dynamics simulation where, at every step, particles on the left half of the simulation box are assigned to species 0, while particles on the right half are assigned to species 1."
]
},
{
"cell_type": "code",
"metadata": {
"id": "HlT0lZ0IO9rx"
},
"source": [
"def run_brownian(energy_fn, R_init, shift, key, num_steps):\n",
" init, apply = simulate.brownian(energy_fn, shift, dt=0.00001, kT=1.0, gamma=0.1)\n",
" # apply = jit(apply)\n",
"\n",
" # Define a function to recalculate the species each step\n",
" def get_species(R):\n",
" return np.where(R[:,0] < box_size / 2, 0, 1)\n",
"\n",
" @jit\n",
" def scan_fn(state, t):\n",
" # Recalculate the species list\n",
" species = get_species(state.position)\n",
" # Dynamically pass species to apply, which passes it on to energy_fn\n",
" return apply(state, species=species, species_count=2), 0\n",
"\n",
" key, split = random.split(key)\n",
" state = init(split, R_init)\n",
"\n",
" state, _ = lax.scan(scan_fn,state,np.arange(num_steps))\n",
" return state.position,np.amax(np.abs(-grad(energy_fn)(state.position,\n",
" species=get_species(state.position), \n",
" species_count=2)))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "L1Lso1xbXm10"
},
"source": [
"When we run this, we see that particles on the left side form clusters while particles on the right side do not."
]
},
{
"cell_type": "code",
"metadata": {
"id": "zshmmAiuROOP"
},
"source": [
"key, split = random.split(key)\n",
"Rfinal, max_force_component = run_brownian(energy_fn, R, shift, split, num_steps=10000)\n",
"plot_system( Rfinal, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bvp_-L3cJYiM"
},
"source": [
"##Efficeiently calculating neighbors"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5NWHAN8MD4Sh"
},
"source": [
"The most computationally expensive part of most MD programs is calculating the force between all pairs of particles. Generically, this scales with $N^2$. However, for systems with isotropic pairwise interactions that are strictly zero beyond a cutoff, there are techniques to dramatically improve the efficiency. The two most common methods are cell list and neighbor lists.\n",
"\n",
"**Cell lists**\n",
"\n",
"The technique here is to divide space into small cells that are just larger than the largest interaction range in the system. Thus, if particle $i$ is in cell $c_i$ and particle $j$ is in cell $c_j$, $i$ and $j$ can only interact if $c_i$ and $c_j$ are neighboring cells. Rather than searching all $N^2$ combinations of particle pairs for non-zero interactions, you only have to search the particles in the neighboring cells. \n",
"\n",
"**Neighbor lists**\n",
"\n",
"Here, for each particle $i$, we make a list of *potential* neighbors: particles $j$ that are within some threshold distance $r_\\mathrm{threshold}$. If $r_\\mathrm{threshold} = r_\\mathrm{cutoff} + \\Delta r_\\mathrm{threshold}$ (where $r_\\mathrm{cutoff}$ is the largest interaction range in the system and $\\Delta r_\\mathrm{threshold}$ is an appropriately chosen buffer size), then all interacting particles will appear in this list as long as no particles moves by more than $\\Delta r_\\mathrm{threhsold}/2$. There is a tradeoff here: smaller $\\Delta r_\\mathrm{threhsold}$ means fewer particles to search over each MD step but the list must be recalculated more often, while larger $\\Delta r_\\mathrm{threhsold}$ means slower force calculates but less frequent neighbor list calculations. \n",
"\n",
"In practice, the most efficient technique is often to use cell lists to calculate neighbor lists. In JAX MD, this occurs under the hood, and so only calls to neighbor-list functionality are necessary."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c8sSH5ayU1N3"
},
"source": [
"To implement neighbor lists, we need two functions: 1) a function to create and update the neighbor list, and 2) an energy function that uses a neighbor list rather than operating on all particle pairs. We create these functions with ```partition.neighbor_list``` and ```smap.pair_neighbor_list```, respectively. \n",
"\n",
"```partition.neighbor_list``` takes basic box information as well as the maximum interaction range ```r_cutoff``` and the buffer size ```dr_threshold```. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "6QIkYw8wNZf4"
},
"source": [
" def harmonic_morse_cutoff_neighbor_list(\n",
" displacement_or_metric,\n",
" box_size,\n",
" species=None,\n",
" D0=5.0, \n",
" alpha=5.0, \n",
" r0=1.0, \n",
" k=50.0,\n",
" r_onset=1.0,\n",
" r_cutoff=1.5, \n",
" dr_threshold=2.0,\n",
" format=partition.OrderedSparse,\n",
" **kwargs): \n",
"\n",
" D0 = np.array(D0, dtype=np.float32)\n",
" alpha = np.array(alpha, dtype=np.float32)\n",
" r0 = np.array(r0, dtype=np.float32)\n",
" k = np.array(k, dtype=np.float32)\n",
" r_onset = np.array(r_onset, dtype=np.float32)\n",
" r_cutoff = np.array(r_cutoff, np.float32)\n",
" dr_threshold = np.float32(dr_threshold)\n",
"\n",
" neighbor_fn = partition.neighbor_list(\n",
" displacement_or_metric, \n",
" box_size, \n",
" r_cutoff, \n",
" dr_threshold,\n",
" format=format)\n",
"\n",
" energy_fn = smap.pair_neighbor_list(\n",
" energy.multiplicative_isotropic_cutoff(harmonic_morse, r_onset, r_cutoff),\n",
" space.canonicalize_displacement_or_metric(displacement_or_metric),\n",
" species=species,\n",
" D0=D0,\n",
" alpha=alpha,\n",
" r0=r0,\n",
" k=k)\n",
"\n",
" return neighbor_fn, energy_fn"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8AB0dK7hWNkt"
},
"source": [
"To test this, we generate our new ```neighbor_fn``` and ```energy_fn```, as well as a comparison energy function using the default approach."
]
},
{
"cell_type": "code",
"metadata": {
"id": "ND2eNJdFQwtr"
},
"source": [
"r_onset = 1.5\n",
"r_cutoff = 2.0\n",
"dr_threshold = 1.0\n",
"\n",
"neighbor_fn, energy_fn = harmonic_morse_cutoff_neighbor_list(\n",
" displacement, box_size, D0=5.0, alpha=10.0, r0=1.0, k=500.0,\n",
" r_onset=r_onset, r_cutoff=r_cutoff, dr_threshold=dr_threshold)\n",
"\n",
"energy_fn_comparison = harmonic_morse_cutoff_pair(\n",
" displacement, D0=5.0, alpha=10.0, r0=1.0, k=500.0,\n",
" r_onset=r_onset, r_cutoff=r_cutoff)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qufh0bh8WcwH"
},
"source": [
"Next, we use ```neighbor_fn.allocate``` and the current set of positions to populate the neighbor list."
]
},
{
"cell_type": "code",
"metadata": {
"id": "aFeZgIoVRGHj"
},
"source": [
"nbrs = neighbor_fn.allocate(R)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "b1UJwsAMWteb"
},
"source": [
"To calculate the energy, we pass `nbrs` to `energy_fn`. The energy matches the comparison."
]
},
{
"cell_type": "code",
"metadata": {
"id": "-d1gVB3ERFTg"
},
"source": [
"print(energy_fn(R, neighbor=nbrs))\n",
"print(energy_fn_comparison(R))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "yJGBnaz2XGt-"
},
"source": [
"Note that by default ```neighbor_fn``` uses a cell list internally to populate the neighbor list. This approach fails when the box size in any dimension is less than 3 times $r_\\mathrm{threhsold} = r_\\mathrm{cutoff} + \\Delta r_\\mathrm{threshold}$. In this case, ```neighbor_fn``` automatically turns off the use of cell lists, and instead searches over all particle pairs. This can also be done manually by passing ```disable_cell_list=True``` to ```partition.neighbor_list```. This can be useful for debugging or for small systems where the overhead of cell lists outweighs the benefit. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3PkSm7d4KTvb"
},
"source": [
"###Updating neighbor lists"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5T_ViIa9Ksgn"
},
"source": [
"The function ```neighbor_fn``` has two different usages, depending on how it is called. When used as above, i.e. ```nbrs = neighbor_fn(R)```, a new neighbor list is generated from scratch. Internally, JAX MD uses the given positions ```R``` to estimate a maximum capacity, i.e. the maximum number of neighbors any particle will have at any point during the use of the neighbor list. This estimate can be adjusted by passing a value of ```capacity_multiplier``` to ```partition.neighbor_list```, which defaults to ```capacity_multiplier=1.25```.\n",
"\n",
"Since the maximum capacity is not known ahead of time, this construction of the neighbor list cannot be compiled. However, once a neighbor list is created in this way, repopulating the list with the same maximum capacity is a simpler operation that *can* be compiled. This is done by calling ```nbrs = neighbor_fn(R, nbrs)```. Internally, this checks if any particle has moved more than $\\Delta r_\\mathrm{threshold}/2$ and, if so, recomputes the neighbor list. If the new neighbor list exceeds the maximum capacity for any particle, the boolean variable ```nbrs.did_buffer_overflow``` is set to ```True```. \n",
"\n",
"These two uses together allow for safe and efficient neighbor list calculations. The example below demonstrates a typical simulation loop that uses neighbor lists. \n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9171zO3pQJnH"
},
"source": [
"def run_brownian_neighbor_list(energy_fn, neighbor_fn, R_init, shift, key, num_steps):\n",
" nbrs = neighbor_fn.allocate(R_init)\n",
"\n",
" init, apply = simulate.brownian(energy_fn, shift, dt=0.00001, kT=1.0, gamma=0.1)\n",
"\n",
" def body_fn(state, t):\n",
" state, nbrs = state\n",
" nbrs = nbrs.update(state.position)\n",
" state = apply(state, neighbor=nbrs)\n",
" return (state, nbrs), 0\n",
"\n",
" key, split = random.split(key)\n",
" state = init(split, R_init)\n",
"\n",
" step = 0\n",
" step_inc=100\n",
" while step < num_steps/step_inc:\n",
" rtn_state, _ = lax.scan(body_fn, (state, nbrs), np.arange(step_inc))\n",
" new_state, nbrs = rtn_state\n",
" # If the neighbor list overflowed, rebuild it and repeat part of \n",
" # the simulation.\n",
" if nbrs.did_buffer_overflow:\n",
" print('Buffer overflow.')\n",
" nbrs = neighbor_fn.allocate(state.position)\n",
" else:\n",
" state = new_state\n",
" step += 1\n",
"\n",
" return state.position"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "C8rzGWg3JM67"
},
"source": [
"To run this, we consider a much larger system than we have to this point. Warning: running this may take a few minutes."
]
},
{
"cell_type": "code",
"metadata": {
"id": "F3BuxR1LKJ8H"
},
"source": [
"Nlarge = 100*N\n",
"box_size_large = 10*box_size\n",
"displacement_large, shift_large = setup_periodic_box(box_size_large)\n",
"\n",
"key, split1, split2 = random.split(key,3)\n",
"Rlarge = random.uniform(split1, (Nlarge,dimension), minval=0.0, maxval=box_size_large, dtype=f64) \n",
"\n",
"dr_threshold = 1.5\n",
"neighbor_fn, energy_fn = harmonic_morse_cutoff_neighbor_list(\n",
" displacement_large, box_size_large, D0=5.0, alpha=10.0, r0=1.0, k=500.0,\n",
" r_onset=r_onset, r_cutoff=r_cutoff, dr_threshold=dr_threshold)\n",
"energy_fn = jit(energy_fn)\n",
"\n",
"start_time = time.process_time()\n",
"Rfinal = run_brownian_neighbor_list(energy_fn, neighbor_fn, Rlarge, shift_large, split2, num_steps=4000)\n",
"end_time = time.process_time()\n",
"print('run time = {}'.format(end_time-start_time))\n",
"\n",
"plot_system( Rfinal, box_size_large, ms=2 )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3LgpOW6bJeia"
},
"source": [
"##Bonds"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u_xSakfJ8TFp"
},
"source": [
"Bonds are a way of specifying potentials between specific pairs of particles that are \"on\" regardless of separation. For example, it is common to employ a two-sided spring potential between specific particle pairs, but JAX MD allows the user to specify arbitrary potentials with static or dynamic parameters. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u9hrkiNlQhyz"
},
"source": [
"### Create and implement a bond potential"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lrugjvd5FKS1"
},
"source": [
"We start by creating a custom potential that corresponds to a bistable spring, taking the form\n",
"\\begin{equation}\n",
"V(r) = a_4(r-r_0)^4 - a_2(r-r_0)^2.\n",
"\\end{equation}\n",
"$V(r)$ has two minima, at $r = r_0 \\pm \\sqrt{\\frac{a_2}{2a_4}}$."
]
},
{
"cell_type": "code",
"metadata": {
"id": "fEUgo2lW_C-z"
},
"source": [
"def bistable_spring(dr, r0=1.0, a2=2, a4=5, **kwargs):\n",
" return a4*(dr-r0)**4 - a2*(dr-r0)**2"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "d69FVa8uGimp"
},
"source": [
"Plot $V(r)$"
]
},
{
"cell_type": "code",
"metadata": {
"id": "T9mAhlt9_W6g"
},
"source": [
"drs = np.arange(0,2,0.01)\n",
"U = bistable_spring(drs)\n",
"plt.plot(drs,U)\n",
"format_plot(r'$r$', r'$V(r)$')\n",
"finalize_plot()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pbAhV1e6G99j"
},
"source": [
"The next step is to promote this function to act on a set of bonds. This is done via ```smap.bond```, which takes our ```bistable_spring``` function, our displacement function, and a list of the bonds. It returns a function that calculates the energy for a given set of positions."
]
},
{
"cell_type": "code",
"metadata": {
"id": "4aFl4IhnB2h8"
},
"source": [
"def bistable_spring_bond(\n",
" displacement_or_metric, bond, bond_type=None, r0=1, a2=2, a4=5):\n",
" \"\"\"Convenience wrapper to compute energy of particles bonded by springs.\"\"\"\n",
" r0 = np.array(r0, f32)\n",
" a2 = np.array(a2, f32)\n",
" a4 = np.array(a4, f32)\n",
" return smap.bond(\n",
" bistable_spring,\n",
" space.canonicalize_displacement_or_metric(displacement_or_metric),\n",
" bond,\n",
" bond_type,\n",
" r0=r0,\n",
" a2=a2,\n",
" a4=a4)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_91khxTSIZ4d"
},
"source": [
"However, in order to implement this, we need a list of bonds. We will do this by taking a system minimized under our original ```harmonic_morse``` potential:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "I6wmOB6YInKg"
},
"source": [
"R_temp, max_force_component = run_minimization(harmonic_morse_pair(displacement,D0=5.0,alpha=10.0,r0=1.0,k=500.0), R, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"plot_system( R_temp, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hVMOKqUaOT_-"
},
"source": [
"We now place a bond between all particle pairs that are separated by less than 1.3. ```calculate_bond_data``` returns a list of such bonds, as well as a list of the corresponding current length of each bond. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "ch34zyUT7vYP"
},
"source": [
"bonds, lengths = calculate_bond_data(displacement, R_temp, 1.3)\n",
"\n",
"print(bonds[:5]) # list of particle index pairs that form bonds\n",
"print(lengths[:5]) # list of the current length of each bond"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "LfK-KuluBLjX"
},
"source": [
"We use this length as the ```r0``` parameter, meaning that initially each bond is at the unstable local maximum $r=r_0$."
]
},
{
"cell_type": "code",
"metadata": {
"id": "p2GYzHgoAoIO"
},
"source": [
"bond_energy_fn = bistable_spring_bond(displacement, bonds, r0=lengths)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "H1QRVSpPPYQO"
},
"source": [
"We now use our new ```bond_energy_fn``` to minimize the energy of the system. The expectation is that nearby particles should either move closer together or further apart, and the choice of which to do should be made collectively due to the constraint of constant volume. This is exactly what we see."
]
},
{
"cell_type": "code",
"metadata": {
"id": "eLwRd59YJJCp"
},
"source": [
"Rfinal, max_force_component = run_minimization(bond_energy_fn, R_temp, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"plot_system( Rfinal, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ha811sZlRjbx"
},
"source": [
"###Specifying bonds dynamically"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "66hlM5BDRnZ_"
},
"source": [
"As with species or parameters, bonds can be specified dynamically, i.e. when the energy function is called. Importantly, note that this does NOT override bonds that were specified statically in ```smap.bond```."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Wg4Ye4RRSCyo"
},
"source": [
"# Specifying the bonds dynamically ADDS additional bonds. \n",
"# Here, we dynamically pass the same bonds that were passed statically, which \n",
"# has the effect of doubling the energy\n",
"print(bond_energy_fn(R))\n",
"print(bond_energy_fn(R,bonds=bonds, r0=lengths))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5iYIcs-nStf6"
},
"source": [
"We won't go thorugh a further example as the implementation is exactly the same as specifying species or parameters dynamically, but the ability to employ bonds both statically and dynamically is a very powerful and general framework."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lmnHa_pDSTCA"
},
"source": [
"## Combining potentials \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "762AxBtvTo7m"
},
"source": [
"Most JAX MD functionality (e.g. simulations, energy minimizations) relies on a function that calculates energy for a set of positions. Importantly, while this cookbook focus on simple and robust ways of defining such functions, JAX MD is not limited to these methods; users can implement energy functions however they like. \n",
"\n",
"As an important example, here we consider the case where the energy includes both a pair potential and a bond potential. Specifically, we combine ```harmonic_morse_pair``` with ```bistable_spring_bond```. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "KowW2fMuV_nY"
},
"source": [
"# Note, the code in the \"Bonds\" section must be run prior to this.\n",
"energy_fn = harmonic_morse_pair(displacement,D0=0.,alpha=10.0,r0=1.0,k=1.0)\n",
"bond_energy_fn = bistable_spring_bond(displacement, bonds, r0=lengths)\n",
"def combined_energy_fn(R):\n",
" return energy_fn(R) + bond_energy_fn(R)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "LR8NW_NkZV0y"
},
"source": [
"Here, we have set $D_0=0$, so the pair potential is just a one-sided repulsive harmonic potential. For particles connected with a bond, this raises the energy of the \"contracted\" minimum relative to the \"extended\" minimum."
]
},
{
"cell_type": "code",
"metadata": {
"id": "vNd0A8CYXCuR"
},
"source": [
"drs = np.arange(0,2,0.01)\n",
"U = harmonic_morse(drs,D0=0.,alpha=10.0,r0=1.0,k=1.0)+bistable_spring(drs)\n",
"plt.plot(drs,U)\n",
"format_plot(r'$r$', r'$V(r)$')\n",
"finalize_plot()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fMU9ethYZzry"
},
"source": [
"This new energy function can be passed to the minimization routine (or any other JAX MD simulation routine) in the usual way."
]
},
{
"cell_type": "code",
"metadata": {
"id": "h67C8SRrWm8J"
},
"source": [
"Rfinal, max_force_component = run_minimization(combined_energy_fn, R_temp, shift)\n",
"print('largest component of force after minimization = {}'.format(max_force_component))\n",
"plot_system( Rfinal, box_size )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7JkkgEY4H19L"
},
"source": [
"##Specifying forces instead of energies"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RXaO8tDcb7aC"
},
"source": [
"So far, we have defined functions that calculate the energy of the system, which we then pass to JAX MD. Internally, JAX MD uses automatic differentiation to convert these into functions that calculate forces, which are necessary to evolve a system under a given dynamics. However, JAX MD has the option to pass force functions directly, rather than energy functions. This creates additional flexibility because some forces cannot be represented as the gradient of a potential.\n",
"\n",
"As a simple example, we create a custom force function that zeros out the force of some particles. During energy minimization, where there is no stochastic noise, this has the effect of fixing the position of these particles.\n",
"\n",
"First, we break the system up into two species, as before."
]
},
{
"cell_type": "code",
"metadata": {
"id": "RzFu0MnmHzp3"
},
"source": [
"N_0 = N // 2 # Half the particles in species 0\n",
"N_1 = N - N_0 # The rest in species 1\n",
"species = np.array([0]*N_0 + [1]*N_1, dtype=np.int32)\n",
"print(species)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hry734J5Zh_F"
},
"source": [
"Next, we we creat our custom force function. Starting with our ```harmonic_morse``` pair potential, we calculate the force manually (i.e. using built-in automatic differentiation), and then multiply the force by the species id, which has the desired effect. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "yZRz8pxVG1O8"
},
"source": [
"energy_fn = harmonic_morse_pair(displacement,D0=5.0,alpha=10.0,r0=1.0,k=500.0)\n",
"force_fn = quantity.force(energy_fn)\n",
"\n",
"def custom_force_fn(R, **kwargs):\n",
" return vmap(lambda a,b: a*b)(force_fn(R),species)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "PGpaSD0BZ-UT"
},
"source": [
"Running simulations with custom forces is as easy as passing this force function to the simulation. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "xhyWE6msitFX"
},
"source": [
"def run_minimization_general(energy_or_force, R_init, shift, num_steps=5000):\n",
" dt_start = 0.001\n",
" dt_max = 0.004\n",
" init,apply=minimize.fire_descent(jit(energy_or_force),shift,dt_start=dt_start,dt_max=dt_max)\n",
" apply = jit(apply)\n",
"\n",
" @jit\n",
" def scan_fn(state, i):\n",
" return apply(state), 0.\n",
"\n",
" state = init(R_init)\n",
" state, _ = lax.scan(scan_fn,state,np.arange(num_steps))\n",
"\n",
" return state.position, np.amax(np.abs(quantity.canonicalize_force(energy_or_force)(state.position)))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "0vZJwbFuja0W"
},
"source": [
"We run this as usual,"
]
},
{
"cell_type": "code",
"metadata": {
"id": "maSN2X6kFbn0"
},
"source": [
"key, split = random.split(key)\n",
"Rfinal, _ = run_minimization_general(custom_force_fn, R, shift)\n",
"plot_system( Rfinal, box_size, species )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pDXzM-vIaZs_"
},
"source": [
"After the above minimization, the blue particles have the same positions as they did initially:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ywwWkJW7JaMt"
},
"source": [
"plot_system( R, box_size, species )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zT2peeMfKKp6"
},
"source": [
"Note, this method for fixing particles only works when there is no stochastic noise (e.g. in Langevin or Brownian dynamics) because such noise affects partices whether or not they have a net force. A safer way to fix particles is to create a custom ```shift``` function."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i0OQMafrnArk"
},
"source": [
"##Coupled ensembles"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0nbe8PfTQEpA"
},
"source": [
"For a final example that demonstrates the flexibility within JAX MD, lets do something that is particularly difficult in most standard MD packages. We will create a \"coupled ensemble\" -- i.e. a set of two identical systems that are connected via a $Nd$ dimensional spring. An extension of this idea is used, for example, in the Doubly Nudged Elastic Band method for finding transition states. \n",
"\n",
"If the \"normal\" energy of each system is \n",
"\\begin{equation}\n",
"U(R) = \\sum_{i,j} V( r_{ij} ),\n",
"\\end{equation}\n",
"where $r_{ij}$ is the distance between the $i$th and $j$th particles in $R$ and the $V(r)$ is a standard pair potential, and if the two sets of positions, $R_0$ and $R_1$, are coupled via the potential\n",
"\\begin{equation}\n",
"U_\\mathrm{spr}(R_0,R_1) = \\frac 12 k_\\mathrm{spr} \\left| R_1 - R_0 \\right|^2,\n",
"\\end{equation}\n",
"so that the total energy of the system is \n",
"\\begin{equation}\n",
"U_\\mathrm{total} = U(R_0) + U(R_1) + U_\\mathrm{spr}(R_0,R_1).\n",
"\\end{equation}\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uTtLhh3dKWAU"
},
"source": [
"energy_fn = harmonic_morse_pair(displacement,D0=5.0,alpha=10.0,r0=0.5,k=500.0)\n",
"def spring_energy_fn(Rall, k_spr=50.0, **kwargs):\n",
" metric = vmap(space.canonicalize_displacement_or_metric(displacement), (0, 0), 0)\n",
" dr = metric(Rall[0],Rall[1])\n",
" return 0.5*k_spr*np.sum((dr)**2)\n",
"def total_energy_fn(Rall, **kwargs):\n",
" return np.sum(vmap(energy_fn)(Rall)) + spring_energy_fn(Rall)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "tNy9BPXATOpU"
},
"source": [
"We now have to define a new shift function that can handle arrays of shape $(2,N,d)$. In addition, we make two copies of our initial positions ```R```, one for each system. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "rfuT7nC7TlLe"
},
"source": [
"def shift_all(Rall, dRall, **kwargs):\n",
" return vmap(shift)(Rall, dRall)\n",
"Rall = np.array([R,R])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3paT1E6zT4uS"
},
"source": [
"Now, all we have to do is pass our custom energy and shift functions, as well as the $(2,N,d)$ dimensional initial position, to JAX MD, and proceed as normal. \n",
"\n",
"As a demonstration, we define a simple and general Brownian Dynamics simulation function, similar to the simulation routines above except without the special cases (e.g. chaning ```r0``` or species). "
]
},
{
"cell_type": "code",
"metadata": {
"id": "iJqEQeF1kBXS"
},
"source": [
"def run_brownian_simple(energy_or_force, R_init, shift, key, num_steps):\n",
" init, apply = simulate.brownian(energy_or_force, shift, dt=0.00001, kT=1.0, gamma=0.1)\n",
" apply = jit(apply)\n",
"\n",
" @jit\n",
" def scan_fn(state, t):\n",
" return apply(state), 0\n",
"\n",
" key, split = random.split(key)\n",
" state = init(split, R_init)\n",
"\n",
" state, _ = lax.scan(scan_fn, state, np.arange(num_steps))\n",
" return state.position"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ug_49XmKkkVk"
},
"source": [
"Note that nowhere in this function is there any indication that we are simulating an ensemble of systems. This comes entirely form the inputs: i.e. the energy function, the shift function, and the set of initial positions. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "sbbX64tvMCJy"
},
"source": [
"key, split = random.split(key)\n",
"Rall_final = run_brownian_simple(total_energy_fn, Rall, shift_all, split, num_steps=10000)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "RrdgrCPMUJzN"
},
"source": [
"The output also has shape $(2,N,d)$. If we display the results, we see that the two systems are in similar, but not identical, positions, showing that we have succeeded in simulating a coupled ensemble. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "m5vSaHU-PKO_"
},
"source": [
"for Ri in Rall_final:\n",
" plot_system( Ri, box_size )\n",
"finalize_plot((0.5,0.5))"
],
"execution_count": null,
"outputs": []
}
]
}
| apache-2.0 |
sophie63/FlyLFM | Notebooks/.ipynb_checkpoints/100106-checkpoint.ipynb | 1 | 3569944 | null | bsd-2-clause |