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jminas/brain_predict_lap.ipynb

Created May 19, 2016 17:19
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brain_predict_lap.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from glob import glob\n",
"import os\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sn\n",
"import nibabel as nb\n",
"import nilearn as nl\n",
"from nilearn.input_data import NiftiMasker"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler, Normalizer\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.svm import LinearSVC, SVR, SVC\n",
"from sklearn.linear_model import (RandomizedLasso, Lasso, LassoCV, LassoLarsCV, \n",
" RidgeCV, LinearRegression, LogisticRegression)\n",
"from sklearn.feature_selection import RFECV, VarianceThreshold, SelectKBest\n",
"from sklearn.ensemble import ExtraTreesRegressor, ExtraTreesClassifier, RandomForestRegressor\n",
"from sklearn.base import RegressorMixin, BaseEstimator\n",
"from sklearn.cluster import KMeans, AgglomerativeClustering\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.cross_validation import KFold, StratifiedKFold, cross_val_score\n",
"\n",
"from sklearn.cross_validation import KFold, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.svm import SVR, SVC\n",
"from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, ExtraTreesRegressor\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import (explained_variance_score, r2_score, f1_score, \n",
" matthews_corrcoef, accuracy_score, roc_auc_score, confusion_matrix,\n",
" classification_report)\n",
"from IPython.display import display\n",
"from sklearn.preprocessing import Imputer\n",
"from sklearn.decomposition import PCA\n",
"\n",
"from nilearn._utils.fixes import f_regression\n",
"from nilearn.mass_univariate import permuted_ols\n",
"from statsmodels.sandbox.stats.multicomp import multipletests, fdrcorrection0\n",
"from nilearn import plotting"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <th>1021</th>\n",
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" <td>10/21/88</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>3/21/13</td>\n",
" <td>4/6/13</td>\n",
" <td>104</td>\n",
" <td>111</td>\n",
" <td>109</td>\n",
" <td>26</td>\n",
" <td>...</td>\n",
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" <td>4.023973</td>\n",
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" <td>0.933333</td>\n",
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" <th>1022</th>\n",
" <td>F</td>\n",
" <td>6/5/92</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>4/10/13</td>\n",
" <td>4/25/13</td>\n",
" <td>126</td>\n",
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" <td>129</td>\n",
" <td>31</td>\n",
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" <td>0.538462</td>\n",
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" <td>0.916667</td>\n",
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" <tr>\n",
" <th>1023</th>\n",
" <td>F</td>\n",
" <td>7/7/84</td>\n",
" <td>29</td>\n",
" <td>15</td>\n",
" <td>3/22/13</td>\n",
" <td>3/29/13</td>\n",
" <td>110</td>\n",
" <td>130</td>\n",
" <td>125</td>\n",
" <td>30</td>\n",
" <td>...</td>\n",
" <td>3.640467</td>\n",
" <td>5.623374</td>\n",
" <td>5.489740</td>\n",
" <td>2.651435</td>\n",
" <td>0.022349</td>\n",
" <td>-0.654522</td>\n",
" <td>0.403846</td>\n",
" <td>0.769231</td>\n",
" <td>0.950000</td>\n",
" <td>0.983333</td>\n",
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" <tr>\n",
" <th>1024</th>\n",
" <td>M</td>\n",
" <td>12/10/85</td>\n",
" <td>27</td>\n",
" <td>15</td>\n",
" <td>4/8/13</td>\n",
" <td>5/6/13</td>\n",
" <td>107</td>\n",
" <td>120</td>\n",
" <td>116</td>\n",
" <td>25</td>\n",
" <td>...</td>\n",
" <td>5.406859</td>\n",
" <td>3.641635</td>\n",
" <td>1.896321</td>\n",
" <td>2.507003</td>\n",
" <td>0.051153</td>\n",
" <td>-0.398913</td>\n",
" <td>0.538462</td>\n",
" <td>0.461538</td>\n",
" <td>0.983333</td>\n",
" <td>0.983333</td>\n",
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" <tr>\n",
" <th>1025</th>\n",
" <td>F</td>\n",
" <td>9/5/91</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>4/24/13</td>\n",
" <td>5/1/13</td>\n",
" <td>123</td>\n",
" <td>130</td>\n",
" <td>131</td>\n",
" <td>31</td>\n",
" <td>...</td>\n",
" <td>5.502573</td>\n",
" <td>5.790365</td>\n",
" <td>9.506849</td>\n",
" <td>9.506849</td>\n",
" <td>0.006867</td>\n",
" <td>-0.289487</td>\n",
" <td>0.750000</td>\n",
" <td>0.538462</td>\n",
" <td>0.983333</td>\n",
" <td>0.983333</td>\n",
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" <tr>\n",
" <th>1026</th>\n",
" <td>F</td>\n",
" <td>1/22/85</td>\n",
" <td>28</td>\n",
" <td>15</td>\n",
" <td>5/20/13</td>\n",
" <td>5/30/13</td>\n",
" <td>107</td>\n",
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" <td>122</td>\n",
" <td>26</td>\n",
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" <td>0.983333</td>\n",
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" <tr>\n",
" <th>1027</th>\n",
" <td>M</td>\n",
" <td>11/6/88</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>5/1/13</td>\n",
" <td>5/10/13</td>\n",
" <td>119</td>\n",
" <td>103</td>\n",
" <td>113</td>\n",
" <td>32</td>\n",
" <td>...</td>\n",
" <td>9.506849</td>\n",
" <td>5.974065</td>\n",
" <td>2.023946</td>\n",
" <td>5.387064</td>\n",
" <td>-0.026725</td>\n",
" <td>-0.568169</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.966667</td>\n",
" <td>1.000000</td>\n",
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" <tr>\n",
" <th>1028</th>\n",
" <td>M</td>\n",
" <td>12/9/89</td>\n",
" <td>23</td>\n",
" <td>15</td>\n",
" <td>5/2/13</td>\n",
" <td>5/3/13</td>\n",
" <td>143</td>\n",
" <td>103</td>\n",
" <td>127</td>\n",
" <td>29</td>\n",
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" <td>3.640467</td>\n",
" <td>3.483508</td>\n",
" <td>1.226942</td>\n",
" <td>1.563768</td>\n",
" <td>0.041532</td>\n",
" <td>-0.595529</td>\n",
" <td>0.538462</td>\n",
" <td>0.423077</td>\n",
" <td>0.933333</td>\n",
" <td>0.983333</td>\n",
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" <tr>\n",
" <th>1029</th>\n",
" <td>M</td>\n",
" <td>7/8/90</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>5/30/13</td>\n",
" <td>6/18/13</td>\n",
" <td>106</td>\n",
" <td>96</td>\n",
" <td>102</td>\n",
" <td>30</td>\n",
" <td>...</td>\n",
" <td>1.306870</td>\n",
" <td>1.207281</td>\n",
" <td>1.569306</td>\n",
" <td>1.970570</td>\n",
" <td>-0.244076</td>\n",
" <td>-0.397760</td>\n",
" <td>0.538462</td>\n",
" <td>0.615385</td>\n",
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" <th>1030</th>\n",
" <td>F</td>\n",
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" <td>24</td>\n",
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" <td>5/21/13</td>\n",
" <td>6/4/13</td>\n",
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" <td>96</td>\n",
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" <td>0.983333</td>\n",
" <td>0.950000</td>\n",
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" <tr>\n",
" <th>1032</th>\n",
" <td>M</td>\n",
" <td>6/30/91</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>5/21/13</td>\n",
" <td>6/4/13</td>\n",
" <td>132</td>\n",
" <td>132</td>\n",
" <td>136</td>\n",
" <td>NaN</td>\n",
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" <td>3.711473</td>\n",
" <td>2.656290</td>\n",
" <td>3.264710</td>\n",
" <td>0.011758</td>\n",
" <td>-0.912715</td>\n",
" <td>0.519231</td>\n",
" <td>0.461538</td>\n",
" <td>0.916667</td>\n",
" <td>0.966667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1035</th>\n",
" <td>F</td>\n",
" <td>5/12/90</td>\n",
" <td>23</td>\n",
" <td>15</td>\n",
" <td>5/15/13</td>\n",
" <td>5/18/13</td>\n",
" <td>139</td>\n",
" <td>125</td>\n",
" <td>136</td>\n",
" <td>30</td>\n",
" <td>...</td>\n",
" <td>2.048156</td>\n",
" <td>3.483508</td>\n",
" <td>1.841575</td>\n",
" <td>2.502192</td>\n",
" <td>-0.063103</td>\n",
" <td>-0.497468</td>\n",
" <td>0.557692</td>\n",
" <td>0.423077</td>\n",
" <td>0.966667</td>\n",
" <td>0.983333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1037</th>\n",
" <td>F</td>\n",
" <td>10/12/90</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>5/31/13</td>\n",
" <td>6/18/13</td>\n",
" <td>90</td>\n",
" <td>130</td>\n",
" <td>112</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.177850</td>\n",
" <td>2.809071</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.461538</td>\n",
" <td>0.500000</td>\n",
" <td>0.966667</td>\n",
" <td>0.966667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1039</th>\n",
" <td>F</td>\n",
" <td>3/21/91</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>6/7/13</td>\n",
" <td>6/12/13</td>\n",
" <td>121</td>\n",
" <td>96</td>\n",
" <td>110</td>\n",
" <td>26</td>\n",
" <td>...</td>\n",
" <td>5.502573</td>\n",
" <td>2.134856</td>\n",
" <td>1.569306</td>\n",
" <td>3.289707</td>\n",
" <td>0.017972</td>\n",
" <td>-0.676889</td>\n",
" <td>0.461538</td>\n",
" <td>0.384615</td>\n",
" <td>0.950000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1041</th>\n",
" <td>F</td>\n",
" <td>3/1/92</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>6/7/13</td>\n",
" <td>6/21/13</td>\n",
" <td>113</td>\n",
" <td>98</td>\n",
" <td>107</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>3.176807</td>\n",
" <td>3.350618</td>\n",
" <td>0.967283</td>\n",
" <td>5.756572</td>\n",
" <td>0.026601</td>\n",
" <td>-0.368407</td>\n",
" <td>0.500000</td>\n",
" <td>0.538462</td>\n",
" <td>0.966667</td>\n",
" <td>0.983333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1042</th>\n",
" <td>F</td>\n",
" <td>6/1/92</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>6/4/13</td>\n",
" <td>6/25/13</td>\n",
" <td>101</td>\n",
" <td>103</td>\n",
" <td>103</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>3.222997</td>\n",
" <td>1.634290</td>\n",
" <td>6.034976</td>\n",
" <td>5.358010</td>\n",
" <td>0.116027</td>\n",
" <td>-0.610784</td>\n",
" <td>0.576923</td>\n",
" <td>0.653846</td>\n",
" <td>0.966667</td>\n",
" <td>0.933333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1045</th>\n",
" <td>F</td>\n",
" <td>10/28/93</td>\n",
" <td>19</td>\n",
" <td>15</td>\n",
" <td>6/27/13</td>\n",
" <td>7/8/13</td>\n",
" <td>135</td>\n",
" <td>130</td>\n",
" <td>137</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>5.502573</td>\n",
" <td>5.477865</td>\n",
" <td>0.777748</td>\n",
" <td>1.198890</td>\n",
" <td>-0.051569</td>\n",
" <td>-0.641819</td>\n",
" <td>0.576923</td>\n",
" <td>0.384615</td>\n",
" <td>0.883333</td>\n",
" <td>0.916667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1046</th>\n",
" <td>F</td>\n",
" <td>7/29/92</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>6/26/13</td>\n",
" <td>6/27/13</td>\n",
" <td>114</td>\n",
" <td>111</td>\n",
" <td>115</td>\n",
" <td>28</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.317985</td>\n",
" <td>6.034976</td>\n",
" <td>0.077565</td>\n",
" <td>-0.447080</td>\n",
" <td>0.480769</td>\n",
" <td>0.423077</td>\n",
" <td>0.983333</td>\n",
" <td>0.950000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1047</th>\n",
" <td>M</td>\n",
" <td>12/21/83</td>\n",
" <td>30</td>\n",
" <td>15</td>\n",
" <td>7/1/13</td>\n",
" <td>6/26/13</td>\n",
" <td>108</td>\n",
" <td>125</td>\n",
" <td>119</td>\n",
" <td>32</td>\n",
" <td>...</td>\n",
" <td>5.502573</td>\n",
" <td>5.790365</td>\n",
" <td>2.486475</td>\n",
" <td>5.789858</td>\n",
" <td>0.004618</td>\n",
" <td>-0.838732</td>\n",
" <td>0.519231</td>\n",
" <td>0.461538</td>\n",
" <td>0.966667</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1049</th>\n",
" <td>F</td>\n",
" <td>12/23/91</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>7/2/13</td>\n",
" <td>7/9/13</td>\n",
" <td>144</td>\n",
" <td>114</td>\n",
" <td>134</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>9.506849</td>\n",
" <td>9.506849</td>\n",
" <td>2.502192</td>\n",
" <td>9.506849</td>\n",
" <td>-0.013917</td>\n",
" <td>-0.630314</td>\n",
" <td>0.480769</td>\n",
" <td>0.653846</td>\n",
" <td>0.950000</td>\n",
" <td>0.983333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1050</th>\n",
" <td>F</td>\n",
" <td>11/19/92</td>\n",
" <td>20</td>\n",
" <td>15</td>\n",
" <td>7/8/13</td>\n",
" <td>7/1/13</td>\n",
" <td>126</td>\n",
" <td>115</td>\n",
" <td>124</td>\n",
" <td>28</td>\n",
" <td>...</td>\n",
" <td>5.406859</td>\n",
" <td>5.790365</td>\n",
" <td>1.677638</td>\n",
" <td>2.926405</td>\n",
" <td>0.072584</td>\n",
" <td>-0.903482</td>\n",
" <td>0.461538</td>\n",
" <td>0.538462</td>\n",
" <td>0.983333</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1052</th>\n",
" <td>F</td>\n",
" <td>11/14/86</td>\n",
" <td>26</td>\n",
" <td>15</td>\n",
" <td>7/3/13</td>\n",
" <td>8/9/13</td>\n",
" <td>129</td>\n",
" <td>125</td>\n",
" <td>131</td>\n",
" <td>33</td>\n",
" <td>...</td>\n",
" <td>3.960057</td>\n",
" <td>5.790365</td>\n",
" <td>3.213075</td>\n",
" <td>6.373281</td>\n",
" <td>0.019271</td>\n",
" <td>-0.475791</td>\n",
" <td>0.346154</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1053</th>\n",
" <td>F</td>\n",
" <td>6/10/13</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>7/18/13</td>\n",
" <td>8/1/13</td>\n",
" <td>121</td>\n",
" <td>125</td>\n",
" <td>127</td>\n",
" <td>31</td>\n",
" <td>...</td>\n",
" <td>9.506849</td>\n",
" <td>5.974065</td>\n",
" <td>1.677638</td>\n",
" <td>9.506849</td>\n",
" <td>0.006768</td>\n",
" <td>-0.657769</td>\n",
" <td>0.788462</td>\n",
" <td>0.730769</td>\n",
" <td>0.966667</td>\n",
" <td>0.916667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1054</th>\n",
" <td>F</td>\n",
" <td>11/6/89</td>\n",
" <td>23</td>\n",
" <td>15</td>\n",
" <td>7/8/13</td>\n",
" <td>7/8/13</td>\n",
" <td>139</td>\n",
" <td>130</td>\n",
" <td>139</td>\n",
" <td>27</td>\n",
" <td>...</td>\n",
" <td>5.261351</td>\n",
" <td>5.351143</td>\n",
" <td>5.756572</td>\n",
" <td>2.926405</td>\n",
" <td>-0.002466</td>\n",
" <td>-0.784725</td>\n",
" <td>0.557692</td>\n",
" <td>0.576923</td>\n",
" <td>0.950000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1055</th>\n",
" <td>F</td>\n",
" <td>6/17/92</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>7/10/13</td>\n",
" <td>7/19/13</td>\n",
" <td>114</td>\n",
" <td>103</td>\n",
" <td>110</td>\n",
" <td>28</td>\n",
" <td>...</td>\n",
" <td>5.502573</td>\n",
" <td>5.790365</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.480769</td>\n",
" <td>0.769231</td>\n",
" <td>0.983333</td>\n",
" <td>0.966667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1056</th>\n",
" <td>F</td>\n",
" <td>5/28/94</td>\n",
" <td>19</td>\n",
" <td>15</td>\n",
" <td>8/16/13</td>\n",
" <td>8/19/13</td>\n",
" <td>120</td>\n",
" <td>120</td>\n",
" <td>123</td>\n",
" <td>32</td>\n",
" <td>...</td>\n",
" <td>5.406859</td>\n",
" <td>5.502573</td>\n",
" <td>2.278494</td>\n",
" <td>5.789858</td>\n",
" <td>-0.008035</td>\n",
" <td>-0.688757</td>\n",
" <td>0.557692</td>\n",
" <td>0.769231</td>\n",
" <td>0.950000</td>\n",
" <td>0.966667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1057</th>\n",
" <td>F</td>\n",
" <td>8/19/92</td>\n",
" <td>20</td>\n",
" <td>15</td>\n",
" <td>7/11/13</td>\n",
" <td>7/11/13</td>\n",
" <td>108</td>\n",
" <td>111</td>\n",
" <td>111</td>\n",
" <td>32</td>\n",
" <td>...</td>\n",
" <td>1.965699</td>\n",
" <td>1.965699</td>\n",
" <td>1.915059</td>\n",
" <td>5.974065</td>\n",
" <td>0.024385</td>\n",
" <td>-0.564570</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.966667</td>\n",
" <td>0.950000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1058</th>\n",
" <td>F</td>\n",
" <td>10/1/92</td>\n",
" <td>20</td>\n",
" <td>15</td>\n",
" <td>7/15/13</td>\n",
" <td>7/18/13</td>\n",
" <td>116</td>\n",
" <td>120</td>\n",
" <td>121</td>\n",
" <td>26</td>\n",
" <td>...</td>\n",
" <td>5.261351</td>\n",
" <td>3.903172</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.148361</td>\n",
" <td>-0.400937</td>\n",
" <td>0.538462</td>\n",
" <td>0.500000</td>\n",
" <td>0.883333</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1060</th>\n",
" <td>M</td>\n",
" <td>12/12/89</td>\n",
" <td>23</td>\n",
" <td>15</td>\n",
" <td>8/5/13</td>\n",
" <td>8/5/13</td>\n",
" <td>107</td>\n",
" <td>100</td>\n",
" <td>104</td>\n",
" <td>30</td>\n",
" <td>...</td>\n",
" <td>0.591736</td>\n",
" <td>0.856804</td>\n",
" <td>1.439199</td>\n",
" <td>2.533671</td>\n",
" <td>0.024278</td>\n",
" <td>-0.239112</td>\n",
" <td>0.442308</td>\n",
" <td>0.461538</td>\n",
" <td>0.916667</td>\n",
" <td>0.983333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1061</th>\n",
" <td>M</td>\n",
" <td>2/3/92</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>8/22/13</td>\n",
" <td>8/8/13</td>\n",
" <td>87</td>\n",
" <td>107</td>\n",
" <td>97</td>\n",
" <td>19</td>\n",
" <td>...</td>\n",
" <td>5.071846</td>\n",
" <td>1.762937</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.040126</td>\n",
" <td>0.445873</td>\n",
" <td>0.500000</td>\n",
" <td>0.384615</td>\n",
" <td>0.866667</td>\n",
" <td>0.916667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1063</th>\n",
" <td>F</td>\n",
" <td>8/26/94</td>\n",
" <td>18</td>\n",
" <td>15</td>\n",
" <td>8/22/13</td>\n",
" <td>8/31/13</td>\n",
" <td>90</td>\n",
" <td>91</td>\n",
" <td>89</td>\n",
" <td>23</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.176274</td>\n",
" <td>-0.572025</td>\n",
" <td>0.538462</td>\n",
" <td>0.423077</td>\n",
" <td>0.933333</td>\n",
" <td>0.950000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1064</th>\n",
" <td>F</td>\n",
" <td>4/10/92</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>8/22/13</td>\n",
" <td>8/23/13</td>\n",
" <td>121</td>\n",
" <td>120</td>\n",
" <td>124</td>\n",
" <td>28</td>\n",
" <td>...</td>\n",
" <td>3.960057</td>\n",
" <td>4.023973</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.050184</td>\n",
" <td>-0.387796</td>\n",
" <td>0.538462</td>\n",
" <td>0.730769</td>\n",
" <td>0.950000</td>\n",
" <td>0.933333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1065</th>\n",
" <td>M</td>\n",
" <td>1/29/94</td>\n",
" <td>18</td>\n",
" <td>15</td>\n",
" <td>9/3/13</td>\n",
" <td>9/10/13</td>\n",
" <td>110</td>\n",
" <td>125</td>\n",
" <td>121</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>3.960057</td>\n",
" <td>3.856982</td>\n",
" <td>1.606331</td>\n",
" <td>1.263946</td>\n",
" <td>0.062724</td>\n",
" <td>-0.624578</td>\n",
" <td>0.480769</td>\n",
" <td>0.653846</td>\n",
" <td>0.983333</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>42 rows × 115 columns</p>\n",
"</div>"
],
"text/plain": [
" Gender DOB Age Yrs_Ed Date_S1 Date_S2 KBIT_verbalstd \\\n",
"participant \n",
"1004 M 9/2/80 33 15 1/17/13 5/16/13 113 \n",
"1006 F 5/2/92 21 15 1/18/13 3/22/13 135 \n",
"1007 M 12/5/87 26 15 1/22/13 2/22/13 112 \n",
"1010 F 9/20/90 23 15 1/25/13 3/25/13 114 \n",
"1013 F 10/20/92 21 15 2/4/13 2/20/13 137 \n",
"1014 M 12/12/91 21 15 2/5/13 3/20/13 135 \n",
"1015 M 12/20/93 19 15 2/12/13 2/26/13 120 \n",
"1016 F 8/12/85 28 15 2/13/13 3/21/13 98 \n",
"1019 M 7/29/81 32 15 3/21/13 3/25/13 104 \n",
"1021 M 10/21/88 25 15 3/21/13 4/6/13 104 \n",
"1022 F 6/5/92 21 15 4/10/13 4/25/13 126 \n",
"1023 F 7/7/84 29 15 3/22/13 3/29/13 110 \n",
"1024 M 12/10/85 27 15 4/8/13 5/6/13 107 \n",
"1025 F 9/5/91 22 15 4/24/13 5/1/13 123 \n",
"1026 F 1/22/85 28 15 5/20/13 5/30/13 107 \n",
"1027 M 11/6/88 25 15 5/1/13 5/10/13 119 \n",
"1028 M 12/9/89 23 15 5/2/13 5/3/13 143 \n",
"1029 M 7/8/90 22 15 5/30/13 6/18/13 106 \n",
"1030 F 7/18/89 24 15 5/21/13 6/4/13 95 \n",
"1032 M 6/30/91 22 15 5/21/13 6/4/13 132 \n",
"1035 F 5/12/90 23 15 5/15/13 5/18/13 139 \n",
"1037 F 10/12/90 22 15 5/31/13 6/18/13 90 \n",
"1039 F 3/21/91 22 15 6/7/13 6/12/13 121 \n",
"1041 F 3/1/92 21 15 6/7/13 6/21/13 113 \n",
"1042 F 6/1/92 21 15 6/4/13 6/25/13 101 \n",
"1045 F 10/28/93 19 15 6/27/13 7/8/13 135 \n",
"1046 F 7/29/92 21 15 6/26/13 6/27/13 114 \n",
"1047 M 12/21/83 30 15 7/1/13 6/26/13 108 \n",
"1049 F 12/23/91 21 15 7/2/13 7/9/13 144 \n",
"1050 F 11/19/92 20 15 7/8/13 7/1/13 126 \n",
"1052 F 11/14/86 26 15 7/3/13 8/9/13 129 \n",
"1053 F 6/10/13 21 15 7/18/13 8/1/13 121 \n",
"1054 F 11/6/89 23 15 7/8/13 7/8/13 139 \n",
"1055 F 6/17/92 21 15 7/10/13 7/19/13 114 \n",
"1056 F 5/28/94 19 15 8/16/13 8/19/13 120 \n",
"1057 F 8/19/92 20 15 7/11/13 7/11/13 108 \n",
"1058 F 10/1/92 20 15 7/15/13 7/18/13 116 \n",
"1060 M 12/12/89 23 15 8/5/13 8/5/13 107 \n",
"1061 M 2/3/92 21 15 8/22/13 8/8/13 87 \n",
"1063 F 8/26/94 18 15 8/22/13 8/31/13 90 \n",
"1064 F 4/10/92 21 15 8/22/13 8/23/13 121 \n",
"1065 M 1/29/94 18 15 9/3/13 9/10/13 110 \n",
"\n",
" KBIT_nonverbalstd KBIT_overall WJ3_raw ... \\\n",
"participant ... \n",
"1004 125 122 24 ... \n",
"1006 130 137 25 ... \n",
"1007 120 119 26 ... \n",
"1010 111 115 30 ... \n",
"1013 129 133 32 ... \n",
"1014 120 132 29 ... \n",
"1015 107 116 30 ... \n",
"1016 112 106 21 ... \n",
"1019 105 106 25 ... \n",
"1021 111 109 26 ... \n",
"1022 125 129 31 ... \n",
"1023 130 125 30 ... \n",
"1024 120 116 25 ... \n",
"1025 130 131 31 ... \n",
"1026 130 122 26 ... \n",
"1027 103 113 32 ... \n",
"1028 103 127 29 ... \n",
"1029 96 102 30 ... \n",
"1030 98 96 27 ... \n",
"1032 132 136 NaN ... \n",
"1035 125 136 30 ... \n",
"1037 130 112 29 ... \n",
"1039 96 110 26 ... \n",
"1041 98 107 29 ... \n",
"1042 103 103 29 ... \n",
"1045 130 137 29 ... \n",
"1046 111 115 28 ... \n",
"1047 125 119 32 ... \n",
"1049 114 134 29 ... \n",
"1050 115 124 28 ... \n",
"1052 125 131 33 ... \n",
"1053 125 127 31 ... \n",
"1054 130 139 27 ... \n",
"1055 103 110 28 ... \n",
"1056 120 123 32 ... \n",
"1057 111 111 32 ... \n",
"1058 120 121 26 ... \n",
"1060 100 104 30 ... \n",
"1061 107 97 19 ... \n",
"1063 91 89 23 ... \n",
"1064 120 124 28 ... \n",
"1065 125 121 29 ... \n",
"\n",
" nback_dPrime_2 nback_dPrime_3 WMfilt_dPrime_4RY \\\n",
"participant \n",
"1004 5.261351 1.788136 6.346643 \n",
"1006 9.506849 5.518134 NaN \n",
"1007 7.512491 5.579108 2.319343 \n",
"1010 9.506849 5.974065 9.506849 \n",
"1013 NaN NaN NaN \n",
"1014 4.655424 5.579108 3.289707 \n",
"1015 1.908446 3.350618 1.421262 \n",
"1016 2.086500 1.668391 1.841030 \n",
"1019 5.477865 1.835382 1.956041 \n",
"1021 1.952442 4.023973 2.648002 \n",
"1022 0.000000 0.000000 2.438951 \n",
"1023 3.640467 5.623374 5.489740 \n",
"1024 5.406859 3.641635 1.896321 \n",
"1025 5.502573 5.790365 9.506849 \n",
"1026 NaN NaN 0.498652 \n",
"1027 9.506849 5.974065 2.023946 \n",
"1028 3.640467 3.483508 1.226942 \n",
"1029 1.306870 1.207281 1.569306 \n",
"1030 0.000000 0.000000 2.533671 \n",
"1032 5.502573 3.711473 2.656290 \n",
"1035 2.048156 3.483508 1.841575 \n",
"1037 0.000000 0.000000 1.177850 \n",
"1039 5.502573 2.134856 1.569306 \n",
"1041 3.176807 3.350618 0.967283 \n",
"1042 3.222997 1.634290 6.034976 \n",
"1045 5.502573 5.477865 0.777748 \n",
"1046 0.000000 0.000000 2.317985 \n",
"1047 5.502573 5.790365 2.486475 \n",
"1049 9.506849 9.506849 2.502192 \n",
"1050 5.406859 5.790365 1.677638 \n",
"1052 3.960057 5.790365 3.213075 \n",
"1053 9.506849 5.974065 1.677638 \n",
"1054 5.261351 5.351143 5.756572 \n",
"1055 5.502573 5.790365 NaN \n",
"1056 5.406859 5.502573 2.278494 \n",
"1057 1.965699 1.965699 1.915059 \n",
"1058 5.261351 3.903172 NaN \n",
"1060 0.591736 0.856804 1.439199 \n",
"1061 5.071846 1.762937 NaN \n",
"1063 NaN NaN NaN \n",
"1064 3.960057 4.023973 NaN \n",
"1065 3.960057 3.856982 1.606331 \n",
"\n",
" WMfilt_dPrime_DRY SRT_medRT_skill_s_all_avg \\\n",
"participant \n",
"1004 6.287545 0.097148 \n",
"1006 NaN 0.021118 \n",
"1007 9.506849 NaN \n",
"1010 6.398278 NaN \n",
"1013 NaN NaN \n",
"1014 5.789858 NaN \n",
"1015 6.034976 NaN \n",
"1016 5.789858 NaN \n",
"1019 5.789858 NaN \n",
"1021 6.034976 0.009253 \n",
"1022 6.373281 0.068931 \n",
"1023 2.651435 0.022349 \n",
"1024 2.507003 0.051153 \n",
"1025 9.506849 0.006867 \n",
"1026 -0.565949 0.064061 \n",
"1027 5.387064 -0.026725 \n",
"1028 1.563768 0.041532 \n",
"1029 1.970570 -0.244076 \n",
"1030 2.533671 NaN \n",
"1032 3.264710 0.011758 \n",
"1035 2.502192 -0.063103 \n",
"1037 2.809071 NaN \n",
"1039 3.289707 0.017972 \n",
"1041 5.756572 0.026601 \n",
"1042 5.358010 0.116027 \n",
"1045 1.198890 -0.051569 \n",
"1046 6.034976 0.077565 \n",
"1047 5.789858 0.004618 \n",
"1049 9.506849 -0.013917 \n",
"1050 2.926405 0.072584 \n",
"1052 6.373281 0.019271 \n",
"1053 9.506849 0.006768 \n",
"1054 2.926405 -0.002466 \n",
"1055 NaN NaN \n",
"1056 5.789858 -0.008035 \n",
"1057 5.974065 0.024385 \n",
"1058 NaN 0.148361 \n",
"1060 2.533671 0.024278 \n",
"1061 NaN -0.040126 \n",
"1063 NaN 0.176274 \n",
"1064 NaN 0.050184 \n",
"1065 1.263946 0.062724 \n",
"\n",
" SRT_medRT_skill_t_all_avg SAT_sem_ACC_9 SAT_syn_ACC_9 \\\n",
"participant \n",
"1004 -0.792416 0.500000 0.538462 \n",
"1006 -0.266436 0.442308 0.346154 \n",
"1007 NaN 0.826923 0.769231 \n",
"1010 NaN 0.519231 0.692308 \n",
"1013 NaN 0.480769 0.730769 \n",
"1014 NaN 0.500000 0.615385 \n",
"1015 NaN 0.576923 0.423077 \n",
"1016 NaN 0.461538 0.461538 \n",
"1019 NaN 0.615385 0.384615 \n",
"1021 -0.501316 0.576923 0.423077 \n",
"1022 -0.512701 0.519231 0.538462 \n",
"1023 -0.654522 0.403846 0.769231 \n",
"1024 -0.398913 0.538462 0.461538 \n",
"1025 -0.289487 0.750000 0.538462 \n",
"1026 -0.167381 0.384615 0.576923 \n",
"1027 -0.568169 0.500000 0.500000 \n",
"1028 -0.595529 0.538462 0.423077 \n",
"1029 -0.397760 0.538462 0.615385 \n",
"1030 NaN 0.538462 0.576923 \n",
"1032 -0.912715 0.519231 0.461538 \n",
"1035 -0.497468 0.557692 0.423077 \n",
"1037 NaN 0.461538 0.500000 \n",
"1039 -0.676889 0.461538 0.384615 \n",
"1041 -0.368407 0.500000 0.538462 \n",
"1042 -0.610784 0.576923 0.653846 \n",
"1045 -0.641819 0.576923 0.384615 \n",
"1046 -0.447080 0.480769 0.423077 \n",
"1047 -0.838732 0.519231 0.461538 \n",
"1049 -0.630314 0.480769 0.653846 \n",
"1050 -0.903482 0.461538 0.538462 \n",
"1052 -0.475791 0.346154 0.500000 \n",
"1053 -0.657769 0.788462 0.730769 \n",
"1054 -0.784725 0.557692 0.576923 \n",
"1055 NaN 0.480769 0.769231 \n",
"1056 -0.688757 0.557692 0.769231 \n",
"1057 -0.564570 NaN NaN \n",
"1058 -0.400937 0.538462 0.500000 \n",
"1060 -0.239112 0.442308 0.461538 \n",
"1061 0.445873 0.500000 0.384615 \n",
"1063 -0.572025 0.538462 0.423077 \n",
"1064 -0.387796 0.538462 0.730769 \n",
"1065 -0.624578 0.480769 0.653846 \n",
"\n",
" SAT_sem_eng SAT_syn_eng \n",
"participant \n",
"1004 0.900000 0.983333 \n",
"1006 0.966667 0.950000 \n",
"1007 0.966667 0.966667 \n",
"1010 0.966667 1.000000 \n",
"1013 0.716667 0.700000 \n",
"1014 0.916667 0.983333 \n",
"1015 0.916667 0.966667 \n",
"1016 0.966667 0.983333 \n",
"1019 0.950000 0.983333 \n",
"1021 0.916667 0.933333 \n",
"1022 0.950000 0.916667 \n",
"1023 0.950000 0.983333 \n",
"1024 0.983333 0.983333 \n",
"1025 0.983333 0.983333 \n",
"1026 0.966667 0.983333 \n",
"1027 0.966667 1.000000 \n",
"1028 0.933333 0.983333 \n",
"1029 0.933333 0.983333 \n",
"1030 0.983333 0.950000 \n",
"1032 0.916667 0.966667 \n",
"1035 0.966667 0.983333 \n",
"1037 0.966667 0.966667 \n",
"1039 0.950000 1.000000 \n",
"1041 0.966667 0.983333 \n",
"1042 0.966667 0.933333 \n",
"1045 0.883333 0.916667 \n",
"1046 0.983333 0.950000 \n",
"1047 0.966667 1.000000 \n",
"1049 0.950000 0.983333 \n",
"1050 0.983333 1.000000 \n",
"1052 1.000000 1.000000 \n",
"1053 0.966667 0.916667 \n",
"1054 0.950000 1.000000 \n",
"1055 0.983333 0.966667 \n",
"1056 0.950000 0.966667 \n",
"1057 0.966667 0.950000 \n",
"1058 0.883333 1.000000 \n",
"1060 0.916667 0.983333 \n",
"1061 0.866667 0.916667 \n",
"1063 0.933333 0.950000 \n",
"1064 0.950000 0.933333 \n",
"1065 0.983333 1.000000 \n",
"\n",
"[42 rows x 115 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lap_sub = pd.read_csv('DS_BOS_06_Students/jminas/project/LAP_MASTER_032514_jm.csv', index_col=0)\n",
"lap_sub\n",
"keys = pd.read_csv('DS_BOS_06_Students/jminas/project/KEY_012814_jm.csv')\n",
"#casl_sub.drop(['CASL13111'], inplace=True)\n",
"#casl_sub.ix[rest_data.SUBJECT, :].groupby('Gender').sum()\n",
"subject = casl_sub.index\n",
"#if need to drop na: patients = casl_sub.colname.dropna().index\n",
"score = casl_sub.finalhsk\n",
"score"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counting each column (number of participants with no missing data): \n",
"[('Age', 42), ('KBIT_verbalstd', 42), ('KBIT_nonverbalstd', 42), ('KBIT_overall', 42), ('WJ3_raw', 41), ('WJ3_std', 41), ('TONI_raw', 42), ('TONI_std', 42), ('CNREPtotalraw', 42), ('Caplan_ACC', 42), ('Caplan_RT', 42), ('CVMT_dPrime', 42), ('PCT_ACC', 41), ('PCT_Opt_Acc', 41), ('RSPAN_Load_%3', 42), ('RSPAN_Load_%4', 42), ('RSPAN_Load_%5', 42), ('RSPAN_Load_%6', 42), ('RSPAN_Load_%7', 42), ('RSPAN_LetterScore', 42), ('RSPAN_sent_acc', 42), ('MLAT_Spelling_Clues_Raw', 42), ('MLAT_WordsSent_Raw', 42), ('MLAT_PairedAssoc_Raw', 42), ('MLAT_Total_Raw', 42), ('MLAT_Total_PercAir', 42), ('MLAT_Total_Perc', 42), ('statcat_WS', 38), ('statcat_3words', 38), ('statcat_2words', 38), ('rotary_delta', 42), ('mirror_deltaE', 41), ('mirror_deltaT', 41), ('CVLT_ListATotal_raw', 42), ('CVLTListATotal_Scaled', 42), ('CVLT_ListA_ShortDelay_raw', 42), ('CVLT_ListA_ShortDelay_scaled', 42), ('CVLT_LongDelay_raw', 42), ('CVLT_LongDelayScaled', 42), ('CVLT_LongDelayHIT', 42), ('CVLT_Discriminability_Recognition', 42), ('Conscientiousness', 42), ('DWECK', 42), ('GRIT', 42), ('Music_exp_years', 42), ('HILAB_Paired.Associates', 42), ('HILAB_Serial.Reaction.Time', 42), ('HILAB_Processing.Speed.SRT', 42), ('HILAB_SRT.Anticipations', 42), ('HILAB_Phonemic.Disc', 42), ('HILAB_MixCost.RT', 41), ('HILAB_SwitchCost.RT', 41), ('HILAB_Processing.Speed.TS', 41), ('HILAB_Letter.Span', 42), ('HILAB_Running.Span', 42), ('HILAB_Stroop.RT', 42), ('HILAB_ALTM', 42), ('HILAB_Phonemic.Cat', 42), ('HILAB_PhonCat.Learning', 42), ('HILAB_NonWord.Span', 42), ('HILAB_Simon', 42)]\n",
"Number of participants left when removing any missing data: 35\n",
"\n",
"Computing participants remaining as columns are removed (starting with the columns with least data)\n",
"No removed, key, number of participants remaining\n",
"statcat_2words 35\n",
"statcat_3words 35\n",
"statcat_WS 38\n",
"HILAB_Processing.Speed.TS 38\n",
"mirror_deltaE 38\n",
"WJ3_raw 38\n",
"WJ3_std 39\n",
"mirror_deltaT 40\n",
"HILAB_SwitchCost.RT 40\n",
"HILAB_MixCost.RT 41\n",
"PCT_ACC 41\n",
"PCT_Opt_Acc 42\n",
"\n",
"Keys removed: ['statcat_2words', 'statcat_3words', 'statcat_WS', 'HILAB_Processing.Speed.TS', 'mirror_deltaE', 'WJ3_raw', 'WJ3_std', 'mirror_deltaT', 'HILAB_SwitchCost.RT', 'HILAB_MixCost.RT', 'PCT_ACC', 'PCT_Opt_Acc']\n",
"Predicting: ['gramm_ACC_4', 'SAT_semantic_dprime_4', 'SAT_syntax_dprime_4', 'SAT_semantic_ACC_4', 'SAT_syntax_ACC_4', 'gramm_ACC_9', 'SAT_semantics_9', 'SAT_synax_9']\n"
]
}
],
"source": [
"data = pd.read_csv('DS_BOS_06_Students/jminas/project/LAP_MASTER_032514_jm.csv')\n",
"data.index = data.participant\n",
"keys = pd.read_csv('DS_BOS_06_Students/jminas/project/KEY_012814_jm.csv')\n",
"ivkeys = ['Age'] + keys.Measures[keys.Type == 'Behavioral'].tolist()\n",
"ivkeys.remove('Videogame_Score')\n",
"dvkeys = [key for key in keys.Measures[keys.Type == 'Training'].tolist() if key.endswith('_4') or key.endswith('_9')]\n",
"dvkeys.remove('vocab_ACC_4') # almost all subject have a perfect score, median split doesn't work\n",
"\n",
"print \"Counting each column (number of participants with no missing data): \"\n",
"print zip(data[ivkeys], data[ivkeys].count().values)\n",
"\n",
"print \"Number of participants left when removing any missing data: \", data[ivkeys].dropna().count().values.min()\n",
"\n",
"sortidx = np.argsort(data[ivkeys].count().values)\n",
"ivkeys = np.array(ivkeys)[sortidx].tolist()\n",
"\n",
"print \"\\nComputing participants remaining as columns are removed (starting with the columns with least data)\" \n",
"print \"No removed, key, number of participants remaining\"\n",
"count_idx = 0\n",
"for idx in range(len(ivkeys)):\n",
" count = data[ivkeys[(idx + 1):]].dropna().count().values.min()\n",
" print ivkeys[idx], count\n",
" if count == 42:\n",
" count_idx = idx + 1\n",
" break\n",
"print \"\\nKeys removed: \", ivkeys[:count_idx]\n",
"ivkeys = ivkeys[count_idx:]\n",
"ivbrainkeys = ivkeys + keys.Measures[keys.Type == 'EEG'].tolist()\n",
"print 'Predicting: %r' % dvkeys"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"41\n",
"41\n",
"set([1013])\n",
"set([1057])\n"
]
}
],
"source": [
"keys_4 = [key for key in keys.Measures[keys.Type == 'Training'].tolist() if key.endswith('_4')]\n",
"keys_4.remove('vocab_ACC_4')\n",
"keys_9 = [key for key in keys.Measures[keys.Type == 'Training'].tolist() if key.endswith('_9')]\n",
"\n",
"subj_4 = set(data[keys_4].dropna().index)\n",
"print len(subj_4)\n",
"\n",
"subj_9 = set(data[keys_9].dropna().index)\n",
"print len(subj_9)\n",
"\n",
"print subj_9.difference(subj_4)\n",
"print subj_4.difference(subj_9)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40, 57)\n"
]
}
],
"source": [
"cleandata = data[ivkeys + dvkeys].dropna()\n",
"print cleandata.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#contrast of parameter estimates\n",
"copes = ['sent:English-v-MAL', 'sent:MAL-v-Rest', 'sent:English-v-Mandarin',\n",
" 'nback:3_gt_1', 'srt:S_gt_R']\n",
"\n",
"def get_fmri_fname(subject, cope):\n",
" parts = cope.split(':')\n",
" if parts[0] == 'sent':\n",
" fname = '/gablab/LAP/Analysis/sent/bips/results/LAP_%s/norm/fwhm_2.0/cope_%s.nii.gz' % (subject, parts[1])\n",
" elif parts[0] == 'nback':\n",
" fname = '/gablab/LAP/Analysis/nback/bips/results/LAP_%s/norm/cope_%s.nii.gz' % (subject, parts[1])\n",
" elif parts[0] == 'srt':\n",
" fname = '/gablab/LAP/Analysis/srt/bips/results/LAP_%s/norm/cope_%s.nii.gz' % (subject, parts[1])\n",
" else:\n",
" raise ValueError('invalid cope')\n",
" return fname"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([1004, 1006, 1007, 1010, 1014, 1015, 1016, 1019, 1021, 1022, 1023,\n",
" 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1032, 1035, 1037, 1039,\n",
" 1041, 1042, 1045, 1046, 1047, 1049, 1050, 1052, 1053, 1054, 1055,\n",
" 1056, 1058, 1060, 1061, 1063, 1064, 1065],\n",
" dtype='int64', name=u'participant')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cleandata.index"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/gablab/LAP/Analysis/sent/bips/results/LAP_1004/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1006/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1007/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1010/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1014/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1015/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1016/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1019/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1021/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1022/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1023/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1024/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1025/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1026/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1027/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1028/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1029/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1030/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1032/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1035/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1037/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1039/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1041/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1042/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1045/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1046/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1047/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1049/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1050/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1052/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1053/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1054/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1055/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1056/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1058/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1060/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1061/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1063/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1064/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1065/norm/fwhm_2.0/cope_English-v-MAL.nii.gz\n",
"sent:English-v-MAL: #subjects: 0, missing: [1004, 1006, 1007, 1010, 1014, 1015, 1016, 1019, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1032, 1035, 1037, 1039, 1041, 1042, 1045, 1046, 1047, 1049, 1050, 1052, 1053, 1054, 1055, 1056, 1058, 1060, 1061, 1063, 1064, 1065]\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1004/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1006/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1007/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1010/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1014/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1015/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1016/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1019/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1021/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1022/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1023/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1024/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1025/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1026/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1027/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1028/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1029/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1030/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1032/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1035/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1037/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1039/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1041/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1042/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1045/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1046/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1047/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1049/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1050/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1052/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1053/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1054/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1055/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1056/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1058/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1060/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1061/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1063/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1064/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1065/norm/fwhm_2.0/cope_MAL-v-Rest.nii.gz\n",
"sent:MAL-v-Rest: #subjects: 0, missing: [1004, 1006, 1007, 1010, 1014, 1015, 1016, 1019, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1032, 1035, 1037, 1039, 1041, 1042, 1045, 1046, 1047, 1049, 1050, 1052, 1053, 1054, 1055, 1056, 1058, 1060, 1061, 1063, 1064, 1065]\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1004/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1006/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1007/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1010/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1014/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1015/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1016/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1019/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1021/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1022/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1023/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1024/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1025/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1026/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1027/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1028/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1029/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1030/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1032/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1035/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1037/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1039/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1041/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1042/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1045/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1046/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1047/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1049/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1050/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1052/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1053/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1054/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1055/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1056/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1058/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1060/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1061/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1063/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1064/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"/gablab/LAP/Analysis/sent/bips/results/LAP_1065/norm/fwhm_2.0/cope_English-v-Mandarin.nii.gz\n",
"sent:English-v-Mandarin: #subjects: 0, missing: [1004, 1006, 1007, 1010, 1014, 1015, 1016, 1019, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1032, 1035, 1037, 1039, 1041, 1042, 1045, 1046, 1047, 1049, 1050, 1052, 1053, 1054, 1055, 1056, 1058, 1060, 1061, 1063, 1064, 1065]\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1004/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1006/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1007/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1010/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1014/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1015/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1016/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1019/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1021/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1022/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1023/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1024/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1025/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1026/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1027/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1028/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1029/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1030/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1032/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1035/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1037/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1039/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1041/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1042/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1045/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1046/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1047/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1049/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1050/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1052/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1053/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1054/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1055/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1056/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1058/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1060/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1061/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1063/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1064/norm/cope_3_gt_1.nii.gz\n",
"/gablab/LAP/Analysis/nback/bips/results/LAP_1065/norm/cope_3_gt_1.nii.gz\n",
"nback:3_gt_1: #subjects: 0, missing: [1004, 1006, 1007, 1010, 1014, 1015, 1016, 1019, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1032, 1035, 1037, 1039, 1041, 1042, 1045, 1046, 1047, 1049, 1050, 1052, 1053, 1054, 1055, 1056, 1058, 1060, 1061, 1063, 1064, 1065]\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1004/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1006/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1007/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1010/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1014/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1015/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1016/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1019/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1021/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1022/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1023/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1024/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1025/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1026/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1027/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1028/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1029/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1030/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1032/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1035/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1037/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1039/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1041/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1042/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1045/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1046/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1047/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1049/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1050/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1052/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1053/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1054/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1055/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1056/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1058/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1060/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1061/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1063/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1064/norm/cope_S_gt_R.nii.gz\n",
"/gablab/LAP/Analysis/srt/bips/results/LAP_1065/norm/cope_S_gt_R.nii.gz\n",
"srt:S_gt_R: #subjects: 0, missing: [1004, 1006, 1007, 1010, 1014, 1015, 1016, 1019, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1032, 1035, 1037, 1039, 1041, 1042, 1045, 1046, 1047, 1049, 1050, 1052, 1053, 1054, 1055, 1056, 1058, 1060, 1061, 1063, 1064, 1065]\n"
]
}
],
"source": [
"# load dataset\n",
"from sklearn.datasets.base import Bunch\n",
"\n",
"dataset = {}\n",
"for cope in copes:\n",
" files = []\n",
" notfound = []\n",
" found = []\n",
" for subject in cleandata.index:\n",
" fname = get_fmri_fname(subject, cope)\n",
" print(fname)\n",
" if os.path.exists(fname):\n",
" files.append(fname)\n",
" found.append(subject)\n",
" else:\n",
" notfound.append(subject)\n",
" print '%s: #subjects: %d, missing: %s' % (cope, len(files), str(notfound))\n",
" \n",
" # resample files:\n",
" newfiles = []\n",
" for filename in files:\n",
" path, name, ext = split_filename(filename)\n",
" newname = os.path.join(tmp_dir + '/' + '/'.join(path.split('/')[-3:]), name + '_2mm' + ext)\n",
" if not os.path.exists(os.path.dirname(newname)):\n",
" os.makedirs(os.path.dirname(newname))\n",
" if not os.path.exists(newname):\n",
" Resample(in_file=filename, resampled_file=newname, voxel_size=(2,2,2)).run()\n",
" newfiles.append(newname)\n",
" \n",
" data2use = cleandata.select(lambda x: x in found)\n",
" \n",
" dataset[cope] = Bunch(func=newfiles,\n",
" dvkeys=dvkeys,\n",
" ivkeys=ivkeys,\n",
" behav=data2use[ivkeys],\n",
" targets=data2use[dvkeys])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def classify(X1, X, y):\n",
" results = [[], []]\n",
" scores = []\n",
" clfs = []\n",
" for train, test in StratifiedKFold(y, 10): #StratifiedKFold(y, 8): #StratifiedShuffleSplit(y, n_iter=200, test_size=0.1):\n",
" clf1 = ExtraTreesClassifier(n_estimators=1000, max_features='sqrt')\n",
" clf = Pipeline([(\"standardize\", StandardScaler()),\n",
" #('select', SelectKBest(k=20000)),\n",
" #('select2', LinearSVC(C=1e-1, penalty=\"l1\", dual=False)),\n",
" (\"forest\", clf1)\n",
" ])\n",
" \n",
" #Feature reduction ====================================================\n",
" #we are adding a random feature (equal distribution that is not correlated to any output)\n",
" #then we filter out the features that were correlated with our random feature\n",
" cc = 1.\n",
" while(abs(cc) > 0.05):\n",
" Xrandom = np.random.randn(len(train), 1)\n",
" cc = np.corrcoef(Xrandom.flatten(), y[train])[0, 1]\n",
"\n",
" Xhat = np.concatenate((X[train], Xrandom), axis=1)\n",
" clf.fit(Xhat, y[train])\n",
" fi = clf.steps[-1][1].feature_importances_\n",
" fi[fi <= fi[-1]] = 0\n",
" fidx = np.nonzero(fi)[0]\n",
" print \"num features: %d\" % len(fidx)\n",
" for i in range(10):\n",
" Xhat = np.concatenate((X[train][:, fidx], Xrandom), axis=1)\n",
" clf.fit(Xhat, y[train])\n",
" fi = clf.steps[-1][1].feature_importances_\n",
" fi[fi <= fi[-1]] = 0\n",
" fidx = fidx[np.nonzero(fi)[0]]\n",
" print \"num features: %d\" % len(fidx)\n",
" \n",
" \n",
" #fidx = range(X.shape[1])\n",
" \n",
" \n",
" clf1 = ExtraTreesClassifier(n_estimators=2000, max_features='sqrt')\n",
" clf = Pipeline([(\"standardize\", StandardScaler()),\n",
" #('select', SelectKBest(k=20000)),\n",
" #('select2', LinearSVC(C=1e-1, penalty=\"l1\", dual=False)),\n",
" (\"forest\", clf1)\n",
" ])\n",
" ytest = clf.fit(X[train][:, fidx], y[train]).predict(X[test][:, fidx])\n",
" #print y[test], ytest\n",
" results[0].extend(y[test])\n",
" results[1].extend(ytest)\n",
" scores.append(f1_score(y[test], ytest))\n",
" importances = np.zeros(X.shape[1])\n",
" importances[fidx] = clf.steps[-1][1].feature_importances_\n",
" #importances[clf.steps[-2][1].get_support()] = clf.steps[-1][1].feature_importances_\n",
" #importances[np.abs(clf.steps[-2][1].coef_.flatten()) > 0] = clf.steps[-1][1].feature_importances_\n",
" clfs.append(importances)\n",
" roc_auc = roc_auc_score(results[0], results[1])\n",
" #print confusion_matrix(results[0], results[1])\n",
" #print classification_report(results[0], results[1])\n",
"\n",
" norm_scores = np.array(scores).dot(np.array(clfs))/np.sum(scores)\n",
" fw = np.zeros(fmri_masked.shape[1])\n",
" fw[X1.keys().values.astype(int)] = norm_scores\n",
" return roc_auc, fw"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_fw(fw, fwhm=6, threshold=0.03):\n",
" fi = np.argsort(fw)[::-1]\n",
" fw_img = nifti_masker.inverse_transform(1. * (fw / fw.max()))\n",
"\n",
" sm_fw_img = nl.image.smooth_img(fw_img, fwhm)\n",
" plotting.plot_glass_brain(sm_fw_img, cmap=plt.cm.spectral,\n",
" threshold=threshold)\n",
" plotting.plot_stat_map(sm_fw_img, #cut_coords=np.linspace(-55, 55, 10), \n",
" display_mode='x', threshold=threshold, cmap=plt.cm.Reds)\n",
" plotting.plot_stat_map(sm_fw_img, #cut_coords=np.linspace(-55, 55, 10), \n",
" display_mode='z', threshold=threshold, cmap=plt.cm.Reds)\n",
" plotting.plot_stat_map(sm_fw_img, #cut_coords=np.linspace(-55, 55, 10), \n",
" display_mode='y', threshold=threshold, cmap=plt.cm.Reds)\n",
" #plt.figure()\n",
" #plt.plot(fw[fi[:100]], '.-')\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#contrast of parameter estimates\n",
"copes = ['sent:English-v-MAL', 'sent:MAL-v-Rest', 'sent:English-v-Mandarin',\n",
" 'nback:3_gt_1', 'srt:S_gt_R']\n",
"\n",
"def get_fmri_fname(subject, cope):\n",
" parts = cope.split(':')\n",
" if parts[0] == 'sent':\n",
" fname = '/gablab/LAP/Analysis/sent/bips/results/LAP_%s/norm/fwhm_2.0/cope_%s.nii.gz' % (subject, parts[1])\n",
" elif parts[0] == 'nback':\n",
" fname = '/gablab/LAP/Analysis/nback/bips/results/LAP_%s/norm/cope_%s.nii.gz' % (subject, parts[1])\n",
" elif parts[0] == 'srt':\n",
" fname = '/gablab/LAP/Analysis/srt/bips/results/LAP_%s/norm/cope_%s.nii.gz' % (subject, parts[1])\n",
" else:\n",
" raise ValueError('invalid cope')\n",
" return fname"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "[Errno 2] No such file or directory: '/gablab/LAP/Analysis/srt/bips/results/LAP_1065/norm/cope_S_gt_R.nii.gz'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-23-eb57f6e5dcb5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/gablab/LAP/Analysis/srt/bips/results/LAP_1065/norm/cope_S_gt_R.nii.gz'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/jminas/anaconda/lib/python2.7/site-packages/nibabel/loadsave.pyc\u001b[0m in \u001b[0;36mload\u001b[1;34m(filename, **kwargs)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[0mImage\u001b[0m \u001b[0mof\u001b[0m \u001b[0mguessed\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m '''\n\u001b[1;32m---> 44\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mguessed_image_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_filename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jminas/anaconda/lib/python2.7/site-packages/nibabel/loadsave.pyc\u001b[0m in \u001b[0;36mguessed_image_type\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMinc2Image\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msignature\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb'\\211HDF'\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mMinc1Image\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mlext\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'.nii'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mBinOpener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[0mbinaryblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m348\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[0mft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwhich_analyze_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbinaryblock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jminas/anaconda/lib/python2.7/site-packages/nibabel/openers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fileish, *args, **kwargs)\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'compresslevel'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marg_names\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m'compresslevel'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compresslevel'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_compresslevel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileish\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 75\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfileish\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mme_opened\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jminas/anaconda/lib/python2.7/site-packages/nibabel/openers.pyc\u001b[0m in \u001b[0;36m_gzip_open\u001b[1;34m(fileish, *args, **kwargs)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;31m# open gzip files with faster reads on large files using larger chunks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m# See https://github.com/nipy/nibabel/pull/210 for discussion\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mgzip_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgzip\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileish\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[0mgzip_file\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_read_chunk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGZIP_MAX_READ_CHUNK\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mgzip_file\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jminas/anaconda/lib/python2.7/gzip.pyc\u001b[0m in \u001b[0;36mopen\u001b[1;34m(filename, mode, compresslevel)\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \"\"\"\n\u001b[1;32m---> 34\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mGzipFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompresslevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mGzipFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBufferedIOBase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jminas/anaconda/lib/python2.7/gzip.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filename, mode, compresslevel, fileobj, mtime)\u001b[0m\n\u001b[0;32m 92\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 93\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfileobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m \u001b[0mfileobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmyfileobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__builtin__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 95\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[1;31m# Issue #13781: os.fdopen() creates a fileobj with a bogus name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/gablab/LAP/Analysis/srt/bips/results/LAP_1065/norm/cope_S_gt_R.nii.gz'"
]
}
],
"source": [
"nb.load('/gablab/LAP/Analysis/srt/bips/results/LAP_1065/norm/cope_S_gt_R.nii.gz')\n",
" '/gablab/LAP/Analysis/srt/bips/results/LAP_1065/norm/cope_S_gt_R.nii.gz'\n",
" \n",
" \n",
" \n",
" ''"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"basedir = '/gablab/p/CASL/Results/Imaging/tone/bips'\n",
"template = '{basedir}/{subj}/smri/warped_image/fwhm_6.0/_imgtype_cope/cope_{task}_wtsimt.nii.gz'\n",
"mask_img = nb.load('/gablab/p/CASL/Analysis/tone/roi/MNI_2mm.nii')\n",
"mask_img = nb.Nifti1Image((mask_img.get_data() > 1).astype(bool), mask_img.affine, header=mask_img.get_header())"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24 Index([u'CASL13101', u'CASL13102', u'CASL13104', u'CASL13106', u'CASL13107', u'CASL13108', u'CASL13109', u'CASL13110', u'CASL13111', u'CASL13112', u'CASL13114', u'CASL13116', u'CASL13117', u'CASL13118', u'CASL13119', u'CASL13120', u'CASL13121', u'CASL13122', u'CASL13123', u'CASL13124', u'CASL13125', u'CASL13126', u'CASL13128', u'CASL13130'], dtype='object')\n",
"(24, 228453)\n",
"(24, 219184)\n",
"[-1 1 1 -1 1 -1 -1 1 1 -1 -1 -1 1 -1 1 -1 1 1 1 1 -1 -1 1 -1]\n",
"num features: 3194\n",
"num features: 494\n",
"num features: 3413\n",
"num features: 358\n",
"num features: 3661\n",
"num features: 480\n",
"num features: 3840\n",
"num features: 601\n",
"num features: 3764\n",
"num features: 354\n",
"num features: 3732\n",
"num features: 745\n",
"num features: 3703\n",
"num features: 489\n",
"num features: 3767\n",
"num features: 660\n",
"num features: 3756\n",
"num features: 557\n",
"num features: 3622\n",
"num features: 570\n",
"{'Spe-v-Wav': [0.625]}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/sklearn/metrics/metrics.py:1771: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n"
]
}
],
"source": [
"### Mask data #################################################################\n",
"nifti_masker = NiftiMasker(\n",
" mask_img=mask_img,\n",
" smoothing_fwhm=2,\n",
" memory='nilearn_cache', memory_level=1) # cache options\n",
"\n",
"roc_data = {\"Spe-v-Wav\": []} # determined by the number of contrasts that are interested?\n",
"fw_data = {\"Spe-v-Wav\": []} \n",
"\n",
"for task in [\"Spe-v-Wav\"]:\n",
" exclude = None\n",
" patients = casl_sub.index\n",
" print len(patients), patients\n",
" \n",
" copes = [template.format(basedir=basedir, task=task, subj=val) for val in patients]\n",
" #print '\\n'.join(copes)\n",
" images = [nb.squeeze_image(nb.load(cope)) for cope in copes]\n",
" fmri_masked = nifti_masker.fit_transform(images)\n",
" print fmri_masked.shape \n",
" \n",
" X1 = pd.DataFrame(fmri_masked.copy())\n",
" X1.index = casl_sub.index\n",
" #X1.ix[:, np.nonzero(np.std(X1, axis=0) < 10)[0]] = np.nan\n",
" X1.ix[:, np.nonzero(np.sum(X1==0., axis=0) > 1)[0]] = np.nan\n",
" X1 = X1.dropna(axis=1)\n",
" #X1 = pd.concat((X1.dropna(axis=1), df2.ix[patients, ['LSAS_PRE_total', 'Age']]), axis=1)\n",
" X = X1.values\n",
" print X.shape\n",
" y = casl_sub.finalhsk\n",
" y = y.ix[patients].values\n",
" \n",
" #if True:\n",
" # y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)/df2.LSAS_PRE_total\n",
" # y = y.ix[patients].values\n",
" # y = 2 * (y > 0.5) - 1\n",
" #else:\n",
" # y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)\n",
" # y = y.ix[patients].values\n",
" \n",
" y = 2 * (y > np.median(y)) - 1\n",
" print y\n",
" \n",
" idx = np.argsort(y)\n",
" roc_auc, fw = classify(X1, X, y)\n",
" roc_data[task].append(roc_auc)\n",
" fw_data[task].append(fw)\n",
" print roc_data"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['Arial'] not found. Falling back to Bitstream Vera Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
{
"data": {
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SuGQZg1qtRiKR1FrN0dq4oX7Xw404Ozvj6elJVlYW2dnZdV5HCoUCpVJpqfoI\nJstbYmIie/fuZfjw4WRmZnLt2jWqqqrw8fEhrF24KZ3YqEar1bJhwwYWLVpkGad53EpBhYCEnJwc\ntm3bxgMPPNCg8/6r+FsIhZspSWowGNiyZQtff/01er0eV1dXnnnmGXr06FFtm6ysLJKTky3BjLGx\nsdx///1MmjTpppSuGW9vbwoKCsjONnUwCwwMrHG80tJSCgsL0Wq1FqFQXl5OYmJitfSh6z+Hy5cv\nU1RURMuWLRu9ypfL5Zbc+ieeeIKEhATGjBlDbm4uhYWFjBsxgYqeWoxHFZRfrqScco5xgH3/O8iB\n//3Kt0e/vGmz2e0qTfpXc/DgQdavX095eTn29vb861//4r777qt1QissLCQjI4PAwMB6Vxiugkns\n/Zs5ACgwrfy0mCa19kJHLhsbHyw6dOhQwsPDef/99zl8+DCPPPLIbclIkEql9OvXj759+3LhwgW2\nbt3KqVOn+Pnnnzl69Cjh4eFMmjTJqpVNpGEcPnyYs2fP4uDgwOOPP17jdXNabnZ2Nvn5+TWquf70\n00/ExsZaXAhmzA9E879fffVVZFIpOnUl0loeQwqFAoPBgEajaZBr82aFAoC/vz95eXkkJyfj4eFh\ndR9zjxSzi9m8CJsyZQpz587Fzc2NsrIyFAoFISEhtGjRotp5+/n50bdvX7744gtefvnlaos4AQHZ\nH5/D5s2bGTJkSKPn71vJnbN8aQIjR46sVpc+MjKyzu11Oh2vvfYa//vf/9Dr9dx3332sX7++mkjQ\naDScPn2ahIQEDAYDbdu2pXv37iQmJjJhwoQmiQQwXcChoaG0atWKrKwsLly4UKP0p9nMd32xktoq\njoEp+Cg6Opr8/Hx8fX0JCgpq0vj69+/PuXPn8PDwIDQ0lN69e5P1ex4KRzmuY+1wf9AepUpJBPfi\ngRcGqlizZg2bN2+udnM0lOtLk/4d0el0rF+/ntdffx2tVoufnx/u7u7s3r2b+fPn895773H8+PFq\n33VFRQV5eXnk5+ffxpGbAr2WLFlCaGgo8+fP57fffrttYxEEgbCwMJYuXcq7777LkCFDcHFx4ciR\nI8ycOZNly5Zx4cKFm7oG/+nodDo+++wzAB599FGrgayenp5otVrLIgdMD+r//e9/JCQkMGfOHMtD\n0Py3OQ3SaDTSrVUvLkbFMtJhIg8nz8SWBdiygFW8xyreo5PQk0d6P4lcLq9RkdYaN37fjREKCoUC\nLy8vBEF2vFxvAAAgAElEQVSwxAlZo1WrVuj1euLj4yktLbV0nLSzs7PMl927d6e3a3/aCx3pTQS9\niUAl2LF8yXK6dOlCnz59ePvtt6uNWWNUU2Esp1+/fmi1Wsv3cKdw11sUGluStLi4mFWrVhEbG4tK\npeLFF1+sUUGrpKSECxcuoNfr8fHxwcfHB7lcTkxMDB07dmw2/5FUKiUwMBCDwUB2djZJSUnVVkS1\nCQWz2et6oWA0GomPj0ej0dCmTZsmt5K+R4gAQIacJ9Y/TwmmOutK7Mg5W0Cn/qEoOyrwmuZC0ZYK\nRhdNIZ7z7NjxHWDyPT7zzDN1prXdKaVJ/wqKioqIjIzkypUryOVy/vOf/zBs2DDL6kej0ZCSksLv\nv//O119/TWhoKPfffz+urq4IgkB+fn69JWddMa3szKsSG0yrMAkmQRtI7Rk2DUUQBAYOHEiXLl34\n5JNP2Lt3L48//niz1dUYJUyhPaYCTyc4BMBvxkN17tOmTRtmzZpFXl4e27dvZ9euXZYUyw4dOjBp\n0qQ7zp1zJ7Nnzx7y8/Px9/evsz6Lra2tpbcCmB7KGzZsQCKRMGvWrGqft4eHB3l5eaSkpCCVSvn5\n559p2d6d0IBQSj7XWXsLSgrKMBgMjY7VaUjBpdrw8vIiLS2N9PT0OjMgPD09KSgosBQWA1Osg5eX\nF/b29pay/DeiVCmRSqW4urrSr18/Nm7cyHfffVfDxfDII49w8uRJDh8+zKhRo6we76/mrhcKjSEj\nI4OlS5eSlZWFu7s7S5YsqfFQLSsrIyYmBqPRSHBwcDXTmpOTE+Xl5c06JkEQCAwMRK1Wk5qaipOT\nkyWX1ixIrveJmW/O68VKeno6eXl5uLq6NimNzvzgmsVSAJK5ii22+NAFgG18CWXguNMF51x7WkW4\n4jhVTsrnebTQeFBBOTY2Nuzfv5+srCwWLFjwjw82y8vLY9GiRaSnp+Pp6cm8efNqpLuqVCpLSe0Z\nM2Zw6tQpli9fztSpU3FwcKCwsJDKyso7op6Ao6Mjs2fPJi4ujrfffpvAwMBmr6vRWNzc3Hj88ceZ\nMmUKO3bs4KeffiIuLo6VK1fSpk0b7r//fvr37y92vKwDrVbLd9+ZhP706dPrfMCae384Ozuj1+t5\n88038fLyYurUqTVirWQyGb6+vly8eJEtW7Zw4cIFxnWZSNHOCuaX/tH0abXpr/OmbEwi0/TY+Mqq\ntUavD7MwuBnXA5hiKOzs7Oqd36VSKaGhoRQVFVFQUIDBYMDJyQmpVMq5c+cs25WjrvZ3K39PXNxd\nLFaahx9+mIULF9K1a9dqsV0tW7Zk4sSJfPPNN3z00Ue8+uqrTbZeNwf/GKmdk5PDwoULycrKol27\ndrz22mu1rryzsrKoqqqiY8eONfxvHh4e1cxtzYVUKiUkJASpVMrVq1ctF3ldrgfzpGcuYGNjY1Nr\nUGRTMGLdfFt0qoyCvWoktgLOIX/6rNeuXYurqysXL15kzpw5pKenN9t47jYyMzN56aWXSE9Px9/f\nn1dffbXehlsSiYSePXuyatUqDh48yO7du6moqLD0E7HGZeNFLhsvEkcMccSg+eNPxR9/Cmhc1kN9\ndOjQgdWrVxMQEMCCBQv45ZdfGjWxm3EVPHAVPOhCL1xxxxV3BjOKwYxCJdihEhoeD+Hg4MDUqVPZ\nuHEjTz75JG5ubiQnJ/P666/z9NNPc/z4cdElYYWdO3dSWFhIQEBAvZlH2dnZ2NjY4ObmZrmmp02b\nZnXucXZ2xsHBgbi4OF544QVyPlZTkaCv8z0UvlKUSuVN++kbKxTM2zbk+jCnvgcEBBAYGIiHhwc2\nNjZW+1kAlvvevAiUSqXMnDmT9evX12gBf//999O+fXsKCgo4fPhwg8d/K/lHCIWCggJeffVV8vLy\nCAkJ4eWXX661qqJerycjIwNHR8daX1cqlVRVVdU7ad8MSqUSb29vBEGwpMuZxUBdMQqZmZkoFAra\ntGnT5BWnuUFLNFFEE0U2GdjjiP6PP+WoKUfNG8ZlvGFcxvLol4jRnGb020P4wLAWo9FIQEAA69at\nIyAggMzMTObMmWMpe/1PIjMzk3nz5pGTk0NQUBAvv/yy1cIsteHg4MD8+fMJCwvj448/5vfff7/j\nHnISiYRBgwaxdu1aCgsLmTt3LleuXLndw8LW1paxY8fy0UcfMWvWLLy9vcnMzGT16tW89NJLxMfH\n3+4h3lFUVFSwZcsWwGRNqG+xUV5ejlqtZv369XTs2JEJEybUub1er+fjjz/mgw8+IDg4GIlGanKR\nhWP68Sgx/TwOPA6XBAcqg1Vs/GgjHg4tG3QOTQlmvP4YN3uPZWVlVcuYSDUmkWpM4jfjIY7q9/Pi\niy/y4IMPVsv+aNWqFQMHDuSXX36pdixbW1tGjRpFfn4+mzZtqmZRvl387YVCaWkpS5YsITExkS5d\nurBkyRKrRWwqKiowGo11VqObMWMGmzdvviVjbdmyJRqNxlLN0exeqE0omF8ztwVu2bJhN1RD0aBG\njhwFdXePzEzPQqfTVVtNurq6smbNGnr37k1ZWRlLlixh8+bNVvu0/90oLi5m2bJlFBQUEBYWxooV\nK27KNC8IAqNGjeK///0v27ZtY/ny5Rw+fJj09HSrn+XXlR+zseQd1rGYdSzmTZbxFpH1+vubglKp\nZPr06Tz//PN88sknbNq0qc7V1fXkG3PIN+aQSzbaP/5oUKNBTTkaytHc9LhkMhlDhgzh3Xff5amn\nnsLJyYnY2FjmzJnD2rVryczMvOlj/53YvXs3xcXFtG/f3mrHw+txdXXlq6++wsnJqUGda3/99ddq\ngeL1Ibcz/aSnZjR4n6YKhaqqKsrKym7ahZaZmVmj5o6ZgoICqqqqalioAe677z4OHDhQQwxERETg\n7+9Pbm4uP/74402NqTn5Wzvt1Go1S5YsITk5GV9fX+bMmdOg1K66FHV4eDg///wzly9fpn379s05\n3BrCoCHBjOY0neYM2Dpg/JklS5bw4YpPuMafq69UY1K17TRGNbGxsWzcuJFhw4bRr/LPznIHMank\nzz//nM2bN/PFF18QGxvL7NmzG5wbfTei1WpZuXIlGRkZtG3blsWLFze5umKnTp148sknqayspLi4\nmE2bNpGVlVXr6kcul2Nra4sePdc7j1544QWCg4Pp2bMnoaGhtyTAz9vbm1WrVrF9+3bmz5/P/Pnz\n74ja9TKZjJEjRxIREcHWrVvZtm0bR48e5cSJE4wePZopU6b8ra/JutBqtWzevBlPT88GWRP0ej0f\nfvghAwYMwM/Pj7NnzxIcHFzDAtteMAXPTuRfHGAH/RlG4B/BqvmYLKZtY04AcG1Nb9NOCaYU91D3\nChxQUqwpummhWFdlxtooKirCxsbmpq/XzMxMBg4cWOtrZnd1bULB1taW7t27c+rUqWpNoSQSCY89\n9hiLFy9m8+bNDBs2rNnLqTeGv61QKCsrY/ny5Vy5coVWrVqxYsWKZgusGzRoECdOnGh2oVBVVYVK\npbIIhoakR9ra2jb7Sr2iooLy8nLk1O/KqM9U969//Yvg4GBef/11oqKimDlzJvPmzWv2z+5OwGAw\n8NprrxEXF4e7uztLly5tukgQTP7i+6YOx6mVI19/8A0VGm29NRFWrFhR7f86nY6LFy9y7NgxPvro\nI7p3787o0aOb/UEukUiYMGECHTp0YPny5XXWwr+eDcZXLFUlzQ+S5na1qFQqHnroIUaMGMGmTZs4\ncOAAP/zwA/v27WPKlCmMGTPmHxfwaLYmtGzZki5dutS7/eeff07Hjh2ZOHEieXl5xMfHc+XKFby8\nvCyu0+spoQg7HLCl4feBxNZ0jMYEjluzKDQ0ELCgoIDy8vJGuQevJycnp1arrrnfj5OTk9W5YNy4\ncbWOs3PnznTt2pUzZ86wefNmnnjiiZsaW3Pwt7wr8vLyWLZsGcnJptLCK1asqLfT4/XUN0E5ODjU\nCEBpDiorK9FoNI2avI1GI2VlZVRVVTVbdKxWq8XW1raGBcEadtgTYOxATwYg/cObZU7XM0/+p7NP\nsnbtWi5fvsxLL73E448/zqhRo+7IBig3g9Fo5MMPP+TEiRPY29sTGRnZqGuuPtKiM3D2diKoW3ui\nj5xv9P4KhYIuXbrQpUsX9Ho9x48fZ82aNXh5eTFhwoQmp9TeSHBwMKNHj+bo0aOMHj26WY/dVNzc\n3Jg1axZjx45l48aNREdHs3HjRg4fPsysWbOa/bO4U6msrGTr1q2AqWhQffdiVFQUGRkZPPbYY4Dp\nc7S1teXixYtcvXoVrVbLxnam4yXwhunvB1NAEsQhnwE4rDU1lkpjJwB9/xCEMxJyq73PCePv9KAL\nW7ZsaXAWV1NcDzqdjuzs7Ab1e7BGZWVlrWnzOTk56HQ6Sw+M2qhLnDz88MOcOXOGPXv2MG3atL+k\nc2xt/O1iFK5du8by5ctJTk7Gx8eHV155hZYtW6LVasnNza3WzfFGGvrQKigouCVpf2YFba5EVtvF\nbhYD5tfM22o0N+/LNWPOR1+5ciWxsbGUlJQ0fOc6xFUrX08EQWDlypWMGTMGvV7Phg0bePvtt+v8\nPu4mtm7dys8//4xcLmfx4sW1lrZtLK6Ch6VgS5v4YOwLnBnQYyAj3MY3OiPgemQyGQMGDOCVV15h\n6NChbNy4kbfeeqvZv4ugoKBqrbLrwxwApjGq0RjVzTqW2mjbti0rVqxg6dKleHh4cPXqVWbPns03\n33xjae72d2b//v3k5+fj5+dXbwyB0Wjkyy+/ZObMmdXmSXt7e7p06YKDgwNpaWmogm54WOYmgGvd\nmT43UpJdSlG6qfdMQzNpbrbgksFg4OrVq5aSy829cMnJyUEqld509kbbtm0JCwujvLyc/fv3N+vY\nGsPfyqJw7Ngx3njjDeRyOd27d+f555/HwcGB4uJiLly4gEwmQ6vV0rVr11qVo/khXNckYW4gNWvW\nrGYfv7l6mdlfaq2n+vWvmc+jpKTkpvysRqORS5cu8eOPP1JcXMyAAQNYuHAhFy5c4L333qtWZa02\nKisrUVeVkWJI5CqxBGAqEGJuSjS43b2Ejg8iMjISnU7Hm2++SUhIiMXke/bsWRYuXHjTfSLuBI4f\nP85nn32GIAi88MILhISENP+bVEHB7jJcpirxHu6O8LXQZNO8ucphaGgox44dY+HChUyZMoW+ffs2\ny4Tp5eVFRkbDA9Lqo6CggJiYGCIiIprtmIIg0L17d959910+++wzfvnlF7788ktOnDjxt7YuVFVV\n8f333wMwefLkeh+o+fn5eHl51bpAUigUbIzYQruHW5M1rB3vX/GGL02WBY69AuteATmUdnsFgC1/\ndKMMZxkAXxjX33DEmVy7do3U1FQuXbpEUFBQg4vc1WdRKCwstAStV1RUUFBQgFqtxsXFpUGtphuD\nXq+nrKwMFxeXJrm0Ro8ezfnz59mxYwejRo26LQXE/hYWBaPRyFdffcWaNWvQarX06tWL+fPn4+Dg\nYFGMBoPBUns7NTW11uPY2Nggk8nqXFnt2rWLsLCwZqtIZ0ar1ZKVlYVSqbQEXNYlFMxK23wRpqam\nNiqPvbKykgMHDjB37lx2797NpEmTWLNmDSNHjsTZ2Zm+ffsSHBxMZGSkRcDciMFgoKysjPy8fKsP\nLd+e3mCEuLg4nJycSEpKol+/fsyZM4fg4GDy8/OZP38+UVFRDR77nURKSgpvvGEysz766KPN2iK2\nHDUF5FBADlUY0KTrKDhXgsrLlkGdh9Aav2Z5H0EQ6NevH6tWreL06dO8/vrrzbKiNrdVby6uXr1q\n9d5tKkqlkqeeeopVq1bRsmVLi3Vh+/btd1xaanMQFRVFVlYWXl5e9OvXr97tU1JS6nyQVpbqKYwp\nwdbWgJvbHzFVRiMI3NRTxlzivKCggFOnTpGamkpZWVmt38X1v7tRKFwveBMSEkhISODy5cskJiaS\nmppKRUUFrVq1uiUBviUlJfVm0TWEXr164e7uTkZGBmfPnm2m0TWOv4VQ+PTTT/n666+RSCQ8/vjj\nzJw506JA8/PzKSsrw8fHh8DAQJRKpVWTuiAIuLq6WiqP3Uh8fDx79+6t1p+9uUhMTKSqqgp/f/8a\nYqAu14NcLsfX1xetVtvgSTQhIYG5c+eSmZnJ/Pnzef7552us6AVBYNy4cYwbN47Fixeza9euGpO+\nRqPBYDCwOHIxGqOazcbP+JL3+ZL3OcEhTkp/RfA0cCX5Cvv27eOJJ54gNzeXsrIyPD09efnllxk6\ndCharZZVq1axd+/em/vwbhNqtZpVq1ZRUVHBwIEDGT9+/C1/z6wjBejL9HTqH46dY/P6Kx0cHJg5\ncybt27dn5cqV1Rqt3QkUFxfX2q64OQkPD+edd95h5MiRlvz/lStXNs4Ndxdg7tJ67733NugBGR0d\nXWc5YW98cNS6kBtsh3GxHT4PrMB73EqeehuSZRAtwNkxczk7Zi587gifOzKVx5nK4wiCUMOCJZFI\nCA4OpkOHDhiNRq5du8alS5c4duwYly9frhYjdmN8wvW/u/7c8vPzqaiooGPHjgQHBzN16lQGDhxI\nUFBQs8R33Tg/mjPUrq+dUNu51odUKmXkyJEA7Nixo4mjvDnueqFgTneSSqUsWrSI8ePHV/sidDod\nRqPRYjJzdnbGYDBYzfM2WyFuzLFOSEjg/fffZ+HChQ3qZtYYUlNTyc7OxsnJqZovq7bI3RtdD2Ay\n8dra2pKSkmIp1lQbBoOBzZs38/HHHzN37lymT59eb8Bd9+7dWbt2LdnZ2cybN4+EhATLa+Z8fmuK\n2cnDkQp1OTlppqqAZteI+QaSyWT897//ZerUqVRVVfHOO+/c1oZDjcFgMLBu3ToyMjLw9/fnueee\na3b/psaoZgufs4XP2coXbOULjmsPsGf/XvSKSrpP7NzgegWNYcyYMURERLB06dK/TQxJYzBbFxYu\nXIi9vT2xsbHMmzfvb1OoqaysjKioKEv/jvowGAycOXOGbt26Wd9ICqpgOW4SLaVGk5ndqIamSFlz\np8pevXoREhKCu7s7tra2ZGZmEh8fbxEDtQmF2hZZCoXC0m+hRYsWZGZmNpvFy9XVtUZDKXOWQ3OU\n/R8+fDhyuZxTp041qzuvodzVQmH//v18+umnAMyePbvWgJwbTVHmfuLWzOmenp4olUqSk5MtAYIJ\nCQnMnj2bvn37Njnd7UbS09O5du0aSqWSkJCQahd7Q2IU4M/64zKZjNjYWPLy8vAR/PAR/Cwlco1G\nI5988glFRUWsWrWq3iZD16NUKnn44Yd57rnn+Pzzz9mwYQNpaWlkZWXh5ORUbZVnDkiLNp5k08mN\njJo6kg9/fB/48+a9/hwFQWDatGnMmDEDo9HIunXr7ojqfvXxzTffEBUVhYODAwsXLqy2arjVZCRk\n8vuxE7Rwc+XChQs3VTq5PiIiIpg8eTLLly9vkmWhRYsWzVb2XBCaHpfRGHr37s1bb71F+/btSU1N\nZd68eezYseOud0UcPXqUyspKwsPDG5RhtWvXLsLDw6362SsrK3EeZ4PMTeBkeQvKdTJ8AM8yk+eh\nCKg8dYrOe6DzHqByGlROYz5hzCeMGTzNDJ62+v4ymQx3d3f8/f3p1q0bnp6eFBYWWqw81jIeoPrc\n6ejoiF6vt8z9SlQoUdECd1pQs8ZBYwgNDa3hFrCzs0Mmk5Genk4v+/50EnoyiJEMYiT3CBHcI0TQ\nXuhIe6FjvcHJjo6OFlH3888/N2msN8NdKxSioqJ45513APj3v/9tVRmb00nKysqAP2ttW5u8ru/o\nGB8fz+nTp1m/fj1r165FEAQWLVrEW2+91eD2p3WRlZVFSkoKdnZ2dOrUqUbATkOFApguyvDwcGxt\nbUlISMDTx7Pa6wcOHECtVvPEE0/cdGCNj48PK1aswM/PjxdeeIG0tDTat29f60raaDSSlZWFVCq1\nCAnzd1Bbis/kyZMtbogVK1ZQVFR0U2P8K4iKiuL7779HIpEwd+7cGvnTN2NetIa5rKy5l0O08STR\nxpMcPHaQJ//vCUun01tR5rV79+6MGDGiRkvcxhAWFsaFCxcatY9Z5Jp/biceHh4sWrSIsWPHWrJ1\nXn311WZvDvdXcujQIcBUD6Y+jh49ym+//cbDDz9c6+ulpaXExMRg4yNHE1/JsfLrLKJqGlE9oWEI\ngmCZP6xZA2pzO4Dpu1SpVOTkNG/fEzDdK+ZukmZkMhnt2rVDr9fT476uCJKbmxO0Wi1arZYxY8YA\npgXyrVgc1MVdKRSys7NZu3YtVVVVlkIp1nBwcECpVFpubJVKhbOzM/n5+VZrIbi4uODp6cm2bdv4\n5JNPWLZsGR07dmTSpEm8/vrr9OnTh+XLl/Pdd9/d9ARdUFDA5cuXAQgJCal1RdoYoQCmDIhHJjzG\n1m+3cf/ESdzjZkqt68kAhg4dyuzZs5v8ADMajXh6ejJt2jQOHDhgte9FaWkparUaDw+Paj0rHB0d\naz1XQRB45pln6NixIwUFBXz88cdNGuetIj09nddeew17e3see+wxOnfuXGObptSMbyiCIBAQEECr\nVq0oKiri9OnTFBYWNvv7DBkyBA8PD/bs2XNT+3fp0oXjx483aFvz6iqcnoTTk0A6EkhHi1Xsr7Yo\nmJHJZDz55JO89NJLKJVKjhw5wvPPP09ycvJfPpamkp2dzcWLF7GxsbFUAjQL2+sFrtFoZNOmTezZ\ns4f58+fXmnVQXFxMdHQ0arWaY4eOsf2n7RheyIYXsjj+WVfOfd8B+Qcg0UC3qu68MxbeGQtI3U0/\nkzxhkichhBNCuOX7r48bq9PW5nqo7f9m12deXh5Go9GShltALgVYd9k2hNatW5OZmVkjCNjDwwMP\nDw9c/JxoP8TfMiYldiixszRCM///esrKyjh79iwnTpzg999/x97entatW6NWq6u5gP8K7sr0yA8/\n/BCtVkvfvn156KGH6txWKpUilUopKCjAaDQiCIJlcjX7l2+kpKSEr776CltbWyZNmkRJSYmlKIYg\nCPTo0YMuXbqwY8cO5syZ0+iUMq1WS1xcHBKJhPDwcKtFNBorFACyM3LY/cNeHrp/Bh3Htuf0pvOU\n6ponEMtoNHL16lUKCwvp0KEDAQEBHDp0iLZt29bYNi0tDcDSKKWyspLCwsJay5iakclkzJo1i2ef\nfZbDhw8zaNCguv2ifzEVFRWsXr0ajUZDly5dGDt27G0dj7lFuYODA1evXuX8+fN4e3vTunXrZnWF\nPPDAA7z00kv06tWr0ZXr/Pz8cHBw4MyZM3Tt2rVJ47hdQsFMv3798Pf3Z/Xq1SQnJ/Piiy+yaNEi\nwsPDb9uYGou5G2Hv3r0tbtQebXrj5dsKHAycP2ey/phTZ6dNm1ZrsKNGo+Hy5csYjUbCwsL44pxJ\n2C8T3gIg8o3NUB7HkiFbWdJiIGg+gst/3Pt2f9w3fzSpnL/FVBFyFc8CMFgYBZhKydeGWq1GoVBY\n5k1rFoQbrxVBEHB0dCQrK8tSVK627W4GQRDw9fUlIyMDX1/far8PCgpi+uPTyM7Oxv1Ndzp06FAj\nePL6MVRVVZGSkmKJAXN1daW8vJyUlBTCwsJIS0vj3LlzdOjQocnjbih3nUXh1KlTnDx5EpVKxb//\n/e8GPZwdHBzQ6/UWX6ubmxtKpZL09PQaVoWEhASLqXHu3Lm4ubmRmJhYI7hRJpMxfvx4li9fTkxM\nDCtXrmyQOdJgMHDp0iUqKytp3759nb0nHB0dadOmTTVXgblTpLVYiQJyOZ98jsRjqaha2NJzeDec\ncKYrfRjJ5CaZxZOSksjIyKBly5YEBgaSlZWFra2t5ZhmH1tpaSm5ubm4uLhYVLw5KK6+Wg9mawXA\ne++9d0sqYN4MRqORd999l+TkZFq3bl2j8Mztwix8u3TpgkqlIi0tjZMnTxIXF2dx9dRHff5RpVLJ\n5MmTLXn3jeXhhx/ms88+qxHsBX+6GSYLjzCIUQxiFKo//uSTa/r5o3HUnYC3tzfr1q2jf//+lJeX\ns2zZMk6ePHm7h9VgzG4Hcy2KqqoquvTpRMceIQSFdGDStEnExMRQXFxMp06drIqEixcvotVq6dCh\ng/VMlFtwf5SXl1NYWIizs3ONRZP5fqzrvpTJZNjZ2d0SwWlO57wRiURCYGAgrq6u5ObmcvLkyVo/\nW51OR0ZGBmfOnCElJQUnJyc6depEaGgobm5uqNVqS6XKxrrzmspdZVHQ6/V89NFHtG7dmvvuu6/B\nJXLNRYnUajVKpRKJRIK/vz8JCQmkpaVZeoXv3buX3bt3s2DBAstKOCgoiLNnz5KQkICtrW2Nm8LZ\n2Zmnn36aX3/9lWXLlrFw4cI6qzZmZmZSUlKCl5dXvQU+MjMzSUtLq2b2k8vlJCcn17uySzmZjpO3\nA94dvLA9pUDIatpNm5mZSUpKCvb29gQGBhIbG8v27dtZvXp1jW3T0tKQyWT4+/9pajMHhjakKde4\nceM4dOgQiYmJ7Ny58y9JO6yPPXv2cPjwYZRKJQsWLGj2oNamYmdnR9euXcnLyyMtLY3s7Gyys7Ox\ntbXF2dkZlUpl+TGLu8bQt29fNm/eTGFhIba2tlRVVWEwGKiqqrL8gGkilslkKBQKbGxskEgkuLi4\n8H//93+8+eabrFix4o4QWE3BxsaGOXPmYG9vz86dO3n55ZeZPXt2gzIIbie5ubmkpqaiUqksLjOd\nTse0/5tKq1at8PLyIiUlhaKiIs6dO4eLi4ulyJJCoUCr1ZKTk0NycjJVVVW0bdu2hoXwKz4EYFWM\ngmwyePuyH3zQC5b3BXOCjnkX81Sw1BTXsDByCACP/dGx9h4hAqBa59Me4T0ZNX4Ux/f+xqXoWPKN\nOVaLK9UmBkpLSykvL29wAaeGohLsqKIKb9rggBMFmIStuQy+VColJCSE1NRU0tLSeOutt5DJZJba\nPWfPnqW0tBSj0YhEIqFNmzb4+vpazsnLy4vU1FRcXFxwd3cnPj4enU7X7OdhjbtKKOzZs4eMjAy8\nvTU7dYAAACAASURBVL0teaUNwWxiuj56283NjdTUVNLT03Fzc+PLL7+kvLycVatW4WJrEiCBdCSB\ni7i5u/HV1i+Jj4+ne/futQYDDhgwADs7O5YuXcqCBQtqNbFXVVWRmpqKXC6vs/a3GfN4r0/HNJuU\nrUWim2+O2cIyDPulqPxUREQMIOybHkiRompAwpJ5ddkJUxaJ0tGWQY/2J1OTztebvmbJqiUUFBSw\ndOnSahUuy9FQWFhITk4O7u7u1awHtZ2LNaRSKTNmzCAyMpLjx49z3333/aVZBTeSnp7ORx99BMDT\nTz/d6PLMNwbkNbSHRmORSCR4eHjg7u5OSUkJmZmZFBcXU1paWi2WRCqVWjqOSiQSJs24H71eT3R0\ntOV3VVVV6PV69Hq95d+tWrVi/fr19O/f3+oYftlhquNvo1Mik0spLiuiMK+QJa8txsXFhf379zN0\n6FBcBZNI/g8vAuCAI1pM1iPzJBttrL5SVyqVtyU1rDYkEglPPfUUdnZ2bNmyhXXr1qFWqxs1L/3V\nmFeh5gwpMC08zA8be3t7QkJCKCsrs6Ram+NeFAqFRRzKZDJCQkLqXagJCEDzrtxrc7lacz3cSElJ\nCcXFxbi6ujZbX5wbqcJ6oTKzAPD29iYtLY38/HxLe2swLTrd3d1xdXWtIQBsbGywt7dHo9GgUqnI\nzc3lypUrt6YKbC3cNULBXAMAYMaMGY2K3Dc/nK43YwuCQJs2bTh69Chz585lzJgxliZF5oZGrriT\nhh3q3HL8/Py4evUqycnJFgvEjXTr1g07OztefvllVq9eXeOhmJ2djVarxd/fH5lMVi1wqDbMD9fr\nV6/mf1sTCuZjLuVNKIbdl+S0D5WzrY09qckCL/3hGFwumDJGqtCTiCkd8QSHACwV/8x/+w7w5PK1\ny+w6vAtduZ4RI0bQoUOHWsd/6dIlgBqlbysqKhAEocE1KLp3706nTp2IiYlh//79t20C1uv1vPba\na2i1WiIiIpq1fPCtQhAEnJycLPUttFotGo2m2o9er7dYBMxpXP/P3pvHR1Wf7f/vM5OZyUwmG1km\n+76SkBAFRBFQZFFQcUFwaavWUq3aH1a7oU+LxWpdqk/V1pVabR9E1Fr32rgAikIEAtkTQvY9M8lk\nmUkms/7+mJxj9swkwdLn+V6+8gpOzpxl5pzP5/rc93Vfd39/P06nU1rV+Pj4IJfLUalU+Pn5sXbt\nWv7whz+wadMmqSZ95A/Az+/6BSqVikRtCpoADT5BMqLio2hoaGD+/Pk8++yzKBQKLrzkAhpqGxHq\nBVxDnk0mmZmZvPvuu6ftc/MWgiBw44034ufnxyuvvMKzzz7LwMAAmzZt+nef2oQoKXE3E8vOzpZe\nE8fRkX4ZImEIDQ3lxIkTkjDZ399fmsgmG3/FrqbXCDfRRw9nE0gKX7H3hRUgero1D/++Zvi3uKZ6\n0L3Pl+5zRzv+i9GVTxrBjxujfkQUsVwaHcmiIiPnChfgwE41FciRk7Y3CxcuMi5NHjUuWSwWysrK\nkMlkozQEc4FcYQk/5yE6aKWUTtJYTupw+GS74LatfpLfAG5vFB8fHxISEry2Bw8MDMRkMpGSkkJD\nQwNlZWX/jyiMxfHjxzEYDERERHDuued69V5R+DJWkdrS0sJf//pXbrzxxmk73EVFRdHW1kZbWxtJ\nSUmThk8zMjLYsGEDzz333Lh+EAaDAZlMRlRUlEfnPdEqfKLoyFQoOQpp2ZCcDk0zEGmb1X0UtNQQ\noY7G0efEF/WUDm2Dg4MoFIpxAk3R9MpTm1RBELjkkksoLi7m7bff5uKLL/63eJzv3r2bU6dOERIS\nwsqVKyVG7w0GcKddPInmnA6oVCpUKtWkueRzzjlH+rdYsTHZZy1uO5GAFeDUiVoAfHBHmgYxI1fI\n2f/6F4REhhAtj+OFX7/EuiUbIAsGHHJMTU7eq1GyoaYfR59LajM9FmK6bceOHd9ahOm+++7jgQce\nmPLe27RpExqNhueee45XXnkFjUZzRkYWRKKwYMEC6TWr1YrNZpswJdjV1UVMTMyMj+eOKMwtzK2D\nGEqMRGSGM89oh0MjYxYTH0+sMLNarWRkZJyWhn4AduzITuOUKkZTkpKS+PTTT6VF2beB/xiiIHbO\nWr16tdcTho+Pj9SgQ0RLSwu7du3illtukfQIIsTQsEbwG9XFLigoiNbWViwWy5Q56osuuogjR45Q\nWloqsXen00lvby/+/v7oFG6iEMPkLVTFsK8YJhYhEoXJhJMii/6V8AQARgMMmCE8Vkk/Wv6L4Z7z\nvxreZwXI3jwKwHbcg/977AXAgoVWRRM3L7sZ+2EZObj7GIgs+QV+DzBKaDY0NDThIN7X1+f1BHvu\nueei0+loa2ujtLT0W1eXl5SUSH4Jt912G2azmdra2lErsv9tmE7sumDBAkpKSrxSXDtsDrqa9XQ2\n68lmKcc5TsKJdualBjGUokYbJ2NFgovwi3yxtjmJKYmkvWpy8WJFRcWEZamnA76+vnR1dU1ZrQOw\nfv16FAoFTz31FM8//zxhYWHTdmT8NmEwGGhvb0ej0UgkT7RGdrlco/RSYqosh8VSishbMekbrpep\nqqriRxl3E0k9lzULfNDsHjOd4rTz0PB3+N3hN0lDqnuM85lkemo9qCc8OpywZcGcE3U2pV9U4Oh0\nMJYoiF44op9LamrqOM+TucB6NvEcOqz0YCSBz7kAcC/kFuA2Yfr/+PW0+zl06BBtbW1cccUVo+Y4\nu91Oc3MzbW1tKJVKFi1axIsvvkhFRQVOp/NbWUD9RxCF/v5+Dh8+jCAIXHTRRTPah6+vLwMDA7hc\nLgYHB/n973/PrbfeisFg8FiYJoZhp7P9FASBLVu2sHv3bmlSEcO6njYIEYmAWq0eNXCL5+p5NYBA\nW5OLmAwnWq0TD4XwuHDRSC2LApaisvlitXl2PLVajclkwmazSXXOVqsVu93utfW1XC7nggsuYO/e\nvRw4cOBbJQomk4knnngCl8vFtddey9KlSyktLaWrq4uenh6vygTPFMX+XCA7O5snn3ySa665ZsK/\ni9cq6lzENN6y4U6BD61fAV1RHI7ogJw18IAFX18HiYmN3JnSiSpZTtLaWOIujKKyspKIiAiSg9MA\nN7G2MsTXX3/NwoUL+Yng3mcwbhM1J+7n8nPyJy2t8xapqalUVlZOSxQA1qxZQ2dnJ6+99hqPPvoo\njzzyyKSRl28b5eXlREdHEx8fL00sLS0tdHd3j+uc6MKFfYpcu6c4XaJVm9lO2+4eQtf6E54extII\nP2reqsRpdREdHIXL6cJkMmG1Wmlvbyc8PJyEhIRvQYBsB2YuLly8eDGvvvoq999/P3fddReBgYG0\ntrZiMBjo6+vD19eXBQsWoNFoCAsLQ6/XU19f/63cY/8RROHAgQPYbDbOOuusGff11mq19PX1MTAw\nwDPPPMPVV1+NRqNBqVROWn0wMpoA7qoJq9XqUdgzKSlJyu35+flhNrv35e/vP+nE8Y0ZhwYXLlZf\netG4YymVSgRBYGhoaEo2+SXuCMydrEfTrkCf4U9sSA8Vvxn+ymOHc70LgnHiFqdp33QLl9KYTxd6\nbITjazmLGv9gjoeq+RS37bMOd8jrR/xi3HF1Oh0Wi4Wuri4pUiNarc6kDfaKFSvYu3cvX331Fbfd\ndptEPk43xHK+jIwMtmzZAkBiYiLd3d3U1taSl5f3H6/enwlCQkLo6emRPElmhOBF0PogZLnr5S0W\nORUVCvoqrAi+cDSzkOQFSVLlxrW3bKb4WClNJU0oHEqqqqqm7Gkyl1i4cCEff/zxlALOkbj++utp\na2vjwIED7Ny5k9///vczHrPmEoWFhbS0tHDxxRcDbiIsWsdnZmYik8lwOp0cPHiQXowoUdHN7MpS\nHQ4HW567nFtvvZUNwmZ+htsTRYwUaIdTTB//zT2BHxmudliPe5HUw+hSwwGXWYpwhAyFwXvQ93UX\nubm5RCZEEBAQwAMPPMDAwADbt29HLpeTkZFBeHj4aX1WLQwQjoN+hjAyPD7FuBdFJc1ugrlp+FpE\nAj12bgF35Pt73/seRUVFbN++naVLl5KUlIRarSY5OZmoqChpvM/LyyM/P5+jR4/+P6Ig4pNPPgHc\njH2mCAgIoLW1lbq6OoxGI+effz7Hjx/H4XB41JFO7A+h1Wo9FlImJCTQ3NxMeno6drtdyhV7Atdw\n5m0sCxYFgYODg1gsFo/C+Y5eJ329oNF4rkC2YUVDEJYuF8KgQHLyEPv3u3A4pn7gQkJCqK6uxmg0\njiMKM8kNxsXFkZiYSF1dHYWFhaPy6acL1dXV5OfnI5fL2bZtmyTU8/PzIzIyktbWVvR6/Zz3r58N\nxIHwh8NVBOIq+5+4vQ/GVhDMBmq1GpvNNmVpljgQfk+4A4C/ie46N33p/n3QBT5Pwe/uAcB/uxEz\nZrDA+4XvAO7JrK2tjetvvo7N37WjVCqJiYnhhqQfsOKJ1aRyNwDviem0YfHbb1HMOGQ+FqmpqTzz\nzDMeEyNBENi2bRsGg4GysjJ+97vf8fDDD39rBHciuFwuioqKAMjNzQXc4WyFQoHdbqevr4/6+nr2\n7NlDeno6AQQhmwOLnZFRxdOFjo4O8vPzWb9+PUFBQcTGxmK32wkMDMTHx+e0k4SRcGEHZne9Ylvq\njRs38tZbb9HZ2cm2bdvGRWMXLVokEYXT0c14LM54otDd3U1NTQ2+vr4sWbJkxvsJCgpCEAT2798v\nrQ4cDodUJjYdGhsbcTgcREdHe3zM4OBgyYBjcHCQoaGhCQfXXMF9XeuHZcDN1DPE5OWEnhAFMfR6\nrnAB8waCuDLwSlICrBx7ZXiDexa5f9u1cHj4n8PhxhTmIyDjY9QUuTQkFUaybFkXuRfZKSwPpqPZ\nXRalHF4RjGTJ4sAgpmdcLhddXV34+PiMKqX0BitWrKCuro7PP//8tBMFp9PJ888/j8vlYuPGjePE\nXPHx8XR0dFBbW3tay6zOZGg0GgYGBmZXw52TDv/8AhZNvono2ZGYmEhrayvNzc3U1tZyyW0X8eEH\n/6S9uQCdbeZjgieQy+XodDra29s9bqSmUCi47777uOuuuzh58iQvvPACd9xxx2k9z6kghq8DAwMl\nwx5/f3+J1N9zzz0EBwdz0003kZWVxdatW+fkuCOJwgeu18f9XdRCpOK2bb6OLBDAHjPAgMHC84N7\nAPjv4bQVTEz8Ojo6+MEPfoCvr6+0OPG2n43L5aK/vx+lUul1ivQD3mALt1BBF8lUEIaDI83uZyNr\nuPrBitt2enBY2DzZOdTX19PY2EhYWBj//d//zWeffcZvf/tbfvWrX41aZC5cuBAfHx+qqqro7++f\nUbTWG5zxzoyiUjcrK2tWA5NKpSIsLEzKlwNTqrtHore3l9bWVrRarVeryEWLFpGeng580yp6bOXF\nZBDzrRPl1aYrkRwLH9WwJ7rN84iCDBmu4Zv76NF5mM1yzs3tIihg6t4WohhOJAq9vb0MDg6OCpt5\nC5HYHTly5LQ3Q/nss8+oqqpi3rx5XHvtteP+rlQqiY2NZWho6Iyp6QekrnTxJBJPInrC0BPGpVzD\npUysJ5gp1Gq1ZKA1Haopo5oy4JT7p2uZ+0dxAZiCYLcF9sAv6GIvf2Yvfx63Dx8fH+Li4jjnnHNI\nSUlBZdWw6cprCFthoynlU7QpAsRogRgghlp8iSFBKu+dLXJzc8d1BpwO/v7+3HvvvSgUCo4cOcJn\nn302J+cyE5w4cQKAnJwc6RkU9VYffPABV111FVdffTWDg4McPnyY2traOXFEnUlEIeTsQNKvSGLR\nj7LZsHEDuojpxYeTGS7B9PbMDoeDhoYGCgoKKCwspKamxuN7e9y+cMyq6kEULAYFBZGXl4e/vz8b\nN27koosu4sEHHxz1najVarKzs3E6nRw7dmzGx/QUZ3xEobi4GBhd0jNThIaGEhcXxwcffCDVw4uG\nMpOtDHt7eykpKUEQBFJSUrwKY6Wmpkr/FhmfXq+XSpHElfgmbgIgCnd9bzedOIdX9xOlKqYzXRqJ\nQ679VFZW8tqf95Lf/ikPtLoZ7o4b3bkzJz7sxG2te3JYezCImUEGWIKeVaTyou1rKj+DCy6FH55v\nIKTNhNPooo4mYHS+zWazjTw8nZ2duFyuWeVpdTodkZGRtLW1UVtbO+pznUuYzWZefvllAL7//e9P\nKn4SG8A0Njai0+m+NXe0MwU2m21uIimxyVBTBZpcjzaXy+VER0dzclcjgRlaUs+5kODgYnya7kcW\n+AOOt6dit8/92icvL49XXnnF65LH5ORk7rzzTp566imeeeYZkpKSvK6dnwuIaYeRlSLNzc38+c9/\n5t577yUmJgabzUZnZyfNzc2SEV1UVBRxcXFTTvZjx8ORE/N0RGEi47HDhw9jtVoJCAhgUa9bB1RX\nVzdKhDkWExkuedIXpL+/n8rKSgYGBlAoFGi1WvR6PaGhoR6ldEde++P8Cjt2clmCg2OsHo6SDOIe\nG18f9qiZ7JyMRqOkGZk/f/6oiMiFF16Iw+Hgqaee4qc//al03EWLFnHixAmOHj162v1d/mOIwlwo\n3kX7y3POOYf777+fnp4ezGYzvr6+BAYGSi1MzznnHC688EIGBweprq5Go9GQkJDgccXCRAgLC6O9\nvZ2mpiZCQ0NHheGVgQqCUvwJi/LH2ulAWazEOei+oaYiCkNDQwwODqLX6zGZTAQFBTFv3rxRoTOx\nLKq2tB5Da5fH56tGQzcGHLhX8A1VcEzlIuUsCL5ORd8HVhjjy+B0Ojl58iQajUYqQ+rr60OpVM46\nNJaVlUVbWxvl5eWnjSjs3r2b3t5esrKyWLFixaTbyeVyEhISqKqqoqGh4bSdjzcQvQdsw6HO1OFg\nYe8cO+OB+57ylPiJ9rv3CY8B8N6P3MY+JaiBcC7ndc6hnb28LJn1iPqCpVzg/v/hygnREGwQM5TD\nDyruJjIlDlVOGGW1f2DLZStoLEokrtbE29TP/kIZPRnccccdXo8Bq1atoqSkhE8++YRHHnmEJ554\n4lu1/3Y6nVJUVtQndHd389hjj3HPPfdIqTWFQkF0dDRRUVEYDAYaGhpobm6mvb2d5OTkcSXknsBm\ns3mdAlAoFDidTnJzczGZTFRVVdHY2Eh3dzeZmZkTTuATdY+Mjo6WnCQnsnZuaGigsbERQLJLbmlp\nwWw2z0JX4ZqRtsPpdFJTU4MgCGRlZU14/NWrV9PQ0MCbb74pVRwtXryYXbt2cezYsSkXu3OBM5oo\n6PX6cbW/s4GPjw8ajYbMzExuvvlmrFYrJSUlmEwmwsLCiImJwWKx8Omnn/Kzn/2MZcuWkZycTFJS\nktdd80SMdC9MSEigqKiIsrIy1Go1l22+FH9/f5Ll0agClKh85Kiy5OgC5lH+cRFJxEy4WhVfs9ls\nVFdXYzAYkMvl6PV65HI5vr6+0jYiW37+rWdQq9XSIHzx8CDcTSfFwyHaBcOqZNGGtJijvMMeLsdd\nktpVrMduDUB/cRCya2R0Vp6i9EQJNtuDmEwmWlpa6OrqIiQkhNDQUBwOBwMDAx6JRafD/Pnz+eST\nTygrK2Pjxo2z3t9YtLa28uGHHyKTybj11lunjRzpdDpaWlpoa2sjKirKox4W/xvgcrlwOp1zNChF\nY2AWYkMXWKrtHKuNIGHh1dS3fEhkVifB2dlo9/lh6h+vLJ8N3n//fW644Qav33fbbbdRXV1NQ0MD\nf/zjH0etCk83SktLMZlMREVFodPpsNlsPPzww2zdunVCh0JBEAgLCyM0NJSOjg7q6uqoqqrC5XKN\n0miI48h3ud39/8NkbmR/BqvV6jVREEXn/f39BAQEkJeXx8bVV5KzaAH2QTu/fvhX47ROE6Uj29vb\nJ0zzulwuqqqq6OjoQKPRkJGRIS1iurq6kMlk0y5qRG3FXexwvw89f+MZAF4uf25KQ7qJ0N7ejtls\nJiYmZspx5KabbuL+++8nJSWFvLw8oqKiiImJobm5mcLCwtPq23FGEwUxmpCdnT1nbEmlUknlXUql\nktzcXOrq6iQlO7hdGDds2MAbb7zB5s2bvRa3TIbAwEDy8vKoqKigp6eHyMhILBYLfY0mumt7kdX7\nkrQlktTsFA7u/wJgSqIg+rCL3Ry7u7vp7u6mp6eHwcFBXC4XWq2WjIyMGa1itATSz+gW1cbKPmo6\nqslcnUZahvvnq6++Ar4hQ2IzE/Ec5sJFLyvLHcorLy+fXWneJNi7dy8Oh4M1a9Z41IdDEASSkpIo\nLi6mrq7u327CJFY1iMJYMT8vrsJ/x8/n5DgdHR0zSiM96HJXY/xVcJ/X0uHzK+BLCjmEhUHp3Lfi\nroRQDzvwiA5/4mT0h2E73Kf4LQAbHNfAMUj2z6PJVsW/tO/w57deJD09fdaGNEtZiRo/nDi58Ts3\n8YPvbGXQ5V0OW6VSsX37dn7yk5/w+eefs2DBAqlM8XTj4MGDgLtFNsCrr77KsmXLpr1fBUEgIiKC\nwMBAioqKqK6uJigoyKtxZGBggJCQEK/ONzw8nNbWVjo6OggICEAul3No/2E6WjtYe+kaiouLyc3N\nHTWhihqFkXOEXC6X0soiWXG5XFRXV9PR0UFwcDBZWVnSeywWi9QHYjaVGmKFl6dwOp3U19ejUCgk\noelkkMvl3H333ezYsUPqPLxmzRr+8pe/8NFHH/3fJQqiReVc6BPAfTP09fURFhYmTTQ+Pj6kpKQQ\nGhoqTbBarZbg4GBOnTpFQ0ODJEj0BiLrHOu+6O/vz+LFixEEQQpvjzSnyatZyH23b2fBkmx27949\nJVEwmUyEhIQwb948lEolERERo0KEE02oE6mGrxFuAr6JJDiGhZS9dNNFJ084d7h7YAyvItKM8zn4\nhgFDbCsxsdHcePP38PPzIzg4eFQJpEqlIigoiO7ubux2u9eri5GIjIwkODgYo9FIS0vLrKxlx6K1\ntZX9+/cjl8u9KjUKDg4mJCSErq4ujEbjnEROznQcPnx4TgekubT5dfa7iD6exqmoUnbt2sUVV1xB\nSkoKmZmZs474yJDhgw92bAwODnpNvKOjo7nzzjt57LHHeOmll1i0aNFp91dwOBx8+aW7HPX888+n\noqKC6upqdu7c6fE+xBr+8vJyDAbDuIZoInkLxH3vr8LtjREihGNhkKPl3pXlBgQEEBAQMCrF0I2e\n7pN6+EBg3ca1FBcXk5OTI32nkxEF8TOAb0iCKBbMysqSxqN5hBEYHMjb+//ukXOjmBITS5ADCCSH\nxZgxUV1d7VVlVn9/PzabjdjYWI/Gx+DgYDZt2sSuXbvYtm0bq1at4m9/+xtFRUVepQS9xRlNFJqa\n3GK5uUg72O12ysrKcDgc4z5MQRAIDg4eN9DHxMTQ1eV5Xt9TTLYalsvlRMW7owzizT8VUbDb7QwN\nDUmeBWP3O9tVt3twVFBZWTlhOK2lqYWWppZJG5OILYZ7enqoqKggOzt7xuckCAIZGRkcOnSImpqa\nOSUKe/fuxel0snbtWq9zsUlJSZIJ01lnnfVvN2GaS7+EiXD48GF++ctfzvj9YwVsWiEAOXJCCZei\nIKIhj3NYHyMS1yKOAJMLwkRy3tzaQO3vannwwQdpbGxkYGCA1NTUCZ+R6dCFHnFN7K4EkvOLX/yC\nRYsWoVKpqK+vx+VyodFoyMrK4qyzzpqUMK5YsYKDBw9y6NAhnnvuOe67777Ter8UFxfT19dHTEwM\nOp2Oe++9l+3bt3sdYQkODkYmk2E0Gr3qnOrE4VF0biQEQWDhwoUTfi61VbVkZGRQWVlJcXExZ599\nttTVEkaXRIrXKP6tvr6etrY2AgMDR0USRPQae8nJyZlWADkVfPGlsrLSq/eIUWFvFhnLly/n4MGD\nHD16lEWLFrFq1Sr27dvHvn37JnVMnS3OWKLgcrkkojDbSUH0/DaZTMTFxXlkxwpIvRZONwZcZoaG\nhqiurqarq4vIyEhaWlqAqYmC2OjHYDBQWVlJamrqjFbtYnhaFMQ1DwvBTrrKKC0tJT8/n8zMzBmZ\n18TGxmIymdDr9dTW1k7ZUGs6iPfBXJYltrS0zCiaIEKj0RAVFUVLS4tXtfb/idDr9chkshnrdSaC\nGrVkLuYuo/xGKS7+Fqtxprv/xpKQZ555hqeffpo9e/Zw+eWXEx8fT1pamlfPiCiwHAmr1cqRI0dQ\nq9UsX74cQRAwmUwUFxfz+OOPI5fL2bp164Tj1q233kpRUREFBQUcOnSI8847z+Nz8RaFhYUolUqW\nL1/OK6+8woYNG2bU68DHxweVSjWqykr8LjYI7mdmEe5GfTLc42U0cbTRPKO07VSVFIIgkJOTw41r\nv8+Hj32M6x9+GOikgho66SPtv90pyiU3uE24HA4HPT09NDU1odVqR7XYnjccDcnFHSETU1+ekm3X\nCKGw2PDtwQcf5MEHH/SYcPT29iKTybwyoxMEgR/96Efs2LGDzMxMli5dSn5+Pvv27WPTpk2nhXye\nsUSht7cXk8mERqOZVUhXJAliWMab8qSR+a3ZQKFUSDeEXC5HJpOhUqmkL9RkMlFSUsLQ0JCU4xfz\n/hPl90eKGbOysqitraW1tRWTyUROTo70HqfTKVk9i+2D/fz8vCI/WVlZ7Nq1S7Ki9haCIJCeno7F\nYqG5uRmZTEZCQsKMbmbR7EokUXOB119/XYomzLRhjGjCVF9fT1hY2JzcM2ci3nzzTS6//PJpt3O5\nXNhsNux2O4IgIJfLUSgUE37ncnwkc7G5hlKp5J577uHAgQO88MILrFu3DrPZTHZ29qwqD5RKJcuW\nLRv3empqKldffTXV1dU8+eST5Obmsnnz5lFkPyQkhBtvvJFnn32W559/fly+fa5gs9n45JNPJOfZ\nmpoabrvtthnvT6FQTOgvsA+3sZuYghB/99Ez590jBUHglzwCxVCSEEZcWhivL86ksqoD+gqoIZlf\nDHeY+p/dv8WGFavVSlNTkxSRHPlsdg8vjNS4P/80kTh4SBjECiMz/VLKxQ9/ksjw2BlUfCZmyEhB\nHgAAIABJREFUEuURUxBiNU5TUxM1NTWkpKR4tS9PcMaOaM3N7qblsbGxM2ZIosK1s7OTefPmkZGR\n4dW+BgcHvRbiiTeIeBN2dHRw6tQpyfREhEqlIjo6GrVaTWNjIzabjYyMDCn0bbW6jY0mEtaIA4/V\nakUul5OSkoJSqaS+vp6qqipiYmLo6+ujpaVlnPJXq9WSkJAwSmQ0UT2zCEEQWLlyJQcOHJhx61y5\nXM6CBQsoLi6WSpJmQhbE9txzRRRmG00QIQqRampqaGxsnHWqTLyHBjFP6An/74AYERo52djtdnp6\neujr62NoaIihoSGsVqtETkX4+vridDqlapigoCBpYCwzFPOnP/2JX/96+u56M8XKlSvJyMjggQce\noLa2FrvdTlZW1mlrN5yamsrDDz/M+++/z44dO/iv//qvUWTg4osvZv/+/VRUVPDKK69w++23z/k5\nFBYWYjKZSExM5JNPPmH79u2zWml6KyC2YkUxiwZJ06HgY9AGwurleurqrbitiL5ZAIkkpaGhAYvF\nQmJi4rdSmaQjmnaaPd5eqVTidDolS21vsHz5cvbt20ddXR0rVqzgvffeY9++ff83icJM0w4jSUJw\ncDDz58/3Oo1QX1/P9ddfP6PjA2RkZFBRUSGFpwHppujq6qKxsRG73Y5MJiMzM3NUSkR04Zoq9SCS\nCUEQiI+PRy6Xc+rUKYxGI0qlUvI6F6MYTqcTvV5PWVkZCxcu9HigXLVqFQ888ACXXHLJjAcbhUJB\nTk6ORBYEQfDafEaMKLS2ts5J5YMYTVi3bt2s289GRUXR3t5Oc3MzYWFhp91S9dvGnj172LhxIz09\nPdKPyWQaRQhEt7/AwECUSiUKhQKXy4Xdbqe3t5e2tjba2tqQy+XMmzdP0grNJi/sKXQ6HU8++STP\nP/88u3bt4uqrr+a8886b0zTKSMjlcjZu3IhOp+P+++9nx44dUlmfTCbjzjvvZNu2bfzzn//kggsu\nmFTnM1McOHAAgIiICEnwLC58ZlIFYrVaJxyLRCIrPotiOH/bb+7khz/84UxPf0IsZSUa3N9XRe5S\nFEM9rD27nrSQVkr2ChAr55EP3H47eegwy/rZ/cIeZE4Zv3/x0XH7E++77YL7b+K+zx5Oo/xKeAKA\nwmGPezF6ImppBobtmNOYj3o49ZBAMp/yPtHEexRREVMzer1emiM8hSAIfPe73+WVV17hhhtu4L33\n3qOgoGDOLLhH4owlCrPRJ4jGPxOVwXizD5PJ5HF/glWCO/T0Q34KQDcGUhZH8+Hb/+TVF1+jyVQP\nfMPMnU4nBoMBs9lMcHDwuAFLdDicKvUgEgURMTEx+Pr6SjXIoghpJMLCwigsLKS5udnjwSkwMJDw\n8HBOnTo1K3MhkSwUFRXR0OB2a4qPj/d4wvf390er1WIymejp6ZlVSqqjo4MDBw4gl8vnRAAkk8lI\nS0vjxIkTnDx5kry8PK8HZFGMJ5YHCghSRYqYpy9yfU13dzfz5s2b9TmLYliLxYLNZsPhcIz6sdls\nDA0NUVpayokTJySiB+6JUDQOE8vm5HL5pN+ly+XCbDZjMBjo6upCr9ej1+vp7++no6ODlpYWQkND\n56SUdjLI5XJuv/12Dh48yB/+8AdaWlq46qqrvC7h8wZLly7Fbrfz17/+dVTkIC4ujk2bNvHaa6/x\nxz/+kSeffHLOGigNDg5SUFAg6bxuvfVWampqaG1tRS6XExcXR3R0tMdjotinxtOFhQvXqKZwpwtl\nzQEsdwmkOocoAZCNjijItTJkcoHKIydnVSLrDRQo0RJALz0omf5ejoqKoqenh7q6OlQq1aT3ohi9\nMxqNDA4OEh0dTUhICElJSfj5+UnVeh0dHbS3t8/5Z3/GEoWRqQdvYLPZqKiokMrVZkISAMlIx1OI\njU18cTPEmKgYAnX+tJW2k2NeQqyQQMB8P5KSkti5cyfJyclT9o0QScBUEYWJ/NhDQ0MJDQ3FbrdT\nXl7OwMAAsbGxkuLb39+fwMBADAYDQ0NDHg/Ma9eu5V//+tesXQhHRhbEsGBqaqpH35EgCERHR1NV\nVUVra+usiMLbb7+Nw+Fg1apVs44miAgICCA6Oprm5maam5snNLSZLSoqKti+fTurVq1i8+bNHg8I\noqiru7ub/v5+iRxMB6vVynvvvccdd9xBRESE5GLq7+/v1eArCAJarVZKfQ0ODtLd3c2pU6cYGhri\n1KlTnDp1ioCAAOkePl0Ohueffz5xcXHs3LmTU6dO8dOf/tRjgfNMj/fll19KKnUR11xzDV988QVN\nTU38/e9/n7C3yExw5MgRrFYrQUFBnHXWWSgUClpaWtBoNLhcLlpbW2ltbSU5OZnQ0FCJ3DmdTgoK\nCuju7iY0NJT58+fj7+9PW1sbwJSf0cio0JEjRygrGy8CnQwiQRYxD/e4KIpbR6bfFKKAsAscyCi2\nhRLnqMPX7sQypADah7cUkPkLWK026svHWMiKWwxf9w7+AMCfcBOhDtwk3B93RUICboO37awGwEIv\nAO/zhrSvPNwlkXbsRBDDYQ6gYnohp1KpJD4+nrIyt3Bcp9MRHh6O1WqVBKQ9PT1S9Z1I4AGJVNxw\nww08/fTTLFiwgEOHDlFUVPR/hyiIH4w3D3B/fz/l5eVYLBbJhGimVQs1NTUkJyfP6L0A2hz3ZN5S\n2C69tmzZMkJCQhgcHOTEiROEh4cTGxs7Ye5MpVIRFxc3oWrYz89PSjVMBLvdTnV1NQMDA1I1xeDg\noJQ7j46Opre3l/b29mlNPkTk5OSwa9euWRvYAJLRVUVFBR0dHZjNZubPn+/RxCA+HGJZ0UzQ29vL\nxx9/DMCVV1454/1MhISEBLq6umhoaCAkJMSrvKgoqhLLAxUomI/bdjcFd/TnivnXoKcdh8PBZ599\nxoUXXsjmzZsnrbaw2+00NzfT29tLT4+7DbNCocDX15egoCBUKpXk5CmXy8f9PP3009x+++1TWlrP\nBGq1mujoaLJjcrH72KgtqOfp157EaDTS19dHbW0tWq1Wcgn0xHvfG8TFxfH444/zzDPPcNddd0nk\n/XThhz/8ITt27CArK0u6z5VKJXfeeSfbt2/nzTff5OKLL56TVMixY8dwuVz09vayadMmhoaG0Gq1\nxMTEEBoaSmtrK42NjZSXl6PRaIiIiCA8PBylUklTUxOBgYFUV1fzzjvv0N3dTWpqKitWrPA48vL1\n11+f9t4DIhqdvsQ6nfjKnFiE0b0eZEoZxo5u7DbPGvHNFSKIxo4dpYf26cHBwURERPDOO+9w/Phx\nnE4narWayy+/XLof/Pz8iIiIIDg4mMrKylHR5OjoaKKjoyU9WlFREevWrZvTazpjiYI4qHny4Lhc\nLtra2qirq0Mul/PQ3Q9TXlghlfvNRBBWU1MjeaN7gmLcCtl5uPOuIVFqbH129J2dw6+HM2Qeoq67\nngULFlBTU4PRaKSjo4PIyEiSk5NHTfydnZ00NjZOGFHw8fGhoaFhwhy/2WymrKwMu92ORqNh4cKF\nlJSU0NTUREREBBqNhnnz5iEIglcuYjKZjNTUVKqrq2dkQDUWCoWC7Oxs6urqMBgMHD9+nLS0tGkN\nQ8T7Qbw/ZoIPPviAoaEhFi1aNOdNeuRyOWlpaRQVFXHy5Elyc3PnNOw5j1C0+LN69Wr27dvHJ598\nwr59+1i+fDkbNmwgPT1dWin19PRQXl6OzWbD19dXErH6+fl5lO559913USqVc04SxsJht3OqrIbs\n7Gzsdjvd3d0YDAa6u7upq6ujrq4OPz8/KdLg6flPh8DAQLZu3Up+fj733XcfP/jBD1i9evUcXNF4\nBAcHs379el577TVuvvlm6fXs7GzOOeccCgoK+Pvf/84tt9wyq+O4XC6OHz+O0Whk48aNBAcH09bW\nhtlsxuVySZ04dTodjY2NdHZ2UltbS11dHcHBwSxduhQfHx8cDgc5OTkYDAb+9a9/8dZbbxEfHz9t\n91yn00llZaVXFRZirl8sMRTLYkWM/K7FyO2Pq91T14t3ROC8YhCt3oystIXbOQXAc+pqgoUgvn/3\nzez+4H8m/awAbhXcrqU3DRP1R4ZFmP0XussmS9a6ty8Rh5xH6gHYMbzdP9gtRT/EOecPzz5BXp67\nRLO/v3/UeBUUFCRpmBobG9mzZw9ms5kVK1ZwxRVXIJfLMZvNqNVqtFot/v7+Erl0OByS0d5IXHfd\nddx33324XC6Ki4vnZEE3EmckUXA4HPT19SEIwrRNWMTyx87OTlQqFRkZGZQXVky6rclkor+/n/7+\nfkwmk9RqWlRmy+VyfHx8OH78OCtXrmRoaAilUundwCQDp92FrXO0B7lGo0Gj0eDv709ubi49PT3U\n19fT0dGBxWIhPT1dSgUMDg4CTBhREG8acRsR/f39nDp1CpvNRlxcHFFRUZJwzG63MzAwgEajQS6X\n4+/vP0qI5gny8vIoLCwkLi4Ok8mEyWTCarVKOW1A+n/xGOLPRIO7TCYjOTmZgIAATp48SVlZGbGx\nsSQmJk76ec+WKFgsFt5//30Arr766hntYzoEBQURFRVFa2srDQ0NHpvOiDX7ol/+Si6RBFFizbYa\nP9T4sW3bNjZv3szrr7/Ovn372L9/P/v37ycxMZH169eTlZVFfX291PU0MjLS64EjMjKS9evXn5a6\nbHGfiaTRh1EKN4sC3PDwcBwOB0ajEb1eT3d3Nw0NDTQ0NKBWq4mMjCQyMnLaUlTJhGm4g9lY4WRo\naCjLly8nNDSUf/7zn5SUlHDbbbedlrTH2rVruffee6mvrx9FUG+44QYKCgr44IMPuOKKK2almaiv\nr8doNGIymfje974HuKNwcrmcpqYmdDqdVJ6dmppKUlISBoOBjo4OjEYj3d3daDQaBgYGpL4Pv/nN\nb6ipqeGhhx5izZo1XHLJJZPeS4cPH2bhwoXfiv8MgMUiYDK5UCphYIR4UDMcyZsr3Ye3uPLKK3ni\niSdISEigvr4ei8Uifa7ieX355Zf09vZy3XXXSRb100EQBOlnJEJDQ8nIyKC9vZ3e3l4aGxvndBF0\nRhKFvr4+QkNDpfDnZBDz8KIeITMzE4VCMW5QsNlstLe309LSMiqvL4ZbrVYrLpeLgYEB6T0tLS00\nNTXR1NSESqUiISEBnU436aApdskLEcLx1/rz3fAbqGyp5DPXB9I2FRUVdHZ20t/fj7+/P8HBwQQG\nBkqdzIqLi8nLy8PHx0cyN5lowJqIKBiNRkpKSlCr1cTFxUnajt7eXqmHxUjSJZfLpZ4XnkwETqeT\n0NBQXnzxxQm1G4Ig4OvrK5EEi8WCyWSS8psajYbo6Gh0Ot247zQsLAytVkt5eTlNTU1YrVbS0tIm\nHIyCgoLQarVee6qL+OSTT/Dx8eGcc87x+OGcCZKSkujt7ZVCuXMhPhyLyMhItm3bxrXXXstHH31E\nfn4+dXV1PP744zgcDs4++2xuuukmqVrEW5xO73hPIAomQ0NDcTqdGI1GDAYDBw4c4LPPPmP58uVE\nRUURHR09q1bfMTExDAwMcNlll9HY2Mi9997Lj3/84zlxhB0JmUzGbbfdxnPPPcdDDz0k3d+JiYmc\nf/75HDx4kNdff50f/ehHMz5GYWEhQ0ND6HQ6KU+tVCqJiYmhoaFBqrMXn3m5XI5Op0On00nCVtFz\nxd/fX5pos7KyePTRR9m9eze/+tWvuOOOOwgPD2dgYEBqtgfuxlnbtm3z6pzHeg2MbKQ3Gb7pMDof\nlUmHzMeEjH084NoDQN+2Bo4cOeJRRc3zLnfVgygcPmdYi1Bw6fAGse8O/2NYE7VpOQC+b1YD7sZ6\nE5WYR0RE8NFHH5GWlkZWVhYqlYquri5eeeUVKisrufzyy9m2bZtX965MJmPevHl0dXWNa+N9ySWX\nsG/fPgYGBigqKvq/QRT0ev2UFQ8ul4uSkhL6+vrQ6XQTTiwmk4mOjg7a2tpwOBwoFApiYmIkMdbY\nSIHL5ZJEX/Hx8SQnJ2Mymejq6pLyvGlpadNOrAql+8tzOkav2KOiohgcHKSsrIz4+Hg0Go3kp2Cz\n2WhqaqK2tpa0tLQpfRTE18aK0cRGV1qtFqPRiNlsprOzk6GhIXJyckbtSyaTSdc73arM6XRSWlrK\n0NAQDoeDoKAgaXL39fWVyi/HfpYWi0UKu3V2dlJdXU1dXR2RkZFSGkSEWq0mNzeX8vJyOjo6cLlc\nE9pG+/v7YzKZZqRRcDqdvPPOOxiNRlatWnVa7XPlcjmZmZkcP36cmpoaNBqNxy51IukUBEEqNxPD\nss1je3vjLv278cYbuf766/noo4/Ys2cPjY2NlJWV8ctf/pKsrCzWr1/Peeed962aQYkRPIvFgkql\nQq1WS4OiOIB3dHTw0ksvsX379in3JZPJCAkJISQkhJqaGum+a2xspKWlhYiICMmXZCQmiySMhCAI\npKamYjabSUhIYOnSpfzxj3/k2muvZcmSJbP5CMYhISGB9PR08vPzRzWGuv766ykoKKCyspLOzs5p\nQ/yTobCwEL1ePyq9AW5ReE9PD62trZKAbixUKtWU4malUslNN93E119/zW9/+1uCgoJYuXKlVPWi\nUCjw9/efM3GwxxBcuJyuUeWI4nWMdJP8trF69WoeeeQRli9fjp+fH++99x6VlZWsWLGCG264AaPR\nSE1NjdfdJv38/Ojq6mJoaGjUmB4ZGUl9fT02m40TJ07MaZfdM5Io9Pe7e9ZPVY4jTpKivWhzc7Pk\nCCcSCFHUExQUJKlJp4pQCIKAj48PHR0dpKWlSURlaGiIiooK2tvbCQwMnFJR2uXqxOl0cujQITZu\nuXzUij0wMJCQkBAaGxs5derUuNC/SqWSclcT+ZeLEK9hrKmNSqXCZrNJJWy+vr4MDQ2RmZk5Kprg\ncDjo7e1Fq9V6NHE0NDRgNBoJDw9n9erVWCyWaStCBEFArVajVqsJDw8nKSmJtrY2DAbDqEhNQECA\n5Pkgl8vx8/Ojt7cXu90+YbRDvI6ZRBQOHz4slQ4tXbrU6/d7Cz8/P1JTUzl58iSVlZXk5OSc1jIt\n0dTo9ttvJyQkhP3797Nv3z7KysooKysjKCiI1atXs3r16hlHGTyFaGksPqe+vr5YLJZxWoPe3l6v\njY96enpIT09n8eLFdHZ20tzcTEtLC62trYSHh5OQkOC1dbCYBjtx4gQWi4WdO3fy8MMP09nZyaWX\nXjr9DrzAddddx89//nPOO+886dpjY2M5//zz2bdvH2+99daMXBQtFgtffvklzc3N49phy+VysrOz\nKSsro6mpCYVC4XXdvsPhkMTiW7Zsoaqqir1797JlyxYsFgv5+fkzasM9Fp5EAUZGIR5//HE63m7j\n5/d+VxovEiIT8VHL+f73vy/5rkyHN1wvA/CQ8CwABeIhYodTQQPDFV9vmgAw4f49mWGduAh84okn\nkMlkrFu3ju3bt6NQKHA6nZw4cQKDwSBFYT3FRBGXgoICdu/ezebNm3n77bclndpcLQzOSKIgTgJT\nmdYoFArUajV9fX2S259arZbC8eKAFB4eTmBgoFerx8bGxlGlbSqVivnz51NQUEBHR8ekRMFoNOJ0\nOqUSr/b29nH9DeLj44mIiKC3txez2Sy1QlUqlVI6Ar4hChPdQOJrI10X1Wo1ixcvpq+vD5PJfQOL\neoiRoS2Hw0FVVRV2u52wsDCGhoYwm83I5fJJ9SAdHR2oVCrS0tIwGo3U1dV51SEN3IQnNjaWmJgY\nuru70ev1UuRI/Lt4PWIp3UTfmXiOvb29Xh0f4B//+AcAGzdu/NbqqnU6nWQ2VF9f71U42xsjIpfL\nRW1treTwqdPpyMzM5KabbmL//v18+OGH1NfX8+abb/Lmm2+Snp7OqlWrWL58+ajnTDRIEqNEM4XD\n4ZDU23FxcVitVikSNFJr0N7e7jVR0Ov1LF26FJlMRkREBDqdjq6uLpqamujo6MBgMJCUlERkZKRX\nn2FgYCBhYWHo9XqsVis7duzgqaeeoqOjg5tvvnnO7hlfX18uu+wyPvzww1ElkZs2bWLfvn3k5+ez\nZcsWr8t/S0pKcDgc+Pn50dHRMU7r4OPjQ0ZGBsePH+fUqVNoNBqPqywcDgclJSX09vYSGhpKcnIy\nK1euZMOGDTz22GOSTXxGRoZX59zT04PFYiE0NHTGk9rQ0BCCIIxaXTsd7u/932Wn3tPTw+uvv05N\nTQ0bN25ky5Yto8Zhsb+D0+n0mijI5XIpGtvb28tLL72E3W7nwQcfRCaT8frrrzMwMEB1dbXX0YrJ\ncEYSBTGiMBVREL27U1JSJG2BTCbDx8cHhUIxqxvEaDSOs8FUKpX4+fnx9z//g+/9cSuXutwmPc5h\nkdnBkH+x9uZVLN+wDK1Wy/z58zGZTHR2dmI2m8nIyBjVzGm60KInREHcZuTrE3XBFDE4OEhVVRVm\ns1lya7vnrp8Spg7H5XLx12f/hm3INoohi5a8YjRGq9ViNs/cVlgQBCmEDO7IkGj2Iyp1RcHlRBAn\nFW+JQm1tLZWVlWi12tOmbJ8MycnJ9Pf309TUJJHIuUZrayudnZ1ERkaOCv2q1WouueQSLr74Yior\nK/n444/58ssvqaqqoqqqihdffJGzzz6bnJwc4uPjMZlMEmETnyexUiY4ONjjigNfX198fHwYHBzE\nbrdLxNvpdNLT04PBYECv11NRUYGPjw8nT54kOjrao3LSsaF5QRAIDQ2VWn5XV1dTXV1NZ2cn6enp\nXgkTExMT6erqora2lrPPPpu7776b3bt38+ijj3L33XfPSgsxEitXruSee+7hqquukvYZFxcnVUB8\n+umnbNq0yat9Hj9+nK6uLm666Saef/55Hn744XHXrlKpyM7OprCwkLq6ukk7NY6FXq+np6eHqKgo\nUlNTpfdER0fzu9/9jrvuuouoqCgGBgamNalzuVwUFRXx7rvvMjQ0hL+/P3q9HofDgUqlIicnh+XL\nl3vsQ2K1WiWiEIM7paJy+CLgRCX3lVwTPcVxCgBY8Ih7rCnh7OG/1AOwY/jvj+K2Hf/tsEEauP13\n3n77balJ3/333092dvaExxkYGGBgYGBGZoBGo5E33niDoqIitmzZMqq5WGZmJu3t7RQVFf3fIAri\npHDs2DEOHz7M8uXLycnJGbWtj4/PnHu2T9ZvXqvVolAp0PhrYEzkOybdHcabN28eRqOR8vJycnJy\nqKuro6GhQapV9hTiYO0NUZgOHR0d6PV6UlJSiIqK4tixY2g0agxNXYTHhxGXHEtNee2o94ikQHz4\nZ0sUxkKhUHilTA4ICEAmk0kTmqeEMD8/H4ALLrhgRh3tZgO5XM78+fMpLCykqqpKqn6ZKzidTimc\nPFnEQhAEMjMzyczM5NZbb6WgoIDPPvuMgoICPvroIz788EM0Gg2LFy+WBmmn04nNZqOvr0+qMlGp\nVISFhREZGTnlNahUKhYuXEhxcTE1NTXY7Xbi4+MlMda8efNITk6ms7MTu90u2TuLjdumIgwmk2nC\nv58nuxCAVaoNhFyoxTdRwXvyD6n4pJpXK/7i0WepVqslcaPov/+d73yHjz/+mF//+tfcd999M7bn\nFgSBnOFOhdWUYcfO+vXrueSSS6RtLr74YgoKCsjPz+fqq6/2KhJ69OhRBgcHueaaa2hvb2fXrl38\n+Mc/HredVqslPDycjo4Ojx1O29vbUSgUE1YkqdVqyab61VdfZevWrZOed0tLC88//zzBwcFs3bp1\nnP+H2WymsLCQ//mf/6Grq4tly5Zx0UUXTRrtFNPNYyMKLocTAZB/C5FDs9nM119/zRdffIHFYuGK\nK65g8+bNlJaWTnqv9Pb2YrVaCQwM9Ioo2Gw2Dhw4wDvvvMNVV13F448/Pm4M3LhxIw899BDFxcVz\nZuJ1RhMF8UOuqKjgo48+IiAgYBxROB0YHBwcN5mkCVksXn0WqxauYSPXsh1xQHaLZf4UEYrS5cOr\nZ/2TeSv90MSq+OcXB8EoJ/w7fpQ9U8Nv39kBTN9RDKbWKIiveUsUDAYDGo2GmJgY9Ho9CoWCygMn\nia/NIuiWEHIjFuFXPvFqV3zwNRrNnBIFbyGG7MRmRJ5UE1itVsn7fu3ataf7FCeEWq0mIyOD0tJS\nKioq5rSErLu7m6GhIeLi4jwiTiqVihUrVpCYmMjSpUspLCyUVuClpaWUlpYSHR3NmjVrWLVqFf7+\n/pJ9rCjsNRgM+Pv7Ex8fP+mk7uvrS25uruTCOTg4SFpamnTdcrmcK6+8EpfLRU9Pj7Tfrq4uwsPD\niY+PH0fYnU7nhOVho7YZctH1kQnfJAVcAjmXzufkyZOjVsJTYaJy1jVr1hAYGMijjz7Kjh075iSk\nLUfOv/71L9atWyelNc466yxCQ0Npa2ujtLSUBQsWeLSvzs5OysvLCQsLIz09nfnz51NcXMx7773H\nZZddNm77wMBA9Hr9hO6uE0G0nR97z4rVB9nqXM7/zlKeeuopbr31Vmw226jPyGq18uabb1JYWMjW\nrVsn9WLx8/Nj+fLlLF++HLPZzOeff87OnTvR6XSsXLlScpoU4XA4pC6lSqVSioa+/PLLvPDCC1xz\nzTX85Cc/GXUMl8uFXq+nrq6O+vp6Ghsb6enpkca1yB8HoFAoOPDET5EjZx1L8EFBPdU4cXDOhw9j\nsVh4pvdPlJeXs3PnTpYsWcItt9wiaX+qq6ulz3kiiCLviIgIWlpapN4ovr6+Ey5kXC4XX3/9NXv2\n7GHRokXccccdBAUFTVjifumll3LvvfdSUVExZzqFM5IojNUoiKukmpqab+X4I7UOIyH3cT8kLtv4\nvKdMI+AadIED+goHCTxLg1+aCplLjkwlo/fY+P1NBW81Cp4gKiqK6upq2tra6O7uxmKxUF1US7TM\n/dAqNeNX9mOjF/39/R73v5hL/OUvf6Gnp4err76awMBAenp66O3t9YgoHDp0CJPJREpKiseeBqcD\nISEhxMbG0tTUxKlTp+bEuArcqz1BELyybRUrbMLDw/nJT36CWq2mrq5O8mRoaWnh5ZdkLATHAAAg\nAElEQVRf5m9/+xtLlixh3bp15OXlkZKSQnd3N+3t7ej1egwGAzqdjsTExAnD8iqViry8PKmaxel0\nkpqaOmpbQRCklJnoey9Gv6Kjo0e5kE60Ahb7rGzkOgC2EwZooRa++9dWUjbI+ezFg7xb9gk//+hO\nXC7XjBpRLVmyhKamJl566aUZNTzKYTFLuQD4poolKyuLw4cPS6FjmUzGmjVr2LNnD/n5+R4ThcLC\nQoxGI2vXrpUmhh//+Mfs3LmTkJCQUaFpcI+xTqfT4+iIn5+f1AhsogiuddCG0+Zi5cqVlJWV8bOf\n/Yy8vDzJSbChoYE1a9bwyCOPeEyQ/fz8pLRZbW0tn3/+Obt37yY5OZmMjAxiY2MJCQmRKhtGEghf\nX1/MZjP79u0jICAAk8mEwWCQFqGhoaEkJiaSkJDAsmXLCA4ORqPRSETSarXy1hPvYseOGRMO7Axh\nQYZMioqJ/TrGjodWq5W+vj4CAgImJQoKhUIql+zq6hrlseDv7y9FoEXt1lNPPYVMJuPXv/418+bN\nk57fo0ePEhsbK3nmwDdW8t3d3bS2ts6JlfwZSRTGRhREolBdXT0nXQOnQ2RkJO3t7aOiF1u4hWCb\nhi+7tPyPPRzuGtYwDHcUPa4pwjog0IgAPdBtVjAUqSYwSI2hw4f8ZpXUMGo6iMIgmLhP+UxTDzqd\njoaGBqlCRKvV4rA78EWNzC4jQB0g9ZMXIQjCqBBzQ0PDabW6nQxfffUV7e3tXHHFFQQFBdHQ0OCx\n6ZJo1/zviiaMREJCAv39/ZKIbzLrZW8gRsA8zcX39fVRV1eHWq0mOztbmrQTExNJTEzkxhtvpLCw\nkPz8fI4cOcKhQ4c4dOgQYWFhUtVEVlaWNKm3t7djMBhITEwkMjJy3PPp4+NDdna25BdiNpvJzs6e\nMHURFBTEwoUL6e7upra2lqamJvr7+0lMTCQgIAC9Xu+Vrbu1HyrecBBwlRVtli9rnWulNNRMcNVV\nV/HYY49x7Ngxzj777OnfMA0uv/xynn766VET+UUXXcSePXs4dOjQuFr5yXDixAmCgoJYs2aN9JqP\njw+//OUv2bFjBzabjZUrVwLuyGJ7eztardbjFFh4eDhtbW10dnaOIgoi4VHjR1+TicC8IFJC03nw\nwQcpKSmhp6eHzZs3ExsbO2MxqCAIJCcnk5yczI033kh1dTU1NTV8/vnnNDQ08OWXX0raNBFqtRpf\nX1/i4uJYvHgxWq2W0NBQ/P39PZo/lEolda7qGZ1ve3s7JpNplEvqWOh0OsLCwqSGW2IDNrFxWnV1\nNfX19cTFxfHCCy+wZMmSUdU3MTExyOVy6uvrqa2tpbGxUfo+Q0NDOffcczlx4gQNDQ3/+4mCeEPq\ndDoCAgLo6+ubsupgrhAdHc3Ro0fHvS7XyggJcWK3j/3yXajVLnp7v3kQmpvggguhs9PFV1/5wXAp\njScQyzTtdjs2m23cSm0qj4WpIJfLiYmJ4eTJk+NKHB2DThR+E5diivlagLKyMtavX+/VcWcLcWIV\n+1+ILN0TotDW1kZRUZEUbv93Q2wpfuzYMU6dOiV1xJwNrFarVz0lxM6dmZmZE0YB5HI5ixcvZvHi\nxRiNRj799FPy8/Npa2tjz549vPbaa+Tl5bFu3ToWL14sif+qq6ul0uKx5yOTyaSoQ01NjWQuNlHd\nvih4DQ4OprGxkba2Nk6cOEFiYiKdnZ0SUcgV3B4H1+G2Pd7O8Or7wgiGW2Sw9003oT/2j8Osuhqu\nWrCFtY4rJMfGyUrbJoMgCGzevHlG5bma4f9GQqfTYbPZ6OnpkSoQdDod8fHxNDQ0UFVVNakYbiRO\nnjzJvHnzxkUgNBoNO3fu5IUXXqC0tJTVq1ej1+vx8fFh/vz5Hk2aoteKQqGQmp1NdN8Y6rsJzPMj\nPjkOjUbjdWWUJ5DJZKSnp0vRODEFWVVVNWo8FAXRGo3mW0lXj4SYwphOtCyTyfDz8xv3rNjtdtrb\n2ykuLuZPf/oTmzdvZsOGDePeHxAQQFxcHK2trRiNRnp7e6WmXxkZGVRVVdHQ0MDy5ctnfU1nJFEY\nK6ATKxy+/vprqqqqpiUKYt5M9Az3ttdDXFwcf//73wH4nnAHAPksZ52Pi9A4La6tqSD6lfiDHBeK\nITUWu5bPhgerrNhe/Pt76AjxpWaTDh7R4cv02gTxerVarRTqGxteF8sfZzLBREdH09fXx+DgIMHB\nwZx0lXGucAEqyzKcvg4Os3/cucA3ng01NTVz7lg3HSorKwGkfhjigOpJ5cMXX3wBuBtyeTOZnk4o\nlUrmz59PUVER5eXl5OXljSN9nkbORNMsT6NsNpsNs9ksra6mQ3BwMJs2beKqq66itLSU/Px8vvrq\nKwoLC91Rh/c/RoGSBkMd3d3dtLW1cfz4cdLT0ydc+UdHRyMIAtXV1ZSWlpKbmztpDlUmk0n9KSoq\nKqitraWiokJScoth/IcYLvH74fC4EAyIX/WwIL26Zyn1gpO+mmaizxpkTcNODNUuyY1PrKH3BDN1\nvCviyIT/Pvfcczl8+PAoA6acnBwaGhooLi6elij09/ej1+ulun2xvXR7ezu+vv8/ee8dH1WZvv+/\nz7RkMpn03ggJSSAFBOmKCFJWEBRwEUXXutbP6lpA/YoFe1lRWax8duWLulhoShUQEREREQghgfTe\nk0mZzCQzmZnz+2NyjgmZ9IB893f5QkLmlJkz5zzP/dz3fV2XO68++zp6b0+GJcXy5JNPMnr0aKZM\nmeKyaVtqXm0vcy8tTCRKdftMphRo+QtBKAuUPLxoCzf+ZXAa6HoDi8WCzWZDoVB0CF7c3d1RKBQu\nS8jnGy0tLahUqn73BqhUKsLDw3nvvfe4+uqr8fb25siRI84McBs7zGw2yyKCra2teHh4oNFosNvt\nMi3YZDLJY+dAcVEGClKtpv1NnJCQIAcKUgrtfMHHx4f6+vouegBEBETEdipgdgRsdgGN2++NJenV\nXqRXe0Fs/8oker2e5uZmlw17jY2NqNXqfgUKCoWCpKSkThORyk2FydK9illNTQ1+fn4XTMddwsmT\nJwFkky4vLy/c3Nx61VSZlpYGMOgKewOFt7c3Q4cOJScnh+zsbBITEzu8vnv3bsrLy1m6dGm3anmC\nIMjZtt40LlVWVmKxWPrsUqhQKBg5ciQjR47EaDRy4MABvv32W0RErFi45557mD17NlOnTqW6upqM\njIwufTvCwsJoaWmhuLiYs2fPkpiY2G1aWq/XM3r0aFJTU8nMzOy31XmrqGD77lBuv7mAYTMU1BfZ\noXe9fOcVkydPZs2aNR0ChVGjRrFt2zZSU1O56aabut2/oKAAQGaVSCWhHd/swGazMWLYCJqbWkj/\n7QzV+bVMeXAKhYWF7N+/H7PZzOo3/4mb0o0rr3aqLN51113A72VH6Xlzd3fHz8/PZYlLatBOT0+n\npqaGlpaWC8Iuah8otA+23dzc/rBAwWq1DphGe+DAASIiIli8eDGVlZWUlpby2muv4efnxw033IBO\np5N9dKSSQ/vnrKWlhaqqKnkuHSguykBB+nLb188kIQ8pQpKyBq50wifiDCQkJ6++QhAEEhMTiVUn\nEMpKACrfHU+VWzlBbt/jPul5mhVHnBuHO3XCzQ2j0SXYIbrtIIFtX5r8PdVj7eWo5CHosGJFjxfz\nN11PAE5efEkbj/fD/WtobW0dkCVt+5vqsON7Dh06hJeXF299/GaH7SRlPY1G0+PqRhAEmbPcH8fO\nrpCamgr8Hij4+PhgsVioqanpdj+bzcaZM06DsPPp69BfBAcHk5mZ2Wki9ReCEBH5dMf/5fHHH2fp\n0qWMHTu2y6yBv78/DQ0NGAyGbim4FouFwsJCNBpNvySCPQTnMl1ayZtpYgQjMVDNlk1b2LJpC/Ou\nnceUKVOIj4+nuLgYURRd9rQMHTqUlpYWufu8p74XtVrNyJEjeeedd7Db7RQXF8sZw2icAX2aNCeM\nAPnRH972nOqdioHGf8BB4RJmqJdTNVXFT0seBOAJ4TUAXhUf7/N16S26eiaCgoJkuXMpy5OcnIxC\noSAzM7PHPoWqqirCwsLkTJ/VasXDw4Pd278lJyuHBGUKDrtDHj/aW0CbTCZWv/lPAiMC0el02O12\nYmJi8PLywtPTs8+LAm9vb2pqajCbzRc8UGgfJEuBgvT6hRReUqvVA5qg6+vr2bJlC6+++ioajYbI\nyEgiIyOZNGkSAKtXr+7xGHv27MHT01N+Bgfa13dh5On6AMmcCToGChK1KS8vr4MX9/nCuHHjaKFj\nNGoWnTebjs4PQJ1dDb30H+8NJN1yG52zGgaDAaDPym1dQXqQXD3Y0rXWaDQcPnz4gkgft4fkGuju\n7i7XJaUAqac6cU5ODhaLhYiIiAEFVecDoijKdVVXndECAnPmzOGpp57i6NGjLF++nMOHD7ukQwUE\nBKBQKMjLy+tS214URXJycrDZbAwbNmxQBk4BAQ90RBCNGg0KlIiiSH5+Ph9++CGffvop33//PaWl\npZ33FQQSEhLw9PSkpKSkV/0mKpUKb29vfHx8yMvLQxfWP4fHNDENlQiKfjAfzhdGjRpFenq6/G+d\nTic7aFZWVna7b0FBAWVlZXJJVqFQYDabSRqaRKAymCJ7HiUUUCtWdVhYSSXOS5nE0rk3k306h182\nOLvo+8rvlyCtpM/1oTlfkNxqD+46xMKxi4kXkogXkuRAwWq1upwvJglXyn8GG3q9HlEUe009PRe7\nd+9m/vz5nRpNe8vWSU9Pp7a2lsjISFpaWgaFzn7RZRQk9zI3N7cON6pWq2XIkCF8vGYd8W6J8qom\nXnCuFCWLXlEU5WyDoZ8ZBYCUlBSGEMsvt7R1IyfHYmwezdzmq/iLmy+a03/Cu6WEtKceRxBEVv/n\nSYYI/mjCo7Ci/F2Q6SfnXxPIYz+Sk+Sybs8dRxIGqlGjIZYEhuGsyUorKMkQabACBYvFgtVqlVct\ndrudiooKjEYjLS0tsqhJXV1dB6MuKUqNw5k2n8Yc+T12lfHpK6RsQnJysjy59baZ8fTp0wC9pphd\nSBQWFsoZAIn5IA1a0r0tCAKiKPLAAw9QU1PDpk2b2LRpE9dddx2XXXaZnK7XarXExsZSVlbG6dOn\nGT58uFyWkmqVxcXF1NTU4O/v329lyDicz9qoNtEgM2a+b7unW0RnUF1RUcGOHTuoqKigrKyMdevW\nsXPnTu666y5mzpzZYWWsVCoZPnw4x48fJysry2WvRnuUl5cTGRlJYmIiJ06coCI5n4NlP/Jw2/2X\n9klbUJ2oAsnKwvyB87WbQBQh5Tko9UohY+JxlgQf45JPnGW91265B4DN54wnFwoJCQmkp6d3CMRD\nQkKoqKigvLy8W4O86upq2d0VnKJvHh4eBEUEc+P9N5Cfn09NZS0GgwGdTtfJDA/A2tRKiHcYxXQO\n6vqKwVKv7A0sFktb8NzZFEqhUGCz2bBYLIMqcNYTlEqlLFbWXdnQFRwOB4cPH+b111/v17mtVitr\n167lySefZOXKlfJzP9CG6YsuUHBVdpCQkJCAHTvNKhMGj0qszVZwEbgOdHIC582uxxviKsAnhMlX\ngM5ciPFZMJtDsc2ejSiKjNn6IpGqYzzfmgGqFHzqWqlqVcJ/2g6U7ayvz6aalW2ugD0hm3Ts2PDA\nkwxOUkQ+AKfaGqAWGJysg8GyLpauuVarlVeEJSVO3qdarUahUJCVldWpi1kKECLkeotz8oCBBWmA\nLOt84sQJ4PeyA/Te70FaofWma/xCora2lsLCQtk0qjdpwYCAAO655x4MBgNbtmxh06ZNTJkyhWnT\npuHn50doaCgWi4WioiKOHz+Ol5dTNEZybwTnxNPed+R8ICQkhDvvvJPFixeze/duNm7cSFlZGatW\nreLzzz/nmmuu4eqrr5ZT7DqdjpiYGAoLC8nPzyc+Pr7LY0uNtJ6enuj1ekaMHM6h736CXrCEHQ5Y\n8zysERUU+U5CpWomJORnSO3cTf5HICEhgc2bN3f4XWhoKCdPnqSioqLbfaurqykrK6OgoICpU6ei\nUCi45JJLeO63F4kbFUv88Hjih//er6NWq9HpdGRnZ+Ph4cE919yPR7A7XvWeGGnoUALpKwRBwGq1\n9kunoj/44n+/IuPEGfwJZhLT8WprbJ0bs4BaXQUTpo93ubLvb1m6NzCbzbIpXl9x+vRp4uPj+122\n2bBhAzNmzCA4OJiAgAA5UBio5fRFFyi4KjtIiI2NxS/Al4QRCUybcBUA9XX1GOuMlJeX9+gO2VdE\nEg05R2HsfABMHgaCv/gCq78/qltuob6+nrzGK/FT5FErOietILWFqtbBqM05B3O7i9KDlHLvSsyj\nr2hvImUwGCgtLSUwMJDo6GgaGxtxd3dnzZo13HHHHYNyPleQKEEGgwGr1YrJZEIURfbu3YvFYumQ\nFZBosz2VHiTDqcHgEQ8WmpubOXv2rExPU6lUcu1fWrHX8nt27Fz4+flx5513YjKZOHToEG+88QYa\njYbp06czceJE/P39yc/PlwV1NBoNISEhhISEDPh+kQLV9hQ/qe59LvR6PX/+85+ZN28eH374IYcO\nHcJgMPDJJ5/w5ZdfMnv2bK677joCAwMJCwujurqa8vJy/Pz8usx45Ofnk5ycLK/W5lx7NS+9/iJX\nKZz88r+1lQT/+eQUeLptaBvhDGYdW34kxQ4/z9tPQ0MDvk+8TOUvFhDbJuesNwBY+MJfBnSN+gu9\nXi93sktjmFRKKC8v73bf6upqQkJCOHToENdddx3e3t6o1WqyT+aQfTIHdHZ8Any556G7aWpqwmQy\ndZDlvvSq0TSbWsjLykeFmiNHjnTQY+gLzmVJnU84r5VCDgQU7SrpAgJ2m/M+uRClagkOh4PGxka0\nWm2/5qIffviB6dOn9+vcOTk5ZGZm8uKLLwLIfjq1tbX9Ol57XLSBgisqm6+vL0PihqDx0HD9HQsB\nWPX6KnyjfVm1ahUmk4nly5cTGRk5KAGDF76w4zgcn8XhtkXp+LeLgWJ4fRIVQcm8c+OfeGREICXb\n3gJKiDqQScYuBQ/QpgWB82HcwL9ZycOuT3QOzKKJH3/8kddff50pU6awfPnyDq9L/x6sFF99fT1K\npRK9Xi9nEiIiImSOryQ3eq41sZaO31EJBX1O2Up86xdffFFuSvLw8CAoKIjq6mrMZjNubm4dPqsU\nqUtlqq465qVAYrC9QPoLyabXZrORmJg4oHSoTqdj9uzZzJ49m/Lycr7//nuWLVtGXFwc06ZN47LL\nLpMld8+3QFl3cHd357rrrmPUqFEYjUaOHTvG8ePH+eabb9ixYwdTp05l0aJFJCQkcPz4cc6ePcuo\nUaNcrmjz8vKYPXs2Z86cwWw2ExYW1uvPtu1HWPonONPUhCAI6NuYNBcToqOjKSgokBs7JXqp1JPk\nClarlbq6Otzc3Lj99ttZu3Ytjz3mFHbr7ll0OBy0tLRgNpsZMWIEOp2OV3UvU1lZyXvvvdfvQEG6\np6XFx/lEa2srWncP7BY7SpTo8JSDWB06sP9uancuzldpqa6ujtbW1n4JqTkcDrKysnjggQf6vK/N\nZuP999/nkUcekcdDadwbjO/iog0UXKVtwsLCCAsLo66uDqvVyqhRo/hs3X9wd3cjflQ8Y8aMobCw\nkIaGBhITE/ssSHQulChBPwWMB1y+7t7izCJ4tXjTiIp6UYVz8TrwtJtUj3fVFCT9bqCfD5w3mNFo\nxMfHB4VCIaep26e+tm/f3u+Bozu0tLSwZMkS/P39UavVREdHExwcLH/248ePo1QqSUpK6lDrUygU\nuLm5YbFYsFgsLu8VURR7ZVd+IZGTk0NTUxORkZEuNQakFXtf07ahoaHcdNNNLFmyhIyMDL777js+\n+ugjJk6cyLRp0wZF/bGn9yVN2K62iYyMpKamBoVCwcMPP4zBYGDz5s38+OOP7N+/n/379zN+/Hhm\nzpxJa2sraWlpXHLJJR0CqaamJgoKCmSDKclUCmC/6OyTmCssBuAFWljzgnM1VfnKcmio4Bn9h6wd\nHcknmw4QUforsXe0TR4FbSdwxvUo/8D+7tjYWPLy8uTPJdWVu2tGk1aLkkzzwYMHe6UaKQXk5war\nwcHBmM1mGhoa+pWB0mq1qNVqWTTvfELKFLS22hBQnZNRUOD4AzIKBoMBrVbbL1ZRZmYmCQkJ/VKw\n3LhxI5MmTeqwmJMW2//VgYKr1VZoaCjDhw9n//79bNy4kYCAAHz9fKkz1FH6SwXlx3aTnZ3Niy++\nyIkTJwgMDMTLy4uZE/6E1kOLn6cvHnotjYoGvv322x7TY2+Jz+EjrGY3mznyXQao3BAir3S+OLeY\nMMHOTQXTaclazLy7f8NrloKQkdUs9E9ld61T2VGqhfW1b0KaLF1pOQxmoFBfX48oinJj5LkDvdls\n5ueff+bNN9/stK80sfm1yT6f+xltNhuZmZmoVCq0Wq1sfCKKIg0NDVRVVXHfffcRFBREVFSUbDxj\nNBplG9WWlhbGjRvXKR2t1WqxWCy0tLS4DBSkNK40cP3RKC8vp6KiAh8fnwHXC7uCQqEgOTmZ5ORk\nLBYLP//8Mx999BFGo5Hx48czefLkbpvizhcUCoXctHj27FnGjRvHY489xs0338zWrVvZu3cvR48e\n5ejRo0RHR8vlhYCAAJRKJUajUaZ1KpVK4uLiCAwM7H2m5Ox+uB9iGmJwb64jsDYbLg7trQ4ICgrq\n4Gej1Wrx9/fvtiFOKh9Iz+/dd98tWxv3tZFOwuzZs/n666/5y1/6XoYRBAG9Xk9dXd15pyVaLBYa\nqafOWosDOMR3zsUdkMVprFhlcaL+KnH2BRJDRavV9kvc7dtvv+1AXe0tioqK+O2333j11Vc7/F5a\nIP3/LlBQq9Vcf/31HD58mJMnT5Kdnc2111+LgFM+NOd0Dvv27WPt2rUUFxdTVFQEwJ8WzMLSYsHD\n3SlK0SyaOXXqVK/ejwoVMcRzJOcgDO+4qvYWvKlzqKlzqAkEanNEQkZCSGIgqT8O6DJcsEChqso5\nuUv1LK1Wi1arpaWlBTc3N7Zv386sWbP6da6amhq5I1upVOLu7t6BvqdSqYiIiCAkJASDwUBVVRX1\n9fXY7XZycnKorq4mLCyMRYsWdYqytVot9fX1ssLkuThXBvyPRH19PTk5OWg0GkaMGCF/Fqk3QfLX\nGMxBzM3NjSuvvJIrr7xStsFdt24dtbW1jBs3jsmTJzNkyJBBK0v0lAXR6XRERUVRUFAgu0OGhIRw\n7733smTJErZv386OHTsoKCggKyuLgwcPMm7cOEaOHIlaraaqqopp06YxYcIEl7oT8HvvxPfskJkj\nNz1Zw0EOUDQpjUv8Slg7ZB//DtcjSmuEH352/v22sxSR1dYf0l7ddTA1QbpDYGAgR44ckf+t0Wio\nra3t9h4+t7zm5+fHrFmz+OKLL/o10QNMmzaNRx99lPnz5/eLVuzl5YXBYMBoNA4aM8sVpEyL1WpF\nhaYD60H6WaFQ0NjYKDOIzicMBgN2u71PXiQSfv75Z0wmU4em7d7A4XDw7rvvcv/993cqt0sZqcHI\n7vw/FSiAU9o0NDRUdpHLyEwnISGB2JExxI6MIa44Fm9vbyIjI2UJ0l/2/Iq5yYx7sw6/UF/ipg4l\nxCPM5fHPxVPi/U6XRe0sLqOCDThpK9UoCLrCwYTxpcSsz+RNViLkC4w3vcTwRC+CDsWDCIq2ZsS5\nwmJ2iF/2+jr0NlCQJpv+DGZWq1UeiKTrLTlnmkwmzGZzt1Sdnh48ySc+KSkJd3d3OQ0oiqJ8ToPB\nwPHjxzs0VAYHB3Po0CF8fX2ZN2+ey5WRVBrpSjfAVQnlj4DRaCQ9PV2+Dq76Shw4OqRNBxs6nY5p\n06Yxbdo0mpubOXbsGF988QVlZWWMGTOGyZMnM2zYsPPeyxASEkJhYSHV1dUdUrM+Pj7cfPPNLFq0\niN27d/P1119TW1vLnj17yM7O5uGHH+ann35i7ty5fX6P+WQQq0tkgm8ZCgH22X8dRLWTwUVgYGAH\nETEpoOwu8+mqsXnOnDk88cQTFBUV9auRV6lUsmTJEt577z2eeOKJPqfCpaCloaHhvAYKTU1NzJs3\nj6ysLBwOB1u2bOmQwVi6dCnl5eU0Nzfj7edFfW2DyxKZRLGfhpMB44tz0WRp09GR5La/Z2enfdtD\nap7ua6BQVVXFhg0beOmll/p8f2/bto2UlBSXrrhSoPBfmVHoabWsUCgYO3Ys+/fvJzc3lx9++IEf\nfviB+KDhXDJ+FJEJEZw6dQovLy+io6OdJkinsgDwIwi9rycKpQJFH5od3d3duZRJHOVHROIQ2gb1\nsDDAJmKrdT7IoijSeLIZ/Sh3NFEKrIX97/ztLlCQfjfQjEJ1dTVqtbqDd4Z0c9XX1/PJJ59w//33\n97tpUnqg3N3dOwxkoihSU1PD2bNnMZvNKBQKwsLCCA8Px8PDA4vFwokTJxAEgauuukreb9++fURF\nRREfHy+XG7qSaJWuX18dNgcTJpOJtLQ0HA4HSUlJnVaGzW1UUhERFeoL4oyq1WqZMmUKU6ZMwWq1\ncvz4cbZt20ZBQQGjRo1i8uTJ/a6T9gQ3NzfUanWXQjRarZbrrruO8ePHs3v3brZu3UpaWhr33Xcf\nDoeDRx55xOV+UibBD2fwUUIh+8Ud2O12dDodNy2+iZGZpdTuaCLk7F8JAX7FeU8/0JZB8MA5QQtt\nlN+RtNk+Y+uXH4TL9ynoug3oPT09OzB5pO+gu3vYVcOuQqHgnnvu4YMPPuDFF1/s13c5ceJE8vLy\nePfdd7n33nv7NNZIrIvKyspBzVq1hyiKGI1G3N3dcTgcCILQaUWtVqtRqVTY7XYCQgKor+3ZG6a/\nsNvtGAwG9Hp9n2iReXl5rF69mvvvv7/PvVTl5eX88MMPvPbaay5f/68OFM6Nouvr68nIyOhgwzpu\n3Dgee+Qxdm/f7bKmXlRURFVVFadOnSIoKIiclkx5VVpbW8vp06e578F7+/S+3mS0jVsAACAASURB\nVBdfY8OGDbLuularJTTsPibedhspb6VQKhQD8FZuGH+5zMaphZHsyQyhLQjlxT5eh/aBwrlR8Bef\nfEk4Q3ja/w2WtR1ZMq9aL77b63PU1NRgtVrl+r9Ux9v0y5ds3ryZ5OTkbnntPUGaENpH+WazmZyc\nHOrq6mQ3y4iIiA5ZgyNHjmA2m4mPj5dr6jabjW+++UZ+KHqy2u4u0LoQaG5uJi0tzam1P2JEt5oX\nWnRYaOHpp5/m9ttvv2A23iqViqSkJCIjI7Hb7ZSWlrJ3717ee+89kpKSuOKKKxgxYsSgDvSuxH4k\n2O12MjMzqa6uZtiwYSxbtozt27dz6NAhWltbufXWW1m+fDmjR4/u9hxKpZKGhgZ27NjBlClT8Pf3\np+47E+azF66prT9o/5wLgiAHwd0F6pKWyLlBaGxsLEOHDmX//v3MmDGjX+/nxhtvZNu2baxYsYKp\nU6cyZsyYXjn3KhQKgoODKSkpoa6ubtD0XtpDUlyUSqYqlarTfaVUKlGpVDgcDj7a8kEnj5DpgjOD\ncBv/A8DTbZkEB1I/lHOCvaFNJ2YFzkWLKzG5xsZG7Ha7/H56giiK7Nq1i/3797Ns2bJOjLLe7P/e\ne+9x9913dxnEeXp6EhwcPChZ1YsuUDh3JajT6fjiiy9ISkqSV6VjxowBBBw4qKysJDg4WN7fw8OD\n4cOHExUVRU5ODlVVVdTW1jJkyBDCw8Px9vZGEAQMBgORkZF9em+LFy+WA4WhQ4eiUCg63RhVVWAw\nQPywJvZliTjo3yArffmuJjqxLXmqoP8UUIvFIqcGz73RcnNzycrK4r777uv38UVRpLa2Fjc3Nzw8\nPLDZbBQXF1NSUoLD4SAoKIiYmBiXZYXvvvsOoEM24dChQ1x66aWdMgldRe8ajYbw8PA/pPTQ0tJC\nWloaFouF+Pj4LlOR5wa5eXl5rF+/Hg8PD2655ZZu2QrdsQx6gvRdVFVVYbFY5GNoNBr+53/+B1EU\nOX36NHv37mXt2rVMnTqV6dOnd1sr720JTHK/OxdWq5XTp09jNBrx8/MjLi4Od3d3Lr/8co4ePcqq\nVasoKSnhb3/7G3PmzOHuu+/Gx8fH+V7rU2lqapL/mEwmTp48yfbt23nmmWdISkrC7w3nZCUF7NL1\nW9322R8WnnP+uy2j4Jgz1rnhKXi+RNWnzyhBCrylfollvMg9gpPa/KHoupyn1+tpamrqRFXuCt1R\ngJcuXcoTTzzBpEmT+tVcJwgC8+fPZ8qUKRw+fJi1a9d24ORLzaWCIMgLPIVCgVKpRKfTYbFYyMvL\n49JLLyUwMJCAgIBBayyW0vzdMTNUKhVKpRKFQkFtbS2xsbHnJVMGfaNiV1VV8fHHH6PX63n55Zf7\nlbH99ttvGTJkiOyB5Aqenp5UVlb+d0o4SyvF9un166+/ng0bNnDvvffKD2swYTTRSGxIHG64d+q4\n9/DwICUlhZqaGnJzcykvL6esrIyoqCi8vLxoaGjAZDL16QFSKpVUVFTw4osvcs0118hdyQDhtAUd\nI0eT1VzNxMgqQhfVUDrWOUmkrfTo00CjVCrZvn078DurQDILCiUSNW4EEMHBtrTrlLZySG+lk+vq\n6hBFscMkViwW8Msvv/D5559z8803U11d3esI+VwYDAYsFou8siguLkatVvPJx59wYN8PZBVlutyv\ntraW1NRUVCqV7KMuiiLbtm3jqaeekrfrqQfBzc2N0tLSCyrdCs7SmSQdHRsb2ydqYkxMDM899xyn\nTp3i7bffJjo6mhtuuGFQV2TV1dVkZWVhs9lwc3MjIiICLy8v6urqKC8vp6SkhMjISEaNGsWoUaMw\nmUz88MMPrFy5kpCQEGbPni0bFvUHarUak8nUQVjIbreTnp6O0WgkIiKig3qkQqFg4sSJfPrpp6xd\nu5avvvqK7du3k5qayo033tipBq5Wq/Hx8aGqqoqEhAQuv/zyP1RHoq+QaL96vZ7iYmeWsr+BgoeH\nB7Nnz2bXrl1cf/31/X5Pvr6+zJ07l7lzf1exlGTd7Xa77EHgcDgQRRGbzYbBYOCnn36irKyMn3/+\nmbq6Onlyj4uLY/jw4YwYMYKgoKA+fz+iKFJWVoZKpepWjly6v/z9/bFYLNTW1nYY7ya0mQc+1Wa6\nxy2XOP+OadugTefqi4+k7Q8jInIZ0zFjYv/+/TgcDmw2G0VFRbLkvUql6hA4SY2UpaWlVFZWUlVV\nxYIFC/rtaFtTU8OuXbu6LDlIaL+oGmhZ86INFNqnlC+//HK2b9/ewVxGjxdNNNKKFQ2uaUCCIBAY\nGIivry8lJSWUlJSQlZUl3+Tp6ekkJyf3aTIJDg7mr3/9KytXruShhx5yuSIuq3V+QaGCtd/K6T3R\nihRt//UXrlgBv/32Gxs3buSFF14gOzubqqoqhgwZ0mcp0oaGBrKzs2lpaaGiogJBEFCr1YSFhfGf\n/7uh2+YsibY6ceJEuWZ34sQJoqOjO0yYPQUK0u8H4yHpLWw2G2lpaZhMJoYMGdJvKuLIkSNJSUnh\n559/5pVXXiEwMJA5c+aQlJQkfw7JpbO3mQV/IYiUS5NJHp3MTbcvIS4ujpCQkA7iLGazmYKCAkpL\nS9Hr9ajVajQaDSkpKYwbN47q6mq+++47/vWvfzFlyhSuuuoqwv2cn1FSlewpUPX19aWxsZGGhgb8\n/PwQRZHc3FxZQKkriWmNRsMDDzzAjBkzeOGFF6ioqODjjz/mrrvuIiUlRZZ1lkob//jHP1i8eHGX\n3/u518sbZ8DhiGvLJCxq6xWY4sVXTzoD/PZS5b2BRI1OZgwAB/FhKt1nJ9zc3OQ+LSlQ6C7z2VXp\nQcLMmTNlBsNgejAIgtDt8fz8/PD09CQzM5PY2Fj5WbBYLOTk5HD27FnWrl1LZWUlI0aMYMKECTLD\npScYDAaam5uJiIjodpyUXvP396e6ulpWnO0TRAdQBKRRxD5EWmmiEg90NDU14ebmhiiKNDc3o1Qq\n5YZtKWhqb+Q0dOhQZsyY0S+NBfntiCLvv/8+d955Z4/ZUpVKhUajkUWnBpJdvegCBVdNaIIgcOut\nt7Ju3Tr5wfITArFiYdY1M3n22Wd7PGZ0dDTh4eFyZsFqtVJVVcXZs2dJTk7u8qaX1P80Go3cGGO1\nWvnzn//Mhg0bmDrVGZW+wBrnDtlQUeIOi1WEtAiy50MyJj7vQ8rSjp2hxOOOlpv4K+AMDlqx8ikf\n4kCJCQW6tjKEvU3wvrd6DUajUdY3EEWRzZs389tvv7FixQo8PT2Jiori9OnT5OTkkJycjCAIeGt8\n8Pb1xm500NJscXkuk8lEZmYmVVVVeHh4oNPpCA8PJzg4WDZL6QoNDQ1s3boVgHnz5sm/37p1K/fc\nc0+HbXsKFNrTMaVU7vmE3W6XU+cREREMGTJkQMcTBIHJkyczadIksrOz2blzJ//+97+ZMWNGv7jW\nl04azdjLxtJY39hJzAicE/Hw4cMpKiqivr5eTjErlUr5WRQEgcsvv5x58+Zx5swZXn75ZVppRYkS\n8RxTnq4QEBBAYWEhubm5eHp6UlZWRnl5Of7+/sTGxvYY0CUkJPDuu+/yxhtvkJqayr/+9S/uvvtu\nrr76annfmpoaqqqquk3LXqzQ6XRyoNCb0oOUVu7K9Eej0TBlyhT27dvHnDlzBvnddg8pG9m+QdPN\nzY2kpCTZ9l3KJv3666+sW7eOCRMmsGDBgm4zvaWlpQiCQFhYWLcBsjSXKJVKAgICqK6u7uBjoZDv\n17ZrJ1VV9CaozoDTp8FcCEQAScRxDSq0+LEfgPnz58vnCg0NxWg0djBrOx/4/vvvCQsLY+TIkb3a\n3sPDA6vVOmDb74suUJAu8rm1+cTERLZu3Up6erpzZYWAuq1zeefOnYwdO7bHY6vVaqKioggPDyc3\nN5eioiJZR33UqFEdumbNZjOlpaWUl5fjcDjQ6/UkJSWRm5uLyWRiwoQJjB07lg8++KDTeczNKhrt\nakJUrql7vYE06Ip0nFgdbf9WDqA/weFwYDKZ8Pb2prm5mdWrVxMYGMjzzz8vP1x+fn4EBQVRVVVF\nbm4u4eHh3Hz7Unz8fLA2t3LqmLMG3z6jYrVaSUtLo6amBp1Ox7Bhw4iMjOz1g/PFF1/Q3NzM2LFj\nZSOnnJwc3NzcOjT7SDe+QqHoNtsxdOhQzpw5Q1ZWVo9KdX2FNCmNZByCQuBP183GP8aHU6dS+XTP\neqBrIaq+nic+Pp74+HiMRiP79u3jySefxEZrtz0q0or1IZ5FP8adG6YvxVJjpfFwCw/f+hjWFitr\nPn+nw4Ds5uYmN3xJKVWLxYLNZsNkMlFXV0d9fT0NDQ2EhoaybNky7r33Xvbs2cPD9z+KBjcaHIZu\nJ3tPT0+GDh1KeXk5P//s1DCQ+op6e594e3uzcuVK1q1bx9atW3n//fcpKyvjjjvuQKFQsGPHjn5R\nKQHIbht3KtpW6F/AkjYr2KfI6Pvx+L2nSIco/9wVJPne1tZWKioqZEZQV5AC7+5W1nPnzuXJJ59k\n9uzZg+qF0xPUajVubm7d1siVSiUjR45k5MiRtLa2cuDAAZ566ikefPBBYmJiOmzrIegIC4hg8W3X\ns/S2m9Bqtd2qLrbPTkdERMhZBSmA/JkDALyCFyIi/9h5EiuncFCHihhCiEZDPEnYgVbiMAEm3uSr\ntjOsk8/l7e1NQ0MDlZWVveotkjKCfaG119TU8PXXX3cSVuoO0vg8UHXKiy5Q6I7Wduutt7J69Wpe\nffVVasUqGhoauPPOO/n111/Jzs7u1NXaFSR1N4vFQnFxMY2NjaSmpuLn54fNZsNsNlNXVyd7HWg0\nGoxGIz/++CNarRY/Pz+io6M7DER5ZBJKJPOanUpL9TvdiIkTeSU/F2zwNV+xgt5pePsRhB07Nqyo\nUPNs24Tj7MZtxB83QrABtSS1BRTfsBGAF3BNIWuP5uZmHA4H1dXVfPzxxyxcuFDuB5AgTVBms5nq\n6mry8vKw2+2cOX2GqJAhjBg+nF9++QU/Pz+0Wq3cMNTU1CQHZK5W1V09GOXl5ezatUvOHknYvHkz\nCxcu7LCt1BDZU+pxxIgRnDlzhoyMjEEPFKQHfZQwjpi5EYTFhGA6a8Vjb7D8WnvzpMGAXq9nwYIF\nXHvttdx6663s2rULg8HA5MmTqa6u7qBW+HecWbbixFBSpis4Uq+j9ccGkq7VEqeKA1GkpKSEhIQE\nl+dSKBRoNBo50+br60tERARWq5XS0lJKS0s5c+YMOp2OG264gRtuuIEvv/yS5cuXs3jxYsaOHdvl\nRC0Fj0VFRfj6+hITE9NnBT+lUsmdd95JbGws77zzDl9//TX19fXcd999HDt2jKVLl/bpeL+2+cH/\nrW11ue4p59/LaGAfzl6hvjaOSve61F0/gan8wA8dXusK0j0eGhrabYpfek/dBUU6nY4JEybwyy+/\ndGCPXQh4enrKQkQ9BSlqtZqZM2eSmJjIqlWr+NOf/tRJOn7UWOdK+tzgydXnb8980uv16PV6qqur\niY6OllfXNlpJ5yR5ZNFKBG6MQUkoAgJusi1pzxTriIgI2cEzMDBw0BUpHQ4Hq1ev5q9//WufSsH/\ntYGC1MXaXnhEQnh4OEOHDuXQoUNMmTIFb29v5s6dy+bNm9mwYQPPPPNMr88jCAIhISHU1tbi4+ND\nY2OjXLcXBAE/Pz/CwsLkunhBQQEmkwk/Pz+ZItTQ0MCUKVMIDg6m7EwuLZZGPOoCsFkUCIq2B1gN\nYj8YeoPBbOgK9fX1bN26FYvFwmOPPUZgYCB5eXk0NDSgUCjw8fHB398fT09PRo0aRUlJCb6+vqSn\nZfDb0d8IEIIYEh/FrQ/8pUMXtEKhwNfXl6ampj77C3z66afYbDauuuoqWeK4srKSuro6RowY0WHb\nwsJCgB7T+4mJiWzevJkzZ8706b30FoIgED07DN8EL5pzrdTubBoMm48eoVAoGDNmDGPGjMFoNPLz\nzz+zevXqTlk4QS2QME2BxQR79mi5a1YT5hqRxppqQkYE9MsGV6PRMHToUMLDw+WMXFpaGkFBQdx5\n553U1dXxxRdfsGnTJm688UaXSnOCIMi02IHiyiuvxMfHh5dffpnCwkIeeeQRrrrqqvMqHXwh8NNP\nzsAlMTGx2+20Wm2XZYf2mDlzJm+99dYFDxR0Oh21tbWYzeZel//Cw8N5+eWXeffdd8nPz+euu+5C\noVAwyWcqExInYiwzMcHvcgCKyceKhesWXtfpOOdSzKOiokhPT6e4uBi9Xs+iNXMoLCzkszc+R4uO\n2UQCp9r+/A5jm97Jmzg9RVwFeZJXTU5ODnl5eV3ax/dXHXLnzp0MGTJEzrT2Fn5+fphMpi6F6XqL\ni+5pkgb/wsJCl86AN954I8888wyTJk1CpVKxcOFCdu7cya+//kpWVlafeP9qtRqtVktAQADJycmY\nTCY0Gg1ubm6dzispX0liQcXFxRiNRl544QWUSiXffrmP46nHEIM3ceWl09Fr9bSUWUltPYMDUVb3\n6g2ySceBg0iGokLFlTi/5P00Ac76pQ0bShS8JC4D4BWWd3U4GQ6Hg3379rF161ZGjx5NTEwMxcXF\nFBcXy3Qmh8NBfX09BQUF+Pr6EhkZKYum/PjLwU7HtNvttLS0yOI2+fn52Gy2PjVO5eTkcPDgQdRq\ndYeV4KZNm1iwYEGn7QsKCgB69EyQUoxZWVm0trYOqueDydFETk4Omz7aSmlhKbZvNIgOMFB1wSR/\nwZllmDVrFrNmzcJgMLDm1few0cpvHCYuNAGNPZKqQjfmzWsgVCtiPm1DTHYjOy+7z9xtCQ6Hg9ra\nWhwOB8HBwTQ1NVFVVUVdXR3x8fH87W9/o7y8nA0bNvDVV19x9913n1er70suuYRnn32WZ555hhMn\nTrBo0aI+H0NSTZVW/9e1NS5+xI4BS2tLplVO9O453b/fWQdvTxF2hebmZpqamnqcgIKCgvDw8KCg\noOC8eY24ghS89rXk4ebmxsMPP8ynn37KqlWr+Pvf/078xFgEhUD+T8W9Osa5Wir+/v5otVr+/e9/\nY7Vauemmm7j//vvZ+saO7g7Ta4SGhsp26e7u7oN2z1dUVPDdd9/1yHJwhZaWFmprawcsPHfRBQre\n3t74+vrKdJr2GgnS65MmTWLv3r1cffXVeHt7c80117Bx40Y+/fRTVq5c2evapDTJKRQKVCpVt5xc\nh8NBVVWV3Inc3NxMSEgIwcHBeHl58T9THyUsNoQAXSBfbfmKMcHj0GcG4LANbInZmdGhQQDs2OR+\nhZ4giiKpqals2LCBhIQE3nzzTWw2G2VlZTLv2cfHB19fXxwOBw0NDZSXl1NTU4PFYkGpVBIVFYW/\nv79LUZP2dW5BEGhpael1sGC32/noIyf/aN68eR2sdXNzc11qOfQ2o+Dt7c3QoUPJz8/n4MGDPQ66\nvYUoiuTn51NWVkZ9aQMntp4m2T5mUI49EPj5+aHBDQ1uXMpkyoyFZP62EZ2nJz7VI6k9GYx+jBqH\nQ+S370/0q17d3NxMdna2LDADzixDdHQ0JSUlZGRkcMkllxAaGsojjzxCXl4e//znPxk3bhwLFy48\nbyv9pKQk7rzzTt566y22bNnCVVdddUHr8YOJtLQ0eeyTmv66wrl08u4wZ84cdu3aNSB9lL5AFEXq\n6+vx9vbuVyOdIAjccsstfPPNNzz33HMExPlRW1ZHWWGZvI0WHXbs7Nq8m0ghWmbfaNGRQwalFHJy\n+2mKxXzMZjObNm1Cq9Vy++23y4vKwbKcVigUJCUlcfLkSfLy8tBoNL0SqOoJ69ev5/bbb+8Xa6U7\nF+I+HWdAe58nREVFUVdXR2FhYadAAZwTypNPPsmMGTNQq9UsWLCAnTt3cuLECQ4cOMC0adN6dR6p\nbtPd4GU2m6mqqqK8vByr1YpSqZTlhts38p20/yL/bDQaCfEKQ8Qhd4P3ZZVpFk388ssvTJl4BaUU\n8iB/AmAKdQB8BVRSy0/sBx7v9ljFxcW89957BAcH89BDD3Wo7bmqTyuVSvz8/OSUVUlJCZWVlaSn\np6PVauXgqCtnOmnVbrVae0U73bBhA2fOnMHf358///nP8u+3bt3Ktdde2ykwkTzbgU7NTq5w7bXX\n8vbbb7Nx40amTZs2KB3JRUVFFBcX4+npyYvrn0P1H5VMCzRQTfsmpwsNQxslr4QCqIcFivk02OpI\n23Waf7dsYHz8WO5bdA/Pv/lcn49tMplITU2ltbWViIgIwsLCMBgMstJmSkoKZ8+e5ezZs4wdOxaF\nQkFMTAyvvPIKmzdv5qWXXuLWW289byvaWbNmsWXLFkpKSvj+++/7pUjYcfUP0Hul08GCJDg2ffr0\nHu9XafLoTQ169OjRrF+/HrPZfEH0RYqLi2Xa60Ceu/nz51NfX88Hqz/Eu7xr3QRXENv+X15ezuuv\nv87111+Pu7s7lZWVREVFDbogm1qtJiUlhczMTHmcGkiwkJmZSUtLS69ZDudCui8Gmk3948zXu4E0\nkEgrx3Oh0+m47LLL2Lt3L+DkEN99990AfPDBBy77G1xBou24qp1JDoa//vorJSUlCILAkCFDmDBh\nQpeKghL0ej3uaNHgho1WbNj6LCUs90u4oJypcX7pjl402Xh7e/O3v/2Nv//97912T7uCTqcjISGB\n8ePHEx4ejsPhID8/n6NHj1JUVOSS6ijVS9tTorrCyZMn+fLLL1EoFDz66KMd3M5OnTrF5Zdf3mmf\ngoICjEYjgYGBveIjT506lYiICARB4PDhwz1u3xNKSkooKChAp9MxcuTIi7sWLoLxFwuKHz0YZRzP\nBL/LULWqWb9+Pa+//jqnT5/udc3UbHY6rtpsNoYPH05sbCxarZbw8HBCQkJobW3FZrPh5+cnm4pJ\nUKlULF68mNtuu413332XTZs29Wjx3h+oVCq5dLVhw4YBr6L+CEhGbOAMFHqClM3rjfqeQqHgyiuv\nlMsa5xN1dXUUFBTg4eHh0rCoLzCZTAwdOpSnX1jB7rJv+J6dnLWnkSWm40cAHniQyGhu40GSWUgy\nC1EyF4EEIolmEtOYHHYFJ9/O5LLLLmPIkCE4HA45OzzYcHd3Jy4uDrVaTWZmJrm5uf2630VRZP36\n9R2au/uK+vp6vLy8+uUC2h4X5SgnpZTz8/O73GbevHk88cQTzJgxA41Gw/Tp0zly5AhHjhzh7bff\n5vnnn+82im1sbKS6uhqtVtspqpQkeKUGnCFDhuDr69unqFiixNlsNr7++muWL1/OHXfc0etmFKPR\nyOxrZnHddddx5513Ar9T3qxYufqaP7FmzZoej+Pl5TVgq2V3d3eGDRtGdHQ0BoOBgoIC8vPzqa2t\nJSUlpcNkGRISwv33309LUwubv9xCVWOly2MaDAZWrVqFKIosWbKElJQU+bVt27Yxd+5cl9c7PT1d\nplT1psSkUqmYP38+7733Hv/5z3+YNGlSv1PSZWVl5ObmotVqSUlJ6RClD4QCOZg4d+KX7plmzJSd\nKCMrK4u4uDiampo6aDNIz5ErNDc3c+rUKVpbWxk+fHinAC0yMpJjx46Rl5cnN/+eu8KVvqvW1lY+\n//xzVqxYwYMPPjgoqdn2uOKKK9i4cSOFhYV8//33zJo1a9COLV1LCeejF+XgwYNYLBZSUlJ6dW3C\nwsJobGyUXXd7wowZM3j66aeZM2fOeeP7WywWzpw5g0KhYMSIEQMOpgsKClAqlUyaNEn+3TPPPMMD\nDzyAgIANG0bqXewpYsPGGVIZRhKaNjq9v78/AQEBlJWVERYW1i95657g4eHBJZdcQkZGBiUlJdTX\n1xMXF4der+9x3JK0eg4dOiTr3OTk5HQQcHI4HKjVanQ6HUFBQS7HNJvNJvcSdadg2RtclIGC1Ol7\n8uRJlw2N4PwiLr/8cvbs2cM111yDIAg88MADnDlzhtTUVDZt2tQhlQ3OC2c0Gqmrq6O0tBSNRtOp\nO9Vut5ORkYHZbGbIkCFERUUN6IFSqVQsWrSIyy+/nI8++og9e/Zw4403umQFSAPRKMZRTQW1VHN2\ney7P3fUC8LsyXD7OlFb7wUFKfUuUvIE2YHX1WYKCgvDz8yM3N5eKigoyMzNJTEyUr6HVaqWyspLc\n3Fxuuf0WCgsL8fHxwdPTU76Z6+vrWbFihZyuXrJkiXyO5uZmjhw5wqpVq1y+h+PHjwP0aAzUHjNn\nzmTz5s0UFxfL9b6+wOFwkJeXR2lpKW5ubowcObLbjNLFivLychQKBQEBAYSFhcnaDLt27eKxxx7j\n2muv7VSeaWlpITU1FYvFQkJCgsssjoeHB5GRkZSXl5OVlSUzZlxBpVJx8803k5mZyauvviqfc7Cg\nUChYuHAhb731Fnv37h3UQOF8o6mpic8++wyA2bNn92ofh8NBUVFRrzJ44Mx2xsfHc+LEiUGnDMPv\nEuZSUNkbRkZ3qK2tpaamBn9/f9nbA+DMmTOsXr0aI42IODOvGjS8izM77KSS69FSRywzGM/vjYUS\n66ampobs7GxGjhyJQqHos5dHd5DGw1GK8aRMSWTkuBT28wNVhirqKxt47M2/d1JutFgsckN5a2sr\n//rXv7jpppu6zKxLvinu7u4u7byrq6ux2+0EBgYOuPRwUQYKYWFhhIaGUl5eTmZmZid6nIR58+bx\n+OOPM2vWLBQKBSaTiWuvvZY1a9awZs0aKisrmTx5Mg6HA4fDQUtLi/ylaLVahg0b1ukCS6nt8PDw\nQa2lBgcHs2LFCk6cOME777xDcHAwN9xwQ5flABvOUoWqm69oMOxD+wOVSkV8fDytra3U1NRQVlbW\noYNe6ivJy8uTGQoKhQI3NzeMRiPr16+nrKwMf39/rrnmGlmlLygoiJ07dzJz5kyXq5DW1lbS0tKw\n2+0dMhC9eb8PP/ww/+f//B82b95MfHw8l112Wa/2tVqtZGRk0NDQIItu/b8UJEiDXkNDA6dOnSIs\nLKxD5kCv17N48WKuvvpqNm7cyLJly1iyZAljx47FarVy6tQpLBaLLPncPhZUOAAAIABJREFUHv5C\nkByYFjnynXa+AQH4+Ph0ukZxbWZL7SWeX3nlFd5//31OnjzJvx/5DM82DQOJIdTfAXvy5Ml8+OGH\nnD17luLi4j6bv3WF881mWb9+PfX19SQmJnbSNekKUrawt4ECwDXXXMPHH3886IGCzWbj9OnTNDU1\nERUV5bK/rC9oamrizJkzqFSqTv1II0aM4JVXXmHLqztobOvd6tzcLaIlGHc6r6a9vb0JCQmhoqKC\nnJycXmvw9BWiQ+TUD+lYz0JgvB9eiVqiRkRQUVHRaVtpIVVSUkJqaio+Pj5MmjRJtmcH50LKaDRS\nX1+P0WiUS36uIJVW+kpVd4WLMlAQBIGxY8dy+PBhTp061WWgoNVqueKKK9i9ezcRERE0NjYSFBTE\n3Llz2bZtG3v37qW1tZVJkybJHH9vb2/0ej3e3t6dMgV2u53y8nI8PDx61SjXn881ZswYRo8eTWpq\nKv/85z8JCgpi+yP7cUfLU7wBwApCgI9RUoaVpSxv+5o+40MA3l7zNrt376ay8ve0fjzd863Px2cZ\n3ia6JKXw2mdmgoODCQgIoKGhgYaGBoxGI6WlpXzwwQeyk9uNN94osy9EUaSqqooDBw7wj3/8w+U5\nMzIysFgsnXwfeoPExETuuOMO1q5dyzvvvNMrmeWGhgYyMjKwWq2EhoYSHBwsZ6Tsdjs2mw273S7/\nrNPpCAkJGVRN/cGCJODT1eCt1+u5/fbbqamp4bPPPuPrr79m6tSpaDQaYmNje+xvEQSBYcOG9ek9\nabVaHnnkEfbv308FrxHBENzpu7bDuZBcJ/fs2cN3333HbbfdNuBjnm+cPXuWXbt2oVQqeeCBB3qd\nxexPoBAVFSXbiveXInsu7HY7WVlZNDU1DcoiSyr/OhwOUlJSumy+zCQNcJZja6jkOZxl5K9opRIj\nbgiE48DugiEWFxdHc3Mza9eu5ccff8Sfjj4QElV2Upv7Z01bo/C5zp8FBQUoFArCw8PlyX5im+HU\ndJzH0FS6QSVYf7RgdRe46927sTvs7ErdQWlpKUVFRWRkZOBwOEhOTiYsLIwJEybg7u4uGxjW1tbK\nfTeCIBAQEEBoaKjLbAIgN1P2RTKgK1yUgQLA+PHj2bZtGwcPHuSGG27ocru5c+eyfPlybr31VhwO\nB9HR0Vx22WUkJyfz/vvvs3//fmJiYrj22mt7PKfEN21vlnM+IAgCl1xyCaNGjeLUqVN8wL+JoT0D\nwYEkPK7AD+g4CEgru/aBwh8BlUpFYGAg5eXlLv0U2jMoioqKWLVqFQ6Hg/Hjx7Ny5Ur0ej1Wq5W8\nvDzKyso4cuQIV1xxRZcr9oMHnToO48aN69f7nTdvHpmZmRw8eJBnnnmGV199tVO0LVG6zp49S1pa\nGmVlZZjNZiorKzEYDL06j7+/PyEhIR3+SIGGZHN+ISG5+Xl7e/eYCg4ICOChhx6SrZ2nTZvGFVdc\n4XJbfwI7Da5dIbtN/vjcHooVwpsAeLCCk+wFEnmBPyMgyDbN/SmjzZw5kz179rB//35uueWWi5oq\nabVaefddJ7ti4cKFfeLf9ydQAKcezbvvvivrwAwEUrnWYDAQFhbWK8+O7iBlJqxWKwkJCV1OhOfi\n3IzC73LZrt+LRGc0GAxMmTKFow3HyDvbdV+cK7S2tvLss8+SnJwsL3gCAwOppgIlSs6ShoAgZ4aN\nNNDY0kAF5YAgL3ITExNZtGgR7u7uGAwGnn/+eaKjo+VSq7u7OxqNhqCgILy9vfH29u5xQXL27FnA\nNbutr7hoA4Xk5GR0Oh1FRUXdRr5arZZp06aRnZ3N0KFDKS0txWAwMG7cOJmj/7//+79UVFRw1113\ndftQSA9bf62Vu4LUBa7RaDo0s0xWOGuzt3AvACtoo3O93gCfaLFrgmm66Qqee7Sg7XVnU+Pfr3+M\nS65J7pC+OtImDdsVWltbMRqN6HS6QU2d+/v7U15eLqfmXeHUqVO89tprNDY2kpyczNNPP41Wq6Ws\nrIyysjJMJhNarZbU1FTeeOMNl8ewWq2yWl1/TJHAGaA99NBD1NfXc+rUKZ566ileeeUVgoODqamp\n4dtvv+Xw4cMUFRVRW1uLWq3Gw8MDtVpNRESEvLKRfO6VSmWHn41GIxUVFZSXl5OTk0N6emd+tq+v\nL3FxccTHx5OQkEBcXNx5aaZqj7q6OhwOR5/ua0lpMS8vj+eff54HH3yw1wN2f6DCD1gKfM8BTsur\nuP4iISGBiIgISkpKOH78eL+Dy/MNURR55513KCgoIDQ0tNtFkStIgUJdXV2f9hsxYgSjR4/mk08+\nGVDGRepJkLK5w4YNG1CQIAUdkgNrTw2doihyyy238Omnn/Ier3KH/kHUOhV/Joo9NSYqbFYCaeSH\nNl+Hc+nkarWaNWvWcPLkSebPn9+h2XwyTtZJeZuz6FBZtt1pD50qHqWlpYWoqCiWLXMK30nS+IdX\nnMSGTXYlVaJABLzwZQQ+5JOFgHM8cjgcVFZWyuq4DoeDiooKwsLC8PHxkQODvvQZOBwOsrOzgf/y\njIJKpWLSpEns27ePffv2dUsRmTNnDsuXL2f+/PnU1tbKaZzhw4fz8MMPs2bNGrZv305FRQXLly/v\nUrpWEAR5IhgsFBUVUVlZKTceSqY43abOa52ucfi4ri1JhlCu6lznQqJ5VlZWyha2w4YNG7Ruc2ky\ndUWzczgcfPXVV/znP//B4XAwduxYnnjiCVQqFenp6fJkPGzYMDIyMpg8eXKXk+axY8cwmUzExMQM\nSPFMo9GwYsUKnnvuOc6cOcOjjz6KSqUiNzcXu90upxAnTJhAUlIScXFxxMTE9Jl3LmUmKioq5D/l\n5eXk5uby66+/cvToUXnb8ePHExUVxcSJE4mLixv0bJYks92XQMHDwwO9Xs/06dMxGAw8++yz3Hzz\nzYwfP17eJktM7/WkcO79IQ22+TwKgPH1cTj70Mby3X0/8R07uIfF+LqoL/cGgiAwY8YM1q1bx4ED\nBy7aQOGLL77g4MGDaLVannrqqT4H8ZIMdlFRUZ/PvWjRIl566SX27dvXZ80JURRpaGggKyuL5uZm\nwsLCugwSRFHk9OnTeHt7d/vsSpmEhoYGgoODe+3AKpWVZ86cycgo56To7vDA85AGtzIVWo0GRaEC\nh901RVGr1ZKcnExqaiqnT59mxIgR3bIEvPz1JF0+gurqaqxWa4cFkkKhIDg4GH+cvTjROMtxUkah\nmWbgd9p7dXU1BQUFmM1mBEHAx8cHQRBITk4eUA+JpGEhNZ8PFBdtoADOzl8pUFi6dGmXE7i7uzvT\npk1jz549LFiwgODgYDIyMigrKyMoKIiVK1fy6quvcuzYMR5//HEef/xxOUMhiiJms5mmpiYqKipo\nbW0dtIHa4XBQUFCASqUiNjZWTmGnp6d30MAX23QRmNj2+WoOg7IZoqPBy4pkgyrZoqpQIwgCJSUl\nWK1WNBqNy4naarXK1sefrP2M4vwiJoycyAHtQX79LBVjVROp/Dogap9CocBoNCKKIm5ubuj1erRa\nLY2Njbz55pscP34cQRBYsmSJ3JOQmpqK0WgkICCA+Ph4lEolb7zxBitXruzyPD/84MyY9Deb0B5a\nrZZnn32WN954g6+//pry8nJmzZrF7NmzmTlzZt89611AEAR8fX3x9fXt1GNjNpvJyckhKyuLrKws\ncnNzOXr0KBs3bsTX15cJEyYwceJERo0aNeCgVRRFDAYDHh4efQp2JDpwQ0MDFouFJUuWsG3bNo4d\nO8Zdd911nvswkoAQfmMlKfTsCtsVJk6cyLp160hNTe2SPfVHYtOmTXz22WcIgsCyZcv6ZU0eGhqK\nm5sbNTU1NDU19YlloFAoWL58Of/4xz9obGxkwYIFXQZ+oihitVoxmUyYTCZqampobGxEEARiYmJk\nrRJXsNvtGAyGbrO5FouFtLQ0TCYToaGhXXolnPueKisrqa+vZ/jw4dx00//H3nmHR1Vtbfw3Lcmk\n955MQhJCCRCaNMFLb4KIlHvFhqJYrqJIVxCVIhaQD7kKFxVUBETggoIIgqAoAQKhhZJCeu9lMpOZ\nzMz3x3CODEnIJCRS9OXJ82gy55w9M+fsvfZa73rfh4mIiMDe3p6pk54lvTgdvUJHYecMPti4hKCg\noHrvAycnJzp27Mj58+e5cOECERERIqE8jGrzGDG3/ObJsukb0ZPKykoKCwvrJAsWXeUzBGPmuumv\nSu9fuuojkWFKJTs7mwsXLiCVSgkKCiIoKAiFQsGZM2duulxw+fJloHnKDnCbBwqRkZGoVCrS0tI4\nfvz4DQ1NRowYwcyZMxk+fDh2dnZ07NiRy5cvU1BQQGBgIO+//z5vvfUWKSkpTJs2jSeeeIIePXqQ\nnp4uOh5WV1eLC1dzQCKRIJFIcHBwECN/Hx8fzp49S3x8PLYOtlSrq2sfWJQPHl4Q2KbO80qREhwc\nTE5ODikpKfXeDEIHR2BgIP/7eicmkwlFhj29J95DQJQvlw4m3fR7tLOzE7+jy5cvYzQaKS8vZ/Pm\nzZSXl+Ps7Mz06dPF6DgxMVEcU6tWrZBIJBw5coT27dvXK6Gdl5fH8ePHkUgk9dbLGwt7e3teeeUV\nMjMzSUtLw8vLiwcffLBJRklNubZgrQvmifTixYvExMRw9OhR9u7dy969e3F1dWXw4MEMHTq0yQzy\n8vJy9Hr9DY8XdvcC3yCTVAAu6M9QWFhIQUEBJSUlDBw4kIsXLzJr1iymT59OcHBwk01uhG6Jiqvt\nbLhXge1VoTT3YMCV+4qH8RsHOHLkSJ3iWw3B398fT09PCgsLSU9P/1M9Dm4Eo9HI559/zq+//oqN\njQ1PP/10kzMeUqkUlUpFQkICqampjTYNsrW1Ze7cuaxbt46VK1cyadIk0V5csBivqqqiurrawi9A\nIpHg4+NDUFBQg6Uz4bj6gt6qqirOnTuHVqtFpVKJ3jI3QllZGSkpKZSVlYl6Ap07dxYzFrG/n6KK\nSkxSE97OqRiNRpKSksjJySEiIqLOucbZ2ZlOnTpx7tw5EhIScO1pT2lMbX0KvU4vvi+ByN1Y6PV6\nUlJSsLGxoUuXLhaZpJSUlJvuGPlLBQoSiYRhw4axZs0atm3bRs+ePevdFdja2jJw4ED27NnD2LFj\nkclktG3bFrVaTXZ2Nvfccw/vv/8+a9eu5eDBgyxfvpzAwEAmTZpESEiISPRSKpXNRjYTgoSKigox\nknVxcSEsLIzExESU7eQkn7iMDnObozQmFhNqTHaZILGBAwGAjqXEAbCTnYA55fvRRx+RmppKYmJi\nnTdDH/kAhjw/AI9KH/Z9fpxXrtoOx+fZI62Q4eEYgDedeZEhN0Uak8vlqFQqPD09KSgoYOPGjRw8\neBC9Xk/Hjh1ZtGiRmMarqKigsLAQd3d3wsLCAPOuYMeOHcydO7feawjOkv/4xz+alT/i7OzMO++8\nw6xZs7h8+TKrV69mxowZzXZ+ayGTyYiKiiIqKoqnnnqK1NRUfvvtN3766Se2bt3Kt99+S9euXRk2\nbBjdunVrVCAr1K6bkn6Uy+UiGVOtVov93KGhobz//vuMGjWKQYMGtRg50w4l/RjKvn37KC0t5f77\n72/U8QJp+KeffuL06dO3RaCQnZ3Nhx9+KLb9vfrqq00Kgq5FSEhIowMFoTRWUVFBRUUFHTt2ZO/e\nvSxatMgisyCXy5HL5Tg6OmJnZ4eDgwOOjo44OjpaXTMX5uy61AnLysqIj4+npqaG8PDwBrswjEaj\nqOEiBCve3t6i/oCAIlM+M2fO5NKlS7z33nuEh4eTlpZGVlYWp0+fxs/Pj7Zt22I0GunPCMDsDwFw\nwukwY8Y9gNu97px3PIf2Zwkmg4lEzJyjS5nxxMbGIpVKycnJ4b777qs1TsFkrLXE7D2hwdxaK8yx\nxcXF1NTUEBISUqvclJqa2uh7/XoIgYJgjHezuK0DBTC7p23dupWEhAR++eWXG6aehw8fzowZMxgy\nZAiOjo6i7PLFixfJyckhJCSEV155hZCQEP7zn/+QmprKxx9/zIgRI5gwYcJNi4PUBRcXFyoqKlCr\n1WIty8vLi8TERLz86qqDXRXXkIcD9T+IQtRdn3ql0tkOha0cbbyljK1rqARbJwm5sc0noStkBTZu\n3EhFRQWOjo507tyZAQMGWOw4s7KyAEvXxyNHjhAWFlZvTfDKlSscOnQIhULBI4880mxjFuDh4cFb\nb73FtGnTOHz4MAMHDmyUmFNzQyKREBoaSmhoKP/61784ceIEP/zwA7GxscTGxuLp6SmWSKwJmgSt\njWvrqEJg6H61jjoW8+d6ETMxLuqqNPhcibkNbKlpFg4ODrRt2xZnZ2eSk5MZN24cJ0+e5PTp07zw\nwguN5nAI5NtFTADg9SkhEGjeDfYoNksYx2H2T9m54GtWrFhBaWkpkyZNqjcwETIj9thz1HQIgI4d\nO/LTTz9x5swZxowZIx7b1ExIU2E0GtmzZw/r16+nuroad3d3ZsyY0Sg9kPoglCwEzZKGxpGXl0d6\nerqF9bBSqWT8+PFs27aNtLQ0HnzwQWxtbbGxsUEikWAymSgqKiIxMZGEhATRGEwIApRKJSqVip49\nexIVFWWRPagrUDCZTKSnp5OWlia2WjckyX5tVsDZ2Znw8HCcnJzEhfZ6yW7hehKJRCz/+vr6kpCQ\nQF5eHkOGDOHHH3+sZQ1fWVHJt5u+ZdTYUURFd0DmZ8PZ7y9wVa7B4rzJyck8+uijDX7u18PW1ha5\nXF7nmJOSkm4qqK2oqCA9PR2FQtFsbf63faCgVCp59NFHWblyJRs2bBAFKOqCjY0N48aN44svvuD5\n558HEFNMgqRsXl4e7u7uzJkzh9jYWI4cOcJ3333Hvn372LXtO2TIGIA5mutAV7Ixk4S+vWr001jR\nFSGVXV1dLU7WCoUCuVyOj6MfgYRwihgAZtKV3/idPG0uXbQhhPADAJ/xBWDpcibcAEKvrACh93eY\nyxg8sONrXDmIHSjNpJquUcU4OBewoyCQdBzowe/0bCLDvLy8nP3797N3716RWNmhQwemTJmCr68v\np0+f5tKlSygUClxdXdHpdEilUvFzUKvVbN26lcWLF9d5fpPJxPr16wFzG+zNpuPqQ0BAABMnTmT/\n/v3s2rXrlgYK10Imk9GzZ0969uxJTk4O+/btY//+/WzcuJHNmzdzzz33MHz4cDp16lRvpk0wAGoO\ngq6gaCeVSklMTOSBBx4gMzOTefPm8cILL7SYaI2w8/7kk0/YtGkTDz/8sNXHClygc+fONdpvpTlg\nMpk4ceIEX331lRjU9+/fn6effrreLqHGoiFvHAFarZb4+HgqKytRKpUEBQXh5uZmkR2IjIxk4cKF\nJCQkIJFISExMFMnQnp6eRERE0K5dOx544AHRP8BkMqHRaEhKSiImJoZPP/2UgQMHcv/99yOXy5FK\npdjZ2VFaWiqqDl68eJGysjLs7e1p27atVZs0QdzN19eX1q1bi/f89XbSAoQF/dpnw8HBgU6dOnHp\n0iUGRw0j4GI4oWlmDpFAEo+mO2hh7ZYPuOfe7sx6cwYjnhkiBhqCkFFNTY1Y9qgP9TlTCutCcXGx\nBW/i/PnztGvXDrlc3uSgNjY2FpPJRFRU1E0rMgq47QMFMJujfP/99yQnJ/PFF1/w9NNP1/vavn37\ncvDgQS5evEjbtm0tolm1Wk1CQgI2Njbcc889DBw4kIkTJ7Jx40ZiYmIwUIOBGi5xFg+80VEHf6CR\nELodrt1xCbU/dZll/asKNblkIkOGirAbnjciIgJbW1tSU1MpLi6ulVo2ac03l6Oj5U12s3wuQanw\n4MGDHDlyRIyI/fz8mDx5Mj179hRv8KioKM6ePcuFCxfEdtVr0+ZfffUVY8aMqXfC/OGHH4iLi8Pe\n3r6WHHdz44EHHmDjxo2iS+jtJprk5+fH448/zsMPPyzyGI4ePcrRo0fx9fVl2LBhDBo0yKL2qtPp\nqKqqEjtchO9FSLUKnIRfME/4BwOvpsCv3kodzh4F4KzEvOsX0ql+fn6kp6eTm5tLnz59iIiIYMWK\nFfTu3btOx8+6IEx+QnZjHmrkmebp6AJnrg7D/Lc5kmUAJHGBy5zHw8PDaoljd3d3VCoVq1evRqFQ\n4H71Pbd0ZsFoNBIXF8emTZvENLCHhwdTp0618CxoDgiBQmpqar1kPZ1Ox/Hjx9FoNPj4+ODn54dC\nYSZFC4x7qVSKRCJh/PjxLF68mMGDBxMeHs69995LQEBAvd+r0C0m8G50Oh27d+9m/vz5zJw5E3d3\nd3x8fEhLS+Po0aOiUq6fnx9hYWFWl9LS09ORSqUWQQL84YxoMBgsvlfhu73+85BKpeaNS0ENdlIl\nxqvL4MGrYk0DrmbUOhi6ojlstHCDzMzMRKPRIJfLSUlJaXJngtBdterDVTz1yBSqE83XLCCHX8+Z\n9WKUNM3h88QJs7Jpc3b63BGBglQq5fnnn2fWrFns2rWL9u3b10tslEgkTJ06lffff59ly5ZZRJmC\ni1ebNm3EiC4kJITXXnuNhIQEDnz/MzXoMWGikDx+YBtueOCJDwGosG2kapzRaKSkpESMqAVkZ2fj\n4ODAkaTDJJEkejgcZi86qjFhYhWL0WAOJOqazGxsbOjQoQOxsbGcOXNG1MvvcVURbE61F1NK9fj0\ncQCXVlwl35JfYQuuoFKVkZ4u4x6qOXGVvFYXDAYDGRkZnD59mri4OOLj46murhY/627dujFixAi6\ndu1a64F0dnYmLCyMhIQEUlJSkEqloj55cnIyGRkZouvn9UhISOC///0vAC+88MJNG1s1BBsbG7y8\nvMjNzaWgoKDZFOuaGwqFgn79+tGvXz8yMjLYu3cvBw8eZP369WzcuJGBAwcybtw4UUVSIpGIu7Xr\nF2ePq5mk+KtmOYy7epGrsca5s2Y29wgsJ0OJRIKXlxeZmZlUVVXh5+fHkiVLWLduHR999BHPP/+8\n1ZP/tbwYQd75MczZQOerA5FcNbkNIhR/ghk2bJjF+xFwxnScuhAVFcX999/PE088wUMPPWTVuJqK\n3NxcDhw4wIEDBygoMDPfXV1dGT9+PMOGDWuRANTZ2Rlvb2/y8/NJTk6uN7Pj6uqKVqsV1VKvhb29\nvYV3jODrIpVKKSgooKioSORyNQQbGxsefPBBwsPDWbhwIS+88ALh4eGixoAg197Y7iKDwYC9vX2t\neUa4167PKAj/f/3rTSaT2RDQyw5tUcP23O7u7nTt2pXU1FRqamqQy+WEh4ezfPlynnzyyUa9BwGC\npYBMLkNdWYkcJSZM6KkRvY6aAkHmHv6CgQKYRSMmT57MunXr+L//+z9CQ0Pr1bD28/OjV69e7Nix\ng759+2JnZ4fBYKC8vJyAgIA6hWNat26NEntMmOhCTzJIoZRiCsmjkDzyyAbg2WefxdfXF29v71o/\nrq6uFjdlbm4uVVVVYroW/mDKymQyriReEV9rxCi20AgpsIYQHR1NbGwsp06dqtNYJy9PQmSUHjuZ\nAe3Vc2ao7dEYZERFVXLihAtozdc2UENiYiJ5eXlkZGSIP1lZWbXqaKGhoXTv3p0hQ4Y0WA7w9fWl\npKSEvLw83NzcsLW1pbS0lLVr1zJt2rQ6dykVFRW888471NTUMHLkyGbrdGgIQqCQn59/2wYK1yIo\nKIinn36axx57jCNHjrBr1y727t3L/v376d+/vzhRtESQJSzSwiQtl8uZOnUq27Zt45133mHGjBkt\n4olx7bORkpJilYWx0HGUnZ3dbOPQ6/Vie1x6ejoZGRnExcVx5cofz7SPjw/Dhw9n5MiRtRxqmxvd\nu3dn9+7dHDt2rM5AQTDA8/f3R6/XW7gQmkwmJBIJNTU14vc6atQokpOTGTVqFFVVVajVak6fPo2n\npyeRkZFWlbI6dOjA/PnzWbp0KZMmTaJr164WNXMhYI3ATPgTum3qS9cbDIY6U+nCWIT3JUCYt64P\nzoqKiqioqOBw7M+cKI/lEcxdC/2uZhSqrmaSM6/ZQNnY2FgIFwllpKb4iOj1emZNmoedoy1ehgCG\nl02kBgNZpGHPH+Z5Tcl2nT59mrKyMrp169aszqx3TKAAMHr0aOLj4zl69ChLly5l2bJl9bazjRkz\nhtmzZ+Pj44NOp0Or1aJQKG7YqyzoCQhM1XZEmyU3KSWFBIyYyMrKEkl510OhUODl5YWjoyNGo1Fs\n3QkLC8PGxobS0lKkUilFRUWoVCq+3b2VsLAw7O3tWbt2LadOnSIwMJDVq1db1fPdrVs31q1bx2+/\n/cbo0aPNvceYKKcMSOb8+XQqTNXYZruj3XkFbIIx5hXyrY+WDoGlRHtX8X16Hu3vNz8A06dPr/M6\nXl5edOjQgc6dOxMdHd0ob3OJREJISAhFRUUUFxej1+vZsWMHnTp1Eifwa1FTU8N7771HQUEBrVu3\nFi22/wwIOxxBoOhOgdDxM2DAAGJjY9m8eTP79+/n22+/JTo6mpCQEItgQdjFj5c8YXkige5i1oTB\nB/PzUELtz0On06FQKCwmbolEwrhx4zhw4AALFy5k3rx5jarDC8+fQEq83r9EMIsymUxkZGSwYsUK\n3nnnnQZ36UL72oULFzh16pRorFNVVYVOpxNT4TKZDK1Wi9FoFDNf1dXVVFZWolarqaiooLKykoqK\nCjGrJkBoEba1taVPnz4MHDiQqKioP027oUePHuzevZuYmJh6Sb9KpdLq9l8HBwfWrVsnBh2VlZWk\npKRQWFgo2mBbU//28vJi4cKFLF68mKqqKqvNruqCra1tnQGX8Lvrv5P6AgWh1TM7remB49atW5kw\nYUKjj9Pr9cTFxeGl8iD9XCYlP+kxGUwYMJBKEgGoOHv1Pm8KDh8+jMlkuqmsRF24owIFQYI3LS2N\nlJQU3n33XV5//fU605xyuZwpU6awevVqRo4ciZOTE5GRkY0idyhQ4I4n7ngST5y5JLBqFQUFBeTl\n5ZGfny/+5OXlUV5eTnZ2tiiXLJVKcXBwICsri8uXLyORSPDz80PWvLAtAAAgAElEQVSv15OUlMTP\nP/9MYGAge/fuBcw7v27durFhwwacnZ1xcnJCJpOJk41UKhV3AEajkcrKStzd3Tl58iRjxoyhU6dO\nnOI8DjgCRgoKjEiSJdSUy6A8AaQKwI+8PBu8JFqk9hJsfMwyxIJUqIeHB8HBwaIASGBg4E1rC9jb\n2xMZGUlCQgIlJSX8/vvvfPzxx7VeZzQaWbVqFXFxcTg7OzNnzpxmI+NYA4GUpFa3rEtgS0EikdC9\ne3e6devGgQMH+Oyzz7h48SIvvvgiffr0YeLEic3WIigw5uv6fgYOHIiLiwtvvPEG8+bNu6HKXVMR\nFBTEwIED2bBhww05S8Jrg4ODOXHiBBkZGahUqjqVDL28vMRyQUOQyWQ4Ojri6upKcHAwbdq0ISQk\nhDZt2twSfosgLy60AN5sRszLy8uiS8HR0ZGoqCiSk5PJysri/PnzREdHW8VHcXZ2ZuHChSxduhS1\nWs2k4Y8BMI4nAHC/qr4ptCfWBWG+u1GgoNFoLH4vENivv0eF72fLz5ssNo5C6UtoZayPuH7+/Hmq\nqqrqNSu8ESaNeJTI6NY4/OaF69EgZFfLasc5QgDBpHC5ybwZrVZLTIyZGN/cWdg7KlAA82S+cOFC\nZsyYQVZWFmvWrOG5556r84Zt27YtQUFB5OXl0atXL6t3wnWnvtaL/1XfZKvVarl06RIXLlxAr9cT\nHByMra0t+/bto6CgACcnJ/r164erq6vIni0vL8fW1las+wptcHXB3d29ljGRsPspLi4mLS2Nckow\nYqALkXjZeOPU3oMzZ3RUGHQYDQac0dCTHhzM/557h/bhqZefxMfHhzZt2rSofbK3tzcSiYRvv/2W\nxx57rNZkajKZ+Prrrzl48CB2dna88cYbzaKQ2BgIhNNra7W3G65P1wq93dfW+iUSCd7e3kydOhUn\nJye2b9/OkSNHOHLkCD179mTixImEh4eLO/SX6AHAa3tOA9AKcxDw6FV1uU/55uqZze2SRqORqqqq\nG7K9u3XrhpOTE2+//TZz5sxplNVtfVyD6zFixAjefvtt4uLibtip4uHhQXZ2NuXl5QwaNIiAgADa\ntGmDvb09tra2SKVS0QZdIANe6+Xh5OQkagc4Ojri5OSEnZ3dn27udSPI5XIGDhzIL7/8QkxMzE1z\nMaRSKaWlpRbEXolEQlhYGDU1NeTn51NUVGR1EKhUKpk/fz5LlixBjw4FjQumBNGmujaFjc0oeHh4\n4OzsTEJCAjk5OYD5nh77yBhc3FwwSY1IJFKOHz8uft+enp4olUrUajVr165lwYIFjRo/mOeVyOjW\nFGQXoI/549nRUEUumfTkH6SQcIMz3BjHjx+nurqaNm3aNHuH2B0XKICZg7BgwQJee+01fvjhBzw9\nPetNA7300kvMnTuXwYMHNypl3lgYjUYyMzMpKytDpVLRvn17XF1dycrK4uzZs3h7e/PGG2+gVqvx\n8PCwEEaZN28eaWlplJSUUF5eLv5UVFRgMBjEDILg1SAoPjo6OuLs7IyzszM1NTX4+Piw+J4PUaDA\nmX7YGMAxxIGSEim2ia25yDYgnWIKkRplxPx4nIUfvEF+fj6xsbGoVCr8/f1bLF164sQJIiMj6xQo\n2bZtG9988w0qlYonn3yyWYxMGgtXV1dUKpWFAt2dCEFZz8vLi3bt2tGlSxcuXrzIli1biImJISYm\nhm7duqGjGhsaHxyWlZVhMBgaNImKjIxk2rRpLFu2jIULFzb78yeRSHjxxRd54403aNeuXb2BriBu\nc88997BkyZJmHcPthE6dOvHdd99x+PDhZiFtBgcHk5aWZsF5EHQ+SktLSUtLa1S2SKFQMGfOHL5c\nuBkf/PHFHDwarwoZCDv5uiAs+nVxIxrKKFwfKEilUiIjI7G1tRU7PiQSCdNmv0RNTY0YAGo0GgoK\nCkQ/hqCgIDZu3MiECROalCVTq9XIkJF2LhOTyVySq6ScMxynkFz2sq1JoncCBJn7uubXm8UdGSiA\neRJ69dVXWbp0KV9++SUeHh4MHDiw1uuUSiUvvfQSK1asYMmSJS2SFtRoNFy8eJGKigqcnJxo27at\nmK7fuXMner1eFPIRHBCvha2tbbMtjIqrIk2t0KJQSnFHgb9WTzEVhNKVT/mFLNLILMrA3d0dk8mE\nh4cHycnJouKZSqXC3d29Wa15U1JSOHjwYJ0T9Q8//MCGDRuQSCRMmDCBLl26NNt1GwMbGxvS0tJu\nSZDSEOwl5h3Iq7wNgM1V/49+DAH+4NUkmOIpLi62aIsEc3ZN6I/fsmULx48fp8P9benUqRNbF6/H\nDiWzGQ4gaod8ym6gtmKnwLWxxk2yVatWPPHEEyxdupSFCxc2u0S2m5ub2D5d3+Io2LG3lA7H7YIu\nXbrg6OhISkoKqampN11m6ty5MydOnKhFjhQ8XQoLC9Hr9Y0qD9ra2hJGWxKJJ49sfKgtf6zT6UTp\ncTAHJ5WVlbW6xwQI99S1AlJGo1Fc9Ouax+zt7a2q4+t0OsrKykhOTmbFihUEBAQ0uZtAJpNRXlyB\njZ358zJh4jLn8cGfMqyzsK8PeXl5nDx5EplMdtNKn3Xh9nJJaSR69eolttetWrWKY8eO1VnfadWq\nFQMGDOCzzz5r1uvrdDqSkpKIjY0V/Quio6PFG9dgMJCZmYmdnR1jxoxBJpOhVCpZvXp1i6UtlTig\nxAEjcmTeCoqQElukwA47/AjAjyDsceTnn38G/khTd+/encDAQKqqqrh06RKxsbGkpaXVSuc1BRqN\nhpUrV/Lyyy/XCtSKi4vZuHEjAM8999yf1uFQF4TSw53KURAgyNvWVbpp3bo18+fPZ+XKlfTp04cz\nZ86QRjK5ZFGNto6zWUJoLRNS8tYgOjqaESNG8O6777aI6NGIESM4fPhwvd9bQUEB/v7+N+U6eidA\noVCIu8mdO3eKvxd2zI1F9+7dxZ786yGUna5dnK1FCOH0YyinOUEJRZgwIXEA73+40mVKe44ePUp8\nfLxomHb58mXS09Oxt7evs4OnrozCtWWHm5lrbWxscHR05NChQ4SGhtKjRw9Onz7dpHnRxcWFsZPH\nMPf/ZrFD8SVf8TEn+Y2D7CbDlHpT2YQdO3ZgMBjo27dvi2TO7+hAAeD+++/nwQcfpKSkhHnz5rFv\n3z4SExPFdKOA4cOHU1paWueOvrHQ6XRcuXKF48ePk5WVJdqUhoWFWaTtU1NTOXfuHK6urmJ07+zs\njKOjY7N6FtQJCSjbyTAaoCTrj+DJFXdssK2l6ChInPbo0YPg4GAMBgOpqakcO3aM+Ph4CgsL69Rq\ntwaffPIJo0ePtuhy0Gg0XLp0iZdffpmYmBhcXFwYPnx4095rM0HQG7gdAwUhAJRf/XcIOw5hJ/6L\noD0RtEej0VBZWYm3t/cNW/JatWrFnDlzWL58OU/NmEzU/W3ImXiR3jvb8pl+JV+YVtc5eVVVVaHR\naHB3d29Uieq+++4jMjKSLVu2WPy+qYvYtbCxsWHUqFFs3769zr9nZ2eTnZ1tVQbkTocgeHX48GHR\n56OpUCqVeHp6kpmZWetvMpkMe3v7Js8JttjRl0Ecl/yKpmMJXg860aZba6QyKV5eXoSFhdGuXTsi\nIyNp164d7du3FyXE6xonWAYt9REZG4vk5GTmzp1L9+7defnllwkPD0er1XL+/PlGB70ymQyVSkVM\nTAyugc7YSJqHE1ZSUsK+ffsAGDduXAOvbhru2NLDtXjkkUd45513KCkpYefOnUycOJHs7Gyxju/i\n4oKzszNTp07lzTffFFnQjYFGo6GwsJDCwkLRVlmpVBISEoKXl1edk11KSgoSicQile3l5cWECRPq\nFRq6WexmKwCvtG+NNECGPKaKrhVaCikH4CJn0KHj6NGjlJaW1oo+bW1tUalUBAUFiZKpwvuWy+V4\neXnh5eUl+qY3hL179yKRSBgwYABGo5GioiJycnLESaysrEzUt7jVEHZJFRUVt3gkTUdeXh56vd7q\nGmpERATLli3j0KFDrF+/nv/+97/8+OOPPP3000RHR9d6fWlpKba2tk1adCdMmMD8+fNF1dTmxIAB\nA5gxYwajRo2qdU8L+gmNIVTeqfDz86NHjx7ExMQQ5K7CFjtGYFY1vbY8ZS169+7NkSNH+Oc//2nx\ne5PJ1GTSb/HVtltvhQ8PjHyAHzO+p7pUzfz354ik58ZACIivDRTqIzJaC6PRyM6dOzl69CizZs0S\nW2wDAgLQ6XSkp6dz+fJl2rdvb/U5CwoKWLduHQaDgf/7z0p8fX1p3779TQczu3btQq/X07NnzyZZ\nlVuDuyJQELQKzpw5Q1paGlVVVbRv315chK6d+Pv168fs2bOZNm0aPj4+2NnZIZPJat2cWq0WjUaD\nVqsVbVbBTIRxd3cXF8wb7apSU1NFprAANzc30XWsOVoP64KtnS3u/Rwwak1UnrRMkUmQIEWKyWTi\n8OHDPPDAA3WeQyqVikJSGo1GbAPNyckhJydHVDJ0dHTE3t4epVJpoU8OEBcXx759+5g2bRqXL1+m\nqKhIJGO6u7vj7++Ps7Nzs+wqmwNCTT8rK6teKdxbBUFjYORVOeXe9ASgDPOuJoZD5tcVFSGXyxvl\nFimVShkwYAC9evViy5Yt7Ny5k/nz59OrVy+eeuopi9p+QUEBOp2uSYGCVCpl2rRpLF68mCVLluDg\n4NBsEsoymYyhQ4dy4MCBWlwFQfekKXbAdyIefPBBYmJi0KNrEln1WvTu3ZsZM2aIJlECbGxskMvl\nVFZWWqXWeD2kCineDzlhG6jgnsp72bP7eyZcfrBJPJK6Sg/1ERkbgslk4tSpU2zZsoX27duzZMmS\nWgTKkJAQUWxLrVY3aLNtMpn48ccf2bt3L1OmTKFdu3YkJyeTnZ1NXFwckZGRTfoMwaxvsWfPHqDl\nsglwlwQKEokEZ2dnAgMDMZlMfPbZZyxfvpwOHTpgMpmorKwUgwZHR0eGDh3Khg0bakXJdUEg0Hh7\ne+Pp6Ymbm5tVqmSCGYzRaLRQIxNYw+fOnSMxMbHZRVmKTPlcvnyZ3NxcIiIiCFgdII4HYAULiYmJ\nYfHixezYsYPevXs32IYoOMMFBwdTWVlJfn4+BQUFtYSn5HK52GJWVVXFxx9/zKOPPiq62gnZCl9f\n31oPd0u2ZloLoQ2qsLCQnJyc2yLL0RjYO5hTwR4eHk26p5RKJU888QSDBw/m008/5ejRo8TGxjJ2\n7FjGjRtHdXU1ZWVleHl5NXkX5O3tzUMPPcTatWt55ZVXmnSO+nDfffcxZ84cxo4dKwaearWalJQU\nMe37V0Dbtm1p3bo13/M9OqrFHXxTYGtrS//+/fnxxx8ZPXq0+HsPDw/RLMrd3b1RG55v2cDYUWMp\nCswh8XQymw58hclkajIfqq72yKaWHlatWoXJZOLVV1+tN2gRzNGKi4vJyckhPDy83vPl5+fz0Ucf\nERQUxDvvvCOONTw8HDs7O1JSUjh9+jS+vr6EhoY2OrDZvXs3VVVVdOrUicjIyEYd2xjcFYECmFt5\nhGAhNjaWpUuX8sEHH6BUKnFycrIgXnXt2pUJEyZgMBjQarV1tsPZ2tqiVCpFjYPGIjY2VqyLXm8l\n6+bmZqHNHh4e3mw76pKSEnJzc3Fxcak31dqjRw8GDx7ML7/8wtq1a3nttdesOrdEIhE/y1atWlFZ\nWYlGoxHr1hqNBr1ej1QqxdnZmZkzZ+Lq6iqKR13fe15VVUVWVhbe3t6NSuG1JEJDQyksLCQlJeW2\nDBQEY6brsZRZ5ObmcvnyZQsuSFPMjwICAliwYAEnTpxg3bp1bNmyhQMHDtC/f3/CwsJumhTYr18/\nDh8+TGJiYrM6TiqVSl555RVRkhjgzJkzGAwGoqKiGm2FfadCIpHw4IMPsnz5csDMrpfQ9Pll+PDh\nzJ4928KrQqFQEBkZyYULF7hw4QLR0dFWd0mFhIQQFBRERkImJ346ielqe+TgwYObNL6GyIyNwbPP\nPmvVMa6uriiVSvLz8+sNFH7//Xe++eYbnn766Vrzm0QiEd07k5KSyM3NRa1Wi4Zd1gT6Wq2WXbt2\nATRJJbIxuGsChYqKCnJzc5k1a5boV7Bq1SpmzJhR54cuTBrNZfV6LXQ6HR9//DH+/v6MHj261gMk\n8Ba0Wi3Z2dnY2dk1STP8euj1ehITE5FKpQwcOJDi4mICqb2LkkgkPPLIIxw5coSYmJgm1YyvDRqa\nAqPRyH/+8x+Ki4sxGAy3DSO9VatWnDhxgpSUlBZpM2pJ5OXliWWd5kD37t3p3Lkzu3bt4osvvuCT\nTz6hZ8+edXIXGotHH32Uzz77jLfeeqsZRvoHrm8JPHnyJMBtYx3+Z6FXr15MmTKF3Nxc9n2/38Ij\nQxDtysRsS91QEKlUKunfvz/bt2+3yMJ6eXkRGBhIZmYmubm5VgfWO3bsQK1W07Vr12bxwGhOMqO1\ngYVEIsHOzq6WuZaAnTt3curUKZYsWWIRoGq1WvLz80VfDYVCgZ+fH87OzuTk5HD58mVycnLo3Llz\ng4HXvn37KC8vJzIystZmtLlx+xRhbxLCl2EymZg3bx4uLi4kJCSIrXd/JgQlRhsbm3qZ/DKZjKio\nKJRKJVeuXCEjI6PJDGIwPxhnz55Fo9GgUqlqKTheD3d3d0aPHo2Liwvfffddk6/bVKxfv57Dhw8j\nlUpRqVQtbppjLYSFJikp6dYOpJHQaDSo1Wq8vb0tyjiBqAhEJeowNBZyuZxRo0bx/PPP06ZNG7FT\nJTEx8abGKzgRnj59+qbOcyPodDqxve9WaXPcKshkMnFRN1Aj7tqbitGjR3Py5Mla0tcqlQqZTEZO\nTo5VWSuhFGxvb99sz3xzZhQag2sl9a+97scff0xOTg7z58+3CBIyMjI4e/asON9nZmaSkpJCWloa\nGRkZ6PV6kRMnBDr1oaamRuzyGT9+fItzvO6aQEEgV2VlZREYGMiMGTMoLCzkm2++YfPmzS3mO18X\njhw5Api/wBulkBQKBVFRUdja2nLlyhXi4uIoLy+36hoeEm9Rm7y8vJwzZ85QWVkp+jQI7nA36s8d\nM2YMBoOBX3/9lbNnzzbuTTYRJpOJzZs3s2PHDuRyOW3btsXe3r7l20WtRLt27ZDJZJw5c+am28v+\nTAhE0eb+HI1GIxcvXsTW1paFCxcyZcoUCgoKmDlzpmhA01RMmjSJjRs33lSAfCMcPHiQkpISQkND\nLQjFfxX079+foKAght8/nG+/30qVSU2VSU0RBRRhnaeFAJlMxnPPPcfq1ast2gKFTijBNKshCJLz\nzclJulFG4c/w3RCegeLiYubPn09AQABTp0614LJVVFSIjpNt27ale/fudO/enU6dOqFSqVCpVAQE\nBBAZGYlKpWowE/Lzzz+L5oLNaSddH+6aQEGQRBYWvOjoaKZPn45EImHjxo3897//bbEJ6VoUFxeT\nmJiIXC6nW7duDb7e3t6ebt26ERgYiFqtJi4ujvPnz5OVlYVara53IlYoFLSKbMWFCxeIi4tDo9EQ\nGhpKaGio1dGlo6MjY8aMAWDdunUNRrE3C61Wy/vvvy9meV566SWMRiMSieS2Uc1zd3ene/fuGAwG\nsTf5ToAwSV/fGigEivUZ3NwIJpOJ5ORkNn75NcuXraCzqhtPP/Qsr7/+OhkZGfzyyy989NFHTRZR\n8vX1RaVScebMmSYdfyOUl5ezefNmwFy/vR26av5sSKVS0Ulyy5YtTRJHuhatWrWid+/ebNiwwWJe\nEsjQ1mxyTCaT2GnWXBCCDq1WK46ruXQU6oPJZKKiokLMGAiOqY888gijR4+udb+lpqZiMplEHwZ7\ne3vs7e1xdXXFx8eHkJAQIiIiCAsLQ6VS3ZAwr9VqxXu7oc1oc+GuCRSEuumZM2fEm6Vfv37MnDkT\nhULBd999x7vvvtvii+HevXsxGAz84x//sJo8JYgddenSBVdXV4qLi0XFx5iYGE6ePEl8fDxnz57l\n1KlTHD16lKenPcWwUUP4/PPP+fjjj+ncuXOT6vyjR4/Gz8+PlJQUPv3000Yfby2ys7OZMWMGv/zy\nC0qlkrlz5xIaGorBYMDLy+uWOO7Vh5EjR6JSqThw4ECLKAm2BCorK7G1tW22ibG6upqzZ8+SnZ1N\nfm4eB/cdBMCIkc8//xwPDw++/PJL9u3bx6pVq5rcUz9s2DB+/PHHZhmzAIPBwMqVKykqKqJt27b0\n7t27Wc9/J6FXr160bt2akpISscQoZBaErGNjMHr0aAwGA1988YX4O2FRs2YjJpVK0Wq1zfpcyeVy\nFAoFBoNBLDm0dOmhqqoKvV5PWVkZc+fOJTU1laVLl1p4+Fz/egcHhzoFoxqLr776ivz8fFq1avWn\n8ajumkBBpVLh7OxMQUGBKLAC0LdvX958800cHBz47bffmDlz5k3XV+uCyWTiwIEDbNmyBYPB0CRj\nDkdHRzp16kSvXr1o164dfn5+KBQKdDodWq2WsrIytFotNjY2pCdlEHv4pMhKbyqpUKlUMnv2bBQK\nBXv27OHzzz8XH7LmgFar5auvvuLf//43aWlpBAYG8sEHH9C7d2/RJbOliTiNRceOHTEYDOTk5Ihl\npNsZBoOBmpqaJvdiXwudTkdmZiYnT56ktLQUX19fDm35FVeDO7bYoMO8a4uIiODLL7+kTZs2HDx4\nkNmzZ1NYWNjo64WHh+Pn59dspUGdTseyZctE57/6yMx/FUgkEh57zGzrvH37diorK2/6fFOmTKG4\nuJht27YBWHSZWHM8WBdUNAbXlx9aOqNw5coVvvnmG/bv38+LL77IrClzCXYMJUgSQpAkxKI0DNSp\n1dMUXL58mV27diGVSnnxxRebNTNzI9w1T5BUKqVTp06AWejnWnTo0IFly5bh6+vLlStXmDFjBitW\nrCA+Pr5ZJqjy8nLee+89PvzwQ4xGIw8//PBNMcMVCgVeXl60bt2abt260atXL7p27Urfvn3p3bs3\nXbt2ZcOOz/nh+G5KSkpu+j2EhYXx/PPPI5WarVVnzpzJxYsXb+phNhgMHDp0iOeee44tW7ag1+sZ\nMGAAH3zwgdjh8euvv+Lu7k6fPn1uavzNDalUyoMPPgjApk2bbvusgkajQafTNbruK+hdFBUVkZmZ\nyblz54iJiSE5ORmTyUTbtm2JjIwkRZfEz4k/MfCR/gy4vz9KpZKlS5fSo0cPpk+fTmBgIKmpqbz6\n6qskJyc3evyPP/54s0yiCQkJzJgxg6NHj+Lo6MiCBQvw9vZu+MC7HJ06daJTp05UVlaKi/vNQCqV\n8tJLL3Hp0iV2794tMvitWbSEboHrnR5vFteWH6DlMgo6nY6tW7eyZMkSoqOjWbRokUU7cn2QyWRo\nNJqbcqatqakRdR7GjBlzQ/2G5sZd0x4JZn2AX3/9lb179zJy5EiLyUelUrFq1So2bdrE//73P+Li\n4jhw4AB+fn5ERUURERFBREQEISEhVkWhJpOJK1eu8Msvv/DDDz+g0Wiws7PjmWeeYdCgQS35NlsE\ngwYNwt/fn5UrV5KcnMysWbNwdHQkKiqKNm3a4OzsjJ2dnajE6ODggK2tLTY2NtjY2GA0GikuLqaw\nsJDTp09z6NAhsfMiPDycZ555xqIFMysriytXrmBvby8GeLcTBgwYwPbt28nKymLv3r3cf//9t3pI\n9ULYJTakECfAZDKRl5dHYmKiaF9eXV2NRCLB1dVVFBcTUsq//fYbK1asoLq6ms6dOzN37lxxB+fn\n58e7777L0qVLOXfuHHPmzGHWrFl/CsFKgFqt5quvvmL37t2YTCa8vb2ZP3/+Tbsn3k147LHHePXV\nV9m5cydDhgy5aTlrmUzGrFmzWLZsGdnZ2bRu3drqrKajoyOFhYXU1NRYJV5nDerLKDRnoJCVlcWy\nZcvo2rUrU6ZMITg4uN614nrLbHd3d8rLyyktLW0y4Xjbtm2kpaXh5+fHww8/3KRzNBV3VaDQp08f\nPv/8c9LS0oiLi6vVEmVnZ8fkyZMZNmwYv/76K3v27CE3N5fc3Fx++uknwFzv8vT0FM2bnJycRLOg\nsrIySktLxZ/KykqUSiV6vZ5u3brxzDPP3NF68u3atePDDz9k06ZNHD16lNzcXGJiYoiJiRFfExQU\nREZGRoPn8vHxITAwkLFjxzJw4MBa6d/du80Wxn369Lmt+AkC5HI5jz/+OEuWLGHTpk0MGDDgthXs\nEXq5rZ2o8/PzSUhIEDNX9vb22Nra4ujoaJGVMJlMbNmyRSSfDho0iBdeeKHW5O7k5MRbb73FqlWr\nOHjwIIsWLWL8+PH885//bLaFoC7k5eXx/fffs2/fPqqqqpDJZIwZM4Z//vOft0277e2C1q1bM2DA\nAA4ePMjatWtZsGDBTWdxFAoFc+bM4aWXXqK4uNhq51cHBwcKCwuprKysRb6Nj48XNVocHR1xcHBo\ncH4wGo3o9XqMRqOYqdDpdGg0GsrLy2vJset0OgoLC8nPz0ehUFgt9ubj48PixYtJT0+nsLCwUZLg\nHh4epKamUlRU1KRAISMjQzRU+/e///2nK9neVYGCXC7n/vvvZ8OGDfzvf/+rt3faz8+PCRMmMG7c\nOBITEy1+BPGQ3NzcBq/n5uZGr169GDFixF0jD6tUKnnyySd58sknycvL49y5c1y5ckX0vZBKpSiV\nStRqNTqdjurqalE61cPDAw8PD0JCQujbty9t2rSpczJSq9ViYDZq1Kg/9f01Bj179qRdu3ZcuHCB\nTZs28dRTT93qIdWCyWSipKREVBK1BkK5qkuXLvVOONXV1axcuZK0tDQkEgmTJ09mzJgx9S4ucrmc\nl19+GX9/fzZu3MiWLVuIi4tj+vTpzapwaTAYiI2NZe/evSQlJVFaWgqYy4vPPPPM31mEG2Dy5Mkc\nO3aM2NhYjh8/To8ePW76nDqdjoceeoj//e9/HDlyhL59+zZ4jMClKS4urhUoCEFsRUUFarWayspK\nC86UTCarM32fnJyMQqGwKD1UV1dz4sQJXn31VYvyrEKhwBcAwSEAACAASURBVNPTE29vbzp27Ch2\nMOTn56PX66mpqSE8PLzW8ySXyzEajRQWFuLm5maRwRO8WAQIuiWCuFW6MQV7e3uRhNiYANpoNPLR\nRx+h1+sZMmQIHTt2tPrY5sJdFSgADB06lN9//51Lly6RnJx8w/5pqVRKZGSkhUa2RqOhpKSEiooK\nKisrLUylXF1dcXV1xcXFRXSkvJuJUj4+Pi3StvjTTz+h0Wjo2LEjoaGhzX7+5oJA3Jo5cyY7d+6k\nR48e9bKabxWEFtrGuO5ptVrs7OzqDRKKiopYtGgRSUlJKJVKFixYYFWrr0QiYeLEibRv357ly5eT\nkJDAtGnTmDJlCkOHDm3yDrampob4+HiOHTvG77//TlFREWBu1+vSpQujR4/+S+okNBaurq488sgj\nrFmzhrVr1xIdHX3TO9P09HTkcjmzZ8/m/fffFz1hGhqHjY0NhYWFtdq5+/fvT//+/es9tqampk5i\n4KJFizh27JhF6cHV1ZWHHnqolkmYAKPRSFZWFrGxsWLXjkKhQK/Xc/r0abp27WqRzTAajSQmJiKR\nSCz8e6yBRCLB399f9MdoTPC8d+9eLly4gJubG5MnT27UdZsLd12g4OTkRPv27UlMTGTr1q3MmTOn\nUccrlcoWcXT8G2ZoNBqRUHWtycztioiICMaPH8/mzZtZsWIFq1atuq1KECUlJeh0Oqs7HoSWrvps\nqEtKSpgzZw65ubn4+voyf/78RrfdRkVFsWrVKtasWcPPP//M6tWr2bNnD2PHjuXee++1ajdVVlbG\n2bNnOXbsGCdPnrRg6wcEBDB06FAGDBjQLJ0efyUMHz6c/fv3c+XKFbZu3SrqLDQFVVVVlJaW4unp\nSUBAANOnT2fFihUsXbr0hqUfYdFMS0ujpKSEQA8zudkarY/67p3r1RmFrMONCJZqtZorV66wfft2\nLl68yNatW0Up5dTUVBITE2nXrh0SiUTUFFGr1QQFBYnl6PqgwRx4XCts5ePjQ25urli2sCZwLiws\nZP369YDZh6Kh67YU7rpAAcw2q7t37+b3338nIyOjWXwU/kbzYNu2bZSUlBAZGfmnEt5uBhMnTuTE\niRMkJyezZs0aXn755dtGwKekpEQkIVoDYcGti89QVVXFwoULyc3NJTw8nDfffLPJfd8ODg5Mnz6d\nbt268emnn5KSksIHH3zAmjVr6Nq1KxERETg4OIgW43q9nszMTFJTU8UF5FoEBwfTo0cPevToQevW\nrW+bz/9Og6CwOHPmTLZv307//v2bXBoSMq8RERFIJBKCg4MZOnQoGzdubLBM5+XlRWpqKpmZmU26\n9vW4nsxojRlaQUEBBoOBXbt2kZqaKgadfn5+lJeXk5eXR1JSEkFBQaSnp5OTk4Ozs3O9ZWa1Wk12\ndjalpaXce++9nD9/nurSP3R75HI5zs7O4msasmo3mUysXr0ajUZDr169bqkeyF0ZKLi7uzNo0CB+\n+OEH1q9fz+uvv/73xHIbID8/nx07dgAwZcqUO6ZsI5fLmT59Oq+88goHDx4kODi43nTmnwmDwYBO\np8PDw8PqmqfQiXJ9AKDX61m8eDFXrlzB39+fN954o1nEYfr160evXr04dOgQO3fupKCggEOHDnH4\n8OEbHmdvb09YWBjdu3enZ8+edzRJ+HZDmzZtGDx4MPv372fNmjW8+eabTZofFQoFNjY2lJSUiAS9\nIUOG8PbbbzfoDGpvb8/Xy78hsLU/z6lmUJmmYaTE7IC4h61A4xxP6/J7aAheXl5kZ2ezf/9+i1ZD\niURCeHg4VVVVZGdnk5ubi9FoxMXFhaioqDqzFAUFBVy6dAmj0YhMJmPx4sXY2dnRpUsXi2fTz8+P\n7OxscnJyGgwUNm/eTGxsLA4ODjz77LNWv6+WwF0ZKIB5F3j48GGOHz9uNcnmb7QcBLdInU5Hv379\naNOmza0eUqMQHBzM9OnTeeedd9iwYQN+fn63XPEvLS1NtKa1Bkajkfz8fJydnS2CAKPRyPLlyzl7\n9ixubm689dZbVmcorIFCoWDw4MEMHjyY3Nxczp49S1JSEjqdTlQHlEql+Pv7ExISgkqlahTn4m80\nHo8//jhHjx4lLi6Offv2MXTo0Eafw9PTEzc3N7KysnB1dcXT0xOpVMpTTz3FihUrmD9/PoWFhWi1\nWgICAmotjOd+uYB/mC9+/T1I3JBJfb5VBoOB3NxcKioqcHJywt3dvVZ5WAgUBGK1NRkFodU7Jyen\nlr+CXC4nOjqajIwMKioq8PT0xMvLq84gIScnR5Ttj4qKwtXVlYyMDFJSUrhy5QqtW7e2uKaLiwuF\nhYVUV1fXyxH59ddf+frrr5FKpcycObPZHGGbirs2UPDw8GDy5MmsXr2aNWvW0KlTp2bZIf2NpuG7\n777j5MmTODk58eSTT97q4TQJffr04fHHH2fDhg0sX74cV1dX2rVrd0vGUlBQQGZmJg4ODlanjouK\nitDpdPj6+lpkczZs2MCRI0ewt7fnzTffbFHfDV9fX3x9fRkyZEiLXeNvNAwXFxeee+453nvvPT79\n9FOio6Mb/b1LpVLCwsKIjY2luLhY5L0EBgbSoUMHPvnkE/r3749Wq6WoqIjAwEBCQ0PFey+gVEVF\nnBa/boGE3xuC/a9m7k8MXuI1ysvLiY+PF3URioqKSElJoVOnThbls+szCtYEChKJBE9PTyorK6ms\nrKwVyAjOtjdCUVERiYmJ2NjY0LFjR5G/FBQURGFhIXl5eahUKouAwN/fn7KyMvLy8urk/yQmJvLh\nhx8C8OSTT9K1a9cbjuHPwJ2R+20ihgwZQocOHSgrK2PdunW3ejh/WSQnJ4uEnGnTpt02TpFNwUMP\nPcTgwYOprq5mwYIFLWJo1BDKy8tJSEjAwcGBdu3aWVXCMZlMpKWliTt3AadOnWL79u3IZDJef/31\n27oL5W80L/r27cu9996LRqNhzZo1TVINtLe3Ry6XWzhHmkwmIiMjOXHiBMHBwXTr1g1HR0cyMzOJ\nj4+3UDrN+C0HXX4NLj2UuHS0JAlXVFSIrw8NDeWee+4hICAAg8EgltAENIWjcO14m6JCW1VVxaVL\nl5DJZBZBgnB9IYBPSkqisLCQoqIiiouLUSgUmEwmsbX3WhQWFrJo0SJ0Oh1Dhw69bQjfd3WgIOhh\n29jY8PPPP3Py5MlbPaS/HAoKCnj77bepqalh5MiRzdK7fSshkUj497//zaBBg6iurubNN9/kxIkT\nf9r1dTod8fHxGI1GwsPDre7AyMvLQ61W4+/vL+5uysrKxJ3LpEmTbjvPjb/RspBIJDz33HN06NCB\n2NhYNm3a1KRzODo6WpiClZWVodPp6NGjBxcvXsTBwYHOnTvj7e1NcXExZ8+eRafTUUQBBfo84raf\np6yiDJshRjRDC8msSiO5JIFz584hlUpp27YtwcHBKJVKkXB4/cIuZBQaEygIGiQODg6N7p6pqanh\nwoUL1NTUEBkZWedz6OnpiaurKzk5OcTHx5OUlMS5c+c4e/ZsnS2eWq2WRYsWUVxcTIcOHXj22Wdv\nm/LbXR0ogJk8MmnSJABWrlxJenr6LR7RXwfl5eUsWLCAoqIi2rdvf8eWHK6HEICOHDlSJAHu3Lmz\n2YyNboTMzEx0Oh3h4eFWT25Go5G0tDRkMpnYAWQymVizZg0lJSVERUXdFuTMv/Hnw9nZmX/+859I\nJBK2bNnSpM2UVCrFaDSK97+g3Dpp0iRRVlsqldKmTRsCAgJEZ1KlqzkLoK+sIeGbNIoySwjuEMjx\n48c5c+YMNTU1tGrVyqKVNycnB/jD2lpAfWTGGz2TWq2W0tJSnJycGq0gmpSUhFqtRqVS1dtqLJPJ\naN++Pe3btyciIoLg4GDCw8MJCQkRZdIFGI1GPvzwQ5KTk/Hz82Pu3LktqmraWNz1gQLAAw88QHR0\nNCUlJcybN4+0tLRbPaS7HqWlpbzxxhtkZmYSGhrK/Pnzb0up5qZCKpUydepUJkyYgMFgYN26dSxb\ntqzJdsvWwGg0UlBQgLOzM76+vlYfl5OTg1arJTAwUPwO9u7dy7FjxwgPD2f69Ol3TAfK32h+dOzY\nUfQOWLZsGQkJCY06Xrh3TCYTGo2G4uJiPDw8CAgIIDg4mMuXLwPmXX5YWBiBgYHmRfZf3lz0PcW3\nrGdjyVre//odnpoxmYCAAHx8fOjSpYtFQKDT6UQNkOv1BK4vPVhzPwutuY11YMzPzycvLw83N7cG\nOQxSqRQ/Pz/8/f3x8/MjICAAlUpFZGSkRSfP119/zW+//YaDgwMLFixoshtwS+EvMTsI9dfOnTtT\nVlbGvHnzSElJudXDumuRkZHBzJkzSUpKwtfXl4ULF1ptWHQnQSKR8OijjzJ37lzs7e357bffeOWV\nV7hw4UKLXK+iogKtVouLi4vVKUm9Xk96ejoKhUJ0ucvIyODTTz9Fp9MxduzYWruzv/HXw/jx47nv\nvvvQaDS88cYbjZofFQoF9vb2mEwmUTVTcO2Miori4sWL4mslEglBQUG0bdsWOzs7Jk6cSEirEPFv\nPj4+hIeH06ZNm1rBQEFBgdgOfD3qIzPeiHtgZ2eHnZ0dBQUF9b7memi1WjIzM1EoFERGRjZLaeDw\n4cNs2bIFmUzG7NmzrXKj/LPxlwgUwGxD+vrrr9OtWzfKy8t57bXXGh05/42GcfLkSWbNmkVubi6t\nW7fm3XffveWtPS2N3r17s2LFCkJCQsjOzmb27NmsXLlSNGtqLggiRA31XwsQHE51Oh0hISHI5XL0\nej0ffPAB1dXVDBgw4O+24b8BmHe+L7/8Mj179qSyspL58+dbLYZkMBioqqrCZDJRWFiIVCoVn/nI\nyMg651nB4XP8+PF88vnHXE6/1GDpTiD/1RUoCLwboT3SWtjY2FhN4qypqeHixYuiyFRzGDOdP3+e\nlStXAmZtmc6dO9/0OVsCf5lAAcw3xbx58+jRowcVFRXMnDmTTz75xIKx+zeaBrVazapVq1i4cCGV\nlZX07NmTJUuWWL2o3enw9/fngw8+4F//+hcKhYKffvqJZ599lm3btqFWNyxNaw1KSkqQyWRWt/nm\n5OSIUsx+fn6YTCY++ugjkpOT8fX1ZerUqc0yrr9xd0AulzNr1iwx8/r666+Tl5fX4HHCrlqr1VJR\nUYGbm5tYX/f39yc7O7vO49zc3IiOjsbOzo4rV65w6dKlBhdtJyenOq2drw8UrO160Ol0Vi341dXV\nnDlzhvLycgICApolC3f27FkWLlyIXq9n+PDhjBw58qbP2VL4SwUK8Ic1quBauHv3bqZOncqePXua\n1B70V4fRaOTQoUO8+OKL7Nu3D4VCwRNPPMHcuXP/dCvUWw0bGxsefvhhPvroIzp37kxlZSXr16/n\nySefZP369bVauhoDo9FIRUUFzs7OVtVUTSYTBQUFKJVKWrVqhUQiYdOmTRw8eBA7OzvmzJlzW3lW\n/I3bAwqFgtdee4327dtTVFTE66+/TmFh4Q2PEYICIYV/7Y5fKpXeMD0vdES4ublRVFTE+fPnRZ7B\n9dDr9VRVVdVZTqhPcOlGpYeampobih6BOVuSkZHBiRMnqKysJDg4uFkMyOLi4njzzTeprq5m4MCB\nt1WHQ12QLVy4cOGtHsSfDalUSteuXenVqxfZ2dmkp6cTGxv7/+3daWxUddvH8e+sXYdO95aWFqlS\nWxUkgFDEopAUFCNKQCFqJEgkxrglxiVA5IUScYtGMTEuCL5REtBKFNkkoUJboWnAttBSwca2s9FO\nZzqdfebcL/rMeajt1Hqj3gWvT3JSQspszMz5nf9yXdTV1ZGUlER+fv64WnE6HkUiEWpqanj99dfZ\nv38/Xq+XqVOnsnnzZioqKsb1m/7vZjKZuP322ykrK6Onp4fOzk7OnDnD3r17aW1tRaPRkJOTM+KV\nUTw+n4+uri4yMzPHNJVjt9vp7OwkLy+PrKwsDh8+zMcff4xWq+Wll14ad10wxfih1+uZN28ep0+f\npqOjg/r6embOnBl3JMvj8dDX10coFCIUCjF16tQhYdZqtXLzzTfHvT+dTkd2djbRaBSbzYbdbsdk\nMg1rLOX1enE6nZhMpmEhNxqNsnv3brRaLStWrODXX3+loaGB4uLiuJ1PHQ4HDoeDgoKCEZ9bf38/\np0+fVtu4T506lYKCgiHfbRaLhfb2drVvhc/nw2g0jho+Tp48yauvvqq2jX7yySfH/WJijfJP7Oka\nxxRFoa6ujk8//RSr1QoMrqC94447uOWWW5g2bdqf+kK/mkWjUdra2jh58iQHDhxQ58zz8vK4//77\nWbhw4Z9eQfxv0NbWxp49e6irq8NoNOL3+9FqtWop5bS0tCENkmJif9ZoNGpQSEtLo7CwkIyMDHJz\nc5k7d+6wL7lIJKLuVZ8xYwYNDQ1s3bqVSCTC448/zl133fWPPn9xZerv7+fll1/m3LlzmEwmNm3a\nRFlZ2bDfczgctLS04Pf7yc3NHTUU/BG73a6uaZg2bdqQ97bf7+f06dMoisLs2bOHnFzD4TD33Xcf\ner2er776itraWrZs2cKcOXPYuHHjiPfV1NSEy+Vi1qxZw07s/f39nDp1img0SnFxMQUFBcMuHn0+\nHydOnECn05GYmEgkEsHn86HRaLjxxhtHDPR1dXW89tprRCIRli5dymOPPTbuQwJcxSWcx0qj0VBR\nUcHMmTP54YcfOHz4MGfPnuXnn3/mu+++Izk5mRtvvJHMzEzMZjNms5n09HTMZjM6nY5gMKgm6Ugk\ngt/vJxgMqkcoFCIYDBKJRIhGo+oRCoVwOp0YDAaysrKYO3fumKvsXY5Ybf3YAYMBIBwO4/f71cPj\n8WCz2bBarerR3d2Nx+MhNTWVgYEBCgsLWb58OXfccYeMwIxi6tSpvPjii7hcLurr6zly5AjNzc04\nnc5hXRJjYq1tL+XxePD7/eh0OnVINSkpiRkzZrBw4UIqKytJTU2lu7sbt9tNbm4uO3fupLq6GoDl\ny5dLSBBjZjKZ2LJlC6+//jonTpxgw4YNPPfcc8N6nGRkZKjVBmO7Hf5bsZG2pqYmfv75Z6ZPn67u\nfkhMTMRkMjEwMEAwGBwy4qDT6dDpdITDYcLhsFqjIN60STQaVUcnRrr6j1VQLCkpiVsnwe12oygK\nU6ZMUdcAxTqgxkpOX+rQoUO89957FBUVMW3aNNatW3fFjLzKt/v/MRqNLFmyhCVLltDV1UVtbS1H\njx7lwoUL/PTTT2O6jdEW7vzepEmT1MIkANXV1WRmZrJmzRoWLFgw5jeQz+fj4MGDOBwOjh07pn5Q\nYkckEhkSCi5XdnY28+bNo7KyUm0vK8YmLS2NqqoqqqqqCIfDuN1u+vr6cLvd6qpx+P8wd2lYUBSF\nSCRCX18fPT099PT00NHRQU9PD3V1dRw/fhy9Xk9ZWRkTJkwgEAjQ3d1NIBCgsLCQxYsXs2zZsv/l\n0xdXoMTERDZs2MCHH37Ivn37eO2113j00UeHvJd0Oh033XQTGo1mTNugI5HIqGsX0tPTKS8vp7m5\nmQsXLgypGBoOhxkYGBh2QaXRaEhLSyMUChEIBNR1Em63e8T7CIVCaLXaYc2lLr0fv98/6hRCQkIC\nKSkpaklqt9utFvT7/dTInj172L59OwAVFRVqkasrhQSFERQUFLBixQpWrFiB1Wrl/Pnz9PX14XQ6\n1Z9OpxNFUdRWq0ajkbS0NEpLS4f8ncFgwGAwoNfr0Wq16pGWlobRaCQcDnPu3DmOHTuGzWbjrbfe\n4tSpUzz44INxkywM7jLYu3cv1dXVeDwejEbjiCl2JLEh7tgbVafTodfrSUhIICkpSf0A5OTkkJeX\nR25urvozIyPjinqDj1d6vZ6MjIzL3jrqcDg4fPgwhw4dor29naamJvX/MyUlhenTp7Nu3TomT578\n1zxw8a+j0+l4/PHHyc7OZufOnXz88cc4HA7Wrl2rnrDHUiAoGo3S3t6O1WpFr9eTnZ3NNddcM+Jo\nZGZmJllZWbjdboLBIEajEa/Xi9/vJyMjY8TibeFwmP7+fsLhsDry63A46OvrG9YNNTaKGm8ENz09\nnc7OTjo7O0ecboHB0byBgQG1HfX58+dRFIUbbrhBnTKJRqPs2LGDPXv2ALB+/XruvvvuP3ytxhsJ\nCn8g1u3u7zR//nzWrFnD4cOHqa6u5tChQ9TW1vLQQw+xaNEiNfUGg0Ha29upqanhyJEj6ra78vJy\n7rzzTkpLS0lISFCDSWw47tK5bjnJX12ys7NZtWoVq1atwuVy0djYiMvlUtvkjrWzpBCj0Wg0rFy5\nkuzsbN59912qq6ux2Ww888wzYy6m1tXVhcvlUr/PYlOZEydOHLGtuMFgIBAIqLvRLBYLXq837q6D\n2Ek/Go2i1WopKCjA6XTS3t4+bEFjYmIiBoMBm82GXq/HZDKRkpJCYmIiGo2G9PR00tPTsdvt6gXS\n7xkMBjQaDQ6Hg56eHnQ6Hddff726Jdzr9fL2229TX1+PTqfj2WefZcGCBWN6rcabf/1ixvGmt7eX\nbdu2qdMdycnJ5OfnEwwGsVgs6jDX5MmTMZlMrF69Wpr5CCH+MadPn2bLli0MDAyQl5fHCy+8wLXX\nXvuH/665uZmLFy9SUlKC2WzG7XZz4cIFwuEwCQkJ5ObmkpubS3JyMsFgkIaGBqLRKBUVFWi1Wpqb\nm3G5XHF3VT3yyCP09vby2WefkZmZySeffMLXX3/NqlWr1H4/l3I6nbS2tqpbKmMXVrGSy6FQiMbG\nRqLRKJMmTSIrKwudTkcoFMLj8dDb20tPTw8+nw+z2cysWbPUhe9dXV28+uqr/Pbbb6Smpqr1Ka5U\nEhTGIUVRqK2tpbq6mpaWFpKSktTVtMXFxdx0000sXLhwTB9OIYT4q1ksFrZu3covv/yCwWBg7dq1\nLF26dNQRS6fTSVNTE9FoFKPRiF6v59prr6W3txebzUYoFAIGr9TD4TCKoqi9IWCwJXogEKCiomLY\nbSuKwsqVKwkEAnz55ZckJyfT0NDA5s2bKS4u5v333x/xMUUiEfr7+/F4PAwMDOB0OgkEAmi1WoqK\nikhPT6ejo2NYDZTk5GS8Xi+pqan4fD5yc3O57rrrgMHtj2+++abaNGrDhg1D+jpciSQojHN2u52B\ngQG1Dnq8xTdCCPFPCgaDfPrpp3z77bfAYCnzp556atSpCL/fj9PpxO12Y7PZ0Gq1TJ8+nZSUFJxO\nJ3a7Ha/Xi1arJTMzk0mTJqnho7GxEZ/PN2zXBQzuCFq9ejVJSUns2rULGFyz8PDDD+PxeNi2bRtF\nRUV/+Jxijdc6OjrQarXk5ORQUFCA3W5Xp0F0Oh2pqalMmDABv99PY2MjxcXFFBcXs3v3bnbu3Imi\nKFRUVPDss89eFd/Z/8qCS1eSlJQUdTum1HMQQowXOp2OWbNmUVRURGNjI+fPn+fHH3+krKxsxH4M\ngLoeICsrC5PJhM1mw+v1kpeXR3JyMmazGYfDQSAQULcopqamotFouHjxItFodMR1NxaLhe+++468\nvDx1saBWq8VisfDLL7+QlpY2pilajUZDamoqWVlZWCwWenp6yMnJISMjA7PZrP5MTk5Gq9Xy22+/\n0d/fj9ls5qOPPuKbb74BBltsr1+//qrpmDv+Kz0IIYQYt+bPn88777xDSUkJVquV559/nl27dqnr\nqeLJyMigsLCQ/v5+tRX6mTNncLvdJCUlodfrOXfuHG1tbWodmng7u2JTA78PKPPnzwegpqbmT20R\nT0hIoLS0FEVR1C2PlwqHw7S2ttLb20tbWxubNm2ipqaG5ORkNm7cyKpVq66IQkpjJbsehBBCXJb8\n/HzeeOMNtm/fzt69e/n88885evQoTzzxRNzthQBFRUVYrVba2trUtQlTpkyhsLCQUCjEmTNnsFqt\nBAIBotFo3JN9rHnV74NCrLpjZ2cnHR0df2qbsNlsJi8vT22sNmHCBILBIG63m+7ubn799VcOHjyo\nTlNcf/31PP300+OyTfTlkjUKQggh/jKnTp3igw8+oLu7G6PRSGVl5ah1YWKLHA0GA8XFxUMW/kWj\nUVpbW/F4PHi9XjQaDZWVlcNu45VXXqG+vp7169erUyGRSEQtCX3hwgWWL1/Ok08++aeei9fr5dSp\nUwSDQbRarVp/4cSJExw9ehSdTkdKSgpr1qyhqqrqqhpFuJQEBSGEEH+pYDDIrl27aGhooL29nYSE\nBJYtW8a99947YnGmQCCAwWAY8UQbCwt2u52cnJxhIxQ+n48HHniArq4uysvL6evrA6CwsFBt1BQL\nIhs2bGDFihWjFrMb6f5PnjyJ0+nk3LlzHD9+HJfLhVarpbKyknXr1qm1E65WEhSEEEL8LSwWCzt2\n7ODYsWPA4NbHiooKFi9erJZ9HgtFUXC5XKSmpg6p5OjxeNi0aZO6JbKsrIysrCxuv/12CgsLMRgM\ntLS0sHv3boLBIMXFxbhcLm699VaWLl1KeXn5qI9BURSamprYt28ftbW16rqL/Px81q9fz8yZMy/j\n1blySFAQQgjxt2ptbeWLL76goaFBXWcwceJEqqqqWLRo0bASy3+kt7eXffv28c033zBhwgTq6+u5\n7bbbWLt2LTNmzBg2MqEoCmfPnuXIkSMcOHBArfYYK7tfWlrK5MmT1YJKFouFs2fP0traSk9PDzC4\ni2L27NnceeedI97H1UyCghBCiH+E3W7n0KFDHDx4UO3sqNPpmDNnDrNnz6a8vJz8/PwRr/KDwSDN\nzc3s37+furo69WR/8803s3r1asrLy8f0GC5evMj333/PwYMHhxVSirm0aV9mZqbazO3PTFlcTSQo\nCCGE+EdFo1EaGhrYv38/J0+eVE/6ACUlJSQmJmI2m9Hr9Wg0GrV9c2zoPxYu7rnnHm644Yb/6jEo\nioLNZqO1tZXW1la6u7tRFAWtVktJSQn5+fmUlpYyceLEf9XowUgkKAghhPif6e3tpaamhpaWFlpa\nWkhMTMRqtQ77PY1Gw6RJk7jtttuoqqq67M6rYuwkUsEyqQAAARRJREFUKAghhBgXFEXh4sWLdHV1\n4fF4iEQihMNhcnNzKSkpuSrKIV+JJCgIIYQQIq5/98SLEEIIIUYlQUEIIYQQcUlQEEIIIURcEhSE\nEEIIEZcEBSGEEELEJUFBCCGEEHFJUBBCCCFEXBIUhBBCCBGXBAUhhBBCxCVBQQghhBBxSVAQQggh\nRFwSFIQQQggRlwQFIYQQQsQlQUEIIYQQcUlQEEIIIURcEhSEEEIIEZcEBSGEEELEJUFBCCGEEHFJ\nUBBCCCFEXBIUhBBCCBGXBAUhhBBCxCVBQQghhBBxSVAQQgghRFwSFIQQQggRlwQFIYQQQsQlQUEI\nIYQQcUlQEEIIIURcEhSEEEIIEZcEBSGEEELE9R8GLZdPsOHI7AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc886ccb090>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Ydz+9m2jdI7LNvPtppgAZNq0yufNO9orftzLMt8qO98BjyEKjZmQ/TTHuDg/S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\nxyqSNk6duEKHHXfcUVK3i5c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v77IYxbhq1apx37Nd+YwZr+d2oQ6z53vrOVfE+HhM9DeD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\nuyPeBWGSCqmX+UaGmXcQubvt3cBYL4rr873r43pyhzDuenPn1K9OyuP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sF/Z74XmmPCG1\nf035r7NM6RkJZD6MVE2qIlnHLap8j/mxpzw0tc5oO6VqwbC/rGkeBI2jlK9meM7bovAt9Xp4PG7D\neNDs9TruiakNeW8Y3xyXbg1h3dB6onVEFcA6/on1L03/z/kAgE9vnF2wz9T90fKJ+ePqPdP2UPet\nfrgsp7Vr1+b3yfqvq1tVuamrGnmeusoKKG4nyinddaVk7NrL+QjzOY+tStSYklNXI2ibqG1ROX/y\n8LOp3wY0nlP3sD7l69HxQ+y7EM9fv+sQHYfqbxTcd9j/6vdl3q+a5gRQ7/fws2HfBGTlreMH9crn\necbiSMes+juR1gtVhYdjTtY7HUuqUlzzOPA6mL8nHHukVhdpvSOp72vhuFb7XC3XhoQVwMYYY4wx\nxhhjjDHGGNNAsQJ4JyGcheAMSLmZVp0FjGUsVx8yqh91Vki9a/Qcwm1VwZfyL3xqyA+q398y+x1T\nTKpiQz2gzLajSttY1uxy/jy8DzqDXMoDODVLp7OgquoiocIjpRRQZQdn+Khsat26NYDMM4+zi+F5\n8jXGIpWZep7lVPPhezrjTPg61UGEs6bqrQSkM82nVF2qTNG6DWSqXKq6VHHH9kHrH7dXdWN4Xql2\nStFYUyUtULNZ2XHjxgEA5s+fX3Bc9e4D4qoxoHz2clWsxjyveE+pwGbZsawYiyklU6nsvJrBnfdy\n+vTpAICLL74YAPD58DMAAKUSMeu1quKxnBdwTLlZTt2iakr1kYupuVLnXU5lH1NwphS/MdVbiPrt\nhwoCVUOowpvwGlX5G1PhaJsTU8WnYByG9Y31NeXFrErVlJdtuE+955q5nfHP+sB9s1x4TmG8pxSE\n2jaoZ632NWFZsk1t27YtgGw1iPZ16hGZUoeGxynl+Qiks1XHFJHaV6SUkeoHzj5NM76Hx9M+QtU3\nukpCx32M6RDeV7ZvurqMn+F9zvsa/tOW8m2SlW+jJoX9Nh/ZnoXEVMHhtYZoX6J9oSptU2PY8P9y\nXte6kkNX7oX9kLb9qtRThZWOKzRDPZCpxTj24SPHN9wHV4+0a9eu4Bz0OkJUVaaqOJ4Xr5H3XlVe\n4fFSq6OI9nXqUxmS8mZN3aNUHxcj5Q/POqLe3SmvbiDrC9h+ceyyNYwaNSr//5NPPgkgux8aF3xU\nRS3vI8+5zY8vLjqOtlkao6p+VdVgrP8lOl5I+aCz7LmiL4RxzT5Br5WPjEVV8nPfYfvJa0it1kn5\nHes+Y57K2i7oChpdfcD2VVeMAcWev7oakeN3xpnWT+6Lnw/fI6r41e/qrAexnAv1hUsvvRQA8PDD\nDwOIrwDRVRYsh1R7rgrtWJ4iXfWlbVmplUvh87B95/cHvR/ad3KfqtTWNhsoVvbyeyHvra4u0nYj\ntiIklZNCv+8xRt9//30AWdyr93RYDqrmJzqGS3nQh/eI91njuiHSuA5/Aa4/+n9jjDHGGGOMMcYY\nY4wxFcUK4O2AsyJAscpO/ZzKZT8NZ0lSmUWJ7kMzpoazg6nM26p60eexWRhVFvAz3Ddn8My2Q8XO\n/vvvD6BwZk29ddTzV/0b1R+3lCcqZxzVL5joLGcpBXDKQzGlpOH5sz5R/RV6KlEhw2vgc14LZyI1\n3lUBEs7Ssnx0llbVCFpuqlIIy0rVLfpc1U46k1nKz1EVRrzfLDc+5zWmPKHD8yEpJbDO0qq6L1T9\ncna4JtADUdVsMS869UhTH7+U6lvPFcjKiuetyhL1omNZqnIilp1X64R693Hfv/rVr6r38fXvAACG\nP1ft3/fm9yfnP9tIVG4p/7pymdTD2f6UF3f+mHLv1d80pv5PqSm1/0gprXSVAlBecZHqq0r5p2tm\ndV1lo8pf9eCMtXt6fF0pEKNfv34AgAcffBBAoVJS62/qOKqgYN1R1Ub4P/fNR16vqtVZPuo3HqL5\nA7TP4PlTucTnVVVVALIyDq+T7Z36m+u919golfFd41y9o7U9IarGifWXqVUtqpxmeWrfHVPC6n1W\nNRAVP1pn+BiOE6kmU9W+nh/3ydd5rat+dAuAwuzeeZXZln2yD47B9p3jmJT/X3idqt5S5a/e85qo\nS7VvV6WYlrl60sdQlbEq8VN5CFjmQFbuVIbr2Ojtt98GkCkLuS8tz1gcqbKR7ZL26bxmHkPbhNg+\nU36q6kupisNYP6SrEvjIa1WFsq6IiHkVp3zh2daw3HXVRsxzs9I+qaxLHN+mVoMSLZd8uf1/0/Lb\n5HM1SB/Ee6k+qjVZgar1LdUf6/c/7TvD8TvLn2NEbqtjNfaHuqpB/XJLnaf2/7Hv2LHrCD+TWjFL\nVGHK7XjNsb5N45jxQM9ktqeau4X3P+yL+V6q/VKFt5YBP8d+oj7B9ojlFcsVwHjV8brmrtFVRylV\nKpDOb5L67aYmXuT8LM9XvxOkfONjY7lUvgxVArPO8Jg6zonlL0mNp3R1Aq+ZcRPLf6FtjsaiXmvq\numKr7XaFPFO2gDDGGGOMMcYYY4wxxpgGipPA7SSEite7774bQLEPWyoru84sxnyIVPmrXkCcFdGZ\n19AzU7Osqloq5Y0TU4mpkk2VSmb74Wxwx44dARQqQVXZofdQfffURyim6laVg2an5z0u57kTxm/K\nU1E96NR/kupM7ovqGCDzFeMsuWbvZh3QLObcJz3IQg8x7j+lMOK2PLZmag3VUURnenXGXVUdKQ/m\nmNeZqg5UvaL+naqIDWdpa6oC1TJRVS2VXgBwySWXFJVHiosuughApoRU363w3FRxlfLF1Rl8KglC\nlQOVMeqPzDhK1SGiSr9wG/WLVU86bYtZln8a+iMAwAGRPkDVi0T7DVV6aJnFXkspnHQFCo+lGYKB\nYnVEStkQ830NjxVTJaRUoKlVK+rJGbad6quo6ng+1/rHx5jijp9leYwdOxY1ZeDAgQCAOXPm5F9j\nDKqKTtv9lHezrgQBitV9LCNdLaT3WPuDsH1PtREx3zogU0WxjKkELtWnqPpVrzXlZx3ukzGlahzC\nvkHVg+rDH8uDQNR/TxXMqqKPeUlrnOs1p1TRuhIk9IDXFTA1VSarp2UYS+pjuHbtWqRYvXo1AODA\nAw8EEFfQ671jfKTaNP1cbLwbU/WF+075aevKnJqsctAVEeXUVKGqmJ/lGCRUB4efoRL4zTffBJAp\nKFUtGu6T5aZqV6KrWnjP+RiOPVRJm1KO8lFVXjFlPs9T+2fGla5sUIUn28lQHad1R5VrOsZjefO8\ndUwVns/WeLuXgmPXAw44oOCctU/U4/K+qAIaKI4tvZd8P9WXxHy0VQ2tj+pvrvWX5x22lfzu2qFD\nBwDZeJznqSvidCWfjtfC89D2MrWCKbVyIGzf9buUjmfKrYyMte+pHBFaTinVakzxzm1T/VEqLwbL\nm+P2IUOGoL6h3w3Ca0kpf3U1p+ZcKuUfzvLVe65ezOpbrW1ejFSOE64C4HP16Oc5hG0P/+d5av/I\nPj3l862K4PA4OpZOeUXzvHWVVPidgO9pjiotr1S/HrtXPM9w1X1DpVEdGvP6B2BjjDHGGGOMMcYY\nY4ypQ6wA3gnhrDSVIDojpf4mKe8eoFjFoCocnd1VhWU4s8JZK50l1Zk0VYfGVIOqDlKlg2a5N1sP\nVZSLFi0CUDjjrSoGjSE+6oxzSjUFxGMmpFz259jnU159qk5MeXdyNjSc8aZShjOQrAOqBuGjKp/U\nbw4A1qxZA6DYZ4xoVmzdh9a78Jp09lW9OHW2WTMEhyocHp/XqsdXFYB+Tmdrw3OuaVZgzUxPJdK/\n/du/lfxcOaiKUeU5UDxzreeu6i22T4wfzhaHccRypTpK77kqgdQzVlUw4WuKelnqTD3v58qVKwEU\nqoq13da6rPc8pYSMKSJ5baqk03jRert+/XoAhYrqtm3bFpxvSmFUrn2J+U6mSHmFqVIjtkJFFUdE\nFZF6n2MZw7kPqpm2hXPPPTf/P7Nfs3xTylVV7zNmY6pNEvYj4T55fbqapJQfW0pxVUplA2Rtt3qW\nA+W98FT1X04JHJ6XqrjZdi1fvhxAttqGSmUqlFme4aou9Xhk3PC4jAVVjDE2WcfDjO6qskypr7Wu\naB8StpM8Ds+Tz1Xxq3Ve1c+xbXn/Ro0aVbQNGTduHABgwYIFBecRxkbK4ztV91SlFPP21JU22kfo\nOKHcioXY+SkpRZO2aeE9Z+yrNznrqfbtVFSzj2NdCvsh9idU9/G98LjhPlhGXHXGz7NNB7J+kqjS\nlqRyCsT6K8YP2znGXOp7haoX1RcZKL4H3Fa9/FMe4jGVJY9Tqe8zVFs++eSTALL7kvKrTqlPY/Ge\n8tzXtkTHEzHFvNYd9Q/WMaq2gdxXONbW/Blt2rQBkNUDxoJesyqBw1Wx/E6g95Z1SsdAWo6xPl2P\np+g4mc913BC2s+pPq/vQ+6ploAr48Dx1FQK3UfUzr4f907e//e3o9dUn2E6E/VpqhZt6H6tKV8dM\nsd81dOWJjom0D1FFcNgmlhsTcVv+XsQ6pKs8w7GlfgfTFamaz0fHwVr3w32mVllqrPJ1jok4lop5\nSbOus6/SPDzlfs8I6yC/J44cObLoOA0NewAbY4wxxhhjjDHGGGNMA8UK4J0Q+v/NmDEDQLF6J6WK\njM3K6Eym+g9ypl497kgYQKqI1Nkhnc1NqcBiaKZIzmaZ7Yfeb6EPrqoQVaGnagf13FWfLaB4xlGV\nHClPaJ3ZrElGVPVB0qy46oMU8xXizKMq7VVBoGoKVS+E56P+wCxzVdqrMoNlEZ4n0VlOnb3lvVAl\nGa8vrNPqNcrn6v+m56cZ4GPecZqFOZUFmcfkTOzQoUOLrnlbGDFiBADgvvvuA1A4i67ti87+q/JX\nPVB5LWG7pKoPbQ91VjrlAxYrS33OuGZc8XX1aI7VnVi/EF67orP/sXPjcRgX77zzTnReu4HEAAAg\nAElEQVSfPD+WG1WPLJvzH5+RbfzVlv5s9+ry+sN3J5S9tvB9nf3fGlLehDHfWo1v9ZDTctbzivny\nM84uuOCCrT73GAMGDAAAzJ8/v+CYVE5oe06FlSp+SynJua3Gv7aTet/C9kiVmRpzqZUH3I7tfhh3\n6t2upJTAek5drr+06L21100DkKlVXn75ZQCZ8lEVtNwXlZShty7LQX3htV3XmFRVV6hA0kzhKa88\nVfXp2C28R1o3yvloa3+pfXX4mZQ6LsZZZ50FIFvRFKpLw744PBe9Xu2DdJVGzKdY4yW1Sk7LOLZS\noVzW8lSbrMrOUBXIOOG568o8jgfY9mr8xPJw8Hjr1q0DkI2JUnVLx408J66MCs+d50FUZUk074Be\nJ1Dst8/zViWnfu/ROhZb0ZSqK6n+VMd2YV2n73KlYb/L+8LrY5nqGEVjNWw7Ut/XUqs0UnEe7lNX\nFhAd13CsyvY87Hf0GBxzcPzIa+c++FxXh2jchzka+D/VxCnPaJJajRTzeOd5alvLfepYk/VD212g\neCVA6r6qd7H2JeH9SH0X0TquKv/66PmbQldrAMXfabQ/ZVumjzp+C/uwlHo+5fWu35n1O0Ls+Cnv\naK2v6rUexqr+dqTjQPVQ53PNPRL2Q6k8Dqm+LpVnpib5X/gdj9+vdR+6wp0xC2S+0Kay+AdgY4wx\nxhhjjDHGGGOMqUNsAWGMMcYYY4wxxhhjjDENlMa2gNh5GTZsGABg2rTqZYe6xEWXEvN5KMtPGfXr\nso/UMp7Ysl8+ppYk6LKTWBKhWBKxcN+6FNRsO0yw8tBDD+Vf0+UshPdF40WXy3CpSGhZoMuHU0mP\nYkl2wu1iSydTyy15HawbmoAplsyIcclluVzewqVXXJLLJWRqk8DlMqWsVlIJyVLL6jUpTniemqCC\n94Dv81p57VxGp0mZwmvV5U5qlq/tgi4pi913vjZ8+HDsSLiMnomwgCx+uGRUlyPp0lyNd8ZEmABL\nE/vpMlhNOKdLonR7oHipO7fVBHJhUhQgu+edOnUCULiEsqbWD4rarYSxq3Xo3XffBQBcddVV0X3d\ncsstBc9PmXE9AOAfH2ZxlNtiAdGkaXWcn3zPDQCApy/6YcF1pBIlkVj/V85eJpVoI5bcJZXEL9XO\npRI6hUtQmRSv0px99tkAgIULFwIoTj6ili88Vy6FDZfWa9ymylbjnMTKQxOJ6RI+nmdqvMA2MUww\nxbqgFhQpSyIdz2R9I4pgu/7qq68CyMZoys033wyg2LIlbDdpF8S6nUoupsvu1YIktqxVryWVvKUm\nCVt1CWeqrmhfwWPGLE90GffW0Lt3bwBZUrjwulLWFtpesgxZB2NWFGxDNfGmHlMT/ej4IIxxjUW9\n56USJ4fnEF5PKgEex0Q8D8YZ+zL2hTEbBJ4z+5OwrYrBY7AeaiJDILvX7EPVLkKtZLRcec3htfO+\n6bhVbeoIP6vlHl6f9o/aVmo7l0pYNnjw4Pw+Tj31VNQGPMYjjzwCILsOtaFK2bSE7ZFeh1q5pOyR\nUvYy4WdTySf12HzU77hM/hRem9p/cJ+MLx03qy1cWLfY/3KfHTp0AJBZV7EcdZysS/pjVlE8Lvet\nbXG5hOhhktmUPQfRdoXfJ7RfiFk7Mm5rsgR/Z4PXREusEI0jjT1NzMZ4j41JUrYoGic8Fl/nsWLj\nBLV/S1mOpfpyvh4moOW1sI/TJJoaR/p9JGZFpFYTqXGBts18zno7ZcqU/D7Hjx8fvSa1SrvtttsA\nFFvhpMZnuwpWABtjjDHGGGOMMcYYY0wDpS6TwDXKqTzDVBSqSjgbyhkdzpzorG/4v85e6YxOyrQ8\nVM2oylFVJprsRpNoheGh+1czdCoHmMzGVBbOhHKWXONCZyw1EYcqFcP/1Sg+lSxDn6sqM3xN1Qiq\nFKC6RRMS8tihakSN7gn3ocbynEXn52J1Q2dfGceqSFE1kSamCBXATPDB+k01giohea1U37BdiCmv\n+L8qXzjjzNlwXqsm62lo6oAHH3yw4DlVvSwHxkL79u0BFMYyy4R1gu0yy52z6ixLVVWq0iDcl8Yo\nY5Pxwn0zXqh6pLorlkQilsAvJJXcJZY4ZO3atQCA1atXA8jUXakZe/LZ0NMBAP9YV33+H637OP8e\nT6v5vlvK5YDqeH529MSCa9I6pH1XOOgpl7gmpWJMPS+1j1i7EKIKsTAxBVWNtc3s2bMBZPdSE0by\nHBkzYdJDVedqgh/uK5VMSJNTAcVlVU5ppe0m4z9sf3nOqYRoqsLkYyl1bMefjQEAzD97HIAs7rnt\npZcWJ4yLceONN+b/5/FYZ1OJb8slbA3PMxXnmshJFWupcWBsHyzX1Moz7XcY72xTgaz8zjnnnOg1\nby8c36QSNqoKT9WnQNbvquIrpcLj66roK7UiQVfLqRJMk9iqYj88P54vr5HjFz7ymvlZ3g9d7RKe\nX6qd1PpINMFPqGRjXW3dujWA4nqq3ydSY85wPKNqXV0VyfLmdlqeXMESKpUb0hhn3rx5AIoV5rEE\nY0S/k6WSSxFtL8P3UwnzFN5zxqoek31LuC3vJRW2OmZlDGjiU47xwlWBjAeOq3gehxxyCIBsHKgJ\n5FTJHNYhxhYThjLGNPE6YR+s6uOw7DTetT3hNa1cuRLAjl+NtzPw2GOPAcjKju1NKkGqKnDDsaEq\n2TUpnI5fNGmjtv/h/nU1KeOFsarno9+zYwphxpPGP+tSKoljbHVLarV5KpGijg/4HYLfe4GG1Rbv\nCFYffWjZbTq+8NeKHCuuPzfGGGOMMcYYY4wxxhiz02MLiFpm3LhxBc/pDcxZSM4IhQobnelVJaL6\nyKQ8YYFiZQ9ncHRmmDM+qiaNKaN0Gz2GqR3oDUnoD6x+bIwTVZFwRj7meaUqXJ15VJWR+oLFZj/V\nD06VrKoCUP+mcKZSZ1lVDct9qf8Y4zs2g6/egywvnenlsVKzpaF3K7fR86EygNfGes9yV1Vv6CGm\nyjrOulKtsKvNuA4cOBAAcNdddwHIZsBTqvVSai5V16j/J+9LyiM1/CwfVRGjqnWqqUKFTLh9SEqZ\nqqtEarJsiGoulldNfYWxxec3t3lL3fsy8Brd8lruy8KVALpvVYqRlLc4UNyOlFNT1qQMUurJVPum\nnoBvvfVW2WNUGsYg44VtG2NVlbU8V6DYD5Gxqf6gqqospQRhm0XU/1Dbah0f8FihV3HK7zZ1z9UL\nWBVwAPCH704AAKx7442C43GfU6dOBQCMHj0apQg9xHkP2J/w+GzPU37xJJZbIeV1nVIFpdTzYZ+t\nyt+U8l3HDtoPUQkH1J7yV1F/QVVLEd7rcKUQr0e9gPlZxmBM+QgUr6IKt9W6oGWqai5VjoX9AM+Z\ndfWku6u909nWonH1Pl77P78EkN1Hxhn3HSoiVTGq6jOS6lO0/wKKfZcZ/1pPVV2peQrCeqne5SkP\nfdYxqjCHDBkSPe+GhrYR6gcdtpv6HVBXX8RWLIXvq1cykMWmtjupPAlaDxgL4T51vMuVb+qPrZ/V\n1S6xsRw/yxVO5PDDDy/Yh7Yfse+rOl7XOsR2Ub87sB7G6hY/w/PUVbYcv5988slFnzVxVIWreUDY\nrmrd4X0Nx946hlbf7FR7qZ67MVK/o/B1XUmh32HCz2nM6eoRXbWj369j9TK1ykjLQFcbk5iHuNk+\n7AFsjDHGGGOMMcYYY4wxDZRGjevuF2D/AFzHqL8PM62H2R41ezxnidQfTJURsdkYzUysarRU5l31\njwHSahvOMIXeYab20RlKxgm9RVX9wvsX3lv1x1JVq84QqoJOVS7hPnVf6q/H9zW7MR9DpYPGuHr8\naSzyvKk04OdDT0NVnui+1cdJFfmxjO48HpVTWt9UaUflgCqZw0zrVGzuakrfFHfffTeArOxUCafZ\nzEOfTtYNvqaf5f3hdupHWcqTV9UImnWX9ZPxxljkTH4qI3DsGOU8umMZp9u2bQsAWLVqFQDgsssu\nK3s8AGi0+5Z60WyLeueLoG/YvEVB3XzLyoHdqs+L94bxz0e2Bezvut94ddHx3v6/N0evRctnazxR\nU/cv9aiem1T+1mUdvO+++wBkfrPaR6cUfqEqkOfP8g7bVKBYZaZq1Nj4IOUhmop/LVvNSxDuXx9V\nFav1VWMgthKFdZjHZd2PjXFihArh+++/H0CxEjNUCQPFfWDK5zckFc9at1P9USkfT72/mr+B94qx\ns379egDAueeeW3SetQXvE33X1RefaNmFMa1ev6qAVH9ZJbaKQJVX6ndb7t5y+/A8+f83pv0MAPDV\n36vbx68+2zKW2PIl8JBJlwMAXp1Q7UPN9pPxFqoaVemlKk9uy/fVQzl2/jxPjkd0hZiqGXkP2d6r\nojx8T8tV1emjRo3Crgivv2XLlgCKxwdhu6nfyXScy/uj9UBjNKwPei95D7Uv13FNKq9MCO+5fldR\nJS2PwVUtumIWyFbCaBkw1tatWwcg8+fV/lO/f4TnpXlLeH7qma+PHL+H434tv5p6z5s0jAsdD2ic\nM4ZZH3QlA1Cc/yY1BtH4UWK+/qqkTa160N969PtteC06ZtMVvup7zLabsRkqgPX7DctT951aRcJ6\nUkoFbbYOK4CNMcYYY4wxxhhjjDGmgdK4Dn8B9g/AO5ixY8eW3ebOO+8EkKkLOQua8kgLVQzqlacz\nUJo1VpU04ayXzrRScUCF4rBhw8pei6kcnM3kLDqz3nK2Tu8xZwTD+FAfSc16r76Jqjrj83AGUH2B\nVbmg6mGdgVXPrPA1PS5RBbDOCMdmMlNZulUNzWOpkke3B7K6qRm1WUeIHpMz1Jyt5Qw3YMUAoSKS\n8cN4531iWavvVuhjyfigqlKzxms7qIqZ2CoLjRPeU41R3mOeD7fjzH1YLw+a9O/V/2xRgr31wykF\n+0x57aqaKjxPVd/WlD2mPV79z7DTq/f9T8F1bfGsbLzltT3veRIAMCCxr8mTJwPI1MjdI9uo73JM\nDQEUKyX1vodlU07xq97irK9vv/02gLqtg7feeisAoH379gAyJZiqjHjuVEKxHwjjjvEetqVA2n86\n9RiiZRbztQWK1YF6f0LFjPYv5bx0dZyi4xygePXH+PHjAQBTplTXJdZ9lveYMWOKrlU5//zzAWT+\n42xHWPbcJ+v6u+++CyCtmgOKPe91HKePWsdj90jVn3oPdOUP+5vzzjuvbBlUGq7oYLyrb7X6nJby\nPuZnNN6JKq34WVUwhfGWGhNrW0w0ltVXMXyN7SeVv19ukvPe8v5hv/weAODFy68HEPeD1WtjGWi9\nUx9k9UMO21n1Tma2d9YtXjvbS666ufrq4pUdpjQPPPAAgCxHgLbnqtYHivN68N7pGJXtD5+rajfm\nrasrT1Xtpz7Wqu4N+xhVDKY8unWVlB4rbOs4jmEbzH1yW1Xqp3xWw32mVgOm1M3aNuj3acC5cSrJ\nb3/7WwDFfaZ+L1XFqtaDsH9gLhXeW+6bfXlKNV8qf0UqL0VqDMs45/asB7HV3FTJqxJYPdz1/PkY\nWwXL4+uj9h08htZLf0etHHWpAC6/5tQYY4wxxhhjjDHGGGN2IhYuXIhDDz0U3bp1ww033BDd5vLL\nL0e3bt3Qs2dPLF26NP96586dcdRRR+GYY45Br1698q//+Mc/Rs+ePXH00Ufj9NNPz9vsffDBBzj1\n1FPRvHnzmlvuNWpU9q9SWAG8EzBixIjo63fccQeATCFE5USoLlHvGVUlqPKTM2Q6IwVksz+cNaJq\nZFfJzlvf4Kwc1Riq1lUlcEzVzfuuiiuqDfioM5I6+xjOuHJWVj0WVT2Z8usj4T7VI1d9j/X8VBHM\nfcUyz/M9nf1XfybNJhzzhuJ7vCd8j7OuVKlqVlVVHYd12FTDe0/FIxXALGOd3VclS4jOxOvseer9\n2My+xjXbTraPVHVT+Uu1FNtrVR9X77Swk9f6mfIHTankwn3UdABBZWS+fTmnegBDxQQADH7qHgCB\nSrgMRUqBLYrDadOm5V/ad8UKAJkCStUeqUzJqjIKlTe8J+pLq0pJKjx3JFSiPvHEEwCKs6+r1yFV\nd7pCCMhiXxUdfFTv2pT/bExNTVSJqf5x6iWpKypin9XVRinFlZ5TGBspFQ6VwDfddBOArM4zBrVN\nDmN20qRJALJ7oSs4VFHXr18/1BSO57SvUgUSr12zeof3SL1Y67NCh2WnClSWrd5bVQuG/rKpbOs6\nviVatto+xPbJ8+D94WdU+Ut0JRQQxGTjwsdGZbKq09eUfV8sd4jmgFDvVr2OlMI6PE9uwz5Ncxeo\nKs1sPWyHdFygysRQra6K3lT7qMo9VRPXxI9dVzbxfHUVAcdpodKQsclHHZencnxomxd+p9C8Aiy3\nMOdDuK9UvoewvurqVh6jlHd7uE8eI+zbuI/U93hTc1jOHOuoP7Z+56xJjiPea8arPqqnrh5LV5IC\nxauc1POa32F4/tpH6O81QHF7rXU5leuAfQW99UNlPuuqfmfS1cOqwNfvODsLmzdvxqWXXorHH38c\nHTp0wPHHH49+/fqhR48e+W0WLFiAFStWYPny5Xjuuecwbtw4LF68GED19f/3f/93/jc3MmHCBFx3\n3XUAgF//+tf46U9/ittvvx177rknfvazn+Hll1/Gyy+/XHcXWkOsADbGGGOMMcYYY4wxxjQYlixZ\ngq5du6Jz587YfffdccEFF2Du3LkF28ybNy9vZ/r1r38dGzduxNq1a/PvxyZn+cM+UD1xQBufvffe\nG9/85je3StDVqFH5v0phBfBOzMiRI6Ovz5o1K/8/Z5pSPsE6c6k+MzF1BWd/qKQxOwbO0qUyfuvs\nI4mpF3lPdYabs9fqsagK8pjSkMfV7M56Himv3VB5Q6ULtw2XWADZrCa3U2UwH0MVTCpDOs9D/YNZ\nh7SMwvLVmV9VSGuWY91elVsm45JLLgEAPPLIIwCKfexUqU1CNYZ6P6o3mMaotpsxBQjrIZW+9P18\n7733Cs5bofel1hMAeOOamwqOS11jym815QsaDlZiytgY9JznIKZLly4AMiVBGJvPHXwwAOBvW7zZ\nhg4dWnLfKYYPH152mxkzZgDI1B+q3KcSIuZ7WxN/1/qGqhPZLnGwqW0a25bwnqtXqGaLV1RBxrIN\nFTN8LeVJq0pzzZyu9Tb8jHosapynvH9jcOwybty46PtUyLA8w0E8kCn1GXdA5jep/pc1id9ypMZz\nDR3GFh9Z7roaho+qegzvm45H+FzbbR3vaJ8Rxp1+4dN9qTpdVzSpjyKQxeaSURMBAMdP+XHBZ0mj\nPbeM13evPubq1asBFHvZA1m7zj5LM8qX8/mOqXf5nq6MUc9kKtl4L+bNmwcga8P69u1btG9TiLbz\nKf/ZcLyp4xaWt66y42d5n9TDM/yepzk7dOWNevJrP8DzC1cKcUykdZr1juelq+zUyzVc4RFTBYfn\nkaqn+n64na6I4XscW6QU1tpvhWjbYrYdjRtV5bL8eb8Y1xqb4dhDxwGa24jjej5nnapJ/qYUt9xy\nC4BsbM3faVKrq4Di78e6QoYxyfPXlTS8Trbl4T71NwM+VwWwrmRNfbepr1RVVaFTp0755x07dsRz\nzz1Xdpuqqiq0bdsWjRo1whlnnIEmTZpgzJgxBdf/ox/9CHfddRf23nvvvGKYbI1tQyUtHsphBbAx\nxhhjjDHGGGOMMabBUNMfV1MWTM888wyWLl2KRx99FL/5zW/w9NNP59/7+c9/jrfffhsXX3wxrrrq\nqu04RyuAzXYwaNCgotdmzpwJoDjDsnqjUUVkX9/6j6pJU56HOqsXzgBy1pOzmupFm8pArvsOZy7V\ni0hn8/mc26lqJ6YA5jaqcEipu9S/lDP7oQdwuWzrnB1Vj0WWH98PffjUn4mPPJb6BvKR95CzzfbS\nS6NxQtQrMuaTqz69mh1b1WXqRRpTRK5fvx4AsGbNGgCZ8pf1MwXjJpZ9PeV5qgMTrZepTMVANouf\nUitOnTq1YB+c5VcVXdh+tG3bFkCm8KlNuCxLYWZolgXbsJ1R9RtDvdLZBqoXacz3mXHEe6be4zGl\nePi5mL+sflZVJNqOqiKRhOdZLs7LKX/Vtx3IVocot912G4Csb1DfXu5bVdPhcXgeDSXGdiT0J372\n2WcBZPHEekzVEdshqr9i/tS8Zxznsg1TJSRJqdfDvkVzA2jspVTo3FcsZvk/+4iH+lXH0bm/u6Ng\nH3vMeKLg+Wf/7/8ByNSVYT3hNXKso2o3VcHpahL12w5hHVbva80VoqumnMug5sRWcIRoexr+r5/R\nOqQqWVWHh6uC+L+OYXVln8aV+kWH/qXcR7nvlezLOa7Q1S6hAricTzzjWn15NYdAWHZ8j2MzxrPm\nRNG8KiQ2blcVs9l2NNYY1xpzev9iSneibRXbT9Yt7ourLyqxUkfVww8++CAAYP/99wdQvGoqfE1z\nQfBRvbpVtc6YDVfM8FrD77BAsfJdfy9KjcXrOx06dMivHgaqVxJ37Nix5DarV69Ghw4dAADt27cH\nUO2nfM4552DJkiU48cQTCz4/ePBgnHXWWdt8jlYAG2OMMcYYY4wxxhhjzDZw3HHHYfny5fjb3/6G\nL774ArNmzSpK1NuvX7/8RNTixYux7777om3btvj73/+et/z65JNPsGjRIhx55JEAgOXLl+c/P3fu\nXBxzzDEF+0xN6sVo1Lj8X6WwAngXYfDgwdHXJ0+eDKB+Z4k2cag+WrhwIYDiWWlVnZJwFlS3oSpK\nvX/UN1RVXjqrHpLKbKpZ4tVXLpw55zVRDVTOe0iVWVQ3htk7OfupnpUkpdJlmcRUxdyXKkU1Uysf\nNQuzerOZYngfVBmpiuCY/yPviyreVd3N2XZ+luocPg99cPke7z3VZ6mM0UR9rUOVTjnFo9a7cr6U\nQPmMvSw/bjdq1CgAmWLyoIMOApC1EeG2559/fsl91ybb6jtc32GMqfpC1XYktkoj5dmufoja9mnc\nhbETZncHsnaQca8KZVUmx1S+qrLU46dUlnr+rM9ApsRX1OtXlTLqbRl6OKa8iU3lSPn2q6+jriQK\n/1dPWsY971vK/19Vg+H/Ghep9zXe9RHI+hnW4bw3qih+FcYiyyY8T345ZRmon6qOw7Q+xuqYerYS\nlqeqztSD0wrgmsOVRO3atQOQjQ9Sqx2ArPzZ5moeBD6qcjWVqyLch45hdczK2FXVd2xMEqqWS8G+\n/OabbwYAdO7cGUBWFuF+tO4SPW9dGUb4ejjW1tWLvDaWI4+v7QnLhPsK26Ry40BTc7T91lU7qlZn\nLDM29btc+Bkd4/OzjL0DDjig4tdDBg4cCCDLN0DFafidQFdz6XdGHSdqToyYF72udlKf/ZR3/s7K\nbrvthsmTJ6NPnz7YvHkzRo4ciR49euRXDI8ZMwZnnXUWFixYgK5du6Jp06aYNm0agOpVjrxPX375\nJS688EL07t0bAPDDH/4Qr732Gpo0aYKDDz44334B1W3YRx99hC+++AJz587FokWLcOihhybPsS4V\nwP4B2BhjjDHGGGOMMcYY06A488wzceaZZxa8poIxCiNDunTpghdeeCG6z9mzZyeP97e//W3rT7KO\n8A/AuzhW/u78pDL8cgaQs50xXzad3ecsImcZqaBJqVxS3qMxUjNbqsbRLKYh77//ftnjxBg9ejQA\n4Pbbb8+/xllPzqSqjyTLRrMNqzKYGVzDfarnmvpgqpqJx+TMtetlGvokMwM61RmML8Z7LN54z1ju\nvA+qMGFMcDvWD74fU2hr/eK2v/rVrwAAV1xxBYAsA/Cxxx5bcJ5hHSqVDThGTPEbXi+QVkTyfBh7\nPE+ys2X6bShQacs4Z3ww9tS3NOZZm/IvVaWTqnZLrehQf2xVFPIY7EvK+faG/6eUv+X6F14nVXRA\nseKdSg62FyxH9QXnc1UZAVnbMn78+JLnY7YetrXaBuvKIb0/YZsXU/UB2ThG64HGpKrsw/2rslcV\niDVVAoeo8rgcHMfEuPvuuwFkYw+Wp3oCp1Y8xUidu6620Qz0rHv9+/cvewxTDduU+fPnAyhuu1nm\n4fhdx6Y6jldfU6Jj3HC8qu/xOesjx0xUTKr3dEwBnFrBkYK5PaZMmQIAOPDAAwEUxqHGIM9Tlcqp\nlWK8Po4nw2siqqTWOqTff2KrW7yar3JwTKQqdB1r6MrLlEdw+BlVc2t+EPZLVOnWhg8u9zl9+nQA\nKPCn1X5GVbtab3mtvA4+hmM7lo/WT20XGMMNdbVdvaKxFcDGGGOMMcYYY4wxxhjTMLEFhDGmpnDW\nuk2bNgCyGUHOWnNGXFUC4WucFVSVK2cZVUVCVPUFpBUzKQ9IzlxyllGVBUCW9bpcFuFy0NcUAB56\n6CEAmTpI1W/qB8hrVD9kVQ0A2Uy1zrqqd5+Wje7bFMMsvFTKaEyqB3aI+gerx5t6ilHJxNn/2Iw5\n6wZf02Pw/G688UYAQPfu3Qv2qbEApL1ZU0oTVf7ycx9++GH+tUGDBhWVR7jPsG6YHQ+zTjMztKoF\nGbtUemj2diCLScYgn2ubpdnWte0L/dj5v8acKmrpFa2KsJjasZwfaUpBxn2zva2qqsq/x3L49a9/\nDSBbqcE6Qc9UVXWxHC+77LLoMU3twBUK++67L4DiNlrvjyqBQ1Ke6Kra1e34fhhvuppH95k6phLW\nF8ZmpcY14T6odCesGykVI/s8PucjkL4mXgu3VfV8uCrKbB3qy6n+sqGiVJW+ej+4LdtsXfUW+psT\nHZfr9wZ+VscY3I7qytCPfe3atTW7eIGqaOYhCMd6uiKAjxx/8Xx1jK0+vmH91u8/6vnO11nuOm5X\ntSUAfPDBB1t93SYOx/5z584FUOx9rflliN7HVJsOZG0e40SV5aVWR1WKiy++GADwwAMP5F9LrdxQ\nn2Oer47hGKPhtfOa1B9YVxGnVg+aymMPYGOMMcYYY4wxxhhjjGmo2ALCGGOMMcYYY4wxxhhjGihW\nABtjaso777wDAGjXrh2A4mVefCy17EXRpU18rEnSN01eoUsadEkWl6kxWyaXTIvOYcYAAB4fSURB\nVIVLEWsjkQITBnH5M5d1pZIX6WMsgYsuww6XgoXPWTbcnkttwoQUpjSMe13eyPsXW3KuS4HV+oHo\ncktd9hXGZuo9XS7YokULAMABBxxQ8LmaJEjROqTLNPVceOxwSXyKcKmmqT8wEeS8efMAFLfvGrOa\nUBIoXq6r7blaRLDd4uv8PJdDhqjlCW0l+KhWRKTUEjddml/O+oGxu2rVKgCF7SfLh0uSWT7cZsKE\nCcnzMHUPLWqee+45AFnsMZ55P9WCJByLaGJEtR9Ru52UFVPMjoekEsipJYomqQutWbikdt26dQAy\ne6CrrroK28vw4cMBAHfccUfBeWr7ENq6hOcbQ8tA+x+18eI5mK2Hlh2dOnUCkLWfMQsytR7QcTrr\njC5jZ/1gDIR9idYdtuNqe8DYVYsD2n+Er29vUmMmop05c2b+NR0/8ZrVAkJf1zF5rK7rdyW+rnVE\n6zb3HS6ZdxLdysPkkgsWLACQJcpNWaaV+t6asiTU1xlndTlePu+88/L/0/JObRd1PKjJgPX8w35I\ny4Vxr3Hs9rzuaFSHCuDSplXGGGOMMcYYY4wxxhhjdlqsADZmJ4eJEh577DEA2QygJsOKJcVSOLuo\nCdHUgD410xpuG3svPC8+MhkP1SNUMYTH0H1WgtGjRwMA5syZU3AMVU6rkkeVP7Fz02tW5R3hbCyT\nZPBemvJQWTF9+nQAQNu2bQFkim4SqjlSSnbeW27LWX69b5psBShOLMRH1kOqJ6lSSCnMY6SSvOnr\nqphcs2YNAOA73/lOct/k8ssvL7uN2XH069cPAPDMM88AyGKPShVVPMUS5aSSnzBuGO+60qJjx44A\nCtWCqkTTJCNUGvJYNVX1httoXGt/w2tmojy2n2G7S+Uvz4vtvanfsO2iAlJXbTC+2a6G91zbcX2d\n9UD7bG1fS5GKY1UVa2K0sG7xPa4Kueiii2p8/JrC42k5qZK6XPK6kJRajnXeSrHtZ/DgwQCAZ599\nFkCWZJbxH7bFmrypXBxr/eBjGAP8P6Wi57GoTGayTyaDY/2tjVhg2QDArFmzAKTHVara1ZjV7zix\nz+gqLk2orXWdqueajLvM9rNy5UoAWfkzgaiO03UcEVs1ovAec0UTVw7tqGTJ7777bsFzTc4Y+24C\nFF8z22ogi2+2KRxXMY6HDRtWuQswNcMWEMYYY4wxxhhjjDHGGNMwqUsLCP8AbEwD4a233gKQqUo4\n46czhFQmAsWeVuqhR1KebzFvpZTPnr7PGUzuK6VcBoBNmzYlr3tbmTJlCoCsXPhIxUXKY4llE1Nb\nqCqOj1TY8ZHXzlnlsWPHVuaidkEuvvhiAMC0adMAZPHE+xnzaOY95D1WRQzvE+uH3vuYX6LGgyqu\nGFc1IeXxq3VKFZP05VuxYgUA4IwzzqjxMU39hh7pBx98MIAsrhizjM1QAax9ALdVP0e+r3WnJp7v\nWkf0mFoveAyqdsPj7b///gX70L6C6hWqzKiK2V6PSVN/OOeccwBkincqubXtVe9pIIsXbsO+WxXB\nVC/qKgyNZSAd+6o4ZJ/BFU3q9x/2GVTl1oXnv9Z1XeVFYt7G6jep6v8NGzYAKFRmmsrw/vvvAygc\nrwOFY3PGUcq/VEnl44gpgFPe19pH0POX51mTvAOVgJ7hVAK3bNkSQFYm2oexHqhaOrZ6kY/sb1K+\n36wHvFeuB3UL+/2bb74ZQHZP+R04tXK0lMe7rmiiJ3dt5KHZGlR5fPfddwPIFPjs09TbXZXCoQJY\nfeHtV10PsALYGGOMMcYYY4wxxhhjGih1qABulNsa8ytjTL1n4cKFAIADDjgAQPEsaDiLrbPhVIVw\nFl0VYtxe/XJjCuBU08LZRs6wcmaVs5GccaWSBgDOP//8Upe8XXAmldBHimWgnnmqwoyVpyrWqBCo\nDa8/UwiV3TEVgHreUTXC2XOqthiDfJ2+wur3q/+Hx6C6i/ts3759wT7VVzuMI1X+qopLVQrr168H\nALz55psAgBEjRsA0TKh46tChA4AsNqh4isUm22/Gi662UN9etn1sC0NVCeuOev7ykf5xrDNt2rQp\nOH++/9JLL+Vf0zrSunXrgvNnX8DPDh06NFY0pgHBfrlHjx4AshiMZTMnrAuqjGR8sf3magy+rgrh\nUEXPuhJr+8NjsI+nKpbbqSILyFTLb7/9NoBsFUttcP/99wPI+ptUTghduQUUq+NYThyznXvuubVw\nxibk+eefB5DFdDieUXV5akVf6pGUGsOyndc+RMco/BxXIl5wwQU1vsZK8PDDDwMo7rO0bHS8FVsh\nxr6MK6o4HuQ+WDbO2VG/mDp1KoBsHMHxfU3y3zAe9DsAV5/y9frqi8sVkFQEM1a5MoUe3c6FUL/5\npN83ym7TdN6zFTmWFcDGGGOMMcYYY4wxxhhTl1gBbIzZXv70pz8ByBSHOqMPZLOEVBVQ7cLnnPXk\nZzRDsCrJwm1VIZDyEVb/VX6e6higbmZdf/nLXwLIlKNU2KWyqsaUSDx3KnyGDBlSi2dsasLkyZPz\n/zPeec9UEUZUvah+cjFUlUu1ItUHbdu2BZApU1QBHNYXzVavPpOqLKfqrDaVZKZ+oUpg9YoEiv1S\nGb/qjRr6wgFZjLLvCBU02h7qCg7GPesUs7SzvaRHJNXq4fldccUVpS7Z7IIsWLAAQNZ+6vihlFqX\nccVtqaJkfKsHYiwHArfVVRjqh8t9vffeewCyMQDrXKiypKqsLscHM2fOLDgf1k9eu+YnCP/Xsdm4\ncePq4IwNkClbDzroIACF6t3U13feY27LOqJKSL2/sde0brBuqU87433ZsmUAij1L64pHHnkEQPGK\nvZRyP7x2qj45rnKc75yoEpiq8NgqKW3/2DYzFr773e/WwRkbU80nA04ou03Th/9UkWNZAWyMMcYY\nY4wxxhhjjDF1SR0mgbMC2JgGyl133QUA6NatG4C4elH94PhIpQcVNGwm1A+XSplwFp2f1cy5NX3k\nTOzy5cvz+9wRM/HTp08HkHkp09OVCh9nnm+4zJ07F0CmAFZFTUyFo36TjBP6kPFRVfckrEPcF+sZ\nVZV//etfAQDDhw/fnsszDQi281S7NGvWLP8e45axpb6IquzTPoKKmdBbUjOlqwKS7ST7CnrSUXW/\natWqgu2BrO2vr/56ZsezaNEiAMB+++0HoNjTFsjilDHJR75ORTvbb6rXSymAuX++pl6RPAbrEl+n\nb/XatWsLjgVYWWi2nieeeAJANo4AihXAukJP23tVvcZW6zG+WRfUZ5jwM4z3d955BwDQv3//bbm8\nisOVXx6nm3vvvRdAcS4PoDgnDR9rM++MMSn+fu43y26z95w/VuRYVgAbY4wxxhhjjDHGGGNMXVKH\nCmD/AGxMA+Wiiy4CkGWB7tKlC4DCrNQpP9uUgkZVAeqDB2TqAr6n3r+hF164T0IFMP36dhT2U911\nSalfNFaBLL41q7R6Lap3HvcVW4Sj9ZLZqK38NQrbeULPSCBTvFDJm8rczpjko/qzhzGq7bnC2OVn\n6aXHdp19Rdi+h/2HMTF69+4NIFNC0tcxXDmh6lxdSUF0LKJtcPhcxzg6RlJPYtYhHtO+uaYSnH76\n6QCAp556Kv8a1fDqz6uPGsOqWg+helgVv7oPfpZK9w8//HAbr6x2sPLXkFI+vlOmTAGQrQ7x9z6z\nI2lUh0ng/AOwMcYYY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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc886ccb6d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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a7KpfX1NTU/SZqq24Dt4be3t7Y55sjC1VIlq1dSguQv5xhUIh2qatjmn3derU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\nuSzroaqrNm3ahNdeew0AMHr0a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512Gm644QZ86Utfwpo1azBixAh84xvf2OZBLCBtRk0J\n+/Dhw5OXeqpU7NQ+oPCCz+mD69at264XyIsuumib13XCbG8d3Bl9qp1u+vOf/xxAYaoflVKcXmqn\nHLI+8lMHsJjd8/Of/3x031QDst5zQLe6ujoZuKIyki+17r/Ue7a3/vXUp86ePRtXXXUVRo0ahcbG\nRlx11VU+iOUkbE/9Ky8vT6a5A8DQoUOLvnv++efx29/+FnV1dUlfCQCLFy/G8ccf34dH4QxUdmT/\nO3ToUMybNw/f+MY3cPXVV6Ourg5nnXUWbrzxxh1yLI7TVwwoRZbjOI4zcNiVA1mO09uBrOrq6pR3\nWmwgqzf4QJbjOI7jOM6OxQeyHMdxHMcZdNBLoqamJpnKwwFVH0R1HMdxHMfpv5Tt6gI4juM4juM4\njuM4juM4Tm9wRZbjOI7jOI7jOI7jOI4zIHBFluM4juM4juM4juM4jjMg8IEsx3Ecx3Ecx3Ecx3Ec\nZ0DgA1mO4ziO4ziO4ziO4zjOgMAHshzHcRzHcRzHcRzHcZwBgQ9kOY7jOI7jOI7jOI7jOAMCH8hy\nHMdxHMdxHMdxHMdxBgQ+kOU4juM4juM4juM4juMMCHwgy3Ecx3Ecx3Ecx3EcxxkQ+ECW4ziO4ziO\n4ziO4ziOMyDwgSzHcRzHcRzHcRzHcRxnQOADWY7jOI7jOI7jOI7jOM6AwAeyHMdxHMdxHMdxHMdx\nnAGBD2Q5juM4juM4juM4juM4AwIfyHIcx3Ecx3Ecx3Ecx3EGBD6Q5TiO4ziO4ziO4ziO4wwIfCDL\ncRzHcRzHcRzHcRzHGRD4QJbjOI7jOI7jOI7jOI4zIPCBLMdxHMdxHMdxHMdxHGdA4ANZjuM4juM4\njuM4juM4zoDAB7Icx3Ecx3Ecx3Ecx3GcAYEPZDmO4ziO4ziO4ziO4zgDAh/IchzHcRzHcRzHcRzH\ncQYEPpDlOI7jOI7jOI7jOI7jDAj+f4R6d9HBD05sAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc885a90f10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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1PNTe4npUdqq2O7Ji2M+MIRSPUbuMZbW9kWHGMsr0yRg47APriZom/QHu+0Cl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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc88692c190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_fw(fw_data['Spe-v-Wav'][0], threshold=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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fb/1c11O7zLo27of2EHBr+GEC0AFoLCKZxpgdQBbgBwpFJOqbq6mvlFJKOeYN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\n0IQQQkSE/wFAkRb2bg4XoAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2aab23671f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for key in roc_data:\n",
" ax = plt.subplot(5, 1, key)\n",
" sn.heatmap(np.array(roc_data[key])[None, :], annot=True, cmap='cubehelix', vmin=0.5, vmax=0.78,\n",
" yticklabels=False, xticklabels=False)\n",
" ax.set_yticklabels('%d' % key, rotation=0)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"43 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P07' 'SAD_P08' 'SAD_P09'\n",
" 'SAD_P10' 'SAD_P11' 'SAD_P13' 'SAD_P14' 'SAD_P16' 'SAD_P17' 'SAD_P19'\n",
" 'SAD_P20' 'SAD_P21' 'SAD_P22' 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27'\n",
" 'SAD_P28' 'SAD_P29' 'SAD_P30' 'SAD_P31' 'SAD_P34' 'SAD_P35' 'SAD_P36'\n",
" 'SAD_P37' 'SAD_P38' 'SAD_P39' 'SAD_P41' 'SAD_P44' 'SAD_P45' 'SAD_P47'\n",
" 'SAD_P48' 'SAD_P50' 'SAD_P51' 'SAD_P52' 'SAD_P53' 'SAD_P54' 'SAD_P56'\n",
" 'SAD_P58']\n",
"[[13 9]\n",
" [ 8 13]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.62 0.59 0.60 22\n",
" 1 0.59 0.62 0.60 21\n",
"\n",
"avg / total 0.61 0.60 0.60 43\n",
"\n",
"{1: [0.60497835497835495], 2: [], 3: [], 4: [], 5: []}\n",
"[[13 9]\n",
" [ 7 14]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.65 0.59 0.62 22\n",
" 1 0.61 0.67 0.64 21\n",
"\n",
"avg / total 0.63 0.63 0.63 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867], 2: [], 3: [], 4: [], 5: []}\n",
"[[17 5]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.74 0.77 0.76 22\n",
" 1 0.75 0.71 0.73 21\n",
"\n",
"avg / total 0.74 0.74 0.74 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345], 2: [], 3: [], 4: [], 5: []}\n",
"[[12 10]\n",
" [ 9 12]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.57 0.55 0.56 22\n",
" 1 0.55 0.57 0.56 21\n",
"\n",
"avg / total 0.56 0.56 0.56 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841], 2: [], 3: [], 4: [], 5: []}\n",
"[[11 11]\n",
" [15 6]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.42 0.50 0.46 22\n",
" 1 0.35 0.29 0.32 21\n",
"\n",
"avg / total 0.39 0.40 0.39 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [], 3: [], 4: [], 5: []}\n",
"42 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P07' 'SAD_P08' 'SAD_P09'\n",
" 'SAD_P11' 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20' 'SAD_P21'\n",
" 'SAD_P22' 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28' 'SAD_P29'\n",
" 'SAD_P30' 'SAD_P31' 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37' 'SAD_P38'\n",
" 'SAD_P39' 'SAD_P41' 'SAD_P44' 'SAD_P45' 'SAD_P46' 'SAD_P47' 'SAD_P48'\n",
" 'SAD_P50' 'SAD_P51' 'SAD_P52' 'SAD_P53' 'SAD_P54' 'SAD_P56' 'SAD_P58']\n",
"[[13 9]\n",
" [11 9]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.54 0.59 0.57 22\n",
" 1 0.50 0.45 0.47 20\n",
"\n",
"avg / total 0.52 0.52 0.52 42\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539], 3: [], 4: [], 5: []}\n",
"[[12 10]\n",
" [12 8]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.50 0.55 0.52 22\n",
" 1 0.44 0.40 0.42 20\n",
"\n",
"avg / total 0.47 0.48 0.47 42\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266], 3: [], 4: [], 5: []}\n",
"[[17 5]\n",
" [ 6 14]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.74 0.77 0.76 22\n",
" 1 0.74 0.70 0.72 20\n",
"\n",
"avg / total 0.74 0.74 0.74 42\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [], 4: [], 5: []}\n",
"43 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P07' 'SAD_P08' 'SAD_P09'\n",
" 'SAD_P10' 'SAD_P11' 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20'\n",
" 'SAD_P21' 'SAD_P22' 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28'\n",
" 'SAD_P29' 'SAD_P30' 'SAD_P31' 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37'\n",
" 'SAD_P38' 'SAD_P39' 'SAD_P41' 'SAD_P44' 'SAD_P45' 'SAD_P46' 'SAD_P47'\n",
" 'SAD_P48' 'SAD_P50' 'SAD_P51' 'SAD_P52' 'SAD_P53' 'SAD_P54' 'SAD_P56'\n",
" 'SAD_P58']\n",
"[[17 6]\n",
" [ 9 11]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.65 0.74 0.69 23\n",
" 1 0.65 0.55 0.59 20\n",
"\n",
"avg / total 0.65 0.65 0.65 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435], 4: [], 5: []}\n",
"[[13 10]\n",
" [10 10]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.57 0.57 0.57 23\n",
" 1 0.50 0.50 0.50 20\n",
"\n",
"avg / total 0.53 0.53 0.53 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395], 4: [], 5: []}\n",
"[[13 10]\n",
" [11 9]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.54 0.57 0.55 23\n",
" 1 0.47 0.45 0.46 20\n",
"\n",
"avg / total 0.51 0.51 0.51 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392], 4: [], 5: []}\n",
"[[17 6]\n",
" [12 8]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.59 0.74 0.65 23\n",
" 1 0.57 0.40 0.47 20\n",
"\n",
"avg / total 0.58 0.58 0.57 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428], 4: [], 5: []}\n",
"[[10 13]\n",
" [14 6]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.42 0.43 0.43 23\n",
" 1 0.32 0.30 0.31 20\n",
"\n",
"avg / total 0.37 0.37 0.37 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613], 4: [], 5: []}\n",
"[[17 6]\n",
" [10 10]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.63 0.74 0.68 23\n",
" 1 0.62 0.50 0.56 20\n",
"\n",
"avg / total 0.63 0.63 0.62 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432], 4: [], 5: []}\n",
"[[13 10]\n",
" [13 7]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.50 0.57 0.53 23\n",
" 1 0.41 0.35 0.38 20\n",
"\n",
"avg / total 0.46 0.47 0.46 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399], 4: [], 5: []}\n",
"[[15 8]\n",
" [14 6]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.52 0.65 0.58 23\n",
" 1 0.43 0.30 0.35 20\n",
"\n",
"avg / total 0.48 0.49 0.47 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391], 4: [], 5: []}\n",
"[[13 10]\n",
" [11 9]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.54 0.57 0.55 23\n",
" 1 0.47 0.45 0.46 20\n",
"\n",
"avg / total 0.51 0.51 0.51 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392], 4: [], 5: []}\n",
"[[15 8]\n",
" [ 9 11]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.62 0.65 0.64 23\n",
" 1 0.58 0.55 0.56 20\n",
"\n",
"avg / total 0.60 0.60 0.60 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [], 5: []}\n",
"43 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P07' 'SAD_P08' 'SAD_P09'\n",
" 'SAD_P10' 'SAD_P11' 'SAD_P13' 'SAD_P14' 'SAD_P16' 'SAD_P17' 'SAD_P19'\n",
" 'SAD_P20' 'SAD_P21' 'SAD_P22' 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27'\n",
" 'SAD_P28' 'SAD_P29' 'SAD_P30' 'SAD_P31' 'SAD_P34' 'SAD_P35' 'SAD_P36'\n",
" 'SAD_P37' 'SAD_P38' 'SAD_P39' 'SAD_P41' 'SAD_P44' 'SAD_P45' 'SAD_P46'\n",
" 'SAD_P47' 'SAD_P48' 'SAD_P50' 'SAD_P52' 'SAD_P53' 'SAD_P54' 'SAD_P56'\n",
" 'SAD_P58']\n",
"[[13 9]\n",
" [ 9 12]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.59 0.59 0.59 22\n",
" 1 0.57 0.57 0.57 21\n",
"\n",
"avg / total 0.58 0.58 0.58 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [0.58116883116883111], 5: []}\n",
"[[14 8]\n",
" [10 11]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.58 0.64 0.61 22\n",
" 1 0.58 0.52 0.55 21\n",
"\n",
"avg / total 0.58 0.58 0.58 43\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [0.58116883116883111, 0.58008658008658009], 5: []}\n",
"41 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P08' 'SAD_P09' 'SAD_P10'\n",
" 'SAD_P11' 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20' 'SAD_P21'\n",
" 'SAD_P22' 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28' 'SAD_P30'\n",
" 'SAD_P31' 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37' 'SAD_P38' 'SAD_P39'\n",
" 'SAD_P41' 'SAD_P44' 'SAD_P45' 'SAD_P46' 'SAD_P47' 'SAD_P48' 'SAD_P50'\n",
" 'SAD_P51' 'SAD_P52' 'SAD_P53' 'SAD_P54' 'SAD_P56' 'SAD_P58']\n",
"[[15 7]\n",
" [ 9 10]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.62 0.68 0.65 22\n",
" 1 0.59 0.53 0.56 19\n",
"\n",
"avg / total 0.61 0.61 0.61 41\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [0.58116883116883111, 0.58008658008658009], 5: [0.60406698564593309]}\n",
"[[15 7]\n",
" [11 8]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.58 0.68 0.62 22\n",
" 1 0.53 0.42 0.47 19\n",
"\n",
"avg / total 0.56 0.56 0.55 41\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [0.58116883116883111, 0.58008658008658009], 5: [0.60406698564593309, 0.55143540669856461]}\n",
"[[14 8]\n",
" [11 8]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.56 0.64 0.60 22\n",
" 1 0.50 0.42 0.46 19\n",
"\n",
"avg / total 0.53 0.54 0.53 41\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [0.58116883116883111, 0.58008658008658009], 5: [0.60406698564593309, 0.55143540669856461, 0.52870813397129179]}\n",
"[[14 8]\n",
" [11 8]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.56 0.64 0.60 22\n",
" 1 0.50 0.42 0.46 19\n",
"\n",
"avg / total 0.53 0.54 0.53 41\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [0.58116883116883111, 0.58008658008658009], 5: [0.60406698564593309, 0.55143540669856461, 0.52870813397129179, 0.52870813397129179]}\n",
"[[13 9]\n",
" [14 5]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.48 0.59 0.53 22\n",
" 1 0.36 0.26 0.30 19\n",
"\n",
"avg / total 0.42 0.44 0.43 41\n",
"\n",
"{1: [0.60497835497835495, 0.62878787878787867, 0.74350649350649345, 0.55844155844155841, 0.39285714285714279], 2: [0.52045454545454539, 0.47272727272727266, 0.73636363636363633], 3: [0.64456521739130435, 0.53260869565217395, 0.50760869565217392, 0.56956521739130428, 0.36739130434782613, 0.61956521739130432, 0.45760869565217399, 0.4760869565217391, 0.50760869565217392, 0.60108695652173916], 4: [0.58116883116883111, 0.58008658008658009], 5: [0.60406698564593309, 0.55143540669856461, 0.52870813397129179, 0.52870813397129179, 0.42703349282296643]}\n"
]
}
],
"source": [
"basedir = '/om/user/satra/projects/SAD/fmri_results/individual/model01'\n",
"template = '{basedir}/task{task:03d}/{subj}/copes/mni/cope{cope:02d}.nii.gz'\n",
"\n",
"mask_img = nb.load('OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz')\n",
"mask_img = nb.Nifti1Image((mask_img.get_data() > 1).astype(bool), mask_img.affine, header=mask_img.get_header())\n",
"\n",
"\n",
"### Mask data #################################################################\n",
"nifti_masker = NiftiMasker(\n",
" mask_img=mask_img,\n",
" smoothing_fwhm=6,\n",
" memory='nilearn_cache', memory_level=1) # cache options\n",
"\n",
"roc_data = {1: [], 2: [], 3: [], 4: [], 5: []}\n",
"\n",
"for task in range(1, 6):\n",
" exclude = None\n",
" patients = df2.LSAS_POST_total.dropna().index\n",
" if True:\n",
" for grp, df_data in pd.groupby(tsnr_inv > 0.25, 'task%d' % task):\n",
" if grp:\n",
" exclude = np.intersect1d(df_data.index.tolist(), patients.tolist())\n",
" else:\n",
" exclude = tsnr_inv.index[np.nonzero((tsnr_inv > 0.25).sum(axis=1))[0]]\n",
" patients = np.setdiff1d(patients, exclude)\n",
" print len(patients), patients\n",
" for cope in range(1, len(con_data[task]) + 1):\n",
" copes = [template.format(basedir=basedir, task=task, subj=val, cope=cope) for val in patients]\n",
" #print '\\n'.join(copes)\n",
" images = [nb.squeeze_image(nb.load(cope)) for cope in copes]\n",
" fmri_masked = nifti_masker.fit_transform(images)\n",
" \n",
" X1 = pd.DataFrame(fmri_masked.copy())\n",
" X1.index = df2.ix[patients, :].index\n",
" X1.ix[:, np.nonzero(np.sum(X1==0., axis=0) > 1)[0]] = np.nan\n",
" X1 = X1.dropna(axis=1)\n",
" #X1 = pd.concat((X1.dropna(axis=1), df2.ix[patients, ['LSAS_PRE_total', 'Age']]), axis=1)\n",
" X = X1.values\n",
" if False:\n",
" y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)/df2.LSAS_PRE_total\n",
" y = y.ix[patients].values\n",
" y = 2 * (y > 0.5) - 1\n",
" else:\n",
" y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)\n",
" y = y.ix[patients].values\n",
" y = 2 * (y > np.median(y)) - 1\n",
" idx = np.argsort(y)\n",
" roc_auc, fw = classify(X1, X, y)\n",
" roc_data[task].append(roc_auc)\n",
" print roc_data"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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wzOb0GiUEMzMPtwMxWusrgQ+AwPtuXqO17goMAsYopfoGexIJaEIIISyz53qM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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2aab2272a3d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for key in roc_data:\n",
" ax = plt.subplot(5, 1, key)\n",
" sn.heatmap(np.array(roc_data[key])[None, :], annot=True, cmap='cubehelix', vmin=0.5, vmax=0.78,\n",
" yticklabels=False, xticklabels=False)\n",
" ax.set_yticklabels('%d' % key, rotation=0)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"38 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P08' 'SAD_P09' 'SAD_P11'\n",
" 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20' 'SAD_P21' 'SAD_P22'\n",
" 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28' 'SAD_P30' 'SAD_P31'\n",
" 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37' 'SAD_P38' 'SAD_P39' 'SAD_P41'\n",
" 'SAD_P44' 'SAD_P45' 'SAD_P47' 'SAD_P48' 'SAD_P50' 'SAD_P52' 'SAD_P53'\n",
" 'SAD_P54' 'SAD_P56' 'SAD_P58']\n",
"[[ 8 9]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.62 0.47 0.53 17\n",
" 1 0.64 0.76 0.70 21\n",
"\n",
"avg / total 0.63 0.63 0.62 38\n",
"\n",
"{1: [0.61624649859943981], 2: [], 3: [], 4: [], 5: []}\n",
"[[ 7 10]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.54 0.41 0.47 17\n",
" 1 0.60 0.71 0.65 21\n",
"\n",
"avg / total 0.57 0.58 0.57 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361], 2: [], 3: [], 4: [], 5: []}\n",
"[[ 8 9]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.57 0.47 0.52 17\n",
" 1 0.62 0.71 0.67 21\n",
"\n",
"avg / total 0.60 0.61 0.60 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597], 2: [], 3: [], 4: [], 5: []}\n",
"[[ 8 9]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.62 0.47 0.53 17\n",
" 1 0.64 0.76 0.70 21\n",
"\n",
"avg / total 0.63 0.63 0.62 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981], 2: [], 3: [], 4: [], 5: []}\n",
"[[ 7 10]\n",
" [ 7 14]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.50 0.41 0.45 17\n",
" 1 0.58 0.67 0.62 21\n",
"\n",
"avg / total 0.55 0.55 0.55 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [], 3: [], 4: [], 5: []}\n",
"38 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P08' 'SAD_P09' 'SAD_P11'\n",
" 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20' 'SAD_P21' 'SAD_P22'\n",
" 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28' 'SAD_P30' 'SAD_P31'\n",
" 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37' 'SAD_P38' 'SAD_P39' 'SAD_P41'\n",
" 'SAD_P44' 'SAD_P45' 'SAD_P47' 'SAD_P48' 'SAD_P50' 'SAD_P52' 'SAD_P53'\n",
" 'SAD_P54' 'SAD_P56' 'SAD_P58']\n",
"[[ 7 10]\n",
" [ 9 12]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.44 0.41 0.42 17\n",
" 1 0.55 0.57 0.56 21\n",
"\n",
"avg / total 0.50 0.50 0.50 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621], 3: [], 4: [], 5: []}\n",
"[[ 7 10]\n",
" [ 8 13]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.47 0.41 0.44 17\n",
" 1 0.57 0.62 0.59 21\n",
"\n",
"avg / total 0.52 0.53 0.52 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594], 3: [], 4: [], 5: []}\n",
"[[11 6]\n",
" [ 7 14]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.61 0.65 0.63 17\n",
" 1 0.70 0.67 0.68 21\n",
"\n",
"avg / total 0.66 0.66 0.66 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [], 4: [], 5: []}\n",
"38 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P08' 'SAD_P09' 'SAD_P11'\n",
" 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20' 'SAD_P21' 'SAD_P22'\n",
" 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28' 'SAD_P30' 'SAD_P31'\n",
" 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37' 'SAD_P38' 'SAD_P39' 'SAD_P41'\n",
" 'SAD_P44' 'SAD_P45' 'SAD_P47' 'SAD_P48' 'SAD_P50' 'SAD_P52' 'SAD_P53'\n",
" 'SAD_P54' 'SAD_P56' 'SAD_P58']\n",
"[[ 9 8]\n",
" [ 3 18]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.75 0.53 0.62 17\n",
" 1 0.69 0.86 0.77 21\n",
"\n",
"avg / total 0.72 0.71 0.70 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973], 4: [], 5: []}\n",
"[[ 3 14]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.38 0.18 0.24 17\n",
" 1 0.53 0.76 0.63 21\n",
"\n",
"avg / total 0.46 0.50 0.45 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801], 4: [], 5: []}\n",
"[[ 1 16]\n",
" [11 10]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.08 0.06 0.07 17\n",
" 1 0.38 0.48 0.43 21\n",
"\n",
"avg / total 0.25 0.29 0.27 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044], 4: [], 5: []}\n",
"[[ 8 9]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.62 0.47 0.53 17\n",
" 1 0.64 0.76 0.70 21\n",
"\n",
"avg / total 0.63 0.63 0.62 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981], 4: [], 5: []}\n",
"[[ 8 9]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.57 0.47 0.52 17\n",
" 1 0.62 0.71 0.67 21\n",
"\n",
"avg / total 0.60 0.61 0.60 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597], 4: [], 5: []}\n",
"[[ 5 12]\n",
" [ 7 14]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.42 0.29 0.34 17\n",
" 1 0.54 0.67 0.60 21\n",
"\n",
"avg / total 0.48 0.50 0.48 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506], 4: [], 5: []}\n",
"[[ 6 11]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.55 0.35 0.43 17\n",
" 1 0.59 0.76 0.67 21\n",
"\n",
"avg / total 0.57 0.58 0.56 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509], 4: [], 5: []}\n",
"[[ 7 10]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.54 0.41 0.47 17\n",
" 1 0.60 0.71 0.65 21\n",
"\n",
"avg / total 0.57 0.58 0.57 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361], 4: [], 5: []}\n",
"[[ 4 13]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.44 0.24 0.31 17\n",
" 1 0.55 0.76 0.64 21\n",
"\n",
"avg / total 0.50 0.53 0.49 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037], 4: [], 5: []}\n",
"[[ 7 10]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.58 0.41 0.48 17\n",
" 1 0.62 0.76 0.68 21\n",
"\n",
"avg / total 0.60 0.61 0.59 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [], 5: []}\n",
"38 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P08' 'SAD_P09' 'SAD_P11'\n",
" 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20' 'SAD_P21' 'SAD_P22'\n",
" 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28' 'SAD_P30' 'SAD_P31'\n",
" 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37' 'SAD_P38' 'SAD_P39' 'SAD_P41'\n",
" 'SAD_P44' 'SAD_P45' 'SAD_P47' 'SAD_P48' 'SAD_P50' 'SAD_P52' 'SAD_P53'\n",
" 'SAD_P54' 'SAD_P56' 'SAD_P58']\n",
"[[ 7 10]\n",
" [ 4 17]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.64 0.41 0.50 17\n",
" 1 0.63 0.81 0.71 21\n",
"\n",
"avg / total 0.63 0.63 0.62 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [0.61064425770308128], 5: []}\n",
"[[ 9 8]\n",
" [ 8 13]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.53 0.53 0.53 17\n",
" 1 0.62 0.62 0.62 21\n",
"\n",
"avg / total 0.58 0.58 0.58 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [0.61064425770308128, 0.57422969187675077], 5: []}\n",
"38 ['SAD_P03' 'SAD_P04' 'SAD_P05' 'SAD_P06' 'SAD_P08' 'SAD_P09' 'SAD_P11'\n",
" 'SAD_P13' 'SAD_P14' 'SAD_P17' 'SAD_P19' 'SAD_P20' 'SAD_P21' 'SAD_P22'\n",
" 'SAD_P23' 'SAD_P24' 'SAD_P26' 'SAD_P27' 'SAD_P28' 'SAD_P30' 'SAD_P31'\n",
" 'SAD_P34' 'SAD_P35' 'SAD_P36' 'SAD_P37' 'SAD_P38' 'SAD_P39' 'SAD_P41'\n",
" 'SAD_P44' 'SAD_P45' 'SAD_P47' 'SAD_P48' 'SAD_P50' 'SAD_P52' 'SAD_P53'\n",
" 'SAD_P54' 'SAD_P56' 'SAD_P58']\n",
"[[ 7 10]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.58 0.41 0.48 17\n",
" 1 0.62 0.76 0.68 21\n",
"\n",
"avg / total 0.60 0.61 0.59 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [0.61064425770308128, 0.57422969187675077], 5: [0.58683473389355745]}\n",
"[[ 9 8]\n",
" [ 5 16]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.64 0.53 0.58 17\n",
" 1 0.67 0.76 0.71 21\n",
"\n",
"avg / total 0.66 0.66 0.65 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [0.61064425770308128, 0.57422969187675077], 5: [0.58683473389355745, 0.64565826330532206]}\n",
"[[ 9 8]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.60 0.53 0.56 17\n",
" 1 0.65 0.71 0.68 21\n",
"\n",
"avg / total 0.63 0.63 0.63 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [0.61064425770308128, 0.57422969187675077], 5: [0.58683473389355745, 0.64565826330532206, 0.62184873949579833]}\n",
"[[ 9 8]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.60 0.53 0.56 17\n",
" 1 0.65 0.71 0.68 21\n",
"\n",
"avg / total 0.63 0.63 0.63 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [0.61064425770308128, 0.57422969187675077], 5: [0.58683473389355745, 0.64565826330532206, 0.62184873949579833, 0.62184873949579833]}\n",
"[[ 8 9]\n",
" [ 6 15]]\n",
" precision recall f1-score support\n",
"\n",
" -1 0.57 0.47 0.52 17\n",
" 1 0.62 0.71 0.67 21\n",
"\n",
"avg / total 0.60 0.61 0.60 38\n",
"\n",
"{1: [0.61624649859943981, 0.56302521008403361, 0.59243697478991597, 0.61624649859943981, 0.53921568627450978], 2: [0.4915966386554621, 0.51540616246498594, 0.6568627450980391], 3: [0.69327731092436973, 0.46918767507002801, 0.26750700280112044, 0.61624649859943981, 0.59243697478991597, 0.48039215686274506, 0.55742296918767509, 0.56302521008403361, 0.49859943977591037, 0.58683473389355745], 4: [0.61064425770308128, 0.57422969187675077], 5: [0.58683473389355745, 0.64565826330532206, 0.62184873949579833, 0.62184873949579833, 0.59243697478991597]}\n"
]
}
],
"source": [
"basedir = '/om/user/satra/projects/SAD/fmri_results/individual/model01'\n",
"template = '{basedir}/task{task:03d}/{subj}/copes/mni/cope{cope:02d}.nii.gz'\n",
"\n",
"mask_img = nb.load('OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz')\n",
"mask_img = nb.Nifti1Image((mask_img.get_data() > 1).astype(bool), mask_img.affine, header=mask_img.get_header())\n",
"\n",
"\n",
"### Mask data #################################################################\n",
"nifti_masker = NiftiMasker(\n",
" mask_img=mask_img,\n",
" smoothing_fwhm=6,\n",
" memory='nilearn_cache', memory_level=1) # cache options\n",
"\n",
"roc_data = {1: [], 2: [], 3: [], 4: [], 5: []}\n",
"\n",
"for task in range(1, 6):\n",
" exclude = None\n",
" patients = df2.LSAS_POST_total.dropna().index\n",
" if False:\n",
" for grp, df_data in pd.groupby(tsnr_inv > 0.25, 'task%d' % task):\n",
" if grp:\n",
" exclude = np.intersect1d(df_data.index.tolist(), patients.tolist())\n",
" else:\n",
" exclude = tsnr_inv.index[np.nonzero((tsnr_inv > 0.25).sum(axis=1))[0]]\n",
" patients = np.setdiff1d(patients, exclude)\n",
" print len(patients), patients\n",
" for cope in range(1, len(con_data[task]) + 1):\n",
" copes = [template.format(basedir=basedir, task=task, subj=val, cope=cope) for val in patients]\n",
" #print '\\n'.join(copes)\n",
" images = [nb.squeeze_image(nb.load(cope)) for cope in copes]\n",
" fmri_masked = nifti_masker.fit_transform(images)\n",
" \n",
" X1 = pd.DataFrame(fmri_masked.copy())\n",
" X1.index = df2.ix[patients, :].index\n",
" X1.ix[:, np.nonzero(np.sum(X1==0., axis=0) > 1)[0]] = np.nan\n",
" X1 = X1.dropna(axis=1)\n",
" #X1 = pd.concat((X1.dropna(axis=1), df2.ix[patients, ['LSAS_PRE_total', 'Age']]), axis=1)\n",
" X = X1.values\n",
" if True:\n",
" y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)/df2.LSAS_PRE_total\n",
" y = y.ix[patients].values\n",
" y = 2 * (y > 0.5) - 1\n",
" else:\n",
" y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)\n",
" y = y.ix[patients].values\n",
" y = 2 * (y > np.median(y)) - 1\n",
" idx = np.argsort(y)\n",
" roc_auc, fw = classify(X1, X, y)\n",
" roc_data[task].append(roc_auc)\n",
" print roc_data"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RGW2lbSQNaEoppWzzeXMs1StLphAbbQEdtq+UUqoUPN7scImhLJlCrLYFKuAI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\noAkhhGgTJKAJIYRoE/4HMJuD2mjY5nAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2aab22bad110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for key in roc_data:\n",
" ax = plt.subplot(5, 1, key)\n",
" sn.heatmap(np.array(roc_data[key])[None, :], annot=True, cmap='cubehelix', vmin=0.5, vmax=0.78,\n",
" yticklabels=False, xticklabels=False)\n",
" ax.set_yticklabels('%d' % key, rotation=0)"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def \n",
"_, pvals_anova = f_regression(fmri_masked, tested_var,\n",
" center=False) # do not remove intercept\n",
"#pvals_anova *= fmri_masked.shape[1]\n",
"_, pvals_anova, _, _ = multipletests(pvals_anova, 0.05, 'fdr_bh')\n",
"\n",
"pvals_anova[np.isnan(pvals_anova)] = 1\n",
"pvals_anova[pvals_anova > 1] = 1\n",
"neg_log_pvals_anova = -np.log10(pvals_anova)\n",
"neg_log_pvals_anova_unmasked = nifti_masker.inverse_transform(\n",
" neg_log_pvals_anova)"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<nilearn.plotting.displays.OrthoProjector at 0x2aab7f715410>"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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QIQyATnRHgxOdbe3vaGEKAFlcAODr5M+YP38++/fvJyQkhAcffLDJ/TWHr68vjzzyCMuX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\nux7HJ6+NfB5lKaj8GyNMdEeSHY1xSKShTkSP5+A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BR0cHDz30EHfccQdRUVFUV1fT0NBA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B33vih08ZeKa9AoztpP+S/rvTL/fZwAnISeiV7mzEmwMtHJ62ezYbbANCXNQoyN4nbvE2t40A52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\n/j8lVKf1l3bftAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2aab7d232250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plotting.plot_glass_brain(neg_log_pvals_anova_unmasked, cmap=plt.cm.spectral,\n",
" threshold= -np.log10(0.05))"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(43, 221600)\n",
"[ 0.25 0.6375 0.77941176 0.63768116 0.28205128 0.67032967\n",
" 0.27659574 0.42253521 0.53409091 0.30263158 0.43956044 0.56962025\n",
" 0.36231884 0.73333333 0.66666667 0.25333333 0.26229508 0.43939394\n",
" 0.55 0.52688172 0.56756757 0.81428571 0.57471264 0.2739726\n",
" 0.82417582 0.1443299 0.74025974 0.59302326 0.37634409 -0.03333333\n",
" 0.46938776 0.38961039 0.39669421 0.28395062 0.85294118 0.38888889\n",
" 0.77669903 0.46969697 0.69863014 0.55789474 0.4 0.58064516\n",
" 0.71428571]\n",
"(43, 221378) (43, 221378)\n",
"[-1 1 1 1 -1 1 -1 -1 1 -1 -1 1 -1 1 1 -1 -1 -1 1 1 1 1 1 -1 1\n",
" -1 1 1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 1 1 -1 1 1]\n"
]
}
],
"source": [
"print fmri_masked.shape\n",
"X1 = pd.DataFrame(fmri_masked.copy())\n",
"X1.index = df2.ix[patients, :].index\n",
"X1.ix[:, np.nonzero(np.sum(X1==0., axis=0) > 1)[0]] = np.nan\n",
"X1 = X1.dropna(axis=1)\n",
"#X1 = pd.concat((X1.dropna(axis=1), df2.ix[patients, ['LSAS_PRE_total', 'Age']]), axis=1)\n",
"X = X1.values\n",
"if True:\n",
" y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)/df2.LSAS_PRE_total\n",
" y = y.ix[patients].values\n",
" print y\n",
" y = 2 * (y > 0.5) - 1\n",
"else:\n",
" y = (df2.LSAS_PRE_total - df2.LSAS_POST_total)\n",
" y = y.ix[patients].values\n",
" print y\n",
" y = 2 * (y > np.median(y)) - 1\n",
"print X.shape, X1.shape\n",
"idx = np.argsort(y)\n",
"print y"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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2rwd+BxwnIp8scqhsH8wGfRhF5FPY6OrcPnZRIh//fHYAFaGfZ3abanZNiJ5U7DnJVdbz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\nItIw/g+zsICj/v2MKwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2aab80535410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,10))\n",
"sn.set(style='white', palette=\"Set3\")\n",
"cmap = sn.cubehelix_palette(light=1, reverse=True, as_cmap=True)\n",
"im = plt.imshow(np.corrcoef(X[idx, :]), interpolation='nearest', cmap=cmap)\n",
"plt.axis('tight')\n",
"plt.xticks(range(len(patients)), y[idx]) # size='xx-large', \n",
"plt.yticks(range(len(patients)), patients[idx]) #, size='x-large')\n",
"plt.title('Feature correlation matrix (Participant x Participant)', size='xx-large')\n",
"divider = make_axes_locatable(plt.gca())\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1)\n",
"cbar = plt.colorbar(im, cax=cax)\n",
"cbar.ax.tick_params(labelsize=20) "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def null_variance_score(y_true, y_predict):\n",
" y_true = np.array(y_true)\n",
" y_predict = np.array(y_predict)\n",
" ynull = []\n",
" for train, test in LeaveOneOut(len(y_true)):\n",
" ynull.append(np.average(y_true[train]))\n",
" return 1 - np.sum((y_true - y_predict)**2)/np.sum((np.array(ynull) - y_true)**2)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def regress(X1, X, y):\n",
" results = [[], []]\n",
" scores = []\n",
" clfs = []\n",
" for train, test in KFold(len(y), 10): #StratifiedKFold(y, 8): #StratifiedShuffleSplit(y, n_iter=200, test_size=0.1):\n",
" clf1 = ExtraTreesRegressor(n_estimators=1000, max_features='sqrt')\n",
" clf = Pipeline([(\"standardize\", StandardScaler()),\n",
" #('select', SelectKBest(k=20000)),\n",
" #('select2', LinearSVC(C=1e-1, penalty=\"l1\", dual=False)),\n",
" (\"forest\", clf1)\n",
" ])\n",
" Xhat = np.concatenate((X[train], np.random.randn(len(train), 1)), axis=1)\n",
" clf.fit(Xhat, y[train])\n",
" fi = clf.steps[-1][1].feature_importances_\n",
" fi[fi <= fi[-1]] = 0\n",
" fidx = np.nonzero(fi)[0]\n",
" #print len(fidx)\n",
" #fidx = range(X.shape[1])\n",
" clf1 = ExtraTreesRegressor(n_estimators=2000, max_features='sqrt')\n",
" clf = Pipeline([(\"standardize\", StandardScaler()),\n",
" #('select', SelectKBest(k=20000)),\n",
" #('select2', LinearSVC(C=1e-1, penalty=\"l1\", dual=False)),\n",
" (\"forest\", clf1)\n",
" ])\n",
" ytest = clf.fit(X[train][:, fidx], y[train]).predict(X[test][:, fidx])\n",
" #print y[test], ytest\n",
" results[0].extend(y[test])\n",
" results[1].extend(ytest)\n",
" scores.append(null_variance_score(y[test], ytest))\n",
" importances = np.zeros(X.shape[1])\n",
" importances[fidx] = clf.steps[-1][1].feature_importances_\n",
" #importances[clf.steps[-2][1].get_support()] = clf.steps[-1][1].feature_importances_\n",
" #importances[np.abs(clf.steps[-2][1].coef_.flatten()) > 0] = clf.steps[-1][1].feature_importances_\n",
" clfs.append(importances)\n",
" nv_score = null_variance_score(results[0], results[1])\n",
"\n",
" norm_scores = np.array(scores).dot(np.array(clfs))/np.sum(scores)\n",
" fw = np.zeros(fmri_masked.shape[1])\n",
" fw[X1.keys().values.astype(int)] = norm_scores\n",
" return nv_score, fw"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24 Index([u'CASL13101', u'CASL13102', u'CASL13104', u'CASL13106', u'CASL13107', u'CASL13108', u'CASL13109', u'CASL13110', u'CASL13111', u'CASL13112', u'CASL13114', u'CASL13116', u'CASL13117', u'CASL13118', u'CASL13119', u'CASL13120', u'CASL13121', u'CASL13122', u'CASL13123', u'CASL13124', u'CASL13125', u'CASL13126', u'CASL13128', u'CASL13130'], dtype='object')\n",
"{'Spe-v-Wav': [-0.11087612100005328]}\n"
]
}
],
"source": [
"basedir = '/gablab/p/CASL/Results/Imaging/tone/bips'\n",
"template = '{basedir}/{subj}/smri/warped_image/fwhm_6.0/_imgtype_cope/cope_{task}_wtsimt.nii.gz'\n",
"mask_img = nb.load('/gablab/p/CASL/Analysis/tone/roi/MNI_2mm.nii')\n",
"mask_img = nb.Nifti1Image((mask_img.get_data() > 1).astype(bool), mask_img.affine, header=mask_img.get_header())\n",
"\n",
"\n",
"### Mask data #################################################################\n",
"nifti_masker = NiftiMasker(\n",
" mask_img=mask_img,\n",
" smoothing_fwhm=2,\n",
" memory='nilearn_cache', memory_level=1) # cache options\n",
"\n",
"nv_data = {\"Spe-v-Wav\": []}\n",
"\n",
"for task in [\"Spe-v-Wav\"]:\n",
" exclude = None\n",
" patients = casl_sub.index\n",
" #if True:\n",
" # for grp, df_data in pd.groupby(tsnr_inv > 0.25, 'task%d' % task):\n",
" # if grp:\n",
" # exclude = np.intersect1d(df_data.index.tolist(), patients.tolist())\n",
" #else:\n",
" # exclude = tsnr_inv.index[np.nonzero((tsnr_inv > 0.25).sum(axis=1))[0]]\n",
" #patients = np.setdiff1d(patients, exclude)\n",
" print len(patients), patients\n",
" copes = [template.format(basedir=basedir, task=task, subj=val, cope=cope) for val in patients]\n",
" #print '\\n'.join(copes)\n",
" images = [nb.squeeze_image(nb.load(cope)) for cope in copes]\n",
" fmri_masked = nifti_masker.fit_transform(images)\n",
" \n",
" X1 = pd.DataFrame(fmri_masked.copy())\n",
" X1.index = casl_sub.ix[patients, :].index\n",
" X1.ix[:, np.nonzero(np.sum(X1==0., axis=0) > 1)[0]] = np.nan\n",
" X1 = X1.dropna(axis=1)\n",
" #X1 = pd.concat((X1.dropna(axis=1), df2.ix[patients, ['LSAS_PRE_total', 'Age']]), axis=1)\n",
" X = X1.values\n",
" y = casl_sub.finalhsk #/df2.LSAS_PRE_total\n",
" y = y.ix[patients].values\n",
" nv_score, fw = regress(X1, X, y)\n",
" nv_data[task].append(nv_score)\n",
" print nv_data"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "invalid literal for int() with base 10: 'Spe-v-Wav'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-38-cae9f6c703ae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnv_data\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Spe-v-Wav'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m sn.heatmap(np.array(nv_data[key])[None, :], annot=True, cmap='cubehelix', vmin=0.1, vmax=0.2,\n\u001b[0;32m 4\u001b[0m yticklabels=False, xticklabels=False)\n\u001b[0;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%d'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrotation\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[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36msubplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 895\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 896\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 897\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 898\u001b[0m \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 899\u001b[0m \u001b[0mbyebye\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/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 912\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 913\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 914\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\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[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 916\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m 9244\u001b[0m \u001b[0mrows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9245\u001b[0m \u001b[0mrows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 9246\u001b[1;33m \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9247\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\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[0;32m 9248\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnum\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Spe-v-Wav'"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc884641f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for key in nv_data:\n",
" ax = plt.subplot(5, 'Spe-v-Wav', key)\n",
" sn.heatmap(np.array(nv_data[key])[None, :], annot=True, cmap='cubehelix', vmin=0.1, vmax=0.2,\n",
" yticklabels=False, xticklabels=False)\n",
" ax.set_yticklabels('%d' % key, rotation=0)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24 Index([u'CASL13101', u'CASL13102', u'CASL13104', u'CASL13106', u'CASL13107', u'CASL13108', u'CASL13109', u'CASL13110', u'CASL13111', u'CASL13112', u'CASL13114', u'CASL13116', u'CASL13117', u'CASL13118', u'CASL13119', u'CASL13120', u'CASL13121', u'CASL13122', u'CASL13123', u'CASL13124', u'CASL13125', u'CASL13126', u'CASL13128', u'CASL13130'], dtype='object')\n",
"{'Spe-v-Wav': [-0.13139486199330097]}\n"
]
}
],
"source": [
"basedir = '/gablab/p/CASL/Results/Imaging/tone/bips'\n",
"template = '{basedir}/{subj}/smri/warped_image/fwhm_6.0/_imgtype_cope/cope_{task}_wtsimt.nii.gz'\n",
"mask_img = nb.load('/gablab/p/CASL/Analysis/tone/roi/MNI_2mm.nii')\n",
"mask_img = nb.Nifti1Image((mask_img.get_data() > 1).astype(bool), mask_img.affine, header=mask_img.get_header())\n",
"\n",
"\n",
"### Mask data #################################################################\n",
"nifti_masker = NiftiMasker(\n",
" mask_img=mask_img,\n",
" smoothing_fwhm=2,\n",
" memory='nilearn_cache', memory_level=1) # cache options\n",
"\n",
"nv_data = {'Spe-v-Wav': []}\n",
"\n",
"for task in ['Spe-v-Wav']:\n",
" exclude = None\n",
" patients = casl_sub.index\n",
" #if True:\n",
" # for grp, df_data in pd.groupby(tsnr_inv > 0.25, 'task%d' % task):\n",
" # if grp:\n",
" # exclude = np.intersect1d(df_data.index.tolist(), patients.tolist())\n",
" #else:\n",
" # exclude = tsnr_inv.index[np.nonzero((tsnr_inv > 0.25).sum(axis=1))[0]]\n",
" #patients = np.setdiff1d(patients, exclude)\n",
" print len(patients), patients\n",
" copes = [template.format(basedir=basedir, task=task, subj=val, cope=cope) for val in patients]\n",
" #print '\\n'.join(copes)\n",
" images = [nb.squeeze_image(nb.load(cope)) for cope in copes]\n",
" fmri_masked = nifti_masker.fit_transform(images)\n",
" \n",
" X1 = pd.DataFrame(fmri_masked.copy())\n",
" X1.index = casl_sub.ix[patients, :].index\n",
" X1.ix[:, np.nonzero(np.sum(X1==0., axis=0) > 1)[0]] = np.nan\n",
" X1 = X1.dropna(axis=1)\n",
" #X1 = pd.concat((X1.dropna(axis=1), df2.ix[patients, ['LSAS_PRE_total', 'Age']]), axis=1)\n",
" X = X1.values\n",
" y = casl_sub.finalhsk\n",
" y = y.ix[patients].values\n",
" nv_score, fw = regress(X1, X, y)\n",
" nv_data[task].append(nv_score)\n",
" print nv_data"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "invalid literal for int() with base 10: 'Spe-v-Wav'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-41-5262d5b382f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnv_data\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m sn.heatmap(np.array(nv_data[key])[None, :], annot=True, cmap='cubehelix', vmin=0.1, vmax=0.2,\n\u001b[0;32m 4\u001b[0m yticklabels=False, xticklabels=False)\n\u001b[0;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%d'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrotation\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[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36msubplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 895\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 896\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 897\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 898\u001b[0m \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 899\u001b[0m \u001b[0mbyebye\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/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 912\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 913\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 914\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\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[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 916\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m 9249\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_subplotspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSpec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnum\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[0m\n\u001b[0;32m 9250\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 9251\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_subplotspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSpec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9252\u001b[0m \u001b[1;31m# num - 1 for converting from MATLAB to python indexing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9253\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Spe-v-Wav'"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc884f22cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for key in nv_data:\n",
" ax = plt.subplot(1, 1, key)\n",
" sn.heatmap(np.array(nv_data[key])[None, :], annot=True, cmap='cubehelix', vmin=0.1, vmax=0.2,\n",
" yticklabels=False, xticklabels=False)\n",
" ax.set_yticklabels('%d' % key, rotation=0)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'results' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-42-547717a6a3a7>\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[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspearmanr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\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[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpearsonr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\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[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'results' is not defined"
]
}
],
"source": [
"import scipy.stats as stats\n",
"print stats.spearmanr(results[0], results[1])\n",
"print stats.pearsonr(results[0], results[1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def regress(X1, X, y, nobehav=True):\n",
" results = [[], []]\n",
" scores = []\n",
" clfs = []\n",
" subs = []\n",
" for train, test in KFold(len(y), 10): #StratifiedKFold(y, 8): #StratifiedShuffleSplit(y, n_iter=200, test_size=0.1):\n",
" clf1 = ExtraTreesRegressor(n_estimators=1000, max_features='sqrt')\n",
" clf = Pipeline([(\"standardize\", StandardScaler()),\n",
" #('select', SelectKBest(k=20000)),\n",
" #('select2', LinearSVC(C=1e-1, penalty=\"l1\", dual=False)),\n",
" (\"forest\", clf1)\n",
" ])\n",
"\n",
" cc = 1.\n",
" while(abs(cc) > 0.05):\n",
" Xrandom = np.random.randn(len(train), 1)\n",
" cc = np.corrcoef(Xrandom.flatten(), y[train])[0, 1]\n",
"\n",
" if nobehav:\n",
" Xhat = np.concatenate((X[train], Xrandom), axis=1)\n",
" else:\n",
" Xhat = np.concatenate((X[train][:, :-2], Xrandom), axis=1)\n",
" clf.fit(Xhat, y[train])\n",
" fi = clf.steps[-1][1].feature_importances_\n",
" fi[fi <= fi[-1]] = 0\n",
" fidx = np.nonzero(fi)[0]\n",
" for i in range(10):\n",
" Xhat = np.concatenate((X[train][:, fidx], Xrandom), axis=1)\n",
" clf.fit(Xhat, y[train])\n",
" fi = clf.steps[-1][1].feature_importances_\n",
" fi[fi <= fi[-1]] = 0\n",
" fidx = fidx[np.nonzero(fi)[0]]\n",
" \n",
" print len(fidx)\n",
" #fidx = range(X.shape[1])\n",
" if not nobehav:\n",
" behavidx = np.arange(2) + X.shape[1] - 2\n",
" fidx = np.concatenate((fidx, behavidx))\n",
" clf1 = ExtraTreesRegressor(n_estimators=2000, max_features=None)\n",
" clf = Pipeline([(\"standardize\", StandardScaler()),\n",
" #('select', SelectKBest(k=20000)),\n",
" #('select2', LinearSVC(C=1e-1, penalty=\"l1\", dual=False)),\n",
" (\"forest\", clf1)\n",
" ])\n",
" ytest = clf.fit(X[train][:, fidx], y[train]).predict(X[test][:, fidx])\n",
" #print y[test], ytest\n",
" results[0].extend(y[test])\n",
" results[1].extend(ytest)\n",
" scores.append(null_variance_score(y[test], ytest))\n",
" importances = np.zeros(X.shape[1])\n",
" if nobehav:\n",
" importances[fidx] = clf.steps[-1][1].feature_importances_\n",
" else:\n",
" importances[fidx] = clf.steps[-1][1].feature_importances_[:-2]\n",
" subs.extend(X1.index[test].tolist())\n",
" #importances[clf.steps[-2][1].get_support()] = clf.steps[-1][1].feature_importances_\n",
" #importances[np.abs(clf.steps[-2][1].coef_.flatten()) > 0] = clf.steps[-1][1].feature_importances_\n",
" clfs.append(importances)\n",
" nv_score = explained_variance_score(results[0], results[1])\n",
"\n",
" norm_scores = np.array(scores).dot(np.array(clfs))/np.sum(scores)\n",
" fw = np.zeros(fmri_masked.shape[1])\n",
" fw[X1.keys()[:-2].values.astype(int)] = norm_scores\n",
" return nv_score, fw, pd.DataFrame(results[1], index=subs, columns=['LSAS']) "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24 Index([u'CASL13101', u'CASL13102', u'CASL13104', u'CASL13106', u'CASL13107', u'CASL13108', u'CASL13109', u'CASL13110', u'CASL13111', u'CASL13112', u'CASL13114', u'CASL13116', u'CASL13117', u'CASL13118', u'CASL13119', u'CASL13120', u'CASL13121', u'CASL13122', u'CASL13123', u'CASL13124', u'CASL13125', u'CASL13126', u'CASL13128', u'CASL13130'], dtype='object')\n",
"(24, 219184)\n",
"295\n",
"783\n",
"488\n",
"771\n",
"1108\n",
"881\n",
"881\n",
"1156\n",
"916\n",
"424"
]
},
{
"ename": "TypeError",
"evalue": "Cannot concatenate list of ['DataFrame', 'DataFrame', 'Series']",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-15-bbc62e53ab3b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 44\u001b[0m df_computed = pd.concat((pred_scores, casl_sub.ix[patients], \n\u001b[1;32m---> 45\u001b[1;33m (casl_sub.finalhsk).ix[patients]), axis=1)\n\u001b[0m\u001b[0;32m 46\u001b[0m \u001b[0mXc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_computed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[0myc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_computed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\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[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity)\u001b[0m\n\u001b[0;32m 927\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 928\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 929\u001b[1;33m verify_integrity=verify_integrity)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity)\u001b[0m\n\u001b[0;32m 1003\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverify_integrity\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1005\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_new_axes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1006\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1007\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_result\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[0m\n",
"\u001b[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36m_get_new_axes\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1235\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1236\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1237\u001b[1;33m \u001b[0mnew_axes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_comb_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1238\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1239\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin_axes\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mndim\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/mindhive/gablab/u/zqi/envs/zqi2/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36m_get_comb_axis\u001b[1;34m(self, i)\u001b[0m\n\u001b[0;32m 1265\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1266\u001b[0m \u001b[0mtypes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1267\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot concatenate list of %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtypes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1269\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_get_combined_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_indexes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mintersect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintersect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: Cannot concatenate list of ['DataFrame', 'DataFrame', 'Series']"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"basedir = '/gablab/p/CASL/Results/Imaging/tone/bips'\n",
"template = '{basedir}/{subj}/smri/warped_image/fwhm_6.0/_imgtype_cope/cope_{task}_wtsimt.nii.gz'\n",
"mask_img = nb.load('/gablab/p/CASL/Analysis/tone/roi/MNI_2mm.nii')\n",
"mask_img = nb.Nifti1Image((mask_img.get_data() > 1).astype(bool), mask_img.affine, header=mask_img.get_header())\n",
"\n",
"\n",
"### Mask data #################################################################\n",
"nifti_masker = NiftiMasker(\n",
" mask_img=mask_img,\n",
" smoothing_fwhm=2,\n",
" memory='nilearn_cache', memory_level=1) # cache options\n",
"\n",
"nv_data = {'Spe-v-Wav': []}\n",
"fw_data = {'Spe-v-Wav': []}\n",
"\n",
"for task in ['Spe-v-Wav']: #range(1, 6):\n",
" exclude = None\n",
" patients = casl_sub.index\n",
" #if True:\n",
" # for grp, df_data in pd.groupby(tsnr_inv > 0.25, 'task%d' % task):\n",
" # if grp:\n",
" # exclude = np.intersect1d(df_data.index.tolist(), patients.tolist())\n",
" #else:\n",
" # exclude = tsnr_inv.index[np.nonzero((tsnr_inv > 0.25).sum(axis=1))[0]]\n",
" #patients = np.setdiff1d(patients, exclude)\n",
" print len(patients), patients\n",
" copes = [template.format(basedir=basedir, task=task, subj=val) for val in patients]\n",
" #print '\\n'.join(copes)\n",
" images = [nb.squeeze_image(nb.load(cope)) for cope in copes]\n",
" fmri_masked = nifti_masker.fit_transform(images)\n",
" \n",
" X1 = pd.DataFrame(fmri_masked.copy())\n",
" X1.index = casl_sub.ix[patients, :].index\n",
" #X1.ix[:, np.nonzero(np.std(X1, axis=0) < 10)[0]] = np.nan\n",
" X1.ix[:, np.nonzero(np.sum(X1==0., axis=0) > 1)[0]] = np.nan\n",
" X1 = X1.dropna(axis=1)\n",
" #X1 = pd.concat((X1.dropna(axis=1), df2.ix[patients, ['LSAS_PRE_total', 'Age']]), axis=1)\n",
" X = X1.values\n",
" print X.shape\n",
" y = casl_sub.finalhsk #/df2.LSAS_PRE_total\n",
" y = y.ix[patients].values\n",
" nv_score, fw, pred_scores = regress(X1, X, y, nobehav=True)\n",
" '''\n",
" df_computed = pd.concat((pred_scores, casl_sub.ix[patients], \n",
" (casl_sub.finalhsk).ix[patients]), axis=1)\n",
" Xc = df_computed.ix[:, :3].values\n",
" yc = df_computed.ix[:, -1].values\n",
"\n",
" result = []\n",
" for train, test in LeaveOneOut(len(y)):\n",
" result.append([yc[test], LinearRegression().fit(Xc[train], yc[train]).predict(Xc[test])])\n",
" result = np.array(result)\n",
" nv_score = explained_variance_score(result[:, 0], result[:, 1])\n",
" '''\n",
" nv_data[task].append(nv_score)\n",
" fw_data[task].append(fw)\n",
" print nv_data"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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JCQkhODiYPn36tNnNGQwGamtrqa6upqamhtraWurq6qitraWhoYHa2lq+++47brvtNnQ6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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2aab0cf62350>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7O487qPCUhv7ZWaxcd8Xncjvgz31Mn8P13uOlQw89VNIw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SxS/USfSO3jxuAsDO03ZP1Oy1zsBjPmBcx6MxaHd955J4D0/PGYgPQcaR+FM6UqURfrTfDkyZMB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