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et.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multivariate Prediction LAP Dataset - 2mm Smoothing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Needs nilearn from github https://github.com/nilearn/nilearn\n",
"- Parallel CV code adapted from https://github.com/ogrisel/parallel_ml_tutorial"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named nipype.utils.filemanip",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-3b79bf01a512>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfixes\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mnipype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilemanip\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msplit_filename\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnipype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfreesurfer\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mResample\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparallel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mClient\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: No module named nipype.utils.filemanip"
]
}
],
"source": [
"from glob import glob\n",
"import os\n",
"import pwd\n",
"import time\n",
"import pwd\n",
"import warnings\n",
"\n",
"from sklearn.externals import joblib\n",
"import matplotlib.pyplot as plt\n",
"import nibabel\n",
"import numpy as np\n",
"import sklearn.utils.fixes\n",
"from nipype.utils.filemanip import split_filename\n",
"from nipype.interfaces.freesurfer import Resample\n",
"from IPython.parallel import Client\n",
"\n",
"\n",
"\n",
"if not os.path.exists(tmp_dir):\n",
" os.makedirs(tmp_dir)\n",
" \n",
"#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": 15,
"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": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"data = pd.read_csv('LAP_MASTER_012814_jm.csv')\n",
"data.index = data.participant\n",
"keys = pd.read_csv('KEY_012814_jm.csv')\n",
"\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": 16,
"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": 17,
"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": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sent:English-v-MAL: #subjects: 39, missing: [1007]\n",
"sent:MAL-v-Rest: #subjects: 39, missing: [1007]\n",
"sent:English-v-Mandarin: #subjects: 39, missing: [1007]\n",
"nback:3_gt_1: #subjects: 35, missing: [1014, 1015, 1016, 1026, 1064]\n",
"srt:S_gt_R: #subjects: 33, missing: [1007, 1010, 1014, 1015, 1016, 1019, 1026]\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",
" 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",
" # 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": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Load the mask #############################################################\n",
"from nilearn.input_data import NiftiMasker#\n",
"mask_vol = '/usr/share/fsl/data/standard/MNI152_T1_2mm_brain_mask.nii.gz'\n",
"nifti_masker = NiftiMasker(mask=mask_vol)\n",
"\n",
"# We give the nifti_masker a filename and retrieve a 2D array ready\n",
"# for machine learning with scikit-learn\n",
"fmri_masked = {}\n",
"for cope in copes:\n",
" fmri_masked[cope] = nifti_masker.fit_transform(dataset[cope].func)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"samples: 39 features: 228483\n",
"(91, 109, 91)\n"
]
},
{
"data": {
"image/png": 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LWhzpTKbNJDU9X8OftFE8ECdpRoHMkd3ZqTiS0q4Un44pKnkQ2UtR+TzSlMQb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\nZ0xhfNMZU5j/B8AObvYRn4+DAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7424fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, n_features = fmri_masked[copes[0]].shape\n",
"print 'samples: %d features: %d' % fmri_masked[copes[0]].shape\n",
"\n",
"data_shape = nifti_masker.mask_img_.shape\n",
"print data_shape\n",
"mask = nifti_masker.mask_img_.get_data().astype(np.bool)\n",
"\n",
"### Visualize the mask ###\n",
"plt.figure()\n",
"plt.axis('off')\n",
"plt.imshow(np.rot90(nibabel.load(dataset[copes[0]].func[0]).get_data()[..., 27]),\n",
" interpolation='nearest', cmap=plt.cm.gray)\n",
"ma = np.ma.masked_equal(mask, False)\n",
"plt.imshow(np.rot90(ma[..., 27]), interpolation='nearest', cmap=plt.cm.autumn,\n",
" alpha=0.5)\n",
"plt.title(\"Mask\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.cross_validation import KFold, ShuffleSplit, StratifiedShuffleSplit\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.svm import SVR\n",
"from sklearn.ensemble import ExtraTreesRegressor, ExtraTreesClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import explained_variance_score, r2_score, f1_score\n",
"from IPython.display import display"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create persistent CV splits for parallel execution\n",
"def persist_cv_splits(X, Xb, y, n_iter=5, data_dir='./', name='data',\n",
" suffix=\"_cv_%03d.pkl\", test_size=0.2, random_state=None):\n",
" \"\"\"Materialize randomized train test splits of a dataset.\"\"\"\n",
"\n",
" cv = StratifiedShuffleSplit(y, n_iter=n_iter, test_size=test_size, random_state=random_state)\n",
" cv_split_filenames = []\n",
" for ii, (train, test) in enumerate(cv):\n",
" cv_split_filename = os.path.abspath(os.path.join(data_dir, name + suffix % ii))\n",
" if not os.path.exists(cv_split_filename):\n",
" cv_fold = (X[train], Xb[train], y[train], X[test], Xb[test], y[test])\n",
" joblib.dump(cv_fold, cv_split_filename)\n",
" cv_split_filenames.append(cv_split_filename)\n",
" \n",
" return cv_split_filenames\n",
"\n",
"cv_split_names = {}\n",
"for cope in copes:\n",
" cope_cv_split_names = {}\n",
" X = fmri_masked[cope]\n",
" Xb = np.array(dataset[cope].behav)\n",
" for key in dataset[cope].dvkeys:\n",
" y = 2 * (np.array(dataset[cope].targets[key]) > cleandata[key].median()) - 1\n",
" fnames= persist_cv_splits(X, Xb, y, n_iter=200, data_dir=tmp_dir, name='%s_%s' % (cope, key), suffix=\"_cv_%03d.pkl\",\n",
" test_size=0.2, random_state=None)\n",
" cope_cv_split_names[key] = fnames\n",
" cv_split_names[cope] = cope_cv_split_names"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'iterative_tree' from 'iterative_tree.pyc'>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import iterative_tree as itree\n",
"reload(itree) #for parallelization? "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/sklearn/ensemble/forest.py:1064: DeprecationWarning: Setting compute_importances is no longer required as version 0.14. Variable importances are now computed on the fly when accessing the feature_importances_ attribute. This parameter will be removed in 0.16.\n",
" DeprecationWarning)\n"
]
}
],
"source": [
"def compute_evaluation(cv_split_filename, model, return_weights, params=None):\n",
" \"\"\"Function executed by a worker to evaluate a model on a CV split\"\"\"\n",
" # All module imports should be executed in the worker namespace\n",
" import numpy as np\n",
" from sklearn.externals import joblib\n",
" from sklearn import metrics\n",
" from copy import deepcopy\n",
" \n",
" #OK, so taking predetermined cv splits and loading them into jobs for parallelization\n",
" X_train, Xb_train, y_train, X_test, Xb_test, y_test = joblib.load(\n",
" cv_split_filename, mmap_mode='c') \n",
" \n",
" if params is not None:\n",
" model.set_params(**params)\n",
" \n",
" # model == clf, \n",
" model_brain = deepcopy(model) #also why deepcopy?\n",
" model_behav = deepcopy(model)\n",
" \n",
" #fit the model\n",
" model_brain.fit(X_train, y_train) \n",
" model_behav.fit(Xb_train, y_train)\n",
" \n",
" #feature weights\n",
" active_behav = np.where(model_behav.feature_weights > 0)[0] #np.percentile(model_behav.feature_weights, 10))[0]\n",
" active_brain = np.where(model_brain.feature_weights > 0)[0] #np.percentile(model_brain.feature_weights, 10))[0]\n",
" print \"active_brain: %d\" % len(active_brain)\n",
" print \"active_behav: %d\" % len(active_behav)\n",
" \n",
" #combining brain and behav\n",
" Xcombined_train = np.hstack((Xb_train[:, active_behav], X_train[:, active_brain]))\n",
" Xcombined_test = np.hstack((Xb_test[:, active_behav], X_test[:, active_brain]))\n",
" \n",
" #combo modelfit\n",
" model.max_features = None\n",
" model.fit(Xcombined_train, y_train)\n",
" \n",
" #f1 scores\n",
" brain_validation_score = metrics.f1_score(y_test, model_brain.predict(X_test))\n",
" behav_validation_score = metrics.f1_score(y_test, model_behav.predict(Xb_test))\n",
" combined_validation_score = metrics.f1_score(y_test, model.predict(Xcombined_test))\n",
" validation_score = (brain_validation_score, behav_validation_score, combined_validation_score)\n",
" \n",
" # get the feature weights for prediction from brain data\n",
" if return_weights:\n",
" if hasattr(model_brain, 'feature_weights'):\n",
" weights = model_brain.feature_weights\n",
" elif hasattr(model_brain, 'feature_importances_'):\n",
" weights = model_brain.feature_importances_\n",
" active = np.where(weights > 0)[0]\n",
" weights = weights[active]\n",
" \n",
" #why is this commented out?\n",
" '''\n",
" # behavioral data only\n",
" model.fit(Xb_train, y_train)\n",
" behav_validation_score = metrics.f1_score(y_test, model.predict(Xb_test))\n",
" \n",
" # brain and behavioral combined\n",
" Xcomb_train = np.c_[X_train, Xb_train]\n",
" Xcomb_test = np.c_[X_test, Xb_test]\n",
" model.fit(Xcomb_train, y_train)\n",
" comb_validation_score = metrics.f1_score(y_test, model.predict(Xcomb_test))\n",
"\n",
" validation_score = (brain_validation_score, behav_validation_score, comb_validation_score)\n",
" '''\n",
" \n",
" if return_weights:\n",
" return validation_score, active, weights\n",
" \n",
" return validation_score\n",
"\n",
"#Classify these bitches:\n",
"from iterative_tree import IterativeExtraTreesClassifier \n",
"clf = IterativeExtraTreesClassifier(n_estimators=500, max_depth=None, \n",
" max_features=0.2,\n",
" min_samples_split=1, n_jobs=1)\n",
"\n",
"#why not make a pipeline with standard scaler like:\n",
"'''clf1 = Pipeline([('standardize', StandardScaler()), ('forrest', clf)])'''\n",
"\n",
"# try normal ExtraTreesClassifier and iterative version\n",
"clf_params = [{'max_iterations':1}, {'max_iterations':10}]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# WHERE ARE WE REDUCING FEATURE SPACE? CLUSTERING, PCA???"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if 0:\n",
" #os.system('qsub -q max30 -t 1-30 pbs_engines')\n",
" #os.system('qsub -q max10 -t 1-10 pbs_engines')\n",
" os.system('qsub -q many -t 1-150 pbs_engines')\n",
" time.sleep(10)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# run all the tasks\n",
"client = Client(profile='default')\n",
"lb_view = client.load_balanced_view()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"'\\nfrom iterative_tree import IterativeExtraTreesClassifier \\nclf1 = IterativeExtraTreesClassifier(n_estimators=500, max_depth=None, \\n max_features=0.1,\\n min_samples_split=1, n_jobs=1)\\ncompute_evaluation(cv_split_names[cope][key][0], clf1, True, clf_params[1])\\n'"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"from iterative_tree import IterativeExtraTreesClassifier \n",
"clf1 = IterativeExtraTreesClassifier(n_estimators=500, max_depth=None, \n",
" max_features=0.1,\n",
" min_samples_split=1, n_jobs=1)\n",
"compute_evaluation(cv_split_names[cope][key][0], clf1, True, clf_params[1])\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Started 8000 tasks\n"
]
}
],
"source": [
"tasks_linear = []\n",
"tasks_copes = {}\n",
"for cope in copes:\n",
" tasks_keys = {}\n",
" for key in dataset[cope].dvkeys:\n",
" key_tasks = []\n",
" for fname in cv_split_names[cope][key]:\n",
" param_tasks = []\n",
" for params in clf_params[:1]:\n",
" task = lb_view.apply(compute_evaluation, fname, clf, True, params)\n",
" param_tasks.append(task)\n",
" tasks_linear.append(task)\n",
" key_tasks.append(param_tasks)\n",
" tasks_keys[key] = key_tasks\n",
" tasks_copes[cope] = tasks_keys\n",
"\n",
"n_tasks = len(tasks_linear)\n",
"print 'Started %d tasks' % n_tasks"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"while True:\n",
" n_completed = np.sum([task.ready() for task in tasks_linear])\n",
" print(\"Tasks completed: {0}%\".format(100 * n_completed / float(n_tasks)))\n",
" if n_completed == n_tasks:\n",
" break\n",
" time.sleep(60)\n",
" \n",
"if kill_cluster_engines:\n",
" client.shutdown()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# retrieve results\n",
"coperesults = {}\n",
"\n",
"for cope, cope_tasks in tasks_copes.iteritems():\n",
" coperesults[cope] = {}\n",
" for key, param_tasks in cope_tasks.iteritems():\n",
" coperesults[cope][key] = {}\n",
" this_scores = []\n",
" this_weights = []\n",
" for ii in range(len(param_tasks[0])):\n",
" this_scores.append(np.array([task[ii].get()[0] for task in param_tasks]))\n",
" avg_weights = np.zeros(n_features, dtype=np.float)\n",
" for task in param_tasks:\n",
" _, active, weights = task[ii].get()\n",
" avg_weights[active] += weights\n",
" avg_weights /= len(param_tasks)\n",
" this_weights.append(avg_weights)\n",
" coperesults[cope][key]['scores'] = this_scores\n",
" coperesults[cope][key]['weights'] = this_weights"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pd.set_option('float_format', lambda x: '%.3f' % (np.round(100*x)/100.))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: sent:English-v-MAL\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 5%</th>\n",
" <th>(br&gt;c) 95%</th>\n",
" <th>(bv&gt;c) 5%</th>\n",
" <th>(bv&gt;c) 95%</th>\n",
" <th>5%</th>\n",
" <th>95%</th>\n",
" <th>b5%</th>\n",
" <th>b95%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td> 0.000</td>\n",
" <td>0.390</td>\n",
" <td>-0.670</td>\n",
" <td>0.220</td>\n",
" <td>-0.670</td>\n",
" <td>0.180</td>\n",
" <td>0.570</td>\n",
" <td>0.750</td>\n",
" <td>0.570</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.460</td>\n",
" <td>0.500</td>\n",
" <td>-0.360</td>\n",
" <td>0.470</td>\n",
" <td>0.570</td>\n",
" <td>0.550</td>\n",
" <td>0.560</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.350</td>\n",
" <td>0.460</td>\n",
" <td>-0.420</td>\n",
" <td>0.400</td>\n",
" <td>0.500</td>\n",
" <td>0.440</td>\n",
" <td>0.500</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.250</td>\n",
" <td>0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.400</td>\n",
" <td>0.420</td>\n",
" <td>-0.420</td>\n",
" <td>0.350</td>\n",
" <td>0.440</td>\n",
" <td>0.440</td>\n",
" <td>0.500</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.210</td>\n",
" <td>0.360</td>\n",
" <td>-0.570</td>\n",
" <td>0.170</td>\n",
" <td>-0.570</td>\n",
" <td>0.180</td>\n",
" <td>0.440</td>\n",
" <td>0.630</td>\n",
" <td>0.400</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.060</td>\n",
" <td>0.390</td>\n",
" <td>-0.800</td>\n",
" <td>0.000</td>\n",
" <td>-0.800</td>\n",
" <td>0.000</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.860</td>\n",
" <td>0.080</td>\n",
" <td>-0.860</td>\n",
" <td>0.070</td>\n",
" <td>0.330</td>\n",
" <td>0.750</td>\n",
" <td>0.330</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.060</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.000</td>\n",
" <td>-0.860</td>\n",
" <td>0.000</td>\n",
" <td>0.330</td>\n",
" <td>0.750</td>\n",
" <td>0.330</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 5% (br>c) 95% (bv>c) 5% (bv>c) 95% 5% 95% b5% b95% \\\n",
"6 -0.500 0.360 0.000 0.390 -0.670 0.220 -0.670 0.180 \n",
"7 -0.500 0.250 -0.500 0.250 -0.460 0.500 -0.360 0.470 \n",
"4 -0.500 0.250 -0.500 0.250 -0.350 0.460 -0.420 0.400 \n",
"3 -0.250 0.250 -0.500 0.250 -0.400 0.420 -0.420 0.350 \n",
"1 -0.500 0.250 -0.210 0.360 -0.570 0.170 -0.570 0.180 \n",
"0 -0.500 0.170 -0.060 0.390 -0.800 0.000 -0.800 0.000 \n",
"5 -0.500 0.170 -0.170 0.500 -0.860 0.080 -0.860 0.070 \n",
"2 -0.500 0.170 -0.060 0.500 -0.890 0.000 -0.860 0.000 \n",
"\n",
" bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.570 0.750 0.570 gramm_ACC_4 \n",
"7 0.570 0.550 0.560 SAT_synax_9 \n",
"4 0.500 0.440 0.500 SAT_semantic_ACC_4 \n",
"3 0.440 0.440 0.500 SAT_semantic_dprime_4 \n",
"1 0.440 0.630 0.400 SAT_semantics_9 \n",
"0 0.400 0.750 0.400 SAT_syntax_ACC_4 \n",
"5 0.330 0.750 0.330 gramm_ACC_9 \n",
"2 0.330 0.750 0.330 SAT_syntax_dprime_4 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: sent:MAL-v-Rest\n"
]
},
{
"data": {
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 5%</th>\n",
" <th>(br&gt;c) 95%</th>\n",
" <th>(bv&gt;c) 5%</th>\n",
" <th>(bv&gt;c) 95%</th>\n",
" <th>5%</th>\n",
" <th>95%</th>\n",
" <th>b5%</th>\n",
" <th>b95%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.060</td>\n",
" <td>0.500</td>\n",
" <td>-0.860</td>\n",
" <td> 0.100</td>\n",
" <td>-0.800</td>\n",
" <td>0.030</td>\n",
" <td>0.420</td>\n",
" <td>0.750</td>\n",
" <td>0.440</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.210</td>\n",
" <td>0.360</td>\n",
" <td>-0.670</td>\n",
" <td> 0.290</td>\n",
" <td>-0.670</td>\n",
" <td>0.250</td>\n",
" <td>0.400</td>\n",
" <td>0.630</td>\n",
" <td>0.400</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.500</td>\n",
" <td>0.230</td>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.860</td>\n",
" <td> 0.100</td>\n",
" <td>-0.800</td>\n",
" <td>0.080</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.600</td>\n",
" <td> 0.450</td>\n",
" <td>-0.600</td>\n",
" <td>0.350</td>\n",
" <td>0.330</td>\n",
" <td>0.500</td>\n",
" <td>0.330</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.070</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.670</td>\n",
" <td> 0.290</td>\n",
" <td>-0.670</td>\n",
" <td>0.290</td>\n",
" <td>0.290</td>\n",
" <td>0.500</td>\n",
" <td>0.290</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.100</td>\n",
" <td>0.390</td>\n",
" <td>-0.860</td>\n",
" <td>-0.070</td>\n",
" <td>-0.800</td>\n",
" <td>0.080</td>\n",
" <td>0.330</td>\n",
" <td>0.740</td>\n",
" <td>0.290</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.670</td>\n",
" <td> 0.380</td>\n",
" <td>-0.570</td>\n",
" <td>0.290</td>\n",
" <td>0.290</td>\n",
" <td>0.500</td>\n",
" <td>0.290</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.100</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td> 0.000</td>\n",
" <td>-0.750</td>\n",
" <td>0.150</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.290</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 5% (br>c) 95% (bv>c) 5% (bv>c) 95% 5% 95% b5% b95% \\\n",
"6 -0.500 0.250 -0.060 0.500 -0.860 0.100 -0.800 0.030 \n",
"1 -0.500 0.250 -0.210 0.360 -0.670 0.290 -0.670 0.250 \n",
"0 -0.500 0.230 0.000 0.500 -0.860 0.100 -0.800 0.080 \n",
"4 -0.500 0.170 -0.500 0.170 -0.600 0.450 -0.600 0.350 \n",
"7 -0.500 0.070 -0.500 0.250 -0.670 0.290 -0.670 0.290 \n",
"5 -0.500 0.170 -0.100 0.390 -0.860 -0.070 -0.800 0.080 \n",
"3 -0.500 0.170 -0.500 0.250 -0.670 0.380 -0.570 0.290 \n",
"2 -0.500 0.170 -0.100 0.500 -0.890 0.000 -0.750 0.150 \n",
"\n",
" bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.420 0.750 0.440 gramm_ACC_4 \n",
"1 0.400 0.630 0.400 SAT_semantics_9 \n",
"0 0.400 0.750 0.400 SAT_syntax_ACC_4 \n",
"4 0.330 0.500 0.330 SAT_semantic_ACC_4 \n",
"7 0.290 0.500 0.290 SAT_synax_9 \n",
"5 0.330 0.740 0.290 gramm_ACC_9 \n",
"3 0.290 0.500 0.290 SAT_semantic_dprime_4 \n",
"2 0.400 0.750 0.290 SAT_syntax_dprime_4 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: sent:English-v-Mandarin\n"
]
},
{
"data": {
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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" <th></th>\n",
" <th>(br&gt;c) 5%</th>\n",
" <th>(br&gt;c) 95%</th>\n",
" <th>(bv&gt;c) 5%</th>\n",
" <th>(bv&gt;c) 95%</th>\n",
" <th>5%</th>\n",
" <th>95%</th>\n",
" <th>b5%</th>\n",
" <th>b95%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.280</td>\n",
" <td> 0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.380</td>\n",
" <td> 0.420</td>\n",
" <td>-0.360</td>\n",
" <td> 0.420</td>\n",
" <td>0.500</td>\n",
" <td>0.500</td>\n",
" <td>0.560</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.250</td>\n",
" <td> 0.300</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.340</td>\n",
" <td> 0.450</td>\n",
" <td>-0.380</td>\n",
" <td> 0.440</td>\n",
" <td>0.550</td>\n",
" <td>0.500</td>\n",
" <td>0.500</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td> 0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.670</td>\n",
" <td> 0.440</td>\n",
" <td>-0.670</td>\n",
" <td> 0.380</td>\n",
" <td>0.440</td>\n",
" <td>0.500</td>\n",
" <td>0.440</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.500</td>\n",
" <td> 0.170</td>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.750</td>\n",
" <td> 0.000</td>\n",
" <td>-0.670</td>\n",
" <td> 0.080</td>\n",
" <td>0.500</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td> 0.170</td>\n",
" <td>-0.170</td>\n",
" <td>0.390</td>\n",
" <td>-0.750</td>\n",
" <td> 0.070</td>\n",
" <td>-0.670</td>\n",
" <td> 0.170</td>\n",
" <td>0.330</td>\n",
" <td>0.670</td>\n",
" <td>0.330</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td> 0.070</td>\n",
" <td>-0.100</td>\n",
" <td>0.390</td>\n",
" <td>-0.860</td>\n",
" <td> 0.000</td>\n",
" <td>-0.750</td>\n",
" <td> 0.000</td>\n",
" <td>0.330</td>\n",
" <td>0.670</td>\n",
" <td>0.290</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.500</td>\n",
" <td> 0.070</td>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>-0.070</td>\n",
" <td>-0.860</td>\n",
" <td>-0.080</td>\n",
" <td>0.290</td>\n",
" <td>0.750</td>\n",
" <td>0.290</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>-0.100</td>\n",
" <td>-0.100</td>\n",
" <td>0.390</td>\n",
" <td>-0.890</td>\n",
" <td>-0.080</td>\n",
" <td>-0.860</td>\n",
" <td>-0.070</td>\n",
" <td>0.290</td>\n",
" <td>0.750</td>\n",
" <td>0.000</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 5% (br>c) 95% (bv>c) 5% (bv>c) 95% 5% 95% b5% b95% \\\n",
"3 -0.280 0.250 -0.500 0.250 -0.380 0.420 -0.360 0.420 \n",
"4 -0.250 0.300 -0.500 0.250 -0.340 0.450 -0.380 0.440 \n",
"7 -0.500 0.250 -0.500 0.300 -0.670 0.440 -0.670 0.380 \n",
"6 -0.500 0.170 0.000 0.500 -0.750 0.000 -0.670 0.080 \n",
"1 -0.500 0.170 -0.170 0.390 -0.750 0.070 -0.670 0.170 \n",
"5 -0.500 0.070 -0.100 0.390 -0.860 0.000 -0.750 0.000 \n",
"0 -0.500 0.070 0.000 0.500 -0.890 -0.070 -0.860 -0.080 \n",
"2 -0.500 -0.100 -0.100 0.390 -0.890 -0.080 -0.860 -0.070 \n",
"\n",
" bb_medianf1 bv_medianf1 medianf1 name \n",
"3 0.500 0.500 0.560 SAT_semantic_dprime_4 \n",
"4 0.550 0.500 0.500 SAT_semantic_ACC_4 \n",
"7 0.440 0.500 0.440 SAT_synax_9 \n",
"6 0.500 0.750 0.400 gramm_ACC_4 \n",
"1 0.330 0.670 0.330 SAT_semantics_9 \n",
"5 0.330 0.670 0.290 gramm_ACC_9 \n",
"0 0.290 0.750 0.290 SAT_syntax_ACC_4 \n",
"2 0.290 0.750 0.000 SAT_syntax_dprime_4 "
]
},
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},
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"text": [
"cope: nback:3_gt_1\n"
]
},
{
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" <th></th>\n",
" <th>(br&gt;c) 5%</th>\n",
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" <th>(bv&gt;c) 95%</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.210</td>\n",
" <td>0.390</td>\n",
" <td> 0.170</td>\n",
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" <td>-0.560</td>\n",
" <td>0.050</td>\n",
" <td>-0.560</td>\n",
" <td>0.130</td>\n",
" <td>0.670</td>\n",
" <td>0.860</td>\n",
" <td>0.670</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.100</td>\n",
" <td>0.390</td>\n",
" <td>-0.250</td>\n",
" <td>0.300</td>\n",
" <td>-0.210</td>\n",
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" <td>-0.270</td>\n",
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" <td>0.600</td>\n",
" <td>0.670</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.170</td>\n",
" <td>0.390</td>\n",
" <td>-0.250</td>\n",
" <td>0.300</td>\n",
" <td>-0.170</td>\n",
" <td>0.520</td>\n",
" <td>-0.220</td>\n",
" <td>0.470</td>\n",
" <td>0.670</td>\n",
" <td>0.570</td>\n",
" <td>0.670</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.420</td>\n",
" <td>0.670</td>\n",
" <td>-0.470</td>\n",
" <td>0.570</td>\n",
" <td>0.570</td>\n",
" <td>0.400</td>\n",
" <td>0.570</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.100</td>\n",
" <td>0.500</td>\n",
" <td>-0.750</td>\n",
" <td>0.190</td>\n",
" <td>-0.670</td>\n",
" <td>0.190</td>\n",
" <td>0.570</td>\n",
" <td>0.750</td>\n",
" <td>0.570</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.210</td>\n",
" <td>0.360</td>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.600</td>\n",
" <td>0.170</td>\n",
" <td>-0.600</td>\n",
" <td>0.180</td>\n",
" <td>0.590</td>\n",
" <td>0.800</td>\n",
" <td>0.570</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td> 0.070</td>\n",
" <td>0.500</td>\n",
" <td>-0.670</td>\n",
" <td>0.000</td>\n",
" <td>-0.670</td>\n",
" <td>0.100</td>\n",
" <td>0.500</td>\n",
" <td>0.800</td>\n",
" <td>0.500</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.210</td>\n",
" <td>0.390</td>\n",
" <td>-0.800</td>\n",
" <td>0.270</td>\n",
" <td>-0.800</td>\n",
" <td>0.240</td>\n",
" <td>0.330</td>\n",
" <td>0.670</td>\n",
" <td>0.330</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 5% (br>c) 95% (bv>c) 5% (bv>c) 95% 5% 95% b5% b95% \\\n",
"6 -0.210 0.390 0.170 0.500 -0.560 0.050 -0.560 0.130 \n",
"4 -0.100 0.390 -0.250 0.300 -0.210 0.480 -0.270 0.460 \n",
"3 -0.170 0.390 -0.250 0.300 -0.170 0.520 -0.220 0.470 \n",
"7 -0.500 0.300 -0.500 0.250 -0.420 0.670 -0.470 0.570 \n",
"2 -0.500 0.360 -0.100 0.500 -0.750 0.190 -0.670 0.190 \n",
"0 -0.210 0.360 0.000 0.500 -0.600 0.170 -0.600 0.180 \n",
"5 -0.500 0.250 0.070 0.500 -0.670 0.000 -0.670 0.100 \n",
"1 -0.500 0.170 -0.210 0.390 -0.800 0.270 -0.800 0.240 \n",
"\n",
" bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.670 0.860 0.670 gramm_ACC_4 \n",
"4 0.670 0.600 0.670 SAT_semantic_ACC_4 \n",
"3 0.670 0.570 0.670 SAT_semantic_dprime_4 \n",
"7 0.570 0.400 0.570 SAT_synax_9 \n",
"2 0.570 0.750 0.570 SAT_syntax_dprime_4 \n",
"0 0.590 0.800 0.570 SAT_syntax_ACC_4 \n",
"5 0.500 0.800 0.500 gramm_ACC_9 \n",
"1 0.330 0.670 0.330 SAT_semantics_9 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: srt:S_gt_R\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 5%</th>\n",
" <th>(br&gt;c) 95%</th>\n",
" <th>(bv&gt;c) 5%</th>\n",
" <th>(bv&gt;c) 95%</th>\n",
" <th>5%</th>\n",
" <th>95%</th>\n",
" <th>b5%</th>\n",
" <th>b95%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.100</td>\n",
" <td>0.390</td>\n",
" <td> 0.070</td>\n",
" <td>0.500</td>\n",
" <td>-0.490</td>\n",
" <td>0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.230</td>\n",
" <td>0.730</td>\n",
" <td>0.860</td>\n",
" <td>0.750</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.160</td>\n",
" <td>0.670</td>\n",
" <td>-0.290</td>\n",
" <td>0.570</td>\n",
" <td>0.670</td>\n",
" <td>0.500</td>\n",
" <td>0.750</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.260</td>\n",
" <td>0.300</td>\n",
" <td>-0.170</td>\n",
" <td>0.670</td>\n",
" <td>-0.220</td>\n",
" <td>0.670</td>\n",
" <td>0.750</td>\n",
" <td>0.500</td>\n",
" <td>0.750</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.170</td>\n",
" <td>0.390</td>\n",
" <td> 0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.560</td>\n",
" <td>0.130</td>\n",
" <td>-0.600</td>\n",
" <td>0.140</td>\n",
" <td>0.730</td>\n",
" <td>0.890</td>\n",
" <td>0.670</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.210</td>\n",
" <td>0.360</td>\n",
" <td> 0.070</td>\n",
" <td>0.500</td>\n",
" <td>-0.570</td>\n",
" <td>0.130</td>\n",
" <td>-0.560</td>\n",
" <td>0.180</td>\n",
" <td>0.670</td>\n",
" <td>0.860</td>\n",
" <td>0.670</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.330</td>\n",
" <td>0.570</td>\n",
" <td>-0.330</td>\n",
" <td>0.670</td>\n",
" <td>0.570</td>\n",
" <td>0.400</td>\n",
" <td>0.570</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td> 0.000</td>\n",
" <td>0.390</td>\n",
" <td>-0.670</td>\n",
" <td>0.130</td>\n",
" <td>-0.670</td>\n",
" <td>0.170</td>\n",
" <td>0.570</td>\n",
" <td>0.750</td>\n",
" <td>0.500</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.170</td>\n",
" <td>0.390</td>\n",
" <td>-0.750</td>\n",
" <td>0.170</td>\n",
" <td>-0.750</td>\n",
" <td>0.160</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 5% (br>c) 95% (bv>c) 5% (bv>c) 95% 5% 95% b5% b95% \\\n",
"6 -0.100 0.390 0.070 0.500 -0.490 0.250 -0.500 0.230 \n",
"4 0.000 0.500 -0.500 0.300 -0.160 0.670 -0.290 0.570 \n",
"3 0.000 0.500 -0.260 0.300 -0.170 0.670 -0.220 0.670 \n",
"0 -0.170 0.390 0.170 0.500 -0.560 0.130 -0.600 0.140 \n",
"2 -0.210 0.360 0.070 0.500 -0.570 0.130 -0.560 0.180 \n",
"7 -0.500 0.360 -0.500 0.170 -0.330 0.570 -0.330 0.670 \n",
"5 -0.500 0.250 0.000 0.390 -0.670 0.130 -0.670 0.170 \n",
"1 -0.500 0.250 -0.170 0.390 -0.750 0.170 -0.750 0.160 \n",
"\n",
" bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.730 0.860 0.750 gramm_ACC_4 \n",
"4 0.670 0.500 0.750 SAT_semantic_ACC_4 \n",
"3 0.750 0.500 0.750 SAT_semantic_dprime_4 \n",
"0 0.730 0.890 0.670 SAT_syntax_ACC_4 \n",
"2 0.670 0.860 0.670 SAT_syntax_dprime_4 \n",
"7 0.570 0.400 0.570 SAT_synax_9 \n",
"5 0.570 0.750 0.500 gramm_ACC_9 \n",
"1 0.400 0.750 0.400 SAT_semantics_9 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with warnings.catch_warnings():\n",
" warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
"\n",
" f1_results = {}\n",
" for cope in copes:\n",
" print 'cope: %s' % cope\n",
" f1_results[cope] = pd.DataFrame([{'name': k, \n",
" 'medianf1': np.median(coperesults[cope][k]['scores'][0][:, 0]),\n",
" 'bv_medianf1': np.median(coperesults[cope][k]['scores'][0][:, 1]),\n",
" 'bb_medianf1': np.median(coperesults[cope][k]['scores'][0][:, 2]),\n",
" '5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - coperesults[cope][k]['scores'][0][:, 1], 5),\n",
" '95%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - coperesults[cope][k]['scores'][0][:, 1], 95),\n",
" 'b5%': np.percentile(coperesults[cope][k]['scores'][0][:, 2] - coperesults[cope][k]['scores'][0][:, 1], 5),\n",
" 'b95%': np.percentile(coperesults[cope][k]['scores'][0][:, 2] - coperesults[cope][k]['scores'][0][:, 1], 95),\n",
" '(br>c) 5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - 0.5, 5),\n",
" '(br>c) 95%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - 0.5, 95),\n",
" '(bv>c) 5%': np.percentile(coperesults[cope][k]['scores'][0][:, 1] - 0.5, 5),\n",
" '(bv>c) 95%': np.percentile(coperesults[cope][k]['scores'][0][:, 1] - 0.5, 95),\n",
" } \n",
" for k in coperesults[cope].keys()]).sort('medianf1', ascending=False)\n",
" display(f1_results[cope])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: sent:English-v-MAL\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 2.5%</th>\n",
" <th>(br&gt;c) 97.5%</th>\n",
" <th>(bv&gt;c) 2.5%</th>\n",
" <th>(bv&gt;c) 97.5%</th>\n",
" <th>2.5%</th>\n",
" <th>97.5%</th>\n",
" <th>b2.5%</th>\n",
" <th>b97.5%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.500</td>\n",
" <td>0.390</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.860</td>\n",
" <td>0.250</td>\n",
" <td>-0.860</td>\n",
" <td>0.250</td>\n",
" <td>0.570</td>\n",
" <td>0.750</td>\n",
" <td>0.570</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.560</td>\n",
" <td>0.570</td>\n",
" <td>-0.420</td>\n",
" <td>0.520</td>\n",
" <td>0.570</td>\n",
" <td>0.550</td>\n",
" <td>0.560</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.550</td>\n",
" <td>0.500</td>\n",
" <td>-0.600</td>\n",
" <td>0.500</td>\n",
" <td>0.500</td>\n",
" <td>0.440</td>\n",
" <td>0.500</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.500</td>\n",
" <td>0.500</td>\n",
" <td>-0.470</td>\n",
" <td>0.500</td>\n",
" <td>0.440</td>\n",
" <td>0.440</td>\n",
" <td>0.500</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.250</td>\n",
" <td>0.360</td>\n",
" <td>-0.670</td>\n",
" <td>0.250</td>\n",
" <td>-0.670</td>\n",
" <td>0.290</td>\n",
" <td>0.440</td>\n",
" <td>0.630</td>\n",
" <td>0.400</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.860</td>\n",
" <td>0.000</td>\n",
" <td>-0.860</td>\n",
" <td>0.100</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.250</td>\n",
" <td>-0.890</td>\n",
" <td>0.190</td>\n",
" <td>0.330</td>\n",
" <td>0.750</td>\n",
" <td>0.330</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.100</td>\n",
" <td>-0.890</td>\n",
" <td>0.170</td>\n",
" <td>0.330</td>\n",
" <td>0.750</td>\n",
" <td>0.330</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 2.5% (br>c) 97.5% (bv>c) 2.5% (bv>c) 97.5% 2.5% 97.5% b2.5% \\\n",
"6 -0.500 0.390 -0.170 0.500 -0.860 0.250 -0.860 \n",
"7 -0.500 0.360 -0.500 0.360 -0.560 0.570 -0.420 \n",
"4 -0.500 0.300 -0.500 0.300 -0.550 0.500 -0.600 \n",
"3 -0.500 0.300 -0.500 0.300 -0.500 0.500 -0.470 \n",
"1 -0.500 0.360 -0.250 0.360 -0.670 0.250 -0.670 \n",
"0 -0.500 0.250 -0.170 0.500 -0.860 0.000 -0.860 \n",
"5 -0.500 0.250 -0.170 0.500 -0.890 0.250 -0.890 \n",
"2 -0.500 0.170 -0.170 0.500 -0.890 0.100 -0.890 \n",
"\n",
" b97.5% bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.250 0.570 0.750 0.570 gramm_ACC_4 \n",
"7 0.520 0.570 0.550 0.560 SAT_synax_9 \n",
"4 0.500 0.500 0.440 0.500 SAT_semantic_ACC_4 \n",
"3 0.500 0.440 0.440 0.500 SAT_semantic_dprime_4 \n",
"1 0.290 0.440 0.630 0.400 SAT_semantics_9 \n",
"0 0.100 0.400 0.750 0.400 SAT_syntax_ACC_4 \n",
"5 0.190 0.330 0.750 0.330 gramm_ACC_9 \n",
"2 0.170 0.330 0.750 0.330 SAT_syntax_dprime_4 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: sent:MAL-v-Rest\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 2.5%</th>\n",
" <th>(br&gt;c) 97.5%</th>\n",
" <th>(bv&gt;c) 2.5%</th>\n",
" <th>(bv&gt;c) 97.5%</th>\n",
" <th>2.5%</th>\n",
" <th>97.5%</th>\n",
" <th>b2.5%</th>\n",
" <th>b97.5%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.100</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.180</td>\n",
" <td>-0.890</td>\n",
" <td>0.170</td>\n",
" <td>0.420</td>\n",
" <td>0.750</td>\n",
" <td>0.440</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.250</td>\n",
" <td>0.360</td>\n",
" <td>-0.750</td>\n",
" <td>0.380</td>\n",
" <td>-0.670</td>\n",
" <td>0.330</td>\n",
" <td>0.400</td>\n",
" <td>0.630</td>\n",
" <td>0.400</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.180</td>\n",
" <td>-0.890</td>\n",
" <td>0.150</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.670</td>\n",
" <td>0.500</td>\n",
" <td>-0.670</td>\n",
" <td>0.470</td>\n",
" <td>0.330</td>\n",
" <td>0.500</td>\n",
" <td>0.330</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.750</td>\n",
" <td>0.400</td>\n",
" <td>-0.750</td>\n",
" <td>0.340</td>\n",
" <td>0.290</td>\n",
" <td>0.500</td>\n",
" <td>0.290</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.100</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.050</td>\n",
" <td>-0.890</td>\n",
" <td>0.100</td>\n",
" <td>0.330</td>\n",
" <td>0.740</td>\n",
" <td>0.290</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.670</td>\n",
" <td>0.450</td>\n",
" <td>-0.670</td>\n",
" <td>0.450</td>\n",
" <td>0.290</td>\n",
" <td>0.500</td>\n",
" <td>0.290</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.070</td>\n",
" <td>-0.800</td>\n",
" <td>0.180</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.290</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 2.5% (br>c) 97.5% (bv>c) 2.5% (bv>c) 97.5% 2.5% 97.5% b2.5% \\\n",
"6 -0.500 0.300 -0.100 0.500 -0.890 0.180 -0.890 \n",
"1 -0.500 0.360 -0.250 0.360 -0.750 0.380 -0.670 \n",
"0 -0.500 0.250 -0.000 0.500 -0.890 0.180 -0.890 \n",
"4 -0.500 0.250 -0.500 0.250 -0.670 0.500 -0.670 \n",
"7 -0.500 0.170 -0.500 0.300 -0.750 0.400 -0.750 \n",
"5 -0.500 0.170 -0.100 0.500 -0.890 0.050 -0.890 \n",
"3 -0.500 0.170 -0.500 0.300 -0.670 0.450 -0.670 \n",
"2 -0.500 0.250 -0.170 0.500 -0.890 0.070 -0.800 \n",
"\n",
" b97.5% bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.170 0.420 0.750 0.440 gramm_ACC_4 \n",
"1 0.330 0.400 0.630 0.400 SAT_semantics_9 \n",
"0 0.150 0.400 0.750 0.400 SAT_syntax_ACC_4 \n",
"4 0.470 0.330 0.500 0.330 SAT_semantic_ACC_4 \n",
"7 0.340 0.290 0.500 0.290 SAT_synax_9 \n",
"5 0.100 0.330 0.740 0.290 gramm_ACC_9 \n",
"3 0.450 0.290 0.500 0.290 SAT_semantic_dprime_4 \n",
"2 0.180 0.400 0.750 0.290 SAT_syntax_dprime_4 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: sent:English-v-Mandarin\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 2.5%</th>\n",
" <th>(br&gt;c) 97.5%</th>\n",
" <th>(bv&gt;c) 2.5%</th>\n",
" <th>(bv&gt;c) 97.5%</th>\n",
" <th>2.5%</th>\n",
" <th>97.5%</th>\n",
" <th>b2.5%</th>\n",
" <th>b97.5%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.520</td>\n",
" <td>0.470</td>\n",
" <td>-0.500</td>\n",
" <td>0.500</td>\n",
" <td>0.500</td>\n",
" <td>0.500</td>\n",
" <td>0.560</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.500</td>\n",
" <td>0.570</td>\n",
" <td>-0.490</td>\n",
" <td>0.500</td>\n",
" <td>0.550</td>\n",
" <td>0.500</td>\n",
" <td>0.500</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.670</td>\n",
" <td>0.480</td>\n",
" <td>-0.670</td>\n",
" <td>0.450</td>\n",
" <td>0.440</td>\n",
" <td>0.500</td>\n",
" <td>0.440</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.860</td>\n",
" <td>0.080</td>\n",
" <td>-0.860</td>\n",
" <td>0.110</td>\n",
" <td>0.500</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.210</td>\n",
" <td>0.390</td>\n",
" <td>-0.800</td>\n",
" <td>0.130</td>\n",
" <td>-0.750</td>\n",
" <td>0.190</td>\n",
" <td>0.330</td>\n",
" <td>0.670</td>\n",
" <td>0.330</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.170</td>\n",
" <td>-0.170</td>\n",
" <td>0.390</td>\n",
" <td>-0.890</td>\n",
" <td>0.050</td>\n",
" <td>-0.860</td>\n",
" <td>0.070</td>\n",
" <td>0.330</td>\n",
" <td>0.670</td>\n",
" <td>0.290</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.500</td>\n",
" <td>0.100</td>\n",
" <td>-0.060</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.000</td>\n",
" <td>-0.890</td>\n",
" <td>0.000</td>\n",
" <td>0.290</td>\n",
" <td>0.750</td>\n",
" <td>0.290</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.070</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.890</td>\n",
" <td>0.000</td>\n",
" <td>-0.890</td>\n",
" <td>0.000</td>\n",
" <td>0.290</td>\n",
" <td>0.750</td>\n",
" <td>0.000</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 2.5% (br>c) 97.5% (bv>c) 2.5% (bv>c) 97.5% 2.5% 97.5% b2.5% \\\n",
"3 -0.500 0.300 -0.500 0.360 -0.520 0.470 -0.500 \n",
"4 -0.500 0.360 -0.500 0.360 -0.500 0.570 -0.490 \n",
"7 -0.500 0.250 -0.500 0.360 -0.670 0.480 -0.670 \n",
"6 -0.500 0.250 0.000 0.500 -0.860 0.080 -0.860 \n",
"1 -0.500 0.170 -0.210 0.390 -0.800 0.130 -0.750 \n",
"5 -0.500 0.170 -0.170 0.390 -0.890 0.050 -0.860 \n",
"0 -0.500 0.100 -0.060 0.500 -0.890 0.000 -0.890 \n",
"2 -0.500 0.070 -0.170 0.500 -0.890 0.000 -0.890 \n",
"\n",
" b97.5% bb_medianf1 bv_medianf1 medianf1 name \n",
"3 0.500 0.500 0.500 0.560 SAT_semantic_dprime_4 \n",
"4 0.500 0.550 0.500 0.500 SAT_semantic_ACC_4 \n",
"7 0.450 0.440 0.500 0.440 SAT_synax_9 \n",
"6 0.110 0.500 0.750 0.400 gramm_ACC_4 \n",
"1 0.190 0.330 0.670 0.330 SAT_semantics_9 \n",
"5 0.070 0.330 0.670 0.290 gramm_ACC_9 \n",
"0 0.000 0.290 0.750 0.290 SAT_syntax_ACC_4 \n",
"2 0.000 0.290 0.750 0.000 SAT_syntax_dprime_4 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: nback:3_gt_1\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 2.5%</th>\n",
" <th>(br&gt;c) 97.5%</th>\n",
" <th>(bv&gt;c) 2.5%</th>\n",
" <th>(bv&gt;c) 97.5%</th>\n",
" <th>2.5%</th>\n",
" <th>97.5%</th>\n",
" <th>b2.5%</th>\n",
" <th>b97.5%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.210</td>\n",
" <td>0.390</td>\n",
" <td> 0.070</td>\n",
" <td>0.500</td>\n",
" <td>-0.640</td>\n",
" <td>0.130</td>\n",
" <td>-0.670</td>\n",
" <td>0.140</td>\n",
" <td>0.670</td>\n",
" <td>0.860</td>\n",
" <td>0.670</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.210</td>\n",
" <td>0.390</td>\n",
" <td>-0.250</td>\n",
" <td>0.300</td>\n",
" <td>-0.270</td>\n",
" <td>0.550</td>\n",
" <td>-0.330</td>\n",
" <td>0.520</td>\n",
" <td>0.670</td>\n",
" <td>0.600</td>\n",
" <td>0.670</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.210</td>\n",
" <td>0.390</td>\n",
" <td>-0.250</td>\n",
" <td>0.300</td>\n",
" <td>-0.220</td>\n",
" <td>0.570</td>\n",
" <td>-0.390</td>\n",
" <td>0.570</td>\n",
" <td>0.670</td>\n",
" <td>0.570</td>\n",
" <td>0.670</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.570</td>\n",
" <td>0.670</td>\n",
" <td>-0.570</td>\n",
" <td>0.670</td>\n",
" <td>0.570</td>\n",
" <td>0.400</td>\n",
" <td>0.570</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-1.000</td>\n",
" <td>0.290</td>\n",
" <td>-0.860</td>\n",
" <td>0.290</td>\n",
" <td>0.570</td>\n",
" <td>0.750</td>\n",
" <td>0.570</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.260</td>\n",
" <td>0.390</td>\n",
" <td>-0.100</td>\n",
" <td>0.500</td>\n",
" <td>-0.670</td>\n",
" <td>0.220</td>\n",
" <td>-0.750</td>\n",
" <td>0.220</td>\n",
" <td>0.590</td>\n",
" <td>0.800</td>\n",
" <td>0.570</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.860</td>\n",
" <td>0.070</td>\n",
" <td>-0.750</td>\n",
" <td>0.180</td>\n",
" <td>0.500</td>\n",
" <td>0.800</td>\n",
" <td>0.500</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.260</td>\n",
" <td>0.390</td>\n",
" <td>-0.890</td>\n",
" <td>0.330</td>\n",
" <td>-0.860</td>\n",
" <td>0.290</td>\n",
" <td>0.330</td>\n",
" <td>0.670</td>\n",
" <td>0.330</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 2.5% (br>c) 97.5% (bv>c) 2.5% (bv>c) 97.5% 2.5% 97.5% b2.5% \\\n",
"6 -0.210 0.390 0.070 0.500 -0.640 0.130 -0.670 \n",
"4 -0.210 0.390 -0.250 0.300 -0.270 0.550 -0.330 \n",
"3 -0.210 0.390 -0.250 0.300 -0.220 0.570 -0.390 \n",
"7 -0.500 0.360 -0.500 0.300 -0.570 0.670 -0.570 \n",
"2 -0.500 0.360 -0.170 0.500 -1.000 0.290 -0.860 \n",
"0 -0.260 0.390 -0.100 0.500 -0.670 0.220 -0.750 \n",
"5 -0.500 0.360 -0.000 0.500 -0.860 0.070 -0.750 \n",
"1 -0.500 0.250 -0.260 0.390 -0.890 0.330 -0.860 \n",
"\n",
" b97.5% bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.140 0.670 0.860 0.670 gramm_ACC_4 \n",
"4 0.520 0.670 0.600 0.670 SAT_semantic_ACC_4 \n",
"3 0.570 0.670 0.570 0.670 SAT_semantic_dprime_4 \n",
"7 0.670 0.570 0.400 0.570 SAT_synax_9 \n",
"2 0.290 0.570 0.750 0.570 SAT_syntax_dprime_4 \n",
"0 0.220 0.590 0.800 0.570 SAT_syntax_ACC_4 \n",
"5 0.180 0.500 0.800 0.500 gramm_ACC_9 \n",
"1 0.290 0.330 0.670 0.330 SAT_semantics_9 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cope: srt:S_gt_R\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(br&gt;c) 2.5%</th>\n",
" <th>(br&gt;c) 97.5%</th>\n",
" <th>(bv&gt;c) 2.5%</th>\n",
" <th>(bv&gt;c) 97.5%</th>\n",
" <th>2.5%</th>\n",
" <th>97.5%</th>\n",
" <th>b2.5%</th>\n",
" <th>b97.5%</th>\n",
" <th>bb_medianf1</th>\n",
" <th>bv_medianf1</th>\n",
" <th>medianf1</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.500</td>\n",
" <td>0.350</td>\n",
" <td>-0.560</td>\n",
" <td>0.330</td>\n",
" <td>0.730</td>\n",
" <td>0.860</td>\n",
" <td>0.750</td>\n",
" <td> gramm_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.300</td>\n",
" <td>0.720</td>\n",
" <td>-0.380</td>\n",
" <td>0.670</td>\n",
" <td>0.670</td>\n",
" <td>0.500</td>\n",
" <td>0.750</td>\n",
" <td> SAT_semantic_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.060</td>\n",
" <td>0.500</td>\n",
" <td>-0.500</td>\n",
" <td>0.360</td>\n",
" <td>-0.220</td>\n",
" <td>0.800</td>\n",
" <td>-0.270</td>\n",
" <td>0.800</td>\n",
" <td>0.750</td>\n",
" <td>0.500</td>\n",
" <td>0.750</td>\n",
" <td> SAT_semantic_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.210</td>\n",
" <td>0.390</td>\n",
" <td> 0.070</td>\n",
" <td>0.500</td>\n",
" <td>-0.640</td>\n",
" <td>0.160</td>\n",
" <td>-0.670</td>\n",
" <td>0.180</td>\n",
" <td>0.730</td>\n",
" <td>0.890</td>\n",
" <td>0.670</td>\n",
" <td> SAT_syntax_ACC_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.500</td>\n",
" <td>0.390</td>\n",
" <td> 0.000</td>\n",
" <td>0.500</td>\n",
" <td>-0.720</td>\n",
" <td>0.230</td>\n",
" <td>-0.670</td>\n",
" <td>0.230</td>\n",
" <td>0.670</td>\n",
" <td>0.860</td>\n",
" <td>0.670</td>\n",
" <td> SAT_syntax_dprime_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.500</td>\n",
" <td>0.500</td>\n",
" <td>-0.500</td>\n",
" <td>0.250</td>\n",
" <td>-0.330</td>\n",
" <td>0.670</td>\n",
" <td>-0.380</td>\n",
" <td>0.720</td>\n",
" <td>0.570</td>\n",
" <td>0.400</td>\n",
" <td>0.570</td>\n",
" <td> SAT_synax_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.100</td>\n",
" <td>0.500</td>\n",
" <td>-0.750</td>\n",
" <td>0.230</td>\n",
" <td>-0.750</td>\n",
" <td>0.230</td>\n",
" <td>0.570</td>\n",
" <td>0.750</td>\n",
" <td>0.500</td>\n",
" <td> gramm_ACC_9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.500</td>\n",
" <td>0.300</td>\n",
" <td>-0.170</td>\n",
" <td>0.500</td>\n",
" <td>-0.800</td>\n",
" <td>0.240</td>\n",
" <td>-0.800</td>\n",
" <td>0.250</td>\n",
" <td>0.400</td>\n",
" <td>0.750</td>\n",
" <td>0.400</td>\n",
" <td> SAT_semantics_9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (br>c) 2.5% (br>c) 97.5% (bv>c) 2.5% (bv>c) 97.5% 2.5% 97.5% b2.5% \\\n",
"6 -0.170 0.500 -0.000 0.500 -0.500 0.350 -0.560 \n",
"4 -0.170 0.500 -0.500 0.360 -0.300 0.720 -0.380 \n",
"3 -0.060 0.500 -0.500 0.360 -0.220 0.800 -0.270 \n",
"0 -0.210 0.390 0.070 0.500 -0.640 0.160 -0.670 \n",
"2 -0.500 0.390 0.000 0.500 -0.720 0.230 -0.670 \n",
"7 -0.500 0.500 -0.500 0.250 -0.330 0.670 -0.380 \n",
"5 -0.500 0.300 -0.100 0.500 -0.750 0.230 -0.750 \n",
"1 -0.500 0.300 -0.170 0.500 -0.800 0.240 -0.800 \n",
"\n",
" b97.5% bb_medianf1 bv_medianf1 medianf1 name \n",
"6 0.330 0.730 0.860 0.750 gramm_ACC_4 \n",
"4 0.670 0.670 0.500 0.750 SAT_semantic_ACC_4 \n",
"3 0.800 0.750 0.500 0.750 SAT_semantic_dprime_4 \n",
"0 0.180 0.730 0.890 0.670 SAT_syntax_ACC_4 \n",
"2 0.230 0.670 0.860 0.670 SAT_syntax_dprime_4 \n",
"7 0.720 0.570 0.400 0.570 SAT_synax_9 \n",
"5 0.230 0.570 0.750 0.500 gramm_ACC_9 \n",
"1 0.250 0.400 0.750 0.400 SAT_semantics_9 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with warnings.catch_warnings():\n",
" warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
"\n",
" f1_results = {}\n",
" for cope in copes:\n",
" print 'cope: %s' % cope\n",
" f1_results[cope] = pd.DataFrame([{'name': k, \n",
" 'medianf1': np.median(coperesults[cope][k]['scores'][0][:, 0]),\n",
" 'bv_medianf1': np.median(coperesults[cope][k]['scores'][0][:, 1]),\n",
" 'bb_medianf1': np.median(coperesults[cope][k]['scores'][0][:, 2]),\n",
" '2.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - coperesults[cope][k]['scores'][0][:, 1], 2.5),\n",
" '97.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - coperesults[cope][k]['scores'][0][:, 1], 97.5),\n",
" 'b2.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 2] - coperesults[cope][k]['scores'][0][:, 1], 2.5),\n",
" 'b97.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 2] - coperesults[cope][k]['scores'][0][:, 1], 97.5),\n",
" '(br>c) 2.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - 0.5, 2.5),\n",
" '(br>c) 97.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - 0.5, 97.5),\n",
" '(bv>c) 2.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 1] - 0.5, 2.5),\n",
" '(bv>c) 97.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 1] - 0.5, 97.5),\n",
" } \n",
" for k in coperesults[cope].keys()]).sort('medianf1', ascending=False)\n",
" display(f1_results[cope])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with warnings.catch_warnings():\n",
" warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
"\n",
" f1_results = {}\n",
" for cope in copes:\n",
" print 'cope: %s' % cope\n",
" f1_results[cope] = pd.DataFrame([{'name': k, \n",
" 'medianf1': np.median(coperesults[cope][k]['scores'][0][:, 0]),\n",
" 'medianf1_i': np.median(coperesults[cope][k]['scores'][1][:, 0]),\n",
" 'bv_medianf1': np.median(coperesults[cope][k]['scores'][0][:, 1]),\n",
" 'bv_medianf1_i': np.median(coperesults[cope][k]['scores'][1][:, 1]),\n",
" 'bb_medianf1': np.median(coperesults[cope][k]['scores'][0][:, 2]),\n",
" 'bb_medianf1_i': np.median(coperesults[cope][k]['scores'][1][:, 2]),\n",
" '2.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - coperesults[cope][k]['scores'][0][:, 1], 2.5),\n",
" '97.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 0] - coperesults[cope][k]['scores'][0][:, 1], 97.5),\n",
" 'b2.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 2] - coperesults[cope][k]['scores'][0][:, 1], 2.5),\n",
" 'b97.5%': np.percentile(coperesults[cope][k]['scores'][0][:, 2] - coperesults[cope][k]['scores'][0][:, 1], 97.5),\n",
" } \n",
" for k in coperesults[cope].keys()]).sort('medianf1', ascending=False)\n",
" display(f1_results[cope])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/software/python/anaconda/envs/nipype-0.9/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",
"/software/python/anaconda/envs/nipype-0.9/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",
"/software/python/anaconda/envs/nipype-0.9/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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ELy1cl8O331rWakVRUREjhg1FpdMQ5mzHmZxbBIQ2YNLPU80tWjmmTZvK6tWr+Oijj+nV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\n58L2SgnwtX/GurrkV/4cOrSfi5eqWHHPElk6c/2RnprCpPxcvvxyHZ2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oLDzI5IkT8PNC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0i6ptYQym+CsYr77pW+rNxROrqdUKnuTADJiIjSntawoBCK9e0XIC/SWW6eAwIcm+QzbfosRGD1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lZqJ4pLSomKjCQy0r8nnMHBjnKC0pITgzLwER/vOH3+4p1XEVQqdIYAomJi/Rao9IZztdUg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WcSHtIg+PuRk3jbrxDYAAbz2Tb+nP4cOHOHBgbxtbWD9ZWdYgR2R4GBZRJCfHtWVXP8z/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LJ7\n927iExLx9u4dsJ4xaw57d+1g1669zJljuaDJSNBqteTk5DB6wtTuz2VYTDwSqZRDhw4TETHaqvc/\nciSVU4UF+E2+GZlq6KxQiZNxA2Ww7CMTblFJNJ07zrLly0lKmtzrmWsNjh41+kCDs1/3n3WV2Zw9\nexFv75FJCfRkMD9h1QwkmUzGkiVLWLBgAWPGjOG+++4jPj6eDz/8kA8//BCAv/71r2RmZpKUlMSN\nN97IG2+8gZfXte1gXk1lZQX79+8hth/to0c6hV7BIxPGLCSBr1d8YbMspPr6OjKOpTPJ1x2F9Mqv\n5oVpY3oFjwACXFREuruwY+smu9TaiqJIcfEpIgP6fkDlMimhfl4UF9m27r61tYUVKz4jPjaGG2bN\n6HN+2sTxfYJHJu5YOJ+QoCA+/+wjOjs7rW1qH/LyjFk8Y5ON9o3r+jM317bZPQNRV1dL0amTxCZP\n7pUZF5s8mZIzp4clzHotrFmzijPFp/CdtACFq+egY33Gzek3eGRC5uSC39TbqLh0kU8+tX62Q0+K\ni08jqNxBqkCv8uaUFXdJHR1H8RMmqqurOHnqJFFaEaHLX9zSKXQHjwDC9CDHuuLKA1FbW0ve8Twm\n+nkg6fFdfCg+vFfwCCDB2xVnuYy9u623G5aVlcHhI6ksmpGEt9vgmSqr//bLQYNHJhZMTiDQ24Pl\nH75ntR3TnTu3cq70LD97YDGKHinzj953d59FQXxsNLOnT2HD+rUW6RRTWHiS2NHxhIaNLPCx8ruN\nIwoemZg770YkEsmgOl4mXZTIMeNGdI+RdmEzETlmHJ0azaAaXqeLTyNTOSNzGVkQ7lq6sAGovI2b\nKSUlZwYcY9I/MukRDZdr6cIGxgWFcb7meG28h4Oj+Yndu7fT0dnJ2B6SGPd2Ct3BIzBmJjkLEtbb\nQWNTo9Hw9covCHV1JtH7SqmqqQubCWe5jLkhPuTm5dhcxBjg69Vf4aJScuOEK2uc+6+f0h08AvB0\ndWFOUiy792zv7jhlTcrLL7By5RdMmZDMzTf0P4fsz0+YcFW78Ienfs6lS+WsXPmFNU3tRX5+Lpcv\nX2L29X31/EbHx+MfEMjOnVttZs9AnDp1Ek1HBxFxY7uPKVQqgiNjyM61buWFVqvl8y8/Q+nhi9so\n83zb1R2dB0MQBHzHz6O1uYmNm9Zfi6lmkZefh6BQQ5cchuhiDHaePGn977J12xMACxcuZOHChb2O\n/fKXVyaQPj4+bN682ao2fLtmFYIoMrafpjlXayCZEBBI1ojsr6ni8OFDzJo116o2AuzatR2DwcC0\nwN4O72oNJBPTA7xYVXSB3NxsJk4cfOfX0ly6VE5tXT13TTM+AEzaFmonJZ/+8TESI4LZnnmCjo4O\nqwmDXs2aNatobm7m1395pt/yv4X3XakJ3vZN74e6XCbjqcce4m+v/IuUlA3cdde9Vre3J5mZGQQF\nh/D7p34BgKenF37+AWRmHmXBgltsakt/7N+/B1EUGZ1s/JztXvMlAH5hEQDs3buLBx6wbr338eN5\nrFu3FrfIsbhFDJ31YY6+hUtgJJ7x09i3dxdJ48Yzc+bIOzGYiyiKFJ0+hU7p3a1vUd1sFNJW26iU\nxdFwBD9hYv/+PYhAVI+4fE9ti3s7BeQIhOtEDqcd5LHHn7T6LlNPDhwwfhcn+vXWnjP5CZkAr8w0\nthqWSSQk+7iRcSyd5ubmQbVARkJrayvLP1xCqJ8Xd103dKaQyU8ArHl+4J1khUzGU7fN4YUvN7Jq\n1Rc88YRld51ra2v5evVXjB+XwJwZU3udM/kJQYCtX1/xE7945AGO5ebz8UdL+dtzL12TvoGrqysX\nLp5Hr9ebXRrRk5FoIPWkuroKg8EwqAbSmZJiXD29ULuPrHPYtWggAQR1NWsoKSlmzJjEfsc0NTcj\nValH/Lu4Fg0kAKlChUQmH7QJwukQNksAACAASURBVPnzpSBIRtyB7Vo0kABElXE+WVZWapcsGEvi\nKH5Cq9WyeeN3BImS7ixVuOIn3A3wk04BGQLxnQayCo5z9uwZRo2KtrptJrZu3UxdYwP3JEb0+n6Y\n/IS7QsZfp8QBMDPQm8OX6/jys+W89sY7SEagJzYSCgsLyM7J4oHrp+CiUnYfN/kJuUzCyr88CcA9\nsyZyML+Yb75ZwW9+87TVbNLr9SxZ8jYqpYL//eVjAz5bBltPAIwfm8AdN89n07YUpk6dQULC2D5j\nLM22bZtxc3dn2szrgCt+Qq125cMvVjB/4S2s+OwTSkrOEBVlu8/i1eTkZCGVygiNiev2EzKlkmnz\nbyM15Ttqa2stkj3THwcO7KWupoqgOYvN1s0brp9Q+QShDotj06b13HbrIqvN6zs7O8nPz0PnHIy8\nwJiNaVB5gkzFsWMZVo9b2OYpYUcuX77EwUP7+80+GopwA3iKtslC0mq17NqeQqynGh8n5dAXAIne\nbrgp5GzbssGqtvWHKWMmaVT/AsbJUSHodHoKC0/YxJ7z58vYvn0LC2+cS/SoiBG9x4RxicyYMol1\n69bYZJfDRH19PQUFJ5g6/UrWlCAITJk+g+PH82hubrKZLf3R3t7G5s0biIwfi5d/YK9zLq5uRI+d\nwNZtm63aSayxsZG3//MmCjcvfCddW0nF1XiPm4XKO4ily96zSSZVTU01Lc1NiM69U4xLSoqtfu8f\nGZzOzk62pWwkWA+uQ2jljdYZu7HttWEWkiiK7Nm1jVHuLmb7icn+nuj0elJTD1jcnq++/IT6hgae\nun1un+4510pcWCALJiWyY8fW7iwOS2AwGHj//bfRG/T89olHzQ4++Hh58dgDi8nLz73mXdwbb1xA\ndVUV69Z8fU3vMxJ0Wi2ffLAUpVI1aHl+Y2MDajfbtp3vicpFjUQipbm5ecAxCrkCg05jQ6t6Y9Dr\nEPX6QUVfK6uqQe5k1COyB1IFSKTU1f1wdfYszcGD+2hsbiaxc+gOtLE6kAsCGzd8ZwPLjFRWVvDt\n2lWM8XIlymPoxatcKuHmcD/OlpWya9c2G1gIOp2Oj5YvwdtNzcIpQwdXfNxduWXKWPbv32tRf3A1\nmzZ+x5kzxfzmiUfx8ri2599jDywmKMCfpe//x+raQ5WVFWRnZ3LD/AUDPo/mXD8PpUrF9u0pVrVl\nKLKyMwkaFYPiqtK+yK6MpFwrZSHp9Xq+W/8tKq8AnAOt2/jHa8wMtJ0aduzYYrV75OVlo+3swOAe\nduWgIKB3DeFY5lGrdI7tyX99AGntNysHzD4CY+bR1dlHJgQEkjtFqupqrDL57kl6ehoNzc3MDOwb\ndXWWSfpkH4FRT2JqgCd5x/O5eNG2bQNzso8R4OWOn6dxB1PtpOzOPgKICw1ELpORnZ1pdVtEUeTT\nT5bh4uzMo/cN3e2iv90CE08+fD+iwcCXX35iSRMHJT09DVE0MO26WXh5eePl5c17H33K9Jmz0Ov1\nHD16xGa29Mf27VtoaWlm2k23dx+LTBhHZMI4ohKSmbbgdjra29myZeQlFYMhiiIffPgeLS3NBMy4\nA4msfz2MgRhqx0CQSAmYcQd6vYH/vPsWBsPQk8Jr4cyZLv0jJ28MClcMcnXX8e93icF/A4cO7aO5\nrZVEXe/jzoYr2UcmfEUBf1Fg07q16HRXXWAlTp48QVVNDZP9+05sZULv7CMTQWongtVO7Nm5xaJi\nrseP57Fn7y5unzaO6KDhtVAfLPuoJw/eMBUfNzVL33/bYqXFW1I2cPx4Hr989KF+tS0EoW/2kYnb\nbprHxOSxfPnFJ1y4MPJWuZMmTeX6629kw7dr2JayacTvM9zsI51Ox3tvv0nhyRM8+eSv8fQcuLxH\nIpFaZONspGVsosGAiDhogG9MfDzalka0LQ0jNQ8YuQZSR/VFRNFAbGzcwGM0HSAZeVchAyPPPgJA\nEBCkctrbrbuY+KFgMBjYsG4N3kgIvGqq4G64kn1kQonA6E6RI+lpNtmgEkWR5R8uQTAYWBQV2Oe8\nu0LWK/vIxHhfD6I91Kz46jNqa2utbufWrZu4cPEijy2YiUrR+/shl0l6ZR+ZuGf2RLzd1Hy0fIlV\nfG51dRXffvs1M6dO6pOZOhCDrSdUKiV/eOrnVFVXWb2McfuOLUgkEubddKVTnVrt2p19BODs4sKs\nOdeTlnaQxsZre2aOlJqaai6VXyAiritLWqlEplTyu9fexzswGLW7p9XWjbm5WdRUVeARN3VEWavD\n8RNKTz+cA0exeYv1ZGZSUw8hyFSILgEYnLwwOHmhH7UAg1s4Om0n2VZuxPRfHUC6dKmc1LSDjNaB\n8wDZRzKJ8bVf3v/EOswAXqLANyutl4UkiiKbNqzFz1lFrGff3YI2nYE2nYFXM/oq+k8L9EImkbAl\nxXZZSA0N9eQfz2dq3JXOJ5/+8bHu4BGAQi5jQnQYR9IOWn1xdeRIKgUnT/DIfXfh5jrwbotMJkMm\nk/HM//1jwDH+fr7cu+hWjhxJ48SJfGuY24e0tIOEhoUTEhrGex99ynsffQpAeGQkgUFBpKUN3onG\nmrS3t7Fx0zoi48cSEH4lYh+VkExUgjEd3jcolJhxE9myZZNVspBSUw+QdewoXmNnofTsu+AbCIlc\niUSu5FLa0HXIcrUHPhNu5MzpU1YLhJkoLi5CkEgRlR7oY+5AH7sIqZPHjwEkO6PX61m/9mu8RYGA\nqxYGcsH4ypb19hOJnSJ1TY02+45u37oZJ5mURO++mi9i1+uDvL7ddCb7e1B28SKnT1umK0xHRwfL\nlv6HAC937p1jfvm0KTjz9FLzMm9UCjm/vG02lysqWLtm1UjN7ebcubOsWvUlMyZPHFDb4uYb5nLz\nDXNJz8rpc04QBP7w1M9xclLxzn/eRKsdYGdqCARB4Mknf8OUqdNZ8dknHNy3d0Tv88RD95s9VhRF\nPv7gfTKPpvOzx37B7NnXDzrez8+PproaxGsMqC99/n9HdF1TQx2iwYCPz8DaQZMmTUUQJDQUX5tW\nYPHat0Z0XcPpTFTOLoOWp/h6e4O2FUYYvJV0vaTFIww0GnSI2g6rlYT80MjMPEpFVSWJnYZujTwT\nzRLja6ui9+96jA4EUWTzZutroqSmHiD/eB4Lwv3wUPbdbGvR6mjR6vr4CUEQuCsq0Jih+LF1OxLX\n1FSzZs1KJsSEM3l0RJ/zK//yZJ/gERj9wWMLZnL+wgW2bh154H0gVnz1mfHZ/MiDQ451cXbGxdmZ\nV//z/qDjEuJimTdrBilbNlotgNjU1MiunduZOmMmXj2+527u7ri5u7N21YruYwtuvQ2dTsfmzbav\nXAGjXAdA5BjjM/N3r73P714z/gwFQSAyfix5eTlW0aLdvWc3MpVzd4MdcxnOWqIn7tHJtDY3kZfX\ndy5xrWg0HRw7lo7ONcSY3aryAJUHQvNFRBc/BLkTqVael5oVQGptbeW5557jwQeNX6pTp06xYYN9\nPnzDYc3XK5DCgNlH5mDKQqqur+PgwX0Ws60nhYUFlJ4/z3VBXr1EUc1BLZcxwdedAwf20tTUaBX7\nriY19QAGg4HZYwf/Es4eF0Njc7NVhaA7Ojr48stPGBURxsIbB58Qm8s9i27F39eHTz9ZZvXgV3V1\nFUVFhd01yz0RBIFpM66joOAE9fV1VrVjILZv30JrSwvTFtwx6LipN91Ge3sbKRYOZLa1tfHxp8tR\neQfhGWfd7niukYm4BEezavUK6uqstwNXXHwawdkLJFdKfnRKL4qKiuzW7tcSfF/9hIk9e3ZSWVvD\nuE6xz8JgIEIM4C0KrPryMzQa65bS1NRUk3Esncn+nr2aLJjDBD8PVDIpKRaaNH69+kuqamr41W1z\nUMitK6U4blQoc5NGs2nzes6eHViseCg0mg7e+c8buLu58b+/fHzEujleHh48/aufU3a+9JoEUmUy\nGb//3/9H4thxfLzsfYoKrVeWAbB5/Xcc2r+Xxfc+wK23DP48BxgdG0dHWys1l8utatdAXCw2BjtH\nj44fcIy/fwDTZ86isTibzibb+si2ilJay89w+22LBtV5jIsbA3otQrt9SsiEFqPoe2ysdTsbmcv3\n2U+Iosi6b7/BVZAQPoz9ZGcEonSwd/dOGhutN09vbm7is0+WEerqzPTA4YuHezspmR/my7HMDKtm\nvn/+2XJEg4HHb5457Ofw5NERTIgJZ82alRaVmjh16iSHj6Ry9+0L8fOxbLD1Zw8uRiIR+OqrTy36\nviZSUjbS2alh0d2LhxwbFBzCtBnXsX37FrvIYxzNOIKXXwDe/kH9no8eOwGNpsPiG/itra1kZx9D\nHTYGQWLZcvuBcAmMQqp0Yt/+PRZ/7+zsLLTaTgxu/TTiECToXEPJzjpm1dJJs2ahTz31FFqtltzc\nXACCg4N58cUXrWaUJaitreVIehqxOnAaZDEQpDe+5moHHhPalYW0Ye3XVlngbd60Dme5jAm+/dfb\nDpRyauK6YG+0Oh27dm23uG1Xo9fr2b51EzHB/oT6De6gxkeH4al2YdtW62V0bNxoFFx76rGHu1uA\nDkRsVCSxUZG89dJzg45TKhT84pEHuHDxgtXrwU1ByRnX9a9FMWPWHETRwKFD+61qR3+Yso9GjRlH\nQFjkoGNNWUhbt24eVLNiuKzf8C1tLc34TpxvtuCdCafACJwCIwia+ROzxguCgM/4eej1Olau/mok\n5g6JXq+npKQYraL3d0ev8qK5udGm2luW5vvoJ0y0tbXx9cov8BcFwvpJuAjXG18TdL39hIDApE6R\nuqYGtm61rnjr9u0piKLIjAEWBqFqJ0LVTjyVFNXnnFIqZYq/JxkZR675M3b+fBlbt6Vw08QxjAnv\nfxI4EEFeHgR5efD2r83PnAF4ZP4M3J2d+Hj5khH74C8+/5jyS+U88+tfDJqpOmViMlMmJg/YsRNg\n6sRkbl8wjy1bNpKTM/I0cblczh+f+QveXt58/tGHZv/fVConVConPllpXiZXXW0t332zmmnTZ7L4\nngfMuiY5eSKCIFCUe8ys8VejdHFB6eLCr19+Z0TXF+Uew8vHl9DQsEHH/eyRx1EqFFSmpww/W0om\nB5mcmMXPDOsyvVZDVcZWfPwCWHTHXYOOnTRpChKpDEnDueHZ1oVB4YpB4Yo+ZuigX39IG0tROatJ\nTEwa0fWW5vvsJ06dOknJuRISNAYk/awrfAzG1y39SGIk6ECr17Ftm/X8xJdffExrWxt3RwcNuBk9\nmJ8AmBXkQ6CL8Vnb2tpqcRtzcrI4mpHO3ddNwM9jYBH/gRAEgccXzMSg1/PFZ5brnrt27So83N1Z\nfMetZo2fkJTIhKRE/vr73ww51sfLi3tuv4WjR49QVlZ6jZb2pqWlhe3bU5g8dTohVz0rp0ybzpRp\n01n84E97HV90z2I0mg5SrJxtfzWtrS2cPHmCUYkDi/mHxIxGoVSRkWHZAOaJE/kY9DrUocMPpA93\nLWFCkEpxCY4hJzfb4hVMqakHEOROiC5G+QCDazAG12BEV6MuscEtDJ1OS1ZWhkXv2xOzVmT5+fm8\n/vrrKJVG0U5XV1eH3ynfvWsbBlEkbogEkrlaYdDgERgXCXFakUvVlRYXb7t0qZysrGNMC/BEPsCu\n8l+nxA0YPALw7yp925qyweq74JmZGVRWV3P7tKHbH8qkUm6enED+8XzKykY2eRqM2toaNm1ax+zp\nU0mMGzol8a2XnhsyeGRixuSJjEuIZ+3a1bS2Wkcc2mAwsG/fbuITEvELCOh3TFBICDGxo9m7d5fN\nv3PbtqXQ2tLC1B7aR4NhykKyVAlYS0sLKSkbcA0f090ueTgEzfzJsB/4CldP3GMmcnD/XqukG1+8\neB6tthPRqfcOl0lQ26SP9H3k++gnTKz77hua21qZrOk/+2iCTugTPDIRaBAI1cO6tautlinY0dHB\n7p3bSPB2w1PVvwbYU0lRAy4KAGYEeiGK4jULaK75ZgUqhbxXm2VzefvX9w87eARGjb3FcyZSXFIy\nooBNenoau3bv4J47bmH8uME7OE6bOH7Q4JGJJ356P+GhIbz//tvU19cP2yYTLi5q7rzzbs6XlVJu\nppbhJyu/Njt4BJB5NB2dTseDDzxi9o6/p6cn48dPouBoKroRlBP8+uV3Rhw8qquq4Pzpk9x4w/wh\n7fX09OLJXzxFR+0l6grShnWfmMXPDDt4JIoi1cd2oGtr5un/fab7eTcQTk7OTJ9+HdLGs6BtG9a9\nAGOZ8wiDR2gakTRdYN71N46o2581+F77iW+/RoVA9ABrwVs6hX6DRwAeotFPbEvZaJWsgOPH89h/\nYB+zg70JdBk4I24oPyGVCNwdHUhjUxOrVn5uURs7Ozv55OOlBHl7cPv0kQc0/TzduOu6CaRnpHc3\n9LkWSkrOkJ+fx123LkClMq85xV9//xuzgkcmFi28CSeVig3r147UzH7ZssX4ebpzcd/O0Ysf/Gmf\n4BFAaFg4k6dOZ9vWFItu+A7F0aNHMOj1RI8duGOrTCYncsw40o8eGXGJeH/k5ecgkSlQ+Qxv0wtG\ntpYw4ewfgaa9jdLSsyO6vj/a29vJys5Epw7pbs4guoZ0B48ARGc/BLkzhw5ZT7/ZrADS1Q6yo6PD\n6kKz14Jer2fn1s0E6cFtiE465hKpBwWw1cKdFNauWYlcKulXPHs4XB/iS1NLC7t3Wy8LyWAw8O2a\nlfh7ujE5bvCMFBPzJ45BpZDz7drVFrdn9eqvEEWRxx8cOm1zuAiCwC8efoCWlhbWWUn8rrCwgMrK\nCubOG7yr2Nx58ykvv0hxcZFV7OiPtrY2Nm1aT6QZ2UcmfINCiUmayJatmyzilA4c2ItOq8XDyqVr\nV+MxejIIsGv3Dou/t0nnyHB1AEnpAYKEM2ds9zu2NN83P2GipOQMmzevJ1oHPiP0F5O0xk6aH37w\nnlUWQ1u3bqK1vZ1ZwT5DDx4AT5WCsT5ubN+WMmIBzerqKo5mpLNwciJqp4EXKdZgbtJofN1dSdk0\nPB9cXV3FsmXvERsVySP33W0xe5QKBX/536dob2vj/SX/vqbPemyssUyr7JzlJpk9KT13Fjc3dwIC\nhheIX7ToLtpamsg7vN8qdg1E+o5NKBRKbrpp4dCDgeuum8N1s+ZSdyKN5vOW0fkaiPrCdJrLTrL4\n3gcHFc/uyQP3P4QASKtso6sIgCgiq8hBrlBy111DNxexFd9XP3H+fBm5+bnEa0Vkw+zobGKsDto0\nHRbv3KnRaFj2/tt4Oym5MXR4DQ36I9TVmRlB3uzctd2im+YbN35HZVUVTyy87pq7dt4xPZlAL3c+\n+WjpNQcaNqxfi4uzM7fMv+Ga3mcwXNUuLLxxLocPH7LY5mRtbQ2bN69nyrQZhEeYN083cff9D9Ch\n6WCtFdZmA7Fz13a8/AOHXFMkTJlJa0szGRnpFrt3Vk42Kr9Qm5WvmXAKMJaY5efnWuw9s7Iy0Ou0\nGNz7KV8zIQjoXEPJzcu2SiYhmBlAmj17Nq+88godHR3s37+fxYsXs2jRIqsYZAkyM4/S1N42ZPYR\nwOcq0fhSDD7pl3fVMB/LybJYDXN5+QXSDqcyPcALtWJgHYlnU090vwZilLsLUe4urPv2a6u17jty\nJI3S82Usnj2pT7nYvS8v496Xl3Hfy8t6HVc7qbhtqjGaXFIycv2KqykrO8fBg/tYtHA+/n4Di2z2\nZOF9j3a/zCE6MpwbZ89k69bNVikt2rN3J07OzkyeNr372EN3L+p+mZg6cyZKpZI9NmwXvm3bZlpb\nW3p1XuvJ23/4eferJ9PmW64j287dO1F5BaLy6j87ayiKV/+z+zUc5C5uuARFsccKWV9Fp4uMbZUV\nrsZ7FaxEXrASaeHXiE5eFBQWWvR+tuT75ifAGPR57z//QgVMHmQOOpSfcBcFxmtFsnIyLS6o3dzc\nxIZ1a4j3ciXCzXnAceb4iZvC/NFqtXz77cjax2dmHgVg9rjhiVCaMPmJe6/yE+Ygk0qZmRBNQeFJ\ns8X69Xo9777zLwwGPc/+z1PIZUPrNQ3HT4SHhvDLRx8iLz/3mgRyQ0JCcXJyprCgwKzx/fmJwSgs\nOEFMTOyw9UbGjElk7LjxpO/cTFP98HThBvIRQ1F2+iRFORnceusduLub10ZbEASe+tXviIyKoSo9\nhY7ay2ZdN1wf0XKhiNq8g0yddh333H2fWdeAUavplltuR9pQgtA8PE2pbh9RsHJY10kazyFpKefe\nxffj5tZXdN9efB/9BMD6775BhjDoumIoP+FnEPA3wMbvvrFodsXaNSupqq3l7qjAASsZTJjjJ4Bu\nEe4PlvzbIrY2NTWyceO3TIsfxdjIkEHHmuMn5DIpP1swk8uVlezZs3PEdtXV1ZJxLJ2F8+bi4uxk\n9nXDXU8A/OTWBSAIFpMbWbnyCwwGAw888rN+zw/mJ0LDwrnhxpvYsWOLTbp4l5Wdo+TMacZOm93L\nD5l8xDt/utKZNSwmHncvH3bs3GqRezc1NVJXXYmz3+Dl0AMx0rUEgEzlgtLdhxMnzfPt5nDw0EEE\nuTOi85VgcX9+wuAejkGv7563WRqzAkivvvoqoiji6urKn/70J6ZOnerQNcsp363BWTQKnFqS0TrQ\niwb2WCjLZ/XKL5BLBGaHjHxXuSfzw/z+P3vvHRXXde79f85Ueu9NdJAQCAlVq6FeLNmWrWLLduIk\nju2bOIljJ3GS68S5Sd68zs2b5K6067jFji2rWbJk9V5BAvUCEkIICQSid2aAKef3xzAYScCcc2ZA\neK3fZ61ZSx72PmcbZvZz9lO+Dy1tbezc6VyJQl9YLBbWr/2I6JAApo1OlDV3yZQxeHu4sfbTD122\nnk2frcPNTc/KR5e47Jp98czKx0EU2bLlM5det62tjbyTuUyZNl1SCvykh6aSm3MUg0F+CrxcDAYD\n27ZtkZV9ZCcoIsolWUitrS1U3r6FZ1SS4ms4g2dkEm0tzVRW3nbpdQuvXMHqFmhrRXUPVrdAbt28\nMWjdHgebr5qdANi0aR0VVZVM6RDRK4wq2xllhmBR4N1//p2mJuUlTfeyefMGOro6WThCegfC/gj2\n0DMh1I99e3dRVSXtkN2by5cuEOrvQ0SgtIO9qxmbFIPVaqWoSFpUfMvnG7ladJXvPf8cEWHO//76\nYtHcbKZOGs/atR9TqjCDSK1Wk5k5lrzcHJqbXNte+fzZM9RUV5GVpSyT88UXvgOilX3rPxz0UqNO\no4F96z4kNDyCJ2Q4aAB0Oh0//+kv8PXzo/LIBjqbXBv0ab9TSlXuF8TGJ/C9l1+R7Yxb/dSzhIZH\noa08qaiUTRadLWjunCIhMZWlSx8b3HvJ5KtoJ+7cqSQn9zgpJuftRLoJGltbOHJEWefFeyktLWHb\nti1MCPUnwa9/bTe56NVqliWEU1ldzebN652+3tatm+nqMrEqW3rXTkdkJkSTGh3G5k3rFHftOnRw\nH1arlUVzs122rv4ICghg0rhMDh/e77RTrri4iGPHDrNo6aOEhCqzbcufXI3ezY2P/v2+U2uRwsaN\n69Dp9YwaP8XhWEGlImPqLK4UXubKFecdL2VltwDQ+TmfnacErW8wN12kfWUwGLhw4Sxm7+g+zxG9\nEd2DQOfJkaODU8bm0IFksVj4zne+wxtvvEF+fj75+fm88cYbaCRE8h4Ed+5UcrW0hBQzfYrc3YfV\n9nqun7rl3viJAqEW2LN9q9MptwUFl8g7lceMyCC8JHax+f200QP+PM7Xk5H+3mz6zPVaHAcP7uNO\ndTVPZU9E1YeYsdD9Wv+Ll+77mYdex2MPjeXCxQtcunTB6bXcvl3OybwTPLJwHt5enrLn71ovvXNO\nSFAgc2dO4+DBfS79ne4/sJuuri7mLug7RX/NprszeOYuXExHRweHDg1+FlJP9pGDzmsAP/zTe/e9\nN3n+I05nIV29asvEcQ+JVnwNO0lP/VT2HPt9C10YNejo6KC6qgLR48vyte7tB0va04jugZhNXUMS\nDXI1XzU7AbYH7883byTBDNFWB/u/BDuhQmBqp0hnh5F33lYu9tyb2toadu/aTlaIP2ED6Fr0xpGd\nmBsTgloQWKtAKL6xvpYQP2/Z8+5lQx92Qgr2ezc0ON6LKypu89mm9cyYMpFZ0xw/tN6LVDshCAI/\neOEbeHt58s+3/6rYAbxy5dOYzSb+/N//lw6JGin32ol7qbhdztt//R+ioqLJzp6jaF2hoWF8/Wvf\nouzaFS7mHpY9vy8b0R9Htm6grbmRH3zvVYeBlb7w8/Pn17/6HR5ueioPraOrVZrNdmQjjDXlVB3b\nRFhEJL9849eK1qbT6Xj9xz9DI1jRlh8Bq7QOr71thCTMnejKD+Om1/Oj134ybLSP4KtpJ8AWsFQD\nox39ySTYiUirLdDw2fo1Tnf5tVqtvP33/8FDq2FxnLxMbUd2AiA1wJvMYF8+37yRykrl3RiNRiP7\n9u5kyqgEIoP8Jc9zZCcEQWDFzPE0NjWRm3tM9rosFgv7D+whMz1NcYBBznkCbAGH5uZmp7JCRFHk\nX/96Fz8/fx553HFZdn92wsfXl2UrVnH+3BnOnTuteD2OuHHjOnl5uYydOR83z7udnCqNBpVGww/+\n++5sszFTs/H08WXNpx87/SxVVnYTAL2ftGqV/lBylrDft6Wx3iUJAKdP52G1mO8rX+vTTggCFu8Y\nLl++IDlrWw4OHUhqtZqLF5XXbe/evZvU1FSSkpL4/e9/3+eYw4cPM3bsWEaPHk12drbiewHs3vkF\nKiBJ4r78XJcgyXlkJ9UCDW2tTgm3WSwWPnj3H/i56ZgpQdPi99NGS9rsAZbEh2E2mVjzyYeK13cv\nRqORDes+JjU6jKzkvmsu1//ipT6dR3YWTEgjyNebT/79vtPOt883b0Cv07Fs8QJZ83at/0j2Zg+w\n8rElWCwWvvhis+y5fWE2m9m1cztp6Rn31S2v2bS1z80+ITGJlNRR7NjxxaBmqHR2drJ9xxfEpo4m\nLDq233E//NN7/R4MgsIjP5tuUAAAIABJREFUSUwfx67d2xWLRdqzI3S+yjf8pKd+qnjD13r5I6jU\nirI0+qO4uAhE8S79I0va0z0bvtXdthdcveo6p9VQ8VWzEyaTib/++Q+4iTBRQiBQqp3wEwUyTXDq\nTD65ucedWiPAunWfgCgyL8bx90CqnfDRaZkWEUBu7jHZZcXGDiPu+r5FvKWw4RcvKXYeAT33NhoH\nfhATRZH33vsHer2OF5+TePDuRomd8Pby4sWvr6bkxnX27VXWuTMqKprvfe9Vrl+7xh/f+j8DRtT7\nsxO9qa66w+9+9QtUgoqf/OQNtFqtonUBzJu3kNHpmRzbtpGmuhpJcwayEX1xo+ACBfnHefSxJ0hK\nUt5yPiwsnF//6nfo1AKVB9dhau9fckCKjeiov0Pl0Y0EBgXz6zd/i7e3cgdqdHQMP3rtdYSORjS3\nc0B0/CzU20Y4xGpBW34EldnIf/78lwQFOXdgcjVfNTsBtmeRY8cOk2wWB+zoDNLshIDAmC6R+qZG\np7OQcnKOcuPWTRaPCMFDI81RKOc8AbAkLhyNIPDJR9K/y/eSm3sUY0cHCycM3MDAjhw7MTo2kohA\nP/bt2SF7XRcvnqeuro7Fc7Jlz1V6nhg3Jp3goECn9GqPHz9CcXERK59+Bnf3/svapdiJBYseJjQs\nnI8+et9ph2ZfiKLIhx99gJuHJ1kz79d7/cF/v32f8whAq9Mzce7DFF0tcLoEq7y8HLXeHbWb/IQD\ncO4sAV+eYyoqnA8OHzl6BLQetuyiXvRnJ6w+MYhWC6dOuU5Pyo6kErbZs2fz8ssvk5+fT2FhYc/L\nERaLhZdffpndu3dTWFjI2rVruXKPxkdTUxPf/e532bZtG5cvX+azz5SXCnV2dnLo4H6iLeDhZJpp\nf8RYwF10Tkz7wIG9lFXc5uHYUHQO6pXlEuSuZ1pEIEeOHqK4+JpLrrl9+xaaWlp4Zu4U2WnbdnQa\nDU9mT+DGzVJFkQI7zc3N5OYeY/6sGfj6OB8Jl0J4aAjTJ0/g0KH9Lulyd+zYYRoa6lm8VF7d/6JH\nHqG2tsap358jjh49SFtrC+NnLXTqOuNnLcDQ3s7Bg8pq0xsb622tj7XyI72uQBAENB5e1NXXueya\nubnHEVQaRM9+IoU6LwQ3X47nOO94eBB8VewE2JzQ5XcqmNzpfEnCvaR1i3G/+/ZfndLLu3WrlGNH\nDzE1PAA/J5w2fTEjMggPrYY1H38ga563lw/txsHt9DkQbUabvp+398Dtny9fvsjly5d4dsUyAvyG\nptxu5kOTyRiVyqbN6xXbicmTp/Ld7/6AwsuXePsv/6M42NLS3Mzvf/NfmE1mfvnL3xIeLr/zTG8E\nQeDl7/4AjVrD3nX/QnSx6HGHoZ39G/9NVHQMK1esdvp6UVHR/OrN/4PKaqLy8HrMHcoiv10t9dw5\nuhFfHx9+81+/w89PevZEf2RlTeSb3/g2qtbbqKvOOX29HkQRdeVJBEMt3//eD0lNHeW6a7uQr5Kd\nANi0aT0CMNp1kkVEWiEIFZ9t+FTxob2rq4s1/36fCC93xoYM3h7nrdMwMzKQU2dPKy4nOnJoP5FB\n/qREKdOzHAhBEJg9NpVr14tlB/xyco7i6eHBpPGOu266CrVKxZzpD3H50kVFzSw6OztYs+ZDYuPi\nmZ7tvOi3Rqtl9de/QUXFbZdpM/Xm4MF9XCm8xNTFj6MfwNnVF+mTZxAUHsk77/6vU0LQNXW1aDx8\nFJ9fnUXjYc+clqcjeC/t7e1cvnQei0+Mw/I1O6J74KCVsUnyXqxdu5YdO3awatUqHn744Z6XI/Lz\n80lMTCQ2NhatVsuTTz7J1q13e0M//fRTnnjiCaKibKJqQUHK9YBOnszB2NUpSTxbKWoEksxwofAy\ntbXSInG9aWtrY+2aD4n39SQ9cOCHYKXMiQ7GW6fl/Xf/7nS2T1NTI1u32ITvkqOc05CYlp5EbGgQ\nn675l+L638OH92O2WFg8b5ZTa5HL4nmzaW9v58QJ5w73JpOJjRvXEp+QyJhxWbLmZk2YRMyIWNa7\nIPW5L6xWK1u2fk5I1AiiEpVHgAHCYxOIiEvii+1bFWVMGY0dqLT6B7bhA6i0egwG17TbtVgs5J44\njsU7ElT9pOsLAmbvGIquFjjVFvxB8VWxE7dulbJp03rizRDjqHRNAfZSNmOHkXff/qvi66z5+F+4\naTVku0gjrzfuGjWzo4K4VHCJCxekH2L9g4KpaRq6tr/3Yr93QEDAgOM++2wtgf5+LFIQWVaKIAg8\nvWIZTU1NHHCig+PMmbN55tlvkHcih327lYmIvv/232mor+P1198gJmaATi0yCAwM4hvf+DYVN4q5\nnOdaJ/fx7ZswtrXyvZd/6FSmVG/i4uL5z5+/idXQyp0jG7Ca5Dn1TIYWKg+vR6/V8F9v/h8CA133\nPVy0aCnzFzyMuuEqqsYSl1xTVVeIuvkmK1c+zdSpM1xyzcHgq2InAKqrqzh65CDJJtGlQWkBgTGd\nVuoa6jl27LCia+zatZ36piYejg1FNcjPSdMjg/DRa/nog3/KLidqbW2lqPgaE1NiB+15blJqPABn\nzpySPMdkMnH6dB6Tx49F56I9RyrTJ0/EKork55+QPfeLLz6nvr6eZ7/5fJ9yIkrImjCRtPQMNmz4\n1CUdlO00NNTz0b/fJyohmfTJ02XPV2s0zFv1HM1NjXz8yb+Ur6OxEbVenvPKlWjcbWV7zmpjnj6d\nh9Vqweojw6Z3l7EVFl5yeRmbpE/fzZs3KS0tve/liIqKCqKjv9QwiYqKoqLi7jra4uJiGhoamDVr\nFuPHj+fjj+XrMtjZuXUzvqJA2CB3BE22gAiKvLUbN6yh3WBgaVzYoG2meo2ahSNCKCm9wfHjznkd\nN25ci9ls5qlZzrdSVwkCz8ydTG1dPXsUpJsCnMg9RkpiAiOiIp1ejxzSR6YQFhpMrpMdlvbv301t\nbQ0rn35W9t9fpVKxYvUzVFdXcejQfqfW0RdXrhRQU32HcTPmuuSzOW7GXBrqahXpXgkqAdu37AEi\nii4z0JcvX8DQ3obFwcZv9YlBFEVOnsxxyX2Hkq+CnbBYLPzlz/+NXmLpmlL8RYExJsg7nUdenvyH\nxIKCS5y7cI7syEA8JGrkyWVKeAD+bjo+/ug9yYGG+PhEaptbezKBhpqbVbaMwNjY+H7HVFdXUVhY\nwKOL5qPTuTZzyxEZo1JJSUzg8GHn9udHli5j1KjR7NomX0euprqa0/l5LFu2wuVZKNnZcxg5Mo1j\n2z+jvdU13WgrS69z6eRRFi9+hPh4eQ06HDFyZBo/+tFP6WysoTpvp+TDr2ixUHX8cwRzF2/+4tdO\nZ3D1xTe/8W1GjkpHcycfweBcpqvQWomm5jyTJk9l+XJ54uNDzVfBTtjZvHkDAjbha1cTZYUgQc1n\nGz6VHWQzmUxs27KRRD8vEl0onN0fOrWKOVHBlNwspbBw4O5t91JQcBGr1cq4JNc4svsi1N+HyCB/\nLsjQ8SksvER7ezvTJzt/tpFL3IhoIsPDyJf5bFBfX8/WrZuYNGUqqaOklQNKQRAEnnnuW7Qb2vns\ns7UuuaYoirzz7j8wmUzMW/l1BIXP0mExcYzLns+B/Xu4fFlZ+WtXVxeCZmidhL0R1JrudTi3kRw9\ndhS0nrasIhlYfUcMShmb5CfTwsJCDh48aEsXnD2bkSNHOpwj5RBqMpk4e/YsBw4cwGAwMGXKFCZP\nnkxSUv/dl3x972+1WFJynRvlt5hgsnn3pfKhW/cDhUQhbQAvUSDKInJgz06ef/6bkiNmFRW32bNn\nJxNC/Ynwkt4usne7Tam1y+NC/Mi908CnH3/A/PlzFIk+NjY2cujgPrLHpBDuoOvO6t+9A0BCeDC/\n+cayfsdlxEeRFhvB9i82s3LlclnRxvr6OkpulPDck8slz+mNvd2mt5cnG97/h6y5giAwOWssO/Yd\nQqcDd3fpfz87RqORTZs3MGp0OqMzxvQ55uurbIJ48QlJvPm7+1tGjs0aT1JKKp9tWsfSpYsV/V37\n48SJo2h1ehLTxzkcu3/DvwGIS8sgIS2zzzFxaRno3T3IzT3CzJlTZa3Fx9sTq9mEKIqKnVk9LTc1\nWpJWvCZ7vmg24eXl3ud+I5e8vBwEtRbR6+6DiKZ4GwBWn2isoZmIbn7g5sexnKM8+eQKp+871Ax3\nO7Fjx3bKKm4zswvcBtlOpJvhpkbgX+/+g+zsaZKdGaIo8unH7+Or1zE1XPqDglw7oVGpmB8Twvpr\nZZw7d5LZsx2LLGdmpvPJJ3Cl7A4TUuR1aAR62jIHeHvy9ivPyp5feOsOocFBxMSE9zvmwAGbPuHU\nSeNlXx++tBOCADvXyde4mDZpPO+vWU9HRwuhCrvjAEyaNJF//esDOjo6cHO7W0B9IDtxu7vjzNSp\nU1yyd93La6+9xgsvPM/Zw/uYvrR/WyzFRgDk7t6Cf0AA3/72txTZVUfMnj2Turoq3nvvXVpKzuOb\n+GW5SnW+LQjoGZmAV+SXe0ndxSN01N/hl7/8FZmZ0vVi5PKbX/8Xz3/72zTdOUFX3GJQ3a9jo72y\nAQCrZziWmD6i+JYudHfyCIuM5j9//rP7PivDkeFuJwCqqqo4fPiArOwjOXZCQCCjw8LBulpOn85l\n/vz5ku4BsG/fMZrb2ngiTb5TRsl5AiArxI+9ZTXs3L6Zhx6S7nQpLy9Fo1YRHy5dj0uJnUiOCuVM\nSTE+Pm6SPislJUWoVCrGpDn+7PWF3U5o1Cq2fSovO0YQBMamp3HgWA5eXjrJQvcffLAes8XCk898\nTdL4p5+wyWQEBATy13cHLlePiY0le/Zc9u7dxapVKwkP79/GSuHo0SOcOZ3P9KUr8Avu3w5+9NYb\nACSOyWLqor7PjlMWPELJpXO8/c+/8d6778k+96hUzgXEe84SCCQ99br8C3R/Ht3ctIptssFg4PLl\n81j8kvosX1NX2nSirN6RiN5Rd/1MdAsAnRe5J3N47LGliu7fF5Jcgh9//DHz5s3jwoULnDt3jrlz\n5/LJJ584nBcZGUl5+ZeiUeXl5T2ppXaio6OZP38+7u7uBAYGMmPGDC5ckJ+xsHXLFjQIJA5RB+xU\nM7QY2snNlZ4p8O4//xeNIDB/xOC3ElQJAkviwqhvamLzZmV14Fu3bsFsMbN0St/ODqU8MiWT+sZG\nDh8+JGvexYuXAMjKzHDpeqQyfkwGJpOpp0OYXDZv3kRzUxMrVz+j2CkiCAKrnn6Whvp6tm7douga\nfdHV1cWxY8dISB+L1kVOKY1GS1JGFrm5ubLFtP39/bGaTVjNylqzOosoipiNbQQ7mQIPttLAY8dz\nMHtF9nlAuBezdwzFRVe+cmVsw91OGAwGPnjnHUKtEDsEdkKFwMQukfrmJjZvlq6Zl5eXx7WSEuZG\nB6F1sUbevWQG+xLu6cZHH7wvKQo+cuQo3PQ6LpQMfadAs8VCwa1KsiYMfHi5dPEiEWGhirvqOEtW\nZrptHZeUiwUDPWXKcssr7I5Ki2VwavljYmKYOm06l04eoavTuUy0mooyyouv8sTjTwyK88jO8uUr\nSBudQf3Fo1i6Bl5zV0s9zUWnWbBgEdOmTRu0NQH4+Pjwkx//GDpaUNdeUnQNdfU5MBv5z5/99Cvh\nPBrudsLO2rVrEKzioGQf2Ym2QqCg5pN/fygrC2nX9m0Eu+tJHoLsIztatYqJoX6cOnNG1rNJSfE1\nooMD0EoU+VZKXFgQLa1t1NVJy+YrunqF+BHRuLk9GJ3NkcmJGI0dlJWVSRpfXl7Onj17mDN/ASFh\nrteSAnh85ZMIKhUfffShU9dpb2/nb3//GyFRMYybMdfpdWl1euau+BrVVXf4dO2nsud7eLjLLmF2\nJdYu273d3ZXvz+fPn8NqsWC9xzkkCUHA4hXJpYsXBmzOIRdJGUh/+MMfOHPmDGHdH9qqqirmz5/P\nM888M+C88ePHU1xczM2bN4mIiGD9+vWsXXt3etyjjz7Kyy+/jMViobOzk7y8PF599dUBr9vcfPdh\n1GAwcPDAfuLMCgRRuzP35XRiA5sInjcCm9ZvIDNzksPxJSXXyT2Zx7yYELx1ylLp5EQLAOJ9PRkV\n4M26Tz8lO3shHh7Sa0CtViu7dm5nbGIMEQ6yj8CWeQQMmH1kJzMhmsggf7Z/8QUTJ0qvi7127Toq\nQSAmSlk6ubeXTYFfbvaRnbgRtvTpoqLrxMfLi1q0tDSzYcN6xk+cRFJKar/j4hNskbK+so/sjEwb\nzZixWaxdu5Zp02bj6en8Q8SlSxcwGNpJyZwgaXxcms2JN1BkGSBl7EQu5x0jNzef8eOlR608PX0B\nMLc3o/ZT6HDtTllVkn1k6TRgtZjx9va7b7+RS1XVHTqM7Yh+939/rT62z5Q19Mvfo+gZArVw4cJl\nxo6Vn0URHDw04vL3MtztxLZtW2k1GpjRJS9LFVBsJ8KtApEWkQ2ffsqcOYsdZiGJosiH771LgJuO\nrBBlgr1y7IRKEJgTHcwnV8vZtWsv06dnO5wzalQ650uKFWUHBnjb9mAl2UfXbldj7Oxi1KjMAb+T\nVVVVRIQpD9LY/5eUZB8BRHY7rsrLK53aO5qaWtC7uaHqIzo9kJ1w63bE1NU1Ob139ceihUs5dvQI\nBfk5jJ3ed+aaFBtx9sg+9Ho906bNHrS12vnGc8/zox/9gKaiUwSm2547PCMTAO7KPmq4nINWp2PF\nitWDviaApKQ0Jk+Zzsm8E1gCU0Fz9yHD6mnLBOgz+6irFXVjCQsWPkxoaIys9f7/dsJGX7+z2toa\n9uzeQ7JZxFOOrZBpJ+xZSIdqatixYzczZzoWRW5sbKTgyhVmRwc7JTUg9zwBMCbYj0O369i37yAL\nFiyWNKeqqoooP3l6r0rsRIif7fNcWnobnc7xM3F1dQ3xMcpLUzXdwR252Ud2wkNtNurWrdsEBDh2\nCL333nvodDoeW75S8j0CAmwZzI6yj3rGBwaycPEStm/9nIcfXqZYP+/9D96jqamJp77+nT7tV28S\nx9i0YPvLPrITnZRKatZkNqxfx6SJ04iMlO5ICQoM5naVY6H+/rF9zxRlHwFmg63c28vLX7FNOXYs\nF1Ta+7qv2bF622Rd7s0+6vm5VziWhiJOnjzNmDHSReMHshOSwpuCIPRs9gBhYdL0ezQaDX/7299Y\nsGABo0aNYtWqVYwcOZJ//vOf/POf/wQgNTWVhQsXkpGRwaRJk/j2t7/NqFHy6vZPnsyhy2wmWUnA\nTWV7faiTp7ciIJBkErl6/RrV1VUOx2/fthm9Ws20CHm1i73pnXoqlTnRIRg7Ozl0aJ+seVevFtLY\n1Mz00cmSxhfdrqbodnVP6ulACILA1LRErl4rol5Gl6vq6iqCgwIVC961trXT2tbek3oqF38/X9z0\nekl/73vZvHkjHZ2drHx6YIN4regK14qu9KSe9seqZ57FYGhnyxbnu4yArWuRoFIRlSBNPPuL9//G\nF+//jT+/+vyA4yLiElFrNBQUyIuuxsTEAtDZVCtr3l2YTWA29Uo/lU5X932jo52v27d/XkT9/Rux\nuq7A9ipY0/OeqLM9bFVVyf+cPUiGs52wWCxs3/IZoVYIFhU8dCu0EwCjzdDWYZQklnr+/FlulN1i\nVlQQaoVp13LtRFqgD6Eebny2/hNJUfCJk6ZQ09RCSaX8JhINre00tLZLshP3klNwHZ1WS0Y/5b92\nrFYrKgmZfv0hiraXUjth101T0jzgXqxWK9Y+rjOQnXDFfR2RlJRCUlIK547u71c/y5GNaGtpouhc\nPrNmzXNJEMQRI0bEMTojk9YbF3u6yN05uok7RzdRvO6/AbB0GmkrL2L2rLn4+g5N9z6AVSufAqsF\ndcP9nXNVrWWoWsvushF21HVXUKlVLFumrKz/QTCc7YSdzZvWg2glXe6ZQoGdiLFCACo2rP1Y0nf3\n/PkziEB6kK/Mxd2NkvNEmIeeIHc9p/NzJc9pbGrC31uegLESO2F3OjU2Sut01dLago+3cieq2WLF\nbLEqthP2LtItLS0Ox9bUVHPy5AnmLlwsa19qaKinoaHe4XmiN0seexydTsf2HcoqHCoqytmzezsZ\nU2YSFuO4zD1/3w7y9+1weJYAmPHICjRaHf/68D1Za4qKiKSrrRmrSWn2jQiIis4SAJ1NtnNuWJjy\nssCz589h9Qzpt4pBU3YETdmRPu0EgOgZCoJKkSZtf0hyIMXHx/Pmm29SWVlJRUUFv/rVr4iP71/E\nsjeLFi2iqKiI69ev87Of/QyAF198kRdffLFnzI9+9CMKCgq4dOkS3//+92X/TxzauwsfUSBoiDV3\n47v3+uMODgbt7W2cOJFDVogvboOcxnkvUd7ujPDxYO+ubbLm5eefRKvRkJU8OMJ3D41KQBRFTp/O\nlzzHajEPebeE3giCgFarkf2AXltbw549O5iRPZvIqGjHEyQwIjaOh6bPYOfObU63hgS4ePkioVGx\n6FycAq/RagkfEc9FmeJ3ERGRqDUaOhvktWV1FR0NNufNiBHydV7uxcvL9qAgmKVFHuzjvLyGLj3d\nFQxnO3H9+jUaWppJGcQOnf0RZgVfUeDoQcdO/J3bPsdXr2XcILZlvheVIDArKojK6mpJjt7Jk6eh\n1Wo4fKFoCFZno8tsJufydSZNmoK7g1bAvr5+1Dc8uPJP+72dbfkeF5eAqauL0tIbsuYVXbFFWmNj\nE5y6vyOWLl1Gc30tNwqUPZBeOH4I0Wrl4YcfcfHK+md29mxMhlY6m/p2fhqqbiJaLcyYkT1kawKI\niopmdHom6uZSm/dSClYLmpZbPDRlek+mwVeB4WwnwPa8dujgfpLM4Kkk2CATe0e2mvo6ciQ0ablW\ndAU3jZpQj6EvvRIEgVhvd65fvyZZkN5kNqPTDE4jiN7Y7yG1Q7Fep3NpOY9cOjttZU1S9Hz27t2F\nIMD8RdKyvpzBy9ubqTOzOX7sqKKObJs3b0St0TJloXSnlVQ8vX0ZP3shF86fobRUevfKtLR0EK0Y\na2+7fE1SMNbcwtvXn9BQZaWHnZ2dNNbX2rSMlKLSIOp8KLp+Xfk17r2klEFvv/02V69eJSMjgzFj\nxnD16tUej/+Dpr6+jqKSYuLNovyyhF5kKOjc5iUKhFrhkINubBcvXsBitZLhZMTAV6dsE04P9KGy\nupqammrJc64WXiQxIhg3meV2G37xkqRx4YG++Ht5cvWK9CiICFhE51vsvfmTVxTPtYoiyPyc7djx\nBaIo8vjKJyXPWbPJcfed5atWYzKb2SXTOXgvnZ0dlJYUE5UgLdusNz/8k+NIQGRCCuW3Smlvb5d8\nXY1GQ0JSKsZqafXhA+GfNkX2HGP1TULCIvD1de47CxAbG4dGq0dov//7J3a/rDEze96zjxs50nVd\nNoaC4Wwnzp49jQBEOpmcoeTTICAQZRYpun5tQC2wxsZGLl6+yLhgXzROdP+bHSVft2t0kA9uGjWH\nDu51ONbT05NJE6eQU1BCh8KuIlLthJ38K6UYOruYNdux0GxsXDw3y2/TbjAoWpudUSn9C+8OREFR\nMWBrI+8MmZnj0Ovd2LOj//39tZ/+513/bTaZ2L9nFykpI/H3d86B5YiJEycTGBTM6UO7BzxM9mUj\nujo6uJh7mKzxk5yKyspl1Chb2Y6xpltLR1CBoCLpyZ/Y3q8tR6fXu7wbnBRmTJ8BXW0IHQ13vW/t\nflnSnr7rfaG9CtHSxbRpM4ZukS5gONsJgC+2bkZUkn3UC7kFtDFWCBAFNq79xKFj5kZxEZGebqic\n7JQb4q6sQ2WUtzttBiN1ddKyw0Unntnl2gnb/aQ5try9fahvbJJ9/XtRaicaGpt71jEQoiiSm3uM\njMyxBAZJFyLvjZTzRG9mzZmHydTFmTPSA/wAbW1t5OQcZfSkaXh4ycvuSsqUJteQMXUWOr2eXbu2\nS752Skoqao2G9krnnCdJT/1U9hyrxYyx6iaZGZmKS04rK28DIlZ9/0+g/dmJu8bofSkvd/5MZUeS\nRyI0NJT169e77Kau5OLF84jACIUHA18ns5ZGmCG/oZ66ulqC+vlyFxZeQqdWEeMjL43TVSR1C+0V\nFl4mJMSxsGhXVxc3b91i6WTp4tlyN3pBEEiJDuVakXRB6qCgEE6fzsditaJWcMBaNCdb9pzetLa1\n095u6Pfv3BdGo4GDB/cyeeo0goIdz5Oz0YeEhTFh0mT27dvN8uVPotcryx66dq0Ii8VCVKK08jWQ\n5jiyE52QQt7ebVy9WkhWljSNJYDxY8dx7cq/MRla0HrIq6EH0Pooi8paTZ0Ya28zc/4iRfPvRa1W\nk5U1nrxTp7CEZYHqy21X9L/noCKKaJpvEB41QtbnbDgwnO1E8ZUC/EXka+S5iDArFFitlJXdIqUf\nDbTz589gFUXGBCvLPgpWeCAA0KpUjA7w5szp/O4SsIH314WLlnI85xj7zhTKarKg5EBgFUU+zzlH\nZHi4LZLogIkTp7BlyyaOnzzFgtkzHY6/F42T0fLDOScJDAx02gnh7e3D/PmL2L5jK4+vfIqwXl1x\nZs9b0Oec40cOU19Xx4svvOzUvaWgVqtZ9thy3nvvfym7VsiIlLsd3gPZiPPHD9BhaOfxZUPbaTIw\nMIiAoBCMteX4p07ocRzZ6awtJyV5pOSuSK5k/PhJCCoVqpYyLL3aNPd3IFC1lKHTu5GRMbAO4XBj\nONuJ9vZ2Dh7YS5zZFiCWi1K3v4BAmknkWF0NFy+eY8yY/jvhNjY1kOCmPBPfWQvop7fZmaamRoKD\nHbvKfL19aGqT58xXYica22wBSh8faWGe+IQkTp48Lsne9YWzduLq9RIEQSAubuBM0bKym9TW1vDo\ncvl7pVzHkZ24hEQCg4LIzz9Bdrbj7qx2Tp/Ow2KxMDJrsuQ5ejd5zRPc3D2IGzWG/FN5vGixSNqr\n9Xo3xo+fzOmzpwkaOxuVWt7fTonjyE57RTGWrg5mzsxWfA2700fU9/9sOJDjyI7o5kt7zS2MRqNL\nmlZI+ta89dZb1Nce6i8RAAAgAElEQVR/WSZTX1/PH/7wB6dv7gquXilEh4CfQkfQCIvtNc6sbFsN\n6XauFw3gCGmorcZPr0Ot0Pvoq9Pgq9Pw84n9iy8PRICbbcNvbGxwMNJGTU01FquVaIUirlKJCvan\ntr4ek0laFDsyMhKTyUR1jTJdnIlZmUzMymRylnQBsd6UV1QCtvIqqZw+nY/RaGTO/IWK7umIuQsW\n0d7eztmzpxVfw65/FBE3OFHX8BHxqNXydZAeesgmGtpyQ1k3I6/oZLyikwnKkHeIbL1ViGgxM326\n/MNnfzy8eClYulA137rrfat35F1tNwVjLXQ08egS17XaHCqGs52ora7C24nkRV+r7bVMpoi2Ha9u\n+1Rf3//edfVKAe5OlCakB/qQHujDglhladJxvp4YOzupqHCc5p2SMpL0tNF8ceI8hs7BLQM4WVhC\neW0DT6xYLelBPzExmYjwCPYcOio5Gt2b5IQ4khPi+OOv35A9t7a+gTMXLjF9eraiQ8m9LF26DI1a\nw/Ytm+96f2zWeMZmjWdcr450VquVLz7fRHxCIpmZ/R9AXcns2fPwDwjkxO6tkn/XnR1Gzhzew5ix\nWSQlyc96dZb0tHQ6a8vvW6+l00hHUy2jRzt2Ug4G3t7eJCalom6XoH0niqjbq8jMHIf2AZb1K2E4\n24nc3GN0mU2MVJh9FGS1vRYrsBOxFnBHYO/uHQOOa2034KWwGgFsgYZgdx2vZSn77nlpbQf25uZm\nSePDQkKobpQ21hmqG21aQlIzGkeOTKO93UDRdXklwnacsRMAZy5cInZErMPmRteuXQUgLd213bAH\nQhAE0tIzuHbtqiwbevnyJTy8vAmVoH1kJyY1jZjUNJZ8TbrTMH7UGNrbWmVl08yftwBLVwdtZVcl\nz3EFzdfP4+MXwOjRyv9+t27dsmXL9qGjKge7A6qiwjVddCU94axdu5bAwC8jIoGBgaxZ07dQ01BT\nXXEbH6vy8rVxZkGx8wi+zGAaKJ2zra0NdyfaMf98Yqpi5xGAViWgVgmS61nv3LE5SsIDnC/fGYjw\nAF9EUZQsSp2cbOt8dqFAetZSbyZnjVXsPOp9XzkPvbm5xwgIDByw85ozjByVhp+fPzm5xxRf49Ll\ni4RExsiOBEhFo9MRpkAHKTQ0jJRRGbSUXOgRPZVDUMZM2c4jURRpLj5HaEQ0iYmuO9ykpo4iMCjU\npnHR+37eUXd1TVA1laLR6njooa9WWQIMbzth6OhAgf51D8u6BMXOI6Dn3gOVcZbdLCHCidKEBbFh\nip1HAJFetu9/efktByNtrH7mG7QYOth4RLnz2hEdXSb+ve8EsTEjehzKjhAEgcWLH+HKteucuSC/\nNfoff/2G4kPBp5u2oFKpmDfPNdmL/v7+zJiRTc7Rw7T1st/jJky8y3kEcPHcWaqr7vDI0mVOdWeS\ng1arZcXyVdy5dYObV6WVo587up8Og4GnVg3cdWuwSEsbjbnTSFfL3Q08jLW2h+oHWTo8dkwmGBvA\n4sApa2oDk4GMITxUuorhbCfO5J3AE4FAhbZicZegyHkEoO4udb504fyAOptKnOK9eS0rWbHzCL7s\nYCp1HSFhEVQ1OhaKdpbqxhbUKpXkzO0JEyaj02o5eEy6IHhvnLETlVXVXLl2XdJz3o3SG3h4ehIc\noryzqBJGxMXT3NwsOfEA4FbZTYIiomTZnyVfe0mW8wggqLsDW1nZTclzRo/OICwiisbCE05/h6Ri\nrKvAWH2LR5Y86lRW6/UbNxB13jYnkhOI3SVwripjU7yaoej0IYWODiOaIRbP7o09DtDR0dHvGF8/\nf9rMD+731WGxYrGK+PlJK42oqrIJF4cNsgPJfn37/RwRFRVNUGAQp865TkVeDqfPXyQhPkGyOKrV\naqWwsIAxY7NcEo3uC5VaTXrmWK4UFijaFI1GIyUlxcQkjRyE1X1JdFIqZTdvyBblW7p4CWZDK+13\npAvmOUNnwx06m2pYuniJSw9hgiCQPTPbpm9k6T/jTt1Wybhx412SXjocGC52Qq/VoUytxzXYYxRu\nA4jUG9racB/iJgu9sd9bqlZZYmIyc2bPY9epS9y440S3xAFYdzifhtZ2nn/hu7IewGbPmU9oaCj/\n+nRjv13CXM3tyjvsPXSM+fMWSSoVl8rChUvo6uriyKEDA47bu2sn/v7+TJwoX/PNGbKz5xIQFCwp\nC6nDaODskX2My5pIQsLQ6wyB7SABYKy621FqqLqJRqtzaeBALnaNJsEw8PdJ1V5z1/ivOsPFTlwt\nKiTCST1VZwi3gtHURVlZ/058nVaDyfLgDj2m7v1Up5NWMh0aHklzu1GxXp5UqhtbCAoMkGwnPDw8\nmDTpIQ4cy6FNhj6nK/hi937UajXTJYj119XVEhoqrVOhKwntzuSS0ym7paUFT5/BbwDi5W07O8o5\nT6hUKp5cuZqulnrayoemAUjD5RzcPb2Y76QcRvntMqwDlK9JRucFgnrA/UUOkvIgExMT+eMf/8gP\nf/hDRFHkz3/+M4mJD8b434u7hyfNTnyxPnTr3oit8JyCyIF9SxzoYBAaHkl+/kk6LRb0CryQvdtt\n/n6a/AeGGoNN7V/qQ21VVSWe7nq83KVr6vRutym1fjnM37YJ2DOeHCEIAhMmTGb/gT20trXj7eUp\neX1wd1vmXes/kjW3uqaWwqJiVq1cLXlOZWUFBkO7rOyj3u02pdYvJ6ekcuzwQaqrq2QLkl65UoDV\nYiEmWV6r294tN6XoIcUkj+Tkni8oLLzEpEkPSb5PVtYEPL19ab5+Hq9IeWKFvVtuSq1hbi4+h0ar\nY/r0bFn3kkJkd9QEswHUts++trvlplXjjiV5GZgMRLuoU99QM5zthK+fH62NyjtzOWsnDN1TBtJn\nMFssqFTKbZmzdsJeYi21iw3A0898g3NnT/GXzw/w+28/gd5BOY0cO3HxRjk78y6xYP4iUlLkObi1\nWi2rVj3DX/7yR46eyCN7qnSnilI78dG6Teh0Oh5/YqWstToiNjaOlJSRHNy7h0VLHkGlUvXYCZVK\nxccbP6emupqL58/y+OMrh7ykSavVsnL5k7z99l8pvXKJ+FE2B43dRggqFa/8v3cAOH/0AJ1GA0+u\nkm5HXU1wcAj+QSEYqm9Se3a/7U2NFp2HD8mpox5oSVhiYjIqtRpVezUWb1upfI+NQMCSZvu9CYYa\n3Nw9ifoK2orhaifMZjPtHR095cZKcNZO2O/d1NQA9C3C7+3lTYtE2Ye+cNZOtHTZ7IPUgHRYWARg\nc/CMCJWmS6nkPFHd2EJYqLzn30cefYJjx4+wbfd+npLR6h6U24mmlhZ2HzjMtGkzCQx03PCipaUZ\nb4XNXJScJ+z4+Pj03F8qFqsFVT9t5vtD7lkC6NEwsljk1ZpOnvwQwaHhNBbk4hWdItkpp+Qs0VF/\nB8OdGzy1+mtOBYQ7OjpobapHDB5YOuVLOzGAHpKgQtT7cKO0tO+fy0RSWsRf/vIXtm/fjoeHB56e\nnuzcuZO///3vLlmAsySkpNCgEuniwXjkq7t/g7Gx/XdcycjIxCKKFDcOrZfbzpWGVlSCQFpahqTx\nt8tuEhEw+F5kbw83vD3cuX1bej1m9qw5mEwmjp7IG8SV3c+B7jTXmTIE5eyOsajowX3Ii4iyOSak\nZnL15vz5s6g1GiIGueVzWEwcOr2ec+fPypqn0WhYMG8BhsobmNoHt47e0tVBa9kVpk6b6bAuXQmV\nlRW2f2gGcMxq3KioqHD5vYeC4Wwn4pNTaFAJiA/ITjT02In+tQH8/f1pHeQo7UC0dN87IEB6q1gv\nLy+++73XuNPQzMf7TrhsLa2GDv7+xWEiIyJ49mvfUnSNqVNnMCJmBB+t34RJhlNMCUXXb3A87xRL\nlzyGr6/rbef8+YuoulNJ4eW+S/IO7d8LCMyd27e49mAzc+ZsAoOCydu7vd8spM4OI2eP7iNr/ESH\nwrGDzbjMcRhreqXxiyJdLfWMHzs02lH9odfriYyKva8T272oO+pJTk4d8qwEVzBc7YRKpUJA4EHm\nQtnvrR5A5Dc8IpL6jgdnJ+qMtoC01GBlWJitrLp6kMvYqppaCA2PcjywF7GxcYwbN54tu/bS0dE5\nSCu7m60799JlMvHYY8sljTebTOgegFNbq7VlmEnVqAVbJqFqCBoQ2O8hN3NRrVbz1KrVdDbV0H77\n2mAsrYeGghzcPDxZtPBhp65z69ZNAEQ31zxXWPV+lN684ZIyPkkOpMjISA4dOkRdXR11dXUcPHiQ\niIgIp2/uCsaPn4QVKFf6me3ufackWgBwSw16jXbAVOKRI9PwcHPjbK1zLSOVRAssosiFumZSklLw\n9nYswCWKIrfKbkqOFNyL3O4JI0ICuHVTemvFuLgEYmJi2HtYueaP3OwjURTZd+QYo0enS+o6Yae2\n1taOPUhB7bKcaIG9Ntp+P6lYrVZOnMwhNnU0GonpyPciNWKgVmuIHZlBXt4J2Zv+3LkLEARoKVFW\nuig1YtB6swDRYmbRgsWK7jMQTU2N7Ni5HatnGKi/FEm2atxt2Ucpj4MgYPGJIS//RI/R+CoxnO1E\nUnIqJkQalJ63nLQTVSoI8PYZ0LkQFhlNjbELq5OGXYmdAPmZqnbS08ew5OFH2HumkLPF0lKjB7IT\noijyzo6jtBg6+MErP0GvVyYqrlKpWP30c1RV17LnwBHZ8+XYiQ/XfYaPtw9Lli6TfR8pTJ48FS8v\nb/bv2QXY/t/s2Udmk4kjB/czblzWA+vcqNFoeOLxlVSV3aDsWiFgyzzqnX104fhBOo0GVix/6oGs\nsTeZYzKxmrpArQaNltDxNsfbcOholpKUhLqjEbr3ASvCXdlHWM3Q0ULSMMjaUcJwtRMqlYogf38a\nnVEbcNJONHVPCw/v//cxIi6BakNHTymZUpTaicr2DoL8/CR3/Q3tzgqqUiCkLfU80WbspN3YeVen\nSqk8/vhKWlrb2HXgkOy5IM9OtBsMbNtzgIkTJ0vOHhQEFc6e9ZV0Y7MH2wQZujtWs1mx1o+czs52\nB5KcbGk7Dz00ncDgUEVaSFLPEp2N1bRXXGfpkkdxd3cuGH39uq3cTnQfOFute+tx2I1NdA/E2N46\noG6zVCR9Mo4cOUJrayteXl6sW7eOl156iVIXpUA5S1JSCuFBwZzTgllBdPm5LkHxZt8giJRoYMbM\n2QOmPWs0GhYuWkphfUvPQ7ocfj9ttOLN/lJdMw0dXSx59HFJ4+vr62g3GBkRKj0KDbaNXknrzZjQ\nAMrLyyU7FQRBIDt7Lteu3+DWbXmZGrvWfyTbeQRw+UoRVdW1ZGfPlTWvpqYGvV4vua0o2DZ6uZu9\nv38Aao2GmpoaWfOuXbtKU2MDyWMmyJoHts1ezoYPkJw5nrbWFgoLpYmt2gkODiF9zDhablxEtEp3\nPiU99VPJG74oirRcP0f0iHiXa3O0trbyX795E2NHB5awuyPclpTHbc4j+38HpyOqdfz6t29KLu0c\nLgxnOzF27HgEoExhoMEZO2FCpFINE6dMG3BcesZY2k1m7rT3r6c3EM7YCYCixla83N0ZMUJ6BxU7\nT63+OiOio/nfbUdoNfS/fil24vjlYvKu3mDVqmeczlQZOzaL1JSRrN+6XfLDplw7UVhUzPlLBTy2\nbPmgZC6CTW9k9uy5nMnPo76ulo83fs7HGz8HIO9EDs1NTSxcuGRQ7i2V7Ow5+AcEcvrQbgBe+X/v\n9DiPLGYz544eYEzmuAemfdQbWza2QMDIKSSteA1D9U08vLyJjh7xoJdGfHw8oqULTLaMdUva6i+d\nR4DQ2QyIDzyLSynD2U5kZk3gjlqgU2GmqjN2AuCmBoL9AwYMVI4cORqLVaS81ajoHs7YCVEUKW0x\nMGq0tGoGsGWp6vU6GlsNkufIPU80tdm+KwEBjkvC7iUlZSSjRqaxecceWQ4JJeeJnfsO0W4wsGyZ\n9DJnQQBRVOYsVHKesCNabd8BOWX1SjKQlJwlVCqVLeCqQDtNrVaz4okVdDRUYai6KWmOnLMEQEPh\nSbQ6PYsXOd9N+dTpU7bua9qBy+AsaU87dB4BiJ62veW8zGqQvpDkQHr55Zfx8vKioKCAP/3pT8TE\nxPCtbylLK3c1arWaF1/+IW0CXFTe2VI2IiInteCu0/PU0193OH7xw4+i0Wg4cntwxEb7QhRFDt+u\nIyIkhPHjJ0maU1xsS+uLC5O/ESshLjSILpNJlqjXjBmzUKvV7HMiC0kOew4dxd3dncmTpWv3ANTW\n1RAYHDzoaeYqtZrAwCBqa+U5kHJzj6PWaIhPG5pOLnGp6Wh1enJyjsqeu2jBYszGNtorlbVcdURn\n/R06m+tYvNC12UelpSW89qMfcLv8FqaoqYhuDgTYNW6YorNpaWvnx6//kHPnzrh0PYPJcLYTvr6+\nJMcncksz9CUfFSpbacKkKVMHHDdmjK1DZFGjPKF5V2AVRa41tZORkalI8F+r1fLy939MW0cnH+w+\nrngdDa3tfLAnh5SkZB55xPlsHkEQWPb4SurqGzicc9Lp6/XFxi924OXl5bLOa/2xYMHDiCIc2LP7\nrvf37NxBeHjEA8+e0Wq1zJ0zn7LiqzQ33C28Wlp4EUNbC4sesJPLjre3N1ExI+ioKUMURTpqyklP\nyxi0ZhdyiIiwleEIXW19/lzoau0eN7AmxnBlONuJ+QsexozItSE8S9ipF0SqVbBwycBaPKmpoxCA\n0uahl8SoMXbSbjIzSmZLcg83d4xdDjoLOoGh01ZmpdSB/+hjywfVRgB0mUxs2bWX9NEZspzogqBy\nOitZCV86raQ9M4miiMlsQq0ZmnI7rVZLZ6eyssMZM2bj4+dPU6Hryu7tdLU20lZ+lUULH8bLy8up\na7W2tlBYeAmLl+tkUES9H+i9OXpM/jnsXiRZS41GgyAI7Nq1i5deeomf//znNEoUJN29ezepqakk\nJSXx+9//vt9xp06dQqPRsHnzZmkr70VaWjqTMrO4rIUWQd4X7UM30faS2eP5uhpq1PC1556XVBrm\n6+vL7NnzOFfbTFOnvI309eOXe15yKGps4057B8tWrJb8YHTmTD6e7noSIuSVXa38zdus/M3bPP1/\n35E1b0yC7Ytx9uwpyXN8ff3IGjeBA0dzZEUMFq36es9LKu0GI8dPnmLq1BmSU3bt1NXWEBQk7/f4\n9BOP9rzkEBQcTG2ddAeS1Wol98Rx4kamoxtAAL4//vzq8/z51ed559c/ljxHo9MRnzaGk3knZKee\nZmaOw8PLm9ab0r8DxWvfonjtW1zf9D8Ox7bcvIxao2WKgywRqZhMJtatW8PrP32VxtZ2TLFzEb3v\nr8/XXtmA9soG1GVfOkNF9wC6YhfQKer43e9+xT/+8RfJnbEeJMPdTjw0cxZNgkiTTBsByu0E2Mqc\nPd3cHLYH9/PzJzYykqLGvg+OjlBqJwAq2zpoN5nJcqKDV2xsHE88sYqcguucvNK3o9duJ176n4/v\n+5m9dK3LbOU7L7/qVNvb3owdm0V0VDTb9g7cxcyO3UY8/vUXHI6travn5OlzLJi/eMBGGq4gJCSU\nrKzxHDl4gOefeYpvP7uat379K0qKr7FgwcPDwvkxa9ZcBODK6RN89NYbfPTWG+Ts+pyCU7n4+vmT\nmflgNYZ6kzE6nY76O5haGzEZWhg9Ov1BLwmgR1hX6M5Aus9GdL8fFDQ0QT5XM5ztRGxsHGkpIynQ\nKstCcsZOnNOCXqtlzpyBdcy8vLyIDA+ntEXZM4EzdqK02ZZFJLf7nyiKssqw7Hbiyd++7Xhw9/W7\n/yVrXXbGjs0iJjqGz7btklzWJPc8cfj4CRoam3hUovaRHavVqnhvt58lnln+mOy59tI1qdlPBoMB\nq8WCu6e85kZKzhIAbh6etLYpC7ZptVqWPfo4hpoyOhqqHI4v+ezPlHz2ZypzPnc4tvnaaVQqNUuW\nyP+d38uePTsRrVasfrEOx2oL1qAtWIO6+IuBBwoCZp84rl65RHm5c93YJH0qLRYLeXl5bNq0iTlz\nbCLCUg6AFouFl19+md27d1NYWMjatWu5cuVKn+Nef/11Fi5cqFjY6Vvf+QFajYaTWgZdKLUTkTNa\niI+MZtac+ZLnPfrYckRB4FhF/SCu7ksO364j0M+PadNmShpvsVg4d/YUYxOiUQ/Rw6iflwcJESGc\nOS1PFHv2nHk0NbeQf06ZLo5UjuSepLOri9mzpf+dwbbp37lzh5Aw17VzHoiQsDDuVFZI/v4UFV2h\nuamR5Ez55WvOkJw5gfa2VgoKLsqap9FomDFtJu2VJVjNro1kiaJIe3mRreObTOPXFyUl13n1Rz9g\n06Z1mL1H0BW/GNFDpjaJ3puuuIVYgkZx6NB+vveD/xj22UjD3U5MmTINAYHSwdd47MGESLlG4KGp\nMyU5RMZNfIiyViNG89BKuRY1tiIAY8Y4d8BftmwFcSNi+WhvLl0yncRni8s4W3yLp5561qXZFYIg\nMGPmbK5dv0F1jWszgI/n2QIfM2bOdul1+2Py5Kk0NTX2PNQ3NTb0vD8cCA4OIT4hkVtFBT3vWa1W\nyouvMGniFJc5BV1BcvJIrBYT7RXFgE0OYTgQENCtP2nqu+RHMBnR6tyc1tZ4UAx3O/G1b75AJyIX\nhlC3+LZK5LYaVqx6RtIzSFrGWG61Goc8M+VmSzs+Xp6yuv2aTCaaWloI9nUuG2MggrqvrVTXRRAE\nFi1+hFvltym+cdOFK/uSvYePERUZJTtT1GJRrivkDGqNPKFqexMfbz9l+rly8fYPVNQ4yM7MmbNR\nqdW03ip02ZpEq5W2squMz5qAv7+DagMHGI0Gvti2FatXhOPKBZlYA5JBpWHjZ+uduo4kL8FvfvMb\nXnzxRaZMmUJaWhpFRUUkJTluqZ2fn09iYiKxsbFotVqefPJJtm69vx7zr3/9K8uXLyc4WLkApL+/\nP089/RyVarglx/ehQPTujBY6BYHv/PDHsjzDwcEhTH1oGqeqm+hUcECQU7d8u81IaUs7Sx9bjkYj\nLR+3pKSYltZWxiXJ1wHQalRoNSrW/Mxx1PZespJiuF5yneZm6SLjmZlZ+Pv7s/+I/HIJOXXL+48c\nJzoqmsREeS3kKyrKMRoNJCQmy10eIF/0LiEhidbWVskbak7OUTRaHXGjpNey98bTzx9PP39e+OUf\nZM2LTR2NTu/G8ePy0yezsiYgWswYa25LGi/o3BB0biQ+8cqA47qaajB3tDNxgrQyz/6wWCxs2PAp\nP/vZa1TX1mOKmYkl6iHQ9C8CbPUMx+oZjiVm+v0/VKmxhI7FFDef1k4rv/vdr/j7P/5CR4cyjZzB\nZrjbCX//AEaljqRUq5IfZFAojlqutmnzTZ85S9L4zLFZWEWR4iZlWUigTBy1qKmN2JgYfBW2C7aj\n0Wh49uvPU9/Sxt7TBff9PMDbkwBvT95+5dm73reKIusO5xMaEsIiF+gG3Is9s/DkmXMOx7q76XF3\n07P5I8fZtCdOn2NEzIghKyfKzMwCICgklPQxmfj6+RETM4LAwKF5aJdCRnomVbdKiUsbQ+KYLBJG\njcHU1UlGxtCUSkvF3h2qs7m2+7/lC/AOBjqdDo1Wj2C1BUrusxGWTtw9nA90PCiGu52Ij09kVvYc\nrmqQn62qwE5YEDmlFwgNDGLxYml7X3JyKl0WqyJNVTtK7MTt9k6Sk0fKkmW4edOWjRoWIN22qATb\na90b0nSQ/L080Ou0lJRIb8hzL1OmTEWj0XCwu+OyVKScJ6praim4eo3pM2bJlrQwWyyKHUiCICAI\nAp98tkX2XLVKnlB1aWkJAMER8jrhKT1LBIVHcfNmqSIhbQBvbx8yM8fRXnbFoaPZPTwW9/BYIqYO\nXFZvqL6FuaOdGTOkPe8NxLr1azAa2rAES8uMteq8seq8sSQ94niwRo8lIIUTucdka9L2RpL349FH\nH+X8+fP86U9/AiAlJeWu1NDf/va3fc6rqKggulcL86ioqPtaVFdUVLB161b+4z/+A8ApvZiFi5YQ\nHRbBKb2ASeIBQa7oXZ1gq49euGCxIrHRhYuW0mmxcK5WekcCJaJ3J+80oNNqZQk/nzlzCpVKIDNB\nfr3lmp+9oMh5BDAuaQSiKMrKsFCr1UyeNJUz5y9hlHiglit6V9fQwJVr15k6dYbsz+XZs6cBSB01\ncNnKvSgVvUtNS7vrvgNhK1/LIW5UOjqZZXl2XvjlH2Rv+AAarZb40Znk5Z+UvfGPHDkKlVqDsVpa\n2mXiE684dB6BbdMHWzcppdTW1vCz//wJGzeuxewTQ2fC4j5L1u7FEjO9b+dRL0SPILriFmEJHMXh\nQ/t45dXv9Rjr4cRXwU5Mz55DC1bqZU5XKo56Qw1+3j6kpIyUND45ORV3vZ6SJvnlCUrFUTstFspa\nDIwdP1n23L5ITx9D2qg0duZfvi9C/vYrz97nPAK4XFrBrep6Vqx8WnLAQw6hoWEE+AdwrcSxWO/m\nj96R5DyyWq0Ul5QyUmY5hzP4+voSGBjEiNhYvv+jn3C7rIz4+AcvSt2bpKQUrFYLSRlZTF20jJqK\nsp73hxP2jnXm9mZ0ejeXZJ+6Cq1WC90NI+61EYLVMujlkoPJV8FOrH7mOXQ6Pfk6QVawQYmduKKB\nZkS++cJ3B2zG05uEBJvD7XabfCFtpXaiw2yh1tBBYnKqrHknTuSgVqvITIiRPGfdGy9Jdh6BTVQ5\nKzGG/LxcRcLKAJ6eXmRlTeBIbh5WCR3u5JwnjuTaKiykVoP0prWlBS8JMil98clnWxQ5j4Cee7a2\ntkgaf+JkDr4BQfgFy6u6UHqWiE1No7PDyKVLyqtQZkzPxmRoxVhTNuC4iKnLHDqPANpuFuDm7sHY\nsVmK1wQ2Z9zOnduw+CchekgrVbYkPSLNeWQfHzwadF78/X//hslkUrROlzypbdq0iTfeeOO+96Vs\n3q+88gpvvfUWgiB018k63qx9fftXI3/tpz/llVe+zwUNjJdwPv3QzXY/nRVWO9j4RURO6gV8vTx5\n4aUX8PQcWACDhmMAACAASURBVBW9L8aPzyQ6IoILdc1MDpfW6cxeq+yr0/DziY43b6socqm+hRkz\ns4mIkF4nf/5MHilRYXi5y384WfmbL2uV5XZjiwsLwt/bkwvnT/Hoo9JFNufMncWu3ds5fe4i06dM\ndDjeXqusUgnsWPuhw/EnTtlU6ufOmz3gZ+5eRFHk2LFDJKWkEBIqbzPtrX0kx5EUERlFbHwCx3MO\ns3r1qgHHlpbeoLWlmYdGKXeY/PnV53v+LbeDQnzaGK6eOUldXQUpKXIeRtyJjIqmvllamnLx2re6\n/yWQ9NTr/Y7rbKrF28eXuDh5kRM7165d4yevv46howNz5ENY/aQ7lrUFawAJ7TdVaixhY7F6hVNf\neYKf/fzH/PIXv2TKFOWaNUPNcLAT8+bN5p1//o1bapEgGf5Lu50QrPB1iQcEe/e1ZfPn4+8v/XCa\nnJxMean8SKrdTnj8f+ydd3hU5baH3z0lyaT3kEYaLRBCaCEQOiICUqWJiqCCgtgLIirVAtiPx6Me\nhWMBFY4KSBeQ3nsJNZDee89MZvb9YzIhgWQyNQn33vd58miy98xeTNnrW+tb67dkEhbGdDT4cakl\nFYhA166RRt3n9DF6zFjef/9dLiWk0rnW90rnJ+6sQtp77ioO9vY88MBQbGxsLGLDnbRt25b4W40n\nn2trWugLENIzs6iorKRjxw4We90MISgoiONHDhN//ToFBfmEhYU06fUbIyxMW8H85+p/YmunwEah\nQC63ITjY3+oDJYxBVa0lJEikaDSaFvUaym1tQdQGwjU+QmKDOnwiiGoUCkWLsteStAQ/4eKi4PEZ\nM/j6669IkUCggUOwdH7CRQPjDPAT5Yiclwv06NqVQYP0byTVRqHQfscKK40P/IyNJ3QUKrXXCg4O\nNPizV15ezoH9u+kSGoijouFK7DvR+QljqpD6dGrD4bh4Ll48Rf/+xidqAAYM6M+xY0dISEohNFh/\nwsuYeOL0hUuEhobQtm2wUfaUl5dTWlpSo4tmLKbGEwBOzs7IbWwoLi5o9P0uKCjg4oVzdB84zOh7\nvKmxRFCHCGwV9pw4cYiBA03TLh00qD9f/utzihPjsPdpuPPmdixBg9PYNFUqSlOvcd/gwXh5mV7J\nrVar+fKrLxBktqh9DI/RDI4ldEhkqFp1JytpHzt3/snUqQY85s6nMPoRRuDv709ycnLN78nJyQQE\n1A3STp06xZQpUwgJCeG3335jzpw5bNrUiAiUHjp27MiQQYO4LIdSC2shJUi1FUizZs8xebdKEASi\ne2t1LlRq00YzNkZaSQUVVWp6Rhu+o5yfn8/NxESTqo/MRRC0VU9nz54xavegU6cIXF1dOHis8aob\nUzh07CStAwNp3dq4lr74+BskJibS18C2FUvRb8Agbly/TmKi/iDp0iVta4lfSPPsXPsHt6ljhzGE\nhQRTVZxnUXuqinNpHWTa+ObU1BRefe01ylQiypAHjEoemYLo2Apl6HDUcmcWL1nE+fPW1QBrCprS\nTzg5OdG+bVvSm2AaW4ZE68yjoxtPbtemfXhH0ksrmkzfIr1UW8FpbJuuPvr06YONXM6ZG/p39kC7\n4XE2PpnYvn2tljwC8PL2Jr/QsN1UQ8gv0FYRe3sbNyjBXFxdXdFoNDW75K6urk16/cbw9tZW9ujs\nU1ZU4u7h0aKSRwAlJdo2UYmNHVUqJUorTokyFlsbW9A0kOHWVGmP/x+jqeOJ0aPH4OPhyTkb631u\nL8tABcye+5xRj7OxscHezo4SlWntO6ZQqtKuzY2532zcuIGCwiLGxlp/QmSPdkH4e7rx/epVJlch\nRUZqJR3Ox12xmF1KlYrLV6/XTFk1Bt1kZY9mEMwXBAEPD08yMzMbPffgwQNoNBradzVurWMOMpmc\nsIgoDh48ZPK9W6FQ0De2L2Up19CozfsulabFo1YpGTx4iFnPs2nTRhJu3kDl3Q2k1r3Pi04BaJwC\n+eHHH0lNNUwapDZWHVbZo0cPrl+/TkJCAn5+fvz666/8/PPPdc65efP2tJYZM2YwatQoRo/WX4ZV\nWKi/bHP8hEfYu3cf5+UaejeSoLepzuEYUn101kbAz8eH7t37NGqDPkJC2lGl0ZBZVkmAU+OZfBcb\n7dtk6G6Brqw1MDDUYDuPHNGKgXYOMU/LwdjqIx0RwX78ffYK587FGTXiskePGA4d3EelUoltI8GH\nRKJ9jw2pPiooKuJC3BXGjZto9Hu9Zct2ZDIZMX1Mn+hlShtb7379WPP9KrZs2cojj0xv8LyEhCRk\ncjkuHqZrjukwtvoIwNHVDVuFPQkJSUa/tt7evihL9qKpUiKRNRZsat9vfdVHoiiiLMzFv1uU0baI\nosiSpe9SoapCGTIMbIwvM9alkA3aMdAhs0MZNAibWztZvHQZX37xTZ2WBi8v08qdm4um9hMRXbqz\n/to1VIDcwBG1QvUbZWj1EUCmBKQSCQEBYUZ9thwcnNGIIuVVahzkhrtoe5l2P8iY6iOAYmUVEkFA\nIrEzy6/dSWhIKFeT6044cXfSbrzUrj5Kzy2gpLyCNm3CLXr9O5HL7SgpLUUURYOSGY21J5TUTEa0\nsarddyKVypHJZER0jmRPZgYajaRJr98YSqU2ePNo5YdfSBvyMjMoy81qUTYCZGZqh5lIbbVi1BkZ\nubi4tIxknEwmR9BoF68aidbPqcMnAiCIVcjk5n/m/t9PaNH3Oo4cM55Vq74hRwBPsfF7hku1nzCk\n+kiNyDW5QNeu3XB29jRp/SEY6L/q2GhkPKFDd6XSUqVBtpaWlvLrLz/TtU1rOgQapy9WvVQ3uo1t\n8sCefPzfnWzevI2BA40P5G1tnfHx8eF83GXGjtA/OMfQeOLajZsoVSratjXev127pm259jFTn82U\neAK0w3mSk5MbtXvnX7tw9/bF00j9o9qYEku07xpN3InD7Nt3kGgTJ8jGxPRlz57dlKXfwjFA/yZa\nQ9VHAMWJl3BydiUoqK3J9+bs7Cz+/e23aBx90bgEG/VYk2IJoMq3B5L4zbz3wUreXfr+XWsjfX7C\nqgkkmUzGF198wbBhw1Cr1Tz55JOEh4fz9ddfA/D0009b5bre3j7E9u3PkQP76KES9QYJjSWOdGRK\ntH3Kj0182GxFfJ3gZZFSBTSeQDL2Rl+kVCFQa5qHAdy6FY9UKiHE17SkgqmJIx3tA7SClgkJ8UYl\nkGJiYtm1awenzl2gT0/9faeGJI50HD5+Co0oEmPkWHe1Ws3Bg/uI6tbDpL5lU2/0AC4urkRGdWX/\ngb08/PC0BgXeS0pKsVXYm7UjbMrNvjZ2CnuKS4wXCg4ICARElEV52Lm30nuuvsSRDnV5CWpVZfXz\nGsfly5e4dfM6Vb7RJiWPwPibfQ1SW1S+0RQn7OLAgb0MHfqAac/TAmhqPxEYqC1NLxLAw8AiH2MS\nRzqKJODt6WV0VY2jo/azVGZkAsnYxJGOCrUaezs7i4+BDwoJ4+C+3XX+Vp/+UWqOdnhC69bBFr3+\nnTg5OaHRaCgrr8DBvmG/a6iuRXGJNoHk6Gi96UL1UVVVhb2DA8NHjWHPrp0m77ZbC93nqHXbcHoN\nfZBNq75oUdPXdJRWJwCldvY1v7eUBJKtnR3ka4MQXeJIx72ugWQqzRFP9O8/mO//8y0JUo1BLc+G\nJI50ZEqgApH7h40w2i61Wk15ZSUKufGtMsbGEzoU1VO5iosNG52+efMGSsvKmDLQ+KoUYxJHtenV\nIYQQXy/W//oTffsOMElPr2PHzpw4fgS1RqN3GrWh8cS5S5cRBIEOHYzTQwVIS9NWhfj5m5aYMSee\nAPD3D+DKpYtoNJoG1wd5eblcvRJHzP2jTYopzIklWrcNR+HgyIGD+01OIHXuHIW9gyPFiXENJpD0\nJY4A1MoKytJuMmL4g2b5uv98v4oqtUYbUxj5WpocS8jtqfKO4vrVExw9eqhm4IghWDWBBDB8+HCG\nDx9e528N3ehXr15tsesOHnI/+w/sJVkKoRZYX8VLwVYuJyamj9nPpQsQrDWqubxKjZ2trVEf5Nyc\nbDycHPXeMK2Ju7MDggB5eca1J0VEROLk5MSBIycaTSAZw4GjJ/D19SUoKNiox924cZ3CwgJiYptn\nrHJMbF/Onj5FQsLNBsVVRVHT/O0E1RoFxuLvr030KItyG00gGYKySLsLbUoC6fz5syAIVm9bawjR\n3htsHDl77sw9nUCCpvUTrVr5AVBsRALJFIplUsICjW+N1AkayproOyoVBKqskIRwc3OntKISVZUa\nuaxhX1RYWl59vmVH1d6Jg4M20VNSWqo3gWQougqkpk4glVeUY2enwE6h/TdUVLSsyh5doKFrYdNo\nWoC/qYfSUu0GhszOsc7vLQGFnV3DLWxiFfZ2/zv1jxqjqeMJBwcHQoJCyLp5EyzcLZYpAQGBjiaI\n8Gdmais7XW0ME922BC628uprNz7pt6ysjG1bNtKzfTAhvk3XfiUIAhP7d2fFr9s5eHCfSVVIkZFR\n/P33Lm7cTKB9m1CzbTpz4RKhIaE4mbChnJKSjKubG/bNJPDv5x+AUqkkJycbb+/69VxPnz6JKIq0\niezWxNaBRColLKIrZ8+epKqqyqSEoUwmo29sf3bt/guNqhKJ3Pi2sZLkq4gaNf36maa9BZCVlcnx\n44dRu3cAm6ZdU2jc2kLeFX5dv46YmFiD/bVFsgU7duywxNNYlPDwTtjIZGRbKB+SI5MQ3jECWxOn\nVtVGt7CSWGlRJTEhOC8uLsTJvvl2tWRSKfa2thQVGadRIZVKiY7uzbFTZ6i0kIZBQVER5y/GERPT\n1+iF79mzp5BIJERGNf3NFKBL1+4IgsDZs6cbPMfLy5uSokKUzTQOvkqlorggD28v47VDfH39kMpk\nVOY33pdtCLrnMVbnCqCsrBQkMu1PcyAIiFJbiotbTuCjj5biJ3TjuousmCsXESkSNfiaMN5dqdSO\nZraRNk0y30YqQalSmZTQ1YdOK6OwtEzveQUl2uPOzqYLTxqCLoGkqxwyl+KSUgRBQKGwt8jzGUpZ\nWSn2Dg7Y29tX/67/9W1qatY31YkkiUTSZHpexlBTgaRwqvN7S8BeoUAiNpDU1VRhb4EEaEulpfgJ\nHW3ah5NvhaV6vgR8PDxNun/cvKkdsuDv2HRrdoVMiqfClvgb1xo9d/funZSWl/NQP8tt6hpK97ZB\nBPt4svGPdSb5NN003jMXjNfovJPSsnIuX7tBZBfT4oHU1BSTq48sgV+1xlhqanKD55w7dwYnV3c8\nqjfmmprg8AgqysuJj79u8nP06zcAjVpFSfJVkx5ffOsiHl4+Zk1E3b17JwBqD9MqBM1CEKhy70Bq\n8i2jJjybvELt3Llzzf83tYikIUgkEgIDgyiQmb8IVyNSiIYQC43LVVeLdVkrgSQVBNQGjKGsjcLe\ngdKKSqvYYwhVajUVShUKhfELo9jY/pRXVHDw6AmL2LJ73yE0okhsbH+jH5uenoanlzcOTbwrrcPZ\nxQU3d3fS09MaPKdDh44giiRdv9yElt0mJf4qGrWaDh0MG21eG7lcTkDrECpzG/73GUNFbhqu7p4m\ntS54e/uAWgXKZkrgaNQIyiL8/Mzrj7cmLdFPKBQKnB0cKbJiUUSpoPUbvr7GL6p0gpDyJqoGlUsE\nNKJIVZVlt9hdXbUVRQWl+itkCkrLcHJ0NGn30Dh7tN/xvIICizxffkEhzs7OFm/9a4zS0lIcHByw\nrW47bEmJD6CmpU4QJDX/bWltdlC9AQDIFNrd/Zb0Onq4u0FVPRs8GjVUVbY44XRzaYl+QoeLiwsq\nRNQWHsqjlAi4uBs2iflO4uNvIJNI8GniTV8/BzvirzceZB87coBQXy9CTZTEMAdBEBjSrQMpaWl6\n18EN4eLiSmhIKEdPnjHbllPnzqPRaEwS0AYoLi7C1cqVufpwdXOvtqPhtsWMrEw8Wvk1W5WpRyvt\nJl1Wlumbyu3bh+Pp3Yqim+eNfqyyKI/y7GSGDTV+Al1tLly6hGjnDvKm3ZDSoXHSJguvXjVcQF7v\nii0uLq7ev4uiSE5OjhGmNQ/BoW04lHALkYbF5nRjN200DeshFQhagaqgIMu0quiS4oZ+1nRjNwGW\n9zWs3NXYzHvr1sEcO3aYkvJKo8Zt6tCN3QTT9JCSsvJQazQ1+iTGEBERib+fH5t27GJI/4ZbxwwZ\nz6zRaNj8127CO3Q0un0NQJAINTuwpmDO2E0dGrX+loHw8E44Obtw8eh+2nQ2zbGZOnoT4MLRAyjs\nHejc2bTJHJEREWzevAm1sgKpTcMLqMZGb4oaNeVZSfSO7mWSHb169eGHH1YjzbmE2s+05zB69GYt\nJHnXQK0ito/h43+twb3oJ3xb+ZJfbPiOlc5PoIHpBuhc6JJT5iSQZBLjFiOm+AntdSQ115XLLdcS\noUvKFhTfrpDR+QmpRODnBdrWk4KSMlydnS123Ybw9ta2vGZkZuk9zxA/AZCemUUrH/PbaI2lrLSU\nm/E3mD5lAoIgtKjWK7hdgXR42x8c2bERGzsFLk7Wf3+NRZfUSt7xHwA0mpaT5PL2bgXqSlArkV9Z\nD2jFtNWh2lZlLxOqd5ube9FPADWtR5VAY6GdMX6iUhBMrrpMiL9GK3tbpEb6CDDdT4C24ul8QmZN\nErshUlJTiA0PNto2HebGE239te1WKSlJ+JlQBdy37wB++HE1iSmpBAXU/3hD/MTOvw/g6eGp3bQ1\ngcrKSrMmk5obT+iuXVnZcHFBaUkx7v6mJULBvFgCtHqqACUlhmlz1YcgCDxw/wP89NN/UBblYuNc\nVz9YXyxRdPM8giAxqV2yNiUlJYhmTF0zJ5YAaia+6TZWDEFvAikiIoKgBsZb5+bmGmFZ8xAUFMxu\nUUMZYE4Hab7k9vNZAp02kVpjnbJutSgiNbL9ISqqO+vWreXo5Xju62bazc4cDl+KRxAEIiONT2gI\ngsADw0fz3Xdfcf7SZSI7GV/ZouPgsRNkZGbz8NQZJj0+qHUwBw/sIyc7G0+vpt99yUhPp6AgX29L\nlkwmY+SIUfzyy08kX79CYNumK5lMu3WDG+dPMX78JJMdY5/efflz0x+UpFzDJTTSZFvKMhJQV5ab\nnIDx8vJm+PAH2bp1E6K9FxpX8/vlDUUozUSWdZbOkd2IiDD9NbAE96Kf8G8dRGL8Db2bC+ZgTgJJ\nl/xtqqYf3V6DxIRgRB+entr7X3ah/oVddkEJnq2sX6bv6uqKvb09txIbLsc3FI1Gw62kFLp1b7qx\nxTpqaxuKooikxQlU3/3JtXR7pCW4c6PHnI0fS6OrwhFUdRfzgkqbLLwXE0j3op8AcHTUJj8rBbC3\n4Me4EhEnE0Xb09JSCVKYnlgwFa/qzeWMjDTCwhqeWCURhGZtW9Vd29SKkP4DBrP25x/ZuHUnz88y\nLRZISknj9PmLTHhossnCyg6OjhQXmZ4YMZeiokJAv86fm5s7RfnN9/0tytMmn11dTU9iAQwYMJi1\na3+g8MZZvLoZlgzSqKsovnWBLl274eZm3vW9vDxJzzG9Dc9cBKX2c2bM8C29WYbg4GAOHDjArVu3\n7vrx8alfUKslERysrRjK1/OvtNHorz4CyBe0Gj2mBAP1ocvcl6qMaxkwdLegTKU2WmSxTZu2BLcO\n4s8j58wSVDVlt6CorJy/TsfRKzoGFxfTdmQGDboPdzd3vv/1t0YXqw3tFqjVan5c9wcBAYH06mWa\nWHpsbH8kEgkb/rvOpMfrMLX6aMN/1yGVSunTSFLkwQfH4unlw+7//khVtWivKRizY6BWV7H7vz/i\n5u7B2LETTL5mWFhbPLx8KIo/Z1Bg0tAEhcL4s9gp7E0uLwZ49NHptGvfCVnqEW1FkJFoMH7HQChO\nRZ70N55ePrz4wsvNLlB7L/qJ4JAwKhAxWD2m+o0ypPoIIFcC9rZ2RjljHXK5NjBQqk0LaI3dVVZW\nB84ymWUFWV1cXLG1sSEz/7aunVQi1Kk+EkWRzIIifHyN3yU2FkEQiOgUyanzFw26b+irPopPSKSo\nuLhGL6MpsbW1Q6GwJyS0DYJEgp0FdBktia2tHYIg4ODiik/rEFoFBqOwb56yfH3Y2mqDYUm1BpLu\n95aAbiKhUJ6LRmKjrT4Kn4hQnlt93HjNvubmXvQTcLv1tdyQW7+BfkKDSDmiSetdtVpNfmEh7nbm\nJZCM9RNAzTUbaxcKDg7hYkKa2UkkU6c7X7iZUm2HaZt6Li6uDBk8lJ17D5CZla333Ib8xJr/bsDW\n1pYHhj9okg2g3YBK06M/ZCimxhPpqSnVdjTsnztHRJKReJOiPPOSSKZOY7t2Tqs727Gj8VPuauPq\n6kZ0rz4U3TyHWlm/PuydsURxYhxVFaU8OGKUWdcGiO4RDZVFCKX6K6QbwpRYojaSgngwsohDbwIp\nKiqKpKSkeo+NGzfOOOuaAZ2TzdPzr5yqFPQmj3SP9/fxtdgoWhcXVxztFWSUGaY5tLxvhFE3+4yy\nCqNbwQRBYMrUx0nPK2TDobNGPRa0N3pTbvaiKPLDziMoVVVMmmziGEK0i7+HHppM3NXrHD5+qt5z\ntv36vd6gYPuefaSkpTNlyqMmv9deXt6MGDmav3ft5NiRQ0Y/fs1vG02+2R8+sJ8De/cwatTYRgNX\nW1tbnp41h/zsTHb/90ejd4hf+vhbo274oiiy94+fyUlPZeZTz5ikdaVDEATGjRlHRU4q5ZmJDZ7X\n9uE3GkweVRZkUZpynZEjR5vVtiOXy1n4zhIio7ojSz+BNP2EVqfCQNSdHjH8hi+KSLIvIU/eh79/\na5a/v9LqwsOGcC/6ibAwrZ6doUMWpisFg5NHANlSgZCQUJOSe/7VopmZZcaJ3BvrJ3Rkllbg5eZm\n0fY10H5PW3l7k553W3Po5wVP1ySPAIrKKiivVNYIm1ubqK7dyc7J5frNhAbPacxPABw6fqq6Yta0\nNlxzcHd3x9XdjRdeex11VZVJSUprIpFIsFPY0yaiK1Oee4PK8jIc7JtnipA+dBpdnp371vm9JeDn\n54+dvSNCaRbq8ImowycCICnLwt3LF6cW2BLYGPein4DblZQlBtzKDfUTZYK2Tq+hyVaNIYLJdbOm\n+glqXbOx9eKgIcPIyCtk3znTRIlNjSdAuyG9+dh5OneKMKtSb9z4yUglEv794y/1Htf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JUVM/vpt3Fza7iHuCl48skZFBUXsH3bVjSOflphbWUxsoyTdI6MYsGb85t0QdQSaAl+wsVF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\nsaSdqE5JSQmjR49CBjz0yKO88MKLTXLepkSn0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gnGMaglP/70PUlJNxo+wEzEXI9BsHdG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PZd4hDK0t/OLlV3FxUV7uY2bhgkVg0KMuda521t2oS46hUql4cE77BBSl8GO2Ey5aLXbE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\nPXR1dY188C3Mn38HOTk5fPj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OXqa3t/6EXqfBZJQWRIuJiaWt0/vsbV/9CU/muDedTz3k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\noYcxWGZGw25hmz59OjIyMnDHHXfIjnJdT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35+fhKStK6kpAT9+vVr9TWTyYS6ujoXJyIj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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x19c88350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20, 10))\n",
"pos = 1\n",
"for cope, results in coperesults.iteritems():\n",
" for ii, model in enumerate(['T']):\n",
" for jj, feature in enumerate(['brain', 'behav', 'combo']):\n",
" plt.subplot(len(coperesults.keys()), 3, pos)\n",
" \n",
" data = pd.DataFrame({key:results[key]['scores'][ii][:, jj] for key in sorted(results)})\n",
" sns.violinplot(data, inner=\"points\", positions=range(data.shape[1]), \n",
" color=\"RdBu\")\n",
" #maxval = np.max([val for val in ans[0]])\n",
" #plt.plot([0.5, 0.5], [0, maxval], '--')\n",
" #plt.legend(['chance'] + results.keys(), loc='upper left')\n",
" plt.ylabel('f1_score')\n",
" plt.ylim([0, 1])\n",
" if (pos/3. > 4):\n",
" locs, labels = plt.xticks()\n",
" for label in labels:\n",
" label.set_rotation(90)\n",
" else:\n",
" plt.xticks([])\n",
" plt.title('%s | %s | %s' % (cope[:20], model, feature))\n",
" pos += 1"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"\\nplt.figure(figsize=(20, 10))\\npos = 1\\nfor cope, results in coperesults.iteritems():\\n for ii, model in enumerate(['tree', 'iterative tree']):\\n plt.subplot(len(coperesults.keys()), 2, pos)\\n ans = plt.hist([results[key]['scores'][ii][:,0] - results[key]['scores'][ii][:,1] for key in results], 6)\\n maxval = np.max([val for val in ans[0]])\\n #plt.plot([0.5, 0.5], [0, maxval], '--')\\n #plt.legend(['chance'] + results.keys(), loc='upper left')\\n plt.xlabel('f1_score')\\n plt.xlim([-1, 1])\\n plt.title('%s %s' % (cope, model))\\n pos += 1\\n\""
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"plt.figure(figsize=(20, 10))\n",
"pos = 1\n",
"for cope, results in coperesults.iteritems():\n",
" for ii, model in enumerate(['tree', 'iterative tree']):\n",
" plt.subplot(len(coperesults.keys()), 2, pos)\n",
" ans = plt.hist([results[key]['scores'][ii][:,0] - results[key]['scores'][ii][:,1] for key in results], 6)\n",
" maxval = np.max([val for val in ans[0]])\n",
" #plt.plot([0.5, 0.5], [0, maxval], '--')\n",
" #plt.legend(['chance'] + results.keys(), loc='upper left')\n",
" plt.xlabel('f1_score')\n",
" plt.xlim([-1, 1])\n",
" plt.title('%s %s' % (cope, model))\n",
" pos += 1\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "u'no item named median f1 brain'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-48-1c4e5b110f29>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcope\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcoperesults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterkeys\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----> 5\u001b[1;33m \u001b[0mf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf1_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcope\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf1_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcope\u001b[0m\u001b[1;33m]\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;34m'name'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'median f1 brain'\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 6\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf1\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0.6\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 45\u001b[0m \u001b[1;32mpass\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---> 47\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\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 48\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 251\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 252\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 253\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 254\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 255\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 361\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 362\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m_is_label_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m \u001b[0msection\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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 364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 365\u001b[0m \u001b[1;31m# we have yielded a scalar ?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 411\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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 412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 413\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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[0m\u001b[0;32m 414\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 415\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, copy)\u001b[0m\n\u001b[0;32m 2358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2359\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maxis\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-> 2360\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\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 2361\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2362\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2001\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2002\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2003\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\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 2004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2005\u001b[0m \u001b[1;31m# duplicate columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 665\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 666\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 668\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1653\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1654\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1655\u001b[1;33m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1656\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1657\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_find_block\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1934\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_find_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1935\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_have\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1936\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1937\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/software/python/anaconda/envs/nipype-0.9/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_check_have\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1940\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_check_have\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1941\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1942\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no item named %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpprint_thing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\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 1943\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1944\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreindex_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: u'no item named median f1 brain'"
]
}
],
"source": [
"brain = '/usr/share/fsl/data/standard/MNI152_T1_2mm_brain.nii.gz'\n",
"\n",
"for cope, results in coperesults.iteritems():\n",
" for key in results.iterkeys():\n",
" f1 = float(f1_results[cope].ix[f1_results[cope].ix[:, 'name'] == key, 'median f1 brain'])\n",
" if f1 <= 0.6:\n",
" continue\n",
" niimg = nifti_masker.inverse_transform(results[key]['weights'][0])\n",
" act = np.ma.masked_array(niimg.get_data(), niimg.get_data() <= np.percentile(niimg.get_data()[niimg.get_data()>0], 50))\n",
" #act = np.ma.masked_array(niimg.get_data(), niimg.get_data() == 0)\n",
" ### Create the figure\n",
" plt.figure(figsize=(20,15))\n",
" for k in range(15):\n",
" subplot(3,5, k+1);\n",
" plt.axis('off')\n",
" plt.imshow(np.rot90(nibabel.load(brain).get_data()[..., 15 + 3*k]),\n",
" interpolation='nearest', cmap=plt.cm.gray)\n",
" plt.imshow(np.rot90(act[..., 15 + 3*k]), cmap=plt.cm.hot,\n",
" interpolation='nearest')\n",
" plt.text(0, -2, 'R')\n",
" plt.suptitle('Cope: %s ET features: %s f1: %.2f' % (cope, key, f1))\n"
]
}
],
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"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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