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Traditonal_ML_models.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Knee Cartilage Segmentation using Classic Machine Models "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#Import the required packages\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.io import loadmat\n",
"import matplotlib.pyplot as plt\n",
"import imageio"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#Extract the patches of spricified shape and stride and return one dimensional array\n",
"\n",
"def extract_patches(arr,arr_target,patch_shape,stride,num):\n",
" new_shape=((arr.shape[0]-patch_shape)//stride)+1\n",
" #print(\"input shape\",arr.shape)\n",
" #print(\"new shape\",new_shape)\n",
" arr_out=np.zeros((new_shape,new_shape,patch_shape,patch_shape))\n",
" target_out=np.zeros((new_shape,new_shape,patch_shape,patch_shape))\n",
" #print(\"output shape\",arr_out.shape)\n",
" for j in range(arr_out.shape[0]):\n",
" for k in range(arr_out.shape[1]):\n",
" arr_out[j,k] = arr[stride*j:stride*j+patch_shape,stride*k:stride*k+patch_shape]\n",
" target_out[j,k] = arr_target[stride*j:stride*j+patch_shape,stride*k:stride*k+patch_shape]\n",
" print(\"output shape\",target_out.shape)\n",
" output_reshaped=arr_out.reshape(arr_out.shape[0],arr_out.shape[1],-1)\n",
" output_reshaped=output_reshaped.reshape(-1,output_reshaped.shape[2])\n",
" \n",
" target_reshaped=target_out.reshape(target_out.shape[0],target_out.shape[1],-1)\n",
" target_label=target_reshaped[:,:,112]\n",
" target_label=target_label.reshape(-1)\n",
" pos=np.arange(target_label.shape[0])\n",
" pos1=pos//new_shape\n",
" pos2=pos%new_shape\n",
" l=target_label.shape[0]\n",
" nslice=np.array([num]*l)\n",
"\n",
"\n",
" return output_reshaped,(target_label>0).astype('int'),pos1,pos2,nslice"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Function take list of file names and return corresponding one dimensional vector for each image\n",
"def return_dataset(listlabels,dset):\n",
" feature_list=[]\n",
" target_list=[]\n",
" for i in listlabels:\n",
" if dset==0:\n",
" image=loadmat('/beegfs/ark576/Knee Cartilage Data/Train Data/{}'.format(i))\n",
" elif dset==1:\n",
" image=loadmat('/beegfs/ark576/Knee Cartilage Data/Validation Data/{}'.format(i))\n",
" else:\n",
" image=loadmat('/beegfs/ark576/Knee Cartilage Data/Test Data/{}'.format(i))\n",
" \n",
" targets_all=image['SegmentationF']+image['SegmentationT']+image['SegmentationP']\n",
" \n",
" #handle 15 slices which you are not yet doing \n",
" for k in range(15):\n",
" im=image['MDnr'][:,:,k]\n",
" #print(targets_all[:,:,i].shape)\n",
" features,target,pos1,pos2,ls=extract_patches(im,targets_all[:,:,k],15,1,k)\n",
" feature_full=features\n",
" #feature_full=np.insert(features,0,pos1,axis=1)\n",
" #feature_full=np.insert(feature_full,0,pos2,axis=1)\n",
" #feature_full=np.insert(feature_full,0,ls,axis=1)\n",
" feature_list.append(feature_full)\n",
" target_list.append(target)\n",
" final_feature=np.vstack(feature_list)\n",
" final_target=np.concatenate(np.vstack(target_list),axis=0)\n",
" return final_feature,final_target\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"with open(\"/beegfs/ark576/Knee Cartilage Data/Train Data/train_file_names\",'rb') as f:\n",
" train_list=pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#train set\n",
"train_x,train_y=return_dataset(train_list,0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Under Sampling\n",
"list_train_x=[]\n",
"list_train_y=[]\n",
"import random\n",
"zero_index=np.where(train_y==0)[0]\n",
"non_zero_index=np.where(train_y!=0)[0]\n",
"sampled_idx=random.sample(list(zero_index),len(zero_index)//10)\n",
"sampled_train_x_zero=train_x[sampled_idx]\n",
"sampled_train_y_zero=train_y[sampled_idx]\n",
"sampled_train_x_non_z=train_x[non_zero_index]\n",
"sampled_train_y_non_z=train_y[non_zero_index]\n",
"list_train_x.append(sampled_train_x_zero)\n",
"list_train_x.append(sampled_train_x_non_z)\n",
"list_train_y.append(sampled_train_y_zero)\n",
"list_train_y.append(sampled_train_y_non_z)\n",
"final_train_x=np.vstack(list_train_x)\n",
"final_train_y=np.concatenate(list_train_y,axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Normalizing\n",
"train_min=np.min(final_train_x)\n",
"train_max=np.max(final_train_x)\n",
"train_norm_x=(final_train_x-train_min)/train_max"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
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"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n",
"output shape (242, 242, 15, 15)\n"
]
}
],
"source": [
"#Validation set\n",
"import pickle\n",
"with open(\"/beegfs/ark576/Knee Cartilage Data/Validation Data/val_file_names\",'rb') as f:\n",
" val_list=pickle.load(f)\n",
"val_x,val_y=return_dataset(val_list,1)\n",
"val_norm_x=(val_x-train_min)/train_max"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"with open(\"train_x\",'wb') as f:\n",
" pickle.dump(\"train_x\",f)\n",
"with open(\"train_y\",'wb') as f:\n",
" pickle.dump(\"train_y\",f)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(30746100, 228)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_x.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
" criterion='gini', max_depth=None, max_features='auto',\n",
" max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
" min_impurity_split=None, min_samples_leaf=5,\n",
" min_samples_split=10, min_weight_fraction_leaf=0.0,\n",
" n_estimators=128, n_jobs=1, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Train a random forest classifier on trainset\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"random_forest = RandomForestClassifier(n_estimators=128,class_weight='balanced',min_samples_split=10,min_samples_leaf=5)\n",
"random_forest.fit(train_norm_x,final_train_y)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.98839120559447347"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Run the model for validation set\n",
"from sklearn import metrics\n",
"proba_val=random_forest.predict_proba(val_norm_x)[:,1]\n",
"score=metrics.roc_auc_score(val_y,proba_val)\n",
"score"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"fpr_val, tpr_val, thresholds_val=metrics.roc_curve(val_y,proba_val)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### AUC ROC Curve for validation dataset"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b832231bfd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(fpr_val,tpr_val)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing the prediction"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ratio of number of predicted 1/ true 1 = 2.45799553519\n",
"ration of number of predicted 0/ true 0 = 0.991883144492\n"
]
}
],
"source": [
"#For one image, some statistics\n",
"print(\"ratio of number of predicted 1/ true 1 = \",sum(pp)/sum(val_y))\n",
"print(\"ration of number of predicted 0/ true 0 = \",sum(pp==0)/sum(val_y==0))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"reshaped_pp=pp.reshape(7,15,121,121)\n",
"reshaped_val_y=val_y.reshape(7,15,121,121)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2ad54e67c710>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5gfd0n+3jwO9OqJ6TDOYinvs8/02ferwzU1LTLHz1kDTjDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1/S9bb/rkUAUQcwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e59bb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(reshaped_val_y[3,8,:,:])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2ad54e6da320>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ZxAWufxrX0Hqsd9Zz12M9/3qmMQB1AQMxb2cwQPXvwPt6uP5vM+gqfhs43P15O/AbDAYVjwMPAuumXNeb+eVgZm+1AK8BDnU/n88Ba3uu568YhNUjwD8wWHc0tXqAuxmMj5xj0OO6dbHrA+/r3tvHgLdNqZ4TDMYinn0//9049TgzU1LTLHz0kDTjDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1/R8c7E4jajkFvwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e6297f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(reshaped_pp[3,8,:,:])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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NwqOHpBlnUEhqMigkNRkUkpoMCklNBoWkJoNCUpNBIanp/wCbDf7TqGfY1AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54eac62b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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q+kjfpn3Atm55G72xi5Gqqp1Vta568/K3Ap+vqlsmUUtXz2ng6SSv65o205uiP5F66I0FXJNkRff/tpne4Oqk6nnRbOffB2xNsizJei7w/qdh9d2P9a566f1YL7+ecQxAXcBAzA30Bqj+DfjABM7/m/S6il8HDnf/bgB+id6g4nHgEWDVmOt6Kz8dzJxYLcCVwKHu7/P3wMoJ13MHvbB6Avhbevcdja0e4D564yPn6PW4bp3r/MAHutf2MeAdY6rnBL2xiBdfz38zTD3OzJTUNA2XHpKmnEEhqcmgkNRkUEhqMigkNRkUkpoMCklNBoWkpv8D971xM2zAOa0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ec1edd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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ZkpqWw6OHpGXOoJDUZFBIajIoJDUZFJKaDApJTQaFpCaDQlLT/wGb9fk7GR/FXgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ec6cef0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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GHK2qjwytOgDs6J7vYDB2MVZVtbuqNtRgXv524EtVdcM0aunqOQU8nuSVXdNWBlP0p1IPg7GAK5Nc1P27bWUwuDqtep6z0PEPANuTrEqykXO8/6mvofux3lXPvx/rhdcziQGocxiIeQeDAap/Bz4wheP/DoOu4reBI92fdwC/wWBQ8RhwP7B2wnW9hV8NZk6tFuB1wOHu/fk8sGbK9byfQVg9BPwDg/uOJlYPcAeD8ZEzDHpcN57t+MAHup/tR4G3T6ie4wzGIp77ef67PvU4M1NS0yycekiacQaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1GRSSmv4fI6x4l1m7lLwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ed198d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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lNc3CVw9JM86ikNRkUUhqsigkNVkUkposCklNFoWkJotCUtP/A/rJ8KfdF8SQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ed00f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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jwJNJXtk1bWBwif5U6mEwFnBtkku6f7cNDAZXp1XP8+Y7/y5gU5JlSa7iLOc/9TU0H+ud9cL5WOdezyQGoM5iIObtDAao/h14/xTO/9sMuorfBPZ3f94O/BqDQcXDwMPAygnX9WZ+MZg5tVqAq4F93evzT8CKKdfzPgZh9Rjw9wzmHU2sHuAeBuMjpxj0uG490/mB93c/24eAt02oniMMxiKe/3n+uz71eGWmpKZZ+OghacYZFJKaDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGr6f/cvYGUaCNWcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ec06320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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RSGqyKCQ1WRSSmiwKSU0WhaQmi0JSk0Uhqen/APTo8wlACq/KAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ea82a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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q5mnU0tVzGng8ycu6oa0MLtGfSj0M5gKuSXJR9/e2lcHk6rTqec5Cx98PbE+yJsmVnOf9T30N3Y/1jnr+/Vi/ej2TmIA6j4mYtzGYoPp34L1TOP5vM2gVvwUc7v68DfgNBpOKx4EHgfUTrutN/HIyc2q1AK8GDnU/n88C66Zcz18xCKtHgH9gcN/RxOoB7mIwP/IMg47rlnMdH3hv99o+Brx1QvWcYDAX8dzr+e/61OOVmZKaZuGth6QZZ1BIajIoJDUZFJKaDApJTQaFpCaDQlKTQSGp6f8AXw9NwU17kRoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e7d7518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e9ca668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Odn/gvd3P9mHgrTOq5yijsYhnf57/pk89zsyU1DQPjx6S5pxBIanJoJDUZFBIajIoJDUZFJKaDApJTQaFpKb/BeKq9LpVa2A4AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ec06710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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TnR94d/fdfhh444zqOcJoLOKZ7/Pf9anHJzMlNc3DpYekOWdQSGoyKCQ1GRSSmgwKSU0GhaQmg0JSk0Ehqen/APTiAcO4PkQcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e91fcf8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Y5P1PfQ3dj/X2evb9WM+9nklMQC1iIuatDCao/gN43xSO/7sMWsVvAIe7f28FfpPBpOIx4H5g9YTreiO/nMycWi3Aq4FD3dfnX4BLplzPexmE1UPAPzC472hi9QB3MJgfOcug47rxXMcH3te9t48Cb5lQPccZzEU8837+uz71eGWmpKZZOPWQNOMMCklNBoWkJoNCUpNBIanJoJDUZFBIajIoJDX9H9RPHlVehImCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ec9c5f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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bXKGoTU0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ebb4f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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gYPf38y/A2p7r+WsGYfUw8I8M5h1NrR7gLgb3R84y6HHdcq7zAx/sPttHgXdOqZ7jDO5FPP95/vtx6nFkpqSmWbj0kDTjDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1/T+FRwvTUNtPZAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ed2a828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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UZFBIavp/BDbcr9/DW9QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54eb31438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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55WTmYLUArwEOdH8/nwPWDFzPXzEKq4eBf2B03dHM6gHuZDQ/coZRx3XzuY4PvK97bx8B3jajeo4xmot45v38d33q8cxMSU3z8NFD0pwzKCQ1GRSSmgwKSU0GhaQmg0JSk0EhqcmgkNT0fz4TFHxiMA+GAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54eb26588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ed4bf28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Wrp6TwJNJXtU1bWZwi/5U6mEwFnB1kou6f7fNDAZXp1XPcxY6/j5ga5IVSdZzjvOf+hqaj/Wuev58rBdezyQGoM5hIOYdDAao/h34wBSO/9sMuorfBB7t/rwD+DUGg4pHgQeB1ROu6y38cjBzarUArwMOdu/PPwGrplzP+xmE1WPA3zOYdzSxeoC7GIyPnGHQ47r5bMcHPtB9to8Ab59QPccYjEU893n+uz71eGempKZZOPWQNOMMCklNBoWkJoNCUpNBIanJoJDUZFBIajIoJDX9P+4MULOwvQH/AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ea78588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e65e630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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cM9fxJ/bz3fc81h914TVwPdMQFF8DNiRZn+QyehMte8dZQHr3od8NHKmqD/at2gts615vozd3MVJVtbOq1lbvvvytwOeq6tZJ1NLVcwp4MsnLu6HN9G7Rn0g99OYCrk1yeffvtpne5Oqk6jlnruPvBbYmWZ5kPef5/NOw+p7Hens9/3msC69nHBNQ5zER81Z6E1T/Drx3Asf/LXqt4jeAQ91/bwV+md6k4jHgEWDVmOu6np9NZk6sFuA1wMHu+/NPwMoJ1/MeemH1GPD39J47Gls9wL305kfO0uu4bpvv+MB7u5/to8BbxlTPcXpzEed+nv9umHq8M1NS0zScekiacgaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1GRSSmv4f6vB8ppyrUJYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ea03240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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AAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e7c4908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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r06rneWc6/x5gc5JlSdZyjvOf+hqaj/WueuF8rBdfzyQGoM5hIOYdDAao/hX4wBTO/1sMuorfBg50f94B/BqDQcUjwMPAygnX9RZ+OZg5tVqAq4H93evzeWDFlOt5P4Owegz4OwbzjiZWD3APg/GR0wx6XLee7fzAB7r39mHg7ROq5yiDsYjn389/06ce78yU1DQLlx6SZpxBIanJoJDUZFBIajIoJDUZFJKaDApJTQaFpKb/BR5+QWh/kNuqAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e9f0c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2ad4e16beba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Ex7Tq2Qx8paqeqaqTwF3AG6ZYz/NOdfypfX8P3Y/1x114jVzPLATF14GNSTYkOY/BRMu+SRaQwXXotwIHq+rDQ6v2Adu7x9sZzF2MVVXtqqp1Nbgufxvwhaq6fhq1dPUcA55M8opuaAuDS/SnUg+DuYArklzQ/b9tYTC5Oq16nneq4+8DtiVZmWQDp3n/U19D92O9o154P9aZ1zOJCajTmIh5G4MJqn8H3j+F4/8Og1bxm8DD3b+3Ab/BYFLxEPAAsHrCdb2JX01mTq0W4DLgQPf1+Wdg1ZTreR+DsHoE+HsG9x1NrB7gdgbzIycZdFw3LHR84P3d9/bjwFsnVM9hBnMRz38//22ferwyU1LTLJx6SJpxBoWkJoNCUpNBIanJoJDUZFBIajIoJDUZFJKa/hdB5YHdV+mMYwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e9e6908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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UkposCklN/w9DIuk6HmDUAwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54eaf9518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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4IrB+wnW9hV8MZk6tFuBK4FD39/OPwLop1/MXDMLqEeDvGKw7mlg9wJ0MxkfOMuhx3bzU9YH3d9/tx4C3T6ie4wzGIp77Pv9Nn3qcmSmpaRZuPSTNOINCUpNBIanJoJDUZFBIajIoJDUZFJKaDApJTf8Pj5wjpGHQqQEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54eca05f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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cQSGpyaCQ1GRQSGoyKCQ1GRSSmgwKSU0GhaSm/wPCi+vH9wvScQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e98ef98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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agKuAg93X55+BtT3X81cMwuox4B8YrDuaWj3AXQzGR84y6HHdcr77Ax/sXttHgXdOqZ7jDMYinn89/9049TgzU1LTLLz1kDTjDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1/T82Pih7sHZcIAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54e9ad828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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5gfd0n+3jwO9OqJ6TDOYinvs8/02ferwzU1LTLHz1kDTjDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1/S9bb/rkUAUQcwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ebad588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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ZxAWufxrX0Hqsd9Zz12M9/3qmMQB1AQMxb2cwQPXvwPt6uP5vM+gqfhs43P15O/AbDAYVjwMPAuumXNeb+eVgZm+1AK8BDnU/n88Ba3uu568YhNUjwD8wWHc0tXqAuxmMj5xj0OO6dbHrA+/r3tvHgLdNqZ4TDMYinn0//9049TgzU1LTLHz0kDTjDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGoyKCQ1/R8c7E4jajkFvwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54eaba588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": 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AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ec8e080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Av2fw3NHU6gHuYTA+coZBj+vW850feH/32T4CvG1K9RxjMBZx9vP8t+PU48xMSU2zcOkhacYZFJKaDApJTQaFpCaDQlKTQSGpyaCQ1GRQSGr6P3Cm/uuigbVjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad54ed19710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
}
],
"source": [
"i=[4,6,5,3]\n",
"j=[7,5,8,6]\n",
"for a in i:\n",
" for b in j:\n",
" plt.imshow(reshaped_val_y[a,b,:,:])\n",
" plt.show()\n",
" plt.imshow(reshaped_pp[a,b,:,:])\n",
" plt.show()\n",
" print(\"-----------------------------\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing Trained Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"trn_x_mean=np.mean(train_x)\n",
"trn_x_std=np.std(train_x)\n",
"train_norm_x=(train_x-trn_x_mean)/trn_x_std\n",
"random_forest2 = RandomForestClassifier(n_estimators=128,class_weight='balanced',min_samples_split=20,min_samples_leaf=10)\n",
"random_forest2.fit(train_norm_x,train_y)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"random_stride1=pickle.load(open(\"/home/cvh255/Cartilage/jobsubmission/random_stride1\",'rb'))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"val_trained=random_stride1.predict(val_norm_x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_trained=random_stride1.predict(train_norm_x)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97ba642400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1801048>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c189d588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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QkNQwFCQ1DAVJDUNBUsNQkNQwFCQ1/g9WJcXqyKxmHAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c18f9588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c18bcdd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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EnmB5g9o/ZzW/j+VdgyeAx7ufO+a9r8f98YxGSY1Z7z5ImjOGgqSGoSCpYShIahgKkhqGgqSGoSCpYShIavwf2AXPPu5fJRcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c19b9588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1957828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1a5e6d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c19ff7b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1b098d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1b51630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97b9b365f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1aee470>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97ba1d6e80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97ba656c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c18124e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1b944e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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UMBQkNQwFSY3/A0lFy3gBqlK3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c180f7b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1907f28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADm1JREFUeJzt3V+oHOd5x/HvU1k+xo4LVp0KRRa1ArqRA1XCQS7UBBfT2DEFOTdGuSiCmqoXTppAeyEnFzEUQ1pIeueAQkxESa2KJMa6MDW2CJhCiS0HxZbkKlb8B0uWpaQJxPRCsZWnF2dOu+/R2bP/ZnZmjr4fOOzsO7N7Ho3O/PZ9353ZjcxEkpb9XtsFSOoWQ0FSwVCQVDAUJBUMBUkFQ0FSobFQiIj7IuJMRJyNiANN/R5J9YomzlOIiA3Az4A/B84BLwGfz8zTtf8ySbVqqqewGzibmW9k5m+Bw8Cehn6XpBpd19DzbgXeGbh/Drhz2MbXx0LewE0NlSIJ4H1+/cvM/Oio7ZoKhZEiYj+wH+AGbuTOuKetUqRrwvP5/bfH2a6p4cN5YNvA/duqtv+TmQczczEzFzey0FAZkibVVCi8BOyIiO0RcT2wFzja0O+SVKNGhg+Z+WFEfAF4FtgAPJGZp5r4XZLq1dicQmY+AzzT1PNLaoZnNEoqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlw3SwPjoi3gPeBK8CHmbkYEZuAfwNuB94CHszMX89WpqR5qaOn8GeZuSszF6v7B4BjmbkDOFbdl9QTTQwf9gCHquVDwAMN/A5JDZk1FBJ4PiJejoj9VdvmzLxQLb8HbJ7xd0iao5nmFIC7MvN8RPwh8FxE/NfgyszMiMjVHliFyH6AG7hxxjIk1WWmnkJmnq9uLwFPAbuBixGxBaC6vTTksQczczEzFzeyMEsZkmo0dShExE0RcfPyMvAZ4CRwFNhXbbYPeHrWIiXNzyzDh83AUxGx/Dz/mpn/HhEvAUci4iHgbeDB2cuUNC9Th0JmvgH88Srt/w3cM0tRktrjGY2SCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCoaCpIKhIKlgKEzp2XdPtF2C1AhDQVLBUJiBvQWtR4bCDO792K62S5BqZyhMyUDQemUoSCoYCpIKhoKkgqEgqWAoSCoYCpIKhkJNPJFJ64WhIKlgKEgqGAqSCoZCTTztub+cDyoZCpIKhoKuafYSrmYo6JrmsO9qhoKuafYUrmYoNMA/tP4Y7Cn4/7ZkZChExBMRcSkiTg60bYqI5yLi9er2loF1j0TE2Yg4ExH3NlV4l9kl7Sf/35aM01P4LnDfirYDwLHM3AEcq+4TETuBvcAd1WMej4gNtVXbE77i9IthUBoZCpn5AvCrFc17gEPV8iHggYH2w5l5OTPfBM4Cu2uqtVcMBvXVtHMKmzPzQrX8HrC5Wt4KvDOw3bmq7ZphGKjvZp5ozMwEctLHRcT+iDgeEcc/4PKsZUiqybShcDEitgBUt5eq9vPAtoHtbqvarpKZBzNzMTMXN7IwZRmS6jZtKBwF9lXL+4CnB9r3RsRCRGwHdgAvzlZifzh00Hpw3agNIuJJ4G7g1og4B3wN+DpwJCIeAt4GHgTIzFMRcQQ4DXwIPJyZVxqqvdOc0VZfjQyFzPz8kFX3DNn+MeCxWYpaD55994TBUIPl3tc4+3Kwp+a+n55nNGrdMRBmYyioFyaZr3FuZzaGQoP845zNyuHAs++ecJ/OgaGgzrr3Y7uKocCkwwIDZDqGQo0cyzZnsJcweDvswHeid3qGgnpj+SBfHkosWxkMK3sYmoyh0DC7sPVZ2VNYbd2w9RqfoaBeG9YjMBimZyjUzG7rfKy1nx0+zMZQUG954DfDUKiZ3db6efDPl6FQM/+A58sQrp+hoF6Z9MIoTc5QUO/YG2uWoaBeWA4CewHNMxTUC562PD+Gwhz46taMtfar+3x6hkIDfEWr3/L1DoPDCPdzMwyFOfGVa3orr4YcdgHUypBwn0/HUJgj/0gnt3KfjdNDsAcxG0NBvTPqA1odWszGUGiIf5TNWevtSff77AwF9dawXoJmYyg0aOXnC/oqNplpPmrNycbZGQrqnZUfxzZo1KcyaTRDoWH2DqYz7YHsJzHNzlCYA4OhXivPWRjngPf/YHyGgjppmnMR/LKYehgKWhcm+SJarc1QUOeMesVf7cAfdk2EITE5Q0G9N+xtyMEvj9H4DAV1yrRzAl5GXR9DQZ0y6QTjOL0BewqTMRQa5CuU+shQUK+M87ajPYPZGArqvVGXUmsy143aICKeAP4CuJSZn6jaHgX+GvhFtdlXMvOZat0jwEPAFeBvM/PZBurWOjPswB7n8miDoF7j9BS+C9y3Svs/Z+au6mc5EHYCe4E7qsc8HhEb6iq2b9a6cOdaNsnXxk9ypanhUI+RPYXMfCEibh/z+fYAhzPzMvBmRJwFdgP/OXWFWldWBsLK4FzrxCTNxyxzCl+MiFci4omIuKVq2wq8M7DNuartmuUfdGnw1X5UIIyaVLQX1oyRPYUhvgX8A5DV7TeAv5rkCSJiP7Af4AZunLIM9d20w4HBXobBW6+pegqZeTEzr2Tm74BvszREADgPbBvY9LaqbbXnOJiZi5m5uJGFacpQz9T5ym4gNGeqUIiILQN3PwecrJaPAnsjYiEitgM7gBdnK1HrRR0HsG8/Nm+ctySfBO4Gbo2Ic8DXgLsjYhdLw4e3gL8ByMxTEXEEOA18CDycmVeaKV3XopWf5Gww1C8ys+0a+P3YlHfGPW2XoR4YNgQxHEZ7Pr//cmYujtpu2olGaaQmvpdhtXM/DIR6GQpqRFNvFzq52DyvfVDt1jpwV/tuyEkYCM1zTkFzMWpi0B5A85xTUKes9f2P6haHD+oMA6MbDAXN1VpXOvo9kN3gnIJa5dfJz8+4cwr2FNSKta6AtIfQLica1ai1rlUY9tVvapc9Bc3Nyt6BQ4dusqeguRn369wMhnYZCmqUB3j/OHyQVDAUJBUMBUkFQ0GNWO1qSN9u7AcnGjWTwasfR10W7aRjPxgKPdXWpcbjnIXowd9vDh96qssHnkOFfjMU1oHlg7DJg3Ha5zUc+sdQWAeGXXK82gG51rphRn0bk2cnri9eOr2OzPKqvNbFScPWjfO1buqOcS+dNhTWMT8UVYP8PIVr3CSBMBgGzgHIUFinJnnV9+1EDTIU1rFxD/C1PjdR1x5PXlrnPNg1KXsKkgqGgqSCoSCpYChIKhgKkgqGgqSCoSCpYChIKowMhYjYFhE/iojTEXEqIr5UtW+KiOci4vXq9paBxzwSEWcj4kxE3NvkP0BSvcbpKXwI/F1m7gT+BHg4InYCB4BjmbkDOFbdp1q3F7gDuA94PCI2NFG8pPqNDIXMvJCZP6mW3wdeA7YCe4BD1WaHgAeq5T3A4cy8nJlvAmeB3XUXLqkZE80pRMTtwCeBHwObM/NCteo9YHO1vBV4Z+Bh56o2ST0wdihExEeAHwBfzszfDK7LpU9qmejTWiJif0Qcj4jjH3B5kodKatBYoRARG1kKhO9l5g+r5osRsaVavwW4VLWfB7YNPPy2qq2QmQczczEzFzeyMG39kmo2zrsPAXwHeC0zvzmw6iiwr1reBzw90L43IhYiYjuwA3ixvpIlNWmcz1P4U+AvgVcjYvkjer4CfB04EhEPAW8DDwJk5qmIOAKcZumdi4cz80rtlUtqxMhQyMz/AGLI6lU/bTUzHwMem6EuSS3xjEZJBUNBUsFQkFQwFCQVDAVJBUNBUsFQkFQwFCQVDAVJBUNBUsFQkFQwFCQVDAVJBUNBUsFQkFQwFCQVDAVJBUNBUsFQkFQwFCQVDAVJBUNBUsFQkFQwFCQVDAVJBUNBUsFQkFQwFCQVDAVJBUNBUsFQkFQwFCQVIjPbroGI+AXwP8Av265lQrdizfPQt5q7Wu8fZeZHR23UiVAAiIjjmbnYdh2TsOb56FvNfat3JYcPkgqGgqRCl0LhYNsFTMGa56NvNfet3kJn5hQkdUOXegqSOqD1UIiI+yLiTEScjYgDbdczTES8FRGvRsSJiDhetW2KiOci4vXq9paWa3wiIi5FxMmBtqE1RsQj1X4/ExH3dqjmRyPifLWvT0TE/R2reVtE/CgiTkfEqYj4UtXe6X09tsxs7QfYAPwc+DhwPfBTYGebNa1R61vArSva/gk4UC0fAP6x5Ro/DXwKODmqRmBntb8XgO3V/8OGjtT8KPD3q2zblZq3AJ+qlm8GflbV1ul9Pe5P2z2F3cDZzHwjM38LHAb2tFzTJPYAh6rlQ8ADLdZCZr4A/GpF87Aa9wCHM/NyZr4JnGXp/2OuhtQ8TFdqvpCZP6mW3wdeA7bS8X09rrZDYSvwzsD9c1VbFyXwfES8HBH7q7bNmXmhWn4P2NxOaWsaVmPX9/0XI+KVanix3A3vXM0RcTvwSeDH9HdfF9oOhT65KzN3AZ8FHo6ITw+uzKV+YqffyulDjZVvsTSk3AVcAL7Rbjmri4iPAD8AvpyZvxlc16N9fZW2Q+E8sG3g/m1VW+dk5vnq9hLwFEvdv4sRsQWgur3UXoVDDauxs/s+My9m5pXM/B3wbf6/q92ZmiNiI0uB8L3M/GHV3Lt9vZq2Q+ElYEdEbI+I64G9wNGWa7pKRNwUETcvLwOfAU6yVOu+arN9wNPtVLimYTUeBfZGxEJEbAd2AC+2UN9Vlg+syudY2tfQkZojIoDvAK9l5jcHVvVuX6+q7ZlO4H6WZm9/Dny17XqG1PhxlmaPfwqcWq4T+APgGPA68DywqeU6n2Spu/0BS+PWh9aqEfhqtd/PAJ/tUM3/ArwKvMLSAbWlYzXfxdLQ4BXgRPVzf9f39bg/ntEoqdD28EFSxxgKkgqGgqSCoSCpYChIKhgKkgqGgqSCoSCp8L803N+4MA6sSQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c19ece80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1935f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADWpJREFUeJzt3V2oXWedx/Hvb9I0onagHZ0Q0zCNEIR6MVEOdWCKIB1N7U3qjcSLIReF3HREh5mLdLzxpuAIOncKlRbD4LRTfKG5kJY2CGVAbKPEtmknNtpKk6bNOAqWuYi1/ufirKP7OT0n52W/rLXP+X5gs9d+1tp7//dz9vrt51lrn3NSVUjSkj/ruwBJw2IoSGoYCpIahoKkhqEgqWEoSGpMLRSS3J7kXJLzSY5P63kkTVam8T2FJDuAnwEfBy4ATwOfqarnJ/5kkiZqWiOFW4DzVfWLqvod8BBweErPJWmCrpnS4+4FXhm5fQH4yGobX5td9Q7eNaVSJAG8wW9+VVXvXWu7aYXCmpIcA44BvIN38pHc1lcp0rbwRH37l+vZblrTh4vAvpHbN3Ztf1RV91XVQlUt7GTXlMqQtFHTCoWngQNJ9ie5FjgCnJzSc0maoKlMH6rq90n+AXgM2AE8UFVnp/FckiZrascUqur7wPen9fiSpsNvNEpqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlxzTh3TvIy8AbwFvD7qlpIcgPwn8BNwMvAp6vqN+OVKWlWJjFS+FhVHayqhe72ceBUVR0ATnW3Jc2JaUwfDgMnuuUTwJ1TeA5JUzJuKBTwRJIfJznWte2uqkvd8mvA7jGfQ9IMjXVMAbi1qi4m+Uvg8ST/PbqyqipJrXTHLkSOAbyDd45ZhqRJGWukUFUXu+vLwPeAW4DXk+wB6K4vr3Lf+6pqoaoWdrJrnDIkTdCmQyHJu5Jct7QMfAJ4DjgJHO02Owo8Mm6RkmZnnOnDbuB7SZYe5z+q6tEkTwMPJ7kL+CXw6fHLlDQrmw6FqvoF8NcrtP8vcNs4RUnqj99olNQwFCQ1DAVJDUNBUsNQkNQwFCQ1DAVJDUNBUsNQkNQwFCQ1DAVJDUNBUsNQkNQwFCQ1DAVJDUNBUsNQkNQwFCQ1DAVJDUNBUsNQkNQwFCQ1DAVJDUNBUsNQkNQwFCQ1DAVJDUNBUsNQkNQwFCbssVfP9F2CNBZDYYIMBG0FhsIEHXrfwb5LkMZmKEyQIwVtBYaCpIahIKlhKEhqrBkKSR5IcjnJcyNtNyR5PMmL3fX1I+vuSXI+ybkkh6ZVuKTpWM9I4ZvA7cvajgOnquoAcKq7TZKbgSPAB7v7fC3JjolVO2CjBxk94Kh5tmYoVNWTwK+XNR8GTnTLJ4A7R9ofqqorVfUScB64ZUK1SpqBzR5T2F1Vl7rl14Dd3fJe4JWR7S50bVue31HQVjH2gcaqKqA2er8kx5KcTnL6Ta6MW4akCdlsKLyeZA9Ad325a78I7BvZ7sau7W2q6r6qWqiqhZ3s2mQZkiZts6FwEjjaLR8FHhlpP5JkV5L9wAHgqfFKlDRL6zkl+SDwQ+ADSS4kuQv4EvDxJC8Cf9fdpqrOAg8DzwOPAndX1VvTKn7IPAOheXXNWhtU1WdWWXXbKtvfC9w7TlFbxWOvnvEApOaO32iU1DAUpsxphOaNoaC5YcDOhqEwA76ZJ8PjM7NhKMyAb2bNkzXPPkh9GR1hGayz40hBW4pTtfEZCjPgG3U27OfJMBQmaPkQ1yHvbC31t+EwHkNhipbenIbDxm12x7avx2coaMsxGMZjKMyAw9mNsb/6ZShocDw20y+/p6DB8VhMvxwpSGoYChO20qebn3iaJ04fpsgw0DxypDAlBsJ47L/+GApT4Bt6PPZfvwyFKfA8u+aZoTBhBoLmnaEwYf5SjuadoTBhhsFw+LPYHENBUsNQkNQwFCbI4aq2AkNBUsNQmKBD7zv4xws4ctB8MhSmxGDQvDIUpsiv6/bL/t8cQ0FSw1CQ1DAUJDUMhRnwYKPmiaEwRYbB5thv/TIUZsQ3+toee/UMj716xrMGPVszFJI8kORykudG2r6Y5GKSM93ljpF19yQ5n+RckkPTKnweLH9zGwxXN/rFL/VnPSOFbwK3r9D+b1V1sLt8HyDJzcAR4IPdfb6WZMekipU0fWuGQlU9Cfx6nY93GHioqq5U1UvAeeCWMeqTNGPjHFP4bJJnuunF9V3bXuCVkW0udG3blsNhzZvNhsLXgfcDB4FLwFc2+gBJjiU5neT0m1zZZBnzwWDQPNlUKFTV61X1VlX9AfgGf5oiXAT2jWx6Y9e20mPcV1ULVbWwk12bKWMuebBRQ7epUEiyZ+Tmp4ClMxMngSNJdiXZDxwAnhqvxK1hdLRgMGjI1nNK8kHgh8AHklxIchfw5STPJnkG+BjwjwBVdRZ4GHgeeBS4u6remlr1c8Zg0DxY839JVtVnVmi+/yrb3wvcO05R2j5Gw9FjL8PgNxpnbPloYTuOGJZe8/LXvh37YogMhR74ibhyANgvw2Ao9GQ7H19Yaec3EIbDUOiRU4k/2e6vf0gMhYFxx1DfDIWebdffDFzpdW/HfhiiNU9JajYOve/gthglrPYaDYThcKQwQNthfj06UjAQhsWRwoBsp51jNPT8a0vDYihoptz5h8/pg2ZiO0yJtgpDQTNnQAyb0wdN1dV+v2Fp2SnFsDhS0FRsZId31DAsjhS0ps18oq/n9xs86zBMhoLWbb3hsN5PfgNhmAyFbWjpE3r0k3ojQ/jlfxjF4f/Wkqrquwb+PDfUR3Jb32VsWUPaaR0d9OeJ+vaPq2phre0cKWwD436ar3X/0fXu9PPPUNgmlu+4y3fy5dOJJVfb2Ue3Nwy2DqcP29BKxxS09a13+uD3FLYhP911NYaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKmxZigk2ZfkB0meT3I2yee69huSPJ7kxe76+pH73JPkfJJzSQ5N8wVImqz1jBR+D/xTVd0M/A1wd5KbgePAqao6AJzqbtOtOwJ8ELgd+FqSHdMoXtLkrRkKVXWpqn7SLb8BvADsBQ4DJ7rNTgB3dsuHgYeq6kpVvQScB26ZdOGSpmNDxxSS3AR8CPgRsLuqLnWrXgN2d8t7gVdG7naha5M0B9YdCkneDXwH+HxV/XZ0XS3+occN/bHHJMeSnE5y+k2ubOSukqZoXaGQZCeLgfCtqvpu1/x6kj3d+j3A5a79IrBv5O43dm2NqrqvqhaqamEnuzZbv6QJW8/ZhwD3Ay9U1VdHVp0EjnbLR4FHRtqPJNmVZD9wAHhqciVLmqb1/N+HvwX+Hng2ydI/C/gX4EvAw0nuAn4JfBqgqs4meRh4nsUzF3dX1VsTr1zSVKwZClX1X0BWWb3iP2uoqnuBe8eoS1JP/EajpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpIahIKlhKEhqGAqSGqmqvmsgyf8A/wf8qu9aNug9WPMszFvNQ633r6rqvWttNIhQAEhyuqoW+q5jI6x5Nuat5nmrdzmnD5IahoKkxpBC4b6+C9gEa56Neat53uptDOaYgqRhGNJIQdIA9B4KSW5Pci7J+STH+65nNUleTvJskjNJTndtNyR5PMmL3fX1Pdf4QJLLSZ4baVu1xiT3dP1+LsmhAdX8xSQXu74+k+SOgdW8L8kPkjyf5GySz3Xtg+7rdauq3i7ADuDnwPuBa4GfAjf3WdNVan0ZeM+yti8Dx7vl48C/9lzjR4EPA8+tVSNwc9ffu4D93c9hx0Bq/iLwzytsO5Sa9wAf7pavA37W1Tbovl7vpe+Rwi3A+ar6RVX9DngIONxzTRtxGDjRLZ8A7uyxFqrqSeDXy5pXq/Ew8FBVXamql4DzLP48ZmqVmlczlJovVdVPuuU3gBeAvQy8r9er71DYC7wycvtC1zZEBTyR5MdJjnVtu6vqUrf8GrC7n9KuarUah973n03yTDe9WBqGD67mJDcBHwJ+xPz2daPvUJgnt1bVQeCTwN1JPjq6shbHiYM+lTMPNXa+zuKU8iBwCfhKv+WsLMm7ge8An6+q346um6O+fpu+Q+EisG/k9o1d2+BU1cXu+jLwPRaHf68n2QPQXV/ur8JVrVbjYPu+ql6vqreq6g/AN/jTUHswNSfZyWIgfKuqvts1z11fr6TvUHgaOJBkf5JrgSPAyZ5repsk70py3dIy8AngORZrPdptdhR4pJ8Kr2q1Gk8CR5LsSrIfOAA81UN9b7O0Y3U+xWJfw0BqThLgfuCFqvrqyKq56+sV9X2kE7iDxaO3Pwe+0Hc9q9T4fhaPHv8UOLtUJ/AXwCngReAJ4Iae63yQxeH2myzOW++6Wo3AF7p+Pwd8ckA1/zvwLPAMizvUnoHVfCuLU4NngDPd5Y6h9/V6L36jUVKj7+mDpIExFCQ1DAVJDUNBUsNQkNQwFCQ1DAVJDUNBUuP/AXHo+xx/McWlAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1a57ef0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c191dfd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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BV1jaoDZ1rOa7WJoavAKcqH7u7Xpfj/rjGY2SCm1PHyR1jKEgqWAoSCoYCpIKhoKkgqEgqWAoSCoYCpIK/wcQNaX69xz9RQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1c54358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1a09fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADOtJREFUeJzt3U+IXfd5h/HnW1mWieOCVadClkUtg1qQF1XCIBdqQoqbyPFGzsYoi6KFQV24IYF2ITeLemNIS5NuSgIKMVFLatUkMdYixNgiYAolthwUW5KrWPEfLFmWmjoQ04ViK28Xc9Tcn6TRjGbun3Pk5wPDnPu79859c819cs49d0apKiTpgt+Z9QCS+sUoSGoYBUkNoyCpYRQkNYyCpMbEopDk3iTHk5xIsmdSjyNpvDKJzykkWQX8DPg0cBJ4Afh8VR0b+4NJGqtJ7SlsA05U1WtV9WtgP7BjQo8laYyum9DP3QC8NXL5JHDXQje+PmvqBm6c0CiSAN7jl7+oqo8tdrtJRWFRSXYDuwFu4CPclXtmNYr0ofBsfffNpdxuUocPp4CNI5dv69b+X1Xtraq5qppbzZoJjSHpak0qCi8Am5NsSnI9sBM4MKHHkjRGEzl8qKoPkvwV8DSwCnisqo5O4rEkjdfE3lOoqh8AP5jUz5c0GX6iUVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpcd1K7pzkDeA94DzwQVXNJVkL/DtwO/AG8EBV/XJlY0qalnHsKfxZVW2tqrnu8h7gYFVtBg52lyUNxCQOH3YA+7rtfcD9E3gMSROy0igU8GySF5Ps7tbWVdXpbvsdYN0KH0PSFK3oPQXg7qo6leT3gWeS/NfolVVVSepyd+wishvgBj6ywjEkjcuK9hSq6lT3/SzwJLANOJNkPUD3/ewC991bVXNVNbeaNSsZQ9IYLTsKSW5MctOFbeAzwBHgALCru9ku4KmVDilpelZy+LAOeDLJhZ/zb1X1wyQvAE8keRB4E3hg5WNKmpZlR6GqXgP++DLr/wPcs5KhJM2On2icgqffPjzrEaQlMwpTsP3WrbMeQVoyoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jMEF+klFDZBQkNYzChFzYS3BvQUNjFCbAEGjIjIKkhlGQ1DAKY+ahg4bOKEyBodCQGAVJDaMgqWEUJDWMwhj53oGuBUZBUsMoSGoYhTHyT7nrWmAUJDWMwhS4B6EhMQpjZgA0dEZhAkbDsP3WrZ6q1KAYhQm4OALuPWhIjMIEGQMNkVGYsKffPuzhgwbFKEzQaAwMg4bCKIzZlV78hkFDYBSmzDCo74yCpMaiUUjyWJKzSY6MrK1N8kySV7vvN49c93CSE0mOJ9k+qcH7yjMOGrql7Cl8G7j3orU9wMGq2gwc7C6TZAuwE7izu8/Xk6wa27SSJm7RKFTVc8C7Fy3vAPZ12/uA+0fW91fVuap6HTgBbBvTrJKmYLnvKayrqtPd9jvAum57A/DWyO1OdmuSBmLFbzRWVQF1tfdLsjvJoSSH3ufcSsfolcXeV/AMhPpsuVE4k2Q9QPf9bLd+Ctg4crvburVLVNXeqpqrqrnVrFnmGJLGbblROADs6rZ3AU+NrO9MsibJJmAz8PzKRhwm9xY0VEs5Jfk48J/AHyU5meRB4CvAp5O8Cvx5d5mqOgo8ARwDfgg8VFXnJzV833l6UkN03WI3qKrPL3DVPQvc/lHg0ZUMJWl2/ETjhLm3oKExClOy/datlwTC9xXUR0Zhxvx7C+obozBGC724PYTQkBiFMVrKi99AqO+MwpQttDfhIYT6wihM2eXecLzAMKgPjIKkhlGYkYX2GNxb0KwZhR4yDJolozBj7jGob4yCpIZR6An3FtQXRqFHDIP6wCj0jGHQrBmFgTAMmhajIKlhFHrIj0Frlhb9c2yajdEwXOmftPe3LjVu7ikMwJVe+O49aNyMwkBc6bcrpXEyCgOz0ClL/6ybxsUoXGMuhMFAaLky/09BztbvZm3dlcv+MxJawEpf9B6KfPg8W999sarmFrudZx8G6sKLerlx8CyGFuLhw8CN48VsEDTKKFwDfFFrnDx8uEZc6Replnrd6CHF9lu38vTbhycenGk8hq6OewrXsCt9tuHi60ZfnBd/XynPhAyLZx80Ve4ZzM5Szz64p6CpGf0MxeiHrdyT6BejoKm50qGM+sMoaKoMQP959mHCPIa+lM9Hv7mnIKlhFCbM/1fU0CwahSSPJTmb5MjI2iNJTiU53H3dN3Ldw0lOJDmeZPukBh8Kf6VZQ7OU9xS+Dfwz8C8Xrf9TVf3j6EKSLcBO4E7gVuDZJH9YVefHMOsguaegoVl0T6GqngPeXeLP2wHsr6pzVfU6cALYtoL5JE3ZSt5T+EKSl7rDi5u7tQ3AWyO3OdmtSRqI5UbhG8AdwFbgNPDVq/0BSXYnOZTk0PucW+YYksZtWVGoqjNVdb6qfgN8k98eIpwCNo7c9LZu7XI/Y29VzVXV3GrWLGcMSROwrCgkWT9y8XPAhTMTB4CdSdYk2QRsBp5f2YiSpmnRsw9JHgc+BdyS5CTwd8CnkmwFCngD+EuAqjqa5AngGPAB8NCH+cyDNET+6rT0IeGvTktaFqMgqWEUJDWMgqSGf09BM3HxX45WfxgFzYQh6C8PHyQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqLBqFJBuT/CjJsSRHk3yxW1+b5Jkkr3bfbx65z8NJTiQ5nmT7JP8HSBqvpewpfAD8dVVtAf4EeCjJFmAPcLCqNgMHu8t01+0E7gTuBb6eZNUkhpc0fotGoapOV9VPuu33gFeADcAOYF93s33A/d32DmB/VZ2rqteBE8C2cQ8uaTKu6j2FJLcDHwd+DKyrqtPdVe8A67rtDcBbI3c72a1JGoAlRyHJR4HvAV+qql+NXldVBdTVPHCS3UkOJTn0Pueu5q6SJmhJUUiymvkgfKeqvt8tn0myvrt+PXC2Wz8FbBy5+23dWqOq9lbVXFXNrWbNcueXNGZLOfsQ4FvAK1X1tZGrDgC7uu1dwFMj6zuTrEmyCdgMPD++kSVN0nVLuM2fAn8BvJzkcLf2t8BXgCeSPAi8CTwAUFVHkzwBHGP+zMVDVXV+7JNLmohFo1BV/wFkgavvWeA+jwKPrmAuSTPiJxolNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKmRqpr1DCT5b+B/gV/MepardAvOPA1Dm7mv8/5BVX1ssRv1IgoASQ5V1dys57gazjwdQ5t5aPNezMMHSQ2jIKnRpyjsnfUAy+DM0zG0mYc2b6M37ylI6oc+7SlI6oGZRyHJvUmOJzmRZM+s51lIkjeSvJzkcJJD3draJM8kebX7fvOMZ3wsydkkR0bWFpwxycPd8348yfYezfxIklPdc304yX09m3ljkh8lOZbkaJIvduu9fq6XrKpm9gWsAn4O3AFcD/wU2DLLma4w6xvALRet/QOwp9veA/z9jGf8JPAJ4MhiMwJbuud7DbCp+++wqiczPwL8zWVu25eZ1wOf6LZvAn7Wzdbr53qpX7PeU9gGnKiq16rq18B+YMeMZ7oaO4B93fY+4P4ZzkJVPQe8e9HyQjPuAPZX1bmqeh04wfx/j6laYOaF9GXm01X1k277PeAVYAM9f66XatZR2AC8NXL5ZLfWRwU8m+TFJLu7tXVVdbrbfgdYN5vRrmihGfv+3H8hyUvd4cWF3fDezZzkduDjwI8Z7nPdmHUUhuTuqtoKfBZ4KMknR6+s+f3EXp/KGcKMnW8wf0i5FTgNfHW241xeko8C3wO+VFW/Gr1uQM/1JWYdhVPAxpHLt3VrvVNVp7rvZ4Enmd/9O5NkPUD3/ezsJlzQQjP29rmvqjNVdb6qfgN8k9/uavdm5iSrmQ/Cd6rq+93y4J7ry5l1FF4ANifZlOR6YCdwYMYzXSLJjUluurANfAY4wvysu7qb7QKems2EV7TQjAeAnUnWJNkEbAaen8F8l7jwwup8jvnnGnoyc5IA3wJeqaqvjVw1uOf6smb9TidwH/Pv3v4c+PKs51lgxjuYf/f4p8DRC3MCvwccBF4FngXWznjOx5nf3X6f+ePWB680I/Dl7nk/Dny2RzP/K/Ay8BLzL6j1PZv5buYPDV4CDndf9/X9uV7ql59olNSY9eGDpJ4xCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkxv8B+S21TXGAIa8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1cb5f60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9795837c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADLVJREFUeJzt3U2sXPV9h/HnW2OMQqiES2o5xipG8sYs6kRXplJRRIUaEzYmG+QuKi+Q3AWNEqldmGYRNpHSSkl3RHIUFKtKca0kCC9QEViRUDcBEznENnFweBE2xm5KpKAuHHB+XdzjZv7mvt85d86Y5yNdzZn/vP0YNI/OOXd0napCkq76o0kPIGlYjIKkhlGQ1DAKkhpGQVLDKEhq9BaFJPcnOZPkbJIDfb2OpPFKH99TSLIO+CXw18A54CXgb6rq9NhfTNJY9bWnsAs4W1WvV9XvgMPAnp5eS9IY3dDT824B3h65fg64e74735gNdRM39zSKJID3+c2vq+pTi92vrygsKsl+YD/ATXyCu3PfpEaRPhaerx+8tZT79XX4cB7YOnL99m7t/1XVwaqaqaqZ9WzoaQxJy9VXFF4CtifZluRGYC9wtKfXkjRGvRw+VNWHSf4eeBZYBzxRVaf6eC1J49XbOYWqegZ4pq/nl9QPv9EoqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNS4YTUPTvIm8D5wBfiwqmaSbAT+A7gDeBN4qKp+s7oxJa2Vcewp/FVV7ayqme76AeBYVW0HjnXXJU2JPg4f9gCHuu1DwIM9vIaknqw2CgU8n+TlJPu7tU1VdaHbfhfYtMrXkLSGVnVOAbinqs4n+VPguSS/GL2xqipJzfXALiL7AW7iE6scQ9K4rGpPoarOd5eXgKeAXcDFJJsBustL8zz2YFXNVNXMejasZgxJY7TiKCS5OcktV7eBzwMngaPAvu5u+4CnVzukpLWzmsOHTcBTSa4+z79X1X8meQk4kuRh4C3godWPKWmtrDgKVfU68OdzrP8PcN9qhpI0OX6jUVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRR68Ow7J3j2nROTHkNaEaPQI8OgaWQUxswQaNoZhTEyCLoeGAVJDaMgqWEUJDWMQo92f3rnpEeQls0oSGoYhR75JSZNI6PQMw8hNG2MgqSGUZDUMAo985yCpo1R6JnnFDRtjIKkhlGQ1DAKkhpGoWeeaNS0MQprwDBomhiFNWIYNC2MgqSGUZDUMApryEMITYNFo5DkiSSXkpwcWduY5Lkkr3WXt47c9miSs0nOJNnd1+CS+rGUPYXvAfdfs3YAOFZV24Fj3XWS7AD2And1j3k8ybqxTSupd4tGoapeAN67ZnkPcKjbPgQ8OLJ+uKouV9UbwFlg15hmlbQGVnpOYVNVXei23wU2ddtbgLdH7neuW5M0JVZ9orGqCqjlPi7J/iTHkxz/gMurHUPSmKw0CheTbAboLi916+eBrSP3u71b+4iqOlhVM1U1s54NKxxj+vgbCA3dSqNwFNjXbe8Dnh5Z35tkQ5JtwHbgxdWNKGkt3bDYHZI8CdwL3JbkHPA14BvAkSQPA28BDwFU1akkR4DTwIfAI1V1pafZJfUgs6cEJuuPs7Huzn2THmMsrh4ejP7FpbkOGfyLTFprz9cPXq6qmcXu5zca18BcAfDcgobKKEhqGIUx2/3pnXPuGbi3oGlhFHrgh13TzCj0YDknEQ2IhsYorCF/46BpYBQGwL0FDYlRGAjDoKEwCmtsoUOIZ985YRw0cUZhAjy3oCEzChNiGDRURmGC5vuik4cQmiSjMACGQUNiFAbMMGgSjMJAzHeOwTBorRmFAfEcg4bAKEwJw6C1YhQGyEMJTZJRGKiFDiWMg/pkFAZuob2GocRhKHNoPIzCFFjoLzlN8gM51x+p1fQzClPi2g/etTFYyzgYg+ubUZgSz75zYsFDidE9h6UG4ur9Ri/n2x5lDK5v/rsPU2ahD/xSojF6fSV7FwZhei31330wClNquXFY7eGFMZh+S43Cov9snKbP6DG/MdByeU5hSs33PYZRSwnC1ecYfb5rL/Xx4uHDdWyhPQY/8B8/Hj4IaD/8hkBL4eHDdcwgaCWMgpZsru8w6PpjFLSouQLgnsf1yyhoQXN969EgXN880agFGYCPH/cUJDWMgqTGolFI8kSSS0lOjqw9luR8khPdzwMjtz2a5GySM0l29zW4pH4sZU/he8D9c6z/a1Xt7H6eAUiyA9gL3NU95vEk68Y1rKT+LRqFqnoBeG+Jz7cHOFxVl6vqDeAssGsV80laY6s5p/ClJK90hxe3dmtbgLdH7nOuW5M0JVYahW8DdwI7gQvAN5f7BEn2Jzme5PgHXF7hGJLGbUVRqKqLVXWlqn4PfIc/HCKcB7aO3PX2bm2u5zhYVTNVNbOeDSsZQ1IPVhSFJJtHrn4RuPqbiaPA3iQbkmwDtgMvrm5ESWtp0W80JnkSuBe4Lck54GvAvUl2AgW8CfwdQFWdSnIEOA18CDxSVVf6GV1SH/wjK9LHxFL/yIrfaJTUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIaiwahSRbk/w4yekkp5J8uVvfmOS5JK91l7eOPObRJGeTnEmyu8//AEnjtZQ9hQ+Bf6iqHcBfAI8k2QEcAI5V1XbgWHed7ra9wF3A/cDjSdb1Mbyk8Vs0ClV1oap+2m2/D7wKbAH2AIe6ux0CHuy29wCHq+pyVb0BnAV2jXtwSf1Y1jmFJHcAnwF+AmyqqgvdTe8Cm7rtLcDbIw87161JmgJLjkKSTwI/BL5SVb8dva2qCqjlvHCS/UmOJzn+AZeX81BJPVpSFJKsZzYI36+qH3XLF5Ns7m7fDFzq1s8DW0cefnu31qiqg1U1U1Uz69mw0vkljdlSfvsQ4LvAq1X1rZGbjgL7uu19wNMj63uTbEiyDdgOvDi+kSX16YYl3Ocvgb8Ffp7kRLf2T8A3gCNJHgbeAh4CqKpTSY4Ap5n9zcUjVXVl7JNL6sWiUaiq/wIyz833zfOYrwNfX8VckibEbzRKahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVIjVTXpGUjy38D/Ar+e9CzLdBvOvBambeahzvtnVfWpxe40iCgAJDleVTOTnmM5nHltTNvM0zbvtTx8kNQwCpIaQ4rCwUkPsALOvDambeZpm7cxmHMKkoZhSHsKkgZg4lFIcn+SM0nOJjkw6Xnmk+TNJD9PciLJ8W5tY5LnkrzWXd464RmfSHIpycmRtXlnTPJo976fSbJ7QDM/luR8916fSPLAwGbemuTHSU4nOZXky936oN/rJauqif0A64BfAXcCNwI/A3ZMcqYFZn0TuO2atX8BDnTbB4B/nvCMnwM+C5xcbEZgR/d+bwC2df8f1g1k5seAf5zjvkOZeTPw2W77FuCX3WyDfq+X+jPpPYVdwNmqer2qfgccBvZMeKbl2AMc6rYPAQ9OcBaq6gXgvWuW55txD3C4qi5X1RvAWWb/f6ypeWaez1BmvlBVP+223wdeBbYw8Pd6qSYdhS3A2yPXz3VrQ1TA80leTrK/W9tUVRe67XeBTZMZbUHzzTj09/5LSV7pDi+u7oYPbuYkdwCfAX7C9L7XjUlHYZrcU1U7gS8AjyT53OiNNbufOOhf5UzDjJ1vM3tIuRO4AHxzsuPMLckngR8CX6mq347eNkXv9UdMOgrnga0j12/v1ganqs53l5eAp5jd/buYZDNAd3lpchPOa74ZB/veV9XFqrpSVb8HvsMfdrUHM3OS9cwG4ftV9aNueere67lMOgovAduTbEtyI7AXODrhmT4iyc1Jbrm6DXweOMnsrPu6u+0Dnp7MhAuab8ajwN4kG5JsA7YDL05gvo+4+sHqfJHZ9xoGMnOSAN8FXq2qb43cNHXv9ZwmfaYTeIDZs7e/Ar466XnmmfFOZs8e/ww4dXVO4E+AY8BrwPPAxgnP+SSzu9sfMHvc+vBCMwJf7d73M8AXBjTzvwE/B15h9gO1eWAz38PsocErwInu54Ghv9dL/fEbjZIakz58kDQwRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1Pg/ScrSTlbZ+JEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97ba1cacf8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97ba653668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1c9ff98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c181ba90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADL1JREFUeJzt3U+InPd9x/H3p7K8Jo4LVp0KWRa1DKIgH6qERS7UhBQ3keOLnItRDkUHg3pwQwLtQW4O9cWQFpLeHFCIiSipVZPEWAcTY4uAKZTYclBsSa7ijf9gKbLU1IGYHhRb+fawzzbzW2u1f2Zm5xnl/YJlnnlmZuebsfed3/PseDZVhSQt+INJDyCpX4yCpIZRkNQwCpIaRkFSwyhIaowtCknuTXImyVySg+N6HkmjlXG8TyHJBuBnwGeBs8BLwBer6vTIn0zSSI1rpbAbmKuqN6rqN8ARYO+YnkvSCF03pu+7FXhn4PpZ4K6l7nx9ZuoGbhzTKJIA3udXv6yqTyx3v3FFYVlJDgAHAG7gY9yVeyY1ivR74fn63tsrud+4Dh/OAdsGrt/W7ft/VXWoqmaranYjM2MaQ9JqjSsKLwE7kmxPcj2wDzg6pueSNEJjOXyoqg+T/C3wLLABeLyqTo3juSSN1tjOKVTVM8Az4/r+ksbDdzRKahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWuG+bBSd4C3gcuAx9W1WySTcC/A7cDbwEPVNWvhhtT0noZxUrhL6tqV1XNdtcPAseqagdwrLsuaUqM4/BhL3C42z4M3D+G55A0JsNGoYDnk7yc5EC3b3NVne+23wU2D/kcktbRUOcUgLur6lySPwaeS/JfgzdWVSWpKz2wi8gBgBv42JBjSBqVoVYKVXWuu7wIPAXsBi4k2QLQXV5c4rGHqmq2qmY3MjPMGJJGaM1RSHJjkpsWtoHPASeBo8D+7m77gaeHHVLS+hnm8GEz8FSShe/zb1X1wyQvAU8meRB4G3hg+DElrZc1R6Gq3gD+7Ar7/we4Z5ihJE2O72hcB8/+4sSkR5BWzChIahiFdbDn1l2THkFaMaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRGKNnf3HC/0JSU8cojIkx0LQyCpIaw36asxZxhaBp50pBUsMojJgfqKJpZxTGzEho2hgFSQ2jMGaeeNS0MQrrwDBomhiFEbtSADyvoGliFEbMAGjaGQVJDaMgqWEU1oH/taSmiVGQ1DAK68QTkJoWRmGdePigaWEUJDWMwjpytaBpYBQkNYzCOnO1oL5bNgpJHk9yMcnJgX2bkjyX5PXu8uaB2x5OMpfkTJI94xpc0nisZKXwHeDeRfsOAseqagdwrLtOkp3APuDO7jGPJdkwsmmngCsBTbtlo1BVLwDvLdq9FzjcbR8G7h/Yf6SqLlXVm8AcsHtEs04F34+gabfWcwqbq+p8t/0usLnb3gq8M3C/s90+SVNi6BONVVVArfZxSQ4kOZ7k+AdcGnaMqeIhhvpsrVG4kGQLQHd5sdt/Dtg2cL/bun0fUVWHqmq2qmY3MrPGMaaThxjqs7VG4Siwv9veDzw9sH9fkpkk24EdwIvDjTh9Fn7oF19K02Alv5J8AvhP4E+TnE3yIPA14LNJXgf+qrtOVZ0CngROAz8EHqqqy+Mavs+uFgQPH9Rny/7ZuKr64hI33bPE/R8FHh1mqGvRnlt3GQNNBd/RuI4GVw0LH7xiKNQ3/oHZCfE8g/rKlcKEuEJQXxmFCRo8fDASo+Nh2XA8fNA1x0Oz4RiFdbb4X1j/X0194+GDpIZRmLAr/ZpSmiSj0AMeUqhPjEIPGAD1iVHoMWOhSTAKPXC1X6EZBq03o9AThkF9YRR6xDCoD4xCzxgGTZpRmDKGQeNmFHpouffuGwaNk1HoKcOgSTEKPbbn1l3LnmMwDho1ozAFXDVoPRmFa8RqVg2DH+ziakOL+XkKU2JhtXC1H+DVfJz84H4/lESDXClcQxZ/tNtqVw4SGIVrzlJBGFwNLN52paBBRmHKDPMDPBiAwW1XChrkOYUptPjTmkb5/SRXClPuaj/Q/oFbrYUrhWvASsIwjIXVyOIVirG5NrlS0LKu9MNvEK5dRkErYgR+fxgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhrLRiHJ40kuJjk5sO+RJOeSnOi+7hu47eEkc0nOJNkzrsEljcdKVgrfAe69wv5/qapd3dczAEl2AvuAO7vHPJZkw6iGlTR+y0ahql4A3lvh99sLHKmqS1X1JjAH7B5iPknrbJhzCl9K8kp3eHFzt28r8M7Afc52+yRNibVG4ZvAHcAu4Dzw9dV+gyQHkhxPcvwDLq1xDEmjtqYoVNWFqrpcVb8FvsXvDhHOAdsG7npbt+9K3+NQVc1W1exGZtYyhqQxWFMUkmwZuPoFYOE3E0eBfUlmkmwHdgAvDjeipPW07OcpJHkC+AxwS5KzwD8Cn0myCyjgLeBvAKrqVJIngdPAh8BDVXV5PKNLGodU1aRn4A+zqe7KPZMeQ7qmPV/fe7mqZpe7n+9olNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKmhj/2nU/GQVNjH91qp+MgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqTGslFIsi3Jj5KcTnIqyZe7/ZuSPJfk9e7y5oHHPJxkLsmZJHvG+T9A0mitZKXwIfB3VbUT+HPgoSQ7gYPAsaraARzrrtPdtg+4E7gXeCzJhnEML2n0lo1CVZ2vqp902+8DrwFbgb3A4e5uh4H7u+29wJGqulRVbwJzwO5RDy5pPFZ1TiHJ7cAngR8Dm6vqfHfTu8Dmbnsr8M7Aw852+yRNgRVHIcnHge8DX6mqXw/eVlUF1GqeOMmBJMeTHP+AS6t5qKQxWlEUkmxkPgjfraofdLsvJNnS3b4FuNjtPwdsG3j4bd2+RlUdqqrZqprdyMxa55c0Yiv57UOAbwOvVdU3Bm46CuzvtvcDTw/s35dkJsl2YAfw4uhGljRO163gPn8B/DXwapIT3b5/AL4GPJnkQeBt4AGAqjqV5EngNPO/uXioqi6PfHJJY7FsFKrqP4AscfM9SzzmUeDRIeaSNCG+o1FSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhqpqknPQJL/Bv4X+OWkZ1mlW3Dm9TBtM/d13j+pqk8sd6deRAEgyfGqmp30HKvhzOtj2maetnkX8/BBUsMoSGr0KQqHJj3AGjjz+pi2madt3kZvzilI6oc+rRQk9cDEo5Dk3iRnkswlOTjpeZaS5K0kryY5keR4t29TkueSvN5d3jzhGR9PcjHJyYF9S86Y5OHudT+TZE+PZn4kybnutT6R5L6ezbwtyY+SnE5yKsmXu/29fq1XrKom9gVsAH4O3AFcD/wU2DnJma4y61vALYv2/TNwsNs+CPzThGf8NPAp4ORyMwI7u9d7Btje/XPY0JOZHwH+/gr37cvMW4BPdds3AT/rZuv1a73Sr0mvFHYDc1X1RlX9BjgC7J3wTKuxFzjcbR8G7p/gLFTVC8B7i3YvNeNe4EhVXaqqN4E55v95rKslZl5KX2Y+X1U/6bbfB14DttLz13qlJh2FrcA7A9fPdvv6qIDnk7yc5EC3b3NVne+23wU2T2a0q1pqxr6/9l9K8kp3eLGwDO/dzEluBz4J/Jjpfa0bk47CNLm7qnYBnwceSvLpwRtrfp3Y61/lTMOMnW8yf0i5CzgPfH2y41xZko8D3we+UlW/Hrxtil7rj5h0FM4B2wau39bt652qOtddXgSeYn75dyHJFoDu8uLkJlzSUjP29rWvqgtVdbmqfgt8i98ttXszc5KNzAfhu1X1g2731L3WVzLpKLwE7EiyPcn1wD7g6IRn+ogkNya5aWEb+BxwkvlZ93d32w88PZkJr2qpGY8C+5LMJNkO7ABenMB8H7Hwg9X5AvOvNfRk5iQBvg28VlXfGLhp6l7rK5r0mU7gPubP3v4c+Oqk51lixjuYP3v8U+DUwpzAHwHHgNeB54FNE57zCeaX2x8wf9z64NVmBL7ave5ngM/3aOZ/BV4FXmH+B2pLz2a+m/lDg1eAE93XfX1/rVf65TsaJTUmffggqWeMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCp8X9wIKCbCyAgsgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97ba637cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"reshaped_pp=val_trained.reshape(7,15,242,242)\n",
"reshaped_val_y=val_y.reshape(7,15,242,242)\n",
"\n",
"i=[4,6,5,3]\n",
"j=[7,5,8,6]\n",
"for a in i:\n",
" for b in j:\n",
" plt.imshow(reshaped_val_y[a,b,:,:])\n",
" plt.show()\n",
" plt.imshow(reshaped_pp[a,b,:,:])\n",
" plt.show()\n",
" print(\"-----------------------------\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5984333262319529\n",
"0.0837794854263589\n"
]
}
],
"source": [
"import scipy\n",
"print(scipy.spatial.distance.dice(val_y,val_trained))\n",
"print(scipy.spatial.distance.dice(train_y,train_trained))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2b97c1d2a4e0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADJ9JREFUeJzt3U+IXfd5h/HnW1keE8cFK06FLIlaBhGQF1XCIBdqQoqbyPFGzsaoi6KFQV24IYF0ITeLehNIC0l3DijERJTUqkhirIWJsUXAFEpsOSi2JFfxxH+wFFlq6kBMF4qtvF3Mmfr+5BnNv3vn3Cs9Hxjm3N+9d+b1se7jc85cj1JVSNKcP+p7AEnjxShIahgFSQ2jIKlhFCQ1jIKkxsiikOS+JGeSzCQ5MKrvI2m4Mor3KSRZB/wS+DxwFngR+OuqOj30byZpqEZ1pLALmKmq16vq98BhYM+IvpekIbphRF93M/D2wO2zwN0LPfjGTNVN3DyiUSQBvMdvf1NVn1zscaOKwqKS7Af2A9zEx7g79/Y1inRdeK5++NZSHjeq04dzwNaB21u6tf9XVQerarqqptczNaIxJC3XqKLwIrA9ybYkNwJ7gaMj+l6Shmgkpw9V9UGSvwOeAdYBj1fVqVF8L0nDNbJrClX1NPD0qL6+pNHwHY2SGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSY0bVvPkJG8C7wGXgQ+qajrJBuDfgTuAN4EHq+q3qxtT0loZxpHCX1bVzqqa7m4fAI5V1XbgWHdb0oQYxenDHuBQt30IeGAE30PSiKw2CgU8l+SlJPu7tY1Vdb7bfgfYuMrvIWkNreqaAnBPVZ1L8ifAs0n+a/DOqqokNd8Tu4jsB7iJj61yDEnDsqojhao6132+CDwJ7AIuJNkE0H2+uMBzD1bVdFVNr2dqNWNIGqIVRyHJzUlumdsGvgCcBI4C+7qH7QOeWu2QktbOak4fNgJPJpn7Ov9WVT9J8iJwJMlDwFvAg6sfU9JaWXEUqup14M/mWf8f4N7VDCWpP76jUVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpsWgUkjye5GKSkwNrG5I8m+S17vOtA/c9kmQmyZkku0c1uKTRWMqRwveB+65YOwAcq6rtwLHuNkl2AHuBu7rnPJZk3dCmlTRyi0ahqp4H3r1ieQ9wqNs+BDwwsH64qi5V1RvADLBrSLNKWgMrvaawsarOd9vvABu77c3A2wOPO9utSZoQq77QWFUF1HKfl2R/kuNJjr/PpdWOMRae+fWJvkeQVm2lUbiQZBNA9/lit34O2DrwuC3d2kdU1cGqmq6q6fVMrXCM8bL79p2GQRNvpVE4CuzrtvcBTw2s700ylWQbsB14YXUjTh7DoEm2lB9JPgH8J/CpJGeTPAR8E/h8kteAv+puU1WngCPAaeAnwMNVdXlUw48zw6BJldlLAv3642you3Nv32MMzWAQdt++s8dJpA89Vz98qaqmF3uc72gcgcEQeMSgSWMURsQjBE0qo7AGPFrQJDEKI+RphCaRURgxw6BJYxTWgNcXNEmMgiaGR1prwyiskbmjBf9ga9zd0PcA15OFwuDpxeKM6doxCj24Mg6+A/Lq5vaP+2ZtePrQo9237/zIH3T/i9hyf6w9ozAGrozDM78+4YsBj6D64unDGPG04kNGsT9GYQzN94an6ykQXojtl6cPY27u1OJ6eWekQeifUZggg3G4Fq87GITxYBQm0LV2UXK+fwaD0B+jMKHm+1HmJMZhvpkNQr+80DjBlhqGcXqRLRSucZrxemcUJtTVXvxX3tf3ofnVjmCMwfjxF7dOuGGeMqz2BbqcWYzB2lvqL271SGHCDfNHlaO+JmEIJoNRuIYsNxCj/hutjMBkMgrXqKX8/oblnOsvNTKafEbhGrfS/wtzuUcQBuHaYRSuM/O9eIdxCnE9/b8Z1zqjoKG+iK98r4SBmDxGQUO12JGIkRh/vs1ZIzffb5jS+DIKWjOGYTIYBUkNoyCpYRQkNYyCpIZRkNQwCpIai0YhyeNJLiY5ObD2aJJzSU50H/cP3PdIkpkkZ5LsHtXgkkZjKUcK3wfum2f9X6pqZ/fxNECSHcBe4K7uOY8lWTesYSWN3qJRqKrngXeX+PX2AIer6lJVvQHMALtWMZ+kNbaaawpfTvJyd3pxa7e2GXh74DFnuzVJE2KlUfgOcCewEzgPfGu5XyDJ/iTHkxx/n0srHEPSsK0oClV1oaouV9UfgO/y4SnCOWDrwEO3dGvzfY2DVTVdVdPrmVrJGJJGYEVRSLJp4OaXgLmfTBwF9iaZSrIN2A68sLoRJa2lRX+fQpIngM8BtyU5C/wj8LkkO4EC3gT+FqCqTiU5ApwGPgAerqrLoxld0ij49z5I14ml/r0PvqNRUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKmxaBSSbE3y0ySnk5xK8pVufUOSZ5O81n2+deA5jySZSXImye5R/gNIGq6lHCl8AHytqnYAfw48nGQHcAA4VlXbgWPdbbr79gJ3AfcBjyVZN4rhJQ3folGoqvNV9fNu+z3gVWAzsAc41D3sEPBAt70HOFxVl6rqDWAG2DXswSWNxrKuKSS5A/g08DNgY1Wd7+56B9jYbW8G3h542tluTdIEWHIUknwc+BHw1ar63eB9VVVALecbJ9mf5HiS4+9zaTlPlTRCS4pCkvXMBuEHVfXjbvlCkk3d/ZuAi936OWDrwNO3dGuNqjpYVdNVNb2eqZXOL2nIlvLThwDfA16tqm8P3HUU2Ndt7wOeGljfm2QqyTZgO/DC8EaWNEo3LOExfwH8DfBKkhPd2j8A3wSOJHkIeAt4EKCqTiU5Apxm9icXD1fV5aFPLmkkFo1CVf0HkAXuvneB53wD+MYq5pLUE9/RKKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJjVRV3zOQ5L+B/wV+0/csy3QbzrwWJm3mcZ33T6vqk4s9aCyiAJDkeFVN9z3Hcjjz2pi0mSdt3it5+iCpYRQkNcYpCgf7HmAFnHltTNrMkzZvY2yuKUgaD+N0pCBpDPQehST3JTmTZCbJgb7nWUiSN5O8kuREkuPd2oYkzyZ5rft8a88zPp7kYpKTA2sLzpjkkW6/n0mye4xmfjTJuW5fn0hy/5jNvDXJT5OcTnIqyVe69bHe10tWVb19AOuAXwF3AjcCvwB29DnTVWZ9E7jtirV/Bg502weAf+p5xs8CnwFOLjYjsKPb31PAtu7fw7oxmflR4O/neey4zLwJ+Ey3fQvwy262sd7XS/3o+0hhFzBTVa9X1e+Bw8Cenmdajj3AoW77EPBAj7NQVc8D716xvNCMe4DDVXWpqt4AZpj997GmFph5IeMy8/mq+nm3/R7wKrCZMd/XS9V3FDYDbw/cPtutjaMCnkvyUpL93drGqjrfbb8DbOxntKtaaMZx3/dfTvJyd3oxdxg+djMnuQP4NPAzJndfN/qOwiS5p6p2Al8EHk7y2cE7a/Y4cax/lDMJM3a+w+wp5U7gPPCtfseZX5KPAz8CvlpVvxu8b4L29Uf0HYVzwNaB21u6tbFTVee6zxeBJ5k9/LuQZBNA9/lifxMuaKEZx3bfV9WFqrpcVX8AvsuHh9pjM3OS9cwG4QdV9eNueeL29Xz6jsKLwPYk25LcCOwFjvY800ckuTnJLXPbwBeAk8zOuq972D7gqX4mvKqFZjwK7E0ylWQbsB14oYf5PmLuhdX5ErP7GsZk5iQBvge8WlXfHrhr4vb1vPq+0gncz+zV218BX+97ngVmvJPZq8e/AE7NzQl8AjgGvAY8B2zoec4nmD3cfp/Z89aHrjYj8PVuv58BvjhGM/8r8ArwMrMvqE1jNvM9zJ4avAyc6D7uH/d9vdQP39EoqdH36YOkMWMUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUmN/wPxgq4WkZ5weAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b97c1bdeb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"reshaped_train=train_trained.reshape(7,15,242,242)\n",
"reshaped_train_y=train_y.reshape(7,15,242,242)\n",
"\n",
"i=[4,6,5,3]\n",
"j=[7,5,8,6]\n",
"for a in i:\n",
" for b in j:\n",
" plt.imshow(reshaped_train_y[a,b,:,:])\n",
" plt.show()\n",
" plt.imshow(reshaped_train[a,b,:,:])\n",
" plt.show()\n",
" print(\"-----------------------------\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RF with min_split_value = 400"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"random_knees400=pickle.load(open(\"/home/cvh255/Cartilage/jobsubmission/random_split400\",'rb'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"val_trained=random_knees400.predict(val_norm_x)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9798873054132515\n"
]
}
],
"source": [
"import scipy\n",
"print(scipy.spatial.distance.dice(val_y,val_trained))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b930322b748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305a1e668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305a6d780>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b93059f9dd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305a8c438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305b84748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305a42668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305b93e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305c79630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305cd45c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305d284e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9303239518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305d3ff60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305d48cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305d217f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305d48588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADBxJREFUeJzt3UGMlPd9h/HnW8BrxXElE6cIA6qxxAUfSqIVrlQrcoUaiC84F4scKg6W6IFGidQccHOIL5bSSklvjkQUK6hKTVESyxysWjaKZPUSG0fEBhzijY0FBENTR4rVAzHk18O+28wfs95ld96dGfp8pNW+8593dn5+LR6977uDnapCkub8yagHkDRejIKkhlGQ1DAKkhpGQVLDKEhq9BaFJLuSnEkyk+RAX+8jabjSx+cUkqwCfgn8DXAeeBX4UlWdHvqbSRqqvs4UtgMzVfV2Vf0eOAzs7um9JA3R6p5+7gbg3MDj88AD8+18W6bqdu7oaRRJAB/w299U1acX2q+vKCwoyT5gH8DtfIIHsmNUo0j/L7xUP3x3Mfv1dflwAdg08Hhjt/Z/qupgVU1X1fQapnoaQ9LN6isKrwJbkmxOchuwBzja03tJGqJeLh+q6mqSvwdeAFYBT1fVqT7eS9Jw9XZPoaqeB57v6+dL6oefaJTUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIaqxezouTnAU+AK4BV6tqOsla4N+Be4GzwKNV9dvljSlppQzjTOGvq2pbVU13jw8Ax6pqC3CseyxpQvRx+bAbONRtHwIe6eE9JPVkuVEo4KUkryXZ162tq6qL3fZ7wLplvoekFbSsewrAg1V1IcmfAS8m+cXgk1VVSepGL+wisg/gdj6xzDEkDcuyzhSq6kL3/TLwLLAduJRkPUD3/fI8rz1YVdNVNb2GqeWMIWmIlhyFJHckuXNuG/g8cBI4CuztdtsLPLfcISWtnOVcPqwDnk0y93P+rar+I8mrwJEkjwHvAo8uf0xJK2XJUaiqt4G/uMH6fwM7ljOUpNHxE42SGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCj154dcnRj2CtCRGQVLDKEhqGAVJjQWjkOTpJJeTnBxYW5vkxSRvdd/vGnju8SQzSc4k2dnX4OPM+wmaZIs5U/g+sOu6tQPAsaraAhzrHpNkK7AHuL97zVNJVg1tWkm9WzAKVfUy8P51y7uBQ932IeCRgfXDVXWlqt4BZoDtQ5pV0gpY6j2FdVV1sdt+D1jXbW8Azg3sd75bkzQhln2jsaoKqJt9XZJ9SY4nOf4hV5Y7xljy3oIm0VKjcCnJeoDu++Vu/QKwaWC/jd3aR1TVwaqarqrpNUwtcQxJw7bUKBwF9nbbe4HnBtb3JJlKshnYAryyvBElraTVC+2Q5BngIeDuJOeBbwDfBI4keQx4F3gUoKpOJTkCnAauAvur6lpPs0vqwYJRqKovzfPUjnn2fxJ4cjlDTbqd92zzfoImlp9o7Jlx0KQxCj3Zec+2UY8gLYlRkNQwCpIaRkFSwyj0aO6+gjcbNUmMgqSGUZDUMAo981eTmjRGYYV4X0GTwihIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGIUV4sedNSmMQs/8eLMmjVGQ1DAKkhpGoUdzlw7eT9AkMQqSGkZBUsMoSGoYhZ54P0GTyihIahiFHviBJU0yo9AjLx00iYyCpIZRGDJvMGrSGQVJDaPQA88SNMmMwhD5WwfdChaMQpKnk1xOcnJg7YkkF5Kc6L4eHnju8SQzSc4k2dnX4ONo5z3bPEvQxFvMmcL3gV03WP+XqtrWfT0PkGQrsAe4v3vNU0lWDWtYSf1bMApV9TLw/iJ/3m7gcFVdqap3gBlg+zLmmxheOuhWsZx7Cl9O8np3eXFXt7YBODewz/lu7ZZmEHQrWWoUvgPcB2wDLgLfutkfkGRfkuNJjn/IlSWOMR68l6BbyZKiUFWXqupaVf0B+C5/vES4AGwa2HVjt3ajn3GwqqaranoNU0sZQ1IPlhSFJOsHHn4RmPvNxFFgT5KpJJuBLcAryxtR0kpavdAOSZ4BHgLuTnIe+AbwUJJtQAFngb8DqKpTSY4Ap4GrwP6qutbP6JL6kKoa9Qz8adbWA9kx6jGkW9pL9cPXqmp6of38RKOkhlGQ1DAKkhpGQVLDKIwZPx2pUTMKY2bnPdsMg0bKKIwpw6BRMQpjzDBoFIzCGBr8y1Uv/PqEcdCKMgpj6vq/dWkctFKMwhib+yvZ1585SH0yChPC/16DVopRmCBzZw2eLahPRmECedagPhkFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaC0YhyaYkP0lyOsmpJF/p1tcmeTHJW933uwZe83iSmSRnkuzs8x9A0nAt5kzhKvAPVbUV+Etgf5KtwAHgWFVtAY51j+me2wPcD+wCnkqyqo/hJQ3fglGoqotV9bNu+wPgTWADsBs41O12CHik294NHK6qK1X1DjADbB/24JL6cVP3FJLcC3wG+Cmwrqoudk+9B6zrtjcA5wZedr5bkzQBFh2FJJ8EfgR8tap+N/hcVRVQN/PGSfYlOZ7k+IdcuZmXSurRoqKQZA2zQfhBVf24W76UZH33/Hrgcrd+Adg08PKN3Vqjqg5W1XRVTa9haqnzSxqyxfz2IcD3gDer6tsDTx0F9nbbe4HnBtb3JJlKshnYArwyvJEl9Wn1Ivb5K+BvgTeSzP0/0P8R+CZwJMljwLvAowBVdSrJEeA0s7+52F9V14Y+uaReLBiFqvpPIPM8vWOe1zwJPLmMuSSNiJ9olNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkhlGQ1DAKkhpGQVLDKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGkZBUsMoSGoYBUkNoyCpYRQkNYyCpIZRkNQwCpIaRkFSwyhIahgFSQ2jIKlhFCQ1jIKkRqpq1DOQ5L+A/wF+M+pZbtLdOPNKmLSZx3XeP6+qTy+001hEASDJ8aqaHvUcN8OZV8akzTxp817PywdJDaMgqTFOUTg46gGWwJlXxqTNPGnzNsbmnoKk8TBOZwqSxsDIo5BkV5IzSWaSHBj1PPNJcjbJG0lOJDnera1N8mKSt7rvd414xqeTXE5ycmBt3hmTPN4d9zNJdo7RzE8kudAd6xNJHh6zmTcl+UmS00lOJflKtz7Wx3rRqmpkX8Aq4FfAfcBtwM+BraOc6WNmPQvcfd3aPwMHuu0DwD+NeMbPAZ8FTi40I7C1O95TwObu38OqMZn5CeBrN9h3XGZeD3y2274T+GU321gf68V+jfpMYTswU1VvV9XvgcPA7hHPdDN2A4e67UPAIyOchap6GXj/uuX5ZtwNHK6qK1X1DjDD7L+PFTXPzPMZl5kvVtXPuu0PgDeBDYz5sV6sUUdhA3Bu4PH5bm0cFfBSkteS7OvW1lXVxW77PWDdaEb7WPPNOO7H/stJXu8uL+ZOw8du5iT3Ap8BfsrkHuvGqKMwSR6sqm3AF4D9ST43+GTNnieO9a9yJmHGzneYvaTcBlwEvjXacW4sySeBHwFfrarfDT43Qcf6I0YdhQvApoHHG7u1sVNVF7rvl4FnmT39u5RkPUD3/fLoJpzXfDOO7bGvqktVda2q/gB8lz+eao/NzEnWMBuEH1TVj7vliTvWNzLqKLwKbEmyOcltwB7g6Ihn+ogkdyS5c24b+DxwktlZ93a77QWeG82EH2u+GY8Ce5JMJdkMbAFeGcF8HzH3B6vzRWaPNYzJzEkCfA94s6q+PfDUxB3rGxr1nU7gYWbv3v4K+Pqo55lnxvuYvXv8c+DU3JzAp4BjwFvAS8DaEc/5DLOn2x8ye9362MfNCHy9O+5ngC+M0cz/CrwBvM7sH6j1Yzbzg8xeGrwOnOi+Hh73Y73YLz/RKKkx6ssHSWPGKEhqGAVJDaMgqWEUJDWMgqSGUZDUMAqSGv8LPz0wxu65WswAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b930327aeb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305ba1f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305c4beb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305c374e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305befc50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305b3c518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305b37550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305e01518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305e265c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305e999e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305e76390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9300046e48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305e14198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305dc9b38>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b9305da0f28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b93059f0eb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------------\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"reshaped_train=val_trained.reshape(7,15,242,242)\n",
"reshaped_train_y=val_y.reshape(7,15,242,242)\n",
"\n",
"i=[4,6,5,3]\n",
"j=[7,5,8,6]\n",
"for a in i:\n",
" for b in j:\n",
" img =Image.fromarray(reshaped_train_y[a,b,:,:])\n",
" plt.imshow(reshaped_train_y[a,b,:,:])\n",
" plt.show()\n",
" plt.imshow(reshaped_train[a,b,:,:])\n",
" plt.show()\n",
" print(\"-----------------------------\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The RF with split of 100 gave dice loss of 0.85"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"random_stride1=pickle.load(open(\"/home/cvh255/Cartilage/jobsubmission/random_split100\",'rb'))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"val_trained=random_stride1.predict(val_norm_x)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8563867930960705\n"
]
}
],
"source": [
"import scipy\n",
"print(scipy.spatial.distance.dice(val_y,val_trained))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The RF with split = 50 and leaf = 25 gave dice loss = 0.76 "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7633676272987987\n"
]
}
],
"source": [
"import pickle\n",
"random_stride1=pickle.load(open(\"/home/cvh255/Cartilage/jobsubmission/random_split5025\",'rb'))\n",
"\n",
"val_trained=random_stride1.predict(val_norm_x)\n",
"\n",
"import scipy\n",
"print(scipy.spatial.distance.dice(val_y,val_trained))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RF with no positional feature "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The previous model with positional parameter was still ok as it roughly estimated the shape of cartilages. The possibility if that the model is over fitting on positional parameter. Hence, model without positional parameter was trained with same setting but turns out that the model performs worse without positional parameter"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.6026065771393406\n"
]
}
],
"source": [
"import pickle\n",
"random_stride1=pickle.load(open(\"/home/cvh255/Cartilage/jobsubmission/random_without1\",'rb'))\n",
"\n",
"val_trained=random_stride1.predict(val_norm_x)\n",
"\n",
"import scipy\n",
"print(scipy.spatial.distance.dice(val_y,val_trained))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"reshaped_val_y=val_trained.reshape(7,15,242,242)\n",
"reshaped_pp=val_y.reshape(7,15,242,242)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b360afdfb00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b360afd6e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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FbY90AvezOHr7K+CbbdczpMY7WBw9fgU4u1Qn8GfASeAN4ASwueU6n2Kxu/0Ri/utD12vRuCb1Xo/D3y5QzX/O/Aa8CqLG9TWjtV8N4u7Bq8Cp6u/+7u+rsf984xGSYW2dx8kdYyhIKlgKEgqGAqSCoaCpIKhIKlgKEgqGAqSCv8P8KKsZ/eJXbUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b36122d6518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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DFqufh9q+rwf98YhGSYWmhw+SWsZQkFQwFCQVDAVJBUNBUsFQkFQwFCQVDAVJhf8FAQlFRWXH4UUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b3612332518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b3612288198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b3612322978>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b361243c588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b36123d8748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b36124e16d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADd1JREFUeJzt3V+IXOd9xvHvU1eWSeKCXadClkWtgHrhXFQJi3PREFJMI8c3Sm6MclFUMKgXbkighcjNRXJjSAtJ7xJwiIkoaVyTP1gXocY2KaFQYstBcSy7ihX/wZJlq2kCMb1QbOfXiz1bz5F2vbO7c/acM/P9wDJn3jkz89uznGff875nzqSqkKQVv9d3AZKGxVCQ1GIoSGoxFCS1GAqSWgwFSS2dhUKS25OcSXI2ybGu3kfSbKWL8xSSXAX8HPgL4BzwBPCpqnpm5m8maaa66incCpytquer6rfAA8Chjt5L0gz9fkevuwd4eeL+OeBDa618dXbWNby7o1IkAbzOr39ZVe9db72uQmFdSY4CRwGu4V18KLf1VYq0EB6t77w0zXpdHT6cB/ZO3L+paft/VXVfVS1V1dIOdnZUhqSN6ioUngD2J9mX5GrgMHCio/eSNEOdHD5U1ZtJ/gZ4GLgKuL+qTnfxXpJmq7Mxhar6AfCDrl5fUjc8o1FSi6EgqcVQkNRiKEhqMRQktRgKkloMBUkthoKkFkNBUouhIKnFUJDUYihIajEUVvHwK6f6LkHqjaGwBoNBi8pQWMXBGw/0XYLUG0NhDQaDFlVvF24dsslDB8NBi8ZQWIVBoEXm4YOkFkNBUouhIG3CPE9ZGwrSlC4PgnkNBkNBmsJKAKzcHrzxwNwOSBsK0gbMaxBMMhSkKS1CIIChIE1lUQIBDAVpNLZrYNNQkNRiKEhTGMr043bUYShI6xhKIMD2jG0YCpJaDAVpRDx8kLTtDAVpSn2PLWzXqdWGgjSlRTmByVCQ1LKly7EleRF4HXgLeLOqlpJcD/wrcDPwInBnVf16a2VK/VmUHsKKWfQU/ryqDlTVUnP/GPBYVe0HHmvuSxqJLg4fDgHHm+XjwCc6eA9JHdlqKBTwaJInkxxt2nZV1YVm+VVg1xbfQ9I22uol3j9cVeeT/BHwSJL/mnywqipJrfbEJkSOAlzDu7ZYhqRZ2VJPoarON7cXge8DtwKvJdkN0NxeXOO591XVUlUt7WDnVsqQNEObDoUk705y7coy8DHgaeAEcKRZ7Qjw0FaLlLR9tnL4sAv4fpKV1/mXqvq3JE8ADya5C3gJuHPrZUraLpsOhap6HvjTVdr/B7htK0VJfZm8WvOi8oxGqdH3ZxuGwlCQMBAmGQqSWgwFibfHEOb5m5+mZShIXPm1cItsq2c0SnNh0XsHk+wpSGoxFCS1GAqSWgwFqeEg4zJDQaPnzjxbhoJGbxYzBwbL2wwFCackJxkKUsNgWGYoSGoxFCS1GAqSWgwFSS2GgqQWQ0FSi6GgheeJS22GghaagXAlQ0HCcJhkKEhqMRQktRgKkloMBUkthoKEn5CcZChooRkGVzIUJJySnGQoSGoxFKSGvYVlhoKkFkNBC22yd+Cg4zJDQQvNILiS3zqthWcwtK3bU0hyf5KLSZ6eaLs+ySNJnmtur5t47J4kZ5OcSXKwq8K1eB5+5ZSDgdtgmsOHbwK3X9Z2DHisqvYDjzX3SXILcBh4f/Ocrya5ambVSjhL0LV1Dx+q6kdJbr6s+RDw0Wb5OPDvwOea9geq6hLwQpKzwK3Af86mXC0yu/nbY7MDjbuq6kKz/Cqwq1neA7w8sd65pk3SSGx59qGqCqiNPi/J0SQnk5x8g0tbLUPSjGw2FF5Lshugub3YtJ8H9k6sd1PTdoWquq+qlqpqaQc7N1mGpFnbbCicAI40y0eAhybaDyfZmWQfsB94fGslStpO6w40Jvk2y4OKNyQ5B3wB+BLwYJK7gJeAOwGq6nSSB4FngDeBu6vqrY5ql9SBaWYfPrXGQ7etsf69wL1bKUpSfzzNWVKLoSCpxVCQ1GIoSGoxFCS1GAqSWgyFDvgRX42ZoSCpxVDogB/x1ZgZCh14+JVTBoNGy1DoiGMKGitDoQP2EjRmhkJHDAaNlaEgqcVQkNRiKEhqMRQ65AyExshQkNRiKHTM3oLGxlCQ1GIodMQegsbKUOiAgaAxMxQktRgKHfN05/mxKBfPMRQ6YBDMp4M3HliIv62h0IFF+G+i+WUodGgR/qto/qz7XZLaOMNAY2ZPQVKLoSCpxVCQNmGepycNBWkTVsaN5jEYDAVpk+Z1QNlQkLZgHoPBUJDUYihIalk3FJLcn+Rikqcn2r6Y5HySU83PHROP3ZPkbJIzSQ52VbikbkzTU/gmcPsq7f9UVQeanx8AJLkFOAy8v3nOV5NcNatiJXVv3VCoqh8Bv5ry9Q4BD1TVpap6ATgL3LqF+iRts62MKXw6yVPN4cV1Tdse4OWJdc41bZJGYrOh8DXgfcAB4ALw5Y2+QJKjSU4mOfkGlzZZhqRZ21QoVNVrVfVWVf0O+DpvHyKcB/ZOrHpT07baa9xXVUtVtbSDnZspQ1IHNhUKSXZP3P0ksDIzcQI4nGRnkn3AfuDxrZUoaTutez2FJN8GPgrckOQc8AXgo0kOAAW8CPw1QFWdTvIg8AzwJnB3Vb3VTemSupCq6rsG/iDX14dyW99lSHPt0frOk1W1tN56ntEoqcVQkNRiKEhqMRQktRgKkloMBUkthoKkFkOhJ/N8NWCNm6HQk3m+GrDGza+NG4DJYJjHC4FqXOwp9GhRvtpc42IoSGrx8GEA7C1oSOwpSGoxFCS1GAoD5DkM6pOhMGAGg/rgQOMAOfCoPhkKI7HSa+gzMDzJajEYCiPTZzgYBIvBUBiJgzceuGKM4fL7Xey02/EeGhav5jwyGx18XO+DV5M7+XqvbSCM27RXc7anMDJr7Zhr7dDr7ehdz3A8/MqpVYPHgBkupyQ1lc3uxBsNMfXPUJgTXX3icpavu96YiIbBUJgzW9mJJ5+7XR/rNhiGx1CYUxvdoSfX93h/sTn7oG23Wu/AIOqe3yWpwbo8AAyEYTEU1IvJsQsNi6Gg3qycpelg47AYCurVyiyHwTAchoKkFkNBUouhoEFYhPGFsfxuhoIG4/KZiLHsRNMay0zLuqGQZG+SHyZ5JsnpJJ9p2q9P8kiS55rb6yaec0+Ss0nOJDnY5S+g+bKZMyvnvYex3ab56PSbwN9W1U+SXAs8meQR4K+Ax6rqS0mOAceAzyW5BTgMvB+4EXg0yZ9U1Vvd/ApadGP5DzwW6/YUqupCVf2kWX4deBbYAxwCjjerHQc+0SwfAh6oqktV9QJwFrh11oVL6saGxhSS3Ax8APgxsKuqLjQPvQrsapb3AC9PPO1c0yZpBKYOhSTvAb4LfLaqfjP5WC1/qmpDn6xKcjTJySQn3+DSRp4qqUNThUKSHSwHwreq6ntN82tJdjeP7wYuNu3ngb0TT7+paWupqvuqaqmqlnawc7P1S5qxaWYfAnwDeLaqvjLx0AngSLN8BHhoov1wkp1J9gH7gcdnV7KkLk0z+/BnwF8CP0uyMu/z98CXgAeT3AW8BNwJUFWnkzwIPMPyzMXdzjxI47FuKFTVfwBZ4+FVr4xSVfcC926hrt5MXn348isRS4vAMxovs9pZdZ4Yo0ViKEhq8ctg3oGHDlpE9hQktRgKkloMBUkthoJGz9mh2TIUNBecOp4dQ0Gj53dIzJahoNFb6SHYU5gNQ0FSi6GgUbN3MHuGgqQWQ0Fzw4HG2TAUNDc8lJgNQ0Fzw57CbBgKkloMBUkthoKkFkNBUouhoLngIOPsGAoaNcNg9gwFjZ7BMFuGgqQWQ0FSi6EgqcVQkNRiKEhqMRQktRgKkloMBUkthoKkllRV3zWQ5L+B/wV+2XctG3QD1rwdxlbzUOv946p673orDSIUAJKcrKqlvuvYCGveHmOreWz1Xs7DB0kthoKkliGFwn19F7AJ1rw9xlbz2OptGcyYgqRhGFJPQdIA9B4KSW5PcibJ2STH+q5nLUleTPKzJKeSnGzark/ySJLnmtvreq7x/iQXkzw90bZmjUnuabb7mSQHB1TzF5Ocb7b1qSR3DKzmvUl+mOSZJKeTfKZpH/S2nlpV9fYDXAX8AngfcDXwU+CWPmt6h1pfBG64rO0fgWPN8jHgH3qu8SPAB4Gn16sRuKXZ3juBfc3f4aqB1PxF4O9WWXcoNe8GPtgsXwv8vKlt0Nt62p++ewq3Amer6vmq+i3wAHCo55o24hBwvFk+Dnyix1qoqh8Bv7qsea0aDwEPVNWlqnoBOMvy32NbrVHzWoZS84Wq+kmz/DrwLLCHgW/rafUdCnuAlyfun2vahqiAR5M8meRo07arqi40y68Cu/op7R2tVePQt/2nkzzVHF6sdMMHV3OSm4EPAD9mvNu6pe9QGJMPV9UB4OPA3Uk+MvlgLfcTBz2VM4YaG19j+ZDyAHAB+HK/5awuyXuA7wKfrarfTD42om19hb5D4Tywd+L+TU3b4FTV+eb2IvB9lrt/ryXZDdDcXuyvwjWtVeNgt31VvVZVb1XV74Cv83ZXezA1J9nBciB8q6q+1zSPbluvpu9QeALYn2RfkquBw8CJnmu6QpJ3J7l2ZRn4GPA0y7UeaVY7AjzUT4XvaK0aTwCHk+xMsg/YDzzeQ31XWNmxGp9keVvDQGpOEuAbwLNV9ZWJh0a3rVfV90gncAfLo7e/AD7fdz1r1Pg+lkePfwqcXqkT+EPgMeA54FHg+p7r/DbL3e03WD5uveudagQ+32z3M8DHB1TzPwM/A55ieYfaPbCaP8zyocFTwKnm546hb+tpfzyjUVJL34cPkgbGUJDUYihIajEUJLUYCpJaDAVJLYaCpBZDQVLL/wFIKhYhlISUDAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b36078beeb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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03/WsUuM7WJw9fhQ4u1Qn8EfAKeBp4GHg+p7r/DqL3e2XWRy3fvxqNQKfbrb7OeBDA6r5X4HHgcdYfEHtHljN72FxaPAYcKb5u3Po23q9f57RKKml7+GDpIExFCS1GAqSWgwFSS2GgqQWQ0FSi6EgqcVQkNTy/3FNdLVikh0PAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b3612500588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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R43tZmj3+KXB+uU7gT4AzwHPA48DOjuv8Fkvd7ddZGrc+cL0agc802/0C8NEe1fwvwM+Ap1jaoXb3rOYPsjQ0eAo419zu7fu23ujNMxoltXQ9fJDUM4aCpBZDQVKLoSCpxVCQ1GIoSGoxFCS1GAqSWv4fVDd+KZ2DvOYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b360b005160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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LQ4NngLPd6e6+b+v1njyiUVJj1sMHST1jKEhqGAqSGoaCpIahIKlhKEhqGAqSGoaCpMb/A/WdXuUxD0oaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b360af94780>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x2b361226b908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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xFdgGPN9AfR+wvGJVPs3SsoaW1BwRAXwDeCUzvzrwUOeW9Yqa3tIJ3MfS1tufAl9qup5VaryDpa3HPwZOLtcJ/D5wFHgVeBbY0HCdT7DU3X6PpXHrg9eqEfhStdxPA59qUc3/BrwMvMTSCrWpZTXfzdLQ4CXgePVzX9uX9bA/HtEoqdD08EFSyxgKkgqGgqSCoSCpYChIKhgKkgqGgqSCoSCp8P/bEar70yUaFgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b360af9ba20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b36124e42b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"i=[4,6,5,3]\n",
"j=[7,5,8,6]\n",
"for a in i:\n",
" for b in j:\n",
" plt.imshow(reshaped_val_y[a,b,:,:])\n",
" plt.show()\n",
"# plt.imshow(reshaped_pp[a,b,:,:])\n",
"# plt.show()\n",
"# print(\"-----------------------------\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}