MNIST_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "%matplotlib inline\n",
    "from IPython.core.interactiveshell import InteractiveShell #Show all consecutive outputs\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "#Removes all unnecessary warnings by Python\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "import scipy as sp\n",
    "\n",
    "from matplotlib import rcParams\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib as mpl\n",
    "\n",
    "#colorbrewer2 Dark2 qualitative color table\n",
    "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n",
    "                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n",
    "                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n",
    "                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n",
    "                (0.4, 0.6509803921568628, 0.11764705882352941),\n",
    "                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n",
    "                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843)]\n",
    "\n",
    "rcParams['figure.figsize'] = (10, 6)\n",
    "rcParams['figure.dpi'] = 150\n",
    "rcParams['axes.color_cycle'] = dark2_colors\n",
    "rcParams['lines.linewidth'] = 2\n",
    "rcParams['axes.facecolor'] = 'white'\n",
    "rcParams['font.size'] = 14\n",
    "rcParams['patch.edgecolor'] = 'white'\n",
    "rcParams['patch.facecolor'] = dark2_colors[0]\n",
    "rcParams['font.family'] = 'StixGeneral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(x_train,ny_train),(x_test,ny_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_pixels=x_train.shape[1]*x_train.shape[2]\n",
    "x_train=x_train.reshape(x_train.shape[0],image_pixels)\n",
    "x_test=x_test.reshape(x_test.shape[0],image_pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T-SNE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f05187350>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0efd8c69d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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62tYXbH0eNZxuuSJQl8BUaCAcyKwDAOQtL0GqIMsu/WIyYOb0pNzNxMxUcjWd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\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b646LaFqBT2IjzXh55oP4wScbcPzKBU3kj4bOdqwuLw7YB/Wwm7hjVJbm19Qbo0SKmU0R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\n5kUb87uZBhl1hUk+mRBxXEmpms6esjVVM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f05187350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "X_embedded = TSNE(n_components=2).fit_transform(x_train[0:60000])\n",
    "x_emb=pd.DataFrame(X_embedded)\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(x_emb[0],x_emb[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 784)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_train=x_train/255.0\n",
    "x_test=x_test/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.utils import np_utils\n",
    "y_train=np_utils.to_categorical(ny_train)\n",
    "y_test=np_utils.to_categorical(ny_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#making a validation set\n",
    "val_train=x_train[40000:]\n",
    "x_train=x_train[0:40000]\n",
    "val_test=y_train[40000:]\n",
    "y_train=y_train[0:40000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_classes=10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline model of simple ANN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(784, input_dim=784, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 20000 samples\n",
      "Epoch 1/6\n",
      "40000/40000 [==============================] - 11s - loss: 0.2766 - acc: 0.9209 - val_loss: 0.1653 - val_acc: 0.9510\n",
      "Epoch 2/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.1100 - acc: 0.9669 - val_loss: 0.1164 - val_acc: 0.9649\n",
      "Epoch 3/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.0694 - acc: 0.9800 - val_loss: 0.1012 - val_acc: 0.9704\n",
      "Epoch 4/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.0460 - acc: 0.9863 - val_loss: 0.0972 - val_acc: 0.9696\n",
      "Epoch 5/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.0318 - acc: 0.9910 - val_loss: 0.0895 - val_acc: 0.9726\n",
      "Epoch 6/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.0233 - acc: 0.9935 - val_loss: 0.0902 - val_acc: 0.9737\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0f04dd3890>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_data=(val_train, val_test), epochs=6, batch_size=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9952/10000 [============================>.] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "scores=model.evaluate(x_test,y_test,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss  0.0715290525578\n",
      "accuracy  0.9791\n"
     ]
    }
   ],
   "source": [
    "print \"loss \", scores[0]\n",
    "print \"accuracy \", scores[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9504/10000 [===========================>..] - ETA: 0s('number of wrong predictions', 209)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfmKJn1jiJ5b4iSV+YomfWOInlviJ9S+92Pbe1TFMGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022fccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhyzihyzihyzih6zfyoRlq74YxO8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f04c73290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAh0FXB+67lwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022d61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023f1410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023841d0>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02384d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022ceb10>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023ac390>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkPUfqpMjQTCuezsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022f1150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     "execution_count": 25,
     "metadata": {},
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     "data": {
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      "text/plain": [
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      "text/plain": [
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f020bc550>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fd1110>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f5b550>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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      "text/plain": [
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      "text/plain": [
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     "data": {
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     "execution_count": 25,
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc6de490>"
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02415650>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc57d2d0>"
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc589150>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABStJREFUeJzt3c0rrH0cx/GZkyJ5WEjDQpQdeUiU0ChKHlIKJTYsLCxs\nKFnayIJ/QNnJThQpCyRJyQKpWVjY2MhTYqnMvbg39/c37jlHv4vhfN6v3ec0zNXp3dWFa2bC8Xg8\nBCj6leoDAFKF+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL\n+CGL+CGL+CGL+CErLdUH8JXC4TBvVfEXiMfj4SC+D2d+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+\nyCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yJJ6u0L8\nXl5entmZmZlJHx+JRBL+rbm52eyHhwezV1ZWzH59ff3IIQaGMz9kET9kET9kET9k8QPvX6apqcns\n8vJys6PRqNmVlZVmFxUVmZ2TkxPg0f2rsLDQ7Lm5ucCf409w5ocs4ocs4oescDyu8zFVP+EzuTIy\nMsxubGw0u6enx+y+vj6zs7KyzI7FYmbv7++bvbe3Z/b5+bnZNzc3yQ/4D1RXV5t9enpqdjj8sY/Y\n4jO5AE/ED1nED1n8nj/FSkpKzF5YWDC7u7vb7IuLC7Onp6fN3traMvv+/t7zCP0NDg6avb29naIj\nsTjzQxbxQxbxQxbX/J8oOzs74d+mpqbMnpiYMNt9oUdFRYXZl5eXAR3d5xkeHja7tbXV7Pb29i88\nmv/HmR+yiB+yiB+yuOYPkHvv+8bGRsJjSktLzR4YGDB7c3Mz+AP7RO7rB0KhUKizs9Ps+vp6s1P1\ngnUXZ37IIn7IIn7I4n5+DwUFBWavr6+b/fT0lPA1Q0NDZj8+PgZ5SF/uvdf4pqenm313dxfoc3I/\nP+CJ+CGL+CGL3/N7aGtrM9u9/u3o6Ej4mvd+DkgmNzfXbPeNZK+urj70/YL2/Pyc0uf3wZkfsogf\nsogfsrjm99Df32/26uqq2R+9vn/P8vKy2e4HPxwcHJi9traWdL+8vJj99vbme4g/Fmd+yCJ+yCJ+\nyOLeHg8nJyfu9ze7trY2yKcLhUKJ75/f1dVl9sjIiNl1dXVmu68xGB8fN/v6+tr3ED8d9/YAnogf\nsogfsrjm91BWVmb22dmZ2UtLSwlfMzMzY/bt7W2QhxRKS7N/unHvLxodHTXb/btBb2+v2Ts7OwEe\nXTC45gc8ET9kET9kcc0foLGxMbNnZ2cTHuP+f7v3A7n38hweHgZ0dO+bn583232NcU1NjdlBfEaX\nL675AU/ED1nED1lc83+xyclJs1taWsyORqNmu5/Lu7u76/X8kUjE7KqqKrMXFxfNdn+O+Q645gc8\nET9kET9kcc3/zeTn55tdXFxstnuN7mpoaDD76Ogo6eOPj4/NjsViZn/H1/hyzQ94In7IIn7I4pof\nPw7X/IAn4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs\n4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocsqTeqBf6LMz9kET9kET9kET9kET9kET9kET9k\nET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9k\nET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9k\nET9kET9kET9kET9kET9kET9kET9kET9kET9k/QMrX/TgkwZKtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc6beb10>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d8eb50>"
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd8e1190>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f416c7990>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLPET6x/lI84s6vEmIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023dd6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABYNJREFUeJzt3T8otX0cx/FzxFFEEQbKKANCymJj4CyUwaiUwRmERTFZ\nZFGKyZ/VYJAof4pVGRhEUSYkAyEUofMsz+D7O57j0XX+f96v7Xvf9+Vc3b379TvXuVzHHw6HfYCi\nrGSfAJAsxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9Z\nxA9ZxA9ZxA9Z2ck+gUTy+/08qiIDhMNhfyx+Dis/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/\nZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZEk9ukRRYWGhmRsaGswcDAajHl9Q\nUGDmnJwcM4dCoYhjPj4+/nKKScPKD1nED1nED1n+cFjnCX6Z+LjCQCBg5pGRETMPDQ2ZuaysLKav\n39HREfFnOzs7MX0NF48rBDwifsgifsgifsjiQ64kq6mpMXNeXp6Zh4eHzex+yOR+iNXW1hbDs8ts\nrPyQRfyQRfyQxZ4/jurr6yP+zP3Qqbu728z5+fkxPYeDgwMzPz4+mvn29tbMvb29MX39VMbKD1nE\nD1nED1ns+T3Izc018/z8vJk7OzsjjnGvy//V0dGRmc/Pz828urpq5u3tbTO/vr6a2T3n36ytrZl5\nf3//T8enElZ+yCJ+yCJ+yGLP70F2tv3va2xsNPNP+/vLy0szb2xsmNnd0+/u7prZvU7//Pz8/072\nX9XV1Wbu6emJ+u9fXl7MPDk56en1UwkrP2QRP2QRP2Sx5/fAvWbe1NRkZvc9gc/n8319fZn57e0t\n9if2TVaWXd/cX3B3H0rlenh4MPPx8bGZx8fHI44pLy83c3FxsZkHBgbM7L6PSRRWfsgifsgifsji\noVUZbmZmxsyDg4Mx/fnuewKfz+dbWFgw89TUlJm97vF5aBXgEfFDFvFDFtf5U1xpaWnUuaSkxMx9\nfX1m/ulBstG47wHPzs7MvL6+bubZ2dmIn3Fzc/On10wWVn7IIn7IIn7IYs/vgfvczObmZjN3dXVF\nHHN4eGhm99ma7p6+qqoq6uzy++0lcHcP7/793NycmTc3N828tbUV9fXSGSs/ZBE/ZBE/ZHFvjwfu\nfep3d3ex/PFxcX19bea6ujozJ+ve+r/g3h7AI+KHLOKHLK7ze/D09GTmlpYWMy8vL0ccU1lZGddz\ncq/jX11dmTkYDJo5Hfb48cLKD1nED1nED1ns+T1wn8HjPqu+vb094pj+/n4zu/fLV1RUmHlsbMzM\nPz0L6Dv3cxv392lPTk6iHq+ElR+yiB+yiB+yuLcnxQQCATPf39+b+bfv6b24uDBza2urmd3r/umI\ne3sAj4gfsogfsrjOn2JWVlbM/Nse//Pz08yhUMjMmbDHjxdWfsgifsgifshiz59kRUVFZnaf/fOb\n6elpM7vf24v/xsoPWcQPWcQPWcQPWdzYlmDuF1UvLS2Zuba2Nurx7hfAuf8+Xb4YwgtubAM8In7I\nIn7I4kOuBHO/QO63Pb77nmx0dNTMCnv8eGHlhyzihyzihyz2/CnO/WWVxcXFJJ1J5mHlhyzihyzi\nhyz2/Am2t7dn5tPTUzO/v7+beWJiIu7npIqVH7KIH7KIH7K4nx9ph/v5AY+IH7KIH7KIH7KIH7KI\nH7KIH7KkrvMD37HyQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbx\nQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbx\nQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbx\nQ9Y/D5gbvWJKRTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd7a3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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     "metadata": {},
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      "text/plain": [
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     "data": {
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     "metadata": {},
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      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0231b410>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0223bd50>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d98750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022f2050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABi1JREFUeJzt3U1IVHscxvGZzLdwI4oYCC56EwaSdGEFQqCBiwwMhIJc\nuhAxcuGqIHciuFAKcaPgqkUk6CZ8w6DIFzCCRI1wJypKQSQOiDV3F/f3nzg6nuPM2PP97J57deZw\n75fheBznhGOxWAhQdCbVBwCkCvFDFvFDFvFDFvFDFvFDFvFDFvFDFvFDFvFDFvFDFvFDFvFDFvFD\nFvFDFvFDFvFDFvFDFvFDFvFDFvFD1tlUH0AyhcNhPqriHxCLxcJBPA6v/JBF/JBF/JBF/JBF/JBF\n/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JAl9WeM6SA7O9vsa9eu+Xo89ybi\n8/Pzvh5PCa/8kEX8kEX8kMU5f4CuX79udmNjY9zX3Llzx+zLly8HegzT09NmP3jwwOydnZ1An+80\n45UfsogfsogfssLudeJ/md+PK8zIyDC7s7PT7MePH5udl5fn5+kC8fHjR7MrKytTdCTB4eMKAZ+I\nH7KIH7KIH7L4gTcBfX19Zj969Mhs97/lt2/f4h7j7Fn7e8WBgQGzv3z5YvaVK1fMLiwsNPv+/ftm\nuz9k//792+ynT5+a3dXVFXeM6Y4feAGfiB+yiB+yOOf3UFtba/bIyIjZ7969M/vFixdmu79g+ts/\na2lpMXtsbCyRQwxduHDB7Ldv35pdUlJitvvGtkgk4vnv0xHn/IBPxA9ZxA9ZnPN7cP8wZHFx0exn\nz56ZHY1Gza6pqYl7zImJCbP39/fNLioqMvvnz5+ex5ifn2/2xsaG2Tk5OZ7f7x7PwsKC2c+fPzd7\ne3vb8/GSgXN+wCfihyzihyzO+T2418jX19d9H8OHDx/MvnHjhtkzMzNmHxwcmO2+98f9XURZWZnf\nQzTc30u0traaPTc3F+jzHQXn/IBPxA9ZxA9ZnPMnWWlpqdnLy8tmnzt3LtDn29raMrunp8fspqYm\ns8vLyz0fb3V11ezm5ua4r3n//n0ih5gwzvkBn4gfsogfsjjnT7H6+nqzX79+bXZmZmZCj+eeb7e1\ntZn96dMns7Oyssx2P1x3cHDQbPfmGlNTU3HHcPv27aMd7DFxzg/4RPyQRfyQxTl/irkfHDs7O2u2\ne87v3mzC/Rnh169fZruf25Oou3fvmj06Onro91RVVZnt/o2AX5zzAz4RP2QRP2RxQ7oUc6/zu+f4\nKysrZr969cps9xw/aOPj42a7f8f8t5td3Lt3z+ygz/mDwis/ZBE/ZBE/ZHGdP8kKCgrM/vz5s9nn\nz583u7q62uyTfq/8Ydy/Mb5161bc13z9+tXsoG+0zXV+wCfihyzihyyu8yeZe88s9xzflej7+YN2\n8eJFs9337fzNpUuXTupwAsUrP2QRP2QRP2RxnT/J3M/lcd+/f/XqVbMnJyfNrqurM9vv+/VdDQ0N\nZrv36XXvC3wU4XAgl+X/4Do/4BPxQxbxQxbX+ZNsb2/P7N7eXrOHhobMdj8Dx73P19LSktmbm5ue\nz+/ew+vmzZtmd3d3m33YNXv3/gGhUCjU0dHh+T3pgld+yCJ+yCJ+yOI6f4q57+1ZW1szOzc31/P7\n3f9/b9688fz6iooKs4uLiw87RE/u5/2HQid/zs91fsAn4ocs4ocszvnTjPtZnO51/5ycnGQeTtx1\n/P7+frOfPHkS9z27u7snekyc8wM+ET9kET9kET9k8QNvmnN/KfXw4UOz29vbA32+4eFhswcGBsye\nm5sL9PmOgx94AZ+IH7KIH7I45z9lzpyxr1fuL8UikYjZ0WjU7B8/fpj98uVLs79//272Sd/84jg4\n5wd8In7IIn7I4pwfpw7n/IBPxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9Z\nxA9ZUu/nB/6PV37IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7I\nIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7I\nIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7I\n+g/ij4i3jibO/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01eed3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01ec2850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0223b790>"
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     "metadata": {},
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    {
     "data": {
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e11a50>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0243f110>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f7a550>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e93110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzWvNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f13410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAdspAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd776b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABmVJREFUeJzt3UtIFW0AxvFzKqSbBmkgVNRCXbRIWuSiQFtEqy5UdKGg\ngoJyFa2kCApB0KQLhQuJok20yOgKUYuCilCQbBXpqqAghdDuIqRtv+edmPPlnPGcev6/3TPOnPMu\nHl7eOc4lOzExkQEcTSv0AIBCofywRflhi/LDFuWHLcoPW5Qftig/bFF+2KL8sEX5YYvywxblhy3K\nD1uUH7YoP2xRftii/LBF+WGL8sMW5YetGYUewFTKZrM8quIfMDExkc3H5zDzwxblhy3KD1uUH7Yo\nP2xRftii/LBF+WGL8sMW5Yctyg9blB+2KD9sUX7Ysrqk+V+0dOlSyevWrZO8detWyRUVFZJbW1sl\nX79+PX+DK3LM/LBF+WGL8sNW1ukl1H/jbYwLFy6UfPnyZcl1dXWS582bJ/nHjx+Sv3//Lnn+/PmS\ne3p6JK9duzb2+ELgNkYgIcoPW5QftljzF5lsVpezd+7ckTxr1izJN27ckNzX1yf58+fPkr9+/Sr5\n4MGDko8ePSp51apVkru7u3837CnFmh9IiPLDFuWHLdb8RWbZsmWSy8rKJOd7zT179mzJ4TnB7du3\nJW/evDmv3z8ZrPmBhCg/bFF+2KL8sMXNLFMsvPCsq6tL8ooVKyTv2rUr1fGsXLky9u/3799P9fsL\niZkftig/bFF+2GLNn6K5c+dGtt28eVPymjVrJI+MjEh+9+5dXsdUWVkp+cyZM7H7h+ck/xJmftii\n/LBF+WGLNX8elZaWSr5161Zkn3CNf/r06dhjXr16lWhMmzZtktze3i65qqoq9viOjg7JbW1tkl++\nfJlgdIXFzA9blB+2KD9scTNLHq1evVry06dPI/s8evRI8vr16yWPjo5KLi8vlzxjhp6mhdcKHTly\nRPKBAwckj42NSe7t7ZX85MkTyU1NTZk4nZ2dkW2HDx+WPD4+HvsZf4qbWYCEKD9sUX7Y4nf+PKqv\nr8+5z4sXL2L/3tjYKHnfvn2SlyxZInn69OmSZ86cKfnChQuSz58/L/nNmzex47ly5Yrk7du3Sz5+\n/HjkmOfPn0u+du1a7HcUCjM/bFF+2KL8sMXv/HlUU1Mj+fXr1zmPCR8sG97Dm8u3b98kb9y4UfLj\nx4//6PP+1N27dyPbwv8lhC/FS4rf+YGEKD9sUX7YYs2fonD9nclkMs3NzZKXL18uOXw5Rfig2PBa\nm/7+/iRDTCz83T+Tif5voba2VvKHDx8SfSdrfiAhyg9blB+2uLYnRffu3Yts27Nnj+RwzR/eY3vi\nxAnJ4Uuli9GCBQsk/+75RcWAmR+2KD9sUX7YYs2fojlz5kS2bdmyJfaYq1evSi72NX54PdPfhJkf\ntig/bFF+2GLNn6Ldu3fn3OfZs2eS3759m9Zw8qKkpETyhg0bIvsMDAxIHhwcTHVMk8XMD1uUH7Yo\nP2yx5k/Rjh07cu5z7NgxyeE7uYpNeD/B797ju3fvXslfvnxJdUyTxcwPW5Qftig/bLHmT9HDhw8j\n2xoaGgowksm7dOmS5J07d0o+d+5c5Jjw+qRixcwPW5Qftig/bLHmT9Hw8HDOfcLnWIbX+qQtfMfX\n2bNnJYfXJz148EBya2tr5DPz/Q6utDDzwxblhy3KD1s8qzNFpaWlkW2fPn2SHK6Pw+f1nzp1SnL4\nntxc18pXVVVJPnTokORt27ZJXrx4seSOjg7JJ0+elPzx48fY708Dz+oEEqL8sEX5YYvywxYnvCma\nNi06t+zfv19yS0uL5IqKitjPHBoakhyeQIcqKyslhy+pfv/+veTwBPvixYuSf/78Gft9U4ETXiAh\nyg9blB+2WPMXWPhPqOrqasmdnZ2SFy1aJDl8CXVXV5fk8OVv4Qvuuru7//9giwRrfiAhyg9blB+2\nWPPjr8OaH0iI8sMW5Yctyg9blB+2KD9sUX7YovywRflhi/LDFuWHLcoPW5Qftig/bFF+2LK6nh/4\nL2Z+2KL8sEX5YYvywxblhy3KD1uUH7YoP2xRftii/LBF+WGL8sMW5Yctyg9blB+2KD9sUX7Yovyw\nRflhi/LDFuWHLcoPW5Qftig/bFF+2KL8sEX5YYvywxblhy3KD1uUH7YoP2xRftii/LBF+WGL8sMW\n5Yctyg9blB+2KD9sUX7YovywRflhi/LDFuWHLcoPW5Qftig/bFF+2KL8sEX5YYvywxblhy3KD1u/\nAIKAcQ4+kaSWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e30410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNDguIzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01ef9050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02381490>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc617690>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/zEEj+x/gI9uNkOOR6nBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fb42d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f8e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5ef190>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfsgifsgifsgifsgifsgifsj6DxDuN2Gr+a+cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd776c50>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfyw9QcFPFdDPcKwbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f7a910>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABNpJREFUeJzt3T1PFGsYx+FZQqWlmlhpCVpKrZ9BvoCVEiixtEXp6DFU\narQyWmnPS0VC7NBaS7W1ZE/r/XCOyplZYflfV3e7ZjMJv0yemZ2X0Xg87iDRzGlvAJwW8RNL/MQS\nP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPrNnT3oC/aTQa\neVTFOTAej0dDfI89P7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/\nscRPrKjbGKfB5uZmmR88eFDmvb29Mn/8+PFE37+1tfXLzw8ODk70fdPMnp9Y4ieW+Ik1Go9znuYx\nDY8uWVhYKPO7d+/KfOXKlTK3f7/RaNTr8w8fPpS5PaZYX18v86dPn7q/zaNLoCfxE0v8xLLmnzKL\ni4tlfvTo0aDff/369TJfunSpzDMzdX/Z/i7RdV23srIy6Da1rPmhJ/ETS/zEEj+xHPBStAew9+/f\nL3P7I9nXr1+PfcfVq1eH37CfOOCFnsRPLPETy80s4e7cuVPmpaWlMrfHhO2PXG/fvp3Mhv0F9vzE\nEj+xxE8sa/4w7c0wGxsbZW7X+O18dHRUZmt+mELiJ5b4iWXNf861a/zt7e0yz83NlfnHjx9lbm9Y\nb9f4p3ED+1Ds+YklfmKJn1iu5z9n2jX++/fvy3zr1q0yt3//V69elfnevXsDbt0wXM8PPYmfWOIn\nljX/OXN4eFjm9jz+ly9fyvzmzZsyP3z4cDIbNiBrfuhJ/MQSP7Fc2zPlXrx4UeZ2jd8e07XP2WnX\n+L97+cW3b9/+13aeRfb8xBI/scRPLOf5e2jXxxcuXCjz5cuXe3/n3bt3y9w+Z6dd4/d9Id3vPv+T\nl2C3vyU8efLk2P/pw3l+6En8xBI/sZzn7+H58+dlbq+Vb1/m1nWTX5O3hv58fn6+zO21QV13/Njn\nrLLnJ5b4iSV+YjnP30O7/t3Z2Snzaaz523Ps7Tb97tma7e8M7Zr+LFzb4zw/9CR+YomfWNb8A7p2\n7VqZ/+TanmfPnpX55s2bZW7/Pt+/fy9z+yzNly9flvksrNGHZs0PPYmfWOInlmt7BvT58+dfzl3X\ndWtra2Vu1/jtefzW8vJymaf5nVinzZ6fWOInlviJ5Tz/BC0uLh77t9evX5f5pNfuzM46THOeH3oS\nP7HETyxr/gFdvHixzPv7+8f+T3te/+joqMztO7FWV1fLfB6v1Tkpa37oSfzEEj+xxE8sv5gMqP1R\nq32IbNcdP8BtTzg8ffq0zA5wJ8een1jiJ5b4iWXNP6Dbt2+X+d9uTJmZqfubx48fl7l9+QOTY89P\nLPETS/zEsuYf0O7ubpnbc/pd13U3btwo89bW1kS3if9mz08s8RNL/MRyMwtTx80s0JP4iSV+Yomf\nWOInlviJJX5iiZ9Y4ieW+IklfmJFXdsDP7PnJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJ\nn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y\n4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOIn\nlviJ9Q9DCVG/KNazHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02402c10>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrD/hHZInAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02036910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc64f110>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0229e550>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlvELy/iFZfzC+hu9K7g8F7zZywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023bf110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023f9410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e4cb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2OW07OyZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02082410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d6d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f020472d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fd42d0>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fee750>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0201abd0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5Yetn2NTpGk2gb/cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e38250>"
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     "metadata": {},
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     "execution_count": 25,
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      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022f7710>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e8e110>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fa6110>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5e1410>"
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     "data": {
      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5e4b90>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABQ9JREFUeJzt3c+LTQ0cx/Hn8kxWw9Qs/CjJj5o9smSUxawlKbLxB0jk\nj/APKItZTMyCtZISC9NQo5TZKIoFGz8KJb/vs3ye7/F0GWfcO+7n9dp9Yq4zeXc67j1zdLrd7l+Q\naNWgDwAGRfzEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HE\nTyzxE+vvQR9AP3U6HY+qGALdbrezHK/jzE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+x\nxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETK+rRJX+C3bt3l72wsNDz91+/fr3s+/fvl3327Nnl\nObAh5MxPLPETS/zE6nS7OU/wW4mPK9yxY0fZi4uLZZ86dars8+fPl/3w4cOyt23bVvbU1FTZN27c\n+KXjXEk8rhBaEj+xxE8s8RPLh1wD1vwQamRkpOxz586VPTs72/P1Pnz4UPbTp09bHN1wc+YnlviJ\nJX5iuebvs7Vr15Y9OTnZ8/ffvn277M+fP5fd/JBr48aNZTc/9Hr06NHPHGYEZ35iiZ9Y4ieWG9sG\nbH5+vuw9e/aUvXnz5rKfP39e9t69e8u+efNm2ZcuXSr7+PHjv3ScK4kb26Al8RNL/MTyPn+fbdiw\noezt27eX3bx3p3mN3/T48eOy37x5U3ansyyXx0PJmZ9Y4ieW+Inlmr/P9u/fX/b4+HjZV65cWdLr\nPXv2rOzmvT5Jn+MslTM/scRPLPETyzV/n23atKnnr3/69KnV6z948KDsw4cPt3q91atXl71169ay\njx079t3XvHz5suwLFy6U3fZ7XC7O/MQSP7HETyzX/H02MzNT9unTp8uemJgo+9q1a0t6/eY1/4kT\nJ8pes2ZN2evXry/76NGjZR88eLDsnTt3lv1/9w41P1tofg8r5eeInfmJJX5iiZ9Yrvn77MWLF2U3\nn6V55syZsl+9elV283r6yZMnZe/atavnn3/nzp2y161bV/aWLVt6fv3PaL7P/+7du9av+Ts48xNL\n/MQSP7E8t2fADh06VPbly5fLbr6P3vbva6mv1/w3xdWrV8u+e/fud1/T/JmEjx8/LuEIf8xze6Al\n8RNL/MRyzT9gq1bV88/FixfLPnLkSNnLfc2/sLBQdvPeo+np6bLfv3/f6s9fDq75oSXxE0v8xHLN\nv8KMjo6W3byfft++fT2/vvkcn5MnT5Z97969sg8cOFD2169ff+o4B8k1P7QkfmKJn1iu+Ydc816c\nkZGRsqempsr+9u3bbz+mtlzzQ0viJ5b4ieVneMM039cfGxsr+/Xr1/08nIFy5ieW+IklfmK55h9y\nzfv3/b+8/3LmJ5b4iSV+YomfWP7BO+SaNy4m3cj4I878xBI/scRPLPETS/zEEj+xxE8s7/OHmZub\nK/vt27cDOpLBc+YnlviJJX5iueYfcrdu3Sp7cnKy7C9fvvTvYFYYZ35iiZ9Y4ieWB9Xyx/GgWmhJ\n/MQSP7HETyzxE0v8xBI/saLe54f/cuYnlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOIn\nlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4\niSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+In1\nD8PWEQDTCDIqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f36410>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023ef050>"
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc63c2d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02300250>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d652d0>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABMdJREFUeJzt3T9PFFscx+G7cqksSLChk5dAYkGDYGVCgxTYYgIUdjRG\naXglxMRgLCCGqBUJFaHRDkoLEkv+JcRIYcXa3jMmS/bOLuzu93m6H1mGSfzk5Oy6M9NoNpv/QKJ7\nd30CcFfETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zE\nEj+x/r3rE7hNjUbDrSoGQLPZbHTiOFZ+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+\nYomfWOInlviJJX5iiZ9YUZcx3raxsbG/fra9vV3Mjx8/Lubr6+uWx3z16lUxX15etnz97u5uMZ+c\nnLR8fRIrP7HETyzxE6vRbObczeO2b13y5MmTv362t7dXzPfulevPTXv+m1SPd3h4WMybm5vF/Pnz\n52K+uLgo5qurq1rn0w1uXQI1iZ9Y4ieWPX8XjY+P//WzxcXFto4xPT1dzDMzMy1fX/c9xJcvX4r5\n/fv3xfzp06e2jtcN9vxQk/iJJX5iiZ9Y3vD2uImJiWKuflHtwYMHxdzp/zSrvuFdWlqqdbxO8IYX\nahI/scRPLBez9Ji1tbVifv78eTFX9/id9vbt22Ku7vkHiZWfWOInlviJZc9/y6ampor54OCgmOt+\nLl+9WOX8/LyYX79+Xev4g8TKTyzxE0v8xLLnv2XV78ZU9/jt7vmrn8u/fPny/51YICs/scRPLPET\ny56/zy0vLxdz9Ua07969K+YfP350+Yz6h5WfWOInlviJZc9/y75//97V46+vrxfz7OxsMU9OTnb1\n7/cTKz+xxE8s8RPLfXvu2IsXL4q5+u/x7NmzYp6bm2t5vOp9e379+tXy71UfTtEP3LcHahI/scRP\nLHv+PreyslLMGxsbxXzT9QHVh2V8+PChMyfWRfb8UJP4iSV+YtnzD5jT09NiHh0dbfn6nz9/FvPq\n6mox9+J7AHt+qEn8xBI/sez5B0z1GV4fP34s5ocPH7Z1vOHh4drn1Gn2/FCT+IklfmLZ8w+4R48e\nFfPXr1/b+n17fhhA4ieW+Inlvj09bn5+vphvuoa30Si3w/fv3+/4OQ0KKz+xxE8s8RPL5/w9Znx8\nvJiPj4/b+v3qfXvqPtd3aGio1u93g8/5oSbxE0v8xPI5f4+56Zla1fvs3KTdPf/FxUVbr+9nVn5i\niZ9Y4ieW+InlDW+P+f37dzFvbm4Wc/VhFSMjI7X+XvWh1AsLC7WO10+s/MQSP7HETyx7/h63v79f\nzN++fSvmp0+f1jr+7u5uMR8dHdU6Xj+x8hNL/MQSP7FczNJnxsbGinlra6uYp6amivns7KyY37x5\nU8w7OzvFfHV1VfcUu87FLFCT+IklfmLZ89N37PmhJvETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8\nxIr6bg/8l5WfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW\n+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJ\nJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ifWH7sVD9etNm0jAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d4d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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    {
     "data": {
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5edcd0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLPETS/zEEj+x/gbi0CBqm/QZwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01dbbf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE0v8xBI/scRPLPETS/zEEj+xxE+sfwCGf05NWmokCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022fcb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02165e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02102b90>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc643410>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8UMW8UMW8UMW8UMW8UPWb5nD7kMNuTcWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc690050>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nF7KMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02354290>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc58f910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e28f90>"
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e82b10>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhyzihyzihyzihyzih6x/X8wwPPFLUW0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f020eab90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfyQ9RcAVzaw9rs+fwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc52ea10>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02292ad0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfsgifsgifsgifsgifsgifsj6B7x1Es6LPyLxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01dd46d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz0HgJqymAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02184190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc52f510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD1nED1n/AhpNS4wg/FwkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d41290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5753d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8sMW5Yctyg9blB+2KD9s/QJGRq8FXbFCogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02340b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0224e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0201f050>"
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd7e8290>"
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     "data": {
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d74550>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLPETS/zEEj+xxE8s8RNL/MQSP7H+AnqN7fTsFRAeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc6d3290>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd7df2d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
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    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01dc7550>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlviJJX5iiZ9Y4ifWv/kZzgv40rSEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc52fb10>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd9109d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5dd690>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkEX8kEX8kPULtzH0LpxRHfgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f3e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023980d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7mGRefYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd793510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02056290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f020cc3d0>"
      ]
     },
     "metadata": {},
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     "execution_count": 25,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5d4b50>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/zE+gdLAvvszg9FnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f024023d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d32050>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f9e290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn1jiJ5b4ifUHIRnUjkL1iHsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d40450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01df3290>"
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     "metadata": {},
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f94810>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/zEEj+xxE8s8RNL/MQSP7H+BoseOTGVdPhjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02370f10>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngyiEwKtMiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd8359d0>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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      "text/plain": [
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      "text/plain": [
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     "execution_count": 25,
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      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc6b33d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02319b50>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e5a3d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYomfWOInlviJJX5iiZ9Y4ieW+In1BxzNX+WuKN+UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f024000d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01ec1c10>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02423610>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02397690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAAA2JJREFUeJzt3VFO20AUQNG66sJgZcDKYGfpLxmhGndMYuee85ePQlSu\nRs/xTLxcLpdfUPT73m8A7kX8ZImfLPGTJX6yxE+W+MkSP1niJ0v8ZImfLPGTJX6yxE+W+MkSP1ni\nJ0v8ZImfLPGTJX6y/tz7DdzSsiy+quIBXC6XZY+fY+UnS/xkiZ8s8ZMlfrLET5b4yRI/WeInS/xk\niZ8s8ZMlfrLET1ZqS/MZvL6+Xr1+eXnZ9O+fn5+vXn98fEy+o8dl5SdL/GSJn6yl9BDqIx5jnJ3x\n17y9vf3z952RY4wwSfxkiZ8sM/+NPT09Xb1+f3+/6e9/hPsAZn6YJH6yxE+Wmf/Gjvb/vSy7jM83\nZeaHSeInS/xkiZ8sh1lOZutNqrUL7PEm2/jzH5mVnyzxkyV+ssz8BzceRlmb8bdulBs32o2vz7jx\n7bus/GSJnyzxk2XmP7i1mXs8kD7O7FuZ+SFA/GSJnywz/8E98sx9b1Z+ssRPlvjJcoD9xsa9N2uf\nq2/dX7/33/OIX3LlADtMEj9Z4ifLzH9ja/vnZx8esXZNsdXsNchPMPPDJPGTJX6yzPxx4zXB+EC8\nve9D7MHMD5PET5b4yTLzc2XrA/O+mvl/ev+PmR8miZ8s8ZPlDC9Xxnl9/K7QtfsAX/2Mo7LykyV+\nssRPlpn/ztb2259lfj4jKz9Z4idL/GTZ23Nn45nd8XP0ZdllG8u3rb2f7/jp92xvD0wSP1niJ8vn\n/Ac3XpONe23Wnsm19bm9s9/zcyZWfrLET5b4yTLzn8z4ufv/fA6/p/Ea5Eys/GSJnyzxk2XmZ5Px\nvsHs8wTuycpPlvjJEj9Z4ifLYZaDWXtg3a1vah3hYRQjh1lgkvjJEj9ZZv6T2+PA+Wdrh2WOwMwP\nk8RPlvjJMvNzOmZ+mCR+ssRPlvjJEj9Z4idL/GSJnyzxkyV+ssRPlvjJEj9Z4idL/GSl9vPDZ1Z+\nssRPlvjJEj9Z4idL/GSJnyzxkyV+ssRPlvjJEj9Z4idL/GSJnyzxkyV+ssRPlvjJEj9Z4idL/GSJ\nnyzxkyV+ssRPlvjJEj9Z4idL/GSJnyzxkyV+ssRPlvjJEj9Z4idL/GSJnyzxkyV+ssRPlvjJEj9Z\n4idL/GSJnyzxkyV+ssRPlvjJEj9Z4idL/GT9BTPO3FnB5SMHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc693910>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02389d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc6393d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABdBJREFUeJzt3U0o5Xscx3EHZeMhobCQPBULNsRqalJKKRQLG4Voiiwl\nU7OTqCkWkylhZeOx5CFJsrKRbBTKRrGZmknZkIdZ3Lr3fn/H5Z5+Z845fN6v3Xfm+J//4t2/3znn\n/xB4enqKAxTFR3sHgGghfsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgi\nfsgifsgifsgifsgifsgifshKjPYORFIgEOBWFe/A09NTIBzb4cgPWcQPWcQPWcQPWcQPWcQPWcQP\nWcQPWcQPWcQPWcQPWcQPWcQPWcQPWVKnNL8F2dnZZq6srDTz58+fzVxTU/Pi9hobG828urrqsXfv\nC0d+yCJ+yCJ+yAooPYQ6Fi9jTEy0H7s2NjbMXFtb67X98/NzM3d3d5t5b2/Pa/vRwGWMgCfihyzi\nhyzW/BEWH2+PN/v7+2Z2v9d/zfr6uplPT0/N3NfXZ+bd3V0zNzQ0mPnx8TGk948G1vyAJ+KHLOKH\nLM7tCaOMjAwzJyUlBb1mZmbGzKGu8aenp83c09Pz4usLCgrM3NTUZOaPHz+aeWdnJ6T9ecs48kMW\n8UMW8UMW8UMWH3g95OTkmNm9UOTh4SHob6qqqkJ6D/dHKfdHq9ccHx+b2f3A29bWZmY+8AICiB+y\niB+yWPN7SEtLM3NRUZGZU1NTQ96m+yPW4OCgme/u7kLannuxDP7BkR+yiB+yiB+yWBB6ODk5MfPZ\n2ZmZnztp7fr62sxDQ0Nm/v79e5j27i+fPn0K6/beE478kEX8kEX8kMWaP4xaWlrMPDAwEPSab9++\nmdn93PAa99wc94KZ1tZWMycnJ7+4vaWlpZDe/z3hyA9ZxA9ZxA9Z3LQqxmRlZZl5dHTUzO3t7WYO\nBEK7f9Ps7KyZe3t7zXx7exvS9qKBm1YBnogfsogfsvieP8b8+PHDzMXFxWYOdY3v6ujoMPPPnz/N\nPD4+buarqyuv94tlHPkhi/ghi/ghi+/5Y9zU1JSZOzs7/+j7ueca1dfXm/ni4uKPvv//wff8gCfi\nhyzihyzW/DHOvZdmaWmpmd2HU8zPz5vZvWbYfQh1enq6md37/LjXJff395t5e3v7ud3+o1jzA56I\nH7KIH7JY84urq6sz8+TkpJnz8/PN/PXrVzNPTEwEbfPy8jI8O/cfWPMDnogfsogfsljzR9nY2JiZ\nf/36ZeaRkZFI7k5cSUmJmbe2tsycl5dnZvd5AnFxrz8Y2xdrfsAT8UMW8UMW1/BGWGFhoZndc21S\nUlLM7D5Hd3193czPPevXh3suz+HhoZndNX9mZmZY3z+SOPJDFvFDFvFDFmv+CDs/Pzfzzs6OmZub\nm828srJi5i9fvph5eHg4jHsXfL9/996hroqKirC+fyRx5Ics4ocs4ocszu2Jsvh4e/xZWFgws/sM\nrsfHRzPv7e2ZeXFx0czu/fg/fPhgZvd3B/d+/WVlZc/t9t/W1taC/q2xsfHFv/HFuT2AJ+KHLOKH\nLNb8MSY7O9vMm5ubZi4vLw9pe/f392ZOSEgws+/9/qurq4P+7eDgwGubr2HND3gifsgifsgifsji\nA2+My83NNbN7cXhXV9eLr/d1dHRkZveBdXNzc0F/4/4QF2584AU8ET9kET9kseZ/49yLSdwH1tXU\n1JjZ/UywvLxs5pubGzO7N81y/z8aWPMDnogfsogfsljz481hzQ94In7IIn7IIn7IIn7IIn7IIn7I\nIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IkjqfH/g3jvyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQ\nRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQ\nRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQRfyQ\nRfyQRfyQRfyQRfyQRfyQRfyQRfyQ9RvO8kWNO0EudQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f5d510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01dc6210>"
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     "metadata": {},
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f021fd510>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5120d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5e53d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5aab50>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02405050>"
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     "data": {
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd81e290>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc589290>"
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     },
     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc547290>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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     "metadata": {},
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f94f90>"
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      "text/plain": [
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     "execution_count": 25,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd7963d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn1jiJ5b4iSV+YomfWOInlviJJX5i/QNWCCc/62TMvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f021db510>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f930d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABSVJREFUeJzt3csrbW8cx/G9f04G5JYSComBIu1QBgwMRBnxBygDScwY\nGRlIyYSJZCaXZIh/QKFcS3LLjIxcYkCJwTrT8117t45trX35nc/7Nftq7bUe9e7p0dl7n7DjOCFA\n0X+pXgCQKsQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQP\nWcQPWcQPWcQPWb9SvYBkCofDfFXFP8BxnHAQ92Hnhyzihyzihyzihyzihyzihyzihyzihyzihyzi\nhyzihyzihyzihyzihyzihyzihyzihyzihyzihyzihyzihyzihyzihyzihyzihyzihyyprytEKFRa\nWmrm9vb2wJ+xtbVl5peXl8CfEQR2fsgifsgifsgifsjiD940V1ZWZuasrCwzRyIRMw8PD3veLz8/\n38x1dXU+Vhfb3t6emcfGxsy8u7sb+DN/gp0fsogfsogfsjjzp5mJiQkz9/b2mrm8vDyZy/mRqqoq\nM399faVoJd7Y+SGL+CGL+CGLM3+SVVdXm/nk5MTMOTk5CX3+9PS0md1vOmtoaDDz0tKSmefm5swc\n62+Q4uJiM/f19Zn54ODgW2tNNHZ+yCJ+yCJ+yOLMH6DCwkIzLy8vR12Tl5dnZvcZ//393cxPT0+e\nzxwZGTHz29ub5/U7Oztm/vj48Ly+vr7ezJmZmZ7Xh0Kh0Ofnp5kfHh7++ppUYOeHLOKHLOKHLM78\nPpSUlJh5c3PTzI2NjXHfc2BgwMxra2vxLyxABQUFZnb/zo7jRL3m9PTUzOPj48EvLADs/JBF/JBF\n/JDFmT8OFRUVZu7u7jbzd8747s+3LiwsmHl9ff2Hq0uMm5sbM+/v75u5ubk56jWVlZVmbm1tNTOf\n4QVSjPghi/ghizN/HDo7O808MzPjef3GxkbUz/r7+838/Pzsf2EBys7ONrP7/fuxzvhuo6OjZk6X\nM74bOz9kET9kET9kceZPoJWVlaifpdsZ321oaMjM7n/L+I6zs7OglpNQ7PyQRfyQRfyQxZk/DlNT\nU3Fd//r6mqCVfJ/7ezPd8+DgoJnb2to873d+fm7m1dXVqGuur6/jWGHqsPNDFvFDFvFDVjjWZzD/\nVeFw2Ncv6z7Du98Hk5GRYea7u7uoexwdHXk+4/j42Mzu9/v39PSYuaury/N+NTU1Zq6trfW83u3q\n6srzebe3t3HdLwiO44SDuA87P2QRP2QRP2Rx5vfh8PDQzE1NTUHePiXcf5MsLi6aeX5+PomriY0z\nP+AT8UMW8UMWZ34fWlpazDw5OfnX10QiETPn5uYGuaTQxcWFmd2fH7i8vDTz7Oysmd3/R9fj42OA\nqwsGZ37AJ+KHLOKHLM78SdbR0WHmoqKiQO+/vb1t5vv7+0Dvnw448wM+ET9kET9kcebH/w5nfsAn\n4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs\n4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocsqS+qBf7Ezg9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9Z\nxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9Z\nxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9Z\nxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZvwG3sOXsQVo/EwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02149510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfyQ9TfZFhiFEE5PnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f021870d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f024373d0>"
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01deab50>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023ce3d0>"
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     "metadata": {},
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f27050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nACoSFz25HHqyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02336b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc69c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nET9kET9kET9kET9kET9kET9kET9k/QtacC3s4vYNbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd819290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYfa4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd81b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLpuEda6o4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02009ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABOVJREFUeJzt3btrVFsYxuEzxwgWFqJiZW3AwjKFIBiwELTwUqRTsBEU\n/Q8ErQSxtfdWKBYWgq0iCJImFkaEFCIKgqIQU4mQsTx+K5yROJmb7/N0H+wkC/yxWBn33ul0u91/\nING/o14AjIr4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5i\niZ9Y4ifW1KgXMEydTserKv4C3W63sxHfx85PLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/\nscRPLPETK+qW5kE7duxYmR8+fLjmmlOnTpX5zp07A10T/8/OTyzxE0v8xHLm30B79uwp8+rq6ppr\nZmZmyuzMPzp2fmKJn1jiJ5Yz/wa6detWmZeWltZc8/bt22Eth9+w8xNL/MQSP7E63W7OG/wSX1c4\nOztb5q1bt5b5zJkzZT537lyZP378OJiF9cHrCqFP4ieW+IklfmL5T64Jt3v37jLfvHmzzAcOHCjz\n1FTvf/L79++X+d69e3++uDFn5yeW+IklfmI584+5ubm5Ml+7dq3MW7ZsKfPOnTsHvqa/hZ2fWOIn\nlviJ5cw/Yp1OvUfr5MmTZb59+3aZf/c5/cLCQpmvXLlS5vn5+TK/efOmzO0DOJs3by7zjx8/ev78\nSWLnJ5b4iSV+YnmYZcS2b99e5s+fP6/r69sz/JEjR8r89evXMrcPsywvL/f8/k+ePCnzoUOH1rW+\nQfAwC/RJ/MQSP7F8zj9ily5dWtf1L168KPPx48fL3J7xW+79+Y+dn1jiJ5b4ieXMP2Rnz54t8/nz\n53te357xT5w4UeZPnz71/Pr2+1+4cOF3Syw+fPiwrusniZ2fWOInlviJ5d6eIXv37l2Z2/futC5e\nvFjm9+/f97y+ffHs4cOHy9zen9/69u1bmdsX3b58+bLn1w+De3ugT+InlviJ5cw/ZOs983/58qXM\nO3bs2PA1/ar9HePGjRsD/Xl/wpkf+iR+YomfWO7tGbL2DH316tWe1w/6jL+yslLm58+fD/TnjRM7\nP7HETyzxE8uZf8iuX79e5k2bNpW5vZem/Zx/cXGxzHfv3i3z48ePyzw9Pd1zPY8ePSrzONy7Myx2\nfmKJn1jiJ5Z7eybcvn37yty+W3Pbtm1l/v79e5lnZmbK/OrVqw1c3WC4twf6JH5iiZ9YPuefcO2Z\nvT3jt9q/qTUJZ/xBsfMTS/zEEj+xnPkn3OnTp9d1/YMHDwa0kslj5yeW+IklfmI58//llpaWynz5\n8uXRLGQM2fmJJX5iiZ9Y4ieWX3gnXPvA+f79+8u8a9euMh89erTMz549K/Pr1683cHXjzc5PLPET\nS/zE8gD7hNu7d2+Znz59Wub2RbftwywHDx4sc/tHr8eRB9ihT+InlviJ5czPxHHmhz6Jn1jiJ5b4\niSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ1bU/fzwKzs/scRPLPETS/zEEj+xxE8s8RNL\n/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zE\nEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/\nscRPLPETS/zEEj+xxE+sn+ND7yQcGgxnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01df99d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f42dc3950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZBE/ZP0FmxkTlszKCh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fb8290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc565190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e02510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niB+y/gVrx0z9Ch4zjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f020a4ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7XcV5M3U3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02278510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/JBF+SGL8kMW5Ycsyg9Z/wMrO5vBEwCQBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022a00d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022db3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0215fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e3c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABStJREFUeJzt3T8ofX8cx3FH/t1MwnWTyKAMd5DJYJNBWHCLTAZ1Y6AY\njGSmjCwsFimyUDKoOyAWFmS9ZTEQUbqd3/p7f/x+vn+uc/+c1/OxvfTt2/nWs9Pn3s738HzfLwEU\nleb7AoB8IX7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7IIn7I\nIn7IIn7IIn7IKsv3BeSS53m8qiIEfN/3fuLv4c4PWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQP\nWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWcQPWVKvLgmj4eFhs4eGhszu7e01u76+\n3mzft29z8Tz7VpCnpyezJycnzd7b2/v9iy0w3Pkhi/ghi/ghizN/gYnH42ZPTU2ZnUgkzK6pqTG7\ntNTezz4/P80+PT01u7a29tsdi8W+vT7O/EARIn7IIn7IIn7I4gNvjjU3N5s9MzNjdjKZNDsSiZh9\nfX1t9sHBgdmXl5dm7+/vm/34+Gh2VVWV2ZWVlWafn5+XhBV3fsgifsgifsjizB+glpaWLz9zz9DR\naNTs19dXs6enp83e2NgwO5PJZHOJJe/v72a7D7aVlYU3Ee78kEX8kEX8kBXeA10BGBkZ+fKz6upq\ns1dWVsxeX183++Hh4ecv7BsTExNmu58BdnZ2cnk5geLOD1nED1nED1mc+QOUTqe//Kyvr8/sVCqV\nq8v5TxUVFWa7n1NWV1fNvru7C/yacoU7P2QRP2QRP2R57kuLwszzPJ1/7P9w/4O7+5mjtbXV7Pb2\ndrOfn5+DubA/4Pu+9+s/9Wvc+SGL+CGL+CGL7/nFzM/Pm93V1WV2f3+/2YVwxg8Kd37IIn7IIn7I\n4swfcmNjY2YvLy+bvbS0ZPbR0VHg11QouPNDFvFDFvFDFs/2hIz7fP7Z2ZnZV1dXZs/Ozpr99vYW\nzIX9IJ7tAbJE/JBF/JDF9/xFzj3ju+/rb2pqMnt0dNTsYjjjB4U7P2QRP2QRP2Rx5i9ya2trZnd2\ndpo9MDBg9v39feDXVCy480MW8UMW8UMWz/YUmVgsZrb7e3nHx8fNPj4+Dvyaco1ne4AsET9kET9k\n8T1/gWtsbDTbPcO778u/uLgI/JrCgjs/ZBE/ZBE/ZHHmL3A9PT1mu+/Lb2trMzvM79b8adz5IYv4\nIYv4IYtnewrcy8uL2e7z+4uLi2ZnMhmzOzo6zP74+DD79vY2yyvMPZ7tAbJE/JBF/JBF/JDFB94C\n475Uant72+y6ujqzy8vLzU4mk2YvLCyYfXJyYvbg4OBfXWc+8YEXyBLxQxbxQxZn/jxzz+zuL4+I\nx+Nm39zcmB2NRs1uaGgwO51Om51IJMx2f3lFMeDMD2SJ+CGL+CGLM3+eRSIRs//0l0W4D7JtbW2Z\nPTc3Z7b7oFwx4swPZIn4IYv4IYv/wJ5n7pk9lUqZ3d3dbfbu7q7Zm5ubZh8eHv7g1YUbd37IIn7I\nIn7I4nt+FB2+5weyRPyQRfyQRfyQRfyQRfyQRfyQJfU9P/Bv3Pkhi/ghi/ghi/ghi/ghi/ghi/gh\ni/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/gh\ni/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/gh\ni/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/gh6x+w6gVgMKLZZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023e2050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDGWv4+xTRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd799290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02275210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP7HETyzxE0v8xBI/scRPLPETS/zE+gccXPs+n6lngAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc5b6290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pred=model.predict_classes(x_test)\n",
    "act_test_pred=ny_test\n",
    "comp=list(test_pred==act_test_pred)\n",
    "wrong_index=[i for i,x in enumerate(comp) if x==False]\n",
    "wrong_pred=x_test[wrong_index]\n",
    "print(\"number of wrong predictions\",wrong_pred.shape[0])\n",
    "for i in range(wrong_pred.shape[0]):\n",
    "    x=wrong_pred[i]*255.0\n",
    "    im=x.reshape(28,28)\n",
    "    k=plt.figure(figsize=(1,1))\n",
    "    k=plt.gray()\n",
    "    plt.axis('off')\n",
    "    k=plt.imshow(im,aspect=1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout for controlling overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Dropout\n",
    "model = Sequential()\n",
    "model.add(Dense(784, input_dim=784, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 20000 samples\n",
      "Epoch 1/6\n",
      "40000/40000 [==============================] - 11s - loss: 0.3415 - acc: 0.9003 - val_loss: 0.1690 - val_acc: 0.9508\n",
      "Epoch 2/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.1561 - acc: 0.9553 - val_loss: 0.1296 - val_acc: 0.9628\n",
      "Epoch 3/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.1157 - acc: 0.9655 - val_loss: 0.1100 - val_acc: 0.9672\n",
      "Epoch 4/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.0909 - acc: 0.9727 - val_loss: 0.0985 - val_acc: 0.9707\n",
      "Epoch 5/6\n",
      "40000/40000 [==============================] - 10s - loss: 0.0766 - acc: 0.9763 - val_loss: 0.0966 - val_acc: 0.9719\n",
      "Epoch 6/6\n",
      "40000/40000 [==============================] - 11s - loss: 0.0654 - acc: 0.9803 - val_loss: 0.0889 - val_acc: 0.9727\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0f01e7fc50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_data=(val_train, val_test), epochs=6, batch_size=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9888/10000 [============================>.] - ETA: 0s\n",
      "loss  0.0778171791151\n",
      "accuracy  0.9767\n"
     ]
    }
   ],
   "source": [
    "scores=model.evaluate(x_test,y_test,verbose=1)\n",
    "print \"\"\n",
    "print \"loss \", scores[0]\n",
    "print \"accuracy \", scores[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9920/10000 [============================>.] - ETA: 0s('number of wrong predictions', 233)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAh0FXB+67lwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d0df50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc56aad0>"
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     "metadata": {},
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc4a8510>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022b2c90>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9R+AeggX+TkXjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02371650>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f13210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc4af1d0>"
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     },
     "metadata": {},
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    {
     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e2a4d0>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE0v8xBI/scRPrF/cMBrQzConQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02259250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVQoHe4HAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fce690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc48c090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc41b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIn7IIn7IIn7IIn7IIn7IIn7I+g29e+buEragXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2feb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2b9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "metadata": {},
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc07e250>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2418710>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc08bb50>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2b9b10>"
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     "data": {
      "text/plain": [
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f41d10>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJX5iiZ9YfwPPcf1JuorYbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022f1c90>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc0ab090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ9Y/D5gbvWJKRTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2267390>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+IklfmL9AdQc+4BDPzAqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023b2bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYRm/sIxfWMYvrH8BVQOzChAa/XsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023f2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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     "data": {
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02288290>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc253510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/gLNpxcJ0NHYJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2cc0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxA9ZxA9ZxA9ZxA9ZxA9ZxA9ZxA9Z/wGfDkQ1WcJ++wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01fcf3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0200c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01db6c50>"
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22e8810>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0221a990>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsogfsv4Hg7pBqUfpK0kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01ea7410>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2287710>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc3a6050>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwzaiwFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc372350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzWvNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f77ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAdspAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2198350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2387750>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22583d0>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02291510>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22784d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/zEEj+x/gI9uNkOOR6nBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01de8090>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhyzihyzihyzihyzih6zfvx5PQzuCkQAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef230b390>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfyw9QcFPFdDPcKwbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22f70d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1e6510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABPNJREFUeJzt3T9sTQ0cxvFWsNQkxkYMhkZCDCQWg0liIdpICImJGAx2\nxORPEH8WkVikaRMxaDeEGYONxCYiOhoMREKu9f2d9819877ntvfcPp/P9sTVnuGbk3NPT2/He73e\nGCRaM+wDgGERP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRP\nLPETS/zEWjvsA1hJ4+PjPqpiFej1euOD+DrO/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s\n8RNL/MQSP7HETyzxE0v8xBI/saJ+jTHB0aNHy7506VLZDx48KPv69evLfUid5cxPLPETS/zEcs2/\nyty9e7fsTZs2lb1jx46VPJxOc+YnlviJJX5iueYfcQcPHix748aNZS8tLZV99erVZT+mUeHMTyzx\nE0v8xBI/sbzhHTE7d+4s+/79+31ff/r06bLfv38/8GMaVc78xBI/scRPrPFeL+fPVI3i3+Rau7a+\nLXv+/HnZ+/btK/vt27dl79q1a3kObIj8TS5oSfzEEj+x3OfvuDNnzpTdvMb//ft32dPT0wP9/uvW\nrSv70aNHZf/Te8abN2+W/erVq3/9P8PgzE8s8RNL/MRyzd9xExMTff/93r17ZX/69KnV99uwYUPZ\ns7OzZR86dKjsDx8+/O1rHDlypOzXr1+X7Zofhkz8xBI/sTzb0zGTk5NlP3v2rOzmfffmszvfvn3r\n+/W3bNlSdvPnBufOnSt7+/btZf/48aPs5gfjjo2NjS0uLvY9hrY82wMtiZ9Y4ieW+/wds3v37rK3\nbdtWdvNZnqdPn/bdTSdOnCh769atfV//5s2bsi9evFh28/cLRokzP7HETyzxE8t9/o7ZvHlz2c37\n/FNTUwP9fr9+/Sr78uXLZd+5c6fsr1+/DvT7/x/u80NL4ieW+Inlmr/jmu8Bms/vHzhw4D99vY8f\nP5Z97NixspvP3neRa35oSfzEEj+xPNvTcZ8/fy77xYsXZTev+Zvv4W7dulX2hQsXyv7+/XvbQxxZ\nzvzEEj+xxE8s9/k7bmZmpuzHjx/3ff3JkyfLfvjw4aAPaejc54eWxE8s8RPLff6OaX6uzpUrV/q+\n/vbt22XPzc0N+pBWLWd+YomfWOInlvv8Q9b87M2XL1+WvXfv3rKbz/OfPXu27Obn+qxG7vNDS+In\nlviJ5T7/Cluzpp5vbty4UXbzGv/JkydlX7t2reyEa/zl4sxPLPETS/zEcp9/ha1fv77snz9/9n39\n8ePHy/bsjvv80Jr4iSV+YomfWH7I1TELCwtlz8/PD+lIVj9nfmKJn1jiJ5Zr/o7Zs2dP2YcPHy67\n+ccjvnz5suzHtFo58xNL/MQSP7E82Lbyx1D2qVOnyj5//nzZk5OTZb97967s/fv3l720tNT2EDvP\ng23QkviJJX5iueZn5Ljmh5bETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RMr6tke+CtnfmKJn1ji\nJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW\n+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJ\nJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWH8Ap6wAqGNXphgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2abd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1c1dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlvELy/iFZfzC+hu9K7g8F7zZywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0232b590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABiBJREFUeJzt3U1IVHscxvEzF3vBFoELCUwQlSDDhQjpKkh050JQENFF\nEYJQgi+bEBFSUNAEX1FR0Z0b0dRWLQpCaNXOxEJBEGqhGPhSUKh3ERfu73/CGs6Mc/T5fnaPzUwH\n+nI4M82MkZOTEw9Q9E+iDwBIFOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKH\nLOKHLOKHLOKHLOKHLOKHLOKHLOKHrKREH8BZikQifFXFBXBychKJxeNw5ocs4ocs4ocs4ocs4ocs\n4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocsqbc0XwSpqalmV1dXm52ZmWn2kydPzJ6fnzd7Y2PD\n7OnpabN3d3fN/vLly18fa9hx5ocs4ocs4oesiNIvoT6PH2NMT083+/nz52aXl5efev9IxH7iL9p/\n78+fP5s9NTVl9tDQkO8+29vbUf0d0eJjjEBAxA9ZxA9ZXPOHjPs6/ps3b8x2nwMcHh6avbKyYvaH\nDx/MrqysNPvatWtmJycnn3p87nOItrY23226urrMPj4+PvUxo8U1PxAQ8UMW8UMW1/wh09PTY3ZN\nTY3ZpaWlZr9//z7Q35eTk2N2SUmJ2XV1dWbfunXL7N/109LSYnZ3d3eQQ/Thmh8IiPghi/ghi/gh\niye8IXN0dGS2+8a1Fy9enOXheCkpKWa7/+l2584d330ODg7Mzs/PN9v9AE20eMILBET8kEX8kMUH\n2ENmcXHR7K9fvyboSH5xP8B+//59s9+9e+e7T3Z2ttnj4+NmFxUVxejoguHMD1nED1nED1lc84eM\n+0a2vLy8BB3J77nPAd6+feu7TVZWltlh/b8kzvyQRfyQRfyQxTV/yLgfSF9eXk7QkfydV69e+X72\n8OHDBBxJ9DjzQxbxQxbxQxbX/AgkLS3tj7f53fOCMODMD1nED1nED1l8hheBvH792vez3Nxcs933\n+uzt7QX6O/kMLxAQ8UMW8UMWr/PHUEZGhtlXrlzx3ebjx49ndDTxcfXqVbPdX27hef7v9gl6jR8v\nnPkhi/ghi/ghi2v+AK5fv2720tKS2e7313ie583Ozpr9+PFjs8N6ffyf4eFhs93v4fQ8zxsYGDir\nwwmEMz9kET9kET9kcc0fwM2bN82+ffu22d+/f/fdp6qq6tTHmJ+fN3t0dNTsHz9+RH2c0bh8+bLZ\ng4ODZj948MDs1tZW32PMzMzE/LjigTM/ZBE/ZBE/ZHHNH0d9fX2+n+3s7Jjd0dFh9r1798yOROxb\n1xcWFsze3NwMcIT+a3z3dXz3O3jc5yQTExO+xzw+Pg50TGeFMz9kET9kET9kcc0fRz9//vT9rL+/\n3+yXL1+a3dDQYHZzc7PZxcXFZk9OTpod7e/pdV/Hd6/xV1ZWzK6oqIjq8cOMMz9kET9kET9k8b09\nMeR+dtV9r4/ned6NGzfieQheSkqK2c+ePTO7rKzM7EuXLpnd09Njdm9vbwyPLjb43h4gIOKHLOKH\nLF7nj6H19XWzCwsLfbd59OiR2e7r9NFKTU01233vz927d83e2toyu7Gx0Wz3M8YXGWd+yCJ+yCJ+\nyOJ1/hhyv5vz06dPvtu41+hjY2Nmt7e3m/3t2zeznz59anZ9fb3Z7ncJzc3Nmd3W1mb22tqa7xjD\njtf5gYCIH7KIH7KIH7J4whtHtbW1vp+NjIycep/9/X2zV1dXzS4oKDj1z90Pp4yPj//xOM8bnvAC\nARE/ZBE/ZHHNH0dJSf73DXZ2dprd1NRktvslVe6/j/vLLNwvhQ37L7eIBa75gYCIH7KIH7K45se5\nwzU/EBDxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQxbxQ5bU+/mB/+PMD1nE\nD1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nE\nD1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nE\nD1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1nED1n/AtOYXoR1mx0GAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0214b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2OW07OyZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc47d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02009050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f83310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f51e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkPUHQmUXAetHA80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01dd92d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023a6790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5Yetn2NTpGk2gb/cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc18a190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
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     "data": {
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     "metadata": {},
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    {
     "data": {
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1cac10>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef23c5490>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2279050>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef225b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABbpJREFUeJzt3b9LVX8cx/Fzv4qYjaKkg4hLg05uLiqlLSL5C1IUFJoU\nISTIrVQsrEk3p1D/AREHUdACxURB/NGgqIsuUYgl+AuV+12/749fbtzOuXaPr+dje5Uez5fvk8Ox\n+ysSjUY9QNE/f/sEgL+F+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL\n+CGL+CGL+CGL+CGL+CGL+CEr9W+fwG2KRCK8VcUdEI1GI0Echys/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/\nZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZEk9pfkuSk9PN7unp8fshoYGs3/9+mX2ixcvzF5YWAju5JIc\nV37IIn7IIn7Iiih9CPVdfBnjmzdvYu6dnR2z8/PzzV5dXTW7pKQkuJNLEF7GCPhE/JBF/JDFPX/I\nPHv2zOyPHz+aPTw8bPbLly9j/n1bW5vZhYWFZu/t7f3JaSYU9/yAT8QPWcQPWTy3J8nl5uaaPTo6\navbMzIzZ79+/j3m89fV1s9PS0swuLi42Oxnv+YPClR+yiB+yiB+yiB+y+IU3yaSm2v8lIyMjZl9e\nXprd2dlp9vfv32Mef3Z29s9P7o7hyg9ZxA9ZxA9Z3PMnmezsbLMrKirMrq2tNXt/fz+u47sPainj\nyg9ZxA9ZxA9Z3PMnGffFKi73iWx+j391dWX2wcGBr+OHCVd+yCJ+yCJ+yOKeP0BPnz41u6Cg4MbX\njI2NmZ2RkWF2d3e32e4Lzs/Pz/2c4g0XFxdmLy0tBXr8ZMaVH7KIH7KIH7K45w/Qw4cPze7t7b3x\nNY2NjWb39/ebff/+fbOHhobM9vsmYzU1NWZvbm76Ol6YceWHLOKHLOKHLO75A/Thwwez3X/D9zzP\ne/36tdkTExNmHx4emr29ve3rnHJycmLu+fl5X8cPM678kEX8kEX8kMU9fwINDAzc+LP29nazs7Ky\nzP727Vug5/Do0SOzMzMzAz1+mHHlhyzihyzihyzu+RPo/557Pzg4aPbbt2/Nnp6eDvQcKisrzXaf\nG7S2thbozwsTrvyQRfyQRfyQxT3/Lbvt58+npKSYfXx8bPbc3Nxtnk5S4coPWcQPWcQPWdzzh9y9\ne/fMfvz4sdnNzc0xv7+rq8ts90Oul5eXfZxdcuPKD1nED1nED1nc898y99/Z3ffH//nzZ8zvd9/X\n58uXL2YXFRXFdT7u6wueP39utvuZX3V1dWZ//fo1rp+XTLjyQxbxQxbxQ1bE73s/hkkkEkm6/1j3\nHn1vb8/ss7Mzs917dPd1wkdHR2bn5eWZ3dHRYbb7PkH19fVmV1dXx/z6J0+eeK6tra0bfxakaDQa\nCeI4XPkhi/ghi/ghi3v+kDk5OTHb/R3hwYMHZi8uLprd1NRktvs7hauqqsrsyclJs93HGTzP80pL\nS82+vr6O+TPixT0/4BPxQxbxQxbP7Qm53z2XZ3x83Ozf3eO7pqamzC4vLzf78+fPN76nr6/PbPdz\nx+I9h0Thyg9ZxA9ZxA9ZxA9ZPMgVMj9+/DDb/bCJjY0Ns90HnNwX08TLfRMs95dZz/O8V69eme1+\nOPfu7q6vc+BBLsAn4ocs4ocs7vlDprW11ex3796Z3dLSYvanT58Sfk6usrIys1dWVsw+PT31dXzu\n+QGfiB+yiB+yuOdH6HDPD/hE/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF/JBF\n/JAl9Xx+4L+48kMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW\n8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW\n8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UPW\nv11jOdlQLeSZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef225b950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef218af10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2166f50>"
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     "metadata": {},
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e31e10>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f17650>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec558b10>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022be410>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1d82d0>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1e11d0>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef227e450>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxBI/scRPLPETS/zEEj+xxE8s8RPrX9+gEJYS3C5LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc315790>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02094150>"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef23f4450>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef232dbd0>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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     "execution_count": 29,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f78590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc312d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d369d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIn7IIn7IIn7I+g8lwE3ISUfCUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f43390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02003050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhyzihyzihyzihyzih6x/X8wwPPFLUW0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2365290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfyQ9RcAVzaw9rs+fwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2350190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc3c1290>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc350790>"
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e22150>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfsgifsgifsgifsgifsgifsj6B7x1Es6LPyLxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc050750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZBE/ZP0FOZ4ivQTxMVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc0fa150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz0HgJqymAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc318d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2427e50>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+CGL+CGL+CHrB1MDOp8oSFV/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef228ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD1nED1n/AhpNS4wg/FwkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc28e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc3c1ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8sMW5Yctyg9blB+2KD9s/QJGRq8FXbFCogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd835e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2d17d0>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22ab050>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc39c290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01dc9ed0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "data": {
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     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc242bd0>"
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     "metadata": {},
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2291050>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef21be310>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc435ed0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0208da10>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc435290>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxBI/scRPrF/qruecS30BQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1deed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "metadata": {},
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef232c3d0>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc4d1050>"
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     "data": {
      "text/plain": [
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc32d290>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023c4790>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1a6150>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/zEEj+xxE8s8RNL/MQSP7F+AXag0oK/2dL5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01dcf590>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn1jiJ5b4iSV+YomfWOInlviJJX5i/QV93FR9xLdbOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1a1150>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc3f1d10>"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22e84d0>"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2c5350>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d24090>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc228f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8UMW8UMW8UMW8UMW8UMW8UPWv5pxfM1312qcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd76a3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02007150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni/ghi/gh6x++ilLidjPPzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef21c1290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni/ghi/ghi/ghi/ghi/ghi/ghi/gh6zf6pTjyB/0CxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc3b9190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc061290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYv4IYv4IYv4IYv4IYv4IYv4IYv4IetfVt1VnfyejtUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023b26d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc41e150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc480590>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef220b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1fa8d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n32L6jyJUlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc232b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1d7410>"
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     "metadata": {},
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2284050>"
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc3294d0>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc19b350>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1dbdd0>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc452110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABQZJREFUeJzt3T8odQ0Ax3H39WTAIjYkEhl0LZLI6qZkECmJJEUZFJPB\nojAZDJJSBsnKSDb/MihhlMm/QSn5s7jv+v7O4Ml7XNe5v+9n+/U87j31fDude917nlgymcwCHP2T\n7gMA0oX4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4YYv4\nYYv4YYv4YetPug/gJ8ViMW5VkQGSyWTsOx6HMz9sET9sET9sET9sET9sET9sET9sET9sET9sET9s\nET9sET9sET9sET9sET9sET9sET9sET9sET9sET9sET9sET9sWd26BFlZ2dnZsvv6+mQvLS3J3tzc\nlD0yMiL79fX1G4/uZ3Hmhy3ihy3ih61YMulzBz/H2xUWFRXJXl1dld3e3v6lxxsdHZUdfI3wE7hd\nIRAS8cMW8cMW8cMWv+SKuLy8PNldXV2yp6amZFdUVMg+ODiQvbe3J7usrEz229vb/zrO34gzP2wR\nP2wRP2xxzR8xhYWFsk9PT2WXlpbKvr+/l51IJGTv7OzIzsnJkZ2fny87+BojyjjzwxbxwxbxwxbX\n/L9cPB6XvbKyIjv4GmB5eVn23Nyc7Ovr60+fb3x8XPb8/Lzsx8fHT38+SjjzwxbxwxbxwxbX/L9M\n8Msn09PTsmtqamTPzMzInp2dDfX85eXloX4+SjjzwxbxwxbxwxZfYE+z4E2kgp+/n5iYkD02NiZ7\nbW0t1PPn5ubKfnl5CfV4P4EvsAMhET9sET9scc2fZj09PbI3NjZkB79j29TUFOr5JicnZQ8PD8tu\nbW2VfXV1Fer5UoFrfiAk4oct4octPtuTZgUFBZ/++fHx8Zcer7m5WXZ3d7fsxsZG2ZWVlbI7Ojpk\nLywsfOn5o4QzP2wRP2wRP2xxzf/LtbW1yQ5eg7+/v8uura2VfXNzI/vp6ekbjy7aOPPDFvHDFvHD\nFtf8aXZ2dib75OREdn19veyLiwvZt7e3svv7+2VfXl7KLi4ull1dXS378PDwL0ecOTjzwxbxwxbx\nwxbX/Gm2v78ve3BwUPbAwIDs4DX7+vq67KOjo0+f7/n5WXbw9wB/+/lMwpkftogftogftvgOb4ar\nqqqSvbW1JTv4WZ+GhoaUH1NYfIcXCIn4YYv4YYv3+TNcZ2en7OBneYL3/3fCmR+2iB+2iB+2eJ8/\nw21vb8sO/nsPDQ3Jfnh4SPkxhcX7/EBIxA9bxA9bvM+fYUpKSmS3tLTIDv6fXlG4xk8VzvywRfyw\nRfywRfywxQveiIvH47Ln5uZkf3x8yL67u0v5MUUFZ37YIn7YIn7Y4po/4oIfTEskErLPz89l7+7u\npvyYooIzP2wRP2wRP2xxzR8xdXV1snt7ez/9+4uLi7KD7/s748wPW8QPW8QPW1zzR0xZWZnsWEy/\nyz0zMyN7ZWUl5ccUVZz5YYv4YYv4YYubViFyuGkVEBLxwxbxwxbxwxbxwxbxwxbxw5bV+/zAf3Hm\nhy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3i\nhy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3i\nhy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ihy3ih61/AVUi5u+KEX4e\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022b1790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn1jiJ5b4iSV+YomfWP8CUL0XZ00V1AIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02130c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP2wRP2wRP2wRP2wRP2wRP2wRP2z9AkNfkOqaVu0wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0242d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22a5110>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc106210>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc43be50>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH7KIH7KIH7KIH7L+AihrGosr+5AbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc39a210>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d65510>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0238e0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni/gh6zdhfzT2nUeRlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f020ab510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4ocs4ocs4ocs4ocs4ocs4oes37xdIzfg08avAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1f20d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS4ZmYkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef21a53d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd7a5290>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
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     "execution_count": 29,
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc343790>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8NWCvoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef231b210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nET9kET9kET9kET9kET9kET9kET9k/QtacC3s4vYNbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc174e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYfa4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f02256210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01d6d0d0>"
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     "data": {
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     "execution_count": 29,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01f61510>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef21400d0>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f020a73d0>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc397b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLOKHrN9NIwCDVavn4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01e403d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2242050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niB+y/gVrx0z9Ch4zjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2419290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2d3310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/3mT3yV2oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc054290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef214a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWcQPWcQPWcQPWT8A7tTW/mheNJ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f0238d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH7KIH7KIH7KIH7KIH7J+AwYMFeJz99VCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef231a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc260250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/Qf13jdFEZpP0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef21c5750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABfFJREFUeJzt3UsopX8cx/E57rkUg4WdhdQkiwnNRNlYIGXFRrKRqSkL\nC5kazUJNLKambGTFZpooNsxqyi1sRCjlPiUSJZnGZZiZzn/7//6e/x/Hc9zm837tPhPHU/Pu9Die\n85xAMBh8AiiKuO8DAO4L8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW\n8UMW8UMW8UMW8UMW8UMW8UNW1H0fwF0KBALcquIvEAwGA+F4HJ75IYv4IYv4IYv4IYv4IYv4IYv4\nIYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IUvqbYwIXXJystmtra1m9/f3e75ndnb2\nVo8pXHjmhyzihyzihyzO+cVlZWWZXVtba3Z9fb3ZT58+NfvLly+3c2B3gGd+yCJ+yCJ+yOKc/y8X\nFWX/i8vLy83+9OmT2YmJiWYHAvbOgBUVFWYvLS1d+TN///59vYO9YzzzQxbxQxbxQxbxQ1YgGNS5\nZb3i/fm7u7vNbmhoCOn73V94r9PLxMSE2e/evTN7eno6pGNwcX9+wCfihyzihyzO+R+5oqIis6em\npsz2+//b09Nj9tjYmNlJSUme72lvbzd7fn7e7JKSEl/HxDk/4BPxQxbxQxYXtj0ysbGxZn/8+NFs\n9xz/x48fZvf19Zk9MDBg9tzcnNmHh4eXHs/r1689/5aQkGD2ysrKpY9xX3jmhyzihyzihyzO+R+Z\n8/Nzs4eGhsw+PT01230D+rdv38J6PE1NTZ5/c9/MMjw8HNafGS4880MW8UMW8UMW1/Y8MhER9vkq\nPj7e7OPj41v9+S9fvjR7ZGTE8zXu7yWFhYVm+33dn2t7AJ+IH7KIH7J4nf+BcT/swb2JVHR0tNnP\nnj0zu66uzmz3+vtQ5efnmz0+Pm72z58/Pd9TWVlpNtf2AA8M8UMW8UMW5/xh5F7HXlxc7PmazMxM\ns7u6usz2+3eX0dFRszs6Osx++/at2TExMWa7769tbm6+9OsHBwc9x+C+j/ih4pkfsogfsogfsri2\nJ4waGxvN7uzs9HzNwcGB2e758YcPH8xeW1sL6Rjcx//165fZ7vX3R0dHZrsfVuHeq/P9+/dmt7W1\neY7hz58/1zvYG+LaHsAn4ocs4ocszvl9cO8739raavbe3p7ne168eGH2/v5+OA/pycLCgtm5ublm\nu78DuOfncXFxZpeVlZnt/h3hts/v/wvn/IBPxA9ZxA9ZnPOHIDs72+yvX7+avbq6avarV688j7G1\nteXnEDzv4U1JSTF7eXnZ7NTU1Esfb2dnx+zq6mqzZ2ZmQj3EW8c5P+AT8UMW8UMW5/whmJycNNv9\nHaC0tNRs9zX360hLS7v0MXNycsx+8+aN2Vd9bq57v/68vDyzNzc3r3+w94RzfsAn4ocs4ocs3sMb\ngouLC7PT09PNfv78udkZGRlXPmZNTY3ZVVVVZrufweWanZ01+/Pnz2a77+GNjIw0272XvhKe+SGL\n+CGL+CGL1/lD0NLSYrZ7Pn0T379/N9u9r6X7urx7n5ze3l6zk5OTzV5fXzfbvTaooKDAbPf6pIeI\n1/kBn4gfsogfsjjnD4F7L87rvI5/Ffdzc3d3d309nnuM29vbZruf4eW+p3hxcdHXz78LnPMDPhE/\nZBE/ZBE/ZOle1XQDJycnZm9sbNzTkfw/949cPT09Zp+dnZn9GN68clt45ocs4ocs4ocs/siFR4c/\ncgE+ET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9k\nSb2HF/g3nvkhi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/gh\ni/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/gh\ni/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/ghi/gh6x9E\nDGWv4+xTRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22517d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxBI/scRPLPETS/zEEj+xxE+s/wFybaDGti/UdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1361d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP7HETyzxE0v8xBI/scRPLPETS/zE+gccXPs+n6lngAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc12f890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pred=model.predict_classes(x_test)\n",
    "act_test_pred=ny_test\n",
    "comp=list(test_pred==act_test_pred)\n",
    "wrong_index=[i for i,x in enumerate(comp) if x==False]\n",
    "wrong_pred=x_test[wrong_index]\n",
    "print(\"number of wrong predictions\",wrong_pred.shape[0])\n",
    "for i in range(wrong_pred.shape[0]):\n",
    "    x=wrong_pred[i]*255.0\n",
    "    im=x.reshape(28,28)\n",
    "    k=plt.figure(figsize=(1,1))\n",
    "    k=plt.gray()\n",
    "    plt.axis('off')\n",
    "    k=plt.imshow(im,aspect=1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "x_train = x_train.reshape(x_train.shape[0], 28, 28,1)\n",
    "x_test = x_test.reshape(x_test.shape[0],28, 28,1)\n",
    "x_train=x_train/255.0\n",
    "x_test=x_test/255.0\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#getting validation data\n",
    "val_train=x_train[40000:]\n",
    "x_train=x_train[:40000]\n",
    "val_test=y_train[40000:]\n",
    "y_train=y_train[:40000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.layers import Dropout,Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cnn_model = Sequential()\n",
    "cnn_model.add(Conv2D(32, (5, 5), input_shape=(28, 28,1), activation='relu'))\n",
    "cnn_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "cnn_model.add(Dropout(0.5))\n",
    "cnn_model.add(Flatten())\n",
    "cnn_model.add(Dense(128, activation='relu'))\n",
    "cnn_model.add(Dense(10, activation='softmax'))\n",
    "cnn_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 20000 samples\n",
      "Epoch 1/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.3376 - acc: 0.9007 - val_loss: 0.1240 - val_acc: 0.9642\n",
      "Epoch 2/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.1119 - acc: 0.9668 - val_loss: 0.0793 - val_acc: 0.9759\n",
      "Epoch 3/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0816 - acc: 0.9748 - val_loss: 0.0662 - val_acc: 0.9800\n",
      "Epoch 4/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0634 - acc: 0.9803 - val_loss: 0.0640 - val_acc: 0.9800\n",
      "Epoch 5/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0543 - acc: 0.9835 - val_loss: 0.0602 - val_acc: 0.9813\n",
      "Epoch 6/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0448 - acc: 0.9867 - val_loss: 0.0516 - val_acc: 0.9838\n",
      "Epoch 7/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0386 - acc: 0.9870 - val_loss: 0.0489 - val_acc: 0.9854\n",
      "Epoch 8/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0352 - acc: 0.9884 - val_loss: 0.0501 - val_acc: 0.9854\n",
      "Epoch 9/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0316 - acc: 0.9892 - val_loss: 0.0472 - val_acc: 0.9868\n",
      "Epoch 10/10\n",
      "40000/40000 [==============================] - 33s - loss: 0.0279 - acc: 0.9907 - val_loss: 0.0499 - val_acc: 0.9853\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0efc1539d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_model.fit(x_train,y_train,validation_data=(val_train,val_test),epochs=10,batch_size=200,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9856/10000 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.03902165622552857, 0.98770000000000002]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9920/10000 [============================>.] - ETA: 0s('number of wrong predictions', 123)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfmKJn1jiJ5b4iSV+YomfWOInlviJ9S+92Pbe1TFMGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f01df3f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd79f610>"
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2253410>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE0v8xBI/scRPrF/cMBrQzConQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef20ac190>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1fab790>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1f41190>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 37,
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2127250>"
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     "metadata": {},
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1c1310>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc04d550>"
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     },
     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef22bebd0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8UMW8UPWH4L6C/uSEN7UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023a10d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+IklfmL9AdQc+4BDPzAqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022b13d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYRm/sIxfWMYvrH8BVQOzChAa/XsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef20d2b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1c9a110>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
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     "execution_count": 37,
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1b36110>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIKAcQ4+kaSWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1ac9410>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
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     "data": {
      "text/plain": [
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
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    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef18902d0>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1fae850>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef17b2350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef19673d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2OW07OyZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1a9cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1ca73d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc0f4d50>"
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     "metadata": {},
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2139e10>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niB+yiB+yiB+yfgOwXgDusuMkzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022ab350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc12d2d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1e75350>"
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     "metadata": {},
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     "data": {
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1b91110>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1b293d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1c4de50>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nv11jOdlQLeSZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef18a4210>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef20e20d0>"
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     "metadata": {},
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1910510>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1b530d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1de23d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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     "metadata": {},
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2026050>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2070290>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1a617d0>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1de3a10>"
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     "data": {
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef19d9c50>"
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     "data": {
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022d1ed0>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023d4d90>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAPdoeHppT6HaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef176bc10>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc2c12d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1f87110>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023c82d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1ca6790>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz0HgJqymAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efd77c190>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc12c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABSZJREFUeJzt3T+ozX8cx/FzZORSFsnf2b8UsVhsJCzSXRiVTbIqs41J\nkVJiMGGwUEgGZTNTriiTMFCc3/Bbfu/P1Tm/c7/33Ht5PR7bm+vrc/Xs0+ce3/M9/cFg0INEyxZ7\nAbBYxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/\nsZYv9gIWUr/f96iKv8BgMOjPx3Xs/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQS\nP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7GiHlc4aStXrizzvXv3Zn1Nv1+ftHf4\n8OEyT01NlfnEiRNlfvLkSZlfvXo19jr5l52fWOInlviJJX5i9QeDnEfWT/r5/Bs2bCjzmzdvfreG\nMj98+LDMa9asKfPu3bvLfPPmzTJ/+/atzEeOHCnzlStXynznzp0yz8zMzFrjUuf5/NCR+IklfmI5\n83ewbdu2Mt+4caPMu3bt+t0aytz133/c6718+bLMZ8+eLfPHjx/L/Pbt27kvbkKc+aEj8RNL/MRy\n5u/g4MGDZb5///7/WUOZF/rMP8r169fLfObMmTL//Pmz0/XngzM/dCR+YomfWM78HTx//rzMe/fu\nHflnnj59Wub2dfSTJ0+OtYZJ/wyxcePGMr9//77T9eeDMz90JH5iiZ9YzvxjaO+Nb18D//LlS5mP\nHj066xrtG9BHOXXqVJm3b99e5nPnzpX5169fY12/tWxZ3Q8vXrxY5qtXr5b5w4cPnf6+uXDmh47E\nTyzxE8uZfwztQ6gOHTpU5tOnT5e5vU9mEqanp8vcvof3wIEDZW7fI9wa9f8G7fsBjh8/Pusak35f\nsDM/dCR+YomfWB5UO4Znz56VuT3zf/78eSGX0+v1er3bt28Pnbdu3Vrm9evXl/natWtlXrdu3dC/\nb8+ePWXet2/frK+5e/fu0GssFXZ+YomfWOInljP/EO0HR1y4cKHM379/L3P73Myl4PXr10Pn9nt8\n/PhxmVetWjX0+qN+fymz8xNL/MQSP7Hc2zNE+xzLS5culfnFixdl3r9//xxXtnSM+p5b7969m/Vr\nW7Zsmdc1tdzbAx2Jn1jiJ5bX+cfQ3uvePoPnb9R+z63z588v0Ermn52fWOInlviJ5XX+IVasWFHm\n9j6WT58+lfnHjx9zXNnSMe7r/O29Qr1er7dz5855XVPL6/zQkfiJJX5ieZ1/iK9fvw6dE7WfJ9A+\nK/RPYucnlviJJX5iOfNTjPpMsM2bN5d59erVE1zNZNn5iSV+YomfWM78FDt27Cjz33zvl52fWOIn\nlviJJX5i+YGXov0Q6q4far2U2fmJJX5iiZ9YzvwU7Rm//U+uY8eOlfnRo0cTX9Ok2PmJJX5iiZ9Y\nzvyMZXp6uswPHjxYpJV0Z+cnlviJJX5iOfNTzMzMlHnt2rVlnpqaWsjlTJSdn1jiJ5b4ieXMT7Fp\n06YyX758ucy3bt1ayOVMlJ2fWOInlviJ5QPp+OP4QDroSPzEEj+xos788F92fmKJn1jiJ5b4iSV+\nYomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJ\nn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y\n4ieW+IklfmKJn1jiJ5b4iSV+YomfWP8AgGkeMC/9BUkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1d49190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef207b650>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwCpHGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1e59cd0>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABNpJREFUeJzt3T+oT38cx/F7b+oSBsWoDMJlM5juclNsyjWb3NGoGG9J\nKBRuNguDuovRwGQw3UzIYrly6yqU/Kkbvr/19z7q1nW+f+/r8dheun/O8Oz0ufee79d4p9MZg0QT\ng74AGBTxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+x\nxE+sLYO+gH4aHx/3VhWbQKfTGe/G13HnJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1ji\nJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IkV9dYlw2jfvn1lz8zMlH3s2LF1P//IkSNlv379uuy7\nd++W/ebNmw1e4eblzk8s8RNL/MQa73Ry3sGv329X2Dy/j42NjV25cqXsgwcPlr1r166uXsPPnz/L\nvnz5ctm3bt0qe21travfvxe8XSG0JH5iiZ9Y4ieWP3L10OTk5F//tnv37rI/ffpU9p07d8r++vVr\n2YuLi2V/+fKl7KmpqbLn5+fLvnr1atm/fv0q+8aNG39d82blzk8s8RNL/MTyR65Nbvv27WW/ePGi\n7EePHpV97dq1nl9TW/7IBS2Jn1jiJ5Yzf5h79+6V3Xz4rvl3gmHkzA8tiZ9Y4ieWZ3vCbd26ddCX\nMDDu/MQSP7HETyxn/jDNN7mamKj3v+azQN+/f+/5NQ2KOz+xxE8s8RPLmX/E7dy5s+yjR4+WPT09\nXfahQ4fK3rNnT9lv374tu/ma3tu3b//TdQ4jd35iiZ9Y4ieW5/kHbMuW+mPX2bNnyz59+nTZ+/fv\nL7v5bE7zP7to69mzZ2WfOHGiq1//X3ieH1oSP7HETyxn/j47cOBA2U+fPi177969637+x48fy15e\nXl7347dt21Z289me5u/1b968WXbzfX1+/Pix7vfrB2d+aEn8xBI/sTzb02fN5+PPnDmzoc/f6Jn/\n8OHDZb969arsBw8elH3//v0NXc8oc+cnlviJJX5iOfP32YcPH9bd3Xbx4sWefv1R5s5PLPETS/zE\n8mzPJre2tlb2yspK2cePHy/73bt3Pb+mtjzbAy2Jn1jiJ5bf84+4ycnJshcWFspuvkZ4bm6u7FE4\n4/eKOz+xxE8s8RPLmX/EnTp1quxz586V/f79+7JfvnzZ82saFe78xBI/scRPLGf+EXP+/Pmyr1+/\nvu7HN38G+Pz5c9evaVS58xNL/MQSP7HETywvZhlyJ0+eLHtxcbHs5ptYXbp0qezHjx+X/efPny5e\n3WB4MQu0JH5iiZ9YzvxDZseOHWU/f/687N+/f5c9OztbdvNBts3ImR9aEj+xxE8sZ/4h8+TJk7K/\nfftWdvNBtqWlpZ5f07Bx5oeWxE8s8RPLmX/ALly4UPbq6mrZDx8+7OfljARnfmhJ/MQSP7Gc+Rk5\nzvzQkviJJX5iiZ9Y4ieW+IklfmJF/Z4f/s+dn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+Ikl\nfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5i\niZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+Yomf\nWOIn1n/fhAX5cA5jAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1d0b490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLPETS/zEEj+xxE8s8RNL/MQSP7H+AnqN7fTsFRAeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef189f210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nET9kET9kET9kET9k/QtLdUhFSMAIHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef19f5350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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     "execution_count": 37,
     "metadata": {},
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     "data": {
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef23a2350>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1e08790>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJX5iiZ9Y4ieW+IklfmL9A9Ah6/4weeq0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1e823d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef194fe50>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1c2c650>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1a9c410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef19180d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/zE+gdLAvvszg9FnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1e50750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f023d7190>"
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    {
     "data": {
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f022b4490>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef235bf10>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1ad52d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxA9ZxA9ZxA9ZxA9ZxA9Z/wLD3Abv0b9nFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2115790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYomfWOInlviJJX5iiZ9Y4ieW+In1BxzNX+WuKN+UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef224e350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABOVJREFUeJzt3a9rVX8cx3HvV4PTpoIwGGgzqBjEIAbHGAwFwR9/wFWj\nGkxiEBRRDCaXtrKVlZUlk8FmMAgKik1FEYMgiEGDXuv3ffA7HXf3nvv19Xi0F+zHCU8On233nnV6\nvd4GSPRP2xcAbRE/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+x\nxE8s8RNL/MTa1PYFDFOn0/Goir9Ar9frrMfXcecnlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+\nYomfWOInlviJJX5iiZ9Y4ieW+IkV9TbGBDdu3Cj72rVrZXc69R2AHz58KHtycrLsly9fruPVjRZ3\nfmKJn1jiJ1an18t5mscoPrpk//79Zc/MzJR96dKlsp89e1b21NRU2Zs21R/jmmf833nx4kXZ+/bt\nW9PnD4NHl0CfxE8s8RPL7/lb1jzTnzt3btWPHx8fH+TlbNi4ceNAv/4ocecnlviJJX5iiZ9YfuAd\nsrNnz5bd7XbbuZD/8Pjx47YvYWjc+YklfmKJn1jO/EP2+vXrsr9+/Vr2li1bBvr9P378WPb8/HzZ\nt2/fHuj3HyXu/MQSP7HETyxvZmnZ/fv3y26+mWW9vXr1quyjR4+W/e7du4F+//XgzSzQJ/ETS/zE\ncuZv2fnz58tu/t590J48eVL2qVOnyn779u0wL+ePOPNDn8RPLPETy5m/Zdu2bSv76dOnZQ/6DetN\nzZ8BTp48WfYo/B3AmR/6JH5iiZ9YzvwjZmFhoezTp0+XvXXr1rIfPXpU9ufPn8s+dOhQ2c2fMX7n\n5s2bZV+/fn1Nnz8IzvzQJ/ETS/zEcuYfcQcOHCi7+R7f5j+r+PLlS9l79+4t+8KFC2U3X8uzY8eO\nsr99+1b2lStXyp6dnf3VZQ+UMz/0SfzEEj+xnPnDTU9Pl720tFT29u3by37//n3ZExMTg7mwVTjz\nQ5/ETyzxE8uzOsM9ePCg7IcPH5Z95syZYV7OULnzE0v8xBI/sZz5w+3atavsw4cPt3MhLXDnJ5b4\niSV+YjnzhxkbGyv78uXLZQ/7OUFtcucnlviJJX5iOfP/5TZv3lz2nTt3yr548eKavt6nT5/6vqZR\n4c5PLPETS/zEcuZv2ZEjR8rudrtlP3/+vOzl5eVVv97u3bvLbj5n59ixY2u6vu/fv5d969atNX3+\nKHPnJ5b4iSV+YnluT8tWVlbKPnHiREtX8mv37t0ru/laoDZ4bg/0SfzEEj+xxE8sf+Rq2cGDB1v9\n/j9+/Cj7zZs3Zc/NzQ3zcobKnZ9Y4ieW+InlzN+y5pl70Jp/1Lx7927ZV69eHebltMqdn1jiJ5b4\nieWFbS1r/pPp48ePr/rxe/bsKXvnzp1lN9/Msri4WHbzH8otLCz8yWWOFC9sgz6Jn1jiJ5YzP/87\nzvzQJ/ETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xIp6bQ/8mzs/scRPLPETS/zEEj+xxE8s8RNL\n/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zE\nEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/\nscRPLPETS/zEEj+xxE+snx817hDqP1PVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1c2cf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8UMW8UMW8UMW8UMW8UMW8UPWv5pxfM1312qcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc44ae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni/ghi/gh6x++ilLidjPPzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc0f3650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABTNJREFUeJzt3U8obH0cx/GZ68qUUv6lqLEgS1bMDvmTKVlKdlZsWCgs\n/Sk2msiOoqyxUoqaJAlJFhZWsrCTiIUahbnb+/3duoYzf33er92n52meU/fd6ccz51x/PB73AYp+\nZfoCgEwhfsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgifsgi\nfsgifsgifsj6nekLSCe/38+rKn6AeDzuT8bncOeHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKH\nLOKHLOKHLOKHLOKHLOKHLOKHLOKHLOKHLKnHGHNBSUmJ2SMjI2ZPT0+b/fr6avbs7KzZkUjE7Fgs\n5vEKfw7u/JBF/JBF/JDlj8d13uaRC68uOT4+NjsUCnn6vP7+frM3NjY8fV424NUlgEfED1nED1mc\n+TMsHA6bvb29bXZeXp7Z7p/X29ub2fn5+Wa/vLyYHQwGzX56ekr8YrMEZ37AI+KHLOKHLOKHLL7Y\nlmaBQMDs3t5es90fcF2bm5tmDwwMmH1wcGB2U1OT2cPDw2a7X4RTwp0fsogfsogfsjjzp1lXV5fZ\n7pnd9fz8bPba2prZ7v/04mGVxHHnhyzihyzihyzO/GnmfpHtM1tbW2ZHo1Gzy8vLzW5ubv7ehQni\nzg9ZxA9ZxA9ZnPnTbH9/3+zBwUGzPz4+zN7Z2TG7u7vb7JmZmSRenRbu/JBF/JBF/JDFmT/NTk9P\nzXZfInV2dmb24eGh2e5Lrerq6pJ4dVq480MW8UMW8UMWL63KcvX19Wafn5+b/dkzv66HhwezJyYm\nzF5fX//S52UCL60CPCJ+yCJ+yOLMn2MmJyfNnpqaMvvm5sbsiooKswsLC82+v783e2lpyez5+Xmz\n39/fE7/YFOHMD3hE/JBF/JDFmT/HlJaWml1TU2P2Z2f+trY2s0dHR82urq42u7Gx0eyLi4vELzZF\nOPMDHhE/ZBE/ZHHmF1dbW2v27u6u2UVFRWa3traafXV1lZLr+h/O/IBHxA9ZxA9ZPMMr7vr62uy5\nuTmzV1dXzW5paTE7E2f+ZOHOD1nED1nED1n8nh+G+92h29tbs09OTszu6OhI+TW5+D0/4BHxQxbx\nQxa/589yxcXFZvv99rj7+PiY1P+e+14f9+8LcJ8fcJ8X8Pl8vru7u6ReU6pw54cs4ocs4ocszvxZ\nLhKJmN3Z2Wn20dGR2QsLC2a77/b8THt7u9kFBQVmB4NBs8vKyv75DM78QJYjfsgifsjizJ9jqqqq\nzO7r6zO7p6fH7L29PbOj0eh/P39oaMjsr77/P5dw54cs4ocs4ocs4ocsHmbJcu4PnO4D5uPj4+m8\nHF8sFjO7oaHhn3/HfSg+2XiYBfCI+CGL+CGLM3+OqaysNDsUCpnt/gzg/nOvxsbGzF5cXEzq5yeC\nMz/gEfFDFvFDFmf+HyYQCJjtPvzivmQqHA6b7f5lFcvLy2avrKyYfXl5+a3r9IIzP+AR8UMW8UMW\nZ37kHM78gEfED1nED1nED1nED1nED1nED1nED1nED1nED1nED1lS3+0B/sadH7KIH7KIH7KIH7KI\nH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KI\nH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KI\nH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7KIH7L+ABkiG8siHlb3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef212bc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni/hh6182bG83ssgeRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1898490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1bc0050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2092350>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1a02790>"
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     },
     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1d5e210>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1779a90>"
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     "data": {
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1ca63d0>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1fb2610>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef215f990>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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      "text/plain": [
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     "execution_count": 37,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2005090>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc1422d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABfFJREFUeJzt3U8o5H8cx/EZdoXa2utqUyIOauWySq3aYu3BgSLKAaHc\nlJw4rcOeUJsspz04cFmltlY0bS0O/h1MyJ8LJ1Mr61+2yOZ3/P3eH7/Gzn6Hr/F6Pm6vX+ZPv55N\nn7EzX8Grq6sAoCjJ7ycA+IX4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4IYv4\nIYv4IYv4IYv4IYv4IYv4IYv4IeuR30/gLgWDQS5V8QBcXV0F43E/vPJDFvFDFvFDFvFDFvFDFvFD\nFvFDFvFDFvFDFvFDFvFDFvFDFvFDFvFDltRHmhUlJdnXt5cvX8b1/uvr66/9t7S0NLN//vxp9vj4\nuNkLCwtxfU5/ild+yCJ+yCJ+yAoq/RFqxa8x1tXVmT06OurTM/lXOBw2u7CwMKbb8zVGwCPihyzi\nhyx+z//A5efnx/Tzh4eHZqenp5u9tbVl9sHBwbX7+Pr1q9l7e3tmT09Px/Scbguv/JBF/JBF/JDF\n7/kfmObmZrMHBwfN/vHjh9mVlZVmRyIRs1NSUsze3983+9evX3/1PL3g9/yAR8QPWcQPWcQPWbzh\nTXANDQ1mf/r0yexg0L43dL9Y8u3bN7P7+vrM9uuLJtHwhhfwiPghi/ghizN/gqmurja7v7/f7OfP\nn3u6/4uLC7ObmprMHhsb83T/8cCZH/CI+CGL+CGLM/89V1JSYvbk5KTZ7gWiXKFQyGz3yyp5eXlm\nv3jxwmz3PYD7fBYXF6M+/m3gzA94RPyQRfyQxRfY75mWlhazh4aGzE5OTjb75OTE7OHhYbO7u7vN\n/v37t9lPnjwxe3Z21mz3PUB2drbZfpz544VXfsgifsgifsjizO+z4uJis90vnN90xq+oqDB7bm4u\npsc/PT01e3t722z3zF9TU2P2ffisz9/ilR+yiB+yiB+yOPP7rLOz0+zHjx+bfXR0ZPbbt2/NTuTf\ns/uNV37IIn7IIn7I4szvs42NDbPdz9q0traavbu7G9fHdx+vtLQ06s9PTEzE9fH9xCs/ZBE/ZBE/\nZHHm98A9LxcUFJj9+vXra7dZXl422/1O7rt378x2v0MbbxkZGWY/ffo06s+7f5wikfHKD1nED1nE\nD1mc+T34/Pmz2WVlZZ7vMxwOm93T02O2+weez8/PY7p/91qeX758ifrz7nV+dnZ2Ynq8+4xXfsgi\nfsgifsjiWp0euP/v3L26unrtNjk5OWbfdK1N18zMjNlVVVVmu2f0pCT7+uZeB8j97JDL/beK79+/\n/9HzvE1cqxPwiPghi/ghizO/Bzed+evr66/dxj0zl5eXm11bW2v2mzdvoj4H93r57t/NbWtrM/vD\nhw9R729tbc3sV69emX18fBz19neBMz/gEfFDFvFDFmd+D6ampsx2P9szMjJy7TaNjY1R7zMzM9Ps\nm76z615/Pysry2z3ev+u9fV1s933GJFIJOrt/cCZH/CI+CGL+CGLM78HRUVFZru/w3c/VxMIBAID\nAwNmf/z40exnz56Z7X6WJ1bu9fzd6/+/f//e7LOzM0+Pdxc48wMeET9kET9kceaPo97eXrM7Ojpu\n8+H+19LSktnt7e1mz8/P3+XTuRWc+QGPiB+yiB+yiB+yeMMb3/s32/3jcYFAINDV1WW2+0eeU1NT\nzd7c3DTb/TKJ+49UoVDI7MvLyyjPODHxhhfwiPghi/ghizO/z3Jzc81OT083e2Vl5S6fTkLgzA94\nRPyQRfyQxZkfCYczP+AR8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UMW8UOW\n1Of5gf/ilR+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+y\niB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+y\niB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+yiB+y/gF/\n/3mT3yV2oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef201c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABk5JREFUeJzt3UtIVG8cxvEz8reLSmQIuVDaSLa0FhqUQSSC2kpo4QWN\nLu4KFxK2EAPRlS2UNrUZtEVudKHgops70UqQIDSKoChERCzBhZbZtt87/Wc8vHPJnu9n99Q5c17o\n4fCb6bwzkZ2dnQBQlJXpBQCZQvkhi/JDFuWHLMoPWZQfsig/ZFF+yKL8kEX5IYvyQxblhyzKD1mU\nH7IoP2RRfsii/JBF+SGL8kMW5Ycsyg9Z/2V6AekUiUT4qop/wM7OTiQZr8OdH7IoP2RRfsii/JBF\n+SGL8kMW5Ycsyg9ZlB+yKD9kUX7IovyQRfkhi/JDltQjzYj19u1bk/Py8ky+cOGCyYuLiylfU7pw\n54csyg9ZlB+yIko/Qv0vbGPct2+fyfX19SYfPXrU5Kwse3+7cuWKycePH4/7+isrKyYPDw+b/ObN\nm5g1usdsb2/HHOODbYyAJ8oPWZQfspj5/zIHDhwwuampyeSOjg6TT5w4kfI1hfX8+XOTa2pqTN7a\n2vJ6fWZ+wBPlhyzKD1nM/Bnmfq4+MzNj8smTJ+Oev7m5afL+/fvjHv/jxw+Tq6urTf706VPc89va\n2ky+ceNGzDEHDx40eWBgwOT29va410iEmR/wRPkhi/JDFuWHLN7wptmhQ4dMfvz4sckVFRUmLy0t\nmfzw4UOTCwsLTW5paTHZ/fe9evWqydFoNMGK43M3uwRBEDx9+tTkjx8/muy+iV9bWwt1Td7wAp4o\nP2RRfshiA3uaHTlyxGR3xncNDQ3FPf78+fMmv3v3zuTbt2+bPDo6uqt17pY7z//JsWPHTHY30MzO\nziZ1TbvFnR+yKD9kUX7IYuZPs4aGhlDHd3Z2xv17d+auqqoyOdGDaunw7ds3k1dXVzO0Eos7P2RR\nfsii/JDFzL/HuJ/jh92Mkmzus0R/4q7p/fv3qVpOKNz5IYvyQxblhyxm/r/c/fv3Te7p6TH5y5cv\n6VxOzHM6jY2NCc+ZmJhI1XK8cOeHLMoPWZQfstjDm2Y5OTkmt7a2mjw3N2fyq1evTP7582dqFrZL\n169fN/nBgwcJz3F/5G5jY8NrDezhBTxRfsii/JDFzI+4zp07Z7L7mb37PURBEAR37941+datWyb7\nvm9h5gc8UX7IovyQxcyfZsXFxSa7z8o0NzebvLy8bPKTJ09Mfv36tcnr6+te68vPzzd5fHzc5LNn\nz5o8PT0d8xpnzpzxWkMizPyAJ8oPWZQfspj5k+j06dMmd3V1xRxTXl5uckFBgdc13f2xd+7cMXls\nbMxk9zt0IhE7PrvfE9TX12eyu//W/YG6IAiCqamp/19wEjDzA54oP2RRfshi5vdw+PBhk+fn5012\nP8MPgiD4/v27yWHn49LS0oTX+N329rbJdXV1Jrt7cN3v4dna2jLZ3X8wMjIS9/qpwMwPeKL8kEX5\nIYuZ38PAwIDJN2/eNHlhYSHmnPr6epMXFxdDXdN99ubUqVMmd3d3m1xZWRnq9d0Z//LlyyY/evQo\n1OulAjM/4InyQxblhyy+q9NDbm5u3L93v1czCMLP+K61tTWTnz17ZrK7pzbszO8+2/M3zPipwp0f\nsig/ZFF+yGLm32NKSkpMvnbtmsnu/zWE9fnzZ6/z9xLu/JBF+SGL8kMWM38KXbx4MebPwn5u7s70\n7rM7RUVFcc+fnJw0ub+/3+RLly6ZnO7f8c0k7vyQRfkhi/JDFuWHLDazeGhoaDA5Go2anJ2dHXPO\nixcvTH758qXJTU1NJrub5LOy7P3q69evJt+7d89k9w1ypn/QLhnYzAJ4ovyQRfkhi5k/icrKykzu\n7e2NOaa2ttbrGoODgya7m+g/fPjg9fp7ATM/4InyQxblhyxmfuw5zPyAJ8oPWZQfsig/ZFF+yKL8\nkEX5IYvyQxblhyzKD1mUH7IoP2RRfsii/JBF+SFL6nl+4Hfc+SGL8kMW5Ycsyg9ZlB+yKD9kUX7I\novyQRfkhi/JDFuWHLMoPWZQfsig/ZFF+yKL8kEX5IYvyQxblhyzKD1mUH7IoP2RRfsii/JBF+SGL\n8kMW5Ycsyg9ZlB+yKD9kUX7IovyQRfkhi/JDFuWHLMoPWZQfsig/ZFF+yKL8kEX5IYvyQxblhyzK\nD1mUH7IoP2RRfsii/JBF+SGL8kMW5Ycsyg9ZlB+yKD9k/QILoYOh3lzohAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1ea1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH7KIH7KIH7KIH7KIH7J+AwYMFeJz99VCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2257fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDGWv4+xTRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef23a2ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP7HETyzxE0v8xBI/scRPLPETS/zE+gccXPs+n6lngAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef20b0610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pred=cnn_model.predict_classes(x_test)\n",
    "act_test_pred=ny_test\n",
    "comp=list(test_pred==act_test_pred)\n",
    "wrong_index=[i for i,x in enumerate(comp) if x==False]\n",
    "wrong_pred=x_test[wrong_index]\n",
    "print(\"number of wrong predictions\",wrong_pred.shape[0])\n",
    "for i in range(wrong_pred.shape[0]):\n",
    "    x=wrong_pred[i]*255.0\n",
    "    im=x.reshape(28,28)\n",
    "    k=plt.figure(figsize=(1,1))\n",
    "    k=plt.gray()\n",
    "    plt.axis('off')\n",
    "    k=plt.imshow(im,aspect=1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#deep convolutional network\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "x_train = x_train.reshape(x_train.shape[0], 28, 28,1)\n",
    "x_test = x_test.reshape(x_test.shape[0],28, 28,1)\n",
    "x_train=x_train/255.0\n",
    "x_test=x_test/255.0\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#validation set\n",
    "val_train=x_train[40000:]\n",
    "x_train=x_train[:40000]\n",
    "val_test=y_train[40000:]\n",
    "y_train=y_train[:40000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dcnn_model = Sequential()\n",
    "dcnn_model.add(Conv2D(30, (5, 5), input_shape=(28, 28,1), activation='relu'))\n",
    "dcnn_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "dcnn_model.add(Conv2D(15, (3, 3), activation='relu'))\n",
    "dcnn_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "dcnn_model.add(Dropout(0.2))\n",
    "dcnn_model.add(Flatten())\n",
    "dcnn_model.add(Dense(128, activation='relu'))\n",
    "dcnn_model.add(Dense(50, activation='relu'))\n",
    "dcnn_model.add(Dense(num_classes, activation='softmax'))\n",
    "dcnn_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 20000 samples\n",
      "Epoch 1/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.5640 - acc: 0.8236 - val_loss: 0.1368 - val_acc: 0.9599\n",
      "Epoch 2/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.1345 - acc: 0.9590 - val_loss: 0.0906 - val_acc: 0.9723\n",
      "Epoch 3/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0917 - acc: 0.9721 - val_loss: 0.0698 - val_acc: 0.9794\n",
      "Epoch 4/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0720 - acc: 0.9779 - val_loss: 0.0584 - val_acc: 0.9829\n",
      "Epoch 5/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0633 - acc: 0.9807 - val_loss: 0.0507 - val_acc: 0.9852\n",
      "Epoch 6/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0537 - acc: 0.9831 - val_loss: 0.0500 - val_acc: 0.9864\n",
      "Epoch 7/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0464 - acc: 0.9859 - val_loss: 0.0488 - val_acc: 0.9859\n",
      "Epoch 8/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0433 - acc: 0.9866 - val_loss: 0.0442 - val_acc: 0.9867\n",
      "Epoch 9/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0412 - acc: 0.9869 - val_loss: 0.0406 - val_acc: 0.9880\n",
      "Epoch 10/10\n",
      "40000/40000 [==============================] - 36s - loss: 0.0360 - acc: 0.9885 - val_loss: 0.0450 - val_acc: 0.9870\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0efc3a7f10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcnn_model.fit(x_train,y_train,validation_data=(val_train,val_test),epochs=10,batch_size=200,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 3s     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.034389922928315352, 0.98899999999999999]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcnn_model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9888/10000 [============================>.] - ETA: 0s('number of wrong predictions', 110)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyCJ+yCJ+yCJ+yPoDPMw2og0pDqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef222d990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "execution_count": 43,
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef23a05d0>"
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     "execution_count": 43,
     "metadata": {},
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     "execution_count": 43,
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     "execution_count": 43,
     "metadata": {},
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     "data": {
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABStJREFUeJzt3c0rrH0cx/GZkyJ5WEjDQpQdeUiU0ChKHlIKJTYsLCxs\nKFnayIJ/QNnJThQpCyRJyQKpWVjY2MhTYqnMvbg39/c37jlHv4vhfN6v3ec0zNXp3dWFa2bC8Xg8\nBCj6leoDAFKF+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL+CGL\n+CGL+CGL+CGL+CErLdUH8JXC4TBvVfEXiMfj4SC+D2d+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+\nyCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yCJ+yJJ6u0L8\nXl5entmZmZlJHx+JRBL+rbm52eyHhwezV1ZWzH59ff3IIQaGMz9kET9kET9kET9k8QPvX6apqcns\n8vJys6PRqNmVlZVmFxUVmZ2TkxPg0f2rsLDQ7Lm5ucCf409w5ocs4ocs4oescDyu8zFVP+EzuTIy\nMsxubGw0u6enx+y+vj6zs7KyzI7FYmbv7++bvbe3Z/b5+bnZNzc3yQ/4D1RXV5t9enpqdjj8sY/Y\n4jO5AE/ED1nED1n8nj/FSkpKzF5YWDC7u7vb7IuLC7Onp6fN3traMvv+/t7zCP0NDg6avb29naIj\nsTjzQxbxQxbxQxbX/J8oOzs74d+mpqbMnpiYMNt9oUdFRYXZl5eXAR3d5xkeHja7tbXV7Pb29i88\nmv/HmR+yiB+yiB+yuOYPkHvv+8bGRsJjSktLzR4YGDB7c3Mz+AP7RO7rB0KhUKizs9Ps+vp6s1P1\ngnUXZ37IIn7IIn7I4n5+DwUFBWavr6+b/fT0lPA1Q0NDZj8+PgZ5SF/uvdf4pqenm313dxfoc3I/\nP+CJ+CGL+CGL3/N7aGtrM9u9/u3o6Ej4mvd+DkgmNzfXbPeNZK+urj70/YL2/Pyc0uf3wZkfsogf\nsogfsrjm99Df32/26uqq2R+9vn/P8vKy2e4HPxwcHJi9traWdL+8vJj99vbme4g/Fmd+yCJ+yCJ+\nyOLeHg8nJyfu9ze7trY2yKcLhUKJ75/f1dVl9sjIiNl1dXVmu68xGB8fN/v6+tr3ED8d9/YAnogf\nsogfsrjm91BWVmb22dmZ2UtLSwlfMzMzY/bt7W2QhxRKS7N/unHvLxodHTXb/btBb2+v2Ts7OwEe\nXTC45gc8ET9kET9kcc0foLGxMbNnZ2cTHuP+f7v3A7n38hweHgZ0dO+bn583232NcU1NjdlBfEaX\nL675AU/ED1nED1lc83+xyclJs1taWsyORqNmu5/Lu7u76/X8kUjE7KqqKrMXFxfNdn+O+Q645gc8\nET9kET9kcc3/zeTn55tdXFxstnuN7mpoaDD76Ogo6eOPj4/NjsViZn/H1/hyzQ94In7IIn7I4pof\nPw7X/IAn4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs\n4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocs4ocsqTeqBf6LMz9kET9kET9kET9kET9kET9kET9k\nET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9k\nET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9kET9k\nET9kET9kET9kET9kET9kET9kET9kET9kET9k/QMrX/TgkwZKtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef222d310>"
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8UMW8UPWH4L6C/uSEN7UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef156f290>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef194c890>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+g/ij4i3jibO/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef17ff110>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1e6d390>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef202bb10>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef15ec390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5YesX/2nmj4CP0GZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1c84790>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f04cd09d0>"
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1483210>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1417510>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2OW07OyZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef13a9290>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "execution_count": 43,
     "metadata": {},
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     "data": {
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef10b7c10>"
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0f9f250>"
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     "data": {
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
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    {
     "data": {
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0e40250>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0dd3550>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0d69110>"
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     "metadata": {},
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     "data": {
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0c73550>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsogfsogfsogfsogfsogfsv4FuO09WTVdJ58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0c06110>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0b9a410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxBI/scRPLPETS/zEEj+xxE8s8RPrX9+gEJYS3C5LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0aec2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef107a310>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef12b4210>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1603850>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef15b8e50>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1cfb310>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef10607d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAPdoeHppT6HaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef177d1d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef2302350>"
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef159d650>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ5b4ifUHc83myoEO5joAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef15ed390>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0d30490>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0de0350>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1124110>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef107f750>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef14ac210>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1326510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABN1JREFUeJzt3a9rVX8cx/HvZCAYBBEEccEwXRFF1KRgEhEtyqIwZGWg\nQawWg4KwaBeFaTMZFGH+AQvDMIZJ2zY0iDA0+ONav+8Tvl+u5+g92+vxaO+wzznhyYfP7s49GxsM\nBv9Aoh2jvgEYFfETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL\n/MQSP7HET6zxUd/A3zQ2NuZVFdvAYDAY62IdOz+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPET\nS/zEEj+xxE8s8RNL/MQSP7HET6yorzEmmJycLPO5c+fKPD09XeYTJ06U+d69e2Wen5/v8O76xc5P\nLPETS/zEGhsMct7msRVfXTIxMVHmK1eulLl5hj927FiZd+/ePdT1vn//XubTp0+XeWlpaaj1/gSv\nLoGWxE8s8RPL5/wjduTIkTJfvny5zHfu3Cnzjh3d7lfLy8tlXl1dLfOBAwc6vV6f2PmJJX5iiZ9Y\n4ieWX3hHbHZ2tsw3b95std6XL1/K/PTp0zI/e/aszK9evSrzz58/W11/K7HzE0v8xBI/sZz5R+zi\nxYudrvf58+cyP3/+vMwvX74sc/PBt4WFhTJvbGyU+fbt22X++PHjb91nH9j5iSV+YomfWM78I/b+\n/fsyHzp0qNV6+/fvL3Pzc/3FxcUyT01NlfngwYP/uf67d+/KfP/+/SHvsD/s/MQSP7HETyxn/hG7\ndOlSmZtn/rNnzw613tWrV8t8/PjxMp8/f36o9Zp/N1hfXx/q5/vMzk8s8RNL/MTy0qptZt++fWV+\n8+ZNmZt/B/g/165dK/OjR49+67665KVV0JL4iSV+YjnzbzMPHjwo840bN4b6+a9fv5Z5165dre+p\na8780JL4iSV+Yjnzb3HNz+EfPnzYar3mP6c4depUmZt/NxgFZ35oSfzEEj+xPM+/xTTfs3Pr1q1O\n1x8fr0kcPny4zH0483fFzk8s8RNL/MRy5t9i7t69W+bmP7Rrq/l+/rW1tU7X7xM7P7HETyzxE8uz\nPT139OjRMr9+/brMe/fu7fR6nueHAOInlviJ5XP+npubmytz12f8pvn5+T+6fp/Y+YklfmKJn1jO\n/D03MTHxV6/X/L+925mdn1jiJ5b4ieXZnp5pfkf3w4cPZd65c2en11taWirzmTNnyvzt27dOr9cF\nz/ZAS+InlviJ5XP+nrlw4UKZuz7jN3/Hm5mZKXMfz/h/ip2fWOInlviJJX5i+YV3m/vx40eZZ2dn\ny/z27du/eTu9YucnlviJJX5iOfP3zMrKSpk/ffpU5j179gy13osXL8r8+PHj37uxbcjOTyzxE0v8\nxPJllp6bnJws8/Xr18t88uTJMj958qTMCwsLZd7c3Ozw7kbDl1mgJfETS/zEcuZny3Hmh5bETyzx\nE0v8xBI/scRPLPETS/zEEj+xxE8s8RMr6tke+Dc7P7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s\n8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPET\nS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8xBI/scRPLPETS/zEEj+xxE8s8RNL/MQSP7HETyzxE0v8\nxBI/scRPrF/qruecS30BQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef16b40d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef13a12d0>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nET9k/QblehcF0BntCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef13ff0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef15193d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef132b290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLOKHLOKHrN+oVvFvOPUy+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1268b50>"
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     },
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     "execution_count": 43,
     "metadata": {},
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     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
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     "execution_count": 43,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef161aa50>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nscRPLPETS/zEEj+xxE+snx817hDqP1PVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc0e4c10>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8UMW8UMW8UMW8UMW8UMW8UPWv5pxfM1312qcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef16372d0>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef146aad0>"
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     "metadata": {},
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef11dd2d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef195f410>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAXEQAAFxEByibzPwAABOVJREFUeJzt3c+LzXscx/F7bn5NYTMKjWKnKImNokRpZGOhWViwM42i\n+BssFaX8FVj4A9So2YzNsGAyLCzJbkYjSucu7ua+vws3zvie7zmvx2P3ipn5qmffPnN850yv3+//\nBYn+HvYFwLCIn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOIn\nlviJJX5ibRr2BbSp1+t5q4ox0O/3exvxedz5iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1ji\nJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YkW9XWGCzZs3l33w4MGy7969\nW/b09HTZDx8+LPvmzZsbeHXd4s5PLPETS/zEEj+xfMM74g4fPlz2vXv3yj537lzZb9++LXt+fr7s\n79+/b9zFdZw7P7HETyzxE8uZv+MmJyfLvnTpUtl37twp+9WrV2UfOXKk7M+fP5c9MTFR9ocPH37n\nMkeSOz+xxE8s8RPLmb9jZmZmyr527VrZx48fL/v69etlP3nypOwfP36UvXPnzrK3bNnyW9c5Dtz5\niSV+YomfWM78Q/b48eOyjx49Wvb58+fLfv/+/UBfr/m6/759+8q+evVq2bdu3Sp7ZWVloK/fJe78\nxBI/scRPLGf+ljV/IPzFixdlX7lypeyvX78O9PX27t1bdvMH1ptn/ub3GCdPnizbmR/GgPiJJX5i\n9fr9/rCvoTW9Xm/o/9jt27eX/eXLlyFdyb8uXrxY9q5du8peWFgou/kzwMPQ7/d7G/F53PmJJX5i\niZ9YXudv2Uaf8Xfs2FH22bNny96zZ0/ZHz9+/Omfv379uuwunPH/FHd+YomfWOInltf5O2br1q1l\nf/v2rezmsz/Ly8tlb9u2rezm6/THjh0r+9mzZ2WfOXOm7JcvX/7PFbfP6/wwIPETS/zEcuZvWfN1\n+ebz883X2d+8eVP27t27y24+77+6uvrTr//gwYOym98jzM7O/vTju8CZHwYkfmKJn1ie7WnZ2tpa\n2Y8ePfqlj//06dMv/f3m7+zav39/2c3f4ZXEnZ9Y4ieW+InlzD/mbty4UXbzfXqeP3/e5uV0ijs/\nscRPLPETy5l/zFy+fLnsubm5si9cuNDm5XSaOz+xxE8s8RPL8/wjrvmsTvN3fK2vr5d9+vTpP35N\nf5rn+WFA4ieW+Inldf4R13wfnsnJybJnZmbavJyR4s5PLPETS/zEEj+x/CfXiGm+ke3i4mLZ7969\nK3scv+H1n1wwIPETS/zEcuYfMc0H2Q4dOlT20tJS2c1fQDcOnPlhQOInlviJ5cG2jjt16lTZzQfX\nnj592ubljBV3fmKJn1jiJ5Yzf8ccOHCg7Nu3b5d9//79Fq9mvLnzE0v8xBI/sZz5O2ZqaqrsEydO\nlL2wsNDm5Yw1d35iiZ9Y4ieW5/kZOZ7nhwGJn1jiJ1bUmR/+y52fWOInlviJJX5iiZ9Y4ieW+Ikl\nfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5i\niZ9Y4ieW+IklfmKJn1jiJ5b4iSV+YomfWOInlviJJX5iiZ9Y4ieW+IklfmKJn1jiJ5b4iSV+Yomf\nWOInlviJJX5iiZ9Y4ifWP9pY3ENQNJTZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0b0f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1790f10>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1cfc790>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn1jiJ5b4iSV+YomfWP8CUL0XZ00V1AIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef236f490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nET9kET9kET9kET9kET9k/QM0QRpXcZ5rIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1751c10>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+CGL+CGL+CGL+CHrLy8ICwWRs6l9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1c7d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP1jygwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef14c7050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1146790>"
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
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     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0f630d0>"
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     "data": {
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     "execution_count": 43,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0e79510>"
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     "data": {
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlw3yXY4VKkUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0e290d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nACoSFz25HHqyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef10c63d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLpuEda6o4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0fab290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0e79710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0f11110>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn1jiJ5b4iSV+YomfWOInlviJJX5i/Qe8kvoKS1m+wAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0bed990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxA9ZxA9ZxA9Z/wAeABN5t46bwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef14dd390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZBE/ZP0FmxkTlszKCh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0efc0e44d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef19f8f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0f1dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/3mT3yV2oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef121e490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1961790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1315150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9QsbivcMsT2AxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1524590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pred=dcnn_model.predict_classes(x_test)\n",
    "act_test_pred=ny_test\n",
    "comp=list(test_pred==act_test_pred)\n",
    "wrong_index=[i for i,x in enumerate(comp) if x==False]\n",
    "wrong_pred=x_test[wrong_index]\n",
    "print(\"number of wrong predictions\",wrong_pred.shape[0])\n",
    "for i in range(wrong_pred.shape[0]):\n",
    "    x=wrong_pred[i]*255.0\n",
    "    im=x.reshape(28,28)\n",
    "    k=plt.figure(figsize=(1,1))\n",
    "    k=plt.gray()\n",
    "    plt.axis('off')\n",
    "    k=plt.imshow(im,aspect=1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "x_train = x_train.reshape(x_train.shape[0], 28, 28,1)\n",
    "x_test = x_test.reshape(x_test.shape[0],28, 28,1)\n",
    "x_train=x_train/255.0\n",
    "x_test=x_test/255.0\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers.normalization import BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.layers.normalization.BatchNormalization at 0x7f0ef0cf86d0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<keras.layers.normalization.BatchNormalization at 0x7f0f0229fa50>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<keras.layers.normalization.BatchNormalization at 0x7f0ef0b665d0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<keras.layers.normalization.BatchNormalization at 0x7f0ef0f35810>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<keras.layers.normalization.BatchNormalization at 0x7f0ef1293ed0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcnn_model=Sequential()\n",
    "dcnn_model.add(Conv2D(32, (3, 3), input_shape=(28,28,1), activation='relu'))\n",
    "BatchNormalization(axis=1)\n",
    "dcnn_model.add(Conv2D(32, (3, 3),activation='relu'))\n",
    "dcnn_model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "BatchNormalization(axis=1)\n",
    "dcnn_model.add(Conv2D(64,(3, 3),activation='relu'))\n",
    "BatchNormalization(axis=1)\n",
    "dcnn_model.add(Conv2D(64, (3, 3),activation='relu'))\n",
    "dcnn_model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "dcnn_model.add(Flatten())\n",
    "BatchNormalization()\n",
    "dcnn_model.add(Dense(512,activation='relu'))\n",
    "BatchNormalization()\n",
    "dcnn_model.add(Dropout(0.2))\n",
    "dcnn_model.add(Dense(10,activation='softmax'))\n",
    "dcnn_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gen = ImageDataGenerator(rotation_range=8, width_shift_range=0.08, shear_range=0.3,\n",
    "                         height_shift_range=0.08, zoom_range=0.08)\n",
    "test_gen = ImageDataGenerator()\n",
    "train_generator = gen.flow(x_train, y_train, batch_size=64)\n",
    "test_generator = test_gen.flow(x_test, y_test, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "937/937 [==============================] - 234s - loss: 0.0266 - acc: 0.9918 - val_loss: 0.0159 - val_acc: 0.9958\n",
      "Epoch 2/6\n",
      "937/937 [==============================] - 225s - loss: 0.0258 - acc: 0.9917 - val_loss: 0.0151 - val_acc: 0.9947\n",
      "Epoch 3/6\n",
      "937/937 [==============================] - 225s - loss: 0.0247 - acc: 0.9923 - val_loss: 0.0173 - val_acc: 0.9949\n",
      "Epoch 4/6\n",
      "937/937 [==============================] - 233s - loss: 0.0233 - acc: 0.9927 - val_loss: 0.0164 - val_acc: 0.9950\n",
      "Epoch 5/6\n",
      "937/937 [==============================] - 229s - loss: 0.0249 - acc: 0.9923 - val_loss: 0.0193 - val_acc: 0.9943\n",
      "Epoch 6/6\n",
      "937/937 [==============================] - 228s - loss: 0.0230 - acc: 0.9929 - val_loss: 0.0189 - val_acc: 0.9950\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0ef2052690>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcnn_model.fit_generator(train_generator, steps_per_epoch=60000//64, epochs=6,validation_data=test_generator, validation_steps=10000//64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9984/10000 [============================>.] - ETA: 0saccuracy 0.9951\n"
     ]
    }
   ],
   "source": [
    "scores=dcnn_model.evaluate(x_test,y_test)\n",
    "print \"accuracy\",scores[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 11s    \n",
      "('number of wrong predictions', 49)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0f35810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef0539b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
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     "data": {
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     "execution_count": 63,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec5060d0>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec474510>"
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     "metadata": {},
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec3ee290>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhq7yn1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec446fd0>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee7415510>"
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       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee732bc50>"
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    {
     "data": {
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       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "execution_count": 63,
     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec319b50>"
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     "data": {
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec2d73d0>"
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     "data": {
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     "execution_count": 63,
     "metadata": {},
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec1a9050>"
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     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0eec13d290>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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      "text/plain": [
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     "execution_count": 63,
     "metadata": {},
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      "text/plain": [
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     "execution_count": 63,
     "metadata": {},
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      "text/plain": [
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     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee689fbd0>"
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     "data": {
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     "execution_count": 63,
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee6856610>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee67ea050>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
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     "metadata": {},
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    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee661a4d0>"
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     "data": {
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     "metadata": {},
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee652e7d0>"
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee64c5390>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee5e81210>"
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     "metadata": {},
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     "data": {
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee5e6b350>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn1jiJ5b4iSV+YomfWOInlviJJX5i/Qe8kvoKS1m+wAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee5fdab50>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLOKHrN9NIwCDVavn4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee616f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee6335ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ee661a3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAAC+CAYAAABktt6jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPuDNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ef1e9a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pred=dcnn_model.predict_classes(x_test)\n",
    "act_test_pred=ny_test\n",
    "comp=list(test_pred==act_test_pred)\n",
    "wrong_index=[i for i,x in enumerate(comp) if x==False]\n",
    "wrong_pred=x_test[wrong_index]\n",
    "print(\"number of wrong predictions\",wrong_pred.shape[0])\n",
    "for i in range(wrong_pred.shape[0]):\n",
    "    x=wrong_pred[i]*255.0\n",
    "    im=x.reshape(28,28)\n",
    "    k=plt.figure(figsize=(1,1))\n",
    "    k=plt.gray()\n",
    "    plt.axis('off')\n",
    "    k=plt.imshow(im,aspect=1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py27]",
   "language": "python",
   "name": "conda-env-py27-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}