h5_to_image-Comparing_channel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math,sys,os,numpy as np\n",
    "from numpy.linalg import norm\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt, rcParams, rc\n",
    "from scipy.ndimage import imread\n",
    "from skimage.measure import block_reduce\n",
    "import _pickle as pickle\n",
    "from scipy.ndimage.filters import correlate, convolve\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "from ipywidgets.widgets import *\n",
    "rc('animation', html='html5')\n",
    "rcParams['figure.figsize'] = 3, 6\n",
    "%precision 4\n",
    "np.set_printoptions(precision=4, linewidth=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plots(ims, interp=False, titles=None):\n",
    "    ims=np.array(ims)\n",
    "    mn,mx=ims.min(),ims.max()\n",
    "    f = plt.figure(figsize=(12,24))\n",
    "    for i in range(len(ims)):\n",
    "        sp=f.add_subplot(1, len(ims), i+1)\n",
    "        if not titles is None: sp.set_title(titles[i], fontsize=18)\n",
    "        plt.imshow(ims[i], interpolation=None if interp else 'none', vmin=mn,vmax=mx)\n",
    "\n",
    "def plot(im, interp=False):\n",
    "    f = plt.figure(figsize=(3,6), frameon=True)\n",
    "    plt.imshow(im, interpolation=None if interp else 'none')\n",
    "\n",
    "plt.gray()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [i for i in range(0,11)]\n",
    "\n",
    "channel = [0,1,2,3]\n",
    "max_frame_number = 40 - 20\n",
    "instance = [i for i in range(0,25)]\n",
    "session = [i for i in range(0,10)]\n",
    "# there is some confusion between session and user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5_folder_path = '/home/u12215/deep-soli/SoliData_tmp/dsp'\n",
    "# this number of folder we would like to use for training: cross training\n",
    "h5_list = []\n",
    "for x in labels:\n",
    "    for y in session:\n",
    "        for z in instance:\n",
    "            h5_list.append(h5_folder_path+'/{0}_{1}_{2}.h5'.format(x,y,z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = h5_list[1010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2750"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(h5_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_1.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_2.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_3.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_4.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_5.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_6.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_7.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_8.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_9.h5',\n",
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       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_11.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_12.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_13.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_14.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_15.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/0_0_16.h5',\n",
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       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_16.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_17.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_18.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_19.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_20.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_21.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_22.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_23.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_4_24.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_0.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_1.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_2.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_3.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_4.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_5.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_6.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_7.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_8.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_9.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_10.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_11.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_12.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_13.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_14.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_15.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_16.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_17.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_18.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_19.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_20.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_21.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_22.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_23.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_5_24.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_0.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_1.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_2.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_3.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_4.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_5.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_6.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_7.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_8.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_9.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_10.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_11.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_12.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_13.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_14.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_15.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_16.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_17.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_18.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_19.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_20.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_21.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_22.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_23.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_6_24.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_0.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_1.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_2.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_3.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_4.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_5.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_6.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_7.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_8.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_9.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_10.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_11.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_12.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_13.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_14.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_15.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_16.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_17.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_18.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_19.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_20.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_21.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_22.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_23.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_7_24.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_0.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_1.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_2.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_3.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_4.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_5.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_6.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_7.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_8.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_9.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_10.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_11.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_12.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_13.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_14.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_15.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_16.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_17.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_18.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_19.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_20.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_21.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_22.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_23.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_8_24.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_0.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_1.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_2.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_3.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_4.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_5.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_6.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_7.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_8.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_9.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_10.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_11.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_12.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_13.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_14.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_15.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_16.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_17.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_18.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_19.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_20.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_21.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_22.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_23.h5',\n",
       " '/home/u12215/deep-soli/SoliData_tmp/dsp/3_9_24.h5']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5_list[0:1000]\n",
    "#file_name = '/home/u12215/deep-soli/SoliData_tmp/dsp/0_9_0.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demo code to extract data in python\n",
    "import h5py\n",
    "\n",
    "use_channel = 0\n",
    "with h5py.File(file_name, 'r') as f:\n",
    "    # Data and label are numpy arrays\n",
    "    data = f['ch{}'.format(use_channel)][()]\n",
    "    label = f['label'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68, 1024)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def h5file_array(file_name,use_channel = 0):\n",
    "    \n",
    "    with h5py.File(file_name, 'r') as f:\n",
    "    # Data and label are numpy arrays\n",
    "        data = f['ch{}'.format(use_channel)][()]\n",
    "        label = f['label'][()]\n",
    "    return data, label\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57, 1024)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.reshape(data.shape[0],32,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57, 32, 32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 32, 32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:20].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM0AAADKCAYAAAAGucTRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0EhFhkYqMjRS0f8AJKk4DqpwFnoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14656760c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data[0]).set_cmap('hot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=0)\n",
    "data = data.reshape(data.shape[0],32,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8PM+5WN12Oz+HdDcJAADYTjTDAAAM1qya4d0zOu5W4fwscB5W5/w8z7lYnfOzwHlYnfPz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465583e99b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465583e9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14655831bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5WJvz8zznYm1zdX6msmYYAABmgWUSAAAMlmIYAIDB2tZiuKrOrqp7quq+qrp4O489i6rq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\nwGD9Py41uFqVVXuqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465581cc1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(data[:5], titles=label[:5])\n",
    "plots(data[5:10], titles=label[5:10])\n",
    "plots(data[10:15], titles=label[10:15])\n",
    "plots(data[15:20], titles=label[15:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=1)\n",
    "data = data.reshape(data.shape[0],32,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146558480d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14655849bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557ddfcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VPVV14Sz3vR1V1TFVdWNV3VFVt1fV+aP1R1bV9VV19+jxiHmPddZkZZysrExOxsnJ6mRl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\nKYYBABgsxTAAAIOlGAYAYLD+H5Mutv2K+Er2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557c90128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(data[:5], titles=label[:5])\n",
    "plots(data[5:10], titles=label[5:10])\n",
    "plots(data[10:15], titles=label[10:15])\n",
    "plots(data[15:20], titles=label[15:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465900a8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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mds3w6GMS/zvJW5I8nuS+JO9trX1jKgXMoaranWR3a+2rVfULSQ4muTjJZUl+1Fr75Oh/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\n0gwDADBYmmEAAAbr/wFPNqVDT8kNSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557a99978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rtMnKOFlZnpyMk5OVyco4WVmenIwbSk6mds3w6GsS/zvJm5IcTHJPkne21r49lQLmUFXt\nTrK7tfbVqvrlJPcmeUuSy5L8qLX24dH/jKe01j44w1KnSlaOJSvHkpNjycnyZOVYsnIsOTnWUHIy\nzTPDFyZ5sLX23dbaT5N8Nsmbp7j/udNae6y19tXR/WeSHEhyRhaOy77R0/ZlIXhDIitLyMqy5GQJ\nOVmRrCwhK8uSkyWGkpNpNsNnJPn+ovnB0TaSVNWeJK9LcleSl7TWHksWgpjk9NlVNhOysgpZOUpO\nViEnY2RlFbJylJysYifnZJrNcC2zzee6Jamqk5J8LsmVrbWnZ13PHJCVFcjKGDlZgZwcQ1ZWICtj\n5GQFOz0n02yGDyY5a9H8zCSPTnH/c6mqTshCwD7dWrtxtPnx0XU6R67XOTSr+mZEVpYhK8eQk2XI\nybJkZRmycgw5WcYQcjLNZvieJOdX1TlVdWKSdyS5eYr7nztVVUk+meRAa+2jix66Ocne0f29SW6a\ndm0zJitLyMqy5GQJOVmRrCwhK8uSkyWGkpOpfgNdVf1Oko8lOS7Jp1prfzW1nc+hqro4yZeSfDPJ\nz0ebP5SF63GuT/LyJN9L8rbW2o9mUuSMyMo4WVmenIyTk5XJyjhZWZ6cjBtKTnwdMwAAg+Ub6AAA\nGCzNMAAAg6UZBgBgsDTDAAAMlmYYAIDB0gwDADBYmmEAAAbr/wEiuo37RVJjowAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557d6ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WJSdryMmGZGUNWVmXnKwxSk7mWYbPSvLNVduPTfaRpKrOTfLaJPcleWlr7XiyEsQkZy5u\nZgshK5uQlefIySbkZIqsbEJWniMnmzjIOZlnGa519vlctyRV9eIkH0vyx621/170fJaArGxAVqbI\nyQbk5CSysgFZmSInGzjoOZlnGX4syTmrts9O8vgcX38pVdVpWQnYh1trt052f2tyns6J83WeWNT8\nFkRW1iErJ5GTdcjJumRlHbJyEjlZxwg5mWcZvj/JeVX1iqp6QZI3JTkyx9dfOlVVSa5P8mBr7dpV\nNx1Jcnhy/XCS2+Y9twWTlTVkZV1ysoacbEhW1pCVdcnJGqPkZK7fQFdVv5nkfUlOSfLB1tpfzu3F\nl1BVXZTkniRfTPLjye73ZOV8nJuTvCzJN5Jc3lp7aiGTXBBZmSYr65OTaXKyMVmZJivrk5Npo+TE\n1zEDADAs30AHAMCwlGEAAIalDAMAMCxlGACAYSnDAAAMSxkGAGBYyjAAAMP6fzlXgmkOmEvXAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465583f5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=0)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[:5], titles=label[:5])\n",
    "\n",
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=1)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[:5], titles=label[:5])\n",
    "\n",
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=2)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[:5], titles=label[:5])\n",
    "\n",
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=3)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[:5], titles=label[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465582a8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557e79b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6t6ruqar3jtYfW1W3VNX9o8djZj3WaZOVcbKyMjkZJyerk5VxsrIyORk3lJxM7Zrh0dck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\nGKz/Bzf8tA5/LDiBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557bcfe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nUz0zPL/Tql3+anPvHJ85jsO+OT7PcCz2zfGZ4zjsm+PzDMdi37ba8VnR3SQAAGArUQwD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557d5f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=0)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[15:20], titles=label[15:20])\n",
    "\n",
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=1)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[15:20], titles=label[15:20])\n",
    "\n",
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=2)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[15:20], titles=label[15:20])\n",
    "\n",
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=3)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "plots(data[15:20], titles=label[15:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM0AAADKCAYAAAAGucTRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNFKRoZGK/ge3wvzfgYXf1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557efba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM0AAADKCAYAAAAGucTRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nASaZAN9KtqwzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465582dd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_name = h5_list[2010]\n",
    "data, label = h5file_array(file_name,use_channel=1)\n",
    "data1 = data.reshape(data.shape[0],32,32)\n",
    "data, label = h5file_array(file_name,use_channel=3)\n",
    "data2 = data.reshape(data.shape[0],32,32)\n",
    "#plots(data1[:5]-data2[:5], titles=label[:5])\n",
    "plt.imshow(data1[5]).set_cmap('nipy_spectral')\n",
    "plt.show()\n",
    "plt.imshow(data2[5]).set_cmap('nipy_spectral')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.zeros([len(h5_list),20,32,32,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.zeros([len(h5_list),20,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2750, 20, 1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 32, 32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0,:,:,:,ch].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch = 0\n",
    "file_name = h5_list[i]\n",
    "data, label = h5file_array(file_name,use_channel=ch)\n",
    "data = data.reshape(data.shape[0],32,32)\n",
    "data = data[:20,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 32, 32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(h5_list)):\n",
    "    for ch in range(4):\n",
    "        file_name = h5_list[i]\n",
    "        data, label = h5file_array(file_name,use_channel=ch)\n",
    "        data = data.reshape(data.shape[0],32,32)\n",
    "        data = data[:20,:,:]\n",
    "        label = label[:20]\n",
    "        dataset[i,:,:,:,ch] = data\n",
    "        labels[i] = label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2750, 20, 32, 32, 4)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0,:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYSmGAQAYlmIYAIBh/T90uHJvrnK+FQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14655789e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557ad4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557ad4208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naYYBABiWZhgAgGH9PzY3AqjtIrdVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557c28be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[0,:5,:,:,0])\n",
    "plots(dataset[0,:5,:,:,1])\n",
    "plots(dataset[0,:5,:,:,2])\n",
    "plots(dataset[0,:5,:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBgBgWIphAACGpRgGAGBYimEAAIb1/3zfsxgRH9y3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14655849cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557797b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDEszDADAsP4fzL4dRLOpZikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557f47240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nADAsZRgAgGEpwwAADOv/AZvbFVCraG4MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14655773a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[0,5:10,:,:,0])\n",
    "plots(dataset[0,5:10,:,:,1])\n",
    "plots(dataset[0,5:10,:,:,2])\n",
    "plots(dataset[0,5:10,:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAADfRJREFUeJzt3V+sZVV9B/DvT/6kVTECRTMCglUe\nNMSqmYw8qImmRuyLWhmFxDhNTHhpE0n6UOKLadIm9MX2mQQDD2YAYRJIXxpCrNEXcaBFB8h0wAQl\njEIVA1VTsbP6cA/DnDv3zl0z555z7j3r80lW7l77nnv2unu+s+5v9qy7d7XWAgAAI3rDsgcAAADL\nohgGAGBYimEAAIalGAYAYFiKYQAAhqUYBgBgWIphAACGpRgGAGBYMxXDVXV9VR2tqqer6tbtGhSr\nR1boISf0khV6yAldWmvn1JKcl+SZJH+a5MIkjyd53xZf07TVafPKyrK/L23b24vmFK2nmVO0zmZO\n0bpa75wyy5XhfUmebq39pLX2+yR3J/nMDO/H6pKVsT3b+To5oZesjM2cwraapRi+PMnPTuk/N9k3\npapurqrDVXV4hmOxu22ZFTkh5hT6mVPoYU6hy/kzfG1tsK+dtqO125PcniRVddrnGcKWWZETYk6h\nnzmFHuYUusxSDD+X5MpT+lckeX624exO99xzz1T/C1/4wlT/29/+9qafG4SsJLn77run+l/84hen\n+vfdd9/J7f379y9kTDuMnEwcPHhwqn/jjTdO9Q8dOnRy+/Of//xCxrTDyEqSe++9d6q/ft7ws0dO\nXnNqFpLkhhtumOqf+vPppptuWsiYdpJZlkn8MMk1VfWuqrowyY1JHtyeYbFiZIUeckIvWaGHnNDl\nnK8Mt9b+UFV/k+TfsvYbm99srT2xbSNjZcgKPeSEXrJCDzmhV01uJbKYg63oWpy3vOUtU/1Pf/rT\nU/31yyhWRWtto/VYM1vVnKx37bXXTvWPHDmypJHM3aOttb3zeONRsvLGN75xqv/b3/52SSOZL3PK\nbEbJScwpZ+3SSy+d6n/84x+f6p+6TG+V9M4pnkAHAMCwFMMAAAxLMQwAwLBmubUaEx/96Een+p/6\n1Kem+j//+c9Pbn/3u99dyJjYedbfSu3DH/7wVP+OO+44uf3EE+f+Ox6nrmF/+eWXz/l9WJ71t0u7\n7rrrpvq33Xbbye1f/vKXCxkTO8/622Pt27dvqv/1r3/95Pbvfve7hYxpVBdccMFU/9VXX13SSDZ2\nyy23TPX37p1ecn3s2LGT248//vhCxrSTuDIMAMCwFMMAAAzLrdXmoGr6Th6LPMeL5DZIdHIbJLqY\nU+hkTqGLW6sBAMAWFMMAAAxLMQwAwLDcWm0OVnWNMLAcF1100VT/lVdeWdJI2Mne8Y53TPWff/75\nJY2Ene6qq66a6j/77LNLGsnO4MowAADDUgwDADAsxTAAAMOyZngbXHvttVP99Y/dPXjw4MntJ598\nciFjYue5+uqrp/pf/vKXp/qHDh06uX3kyJFFDIkd6v3vf/9Uf//+/VP9Bx544OT24cOHFzImdp73\nvOc9U/0vfelLU/1Tf/YcPXp0IWNiZ/rYxz421f/c5z431b///vtPbn//+99fyJh2EleGAQAYlmIY\nAIBheRzzHOzbt2+q/8gjjyxpJPPl0amz2bt3+mmiK/zf3R6dOqMPfehDU/3HHntsSSOZL3PKbNYv\nr/nRj360pJHMnTllRu9+97un+s8888ySRjJfHscMAABbUAwDADAsxTAAAMOyZphzZn0fnazvo4s5\nhU7mFLpYMwwAAFtQDAMAMCzFMAAAw/I4ZliSN7xh+t+iJ06cWNJIAGBcrgwDADCsLYvhqvpmVb1Q\nVUdO2XdJVT1UVccmHy+e7zDZDWSFHnJCL1mhh5wwq54rw3cmuX7dvluTPNxauybJw5M+3BlZ6Xbi\nxImpNpA7Iyf0uTOywtbujJwwi9bali3J1UmOnNI/mmTPZHtPkqOd79O01Wnzysqyvy9t29thc4rW\n08wpWmczp2hdrefPvLV2zr9A9/bW2vGsHel4Vb1tsxdW1c1Jbj7H47D7dWVFToZnTqGXOYUe5hS6\nzf1uEq2125PcnniyC5uTE3rJCj3khF6ywrneTeIXVbUnSSYfX9i+IbFiZIUeckIvWaGHnNDtXIvh\nB5McmGwfSPLA9gyHFSQr9JATeskKPeSEfh2LyQ8mOZ7k1STPJflKkkuz9tuZxyYfL7Ewfbw2r6ws\n+/vStr0dNqdoPc2conU2c4rW1Xp/ga4mf/gLMcpanIsvnr6d4UsvvbSkkcxXa63m8b6j5OSyyy6b\n6r/44otLGsncPdpa2zuPNx4lK29605um+r/5zW+WNJL5MqfQyZxCl945xRPoAAAYlmIYAIBhKYYB\nABjW3O8zPKJVXSPM9lrhNcJss1VdIwywE7gyDADAsBTDAAAMSzEMAMCwFMMAAAxLMQwAwLAUwwAA\nDEsxDADAsBTDAAAMSzEMAMCwFMMAAAxLMQwAwLAUwwAADEsxDADAsBTDAAAMSzEMAMCwFMMAAAxL\nMQwAwLAUwwAADEsxDADAsBTDAAAMSzEMAMCwFMMAAAxLMQwAwLC2LIar6sqq+k5VPVVVT1TVVyf7\nL6mqh6rq2OTjxfMfLjuVnNBLVuglK/SQE2bVc2X4D0n+trX23iTXJfnrqnpfkluTPNxauybJw5M+\n45ITeskKvWSFHnLCbFprZ9WSPJDkk0mOJtkz2bcnydGOr23a6jQ50TrbYVnRetq8fv4s+/vStr2Z\nU7Su1lvbnp+zUFVXJ/lgkh8keXtr7XjWjna8qt62ydfcnOTmszkOu5uc0EtW6HW2WZGTMZlTOCdn\ncUX4zUkeTfKXk/6v133+Jf/iGqvJidbZNryKIyva+javnz/L/r60bW/mFK2r9da4XXeTqKoLktyf\n5FuttUOT3b+oqj2Tz+9J8kLPe7G65IReskIvWaGHnDCLnrtJVJI7kjzVWvvGKZ96MMmByfaBrK3R\nYVByQi9ZoZes0ENOmFVN/ltg8xdUfSTJ95L8OMmJye6vZW09zr1J3pnkp0n2t9Z+tcV7nflg7Cqt\ntXptW044g0dba3tf68gKmzl1Tkm2LytysnLMKXRZP6dsZstieDsJ2WrpDdnZkpOVM/WDazvJymox\np9DJnEKX3jnFE+gAABiWYhgAgGEphgEAGJZiGACAYSmGAQAYlmIYAIBhKYYBABiWYhgAgGEphgEA\nGJZiGACAYSmGAQAYlmIYAIBhKYYBABiWYhgAgGEphgEAGJZiGACAYSmGAQAYlmIYAIBhKYYBABiW\nYhgAgGEphgEAGJZiGACAYSmGAQAYlmIYAIBhKYYBABiWYhgAgGEphgEAGNaWxXBV/VFVPVJVj1fV\nE1X195P976qqH1TVsaq6p6ounP9w2clkhR5yQi9ZoYecMKueK8P/m+QTrbU/S/KBJNdX1XVJ/inJ\nP7fWrknyUpKvzG+Y7BKyQg85oZes0ENOmMmWxXBb8z+T7gWT1pJ8Isl9k/13JfnsXEbIriEr9JAT\neskKPeSEWXWtGa6q86rqP5O8kOShJM8k+XVr7Q+TlzyX5PJNvvbmqjpcVYe3Y8DsbOeaFTkZizmF\nXuYUephTmEVXMdxa+7/W2geSXJFkX5L3bvSyTb729tba3tba3nMfJrvFuWZFTsZiTqGXOYUe5hRm\ncVZ3k2it/TrJvye5Lslbq+r8yaeuSPL89g6N3UxW6CEn9JIVesgJ56LnbhKXVdVbJ9t/nOTPkzyV\n5DtJbpi87ECSB+Y1SHYHWaGHnNBLVughJ8zq/K1fkj1J7qqq87JWPN/bWvvXqnoyyd1V9Q9J/iPJ\nHXMcJ7uDrNBDTuglK/SQE2ZSrW24hGY+B6ta3MGYu9ZazeN95WTlPDqvtXiyslrMKXQyp9Cld07p\nuTK8nf47ybNJ/mSyzcZ2w/m5ao7vLSd9dsv5kZXl2w3nR06Wb7ecH1lZvt1wfrpzstArwycPWnXY\nb21uzvlZ4zycmfPzOufizJyfNc7DmTk/r3MuzmzVzs9Z3U0CAABWiWIYAIBhLasYvn1Jx90tnJ81\nzsOZOT+vcy7OzPlZ4zycmfPzOufizFbq/CxlzTAAAOwElkkAADCshRbDVXV9VR2tqqer6tZFHnsn\nqqorq+o7VfVUVT1RVV+d7L+kqh6qqmOTjxcve6yLJivTZGVjcjJNTjYnK9NkZWNyMm2UnCxsmcTk\nyTD/leSTSZ5L8sMkN7XWnlzIAHagqtqTZE9r7bGquijJo0k+m+SvkvyqtXbb5C/jxa21v1viUBdK\nVk4nK6eTk9PJycZk5XSycjo5Od0oOVnkleF9SZ5urf2ktfb7JHcn+cwCj7/jtNaOt9Yem2y/krVn\nqV+etfNy1+Rld2UteCORlXVkZUNyso6cbEpW1pGVDcnJOqPkZJHF8OVJfnZK/7nJPpJU1dVJPpjk\nB0ne3lo7nqwFMcnbljeypZCVM5CVk+TkDORkiqycgaycJCdnsMo5WWQxvNHzod3KIklVvTnJ/Ulu\naa29vOzx7ACysglZmSInm5CT08jKJmRlipxsYtVzsshi+LkkV57SvyLJ8ws8/o5UVRdkLWDfaq0d\nmuz+xWSdzmvrdV5Y1viWRFY2ICunkZMNyMmGZGUDsnIaOdnACDlZZDH8wyTXVNW7qurCJDcmeXCB\nx99xqqqS3JHkqdbaN0751INJDky2DyR5YNFjWzJZWUdWNiQn68jJpmRlHVnZkJysM0pOFvrQjar6\niyT/kuS8JN9srf3jwg6+A1XVR5J8L8mPk5yY7P5a1tbj3JvknUl+mmR/a+1XSxnkksjKNFnZmJxM\nk5PNyco0WdmYnEwbJSeeQAcAwLA8gQ4AgGEphgEAGJZiGACAYSmGAQAYlmIYAIBhKYYBABiWYhgA\ngGEphgEAGNb/A8sllz2rwdAOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557e101d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557de9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557a8aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAADIlJREFUeJzt3V+IpeV9B/Dvr/6hhRTUtpFFjVrw\nIgFpIhJEo2isor0wSdGQXm0hIEobUuhFNXeFFlqE2BtRFhS9CK7SBFxKpCxiMeTCuqYN0chWEzAu\nLtqShLY3TdM8vZjjumd2/jw7Z845M+f5fOBhznNm5rzPvPv12a9n3zmnWmsBAIAR/dqyFwAAAMui\nDAMAMCxlGACAYSnDAAAMSxkGAGBYyjAAAMNShgEAGJYyDADAsGYqw1V1R1Udr6q3quqB3VoUq0dW\n6CEn9JIVesgJXVprOxpJzknyoyS/m+T8JN9P8oltvqcZqzPmlZVl/1zGro9/t6cYPcOeYnQOe4rR\nNXr3lFmeGf50krdaaz9urf0iyeEkn5vh8VhdsjK2tzu/Tk7oJStjs6ewq2Ypw5ckeee0+YnJfVOq\n6t6qOlZVx2Y4FvvbtlmRE2JPoZ89hR72FLqcO8P31gb3tTPuaO1QkkNJUlVnfJ4hbJsVOSH2FPrZ\nU+hhT6HLLGX4RJLLTptfmuTd2ZazP913331T8y9+8YtT82efffbU7ccee2wha9pjZCXJ/fffPzXf\nKiePPvroQta0x8jJxFe+8pWp+T333DM1f+aZZ07dfuSRRxaypj1GVpI8+OCDU/O77757an748OFT\ntx966KGFrGmPkZOJhx9+eGr+hS98YWr+9NNPn7q9PlcjmOUyiVeSXFVVV1bV+Um+lOTI7iyLFSMr\n9JATeskKPeSELjt+Zri19suq+tMk/5i139h8orX2+q6tjJUhK/SQE3rJCj3khF6zXCaR1tq3k3x7\nl9ayb91+++1T81tuuWVqfvLkyVO3B71MQlaS3HnnnVPzm2++eWr+zjsf/p7HoJdJyMnErbfeOjW/\n8cYbp+Zvv/3hL9MPepmErCS5/vrrp+bXXHPN1Py1115b5HL2JDlZc/XVV0/NL7/88qn5BRdcsMjl\n7DnegQ4AgGEpwwAADEsZBgBgWDV5+8HFHGyQ1++76667puZHjqzmL6+21jZ6DceZjZKT9dcQP//8\n80taydy92lq7dh4PPEpWbrvttqn50aNHl7SS+bKnzOaGG26Ymn/3u99d0krmzp4yo5tuumlq/tJL\nLy1pJfPVu6d4ZhgAgGEpwwAADMtlEuyYf9Kkk3/SpIs9hU72FLq4TAIAALahDAMAMCxlGACAYSnD\nAAAMSxkGAGBYyjAAAMNShgEAGJYyDADAsJRhAACGpQwDADAsZRgAgGEpwwAADEsZBgBgWMowAADD\nUoYBABiWMgwAwLCUYQAAhqUMAwAwLGUYAIBhKcMAAAxr2zJcVU9U1ftV9dpp911UVUer6s3Jxwvn\nu0z2A1mhh5zQS1boISfMqueZ4SeT3LHuvgeSvNBauyrJC5M5PBlZYXtPRk7o82Rkhe09GTlhFq21\nbUeSK5K8dtr8eJIDk9sHkhzvfJxmrM6YV1aW/XMZuz6O2VOMnmFPMTqHPcXoGj1/5q21nJudubi1\ndjJrRzpZVR/d7Aur6t4k9+7wOOx/XVmRk+HZU+hlT6GHPYVuOy3D3Vprh5IcSpKqavM+HvuTnNBL\nVughJ/SSFXb6ahLvVdWBJJl8fH/3lsSKkRV6yAm9ZIUeckK3nZbhI0kOTm4fTPLc7iyHFSQr9JAT\neskKPeSEfh0Xkz+d5GSS/01yIsmXk/xW1n47883Jx4tcmD7emFdWlv1zGbs+jtlTjJ5hTzE6hz3F\n6Bq9v0BXkz/8hXAtzmpprdU8HldOVs6rrbVr5/HAsrJa7Cl0sqfQpXdP8Q50AAAMSxkGAGBYyjAA\nAMNShgEAGJYyDADAsJRhAACGpQwDADAsZRgAgGEpwwAADEsZBgBgWMowAADDUoYBABiWMgwAwLCU\nYQAAhqUMAwAwLGUYAIBhKcMAAAxLGQYAYFjKMAAAw1KGAQAYljIMAMCwlGEAAIalDAMAMCxlGACA\nYSnDAAAMSxkGAGBYyjAAAMPatgxX1WVV9WJVvVFVr1fVVyf3X1RVR6vqzcnHC+e/XPYqOaGXrNBL\nVughJ8yq55nhXyb589bax5Ncl+RPquoTSR5I8kJr7aokL0zmjEtO6CUr9JIVesgJs2mtndVI8lyS\n25IcT3Jgct+BJMc7vrcZqzPkxOgcx2TF6Bnz+vtn2T+XsevDnmJ0jd5ue27OQlVdkeRTSV5OcnFr\n7WTWjnayqj66yffcm+TeszkO+5uc0EtW6HW2WZGTMdlT2JGzeEb4I0leTfKHk/nP133+Z/6Pa6wh\nJ0bn2PBZHFkx1o95/f2z7J/L2PVhTzG6Rm/H7Xo1iao6L8k3k3yjtfatyd3vVdWByecPJHm/57FY\nXXJCL1mhl6zQQ06YRc+rSVSSx5O80Vr7+mmfOpLk4OT2waxdo8Og5IReskIvWaGHnDCrmvyzwOZf\nUPWZJN9J8oMkv5rc/bWsXY/zbJKPJflJkntaaz/d5rG2Phj7SmutPrgtJ2zh1dbatR9MZIXNnL6n\nJLuXFTlZOfYUuqzfUzazbRneTUK2WnpDdrbkZOVM/cW1m2RltdhT6GRPoUvvnuId6AAAGJYyDADA\nsJRhAACGpQwDADAsZRgAgGEpwwAADEsZBgBgWMowAADDUoYBABiWMgwAwLCUYQAAhqUMAwAwLGUY\nAIBhKcMAAAxLGQYAYFjKMAAAw1KGAQAYljIMAMCwlGEAAIalDAMAMCxlGACAYSnDAAAMSxkGAGBY\nyjAAAMNShgEAGJYyDADAsJRhAACGtW0Zrqpfr6p/rqrvV9XrVfWXk/uvrKqXq+rNqnqmqs6f/3LZ\ny2SFHnJCL1mhh5wwq55nhv8nyWdba7+X5JNJ7qiq65L8bZKHW2tXJflZki/Pb5nsE7JCDzmhl6zQ\nQ06YybZluK3578n0vMloST6b5O8n9z+V5PNzWSH7hqzQQ07oJSv0kBNm1XXNcFWdU1X/muT9JEeT\n/CjJz1trv5x8yYkkl2zyvfdW1bGqOrYbC2Zv22lW5GQs9hR62VPoYU9hFl1luLX2f621Tya5NMmn\nk3x8oy/b5HsPtdauba1du/Nlsl/sNCtyMhZ7Cr3sKfSwpzCLs3o1idbaz5P8U5LrklxQVedOPnVp\nknd3d2nsZ7JCDzmhl6zQQ07YiZ5Xk/idqrpgcvs3kvx+kjeSvJjk7smXHUzy3LwWyf4gK/SQE3rJ\nCj3khFmdu/2X5ECSp6rqnKyV52dba/9QVT9Mcriq/irJvyR5fI7rZH+QFXrICb1khR5ywkyqtQ0v\noZnPwaoWdzDmrrVW83hcOVk5r87rWjxZWS32FDrZU+jSu6f0PDO8m/4jydtJfntym43th/Nz+Rwf\nW0767JfzIyvLtx/Oj5ws3345P7KyfPvh/HTnZKHPDJ86aNUxv7W5OednjfOwNefnQ87F1pyfNc7D\n1pyfDzkXW1u183NWryYBAACrRBkGAGBYyyrDh5Z03P3C+VnjPGzN+fmQc7E152eN87A15+dDzsXW\nVur8LOWaYQAA2AtcJgEAwLAWWoar6o6qOl5Vb1XVA4s89l5UVZdV1YtV9UZVvV5VX53cf1FVHa2q\nNycfL1z2WhdNVqbJysbkZJqcbE5WpsnKxuRk2ig5WdhlEpN3hvm3JLclOZHklSR/1Fr74UIWsAdV\n1YEkB1pr36uq30zyapLPJ/njJD9trf3N5D/GC1trf7HEpS6UrJxJVs4kJ2eSk43Jyplk5UxycqZR\ncrLIZ4Y/neSt1tqPW2u/SHI4yecWePw9p7V2srX2vcnt/8rae6lfkrXz8tTky57KWvBGIivryMqG\n5GQdOdmUrKwjKxuSk3VGyckiy/AlSd45bX5ich9JquqKJJ9K8nKSi1trJ5O1ICb56PJWthSysgVZ\nOUVOtiAnU2RlC7JyipxsYZVzssgyvNH7Q3spiyRV9ZEk30zyZ621/1z2evYAWdmErEyRk03IyRlk\nZROyMkVONrHqOVlkGT6R5LLT5pcmeXeBx9+Tquq8rAXsG621b03ufm9ync4H1+u8v6z1LYmsbEBW\nziAnG5CTDcnKBmTlDHKygRFyssgy/EqSq6rqyqo6P8mXkhxZ4PH3nKqqJI8neaO19vXTPnUkycHJ\n7YNJnlv02pZMVtaRlQ3JyTpysilZWUdWNiQn64ySk4W+6UZV/UGSv0tyTpInWmt/vbCD70FV9Zkk\n30nygyS/mtz9taxdj/Nsko8l+UmSe1prP13KIpdEVqbJysbkZJqcbE5WpsnKxuRk2ig58Q50AAAM\nyzvQAQAwLGUYAIBhKcMAAAxLGQYAYFjKMAAAw1KGAQAYljIMAMCwlGEAAIb1/+qqEFIt9MaZAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557b1c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[0,10:15,:,:,0]) \n",
    "plots(dataset[0,10:15,:,:,1]) \n",
    "plots(dataset[0,10:15,:,:,2]) \n",
    "plots(dataset[0,10:15,:,:,3]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14655773aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557b1c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwAwLM0wAADD+n/2rS0fkp3zkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465577125c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146558374e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[0,15:20,:,:,0])\n",
    "plots(dataset[0,15:20,:,:,1])\n",
    "plots(dataset[0,15:20,:,:,2])\n",
    "plots(dataset[0,15:20,:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.],\n",
       "       [6.],\n",
       "       [6.],\n",
       "       [6.],\n",
       "       [6.]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 1500\n",
    "labels[i,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzTAAAACSRTMMAACAZNEMAwAAIFn/B3imR2jwirkoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557dfe7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557a2a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465579cce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqJ4wsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465578255c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[i,0:5,:,:,0])\n",
    "plots(dataset[i,0:5,:,:,1])\n",
    "plots(dataset[i,0:5,:,:,2])\n",
    "plots(dataset[i,0:5,:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nohgGAABAsiiGAQAAkKz/A/5oSNbmhj2WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465582714e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465582674a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGJZmGACAYf0/xFNzgX0cUa8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557887a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAQAY1v8Ds0vpQ7x2t+YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146558406ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[i,5:10,:,:,0])\n",
    "plots(dataset[i,5:10,:,:,1])\n",
    "plots(dataset[i,5:10,:,:,2])\n",
    "plots(dataset[i,5:10,:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAADxVJREFUeJzt3W2IXNUdx/HfP5tEqwmYWJVFEx8w\nglJohKC+kLwQg6FCEosP0TepBhakBYsVlLwrtGr7wkbwjYtZSDFWg4kYqyAqKeSNqRtr1BjzYMA8\numueiEE0tfn3xdxM9szO7J7dmbl35p7vBw57z8zs3LPXn2f/uXP2XnN3AQAAACmaUvQAAAAAgKJQ\nDAMAACBZFMMAAABIFsUwAAAAkkUxDAAAgGRRDAMAACBZFMMAAABIFsUwAAAAktVUMWxmi81sl5nt\nNbOnWjUolA9ZQQxyglhkBTHICaK4+6SapB5JX0m6TtJ0Sdsl3TTO9zitPK1dWSn656K1vH3LnEKL\nacwptMjGnEKLarFzSjNnhm+RtNfd97n7GUmvSlraxPuhvMhK2r6OfB05QSyykjbmFLRUM8XwlZIO\njOgfzB4LmFmfmQ2a2WAT+0J3Gzcr5ARiTkE85hTEYE5BlKlNfK/VecxHPeDeL6lfksxs1PNIwrhZ\nIScQcwriMacgBnMKojRTDB+UNGdE/ypJh5sbTnfq6+sL+vfff3/QX79+fXW7v78/lzF1GLIi6cEH\nHwz6y5cvD/obN26sbq9duzaXMXUYcpJ57bXXgn7tnLJhw4bq9r333pvLmDoMWZH0yiuvBP3aOYac\nkJNzlixZEvQfeOCBoP/6669Xt994441cxtRJmlkm8ZGkeWZ2rZlNl7Rc0qbWDAslQ1YQg5wgFllB\nDHKCKJM+M+zuP5nZ7yS9q8pfbA64+46WjQylQVYQg5wgFllBDHKCWM0sk5C7vyPpnRaNpWudOnUq\n6A8PDwf9AwcOKHVkRTp06FDQP3r0aNA/ceJEnsPpSOSk4vDh8JPcb775JujXZilFZGV0ToaGhsbs\np4icVNTWJbW/f7799ts8h9NxuAMdAAAAkkUxDAAAgGRRDAMAACBZlt1+MJ+dJXL9voULFwb9kydP\nVrc//fTTvIfTNu5e7xqOTUslJwnZ5u4L2vHGqWTlnnvuCfrbt2+vbu/bty/v4ejWW28N+lu3bm3J\n+zKnTMzVV18d9C+77LKgPzhY7D0kpkwJz7ddf/311e3du3c389bMKYgSO6dwZhgAAADJohgGAABA\nslgmgUnjI01E4iPNJk2fPj3onzlzpqCRtBdzSnNmzZoV9Et8uUbmFERhmQQAAAAwDophAAAAJIti\nGAAAAMlq6nbMAID2K+saYbRWidcII9PT01PdnjlzZvDcyMu4YmI4MwwAAIBkUQwDAAAgWRTDAAAA\nSBZrhidp5O1RL7744uC5devWBf08r+WMznLnnXdWt99///0CR4KiXXDBBUH/6aefDvpHjx6tbj/z\nzDO5jAmdpzYnjzzySNDfvHlzdfvLL7/MZUzoHE888UR1u/a60qtWrQr6Z8+ezWVMZcCZYQAAACSL\nYhgAAADJYplEpJUrVwb9l156qbo9NDQUPPfyyy/nMiZ0noGBgaD/8MMPV7c//PDD4Llly5YF/doc\noVweffTRoH/33XcH/eHh4er2888/Hzz3/ffft29gKNzs2bOr2w899FDw3AsvvBD058yZk8uY0Bku\nvfTSoD9y2cwNN9wQPFd72/bHH3+8fQMrGc4MAwAAIFkUwwAAAEgWxTAAAACSxZrhSGvWrAn606ZN\nq24fP3487+GgQ9VeBmnLli3V7UOHDgXPsUY4LatXrw76e/fuDfoXXXRRdZs1wmkZ+Tukdo3wggUL\ngv7IS/Ch/I4dOxb0ly5dWt2eN29e8Nxbb72Vy5jKiDPDAAAASBbFMAAAAJJFMQwAAIBkWZ63CjYz\n7ktcIu5u7XjfsuZkypTw354J3Spzm7svGP9lE1fWrNS66667gv67775b0EjaizllYnp6eoL+4sWL\ng/7bb7+d53DyxJzSpNrbfv/4448FjaS9YucUzgwDAAAgWeMWw2Y2YGbDZvb5iMdmm9l7ZrYn+zqr\nvcNENyAriEFOEIusIAY5QbPGXSZhZgslnZb0d3f/RfbYXyUdd/dnzewpSbPc/clxd5bIxw+pqP34\noVVZISelE3ykyZyCRphTEIk5BVGil165+7hN0jWSPh/R3yWpN9vulbQr8n2cVp7WrqwU/XPRWt4G\nmVNoMY05hRbZmFNoUS3mv7m7T/qmG1e4+xFV9nTEzC5v9EIz65PUN8n9oPtFZYWcJI85BbGYUxCD\nOQXR2n4HOnfvl9Qv8fEDGiMniEVWEIOcIBZZwWSvJjFkZr2SlH0dbt2QUDJkBTHICWKRFcQgJ4g2\n2WJ4k6QV2fYKSW+2ZjgoIbKCGOQEscgKYpATxItYTP4PSUck/VfSQUkrJV0q6QNJe7Kvs1mYnl5r\nV1aK/rloLW+DzCm0mMacQotszCm0qBb7B3Tcga4NZsyYEfRPnz5d0Ejay7lbVFMuvPDCoP/DDz8U\nNJK2425RE3TdddcF/UWLFgX9F198Mc/h5IY5ZWJqc7JkyZKgPzAwUN0+depULmPKCXNKk+bOnRv0\n9+/fX9BI2it2TuEOdAAAAEgWxTAAAACSRTEMAACAZLFmGJPG+j5EYn0fojCnNGfmzJlB/7vvvito\nJG3HnIIorBkGAAAAxkExDAAAgGRRDAMAACBZU4seAABgbPPnzw/6n3zySUEjQServe7w9u3bCxoJ\n0F04MwwAAIBkUQwDAAAgWSyTAIAOd+DAgaKHgC7AsghgcjgzDAAAgGRRDAMAACBZFMMAAABIFmuG\nAaDDHTt2rOghAEBpcWYYAAAAyaIYBgAAQLIohgEAAJAsimEAAAAki2IYAAAAyaIYBgAAQLIohgEA\nAJAsimEAAAAki2IYAAAAyaIYBgAAQLIohgEAAJAsimEAAAAka9xi2MzmmNlmM9tpZjvM7LHs8dlm\n9p6Z7cm+zmr/cNGpyAlikRXEIiuIQU7QrJgzwz9J+oO73yjpNkm/NbObJD0l6QN3nyfpg6yPdJET\nxCIriEVWEIOcoDnuPqEm6U1JiyTtktSbPdYraVfE9zqtPI2c0CLbIFmhxbR2/f4p+ueitbwxp9Ci\nWmxtO1UTYGbXSLpZ0lZJV7j7EVX2dsTMLm/wPX2S+iayH3Q3coJYZAWxJpoVcpIm5hRMygTOCM+Q\ntE3Sr7P+yZrnT/AvrrQaOaFFtrpnccgKrba16/dP0T8XreWNOYUW1WJr3KirSZjZNEkbJK1z943Z\nw0Nm1ps93ytpOOa9UF7kBLHICmKRFcQgJ2hGzNUkTNIaSTvd/bkRT22StCLbXqHKGh0kipwgFllB\nLLKCGOQEzbLsY4HGLzC7XdIWSZ9JOps9vEqV9TjrJc2VtF/Sfe5+fJz3Gntn6Crubue2yQnGsM3d\nF5zrkBU0MnJOkVqXFXJSOswpiFI7pzQybjHcSoSsXGJDNlHkpHSCX1ytRFbKhTkFkZhTECV2TuEO\ndAAAAEgWxTAAAACSRTEMAACAZFEMAwAAIFkUwwAAAEgWxTAAAACSRTEMAACAZFEMAwAAIFkUwwAA\nAEgWxTAAAACSRTEMAACAZFEMAwAAIFkUwwAAAEgWxTAAAACSRTEMAACAZFEMAwAAIFkUwwAAAEgW\nxTAAAACSRTEMAACAZFEMAwAAIFkUwwAAAEgWxTAAAACSRTEMAACAZFEMAwAAIFkUwwAAAEgWxTAA\nAACSRTEMAACAZI1bDJvZhWb2bzPbbmY7zOyP2ePXmtlWM9tjZq+Z2fT2DxedjKwgBjlBLLKCGOQE\nzYo5M/yjpDvc/ZeS5ktabGa3SfqLpL+5+zxJJyStbN8w0SXICmKQE8QiK4hBTtCUcYthrziddadl\nzSXdIen17PG1kpa1ZYToGmQFMcgJYpEVxCAnaFbUmmEz6zGzTyQNS3pP0leSTrr7T9lLDkq6ssH3\n9pnZoJkNtmLA6GyTzQo5SQtzCmIxpyAGcwqaEVUMu/v/3H2+pKsk3SLpxnova/C9/e6+wN0XTH6Y\n6BaTzQo5SQtzCmIxpyAGcwqaMaGrSbj7SUn/knSbpEvMbGr21FWSDrd2aOhmZAUxyAlikRXEICeY\njJirSVxmZpdk2z+TdKeknZI2S7o3e9kKSW+2a5DoDmQFMcgJYpEVxCAnaNbU8V+iXklrzaxHleJ5\nvbv/08y+kPSqmf1J0n8krWnjONEdyApikBPEIiuIQU7QFHOvu4SmPTszy29naDt3t3a8LzkpnW3t\nWotHVsqFOQWRmFMQJXZOiTkz3EpHJX0t6efZNurrhuNzdRvfm5zE6ZbjQ1aK1w3Hh5wUr1uOD1kp\nXjccn+ic5HpmuLpTs0H+arMxjk8Fx2FsHJ/zOBZj4/hUcBzGxvE5j2MxtrIdnwldTQIAAAAoE4ph\nAAAAJKuoYri/oP12C45PBcdhbByf8zgWY+P4VHAcxsbxOY9jMbZSHZ9C1gwDAAAAnYBlEgAAAEhW\nrsWwmS02s11mttfMnspz353IzOaY2WYz22lmO8zssezx2Wb2npntyb7OKnqseSMrIbJSHzkJkZPG\nyEqIrNRHTkKp5CS3ZRLZnWF2S1ok6aCkjyQ96O5f5DKADmRmvZJ63f1jM5spaZukZZJ+I+m4uz+b\n/c84y92fLHCouSIro5GV0cjJaOSkPrIyGlkZjZyMlkpO8jwzfIukve6+z93PSHpV0tIc999x3P2I\nu3+cbX+nyr3Ur1TluKzNXrZWleClhKzUICt1kZMa5KQhslKDrNRFTmqkkpM8i+ErJR0Y0T+YPQZJ\nZnaNpJslbZV0hbsfkSpBlHR5cSMrBFkZA1mpIidjICcBsjIGslJFTsZQ5pzkWQzXuz80l7KQZGYz\nJG2Q9Ht3P1X0eDoAWWmArATISQPkZBSy0gBZCZCTBsqekzyL4YOS5ozoXyXpcI7770hmNk2VgK1z\n943Zw0PZOp1z63WGixpfQchKHWRlFHJSBzmpi6zUQVZGISd1pJCTPIvhjyTNM7NrzWy6pOWSNuW4\n/45jZiZpjaSd7v7ciKc2SVqRba+Q9GbeYysYWalBVuoiJzXISUNkpQZZqYuc1EglJ7nedMPMfiVp\ntaQeSQPu/ufcdt6BzOx2SVskfSbpbPbwKlXW46yXNFfSfkn3ufvxQgZZELISIiv1kZMQOWmMrITI\nSn3kJJRKTrgDHQAAAJLFHegAAACQLIphAAAAJItiGAAAAMmiGAYAAECyKIYBAACQLIphAAAAJIti\nGAAAAMmiGAYAAECy/g/ahTVPm4QJfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465580c0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465583e2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557cf0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMAAAw9IMAwAwrP8Hz0AHq87zUM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557625a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[i,10:15,:,:,0])\n",
    "plots(dataset[i,10:15,:,:,1])\n",
    "plots(dataset[i,10:15,:,:,2])\n",
    "plots(dataset[i,10:15,:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14655824d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146558333a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465577cdb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAACWCAYAAAA7UIUvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw1KGAQAYljIMAMCw/h9w/ghyFHtWLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146558268e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(dataset[i,15:20,:,:,0])\n",
    "plots(dataset[i,15:20,:,:,1])\n",
    "plots(dataset[i,15:20,:,:,2])\n",
    "plots(dataset[i,15:20,:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset[0,0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM0AAADKCAYAAAAGucTRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146557ee1b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dataset[1000,15,:,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(dataset,open('dataset_with_4_channel.pickle','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(labels,open('labels_with_4_channel.pickle','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}