examples/seti_signal_to_mnist_format.ipynb

{
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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width = 200, align = \"left\"></a>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "--------------------\n",
    "# Convert Signals to Binary Files for Training and Testing\n",
    "\n",
    "In this notebook, we read the Basic 4 dataset and convert signals into a binary files. The format of the output binary files is same as MNIST. The data set is also split for training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /opt/anaconda2/lib/python2.7/site-packages (1.1.0)\n",
      "Requirement already satisfied: ibmseti in /opt/anaconda2/lib/python2.7/site-packages (2.0.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /opt/anaconda2/lib/python2.7/site-packages (from ibmseti) (1.11.0)\n",
      "Requirement already satisfied: numpy>=1.10 in /opt/anaconda2/lib/python2.7/site-packages (from ibmseti) (1.13.3)\n",
      "\u001b[31mtensorflow 1.4.0 requires tensorflow-tensorboard<0.5.0,>=0.4.0rc1, which is not installed.\u001b[0m\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install --user scipy ibmseti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "import requests\n",
    "import json\n",
    "import ibmseti\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import os\n",
    "from PIL import Image\n",
    "from matplotlib import cm\n",
    "from array import array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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    }
   },
   "source": [
    "Let's first create a working folder and set parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "new_sheet": false,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ds_zip_file = 'SETI_ds_64x128.tar.gz'\n",
    "ds_directory = 'tmp/SETI1_data/SETI_ds_64x128/'  # The dataset directory to write the binary files\n",
    "if not os.path.exists(ds_directory):\n",
    "    os.makedirs(ds_directory)\n",
    "# remove its content\n",
    "os.system('rm '+ds_directory+'*')\n",
    "print os.popen(\"ls -lrt \"+ ds_directory).read()\n"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "h and w are the height and width of the images, and lengthRatio is the length of signal in ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
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   },
   "outputs": [],
   "source": [
    "h = 64 # The height of output image (bins)\n",
    "w = 128 # The width of output image\n",
    "lengthRatio = 1.0  # the length-ratio of signal to be read. The higher the ratio, the better resolution. E.g. 0.5 means half of time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Get the file list\n",
    "\n",
    "I have a list of simulated files stored in an OpenStack Object Storage container that is world-readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "collapsed": true,
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   },
   "outputs": [],
   "source": [
    "# Images\n",
    "base_url = 'https://62a44.https.cdn.softlayer.net/seti'\n",
    "container = 'Basic4'\n",
    "# list of images\n",
    "r = requests.get('{}/{}/{}'.format(base_url, container, 'index.csv'))\n",
    "filelist_txt = r.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Let's create a generator to read the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
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   "outputs": [],
   "source": [
    "def filetext_gen(filelist_txt):\n",
    "    results = filelist_txt.splitlines()[1:]\n",
    "    for result in results:\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "This file has 4 classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'narrowband': 2, u'narrowbanddrd': 1, u'noise': 3, u'squiggle': 0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictClass = dict({u'narrowband': 2, u'narrowbanddrd': 1, u'noise': 3, u'squiggle': 0})\n",
    "dictClass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "# Train/Test\n",
    "    \n",
    "We split our data into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found 4000 files\n"
     ]
    }
   ],
   "source": [
    "# At first, use just 80 percent and 20 percent. This will be useful \n",
    "# as you prototype. Then you can come back here and increase these\n",
    "# percentages as needed.\n",
    "\n",
    "training_percentage = 0.80\n",
    "test_percentage = 0.20\n",
    "\n",
    "assert training_percentage + test_percentage <= 1.0\n",
    "\n",
    "uuids_classes_as_list = map(lambda row:row, filetext_gen(filelist_txt))\n",
    "print \"found {} files\".format(len(uuids_classes_as_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's group files by classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "uuids_group_by_class={}\n",
    "for item in filetext_gen(filelist_txt): \n",
    "    uuid, sigclass =  item.split(',')\n",
    "    uuids_group_by_class.setdefault(sigclass, []).append(item)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now, split the file list into test and train based on classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "squiggle: training set size: 800\n",
      "squiggle: test set size: 200\n",
      "narrowband: training set size: 800\n",
      "narrowband: test set size: 200\n",
      "noise: training set size: 800\n",
      "noise: test set size: 200\n",
      "narrowbanddrd: training set size: 800\n",
      "narrowbanddrd: test set size: 200\n"
     ]
    }
   ],
   "source": [
    "training_set_group_by_class = {}\n",
    "test_set_group_by_class = {}\n",
    "for k, v in uuids_group_by_class.iteritems():\n",
    "    \n",
    "    total = len(v)\n",
    "    training_size = int(total * training_percentage)\n",
    "    test_size = int(total * test_percentage)\n",
    "    \n",
    "    training_set = v[:training_size]\n",
    "    test_set = v[-1*test_size:]\n",
    "    \n",
    "    training_set_group_by_class[k] = training_set\n",
    "    test_set_group_by_class[k] = test_set\n",
    "    \n",
    "    print '{}: training set size: {}'.format(k, len(training_set))\n",
    "    print '{}: test set size: {}'.format(k, len(test_set))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "squiggle\n",
      "narrowband\n",
      "noise\n",
      "narrowbanddrd\n",
      "number of files in the training set: 3200\n"
     ]
    }
   ],
   "source": [
    "all_training_set = []\n",
    "for k, v, in training_set_group_by_class.iteritems():\n",
    "    print k\n",
    "    for item in v:\n",
    "        all_training_set.append(item)\n",
    "print 'number of files in the training set:' , len(all_training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "squiggle\n",
      "narrowband\n",
      "noise\n",
      "narrowbanddrd\n",
      "number of files in the test set: 800\n"
     ]
    }
   ],
   "source": [
    "all_test_set = []\n",
    "for k, v, in test_set_group_by_class.iteritems():\n",
    "    print k\n",
    "    for item in v:\n",
    "        all_test_set.append(item)\n",
    "print 'number of files in the test set:' ,  len(all_test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "###  Send request to fetch data, and convert signals to spectogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10026a131a50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CukU3eN6kwvh1r6f7NCootk7jXyVoApaVWuAwTDH2zPIFR8kGvZauyDbHcssO\njt24heeoGpZey2Ndd/uoCUhvUO/AGhsRkfoNSu5bYZ927mIfhqLnun+Ox9sC29YuUDPROSOzSxFJ\nW6QPpOZuxmTG92VVhAzJRLXkRM17OC6LnnbnczGZ8z2hBJc017tbPce4DDbje9o/geOjkLJWi8i2\nS314hf0T17gX5qU2Ow1usWaGklCTeWOQ6PnhPfZhcorrJ2zRfLmjtSv5nLQrQ5ynrRtq26Bhjjms\n+3qf9tMF1vN4g7olEZGa9D3zHTrq9p+SPuZY605q0oL6Lk7UconreDfS+8eE1naWkoEuCUzZkFhE\nJB/itS+5Il3fhPSGW70JcZ9yMt8sIl1jW6/bkBJ9156uH+/gnGrtGhx1abizNmlFO6RhXTQIcKmb\n0wUlYaYk5Ysvar2p1hc3ODw2YL8UGYZhGIZhiN0UGYZhGIZhiIjdFBmGYRiGYYjIK9YU1S0nu3de\nxA/bzygxnOzxR+SwwOSCmxC9FTpTim9HWpfUmuN5cjKFuOqhiGRycK3KiIV9EzDW3J1ijHca6bYM\nHPlqPMUyt6+Tb9NWe9nM76Nuop+jBqKaY73ili6jM8RYcvJ91A1UY9JpaTsL2dTo3eIpQW6ckhbh\noMH0nLyNqph0SqQhaZda2yTPsYzkCOPGIemUZK6/B6xW5Bc1IX3YEudP4bTWq+rj3wYhjTXp1PLX\ndRw92lH8/hn5AVFC2GCk/T16O0pMW+N6yK7x2OmprjQ0MSUM3t2npKJZQwLUPZqH5+TLU+PrrYZE\nveyr0kpIq/IQy6z7Df5RpNXIKPlvcIJ1TxOtkYiekbcPzZdJFzUl/kzPj9YBlhvcI98zStaZVroe\nFelb8hrrUfRxTkWZ1gMFnACYdEmbIfa5v9BtWbdxHk6WOIkK8ktqneq2rDzuY4P3cL2wpmbXoDe9\noUS0SYcSKHexnnGk+6Nf476dkZYrooSng0wncl5XeJ7yAPssXJG3T6L3oGpJ+rAejtP+GfZx0JAc\nIg+w7sGa9HK3qa0r3afbW9iWwRVeX0rS1i4DrePzZFuWbGmdkg62+0Tv69UeaWVTSnZ7B+uxcFpT\n1O5y8l6dzLcJ+6XIMAzDMAxD7KbIMAzDMAxDROymyDAMwzAMQ0ResaaocqGs4hcxyHKAMdBeqX0T\n1oIBSs7NU/SwDNfgIVOlFBenVu91Sf/xQAdsqxA/NF5igqPLzj4c54daM1JdYPuq+6SZmWLMs2Zf\nFhEZPsC5t+XzAAAgAElEQVS6sj9SuCCvn+9rrcb6K+hXEb+NMe/NlvwsAq0J4B7qZtg2JxgDrj/Q\nbUlyam+HNEVbil+/qXNs9Y7wvDdr1Ie1F3gOf1ePbfrHpCt5hsfbCelwKAWZiEi6RzmTWpR3aIvz\nZ/hcaxPCCHUEu1tYRpt1OVvtZfPo9btwPLrAyk7mqE0oWw1bQI7n2b2D7Y9znC+PjvGcIiK3Fmf4\nh4Q8qS6oz9sNc4y8asIS++f0zhEcj2900rEywjnUG9Me4ymfmtN9+lRuw/Fwi306zHEsl/e0zqIX\n4nnDS9Iofoz1rO+oIiTfw7olC9LP9bEt4UL7A1Xk7RSTz1e+RU1RVGkdzkRQ57k4QT1H5xLX3MNf\nuafKOHiE87CifYy1cJVobdPgr3Cz7wn28ZMv47gdXmr/qCLB+e9oGvoh1uv56ECVEZN3XpqTF9aW\n8u9NdJ/WpN2qSLcWXpFHlV5yEtDen7+G86X9ABuXeF2PqzdQC1sssR7dS2ybm+h6OPI6mg1Q+zVe\n4/rJ7uo1V5LPnaOq+jb26WSm85xFcz3/Xwb7pcgwDMMwDEPspsgwDMMwDENE7KbIMAzDMAxDROym\nyDAMwzAMQ0RedULYzEv46IViqqZ7su6oQSVNhnbrMZpLta9JSKv1qxLuo+AqLklceEkCtUMt/Er+\nAkWN1/fHcNyfYd29ziupktd2F2Q8uURxYX2k71kLEnx3Mmx/GeA5igYRG5upVTsya3wd378QbYy1\n9wzFclFB5oSC591NdCLFZYWC1JQM/LoVdeKlNhtbZVhGq8T+6NTYx9WV7tOswjkWDnE+7HpkANhg\nEJpQwkJO7lqTYFO22syyPMFxCDJ8z+6EjAc3uoxOiO1thViP0OE4lW/r/tg9xbGKH+N6iUd4/Fr2\nSJVR0viXEc5bNpZz+hkLidtkxtfHBwCSDs3jay3GDa+xj6IYy3QVmcKVuk/dF7GurTPs0+UcHwBI\nnzSIxu+QkHiDfZzfJtPAkRbBsjGnCyl55/fxHJs3KOmuiMTPyIjzAN/TF1xzfqGKkID6dHCMm+42\nxDIPH6IwW0QkjnA/jZ/j8eo+7vM+0GMb5WR+u8A+7T3Gtqwnuj9YbJx8kZKokhD75IweIBCR5CnW\n3aP+X1b0sFC5bkj2u4djGQnWa9pHAfRwoJ/2iGp6iIRy6NYTMjdtN+wfz3FuV2SyWpPJZHRLJ1lN\nPqIHdygRb02Ja5uShTuHdUseYRnVISU29roem2Meb0sIaxiGYRiG8dLYTZFhGIZhGIbYTZFhGIZh\nGIaIvGJNUeQq2Q9fGKxR2FBOk0P1mUGC8eo0x5ivJ81ReV836bJGTcxejvHYsCAjuYU2PAx7FPMd\nUpw0JVO0mdYECIU484KMJ+/j6/VM37M6MnXbPMPY+/YA4+rdnRY3hWus63wPg7p76xmeU1k1imxq\nMoCkeK1vU+xZdH+0VqQHI8+7okTzwiTWMeGATM2iEM+zfBsLja712ModKpe6rJ3/eJ2SiMjUYR/W\n+zh2cUjnGOjkpeGOkw5jGdEKXw/mWhPQ/RJWvq4p0WRBSXif6fXSpnFxMZ5nVbGJpm7LbkaGl0Ms\nY6/GNbgLtU5rfYBzO92Q5uwRmaF2GhKxtkhzt6Dkvwc4xzrnOmnkUY6mfzWtn2CM41Klet160mK4\nivRAlAAzfqjXy66NdU37ZBi6j/uJ18MinHM7pKSxmxTXtdMetBJRgs/wAbbffQnb1llrwdiGtEwz\nSu46+VM047v+h6jhFBHJSIfTbuPYBWNsW5NRaf4e9WkPry9hn7RvHb3m6ns4dvMRXm82lFF7v8Fo\nkHVo0yMcqHCEfVxkDebA51jX7ATfU4+xnlddNBwWEXntw8dYJukrrwJ0a+xc6utLGZIp8xzf4w5p\n3L6l53pB1/HN6zhf2Px229ea1c0XeU9p0Cw3YL8UGYZhGIZhiN0UGYZhGIZhiIjdFBmGYRiGYYjI\nzyEh7Cx9ofEYbDHGN+xqk6GCEgEmS/K3IN+RXaa1CW3ybqkDiu8PSf8SaQ0N91TPU3wypwB+QxHl\nDuuWhxjzHVeo5QlyXUhwjjHt2d4AjvsL7MOw35AUj5IvjgJMpJlcYB8XIx2/bnPSQ2rwfIT1mvxA\nx9GXQ/Lv6GAns2YinmlNUT0h/5cBxpaTa/yMa+v4NWuZ3IyS2VIsftHTxho9h3HzkPxgtmOMiftM\nCz6qc5zrcYZ1b6Wk22oSjTzBw10Hx66kidykXSlIe+H2sT82A2xLfaO/W7XXqJFwNA+3b2K91hHq\nLkREkktKgDumvaAgP6BrXY/ZL+M8PLiihLjfR/3DzS9gQmERkcNT1BRllKx0ITiBhgNcxyIiO/JY\nGv4ADYC67+F6ygfay8aR9tFTe4NjHKf+M63lqY9JD0WJaDOaLy3Rnkvla6TdOsUyWyGu27KntV6U\nL1rCjHzOhtj+4Zn25XHk08Xat5sV6pD2rvW4sOfYVYSamWCGfb7W00PaHsdu9BDrOvJ4vDzUia3d\nfax7vKK97hQP1+e6jMBhXcMI1/bwCV4bykM9LkFC1wvaHnoj0vieaY3m4ku4HoI51mswI93jnl63\nIXlQlR7rmu9wnsa5vja0HjboR18C+6XIMAzDMAxD7KbIMAzDMAxDROymyDAMwzAMQ0Rede6zqJZ0\n9MJLohhgnLD9THuERG3KB0XxyZsTDPKWTsdJ96eomZm/hj4S3SuMve9CnSMnjTG2vhhiGfFzDL5y\nDi4RkVkXtSijC4w1rwaoq4hzrfdwHmOt0ZrO28fzFi2tTYgcxV9XqAnIyQ+lE2htQnZMHkKk3Zls\nUEO0O9Zar+KMzvMYz+Pfwnplh1rb1JrjuKTPMY68cBjf7oQ6zhxkOGemv4yfiUv8TCa6LZMbbG9x\nB5dWd4Ntmw7JMEZE+jvUqdU9/M7Cee/mCeplRES2R6ipGk1RR5Fm2Jb123qutz7G9wQb8hWhPu82\n+H84kjwU5zgP6y6O7dUGtRwiIu88/QiONwlpmULsn/JAf8frPCftW4vyHJKOrdXV65bzlrH2i+d6\n3uCHM1ijFqMqKKdUn9ridBnhgPyQRtiHnfdwXIpjvRfuyIfIf4G0LJTHq9zXfZo8Jm0b6eOqNWm/\nGrzBojl5YZGPlbxOur6V1s8FtLX5EN+z/zHqx4IGj6GwIu8v0uWsbuNEZs2miEhSkQbzCMeO1206\n1zqtghoTLSnv3z3yOupi20REglNs35z2vl2A+9ZYtMYqzLD9mwrPW3dwPlzc1d6C+0+o3ydYZplQ\nrs22Htv1EM8bbnGcZnfx+tvZaO+4qMkr8CWwX4oMwzAMwzDEbooMwzAMwzBExG6KDMMwDMMwRMRu\nigzDMAzDMETkVQutcy/J0xfip4qEka7SQrgqwyqeHaMgcz9GUddg3WDYRGZ8o6cocF5TcsLgqTZN\n3HoUsNYk6vOkm115ba41zNCwLXUouAtOUUx2/QWdsG+3xXoMZij8y/ZRTBc90OaNzlM/k/i0vI2C\n5s1ai3FZtBhekoA1IwHnQoveogEKFFcl9tnoCoWA8xMtLK4c3deTgLdMyIjw23psyxkK/3pkPJhT\notFbf3Gmysj2sM/yYzovvX/yvjazrEhsm3E2ThIoSoMvZ+cJCg4relDBFTgu7Y/0evE87Wj491Y4\nLrtYC8/r+9iWvI9C0vQDHPvD/EqVsThGoWhdU9JUSkLsGgxT4w2+Z9OhxJHUpe0LLbTekLFkvcF6\n9AIy7rzSc72qyGT0dVqnT0hEHWuRdLLCsQrRU1J2PSwzPtWGdnGFZSzpgZH2ISVpjrUItjjAuV3O\n6ZgfEOjrtoQf42AlN5RgmxJ9b3t6D+rvocB/k5FxK4m1K2yqiIj4ZyRwv4d1HQe0Tj/S/bG9TXOK\nROExXZOyVCcvDWpKBv0W1muwwX1+XejrC6+H3lN8uGN5Bztg1/DAyF6M18ZohXM5cixM19fsTol7\n0JKfutjH/ukudFLZfEbJ0kmIH3fxM8m1XnNl7ye7vbFfigzDMAzDMMRuigzDMAzDMETkJW6KnHN3\nnXP/xjn3fefc95xz/+KTv4+dc3/knPvgk38bssIYhmEYhmH8/eBlgm6liPwX3vu/cM71ReTPnXN/\nJCL/qYj8a+/9151zXxORr4nI7/64gpwTCT+VbLWaYvx21tWaEU7yF2zx2JcUE050k5K3UCOyDskk\ncYux2OC2FmtsKaFj/DHFVikh6najY+BRGz8zP0LNiBtT3DTTmoB2hXHiNCLNxBzbFtTaKCxZ4GfY\nrDFyGJ91pY4bp39JdaNb4nBOCf0iPS7dHbYlSfC8FWloWrHWe5RklFd08TiJSauQ6O8BI9KdRCXF\np7+HGoDqttZIbB2PN2l3plj39UTPj7KNcfTBDWrQVhvU2ISxFtH0Y9QeuGtaPzV+pkkTME1xHY5z\n1BBlnuYta9REpJpjH6WnqKvwKeoKxhkarIqITD2anc6GWK96iWMZea0r6FPi5tUA9Q2DKfbXckjZ\ngUWktcKxSwXbsuujNsM1iJuSR5TI+hrXR/EmzttVpjUjUYDtCyjJbkomo2waKCISrHGskgGNyxkZ\nVR7q9TI9wXEZkL4j5KSiDflgF7dR33J0gZqy2SGO9eCBNgjdCPZR1sN52e7i3pc91OavZYl9FD2l\nupPUrclQt2z/+GSl+S3aX9cN+koa2+oa6xWQ9K99pc0Ky1tYj0JwP2nv4X57Fh6pMpIdztP0BOf+\nigwho0S3ZXuJe9vgMc6PkjRm89t6zQ3/DMfbv437xarG61yW60lWRzx39fWjic/8pch7f+q9/4tP\n/n8pIu+JyG0R+Q0R+cYnb/uGiPzmS53RMAzDMAzjc8jfSlPknHtNRH5FRP5URI6896efvHQmIvq2\n84ef+R3n3Dedc9+81Df7hmEYhmEYnwte+qbIOdcTkf9NRP5z7z38ru+998Lxghev/Z73/qve+68e\n6F/JDMMwDMMwPhe81IP8zrlYfnhD9D977//3T/587py75b0/dc7dEpGLzyrHO5HqU3G+bB9jrcGV\njsXHS4xxnnSew3FeYRmsZRERybsUS16QDmlMCfu6DYYnZxjTLA7I/6WH5z38Y+27klNC09PWARzv\nnaKGpLfU/g31AuuxOyFvjgrj6F5bUUhxH9vLyTkvN2hUcxDotri3sI/cDd4TZ13yTIm0H86iQ0n9\nMkreSXqXFftdiEiPNFaDEn+OXDxAbcKmoxPkxgPss5h0F/mbOG51S3uV9J+Sj8g11rWYUJ8v9feR\n+Bzn+rqFcfOSkoi6RH8PYY8tP8C6LmvypRnqOPsgwz5ctvEzrOuTYcO6rXG8U/LMcSMso7zUmoD0\nDo7LSY3+UKs97J/0r/Ucc+RN43CJybpDZcwaNHgrGpd98i0if6neqV632yPUWZwdYyLNQ9LUDGuq\nqIhsyA+nnePY5R+SR9cdvb23yIdpkOG8LSJcH8tIf5PdI2+09BL7rKRE39WeHtvRY/TD2UWky8px\nfky/rPWmwQrnXceTduWSEijf12UcntHeRlO77mHdr4eopxIR2dtiW9pb8jlb4P7hB3q91DucQ57y\nI5fkc9Wk0xLyyuPk6MES58eQ6i0ikp6Td94EOyRd0Oux3oN8B88T7GhP2sd6dS+1PqoY4djtRuTr\ntSM9XYP+djrWY/UyvMzTZ05E/gcRec97/9996qU/FJHf+uT/f0tE/uAnqoFhGIZhGMbngJf5pegf\nich/IiLfdc5965O//Zci8nUR+X3n3G+LyCMR+ad/N1U0DMMwDMP4u+czb4q8938sOkvB3/DrP9vq\nGIZhGIZh/Hx4pbnP6jCQzfhFbL03Qz1IkOhYqycJSE26ivVt0l043aSbAk103hl8iJ8hv6TspkGI\nQzHcTkY5gs4xErm6i/USEYkFPR2OdyjDKshj6fqtsSqju8U+iy+wzDjB2OqHv/a6KmPyx5jPJ1hj\nv/cKPEc+0P4e1RrrWo2w/e2n2D/lgR6X/AQHd3ODuovDS4z3H84wz52ISNXCgXk+PIbj/jHqDLY7\nXY9yRl4tBzh27TX1x1r3hz/A7w0uxpi3o7C5C3QsPgxJDzfEMgcfYp+y54yIyPIt1DJt29in/QvU\nC5U7LU7Y3aP21ViP8II0Nre151JMMX+5T34nE6znXqX1Dbs+aWhKLKNF2g13qyFnYYZruT/H9k/v\noe4gb/D2YW+ahPRR2wnlCjzQua1kjn24f4prMNxgf2VDPcfqJ5RTTEiTeQf7Z8NJ60QkO8Q1l7TJ\ns6zEMvee6XEp75Nm6A7lucvxHNlK12MwwXI3U1xznXNcc/0PtWYku09avzb2cU2aGs61KSIiT/Az\nOelLd5RLMrnRurXVCOdyNcbzpDucp9FOr9uspDlDUqfNHvbhqEbvMBGR9AH5VJHeNngd5zH7bYmI\nbHtYj2hL2p0JeT9NG+bpIfbprINarnjKPniqCAl6tJZJx9l5H+f6YqIT262P+Bqs8002YWk+DMMw\nDMMwxG6KDMMwDMMwRMRuigzDMAzDMETEbooMwzAMwzBE5BULrZ33En8q4dw2R1GXm2ihZLSkxKtL\nfE96isKv1kab0Q2HaIR2fgtNE1tjMjQ71/lIHLl6hQusR0bGjFWqu/ZyjKaIPUERsOvgOfoP0SRN\nRCQkUXT4GiWZ7aOo7fbpqTBpBwV29ZYSa64omeuevncOW/ie1RhN3lZtPG5fa4Ou9ndJONzGtrFJ\n4tUXtPA8zXD8OzkKNDuUdDZ41vAg5SNKmvprlDSVzMdap1qgWG1Q1BmyQRt54PUKPU+zNYo6V19C\nAeeyi+MQc+JaEWl/hOVWJ5SItIfHN0Pdp60bErk+x+PVL5LhYabbEp+TYRvpMYeXOLfrQs+x8RbF\npCz4jyts/ywl9zoRWRXYhwcjFOuPz/EcRabNPf2aBLxHlDR1Rp+50QaySYJzpuVw3s6HKBT1Ld0f\nQYcTreK8ref0GUrSLCLSviBx+hbHqV3jWM7e1QJWoecdhgXulyFNy16h99OA9Nu9Pr6nGGKfTg/0\ngys1PRSdrGhfC3BNjq+0aPzyHu7JaYT9U7WxT1tPGtb+M3zPOsQ9eEgPEVQTfW0I+5S4eoHvaZ/i\nuLDZo4g2Fa3IVLQgM9ik0G2JIhy8+Qjbsk8i6Xig96DZGh9eGF3jGqs6ZEIbauG5q+h6+yFdbwJc\n10muTVeb5t3LYL8UGYZhGIZhiN0UGYZhGIZhiIjdFBmGYRiGYYjIK9YUSeCkar84ZZxiHJXN+0RE\npMK4Z/QF/ExFcfSqwXytJaQ7eYb6FtblZLU2bwwz0oh0KX6dk2HXVpuNHZD5oCeTq6JNSUMTfc/a\n6mNbFhnG/OcOY8DhUOsbwmP829EpOoUt38B4bVMZrLMZfIs0Inex7vM3tDZh+x7GuMMNljkJsV7J\nqCGeX2J8OllTcsoQX0+/r8tYkmFZ/JAMDdtkePe2Httihe+pyDiudUZJIllkIyKbCc5/T0kgWXcz\nnOmkoSFpRKLHGPPfjnFut73WerGJKhuphTOcD7O+TrTZdaiBCDdkgJhxglhVhGy62B8Z9Vn/OWoG\ngpGep7da53Bcb3DNXR+gDqm70f0RkXaJExn3aqpHrjUSSgMxpaSq93BeJpsGs0LSVBVXeNyiZNAH\nU212mpPZaZlSstLHONZxrnWe6SH2kQ/IuJSMKKO2boun7+N5orVcn6Zfa31IfUPJsdu0jkO6Vlxo\no9JBjuOQs7nlGZbh21qT2CPdoho72sdXezqxdUB7X7eNZfqUEig/1/2VUGLrtMTj7WPcb7Odvs4l\nt3EeLuh64qhpI5mqMkYXqCFifVzZpkS1UYPO8wleC/P7WNdwgXMs9Hqe7n+gDS5fBvulyDAMwzAM\nQ+ymyDAMwzAMQ0TspsgwDMMwDENEXrVPUeUlWb6IWVZv4z1Zd7rhjygtD2uI4hbG++tew33ehxiP\nbR2Tl8uWEsK+phM6DsgzKAixXgklygseaV3BNEL9QjXF7g8ril+PdFuWe6hnqC8wHjtZoY4gPtc+\nEqsaY9p+H+takb/H8NvaL6k6xPfUa9IqkI9GvNC6gv34OX6mj2Vs56Sxmer+6PbQ64nrHi2x/VXD\njG+Rti32+JmANDQy0GPbomSua4d9XHdIP7bQmoCIEga3npMW7jlqObLXdRnFMWmbAhoHIQ+qhtyl\nnXM8D/udOJJldZ5rn6J2QboTtIORymOZWaUrUsX4noCS7JbsBXZbj0vwAa7THSXIbRXYx0WpJ0jd\nwXrsfwd1FPm7pH9ItFZj2UKjquIAx2l4jfqwJg1e6wI7frQhzQQlxN15XY/tBvuZE9EGd7CMbaTH\nJXxO64P81WrSkORbvW6DPmmXlrgGy5gSTtdaDxSTdicOsF6b13GsO6daL1bfwnXZmlIi60usRzTU\n++lFfwLHgxbtl5SHdJ03JchFzdRuRx5+HbredBo0Z5QguKI+LF/HPhw81JrE3VOs252DZ3Acr7H9\nRUuvlyDGeRp7PM6e4tzn+SMiEpNmt0VeaLsU53ZTIvid8Nx9Od8i+6XIMAzDMAxD7KbIMAzDMAxD\nROymyDAMwzAMQ0ResabIByJl98Up88d4+lZH5y8JSL+w7FFOrSnnPtM+NMUdjKVWFCfn2Gp6qOuR\nTzGG6TsYF21d4mdWB9qLordC/YufU04l9jryui3rRxjzXY2oP8jHqMFGQgYxxryLPrYlforx6t24\nwc9iTRqA+6TlmaJWwTd4USy/SLl4voXx6rCPZTjfoNOifFdRRmVQyqSKNEgiIumINFWvYVuWpG1a\nJ5TITESGT8in6TV8vc4oN1qqPXXSFOuel6gRyI7weLvQ49IeYOzdC+m0WlhGeKX7dJtip8VtmofU\nhcW4QdsUY1vKGc7tZIKvp12tS4p+QBqit3G/CA6xjN5We5Vk7MND3k+dDHUmRYPorDjC9tU7nMvB\ngjSLLa2NjErUCLXOsL3pHvZxWOi2sMfS5gj3sdYM1z77GomIRGNaHzmtsRrbsvdU5wvLHfkjkdat\n/GWcc5sGDU38F+RbRT5NnRrLzEPdlmpCHmW09wUl9uHyRO/Jex/j3l+T51I9od8N9JYs0QHuhXmB\ndY1W+Pp4rTflzgrrvqP2xpRQbvemXvsyw7p3r3Aezu+QHrUppyXtQd01jgOPZRjrecrrYT7GXGjt\nPSwzutJ74eYX8TzxnHwBHWklA62xck364pfAfikyDMMwDMMQuykyDMMwDMMQEbspMgzDMAzDEBG7\nKTIMwzAMwxCRVyy0Dkov7csXAsPlnTG83lpqgXNIySmHUxTGlT1KLldr4ahfUOLAEZk1HqCoLW0Q\na7s7lJBvw+aFJPzizHkiEs5JUHaAh5UnQXhX37P2n6HKtZOQIdlHeMgmgiINJoFXlKxUsM+LlhY5\nhhG2Jd/ge5Ie9mHV0+Zr1ZYM2gYkrCWRcLTSgryoh21hoWh0j8SWTpvRxac03uSJ1/Eoio1mWly4\nrcko7hrHhR8Q6BZajCuU4DInc8b5BMsYPNBmZNkaBZi7kswKazxvtq/7g3Mr+h4JqftYz+FCm8C5\npzQOnBQyxrGNr3Sf7k5wTjn0+pQqw7onVcP+QcmMi32cc7tr7K+6px8IqJ9h3XddPG93jX1641BY\nKiJykGFyY0cegLN7mHizW+n54Qrs0/wSx2X1SyiQDxrmaUxmph69+WTdwzK6ka5Hd4B7UE1GnDUJ\nfodzbf5ajymJbIZtW03oIYyqYT+lfYqfw0ieUHLoHj6UISJSk0FoSUa+bJYbTHSf9pbYR9U+mY5+\nkRIqP9Zl+C72R1SSAP6S3p83JFGldbnKce/vfY/GcqDLYHPK8obE7Cc4DvP72mQ0fYztj3r0mR3O\n9U6p51hni/vn7hr3grbgnlx09a1MPNBGmy+D/VJkGIZhGIYhdlNkGIZhGIYhInZTZBiGYRiGISKv\nWFMkpYi7eRH4PQoxeWm+1SZwyy4ZTpFx2voINRPpQOsKxv83GpBFgjHO2SHGOPNC1yN2GONNyOQq\ndJzgUMczS0emZgekQ3mMcVS31PqobB/rxn2WkO4mfk3H4j+dlFdE5PneMRwfv38Bx9OJ1iUVjs67\nR0l2yYiSk5uKiPgJ6Y7IKK39IZYZ1DoWv9lgH3Yj1DtED7APe9/V41LdpcS8S9IAkCEkJ80UESkO\nsIzeQ6xH8A7F6mOt05rdwbYMnqIWo72iRK2x1ml1KelleIDzobvA129yXY/+DZ6XE5zmIxz7sm7I\nKvs6HkYlab/IaLH2ui3plgxCx5TgkqbUtYx0GZ9hihiTyeRioo05izVpDnM8MWtI0kSvOX9OusbX\nscza4+vZnt6D2mNKELwgo85H+P4wbjDFm+AcK7ZkEniMdS9CPS41JXuWANeYJ6PO1S1yUBWt5Ym2\nZE5IiWujtl5zEWkQ/TVpvw6wjL2QxIIiwjtsO9j92DekT7Xe1JORYNGlhKekDwoakrkKJRi/OUD9\nU9/hfhJ2dH+sS9KD9VCrE5AO9PIIE9mKiEyeYvba6oiS+5LOLz/X+8fwgjSGpIcKqO7RuGG9kA6Y\nz5twgnHdHTLNWUN2od/UgP1SZBiGYRiGIXZTZBiGYRiGISJ2U2QYhmEYhiEirzohbCpSvPnilGVK\nXj9zHb/2LQzqsnYjfkaeOzdau3L6S4dwnLYwLuzIryHYai1Ph/Qcm3sYr/bv0wd0qFVCSlCYfgvr\nsUuxzPB1rX+ZefRAGazIq+Z1rDv7CYmIBB18T0qJJDdfQA3JYKN9aMIF+Wig5ZQEpBm5aWm9x16J\nWq/4DNtbJKRdSfX86O0w1r4YoQatE6FGoP2dhiS7LZxT27f4PKR/yfR3iaRLyVzvYN2rmubtTsfR\nU/Le8BF5+XRxnNLrhiSIMY3tCtv74P59OJ58gBoCkQZvqx6W2T7FekRtXY/1HSxjdY7j0nc49kVb\nb0V+Sx4yNa7TznvYX/VXGhJckulStcY+Pb+FuoruOfl+iUh3g9qM9G1sv39K+0erIdlvjeMQVCSC\nuIqoGLgAACAASURBVCY/qUcNnkuC5VYd8pC5Ij1QQ3LbbEj6qBo/07nBtjatuazA/cGxP9AhtjUW\nPT/qOY3VKY3tCPU/m0Qnlc0ekR6KfOC2pNFbD7Q2crTF5KyFJz3QBTWOvOVERMoBtqW6xj5j37td\nR2vwwiXWvX1IPmdr8ijr6D15/wbX8raN5wkOsS0u0PM0v0VJ2klDFV/hPrfntE5r+jYlgM3I5+0p\nnnfltNcR75cRrZ+sjXOwd669jgah7qOXwX4pMgzDMAzDELspMgzDMAzDEJGXuClyzrWcc//OOfdt\n59z3nHP/1Sd/Hzvn/sg598En/+r4iGEYhmEYxt8TnOeEMfwG55yIdL33K+dcLCJ/LCL/QkT+IxG5\n8d5/3Tn3NREZee9/98eV9atvBP7/+a9fxDkdyTtYLyQiUhQU46T8Rp7Cka0HOhZfdzHGu+tiPLK9\nwZine6qKkMUtjOmmMWkEKD67aWlvjlaJceL2Jfn0tOkeVYd8JRuTTwT5jPQq0hg1WMhkFbY//B7O\ngfVXMBY/CnTceBVj+9IN9ns9wz4PQ20kwRqINfUZ55vL+3p+tKY4duEKP7OjDoh+v0Ef9QWsq3uX\nfIsoUVXpdD2qCZVB7Y0p31xV6O8j7HcTX5BGYoRt6V2gnkpEe6SUFJuXIfbPZqfn6f4z1CbUIeo9\nkgrbsj1pENCRT9Uqo9xvx1T3U12EpzKyANtSk6ZmONNjy55K8wA9ydinaCCodRIR8aeULy3EcRhs\n8DN+o3NKBfvY79e38TtkRNoet9Nl9E6xz3Yj6nfa+uKp1iTGpH2rR1ivuvzxmk0RkfIe5SyknFtl\nQq+v9HrpXmNbqhPKFzbHes1zrTvhnIR1i/qswOM003rCG49eNpsAdUeH0TkcLw90PYI+9lGc0H4x\nwzkYbhs2dspl1iINa3tBedzua63XboXzslVj/+TktxUPdH/ke/gej8tF0h/gPD1/B/W6IiJJgnXt\nTslj6Yp0woWeH3Uf50NnTpreHl6jqqAh9xn5nA3++erPvfdfVW8kPvOXIv9D/uZKG3/ynxeR3xCR\nb3zy92+IyG9+VlmGYRiGYRifV15KU+ScC51z35IfWkL+kff+T0XkyHv/N9/vzkTk6O+ojoZhGIZh\nGH/nvNRNkfe+8t7/sojcEZF/4Jz7Cr3uRTumi4iIc+53nHPfdM5982rZ9A7DMAzDMIyfP3+rp8+8\n9zMR+Tci8k9E5Nw5d0tE5JN/GxOLeO9/z3v/Ve/9Vyc6HGsYhmEYhvG54DPNG51zByJSeO9nzrm2\niPxjEflvROQPReS3ROTrn/z7B59VVlWHsly/uDM6XGGmuLzBxEnIGMud4ct5jcIwp/MoSpsSJ4ZD\nbHbyEQqysokuhEV6GSX0TDsoLoufNIgc9yjpYYqCZ3kLf2xr/b9aNJ4UZIRGfmR5hPUKGgTOAZlI\nVvewj7sBCuP8hRZ9ujt4zEkiPYke/Z4uY/sOtn/zGMVzUYhtHV1rwbdcY7mbMSVV3aHwfNrVP2j2\njklIfEhmlo9IrT7RbWmtcY5tBOvRJlPR4kQvvV2On3FdFBdmj8k0j13zRCQ6J4O/Lj2okGA999a6\nT7Maz8NmhDdkzNhbauO0gAz7OoLvqc4piWZbr7mEDC6jIc7bVYH9tdun9SQimcP1MFliEupog237\nwZ03VRknB7jpdDOcU0WIdY8qvfan76Jide+7JOgmvXs91nOs2sM+S1ok6CWzT05ALaKTTK9yHMt2\nivNj9i4a8YmIdDIcy5oeGkivyaywrZ/24GTGLKytKPCQPNV9GpKpqt9RmbcoyW6DoPfg8gqOiw6J\n5smUtlEkvSTzzgHWq4jpehNpgbOjB55CSn5dfBnLcF7v61syNOw8wP1jeoJjOfyBDt1EEa65cIrn\n2da45iYf4XoSEQnuYB/VZzgu1V08jh7osa0pya4/wT4efhvr7gcN16jOj3+I7EfxMo7Wt0TkG865\nUH74y9Lve+//T+fcn4jI7zvnfltEHonIP/2JamAYhmEYhvE54DNvirz33xGRX2n4+7WI/PrfRaUM\nwzAMwzBeNeZobRiGYRiGIa84IWwolfTdC4O1YoSnL3s6Bt6+j/H7mDQAWYnx/BY7mIk2U2vvyNTq\nC5QEkJM1ikgywziwv6TXt/j6bKgTGOYBnsf18TzuERl4HejYs0/pPVNsb36fzNc2Ol6bnLKBG8Vj\nH+NhNdH3zvFzLCNaU6z9V/E4ZxNBEQnOMObbj3CsV2TmWHS07oQTfrJhV5BS8sH/QCeFjL5IZT6h\nxIl3yRDyRGtX/Ed43G6RyShpRrornXg0XmCfLkdoeHgU46S7SkjwICJ1C8dqdIGaodVzrEjIhnci\nIm/jYfsjbAtr0pp2Ee9Jp3aBOpSMdDhBrOP/+TEW3Cd92OAx6gqakpdGx9innrQKeRePT56TaFFE\ngh6tU5pzSYe0PduGfew59mF2gHNod0nJO4dauzJaod5ls8M9Jo303sds9sn07oDW9oa1LboerUvS\n3L3Jex2WsZdpU83pAepbuh+jjjF7A/tjU2qT0XRLehZK3hpw3Ru6JztCzdmKzBvTU9xPukOtn6vI\nHJgfORoMaZ7OG4wGR6RrTcmYE6VP0kl0PcKYtKIpjm1vRp9p0CS2LvCao67JlFS2CPVcr29Ik1iR\nVvYM977gXNeDpEsSRGTke5uupaqEpoTIes9twn4pMgzDMAzDELspMgzDMAzDEBG7KTIMwzAMwxCR\nV6wpcqFI+KmElDl5/Qyer/gjss4wxus9aVmuMW68O9CeGAllnq0+poSfb2E8t264V9yxRogSa8oU\no5r9SCfrrDPyw3F4Xo5NF17Ha5dj7I+9CuPV3Y9QL7X4gtbQLDL0TOk+w1gz6zDWoS6j0yevEtId\nrTz2V/dCx3MD8kyJPfnShKTjivW4+B72abXBsV1tcI7t5rot8hzHarpBvUNyB8UIvUyPbUieMRVp\nqDZtDpI3eB31cOwGZ6RFIP3HcKG1GptLPM/8y6hL8iXOqfVWJ3M9+hi1S6s3sc/iM/IyibVujcP3\n22PSGZD+o8j1VsTJfYse6gh272J/lH29XvIpnjc6xv0ifoxt2exr7cowRy1PlKB2Y0l6lzTVWsBg\nhZ+p3yZ/LUqw3NnqvbC6S+29oj4jm7d5j7J5ikhyjWPVvyG/ly35wezpBLnr2zinyIJK4qeUyPhQ\n78kZ+cultEf3/xrXWHyP/NlEJFzheULSDxZPKTFt3KDl2eF5uxFO3KikZK8f67nOuqzVHq6XzZD2\n7K1et5y4uZtiPfKY9zXt6RfmNMdI19dZ0EBpCyqpMppjc1xT7NdX39P72GaIa65HvncleVTJL+gy\nakrmO72PiXtHj3Fetma4d4qIpDOtDX4Z7JciwzAMwzAMsZsiwzAMwzAMEbGbIsMwDMMwDBF5xZoi\nH4j4T4Ub1w5jrZxDSEQkPscYrruFccLBDcZJ87ShjBjj0W4fXy9vMMY5vaeDrYMLjIvuyIshJV+a\n8Eb7e1TUvjV5YlSUP2xwoXUF4SXqLOouxl63e1iv5PsNPkVd/Ft4/OP9PNb72nMpnmKflhn2YXuL\nMd4mnVbQJw3ABvvH7yin0FzHiLMM25uNUavQIX+cg7/Uub6C21j37TvYXn9GWrCuHtvgFOuWH5BW\nZ0jap6Dh+whZ5NycjLCIHcbRgxvt7+H38TzpjMa/S2thpNsy75AWheZcfhf1L2WDHiha4XlaG9Qi\nbFo4bqNTPS7zW6SHirBt0Y7yvD3T9SgTHIfWR5S3bIhzrmrwSst/gGUsqV79BflrnWjdWpty4wWn\n2Kdd0hCxZkJEpPwK5TY7ovUxxTk1YR8fEXEJ7R9r/Mx2Q2O73+D9RPq54Jpy0n0B108R6z257UkD\nQs1dDyh34AOtGdmR1xNrMntX5I2ltzFx5EkWvEF5u9q0v3qtj6JUgdLN8bzBBvsnTPSa82vS0Izx\nGuRJxhU902W0Ka9hQLnh8i6OQ96gJ1Q6RdpPamp+NtOebS3yAQz2ydeLcgOWR3qOzbe4B3XOUWPl\nRnQNe6SvDU2+hy+D/VJkGIZhGIYhdlNkGIZhGIYhInZTZBiGYRiGISJ2U2QYhmEYhiEir9q8sfIS\nzV8IpLZ9VG0N9rSpVXtFyVtJrFyxgdtWN8n1KMnhDSXfS1AYObjUAmch0W93QwkMqS1uqIVfnjz/\nwgf4nr1bqDYs+7otHUpImFxiWz5+7Q4cH91QJkERaUfYp55OUyyxj0dTLYKt+9jvKSWmDa9IRD3S\nYsuchNVFRAmCW3g83uh6tB0JuhMyxSPjuHil51jdJXH6FYqCO5S8NZvotsghjmXEiXhpOqQNSYc9\naRb3VtjeYkFJVN/QZbQSHIfkfWxL1qdEpF9oSG57hQLFvTnOS0fJGYNlg0lah0TgLABfYz2rhu9n\n4ZbM6OhBhDAlAXi7wYjy8hyOHZmuLnu49kcrPcc6ZKS3uULFbjjAerYrLQou1jh2YQ/XR9LCcSp+\nqcHMcoufGZI4uxziuOWXep4G1P6CkpMOUhzrotD1qBM8j3c4tn5Oe8OZNl5M72AflZQsW9ZYz01D\ngu2ITEM5Ua0ng9SoYc3lb1BiUVq2y0OcH51Em9BGJKQuU+yz9hW2tar0XK/fwr/Fz7HPkr8mg+G+\nLmMT4V7XvY3XCk+GiGlDhtybt/HhDuewbZ0LElGPdJ+6DiWmnWFdFx7PIQ3er+0OJaE+w7kf0CV6\n/po2Kt32WBR/od7ThP1SZBiGYRiGIXZTZBiGYRiGISJ2U2QYhmEYhiEir9y8MZC8/ULDcOfj5/D6\n8h1tela+wQZMGBctWhi/DNY6xllVWEZFsebuJcZel7+gk0K6M4xxJ2NK5kqx+ZCD06JNEpNLeg/n\nGb2tzfk4/lq0MSZ+MkMNRXCi+2M3R+1F/BQL9V8ks8JHWhOQZ1iGH+FnFiNMWDhf6Zjv3QWOv3wJ\n27u+IXPLUz1ds3dJq+HJKO2CTM4atF7JJcbJU9Ip+T62za0a9EDURdGaYuCkZ3CsuRGRmrQZDvOy\nSn6H9HRt/Z2m9SeoeZj/QxwH5/C8+9/WGho/wHpwEuLRFemyvG7L0zu34TgZoEbgcIrGgkXZYBJI\nyZ85qaqssJ7j6EaV4ckkczHEecjajXKm59j0CI304oQGm9Zt/aFuSxBiH/F62QWof9ittUngSHCs\nNnPap46wf3b7uoxugHtduE8GmNdY96DboMM5x7W/+UU8T/990rK8oRN+VixkJBnWVTmB43Gox5Y1\nRMqIknSePacTOcsO65HtYdv2H2Gfu4Ge65sc9U415WpNeM+hvKwiIv6GDA2H2JakIs3ZRuvFihGu\n0+0VnpcNVFl/KaKTUAcx1Z3aVvuGZK4pzqHwiK4vl/iZVq41eK4m7dtdHKeErlmsTxUR6S4bxvsl\nsF+KDMMwDMMwxG6KDMMwDMMwRMRuigzDMAzDMETkVfsU1bWk2xdxYE/6oLjQSe52FcaFRx9hjLcc\nUwy8wWKopmR7qy9idr1FgN3QnepC6gTvH90Vxjzb5F9xE++pMtpLfM/il1HfMHiE8dzoXOuSigOs\na/UlrBcnQSzXDUM8Jv3PHgaK24+wnqXTZcQb8s0gHUrnAssIEq1NCNo/3gNjk+M5dv9+Q2LJ5xgn\nr6ak5yixD5df1m3pDbGMckMxcUpE66c6jr7uoL4j7pBWI8cyk1zrtDZHqE3o7LAPkxo/Ez/W62X5\nBs7tLMT1E5A2zre070oWoHcRz48gx/YPI528dLJBzVD6PexjRzokhxIkEREpxzhWnMx0/RQ1FN2e\nFmvEIfZZ7wmubVdimdG7uk9XzyhJ6or0gzS3vZZGirDMiL6O9gTrlTntH1VTwtfpO6h1GjzCMoJR\nQ8Jgkm8kF9g/mxDb2nneIIAhkifkqZPiWHMiXxGRivQ/6QV+JhlimUHQ0BbSsziSWEUV7R/72sdK\naPqn1B/rGNdkstN7MtvuBHOs62wf9/n1mLK7isjh++ihk1DS2LJDa2Gn+6O9wcbEc2zL2cERnnNJ\nokURSZY0H97C/SOvsA/7bbxmiYiUdFuRT/EzQ/I9kwM9P9yC1iVdf2UfXx+c63pselob/DLYL0WG\nYRiGYRhiN0WGYRiGYRgiYjdFhmEYhmEYIvKKNUVlGMnF8IX/xGCIscXkodZZJAX5iLRJm5HR6x3d\npIC8a7qk7QnbGL+tt/pesfMcg/HuCGOaZ/EBHMdZgx6IYtq7K9QNDPvo/+J02jJZpRiP7ryPbakz\nrHueaG1CMsf4ffeI+iPCum9qnXcofJ36iDQT/grjxO2x9qLYXWPdQk+5nS5wfqy2Ohbfm6PmwZ1g\nGXlO+dT+Ssfit2NsnyctRnKNc2wWN+TZSbCMyRr7MKZ8e9O7qAf54XkoTxnlZMu2lKdprvs0Ocbz\nhORVEvV+vMeOiEhEeqfj7+BErLo49uy5IyLSPse6Zbdw7rsI65VMG3y9Zvg3x6fp4B/Wb+t5GuTY\nh70p+fRscL6cZ+iPIyIyJg1E/iat4xCPw4ZccEWKYzcsscyaFlDvVGt5yl/A9t66QU+y7RD1H4nT\nua3khnzedpR/8BjXy7KjBVKdGY5tK8bzlAXlYPtI70GtDuU+6+Jnuq+hRiR8prVevE9fn4zh+HCF\nOp18ozVFeYB/a3WxLR26VhSR1jXOxriWD8iDq31FeSHneg8qE9LhHGG9kpA0eYUuIyUvo/lt0qzW\neH0p39TXuYzax0supjXZlIPNcaLHBZayvU/7bab3jxb5mi0qbEs3R/1cHOj50dro/fFlsF+KDMMw\nDMMwxG6KDMMwDMMwRMRuigzDMAzDMETEbooMwzAMwzBE5BULrYOqls7shYCwFVCSu4muTkrv8SwU\nPCFTqwbhaPkM3xO0yAQuRDFhnmsxXeeLKHy8CffheL9GcV33Qy2ULAIUE7IBYvUOmbP1tQFk8REK\n8NrJFI7rCZk5TrWpVf0m9mHWQiFkTGLk4KAhMS0ZslUk8PYLPA4aErGmJKIPtvie6ZsoYExrnAsi\nOoFlPcfzhjnWvXvekIh1Tv3Rwf7Iu9jn9aoh4WefEtEKCv+y17BMF+p6JGvsj/PjQzg+fExma2+o\nImS9QxFjm8TYlbAhohY4ZykZtEW4qEoSWs/I3FFEJKopGeWUkuy2sc/zhrXvC0qKucDxj/exv1pP\n9Lj4e3js6GtgkZCwtGGq796g/lihyHPTxj5fHusHAsZPcZ06msoxjYPXulHZPCOBP3rxSY9NZ3ta\nwCqsNb6FhwMyPJxdkjOhiMhtEknHOHbrGE3z2nta8Hr5/7X3ZrGWZNl53r8j4szTnW/evJlZU1dV\nDxTZLRZoaoAgu2mLlgg2HwyKhgm0JRmEAcOmDBtitfxg2ICBBmwY0oMtoEENDYiWRFCi2BAgQu2W\nZRkGSLPIptRdXVNWVlaOd57OPERsP+RtZ/7/iurKqsrOypLX95IZ98bZsffae8eJe9Z//rXE98+2\nGG9WbkmABiVjWeHJSme85vryhYjKkr1/pPvyRYRDMU1clS9qrNh1euEd3penFd4P6RpfYza3baRj\nnv/qQNZ64HmZblvxOuQ7FL0zFvOfNPl+mv2eXWRJi2NaS3nuTusc03Bk7+v9Fq//3ha/B6kJ7Tgt\nEcDXeV9WCxZeN65LMWD7vRVkJYW7Hwb/pMhxHMdxHAf+UOQ4juM4jgPgAzwUhRDSEMK3Qwj/5Px4\nJYTwzRDCW+f/lnzO6jiO4ziO88ngg2iKfhnAawC+n1R8GcC3YoxfDSG8fH78Kz+ogSRG1PP7ucB5\nlFz0j9gCbovXpYsNfo6rD9lcK6/Y57xGECO5Qozkljn3uDzmorMAMJpLsc4jzoE3Vrgfk22bJx08\nw+NbvipmWnd4rJ2x1QM110SrJMaUueRixyNraFd/m+Mx2eD89GSLTeBqq9YEriIFHFsVzolPVqQQ\n6VWbv87m/JrFmhRi3eLf129YbcLBFmsT5j0e/9LrR3RcPbQFUCsiRakMOOcdJhzjTq1EuHaXD6fL\n3I+sI3n0oZ2XSYdjtjll87lpg9dUBVYPpDqt/CIPrj4Vo716iZZHCo+qTisRF8Xm2MZ0eJF1eqeX\n2Ny0u8dru6mVOQEgcNzHUpwzkQLSzdt2Xua6H0QvlYtpYOdt20b/BR5L0efXTMfcZqtkLMm7Uoj3\nGdHkVUUzU2aImYiB7L4IoETnF0vMX4smz+WixfPfvsn3l9UJ7x/AmlXO5xzjrugYJzWrf2nd5Bh1\nuqyH6mesSymW7FovcjF8HHEbkw3eTyjR8gzXeW7nYjxZ9Pg4KxF7FWI+2FgXzZVoSUcl5q/rM47z\nUHRq4Qafn5YUTz874HbXe7wA1iJfQ++3AFCsSfH0Ib9nDTocr5XXrU6re8rzML8kcZe3k0q9pMju\nKu/9oVRZPnuB10dn1+7bRVRtcIlQr4SH+qQohHAJwJ8D8KsP/PhLAL5+/v+vA/i5h7qi4ziO4zjO\nE8jDps/+GoC/ApB/92aM8ft/H+/AfBfiHiGEXwohvBJCeGW/X/LVDsdxHMdxnCeA930oCiH8DIC9\nGOPvv9c5McYIoPSJJ8b4tRjjSzHGl9Y7JV+rdBzHcRzHeQJ4GE3RnwDwsyGEPwugDqAbQvi7AHZD\nCFsxxrshhC0Aez+wFQBFFjBbu5+PrkgB2Mq7NudXTfmcWYfzhHcu8wdUK2+zNwMATC5xbrnZ55xv\nIl42lZrNcWYH3Lf2FudnJzucN682bK51+TpriHBbTvi0eESc2AK585r4ZuR8nO9znrj2tNXhDHLO\nz2qRzLv1C3TcgG0jzvh5OpfjueSrQ0nhwGIi2owLok14hXPTs4b1j1rvcN58diJ+QFfFp6fkz4BJ\nm+OR3OJ8dtzm82fRaiR6BevQRsusCejts85iuW91a2PRXozEc6iyLMWPj60moF7I2j4WzdmQXxOt\nFRZCldfh7FnxDJHitrXbdp2mGY+3vsxCgqwhe+ya/YNpvM37Nr3AezDe4rWfdUu8sOT+kS2kyKzc\ntaZbdm67hzyWRSrXHXCbnazEG6wn+0MKgCbiqaJFigGgIXMLucVMpIBwmJYUHpUCyS3RRvZXxWNo\nZvWEiRTzDSI7mixxP3KtFg2g3ZO9fSD+WeL7lZRlGRa8Hm51eKNu32WhX65FrAHUphxE9aDKu9Kv\naNfYbFXWjFwm3+E2LjTtW+XJFfY2ymeiZZL3pHlhNauJFMOeZnzOdMH9XFy287J+R3RHbe57Y8Fa\nsKRr5yV+mvdy8a4U7n2KC/fmS3ZetEisFoafn8g9W4vQAmglJTrFh+B9PymKMX4lxngpxvg0gF8A\n8M9jjL8I4BsAvnx+2pcB/NaH6oHjOI7jOM4TwEfxKfoqgH83hPAWgJ86P3Ycx3Ecx/lE8oHKfMQY\n/wWAf3H+/0MAX3z0XXIcx3Ecx3n8PNbaZ+miQOvggRy22EhkB1bLM+pyjrv3FutyGu+Kd0fJd+Bm\ngTURg6c4Xz87lFxrzeZa1YslfYdzqcWWeLuUtJEPNE8sGipOk2LyozZvPL8rnigz1gTkBU/pYGLr\nMNXrrBPQOlytBns+HC+s8OTCYJf7dYlj3Drlfo3HViOx/2Ps96napskyL5BZYTVFrW/L+H+cxx9/\nhOdl8BnbxpIUoirmnM9Ocs5Xt89sXbuZ+pn0Rf/SlFpfs5Ktl4rfyYjXXHVPdAU928agyouoflH2\nx3XRre2X1IOai75FfKxiSzx2unatp32pBSc+M/mhaGyu2A+t1YcpORDt35z7Pn5KbigAqrt8jlrM\nLjLu+6JZMpYBz6XqtrJ1qVvWt/qo0YuiOczlXjfm13SHVpe0CKInTPi4EK+jxqmd2yDn5OL7FuSy\no/USn7M3uN1qysfZRHRbDas7mf1xqTl3JvUWh7x+KqkdS7/KOpytgu9JrarcT67b+2mlyVqVuCI+\nToXsa1vmD5HLXqKQBMzShHWuQ1g/vlbKez3Tvd7meMUtG9POGeuhFg3RdV6V+/zE3sf2O+z7Nulw\nzHq5eEG9UKLB+0NeRMUSr7m0yvvp4nd2TBs3wyX+wQtyjcDPAZW2fXZIEq995jiO4ziO86HxhyLH\ncRzHcRz4Q5HjOI7jOA4AfyhyHMdxHMcB8JiF1nEeUOzdfw4LL7AQqpjZZ7TFlMViAxH+1RIpgLlb\n4prd48NqIaaRUihuUrHisWnOP8ukiJ2aR+0nLFgDgOUaC+6Kbe7r/K6ID8dWTJdf4nj0AwupTyus\nBNwoRAUIoDIVE0ARcDb7LMBrTq0gb7DKgt7mqRTErbHotTG1RlqbJyyezE/EFE/EqKHENP3gJTYC\na1RERC5FIOs3rEHoeEvUk2t83fa+iLkbJX9L3OCfNS/weIdi5rjYsYJvNTSMUcTILTFn69h1WpmI\naFyKl55e5qKRy28dmzYWFTFrjLxemt+TgpdP23jkyyLyZH0m5LsPKIoSA7dlnu9cpq6/okJ9uQiA\nyoRfNMqlUPGCr1HNrBFlUP32Ee9bNYDsb1g1bjbkeZmLKZ5++UG/hAIAs74ELeG+t3fkyw0N20je\nEmG1Gqi2JD6p3S95V+6xYn4benwvnC7bdTp5m3+2PON742iV90tx275VtQv5QshFvtHXM96D02D7\n0ZSC4vNEYjwRI0It3AugIoWts7q+n0gR4mqJkW2ffzYGjz/Kra/9lr0nj9r8muqyCOKf57WtX1wA\ngNU7fD8YRhaFJxs8tnRixcyDDfnGkAjtl99h49pZrcSUd0U21U358stUvgyyXbJh7uhcWRPiMvyT\nIsdxHMdxHPhDkeM4juM4DgB/KHIcx3EcxwHwuDVFdWD+3P0cdl7lXGs6s/lJLYKY3+Uc+PyymH5p\n8hVAMednv8qJGD1JQcP6ktW/nFxgA8PqWPQvUjB2tW61Gmcpaw0ac75OaHHf08OSArmis6jPOD7t\nKufZK0clhpiib8lEd1EVjdVwzeZr2yd8nXkmRnIS82SjpKCjGNYFOUVNv7RIIgDM55yPXh/yMOf6\nMgAAIABJREFUZPaleGu4Y7vRFNOzqpg5zrbeX+tV3RJNiFyn0+d4ZdHO7bDgeclHYkQpPnphYPdL\nVuX5Xiy4jeUZ5/PHz5bM7S1Z/6IJyOui80tFQwAAYgLYkHUaxFhttmmN9Tq70ogs5fkdfs1xVZwZ\nAWwlbAxXBF5TtSWet8GBNTtV/U9jwa+ZXJI1VrXro3XK4z+9zveCIOtntG8N/rQfWtz2qM33qHaP\n19y9hvlQzSuD6JTqx7YgbPVQJoJlfeiLC217z95Ph1f4nGGFF3fjLl+3qFstTzrmNdSTwr0QE9ZG\n0/YjiB6q2ua9r0V2k9OS95dMjFkP+X6RisZINa0AkO7wOfll0RNuc5vHb9q1vjzn95zJGe/tzh7f\n56Ztu+fiiug4x9yvxVS0cDO7xkYVXrutBZ8z6sna3rQxzWbcj+odjtnRmugJM6snHH9eNWSuKXIc\nx3Ecx3lo/KHIcRzHcRwH/lDkOI7jOI4D4DFripJZRPPO/bxev8t55eTMaiQqmRTGW5W8+hnnGueX\nbJ40WYh+Q9Kgo03OZ7cPbJ40Ee+NXApHVm5yLnrRtKHtbnERu9nrnPNsTzkvGj5v4zHa5zzxWYV9\nZyqSi01ObB693hKfmSXxKjnlGGrMASAf8/N0tSbXLbjv84H1oggj8TeZ8zz1e6zv6M5t3rg94rkK\nbW5z6f/k359VS7QaHR5/9W2ey2GL16l6VAFAMuTrqidKtSH+SVa6gloULZP4BQUpGDtL7VrPRAMw\nEiFSEXmsK6kI6gBMV8WnqCmFV3Vsu1YjUV/hNZaIduNgyD5eoVmiK5CxqJdRe4nXQ6NECzgY8dw1\n7nC/TtbZ2yas2n40z0TXWOF+dHa4H+qhAgDTZdGZqH5S4jO4bHVa3dusmalOpRBrwvFKOrYfM/Ca\nmS7z8cZb7Gs2XrLePqEh7cr0dya85xYlhZwbhyxuqmphVtEbTge2H8lFjll/jWO2LmMpTku8sNZF\n11hwmycQLWnTaqxW7rLH0kQKsQ6usH6s87Z9f0mXOaaZFE2d7IoH0aotkNuYi2bmgMd2tsr9aJa8\nN+hc5lf43j+b8nqZzKw2UrWyiyrHI5X39HDb7rnGmOM8XeM11E55z2VTe09uHlkvp4fBPylyHMdx\nHMeBPxQ5juM4juMA8Icix3Ecx3EcAI9ZU1QkAaPmfU1MDPxMlgWrTViInmXWktz8d8QDYmJzrZrj\nn4lmojvk/GRJCtxoVyYzznFrPax6YnPP6l8x/yPiEXJDNDZS/gWA8fbJmjz+ZuQ86vyKneJxLvXS\nMs41Vz/FMezu2hy4WPngxtolOq6LL4v61ABAcYHHUp9zzOJdyfenNvfc7bPOYtHjGJ92efy11K6x\nqnj7zOvcRkM0JbORXSAz0ZDVNzmvPppLrbh6iYfMDo/vsLFGx6trrJHYONg3TUwyXpebu7y2jxPW\n0Izb1qeo2+CYjiOfM5cNoucDwGiFNRCdNo+32ucF1Ni1+X+tmXXQYx3ShZu7dJxOrb5h2uW+ji7w\nWNoHfN3K0K6PacL96OyLZuYC72P1DwKsfm7yHPfjOBXflZOSPTflNuay5mZBfN9y249pl6/bvMnX\nGXZ43kKJv9hswdfR8RY9qa+WWW1TSGVfig/NZF20T2OrKeqOed31RHO1WOV+TJfsWj9ZYU3mhT/k\nPbU6YM2dvv8AwPhZ7mv7Fd771a7UHFux6zSKXHK+y9epPc33xlZh5xZyT9a6bvUat7G4UFKz8IjX\ncvddjmmUrX72LMcPADoJD2ZQF13fgO+nZ6ltYy56yXrksTQXUluyZjV41vfQ7u0y/JMix3Ecx3Ec\n+EOR4ziO4zgOAH8ochzHcRzHAeAPRY7jOI7jOAAes9AaAPCAdjZLpXjlVkl3RK9cORCzxhf4NcmB\nFfUNKiL0EtHW5FMs4stKRGw1uW4D3Ea+LAZVqRXTYSoi8ZEYQorwfFy3wsCGiJHzRESOhRS8PLLi\nspjwdWo1VujlZ2LMeGLF61q8tXPE4rrVCQsUJ8EKJU/HLLDrnLHocwlsdpmhJKYy3ZN1KUwrpoDV\niRXA9+siNH+Gx9sWE7C8av+WSERMWjsT0XhXilPuWuH57DkWF27179JxMeHrDm9LhVgA4dN8PJWi\nyx0xPUt0IgEUx2JMeiTFKjMpGJxYkWMy4XbPVvmcUBFjxtTGVAXe66cHdDzd5rG1blux9ly+VJEu\neJ7qC57rUcXGtF6R4qSflxNuifHiwjpzphd5vHUxp6ulYpC5afdLjXXlSDu892PO/aj9a7tvs03u\nRyoGmcPPs7lpbce2Mf0Ux32v4Iqw7SO+NybtEoO/Jq//YiYFUEXQHIMtCBsX/LNFl9dtTYxspzVr\ndrr+h3yfmm7IdaXYa/tf2TW2uMjX3XuGvxBQr/JcZ5l9f9GxxKf5980ziWlJYdp8TdrYE7PXKO+V\nJWbJWqg5jriNQoxLm5mNRxhqZW9uY7jE94K1gr9AAgB3Lm/ScXaV38eCFKnO6vZ9LmQlRcgfAv+k\nyHEcx3EcB/5Q5DiO4ziOA8AfihzHcRzHcQA8Zk1RSCOq3fs56tmY87fTA5tHb1Yll9qSnKcUyTyc\n2Xy+FjTtHXKOe9GWPPKgJH89E9PEEyngF7nN6RWbv87rko8d8NjmojlKKjYnOm9IQVzRPw0zHn+Z\naWKocbvpnPOxi6EUQO1YTYDqjqYTnrujKusMKlWrTegesBOYjmWWcDzGJeZrIfA8pLvc1yyIHoY9\nJgEAy7tc0LGIYhrZElPNns3FJxLThOVAaA049z6t2bVeiFllIZ5m8yrHo/60ndtFi9eHFokcXeHr\nJmV/Fsl0ByksWjkSDVpgTQUAZFIoMrnGbQy6rF1JeLkAsAVPkxvS5rJoKNp233avsYZqusb7Mq9x\n32dVu28rQTQgr8oJLCHB7Blr8Nfc4TbGbSlC3eG9MAg2II111lTlENPIZdEYPWXjkbZEg1jhNvJX\nZf0UrOsDgMWCF02r9YMLgDbfLdE1rv1gvUv1mhSY/pTVrpze5Q3S6PNaP36BDTF7r9mxBNGYTQu+\n9403xKT3M3atV8S8sn3E2kjdH9ZUEKjJniqGci+occzPlqyOb3EmRVOflwLjYg4865as9YLnKm9x\n3/vP8fuLmgUDQM7LFA15Dy/kPXt8ZO/rF2+wgG78DM/DQMwaO31bLDxJbJwfBv+kyHEcx3EcB/5Q\n5DiO4ziOA8AfihzHcRzHcQA8Zk1RzBKMV+7nD/tTzk92X7GFJeNlPj7OuKDl0nXOE9dTm0dcO+Qk\n50IKWibXRA9i5R6YPCteRlJkNi/4+TIp8Tqq7nK+Vr0pqm3OK0+ntiOLqWiKVvk6lSM+znP73Jtu\nszajP+WYiqQG4w2be642eCzLwxM+4Vi8W7ZsDny1w7n3UcY6i4ponfqF1YstL465X6fsCTK+JcUr\np3Z9FKJnOHqWfYtUg9W+ZvPolTrHPYj27eaVi3S8cdV6cwTx9qlKgdPGWLRfXatvSIf8msOnWZvS\nnHDMxxdsPn8WOWbLGc/tEKwH6t6wWo2zFzmGKzdZt6VFMsO8RKe1I7oT8ccp2ry28yO754olPied\ni0/PGR93C3sPgmzDwWXxPRMtSz1nDQUATDKOc5bI2l7iePUOrUYi9qXocEO80vZ4PZxEW2hz/YTX\n3WLOMU6lOPLi0K6x0+UlOm5MuR+9Pq+Hakkhzvw1bnf6OQ7y/Dke63Bq7x9LgdeUav8qcr+tLtl+\nDD7F95xkzOPvvcr3Ey3CCwDFnNdYY4PjoVZgyS2rFR1t8/qoDbivh1uijwp2nRZvSlHdEcd0cpGv\nsXZi70GzOe+xmIi2aUc0aBdsPLILvB92Ni/QcfsNvgctz3geASDmfN3kNt8fliv8mrxt1+mwpZ5j\nJUW4S/BPihzHcRzHceAPRY7jOI7jOAAeMn0WQrgOoI97X9ZdxBhfCiGsAPgHAJ4GcB3Az8cYj9+r\nDcdxHMdxnCeZD6Ip+rdjjA+Kc14G8K0Y41dDCC+fH//KD2ogmRdo3r2fb1VvhrTED2exwl2Mkr+d\n1jkH2jix3i2TFudSJzU+jn1uM5T4SFRvc772tCkagCnn0dPv2rFML0nevMpj64wk51lS6utwhTUi\na0POCzcC57OPtzj/D9h6RpMVjsfKHa4HFColeo99Po5rkie/KDXYdm3Nsf1lNnjpNjhPnt7k624s\n5KIA+tusMxpucm65/nvcZm1hxzJZ5vlfzFhztnrC8ajCei7lc57LUZN1N2sTnqcklHhoJKIpkrp2\n+Yj7OVu1fjj1A45zfcD7obokPjXHVmfRKlgzNchYz9Ga8u/L6qet/AHn/Bc/yvOyEI+yJJQsdpZR\nYLzP6zTwFkRjYdfYPPB1qhnPXdbW+mH2w/P+JR5/siO+VYVoWU5K5lbrB05+cH2oIrEeQ4OneE2F\nq+LblIr/2AUb08EZjyVNRLfW4vVysG79kjZu8T6cvcgxHqzyntQ1CACjddZ75HVeH5s3WQe6t7Fh\n2qhc4JgG8cdJpDDiFFYb2XiD18xCPKm01uR8YbUrSZ/jXshY1LcqPbLzUpzyuitWeP5bh3xfr+Yl\ntb4a4p0nmsRCa2tWbZ2/vCl1D4cc48qavGd/p2QsXb7OhUP2HIpT/n0/tVrR5oTHO1zhddsUHVuE\n3S/VU6sxfBg+SvrsSwC+fv7/rwP4uY/QluM4juM4zsfKwz4URQD/ewjh90MIv3T+s80Y4/d9e3cA\nbJa9MITwSyGEV0IIr+yXfLHDcRzHcRznSeBh02d/MsZ4O4SwAeCbIYTXH/xljDGGUPIZ+r3ffQ3A\n1wDgpWfLz3Ecx3Ecx/m4eahPimKMt8//3QPwmwB+AsBuCGELAM7/3fthddJxHMdxHOeHzft+UhRC\naAFIYoz98///ewD+ewDfAPBlAF89//e33q+tYhYwu31f7DZts/C42bSmeDjiD5caq3xOVhGh14u2\nidpARI5nYlgmxVwXhQ1LkvI5vQ4LSesiGj/6I2yICAD5mNutVVjENxuyIG/StcZ6qx0W7BZiABlF\nO9dMbUxrp9zXToWN4vLnpCBq037AF0dqVskiPxUbtqfWOKu2yuPP3pS5FP+6xVrJvFSlQPCSCFal\nybhZUux3jY8377CQdD7heRnOrTDwwULHALDoSRFI0avm+7Yf+QrH9PpFdi69/NYdOh7DCiUrKyzA\nzLpi1FnjvvcmJQU/O9z3RAScyTu8HsYX7Tpt3pRCzlKEudqRApi79u+z/LL8TPz7qre4DRXuA0Dr\nmNddrcNrblqRArm3rUg6PWbBZlaVIsMiYK1ftUL8ihgHFlIkNG7wa8YjG9N6wYLl6ed5XdZe5X6m\no5LCowmP/91LV+h44zVWK6+s2i8UL56S4q13Zc1JfJLc9mOlYEPQdEfuHyKcfSq5YdpI7vJrxvKF\nmmwga25ixclFVwx0G1K4d4vXx5l8wQYAssj9qInhY1jwfpku27ntXJXirSLeX+Q8ltONkn6IkW/9\nNq+p5r58gWLV3scqt0RoLu9RyTu8r0+q1iC0tiL39RPZPyJmL2r2XjiS975uFO2NvCXNp/a9Ia1/\nOKH1w6TPNgH8Zgjh++f/bzHG3w4h/B6AXw8h/CUA7wL4+Q/VA8dxHMdxnCeA930oijFeA/BjJT8/\nBPDFH0anHMdxHMdxHjfuaO04juM4joPHXBA2pEDo3c+/dnPWMwxqNsfZnnOutSKGZWoamJ5Z/cve\nOmsNqlXOLdeloGNYt20cpWyCWBPTu/pcdDozW9Cx3+Pxdeais5Ev51XessZY409xjjuIpkZjOL9o\np7gqfZ9IxctxxiZntSObi69POG9cKficvMFjGa7ago7JqZiLiYliXYz2yh7hGzOeOzVBG65wfELJ\nik/HfM7+c7xemjucR2/vWX1UWuc2RhU22psNpJDimh2Mmq1VJD5BpmHzzJpZQvwLx1sylw0+YZLa\nosNaNLW1x+PPRF+XlBTZHYrRYJ6KqeaI+1GdWR3O7G0pTil6sSAxL9NHDT7D/Yh3WetXbYou6bNW\nl9Q7Y/1gtsfjH/wor+2gggcAjXd4vOkStzF7g/VBoW7baKdSzPeMNWWxLlrJub1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XAAAP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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10026a3d1f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_spectrogram(fname,h,w,lengthRatio=1.0):\n",
    "    \n",
    "    r = requests.get('{}/{}/{}'.format(base_url, container, fname), timeout=4.0)\n",
    "    if r.status_code != 200:\n",
    "        print 'Failed retrieving {}'.format(fname)\n",
    "        print r\n",
    "        return None\n",
    "    else:\n",
    "        aca = ibmseti.compamp.SimCompamp(r.content)\n",
    "        com_data = aca.complex_data()\n",
    "        ratio = int(np.sqrt(len(com_data) *lengthRatio / (h*w)))\n",
    "        if ratio == 0: \n",
    "            raise ValueError, \"The selected length of signal is less than (Height x Width), select bigger ratio\"\n",
    "        elif ratio == 1:\n",
    "            sig_data = com_data[:h*w].reshape(h,w)\n",
    "            spec = np.abs( np.fft.fftshift( np.fft.fft(sig_data), 1) )**2\n",
    "            spec = np.log(spec)\n",
    "            spec = spec/np.max(spec) # Convert to float (0-1)\n",
    "            image = Image.fromarray(cm.jet(spec, bytes=True)) # convert to RGB\n",
    "        elif ratio > 1: # resize using IPL image\n",
    "            sig_data = com_data[:h*ratio*w*ratio].reshape(h*ratio,w*ratio)\n",
    "            spec = np.abs( np.fft.fftshift( np.fft.fft(sig_data), 1) )**2\n",
    "            spec = np.log(spec) # Convert to float (0-255)\n",
    "            spec = spec/np.max(spec) # Convert to float (0-1)\n",
    "            image = Image.fromarray(cm.jet(spec, bytes=True)) # convert to RGB  \n",
    "            image = image.resize((int(w), int(h)), Image.ANTIALIAS)\n",
    "\n",
    "        \n",
    "        return image\n",
    "\n",
    "# Test\n",
    "img_spec = get_spectrogram('b1cc342f-eae4-442b-91de-10c9a444072e.dat',h,w, 1.0)\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.imshow(img_spec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Grayscale image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIAAAABACAAAAAD3vSCjAAAVx0lEQVR4nAXBV7Jku5UYUGDD47i0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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=128x64 at 0x10026A3D1110>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = img_spec.convert(\"L\")\n",
    "imgarr = np.array(img, dtype=np.uint8) \n",
    "im = Image.fromarray(np.uint8(imgarr))\n",
    "im\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([176, 175, 173, ..., 160, 185, 176], dtype=uint8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgarr.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Write image content into a binary file\n",
    "This binary file uses the famous __MNIST__ dataset format to be read by different image processing algorithms, learning techniques and pattern recognition methods. \n",
    "\n",
    "There are 4 files:  \n",
    "\n",
    "- train-images-idx3-ubyte: training set images \n",
    "- train-labels-idx1-ubyte: training set labels \n",
    "- test-images-idx3-ubyte:  test set images \n",
    "- test-labels-idx1-ubyte:  test set labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Header generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Notice:\n",
    "\n",
    "- Header of label file is 8 bytes\n",
    "- Each number (int-8bit), is one byte in array(B)\n",
    "- For header, the first 4 bytes is 2049 \n",
    "- The second 4 bytes is the number of files\n",
    "- For example, the header for 10 files is [0, 0, 8, 1, 0, 0, 0, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def label_generator(ds_directory, name, data_set):\n",
    "    counter = 0\n",
    "    data_label = array('B')\n",
    "    n = len(data_set)\n",
    "\n",
    "    # number of files in HEX\n",
    "    hexval = \"{0:#0{1}x}\".format(n,6)  # the result is '0x000a'\n",
    "\n",
    "    # header for label array\n",
    "    lb_header = array('B')\n",
    "    # in the next 3 lines we create 10 bytes header\n",
    "    lb_header.extend([0,0,8,1,0,0])  # -> \"{0:#0{1}x}\".format(2049,6) = '0x0801'\n",
    "    lb_header.append(int('0x'+hexval[2:][:2],16)) # add 2 bytes '0x00'\n",
    "    lb_header.append(int('0x'+hexval[2:][2:],16)) # add 2 bytes '0x0a'\n",
    "    print lb_header\n",
    "    with open(ds_directory + name+'-labels-idx1-ubyte','wb+') as f:\n",
    "        f.write(lb_header)\n",
    "        for item in data_set: \n",
    "            uuid, sigclass =  item.split(',')\n",
    "            # print uuid,sigclass\n",
    "            f.write(np.uint8(dictClass[sigclass]))\n",
    "            counter += 1\n",
    "#             if counter > n:\n",
    "#                 break\n",
    "        print 'The number of labels written in the binary file', counter\n",
    "            \n",
    "    os.system('gzip '+ ds_directory + name +'-labels-idx1-ubyte ')\n",
    "    print ('done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('B', [0, 0, 8, 1, 0, 0, 12, 128])\n",
      "The number of labels written in the binary file 3200\n",
      "done!\n",
      "array('B', [0, 0, 8, 1, 0, 0, 3, 32])\n",
      "The number of labels written in the binary file 800\n",
      "done!\n"
     ]
    }
   ],
   "source": [
    "label_generator(ds_directory, 'train', all_training_set)\n",
    "label_generator(ds_directory, 'test', all_test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Image generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def image_generator(ds_directory, name, data_set):\n",
    "    print 'writing to:',  ds_directory , name\n",
    "    counter = 0\n",
    "\n",
    "    data_image = array('B')\n",
    "\n",
    "    n = len(data_set)\n",
    "\n",
    "    # number of files in HEX\n",
    "    hexval = \"{0:#0{1}x}\".format(n,6) \n",
    "\n",
    "    img_header = array('B')\n",
    "    img_header.extend([0,0,8,1,0,0])\n",
    "    img_header.append(int('0x'+hexval[2:][:2],16))\n",
    "    img_header.append(int('0x'+hexval[2:][2:],16))\n",
    "    if max([w,h]) <= 255:\n",
    "        img_header.extend([0,0,0,h,0,0,0,w])\n",
    "    else:\n",
    "        hex_h = \"{0:#0{1}x}\".format(h,6)\n",
    "        img_header.extend([0,0])\n",
    "        img_header.append(int('0x'+hex_h[2:][:2],16))\n",
    "        img_header.append(int('0x'+hex_h[2:][2:],16))\n",
    "        hex_w = \"{0:#0{1}x}\".format(w,6)\n",
    "        img_header.extend([0,0])\n",
    "        img_header.append(int('0x'+hex_w[2:][:2],16))\n",
    "        img_header.append(int('0x'+hex_w[2:][2:],16))\n",
    "        # raise ValueError('Image exceeds maximum size: 256x256 pixels');\n",
    "    img_header[3] = 3 # Changing MSB for image data (0x00000803)\n",
    "    # print img_header\n",
    "\n",
    "\n",
    "    with open(ds_directory+name+'-images-idx3-ubyte','wb+') as f:\n",
    "        f.write(img_header)\n",
    "        for item in data_set: \n",
    "            uuid, sigclass =  item.split(',')\n",
    "            file_name = uuid +'.dat'\n",
    "            # print file_name, sigclass\n",
    "            img_spec = get_spectrogram(file_name, h, w, lengthRatio)\n",
    "            # convert to grayscale: int(0-255)\n",
    "            gimg = img_spec.convert(\"L\")\n",
    "            imgarr = np.array(gimg, dtype=np.uint8) #convert to array\n",
    "            flat_array = imgarr.flatten()\n",
    "            f.write((flat_array))\n",
    "            counter += 1\n",
    "            if counter%100 == 0:\n",
    "                print('Processed files: '+str(counter))\n",
    "#             if counter > n:\n",
    "#                 break\n",
    "        print 'The number of images written in the binary file', counter\n",
    "    print 'Compressing .. '\n",
    "    os.system('gzip '+ ds_directory + name +'-images-idx3-ubyte '+ name +'-images-idx3-ubyte.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "'problematic files, to be filtered'\n",
    "'1c396696-baac-418b-9c14-bd798172ac61.dat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing to: tmp/SETI1_data/SETI_ds_64x128/ train\n",
      "Processed files: 100\n",
      "Processed files: 200\n",
      "Processed files: 300\n",
      "Processed files: 400\n",
      "Processed files: 500\n",
      "Processed files: 600\n",
      "Processed files: 700\n",
      "Processed files: 800\n",
      "Processed files: 900\n",
      "Processed files: 1000\n",
      "Processed files: 1100\n",
      "Processed files: 1200\n",
      "Processed files: 1300\n",
      "Processed files: 1400\n",
      "Processed files: 1500\n",
      "Processed files: 1600\n",
      "Processed files: 1700\n",
      "Processed files: 1800\n",
      "Processed files: 1900\n",
      "Processed files: 2000\n",
      "Processed files: 2100\n",
      "Processed files: 2200\n",
      "Processed files: 2300\n",
      "Processed files: 2400\n",
      "Processed files: 2500\n",
      "Processed files: 2600\n",
      "Processed files: 2700\n",
      "Processed files: 2800\n",
      "Processed files: 2900\n",
      "Processed files: 3000\n",
      "Processed files: 3100\n",
      "Processed files: 3200\n",
      "The number of images written in the binary file 3200\n",
      "Compressing .. \n",
      "writing to: tmp/SETI1_data/SETI_ds_64x128/ test\n",
      "Processed files: 100\n",
      "Processed files: 200\n",
      "Processed files: 300\n",
      "Processed files: 400\n",
      "Processed files: 500\n",
      "Processed files: 600\n",
      "Processed files: 700\n",
      "Processed files: 800\n",
      "The number of images written in the binary file 800\n",
      "Compressing .. \n"
     ]
    }
   ],
   "source": [
    "image_generator(ds_directory, 'train', all_training_set)\n",
    "image_generator(ds_directory, 'test', all_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 21994\n",
      "-rw-r--r-- 1 nimbix nimbix       74 Dec  4 23:48 train-labels-idx1-ubyte.gz\n",
      "-rw-r--r-- 1 nimbix nimbix       64 Dec  4 23:48 test-labels-idx1-ubyte.gz\n",
      "-rw-r--r-- 1 nimbix nimbix 18014274 Dec  4 23:55 train-images-idx3-ubyte.gz\n",
      "-rw-r--r-- 1 nimbix nimbix  4505617 Dec  4 23:57 test-images-idx3-ubyte.gz\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print os.popen(\"ls -lrt \"+ ds_directory).read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Compress everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('tar -cvzf '+ ds_zip_file +' -C '+ ds_directory +' .')\n",
    "os.system('mv '+ ds_zip_file +' '+ \"/\".join(ds_directory.split('/')[0:-2]))\n",
    "#tar -cvzf SETI_ds_64x128.tar.gz -C  tmp1/SETI_ds_64x128/ ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Verify the binary files\n",
    "Let's read the binary file and plot an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 21994\n",
      "-rw-r--r-- 1 nimbix nimbix       74 Dec  4 23:48 train-labels-idx1-ubyte.gz\n",
      "-rw-r--r-- 1 nimbix nimbix       64 Dec  4 23:48 test-labels-idx1-ubyte.gz\n",
      "-rw-r--r-- 1 nimbix nimbix 18014274 Dec  4 23:55 train-images-idx3-ubyte.gz\n",
      "-rw-r--r-- 1 nimbix nimbix  4505617 Dec  4 23:57 test-images-idx3-ubyte.gz\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print os.popen(\"ls -lrt \"+ ds_directory).read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Header read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2049\n",
      "3200\n",
      "[0 0 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import gzip\n",
    "def _read32(bytestream): # this function reads 4 Bytes (32 bit)\n",
    "  dt = np.dtype(np.uint32).newbyteorder('>')\n",
    "  return np.frombuffer(bytestream.read(4), dtype=dt)[0] # read 4 bytes and convert into integer\n",
    "\n",
    "with open(ds_directory+'train-labels-idx1-ubyte.gz', 'rb') as f:\n",
    "    with gzip.GzipFile(fileobj=f ) as bytestream:\n",
    "     print  _read32(bytestream)\n",
    "     num_items = _read32(bytestream)\n",
    "     print num_items\n",
    "     buf = bytestream.read(num_items)\n",
    "     labels = np.frombuffer(buf, dtype=np.uint8)\n",
    "     print labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Image read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2051, 3200, 64, 128)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import gzip\n",
    "def _read32(bytestream):\n",
    "  dt = np.dtype(np.uint32).newbyteorder('>')\n",
    "  return np.frombuffer(bytestream.read(4), dtype=dt)[0]\n",
    "\n",
    "with open(ds_directory+'train-images-idx3-ubyte.gz', 'rb') as f:\n",
    "    with gzip.GzipFile(fileobj=f ) as bytestream:\n",
    "        magic = _read32(bytestream)\n",
    "        if magic != 2051:\n",
    "            raise ValueError('Invalid magic number %d in MNIST image file: %s' %(magic, f.name))\n",
    "        num_images = _read32(bytestream)\n",
    "        rows = _read32(bytestream)\n",
    "        cols = _read32(bytestream)\n",
    "        buf = bytestream.read(rows * cols * num_images)\n",
    "        print(magic,num_images,rows,cols,)\n",
    "        \n",
    "        data = np.frombuffer(buf, dtype=np.uint8)\n",
    "        data = data.reshape(num_images, rows, cols, 1)\n",
    "# magic, num, rows, cols = struct.unpack(\">IIII\", bytestream.read(16))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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11g713s+01g5JGpMMGdZjf1UsVhrj17yHsVaXN4H6H+bmYJsufs0YJ9/DPlyM\nk7FkxuI5Do5TqjUAM1qFSpc0U7yU8dqDBw8urhkTdxorxpY5f9qQeghptBHvuXDhwuLa5Tqi3/Eb\nTd7jxsF1YI4U9uHaqHLZPPfcc4tr52Mca1V0+eLFi0MbXH8WX6R9Zgrkch2oCXAaK9qUGhpeOx/j\n2GizqtCzVBfIpd+6/cK15N6nNsPtfeqfeBZwLk4PVGkwOX+XU4djo43Z78w5RhuzX6fTqrRenNvM\nvq10nzPaN559XBfuBWn0f37ecO1dsXB+BlU57ZyekG1wLTlXlwuL+5L7YWNjY8s+JL9W63C/OJtW\nusYrMfPrsybppyV9sff+t9ZeekLSw6v/fljSp7Y1ghBCCCGEG4CZb4q+T9J/I+nft9Y+t/rbT0h6\nVNInW2sflfS8pA+/NkMMIYQQQnjtKR+Keu//WtL4/dYmD17f4YQQQggh7A47Wvus976IyTL2eujQ\noeEe6gTOnj27uGZs2uksqDuiHob3OF0SY9yMeVY6JamukcRrNxfqfyp9kIuBM7Zc5dhxuSho02pc\nbhzU3TBOThu7GDG1Bhz7TG2rqi4Z75mpKUWNAF+fqTtU9eF0BdwPlZbF6YFY34n3VP7ixkENEdfW\n7bkqDxNzLjm9GH2XY+Xautw+zN3CNnk2UP8heZ9Zh/ZwNmUb1bk1sy5V/TRHpetkTi53ftCnOHaO\ny+lO6Ls8c+gP7gxiLqNKh7Rv376hDc6XbXBuTmPFzyTqBznOd7/73UMbhDbjuNze5xlT1WRzZxDX\ntqrh6PZG9fnKPUYflPxn8Awp8xFCCCGEoDwUhRBCCCFIykNRCCGEEIKkPBSFEEIIIUjaYaH1nj17\nFqJECildATeKGJkIiyI3vi6Ngt5KfOmEgRTLsR8Kw1zxQQrOTp06tbimUNAltOPYKAKlQNGJ2CpB\nJu9xCf4ogOdazhRSpAiawki26daFPsN7KLZzCf7oY1wH/iDACSUp4qR/sA23LlVCO/rYTEFYih7Z\nr/NTCiFdkret+pDqBI8UjjoRLH3mapPTuXu4drSpE0lXNuTrLvEcfZc/KmFCO1cSievAdar2sVT/\n8IA+5cS4TNTK+fIMcj8gYFLRSrzvbMp9WhWmdX7K84NnQVUc2cFzmz8OOnny5HAPPxt4TvHaCc+r\nYr48X1yxcJ5btA9fn9lzHBf91n023HPPPYtr9xm0zsyPCmbJN0UhhBBCCMpDUQghhBCCpDwUhRBC\nCCFI2mH0M4VRAAAgAElEQVRNUWttEVtnnN3FwJ2OZB3GiRmvfLXfdSqdyYwOh/0wjuri6Cxax3g1\n4+xnzpwZ2qDuhon2Zop1VoUTq2KV0qgb4LhoLzcO6ggY4+brLo5OjQR1BByXiz2zDb6n0gtJ43pT\n78C5OA0N50d9C+fi7FElJqUuxdmD60A9B/3WaQG5pyp9kPMx2sjpwSq4l+mXVfJPadwv9OWZwrS0\nUVWIlUV4pXoubGMmoR3nQs2V8zGeMfR17ieXmJNJ/6q5OE0R14VaJ66t27fnzp1bXHNu3D9Oc8a1\nPXz48OKa+8X5+h133LHlPfQpp5ep9GC04XbOdd7jEiTSH3gP18XpkvhZyTZmtIBOLzlDvikKIYQQ\nQlAeikIIIYQQJOWhKIQQQghB0g5rii5durSIvzJu6vImEMbJGVt1miK+h/FZ6htc/heOlcwU/GSs\nnfcwb5GLG7Pd06dPb9mHK6LK+VaFRp3GijF/xoDZL/VS0tUXGp0pVsmYeJW3SPLarXWqnEPSaKMq\nd4vzpyqPCO3ldGtsg+PgPU4zwrFybWf0DeyHuVvoH86mlf6JY3e5fejbVTFKV1SWNuR7OH9njwMH\nDiyuqbGaKShd5SliDh2n1aCNuJbs1/kHfZf3zBTpfvnllxfX1Iww/5w71zl2zp99OF0jtUz0W+pS\nqBeSxrXlmcPPLObokmptG/V1bu9zvXlu0dedrpH24Fy4lq4N9kOfm9ES83zgHuTcqGOSxoLRs+Sb\nohBCCCEE5aEohBBCCEFSHopCCCGEECTtQp6i9VghY/NOh8MYJ2ONjE+6WCvj4ow1Mjbt4qSMizKu\nznG5OCl1BHxPVQ9IGm3EGC/j+24cVW0ajtPpLCptBm3q8khw7Fxr2sNpijhfxpqpK3C5W+gfVTx/\npm4Z76lqf0nj/Gl3xtWd3qOqn0Z9EPVjUp23iTZ3/lHl1KF9nP6F/dL3nV6OVHPh607Lw7HyzOHr\nM224+a7jdDg8g9gG94vT0HBfVnNxcB3ohzwbXI4h7kv6Jc8g6nbcPVXtL5c/6t57711cV7UjZz4b\nqjPZnetVP1UdMwc1Vqy35z4r6TNcS+bPcloerh39heeFO09pQ+5TlxuNuHZnyDdFIYQQQgjKQ1EI\nIYQQgqQ8FIUQQgghSMpDUQghhBCCpB0WWktL0RWFYE48xqRmFO1R5OaSBFIsRnEh+3UiWCb4o/CN\nidUoNpPGgoWcf5W8UBrFY7znpZdeWlw7QSdtSgEiRbFuXSrhOcflxIVVkjcm9HMF/mgjtsH5u3FQ\nXMg26C+uDQoBKzGhE9JWonG24dalKqTJdZpJvEihJMXqTkjL+VUFQF3yRoo4uZYuoR+pkhFWxUzd\nPdWPGVwb7Ie+zB8iOLF2JXCf+YEEBc48C7gO7gziPZUQ/+zZs0MbvIdn7sWLFxfXzz777NDG/fff\nP/xtHZ7JDgp2uXbc666obFXseEbwyzOIQmvuH7e2/Iyij3Ht3RlUFUevfgwjjT9k4nlBG7sfWNGm\nXAfuJ2ePmWS/jnxTFEIIIYSgPBSFEEIIIUjKQ1EIIYQQgqRd0BStxwYZ86N2g++Xxtg773Fanir5\nGnUV1OVIY5yYcXXqDhh7leqisdRmOG0C30Odxb59+xbXLm5cFUmtkkxKo80Ya2esfqbQJuPEtKlL\nAFkV86WGxiUbYzy/Kvjp2qiKIHIuLo5eFeblOrlxVDobl7CNVLosroPzU86PPsV1cfulSopI/3H7\nlpqHqnDvjPaN5wft4xLL8azjWLnWTrvCdnlNjaLT4HGsXDvqTtzach0qjYibC3Um9Fva3BVipQ25\ntrS508/RD3kP/XZjY2Nog3ubdqcfuySSHDv3NsflEnNWhavpty75a6VB5Lk2o52tkmzOfO4T+otL\nqunmN0O+KQohhBBCUB6KQgghhBAk5aEohBBCCEHSDmuKLl++vNAjzBS1Y/yasVVqBlx+D8YsqV+o\ncndIo1aHMUzG82d0OIyLci4uXss4KePqHLvTndCG1CYwZ8hMwT7mImEs2sXAqd2pinO6uVwtLncF\n9Q1cJ47TxbsrLQb7dRoatktNwEyxTrZBfQPbdDqtSiPCPlyeIvp2paly60L/p+6Ge45aBqnWQNA+\nbm2Zd4U4jQjhGVTl9nG6JPbDsVfFbqVxrei3t9566+Laad+cBmQdrrU7Tzm2quiyszHb5ZnEM+j8\n+fNDG7THuXPnFtczfko4N/oc/VYa51KdH+4soM24tvQX5x9VMVvnD6QqfjxzrtOH+Jl0+vTpxfWM\nBm+WfFMUQgghhKA8FIUQQgghSJp4KGqtvbG19u9aa/9fa+13Wmt/ffX3W1trT7bWjq/+fWfVVggh\nhBDCjcqMpugbkv5s7/33W2uvk/SvW2u/KukvSvp07/3R1tojkh6R9PGtGuq9L2KWjN+7GjmMX7Oe\nDeOmTqfE+CSvq9po0hgX5ntYd4axeamuXVXVNXNjdXHhdZzGijHeKmeMi6NXeUWqnCHSmDOn0ly5\nXEdVzouZXCWMaVNnMBPPr2p9UfvlxsF14Ti4Ls7HqjpMHJfTrdGm1CHN1PljjJ/apaq+nDTqSKo6\nZg76ENeSc3P6Oc6Pc5vRXVR17CqtpDTajP4wczZwbNxTM/mS6Mv0U64bdV1SrSHiXJ1N2QbvYZuu\nDWpFudb8fHF5vo4cObK45j6mPZyOjzojamh4RjlNIvcldUqcmzuD6EP8bCDUY0qjf9DuM7pXjq2q\nN+jaqD4br0T5TVHf5NVVfN3qf13SQ5IeX/39cUkf2tYIQgghhBBuAKY0Ra21va21z0k6L+nJ3vtn\nJB3ovb/69chZSQdeozGGEEIIIbzmTD0U9d4v9d7fL+mopO9trX0XXu/a/PZooLX2sdbaU621p9zX\nhiGEEEIINwJX9euz3vvLkn5d0gclnWutHZKk1b9jEojNex7rvT/Qe39gJp9HCCGEEMJuUAqtW2u3\nS/pW7/3l1tqbJP2ApL8h6QlJD0t6dPXvp6q29u7duxDyUQjlRI5VIize40SwbINiMgpWnXCUVKJX\nJ/KqhIFOFE2cjbbqwwklOX+KBylac3OphKPEJfirioayzZlvGivx9kwx10qY7x7umZyyEgY6ODaK\nbTkXZ9OqgDL7oLBWGudCP2Ubbs/xRxMUV3LszvfZT1XI2Ym1KfKlsJjjmDmDaFPa3P0ggPvFJfDb\n6v1urExgx363U2SX98wU2Ob8Z85knkHsp/J91wbnQj925xiF9uyXCQ9Pnjw5tEGRPP2De4F9StL+\n/fsX1xQ48x6KuaXRPzh2Jq9068J+q6SrFKJLo+/y/GC/TgDPfqskzu6zYebMdcz8+uyQpMdba3u1\n+c3SJ3vvv9Ja+zeSPtla+6ik5yV9eFsjCCGEEEK4ASgfinrvn5f0AfP3FyU9+FoMKoQQQghhp0lG\n6xBCCCEE7XBB2N77IjbIOCFjxNIYO2SckPFLF59kzJsxzkpjI436BvbDmLcbB9vguBgndkVUq8SC\nM8U6GQeukuA5bQLnS53NxYsXtxyXNK4l4/mcm4ujs+hftU5OQ0MqbZfTJVV6MWpqXHFKah5YnJI2\nZuI5qU5GSHvM7JcqAaKL3XMt6Yds042DGhlqhui3Fy5cKMfBdeG10/LQZ6hTqs4X9zfakHObKWZJ\n/UeVVFIa7V4lWnT7lmvJBH7s12mseAZx/txjbu9zbNQlzaxLlayQc3nPe94ztMGxb2xsLK65P1yS\nYtqD68B1ckkTaSOeJwcOLLPmuM85anOq5J5Ol1QlM51Jqll9VvKz0Z3r29UU5ZuiEEIIIQTloSiE\nEEIIQVIeikIIIYQQJO2Cpmg9lk5NgCsayngtY5gupkkYO91Om4zhMgbMOKqLxVfF9dim064wHxI1\nJFXhWmnMX0H7MF7rcoSwX86NxQmdbqkqtsiYuNNIMObNe2hD1wbHxnuqAqlSrW/gtct1VOmhiBsH\nx+p0A+s4e1B7wbXlOJ0WkGOnH1a5fxwvvvjiluN046A2g/unyi8l1blbqsKs7j2HDx9eXHMvzGjw\nqKOY2bd8D/thvhyXp6jKGUNtizuDqtxXfN35B+/hOnFcTi/Gs57+wn5PnDgxtFHp1mhjp0uinY8d\nO7ZlG+6zocrRRns4jRV9jPqxat2kcS7c6zPrUumN+fninh22S74pCiGEEEJQHopCCCGEECTloSiE\nEEIIQdIOa4paa4vYIGOJTndCbUKVM8XFJxmPZT+MI7saOYyTMm7OuPJLL700tOHa3Wpczh7UVVS5\nGJxGguOgvuHUqVOLaxevZWyZ60QNhcu7Ql1Npe1x+gb6Q1WrZ6bODv2FugOn02GMu8q55PRA7Ie5\njWZqORHqLGZ0WlVNqZmadNR68XpGl0SbsQ32O6Pl4V5nH84e1GZwbemXLjcY15YaIub1Yh/S6Lvs\nh37sNCO0IefPc8udp8yPVfm2Oz/ohzxPWRvO5TqiVolryzbdutBGfM+M1o3rQt+mvVybVf1N+iDP\nBqnWR/H1mfqcHBfPE5dLr6olybm5fVvpgGc+B53eaYZ8UxRCCCGEoDwUhRBCCCFIykNRCCGEEIKk\nPBSFEEIIIUjaYaG1tBRdVUJByScgW4cirRnRJ6FQziVvpFiQ76kSibl+OHYK0Ny4q0KJ7MOJgikm\nZQHDKsmkNIr0WOSQY7/99tuHNk6fPr1lv2zDCSVpMya0m0nw59rdalyu+KArmrsO/dIJelmwsUoK\n6JLiUcDL/UP/cf5RFVmuhOkOCjJnflTAta2S5DmbHjx4cHFNX+f+cXOpkrvOFB7lOnCslTBfkg4d\nOrS45o8XqiLV0uhD9P0qYahUJ1llG+4HEhTG8pp7zrVBX67OeSfG5Vred999i+sZMf/x48cX11/5\nyle2bMMlbq1+3FD9KEUaCyLThkzM6fY+zxT6B4X47rOCY+OPULgOzqYcB4X31Q+uXL+z5JuiEEII\nIQTloSiEEEIIQVIeikIIIYQQJO2wpmjv3r2LpFNMLuVinIz5M15fXUtjDJOxV8ainaaIbTBezxio\ni+ez3arwJovQSqPNGPPltWuDMV8mjmMbTrvCODmTfHEt3dryHtqsSuDlqIq5utgz+62SfTpNEde2\nSiLJubs2OI4qeZ80+jK1PLShSwJ3tYUlZwrCVnoxl9yTmoBK++X23JkzZxbXnG+lU5LGZISV1s1p\nGSp92Iw+inaviuo6P+V+cMn31nEaK9qZc+HaOy1PpRXl2J09aHfah2N3Wp7nnntucc19O1NgmQW2\nv+d7vmdxzXVxiTm5P2jDkydPbtmmNOo2OX/ay+1baneq5K/8fJbGteLnLX3d+Qf3C/ulDfm5KM19\nXjjyTVEIIYQQgvJQFEIIIYQgKQ9FIYQQQgiSdlhT1HtfxEoZm5+JcfIeagQq3YE0xiOp5XBx0iqP\nBHO7MG+PNMZOeQ/nwpwz0qiZYdyY9nExcMbN2S/1Ha6wHuO1fA+vXfFB5rxgzJ+6Axd7piaCseWq\neKfrh/FsFh90+VAqjQg1Es6m9Du+h3Nzeo9qHPQHp6Gp9FG0FzUV0uhDlZbF+WmliWCbLscO2600\nZs4/qP/i2HkWON3a1ebUcflf2A/Xhfe4osOEuWtoY9cG+6W/8Gxwc6mKcHMtnU3p65X+x50fPPvY\nD/fHTKFe+i0/w1xOM46N/dLm7nOOObgqXRbzokmjBpX9cC3dOcb5V77v9GX8bOS+pKbKnWOuKPsM\n+aYohBBCCEF5KAohhBBCkJSHohBCCCEESTusKbp06dJCJ8O4oYuTUmdR1XZysXpqANjmTC4bxq85\nDsY8XW0axqMZa63yBUljDLzKy+PaYGyZNqSOwOWyqXLqEKf3YMy7ysPidEmMcTNeX+Ucksa50IZO\nM0NoM7ZBnYXLu3K1+h+Xd4VryT1Gv52pW8Z1YR/UXEm1JoT2cPuW93AutIfzD+qBKl0S+3DjqPI0\nOZ0F15tjpf+4daH+h7qcSvcojZoRzp/6FzcX1pPj+nNt3Z5za7VVv06XVGn/WCvOrS3hZwW1cU7n\neeLEicU15/ue97xnce3mQpuxn7vvvntx7fRRpNIlOf0cfYiflfRTZ1OepzwvXB5AQv+vctbN6IBn\nyTdFIYQQQgjKQ1EIIYQQgqQ8FIUQQgghSMpDUQghhBCCpB0WWrfWFgIpiqecaKtKTkixoStwSGFb\nlVzKiZMpWqPYkiI2V4iV91RCWlfQkXPhPRS4uqRWtDPnz8KCLrEehW4U/lGczaKA0iiCdWLsdVwR\nRM6Pc6EY2c2FNqUgnoJONw6KOumHFLA6/6AAkeOiMNIJFisf49rPiB5nkiRW46gEvS4pHgurcv5c\na5cErhIjc9+6xHoULFdJZ51/cP4U8HL+TkRPn2I/FJvOiGA5DtrDrUuVmJWvO3vwPTwLZhJici48\nc7iWro1q/Wlzd47RzvTbI0eOLK6dWJs/suHZT59zSTU5Dt5TFamW6gSY/Ox050f1wwOO0+25qogu\n19LtFycknyHfFIUQQgghKA9FIYQQQgiSruKhqLW2t7X2W621X1ld39pae7K1dnz17xgPCCGEEEK4\nSbgaTdGPSfqipFeDe49I+nTv/dHW2iOr649fTeeMC7oiqkwUxvg146YuYRnvqXQ4VbFGaYxfs00X\na62SVVKn49pgP2yDsWiX5IvxaCYnPHXq1OLa6QoItRvUzFAzINW6LMbzXUK7SutFf6CNXRvUu3Dd\nnFaDWgP6HP3FJbRj/J5ryTacVoPtVkkTnT2ozaGPzRSn3Ldv3+Kaa0n7OD3Q1a6ls2mV3JO6A2eP\nyv85dmpKXD+0GV+n1kca50c/5DlGnYo0nqecL8fl1oVjrfSE7hyr1oF73SXn4znGc5v7mGeUNPoH\ni6TybDx8+PDQBs9L9vvZz352uIdQk8h14F53n3O0M+1TFZx2/VTJgN1+4T1cF671TLJkjp39Ol3S\nTEFkx9Q3Ra21o5L+gqSfWvvzQ5IeX/3345I+tK0RhBBCCCHcAMyGz/62pL8maf3R8kDv/czqv89K\nOjDcJam19rHW2lOttae2++QWQgghhPBaUz4UtdZ+UNL53vtvXOk9fTP+MP72e/O1x3rvD/TeH3A/\nMQ8hhBBCuBGY0RR9n6Qfaq39eUlvlPS21to/kHSutXao936mtXZI0vmqoT179ixigYxpOs0IY9pV\n/hMXe67yEFVtSmPsmW1WRValMe5Z5dRxugLGq6kjYJsujs44OePIHIfTrnCtGOOlPsbZtMpXMRMD\n51ryHq6ta4P9Uu/CQpMzOXXYJv3H6XCqXEZVgUdp9AeuZaVtkcZ1qXJSOV0BNVYcK9fJ5RSp+uHr\nrg3qLLhPqX1ycL3Pn18edVXOIWncLzwfqKFw36rzPZX268477xzaoH9wn7riz6Qqskwtk9NYVVqV\nmULFtAd1a9SyOK0ox8GzjnNzhWxpZ54XHNdLL700tEE9LftlbiNqkKTR/7nH3Gcjoe+yzSrPlzSu\nJe/hOriC0tXnLW3oir+6wrszlN8U9d4/0Xs/2nu/S9JHJP2L3vuPSHpC0sOrtz0s6VPbGkEIIYQQ\nwg3AteQpelTSD7TWjkv6/tV1CCGEEMJNyVWV+ei9/0tJ/3L13y9KevD6DymEEEIIYefZ8dpn67E/\nxred7oTxScZ0GVucqSlFDQDbdPWxGI/l2BnTdHVlOA5qe2bqMDE+X+kuXO2zKi8TX3d5JGiPKr+J\n01lsbGwsrishvlsXUtX3mdHQUM/BdXMamqruEttwmomqvk9Vh0gaY+9so6rrJtUxf66t0/K4XE5b\n4fRzHDttTJ3FTO3EY8eOLa651u4MOnPmzOKaWgWO0+lOqCuhzbi2Tv/CteU1zxPnH+yn0l24fDjs\nl75N/3BtVHUeOXbnH2yX46JGz+VLYv40rjXzFjHPkzSeOZz//v37F9fuM6rKy0R/cbW+nFZpHeqW\nnJ/yvKw0VTO54zh2+razB6HWq8p9JPkaczOkzEcIIYQQgvJQFEIIIYQgKQ9FIYQQQgiS8lAUQggh\nhCBph4XWvXcrQH6VmSKZFFhR6OWEkhRhUZBJoZdLnHb69OnFNQXPHIdLJlUl42PCOyfqozC2KppK\n8aW7pyqA6pJ+cX7stxKSSqPIsSqK6YSBlUi8el2qE+sdOXJkce0EfJWfUuTpxkHfrcblRNJslwJW\n+rbbc0z4WYmmnQi2Skxa/YBCGv2OY+U4XcLUqkBulRxWGtehKjA9sy6cPwXPbl2q4q0Uo7ofN9Av\nOReeDS7ZKfc++6kSzEp1gj/adCbpKs86+r5bF0JRNPeLS4ZLKM52Y6+ofjBy7ty54R7On/1yndyP\nTqpiv/QxJ3CmT/H84Oec+zEQ9yHXjn7skrC6PTRDvikKIYQQQlAeikIIIYQQJOWhKIQQQghB0i5o\nita1FYyRu/gkY4eMP84UuauS81EP4mLAjFlWCR+dNoFj5ziosXExX8Z4GWumJmAmgRXj+VW83/VD\nzQzt4xK4cb7bKSpLvQdj/uzD6bQ4tirBoVtbakLot/Qp2k8a58JEcnzdJd6jj3EubNPF8/m3yuec\nbo1jpR9yP7mEmBwHC7GyDecfXCsWK6XuwOmnOL9Kd+MKKBOOlXvM7bkqgR3vcVqvqogu7eXOV65/\ndSa7/cKx8h76i9NHkap4qfPTah24tk6XRB965plnFteVxkYafZlrW9nHja3SB1XJcqXRhzh2d35U\nRWMrPaqDNuRcZnSes+SbohBCCCEE5aEohBBCCEFSHopCCCGEECTtckFYxvxckTvG7xk3ZWzR5Tth\nXgTGuBnznNGuzOTRIJwL+2EbLu/KjHZpHVdYkvdQz8G5ujg649FV/Nq1QXtwvtQDzcSvq4K5TstD\nPRRtxni1y3/BcVCnxHtcHJ1t3H777Ytrp7mrqDRlTrtS7Q+uvdsv1Mtxvnx9pjgl/YEFYlm4Vho1\nEFwX2tS1wfmx8Cb1Hk67wvnOFOYlVSFarpPT8VVryXVw/sH9Qp0a9TFb5aa70nuqoqKS97t1OBfX\nBnMq0T5cJ+en1N1UWjenBeS5xLWjfzB3mjTajP7Bs5Fzl8azj58NnIv7jKo+19iH+wzjHmMbnKsb\nh/vsmyHfFIUQQgghKA9FIYQQQgiS8lAUQgghhCBphzVFhLH4mRwQjPEyvu1yhDAuXuUDcnkk2C5j\nmIy9uhhnVf+qyhniYHyasWnGkaUxHstxzfTLfngPNSROQ8O4Occ6k/+F7+FcqFOrdAjSqBGocg5J\no3ZnJvcGoa/Th6j/cHqhqmYQ7cN6fFKdt4v7xWm9qKHhPW5/VHB/8NppaFz+o3XoczM6nCq/mtOu\nsJ9q77t9y3Wp8iE5/QvPwioXmNOdcGxskz7m8q2Rs2fPbtmm23PcY9zrzFnmNKvMW0YtC++Zye3D\nsfLanUHcQ5Wm6Pjx40Mbhw4d2rIfnmtO+8a14j2s68a1lmptbHWeSOOzQaXhdTrP1D4LIYQQQrgG\n8lAUQgghhKA8FIUQQgghSMpDUQghhBCCpF0oCLsuBKUQzAmcKfSiIJFiOifgpBiZIjaKxVxyLbbL\nccy0wXso2KSA8bnnnhvaqBKBUWznxOsUeVKwyHE5kSPHUSXKcsUpeQ9tSDE3BfLSKOjlfLnWzscI\nbcpCpE7Ax0SLbGNGNF4ls6R9ZpJZci2ZnND5B9vgHqsErq5fJlqkfVzxRs6vKv7r9j7tznFR5OmE\ntPQx9st96/yUPsT504Yu4SHXvypm69a2OoNmxKm8p9r77nWeH2yzShoojaJo/siAPuf2HNf/2Wef\nXVxzT7qkiZwfxzVTuJg/IOJYqx+hSKP/c1z0W+cfvIdjdz9EIJWQmm3MJJDlfqB/uB8HpSBsCCGE\nEMI1kIeiEEIIIQTloSiEEEIIQdIuFIRdj6fOJLg7fPjw4prFXZnkybXJGC5jq0xgN5Ocj/0yns+4\nsjRqJDguxk2pD3JtcC6VDkOqC8Jy/k7fUCXj4z0ukRxtRntw7DOFWKnN4NrSXtI4f8a8qf9gnF0a\ntRrUBFRJJaVxvlUhTacpqorGUjPg4vnUN1BXwXG65GuVNoX+4sbBfngP18UlGmS7HBfvcZoJtkFd\nCbUZzh6V1ot+6ZJO0qeqgsEzyXCrwppuLhwrfXtG70Efq7RePPelcf1Pnz69uK7ObMnvoXWY3NF9\nNnBtv/M7v3NxXRWddW3QPjwb3bipoeH5Uem4Zu5hv5X9pHEf0x/cutDX+flRJe2VfALUGfJNUQgh\nhBCC8lAUQgghhCApD0UhhBBCCJJ2QVO0HhtkzNfpThgnpw6FsWcXR2cMk3HSqtirNMYnGUtlzNvF\n0VlMryq86fQvlc6EOVWqgpiunyrHjDSOtVonp22q8hJVGhLXRlWs060t145ajapN126VC8rl0Khy\nUFEP5HI/cQ/xHu6Pmfwv1T30a2nMh8T5c62dX1fFS/m6mwvvoU0rv5VGDQhtWhV3dVTvcfuWa+vW\nfx2n1eBe5tirArFSrUOifc6cOTO04fRf61SaPGnUC3JczDl17Nixsp977rlncT1zJlP/U+lynKaI\n+4Nn0MbGxpbjkqT9+/cvrvkZxM/KmWKunEuV90sa50KdHsfhPhtoI+51tuE+G2b0To58UxRCCCGE\noDwUhRBCCCFImgyftdZOSPqapEuSXum9P9Bau1XSP5R0l6QTkj7ce9/6O9EQQgghhBuUq9EU/Zne\n+8W160ckfbr3/mhr7ZHV9ce3auCVV15ZaA0YI3cxcGoPGAdlPNvVt2EclPHKSsshjTFNxsR5j8u7\nUsVSKw2FNM6lqjvkYve857bbbltcU0Pi9FHM1cN+GHt39uB6V/WO3Fyo9ar8wfkYtQd8z8WLFxfX\nzj+qnDqcv9OUsA3On/7g6nQxfk/dHnG6FOoZ6HO0udOdcI/R92ljZ49Tp04trulzVe4SNw769kwe\nK+qMzp49u7jmmeQ0EpX+hdc89xzsh37ptCucP9uYqbHFteLczp07t7h2Z/LTTz+9uL7rrrsW1/RB\nN6/VGtwAABYcSURBVBf6f5WDy/l6pV3hOszkj6rW2uXC4jpw3zKvmdtzPHMqzarTz1U1PLn2Lo8V\noY3pYzOfc1wX1hd0eaxm8iA6riV89pCkx1f//bikD11DWyGEEEIIu8rsQ1GX9M9ba7/RWvvY6m8H\neu+v/qzgrKTxJyiSWmsfa6091Vp7ir+MCiGEEEK4UZgNn/3J3vup1tp+SU+21r60/mLvvbfW7O/f\neu+PSXpMku6+++7t/UYuhBBCCOE1Zuqbot77qdW/5yX9sqTvlXSutXZIklb/nn+tBhlCCCGE8FpT\nflPUWnuLpD2996+t/vs/l/S/S3pC0sOSHl39+6mqrcuXLy9EZlXSK2kUflWiNSdAo5C4EjU6kSOF\nbkyCxtddqLBKQMXXKdiTRkFZVXzRCYtpd4pLKXJzYkuKC2kPjtMJJatkYhTKOVE57cz50h5ubSkm\npFC0KmgojTakPzihOakSc1ZFM6VxrbgfKtG0NO4pri0Fzm4cFH1ybWcSprIYNPulzZ1Y2yWEXYdi\nUyekrQri8jxx+6VK7knRsPtRAYXC9PXKPlca2zpcS9cG147rxHVwxY+/+7u/e3FNu9Ne7vzg3yoR\nsNu39G2udZUAUaqTBM4InCs4LufXFCzThhyHS3TM86IqZO386WqTV7rP7OpHAxw7P9OuNLYZZsJn\nByT98mpQt0j6f3rvv9Za+6ykT7bWPirpeUkf3tYIQgghhBBuAMqHot77s5LeZ/7+oqQHX4tBhRBC\nCCHsNMloHUIIIYSgHS4Iu3fv3kVMkvFaF/NlnJixRcZWXcImaiQYa51J3lhpAFhE08XAOVbGoqll\noRZKGmPglabKaZuqgn183WmbOF9qAqhVcWvLeHWV5MsVMOTfGGumlseNoypMTC2TSzbG+Vfxfecf\nvIf9cq4zydf4HtrD2bQqcMq5Oh+j3qXSGNH3pdHvuC/Zx0zC1Eqb4JJd8h7OheNgMVxpTABK355J\nMsq5cBxcB5eskNoUJsikv9x7771DG66w6jq0l9svHDv3HOcyk4iPc6M/uGSnXG/6Ps9bp32rimPz\nzHHaSL6H46Bmxp1jlb50JvFvpQeqkhY7aLMZrSjXjvdwLjPF5GfJN0UhhBBCCMpDUQghhBCCpDwU\nhRBCCCFI2mFN0eXLlxcxfOoKXJ4WFoFkfJJxZFdsj7F4xokZv3R5ExjDZD+Mxbt8OFUb7Ne1wTgp\nY7rUSDjNSGUPjosFMKVxHdgm49sulwfny9iyy9VCqiKQ1VylUc/C+D1zLh04MFa04Vx4D/UMTndC\n/6DNqNVwOWQqX+baulwejM9zrTkupzmrdGvc6y5fEnOicD9UBXNn+uHrTqfF9/BMmtEDkfPnl7lu\nubbOx9hvtU9dHhr69vvet/xhMbU8p0+fHtqoCnpyLsxjJI1jZ79Vjh1p9HWuU3UWSGNhUa7ljO6V\nY630cydOnBja4PlQzc19VnI/8D3c104/d8cddyyueQZzbd25Xq0dx+XWpcrJxnE4jeaZM2eGv82Q\nb4pCCCGEEJSHohBCCCEESXkoCiGEEEKQtMOaImkZg6Qmwuk9qBlhHJRtOE1AVWuGMU8X42Q8lteV\nhkQa49GMZzMm7OLXfE+VU8jF4tkuY97URDiNFe/hNfMYufwejANznRjfdzkxGNOmNoG5npzGitoV\n9jujF6PNqvpyBw8eHNpgLptKL+ZqStF3OVbOzemBeA/j+9QEsE1pHDv9lHOZqY3HNqi7cBoatkF/\nmanjVuUk4x5zdZhoM9qH54fTQ1CHRH0hzxPn6zxPuS+PHDmyuD569OjQRqUhIm4u3C/0Mfqg83Xa\nneOgX7pcNlwX9su1d2dQlfuLNp/R4bBN3uPGQX+gfWbynPGc4p7jus3UG6zqybn9wns4f/qL8/WZ\nHEqOfFMUQgghhKA8FIUQQgghSMpDUQghhBCCpDwUhRBCCCFI2gWh9boY7tChQ4vXnACN4lOKsSkE\nc0JJisEoymK/rhArBXkstliJuV2/FCxS9DpT0I5J0HjtBIq0GedGIZwTUrJdJvmiuNAJz7lWFAbS\nHk5MR8EdhYCcm2uDwkAKaWfGQYE37cM+nACef2OCxyrpqDTanW3S55yYn2PnPbSxmwt9hutQiXOl\ncS6VkNQVlty/f//imutEYakTfXLtuP7cT65ALsdKG7r9QSiCroToLiEmxejVjztcokH6IZNKchzu\nHOM5RXtUP8KQfALDrdp046jOOr7ufjBS/UCCPsYEiQ76C9fJ/bihmgs/19we5A+ZqmLITsxMH9vO\nDzWqzwIKsd3nvjvbZsg3RSGEEEIIykNRCCGEEIKkPBSFEEIIIUjaYU3Rnj17FnF/xp6dNoGxVMYr\n2YaLcTJmWSWkcsU6eQ91F4y9Ms4sjTFdapkYJ3XJxngPx1XppaRRI0KbVTFg12+V5MzFdxm/ZhFM\njt3FjekPVdIzl3ixKsbJsTt73H777VuOi7F4V7i4Siw4U0CZ2guOnevmEnNWPnbq1KnFtUtEyTao\nQ+G4nJ9WRTDZhrMp/0abziQ7pSakSk7n5lIlJuX+YTJHaTxzOH9qmZzGiokUWayVbbiEutRpVbpO\nnifS6Hf0bfbh9j7PD57jnIvbL9z7HFeVLFca167SE86cY9SD0admEqbyzKEO1iVurfYc5+98jH7J\n91R70vXDPci5uXG4ZK4z5JuiEEIIIQTloSiEEEIIQVIeikIIIYQQJO2wpqi1togFMk7ImLD7G+PE\nVZHIV/tdh/FZpyMgVb4bxnNdLJ4xcJdDaB1qf6Rxfox583UXA2cMl++hfVzsmfFpwri5ez/bZSya\n6+K0Cfwb7cH4tdPQEMaruQ4zhSWpS6PuxOUqoQ9RV0AbunxJXDv6Pu01k9+jyn/j9GKVtqsqACrV\n+aPuvPPOxbXLIVPlM+E4XC4b2pk+RQ0aNVcO6lCqPEZuHNTdsCCs01RwrOyX97j8QPQptsEz2p2v\nlT9w/7gziD7GfqucZa6NKj+QOz/YBvcDfc75Kfc+fYyfHe7zpdJxcpxOg8f1Zr983ennqtxX1b6W\nRl+mDemD7rPUndMz5JuiEEIIIQTloSiEEEIIQVIeikIIIYQQJO2wpujSpUuLuC9jvi4+WcXRGUd2\nbbAfaiSo1XC6pCrWSu2Ci4Ez5svcChy7i/lWMW/GVl0bnB91FBzHyZMnhzaoK2HNKPbrYuBcW45j\nRu9BXVqVt8rlsapyO3GdnNaL+V94D+fv2uA4mKtmRg/FWPtMrpaqDUJNhBsXtSmcG/3Yxf/Z7r33\n3rtlm86mXG++Z0YvRk0E/ZJzdfar6qVxvzjdSVXLi/e4/UJNEedb5aWRRr1PVQ+Le1QaNSIvvPDC\n4po53dxcqMuq9HJOP1dpUiu9peuXe45jn9G6cB14njgNHvvhNf3H7Vt+znEdOFf3+UKqfGLM8ebu\noc04DqdLcr47Q74pCiGEEEJQHopCCCGEECTloSiEEEIIQVIeikIIIYQQJO1C8sZ18RPFlk7ERsEV\nC61SpPXss88ObVD0TNHaTPFSiucoUqMgbaYYHfthmy5xGuFcKAB3oj4WE6yK6x06dGhog+vANri2\nTuBLUTjHRcGqE7BWyQnZr7MpBYmcG8fpktGxXY6DQnQn+qTIsyoi6trgfNkm/cHtOc5vO8lPq71d\nJbyT6kKrvHZtUNRJ3+baOyE+2+C4+OMPV6yTomD+uIPX733ve4c2XKHqdWZE9JxL9UMFJ/imb3/+\n859fXHO/OCEtbfTud797cU1BuLMp15trx/3hzkLudf4gguviih9TrF+J6vlZIo3+8dWvfnVxfeTI\nkcW1Ezhzrbhvq3FJ0rlz5xbXnG+VDFUa9zav+SMd57dcS7bBcbrPW/YzS74pCiGEEEJQHopCCCGE\nECRNPhS11t7RWvvF1tqXWmtfbK39idbara21J1trx1f/jol5QgghhBBuEmY1RX9H0q/13v/L1trr\nJb1Z0k9I+nTv/dHW2iOSHpH08a0a2bNnzyKBFGOeLvka38MEVIyTspCca4NxUWp5XCySyaGqArEu\n2RiLgJ4/f35xPZPwkPHoGT0UqWLPHMeMhobzZ/zetUE9A+/hWrpCm5wv58a1pqZEGufLWHtVIHWm\nje0UyaRf8nVXBLFKpEe/dIV6q+SN7NcV6mWMv0o66vQe9KmqoLJb20q3x3G5pHg8D2gfJu50xVwr\nrRf7cPohtsG1pg86vRjh2tEezk+ZaJFzcWcwoc6TbVCr4s41jt3pwdZxuhPqJanlYZtO/0J/4LnN\ndXH7hZqpqrivS7zIteIZVBUgd1BzRW2kmwv9jjatkvRKdWLOSid8pb/NUH5T1Fp7u6Q/JemnJan3\n/s3e+8uSHpL0+Optj0v60LZGEEIIIYRwAzATPrtb0gVJf7+19luttZ9qrb1F0oHe+6v/F+mspAPu\n5tbax1prT7XWnnLp3kMIIYQQbgRmHopukfTHJP293vsHJH1dm6Gyb9M3vy+z39X23h/rvT/Qe3+A\nX72FEEIIIdwozGiKNiRt9N4/s7r+RW0+FJ1rrR3qvZ9prR2SdP6KLazYs2fPIs43k1OnioufPXt2\nce3ipnwY4zdW1BE4bRNjmNQ7MNbsdBmMGzPmyzixy83B9zD2Tm2C04zQptQHMQbu5kJ7cP4cO2PE\n0mhDzuX06dOLa+cf1FVUOTKcNoH+UeW/4bjdOKgroUbE5UtiP4y9c12cvqHKw8O1dPoo+hj1DVwX\n5x9VPiTuOZ4F0qjnoFaDWgVXyLnSmNGPnRaQa0d/oe7CaVeYx6zKBeXWhXu7mr+zB21IrRvPU5eH\nhv5AG1P/4daWfsqxVuerNO4hjp1nkCvSzXbpczy3XH4g3kNfZxtuv3C9+RlFezjdK+dCP6zyOEmj\nTaviti6PVXVu8Wx0n9kcB/MSsQ2351w+qBnKb4p672clnWyt3b/604OSviDpCUkPr/72sKRPbWsE\nIYQQQgg3ALO/PvsfJf3c6pdnz0r6y9p8oPpka+2jkp6X9OHXZoghhBBCCK89Uw9FvffPSXrAvPTg\n9R1OCCGEEMLusKO1zy5durSI8zFu6OLGVb2WKm+RNMb82e9MnJQwLsyxuxxDVS4fzoW5kaS6phj1\nIG4ujOdTZ8G5uXwPVe4J2phr4MZa1Tpz+jLqO6gjoM2dXozzow6LWh+Xh4XxeepQqLNw+qgqbl7p\nhdzfaDO+7n78QD0D50L9x4zeg/3O5Cqp9ITsw9mUWgOns1nH5Qfi2Cp/cBoa6ioqfdBMHivCuboz\niGPn3mfNMadJZK03vof2crXPuP6sscUcbm4cJ06cWFxzD3JdqOuS6txP1fnqqO5xWh6uC9d65nOO\nPkNf5/nhPhuYg4o2q3RL0rgPuU955jj9HP2S9uEeczmqXJ26GVLmI4QQQghBeSgKIYQQQpCUh6IQ\nQgghBEl5KAohhBBCkLTDQmtpKZ6lKNYltKNojwK8SowqjeIwivyYXMsJRynsogiYbTgxHQWsTFpF\nYaQTn1ZCN97jEoVVSQIplHNiOooHq8K0TtBbieg59hlhMcdFe7lEYVXhVc7NicY5P4oYmUjMiU/p\nQxSFuwSYpBKnc3/MFFGl39Jebm059spf3LpUSVYpJN3Y2BjaoM04N/qHE+NSwErRNOfixNy0B32d\ne8EJelkQmXudc3XFbSkk5znFuVBU7cZKe1T7yb2H4+I574TFFF+/613vWlxTzO7GwfdwnSrhtTT6\nB++hz7kfBHDfVsJi1wY/P+mHPIOc0LraL1XRXak+12eK23KtaB+um/thghPnz5BvikIIIYQQlIei\nEEIIIQRJeSgKIYQQQpAktSoh2HXtrLUL2iwJsk/SKMwI10Jsev2JTa8/sen1Jza9/sSm15/dtumd\nvfdRzAl29KHo25229lTv3ZUNCdskNr3+xKbXn9j0+hObXn9i0+vPzWLThM9CCCGEEJSHohBCCCEE\nSbv3UPTYLvX7HzOx6fUnNr3+xKbXn9j0+hObXn9uCpvuiqYohBBCCOFGI+GzEEIIIQTloSiEEEII\nQdIuPBS11j7YWnu6tfZMa+2Rne7/Zqe1dkdr7ddba19orf1Oa+3HVn+/tbX2ZGvt+Orfd+72WG82\nWmt7W2u/1Vr7ldV1bHoNtNbe0Vr7xdbal1prX2yt/YnY9Nporf3Pq33/2621n2+tvTE2vTpaaz/T\nWjvfWvvttb9d0YattU+sPq+ebq39F7sz6hubK9j0J1d7//OttV9urb1j7bUb1qY7+lDUWtsr6f+S\n9OckvVfSD7fW3ruTY/iPgFck/dXe+3sl/XFJ//3Kho9I+nTv/V2SPr26DlfHj0n64tp1bHpt/B1J\nv9Z7f4+k92nTtrHpNmmtHZH0P0l6oPf+XZL2SvqIYtOr5WclfRB/szZcna0fkfSdq3v+7upzLCz5\nWY02fVLSd/Xev0fSlyV9QrrxbbrT3xR9r6Rneu/P9t6/KekXJD20w2O4qem9n+m9/+bqv7+mzQ+a\nI9q04+Ortz0u6UO7M8Kbk9baUUl/QdJPrf05Nt0mrbW3S/pTkn5aknrv3+y9v6zY9Fq5RdKbWmu3\nSHqzpNOKTa+K3vu/kvQS/nwlGz4k6Rd679/ovT8n6Rltfo6FNZxNe+//rPf+yury30o6uvrvG9qm\nO/1QdETSybXrjdXfwjZord0l6QOSPiPpQO/9zOqls5IO7NKwblb+tqS/Juny2t9i0+1zt6QLkv7+\nKiT5U621tyg23Ta991OS/qakFySdkfS7vfd/ptj0enAlG+Yz6/rwVyT96uq/b2ibRmh9k9Jae6uk\nfyTpx3vvv7f+Wt/Ms5BcC5O01n5Q0vne+29c6T2x6VVzi6Q/Junv9d4/IOnrQlgnNr06VjqXh7T5\nwHlY0ltaaz+y/p7Y9NqJDa8vrbX/VZuyj5/b7bHMsNMPRack3bF2fXT1t3AVtNZep80Hop/rvf/S\n6s/nWmuHVq8fknR+t8Z3E/J9kn6otXZCmyHdP9ta+weKTa+FDUkbvffPrK5/UZsPSbHp9vl+Sc/1\n3i/03r8l6Zck/WeKTa8HV7JhPrOugdbaX5L0g5L+6/4fkiLe0Dbd6Yeiz0p6V2vt7tba67Uptnpi\nh8dwU9Naa9rUaXyx9/631l56QtLDq/9+WNKndnpsNyu990/03o/23u/Spk/+i977jyg23Ta997OS\nTrbW7l/96UFJX1Bsei28IOmPt9bevDoHHtSmpjA2vXauZMMnJH2ktfaG1trdkt4l6d/twvhuOlpr\nH9SmJOGHeu9/sPbSDW3THc9o3Vr789rUb+yV9DO99/9jRwdwk9Na+5OS/l9J/17/Qf/yE9rUFX1S\n0jFJz0v6cO+dYsJQ0Fr705L+l977D7bWblNsum1aa+/XpnD99ZKelfSXtfl/xGLTbdJa++uS/itt\nhiN+S9J/K+mtik2naa39vKQ/LWmfpHOS/jdJ/1hXsOEq/PNXtGnzH++9/6pp9j9prmDTT0h6g6QX\nV2/7t733/271/hvWpinzEUIIIYSgCK1DCCGEECTloSiEEEIIQVIeikIIIYQQJOWhKIQQQghBUh6K\nQgghhBAk5aEohBBCCEFSHopCCCGEECRJ/z+0hIinJo2MMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10026a3d10d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OZEzYaRPYRqf/cG1wvC7GvY3TA3F8nNuu2K201nd0BS05dmmtfaOGin93xY/p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\nsuqTJBgWcx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10026c9fd990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K+oikvb3312XjlySN/wm+ec6TrbUXWmsvbPfJLYQQQgjhy830Q1Fr7c2S/oOk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\nmg0fl/RDvffP995PSjquzftYWMPZtPf+n3vvl1eHPyvpwOr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10026caa28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GkmlD/t3Zw+nQtqG9XAy8K2Y7k9+D2gz6B32dxRslr2fZhn7s5sLcTtSpcZxu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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10026cb18e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pp55aXFPLxXtcbhMWWr3lllsW150OQ1rPj3546NChHccprW3GYq5ca6dr7AqN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10026cbacd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "for i in range(5):\n",
    "    num = random.randint(1,num_images)\n",
    "    # print num\n",
    "    gray_y = data[num].reshape(h,w)\n",
    "    img = Image.fromarray(np.float32(gray_y))\n",
    "    # print(img.mode)\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    ax.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[175, 181, 172, ..., 172, 178, 175],\n",
       "       [165, 170, 183, ..., 163, 182, 162],\n",
       "       [164, 168, 163, ..., 175, 166, 177],\n",
       "       ..., \n",
       "       [179, 172, 169, ..., 174, 187, 195],\n",
       "       [171, 178, 186, ..., 171, 198, 178],\n",
       "       [164, 173, 180, ..., 171, 178, 176]], dtype=uint8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gray_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Verify the binary files by reader class\n",
    "__SETI.py__ is a helper class, identical to MNIST dataset reader, to easily read dataset, one-hot coding, and read images as batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  SETI.zip\n",
      "  inflating: SETI.py                 \n",
      "  inflating: __MACOSX/._SETI.py      \n"
     ]
    }
   ],
   "source": [
    "!wget -q --output-document SETI.zip  https://ibm.box.com/shared/static/jhqdhcblhua5dx2t7ixwm88okitjrl6l.zip\n",
    "!unzip -o SETI.zip\n",
    "import SETI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting tmp/SETI1_data/SETI_ds_64x128/train-images-idx3-ubyte.gz\n",
      "Extracting tmp/SETI1_data/SETI_ds_64x128/train-labels-idx1-ubyte.gz\n",
      "Extracting tmp/SETI1_data/SETI_ds_64x128/test-images-idx3-ubyte.gz\n",
      "Extracting tmp/SETI1_data/SETI_ds_64x128/test-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3200"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SETIds = SETI.read_data_sets(ds_directory, one_hot=True, validation_size=0)\n",
    "SETIds.train.num_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10026cc2d0d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H4vKMsDgltU28r9NYzeRm2cStLW3Ksd52222LtoujM17f5QxxNuV6s821dFov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\nH9v6AcsosR56AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x100271c45490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gray_y = SETIds.train.images[0].reshape(h,w)\n",
    "img = Image.fromarray(gray_y*255)\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Want to learn more?\n",
    "\n",
    "[Deep Learning with TensorFlow](https://cocl.us/SETI_CodePattern_ML0120EN) is a free course in __cognitiveclass.ia__ where you can learn TensorFlow and Deep Learning together.\n",
    "\n",
    "Running deep learning programs usually needs a high performance platform. PowerAI speeds up deep learning and AI. Built on IBM's Power Systems, PowerAI is a scalable software platform that accelerates deep learning and AI with blazing performance for individual users or enterprises. The PowerAI platform supports popular machine learning libraries and dependencies including Tensorflow, Caffe, Torch, and Theano. You can use [PowerAI on IBM Cloud](https://cocl.us/ML0120EN_PAI)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Authors\n",
    "\n",
    "<div class=\"teacher-image\" style=\"    float: left;\n",
    "    width: 115px;\n",
    "    height: 115px;\n",
    "    margin-right: 10px;\n",
    "    margin-bottom: 10px;\n",
    "    border: 1px solid #CCC;\n",
    "    padding: 3px;\n",
    "    border-radius: 3px;\n",
    "    text-align: center;\"><img class=\"alignnone wp-image-2258 \" src=\"https://ibm.box.com/shared/static/tyd41rlrnmfrrk78jx521eb73fljwvv0.jpg\" alt=\"Saeed Aghabozorgi\" width=\"178\" height=\"178\" /></div>\n",
    "    \n",
    "#### Saeed Aghabozorgi\n",
    "\n",
    "[Saeed Aghabozorgi](https://ca.linkedin.com/in/saeedaghabozorgi), PhD is Sr. Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n"
   ]
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