CNN_RNAP_Seq_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "input_data_ordered_raw =  pd.read_csv('rnap_scanned_over_ecoli_genome200k.csv')\n",
    "sequences = input_data_ordered_raw['seq'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>val</th>\n",
       "      <th>seq</th>\n",
       "      <th>left</th>\n",
       "      <th>right</th>\n",
       "      <th>ori</th>\n",
       "      <th>contig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>23.9083</td>\n",
       "      <td>CTATTACCTGCCCACGGTAAAGGCGTTCTGGTTGCCCTACG</td>\n",
       "      <td>3695629</td>\n",
       "      <td>3695669</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>23.5831</td>\n",
       "      <td>TTTCTGCCCGCGGCCTGGCAATTGATACCGCCTTGCCATCG</td>\n",
       "      <td>1109059</td>\n",
       "      <td>1109099</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>23.4205</td>\n",
       "      <td>TTTTTACACGCCTTGCGTAAAGCGGCACGGCGCGGGGTGCG</td>\n",
       "      <td>822216</td>\n",
       "      <td>822256</td>\n",
       "      <td>-</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>23.4098</td>\n",
       "      <td>TTGCTGCCTGGCCGATGGTCTATGCCGCTGCGTTCTCCGGC</td>\n",
       "      <td>772478</td>\n",
       "      <td>772518</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>23.3640</td>\n",
       "      <td>CTTTTACACGCAAAATGCCGTCACGTTCACGGGTCTGGGCT</td>\n",
       "      <td>2748531</td>\n",
       "      <td>2748571</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      val                                        seq     left  \\\n",
       "0           0  23.9083  CTATTACCTGCCCACGGTAAAGGCGTTCTGGTTGCCCTACG  3695629   \n",
       "1           1  23.5831  TTTCTGCCCGCGGCCTGGCAATTGATACCGCCTTGCCATCG  1109059   \n",
       "2           2  23.4205  TTTTTACACGCCTTGCGTAAAGCGGCACGGCGCGGGGTGCG   822216   \n",
       "3           3  23.4098  TTGCTGCCTGGCCGATGGTCTATGCCGCTGCGTTCTCCGGC   772478   \n",
       "4           4  23.3640  CTTTTACACGCAAAATGCCGTCACGTTCACGGGTCTGGGCT  2748531   \n",
       "\n",
       "     right ori     contig  \n",
       "0  3695669   +  MG1655.fa  \n",
       "1  1109099   +  MG1655.fa  \n",
       "2   822256   -  MG1655.fa  \n",
       "3   772518   +  MG1655.fa  \n",
       "4  2748571   +  MG1655.fa  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data_ordered_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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XRMS3O62E5tqUmfVK7alB7QfMzcv/BBwNHAp8uqMLZWZm1p57UFsAIWlP0hh+TwJI2rFT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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unbalanced_pos_labels = np.array(input_data_ordered_raw['val'][0:100000])\n",
    "unbalanced_pos_labels = (unbalanced_pos_labels-min(unbalanced_pos_labels))/(max(unbalanced_pos_labels)-min(unbalanced_pos_labels))\n",
    "\n",
    "plt.hist(unbalanced_pos_labels,bins=100,label='Raw Training Data')\n",
    "plt.legend()\n",
    "plt.ylabel('Counts',fontsize=12)\n",
    "plt.xlabel('Values of Sequences (from MPAthic)',fontsize=12)\n",
    "#plt.title('Convolutional Neural Network Performance')\n",
    "plt.tight_layout()\n",
    "#\n",
    "#plt.savefig('Write_up/raw_data_histogram.png')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomly sample vals and seqs to prevent data imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>val</th>\n",
       "      <th>seq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.9083</td>\n",
       "      <td>CTATTACCTGCCCACGGTAAAGGCGTTCTGGTTGCCCTACG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.5831</td>\n",
       "      <td>TTTCTGCCCGCGGCCTGGCAATTGATACCGCCTTGCCATCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.4205</td>\n",
       "      <td>TTTTTACACGCCTTGCGTAAAGCGGCACGGCGCGGGGTGCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.4098</td>\n",
       "      <td>TTGCTGCCTGGCCGATGGTCTATGCCGCTGCGTTCTCCGGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.3640</td>\n",
       "      <td>CTTTTACACGCAAAATGCCGTCACGTTCACGGGTCTGGGCT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       val                                        seq\n",
       "0  23.9083  CTATTACCTGCCCACGGTAAAGGCGTTCTGGTTGCCCTACG\n",
       "1  23.5831  TTTCTGCCCGCGGCCTGGCAATTGATACCGCCTTGCCATCG\n",
       "2  23.4205  TTTTTACACGCCTTGCGTAAAGCGGCACGGCGCGGGGTGCG\n",
       "3  23.4098  TTGCTGCCTGGCCGATGGTCTATGCCGCTGCGTTCTCCGGC\n",
       "4  23.3640  CTTTTACACGCAAAATGCCGTCACGTTCACGGGTCTGGGCT"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data_ordered_raw[['val','seq']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normally sample high value seqs more\n",
    "# https://stackoverflow.com/questions/33160367/numpy-how-to-generate-a-normally-distributed-set-of-integers\n",
    "bi = np.random.binomial(n=10000, p=0.5, size=1000)\n",
    "beta = np.random.beta(a=19,b=1,size=50000)\n",
    "myList = list(100000*beta)\n",
    "myList = [round(x) for x in myList]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#plt.hist(myList, bins=100)\n",
    "#plt.show()\n",
    "#np.random.seed(1)    # good seeds: 10,5,1\n",
    "# for larger data, seed 2,5 is okay.\n",
    "np.random.seed(6)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           val                                        seq\n",
      "31626  20.4539  CTCATCCACGCTTAAAAAACGCGGGTAAATCATGATCGCGT\n",
      "41187  20.3047  TCACAGCAAATTCGCCTGGTCACGCTCTCCGGTGGCGTCGG\n",
      "4714   21.3827  GAACTGCCCGCCGCGAATGGTTCCCTGCAGGCGGTTCACTT\n",
      "64877  20.0282  TTCGAACTCACCGCCCAGCAGTTCTTCCCAGTTTGGCCCCC\n",
      "41295  20.3031  GGGATAGCTGCGACATCATTTTGGTTACTGGCGACGCGTAT\n",
      "           val                                        seq\n",
      "87941  19.8315  TCTATCGCCGGTTGCGATGCTTTGCTGAACGCAGCCTGAGC\n",
      "2829   21.5992  CTGCTACATTGAACGTATTCGACGTAACGGGATTCTGGCAA\n",
      "51415  20.1728  GGCTTCCCGATTAACGAATATAACTCGCTGCGTGCAGGTCT\n",
      "67730  20.0015  TAGGTTCATGCCAGGCCGTTTGCGACCAGCTGGGGTCCGAA\n",
      "65639  20.0203  GCAAGGAACGCTGCATACTCTGCAACTCGGCTATCCGTGCT\n"
     ]
    }
   ],
   "source": [
    "num_sample = 90000   # number of training samplesq\n",
    "test_size = 5000    # number of test samples\n",
    "\n",
    "\n",
    "#num_sample = 40000   # number of training samplesq\n",
    "#test_size = 1000    # number of test samples\n",
    "\n",
    "binomial_probability_parameter = 0.5\n",
    "\n",
    "index_random_sample_training_data = np.random.randint(num_sample,size=(num_sample))\n",
    "###index_random_sample_training_data = np.random.binomial(n=200000, p=binomial_probability_parameter, size=num_sample)\n",
    "\n",
    "#beta = np.random.beta(a=20,b=1,size=num_sample)\n",
    "#index_random_sample_training_data = list(100000*beta)\n",
    "#index_random_sample_training_data = [round(x) for x in index_random_sample_training_data]\n",
    "\n",
    "#index_random_sample_training_data = set(index_random_sample_training_data)\n",
    "#index_random_sample_training_data = list(index_random_sample_training_data)\n",
    "\n",
    "input_data_raw = input_data_ordered_raw[['val','seq']].loc[index_random_sample_training_data]\n",
    "print(input_data_raw.head())\n",
    "\n",
    "index_random_sample_test_data = np.random.randint(num_sample,size=(test_size))\n",
    "###index_random_sample_test_data = np.random.binomial(n=200000, p=binomial_probability_parameter, size=test_size)\n",
    "\n",
    "#beta_test = np.random.beta(a=20,b=1,size=test_size)\n",
    "#index_random_sample_test_data = list(100000*beta_test)\n",
    "#index_random_sample_test_data = [round(x) for x in index_random_sample_test_data]\n",
    "\n",
    "\n",
    "#index_random_sample_test_data = set(index_random_sample_test_data)\n",
    "#index_random_sample_test_data = list(index_random_sample_test_data)\n",
    "\n",
    "input_data_raw_test = input_data_ordered_raw[['val','seq']].loc[index_random_sample_test_data]\n",
    "\n",
    "print(input_data_raw_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#from random import shuffle\n",
    "#shuffle(index_random_sample_training_data)\n",
    "#shuffle(index_random_sample_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#num_sample = len(set(index_random_sample_training_data))\n",
    "#test_size = len(set(index_random_sample_test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensure no test data got into training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_data_raw = input_data_raw[~input_data_raw.seq.isin(input_data_raw_test.seq)]\n",
    "#input_data_raw = input_data_raw.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "#test_size = len(input_data_raw_test.drop_duplicates())\n",
    "test_size = len(input_data_raw_test)\n",
    "print(test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#num_sample = len(input_data_raw.drop_duplicates())\n",
    "num_sample = len(input_data_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nSnpF0iJJt0raOG/rIemGnL5E0h8k9S/zpzsQeDjv12Q95nM9RdIsYFZO+0w+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\nzKyQHKDMzKyQHKDMzKyQ/j8F3jbq2HMgugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# positive and negative labels\n",
    "# normalize the following between 0 and 1\n",
    "pos_labels = np.array(input_data_raw['val'][0:num_sample])\n",
    "pos_labels = (pos_labels-min(pos_labels))/(max(pos_labels)-min(pos_labels))\n",
    "\n",
    "# test positive and negative labels\n",
    "test_pos_labels = np.array(input_data_raw_test['val'][0:test_size])\n",
    "test_pos_labels = (test_pos_labels-min(test_pos_labels))/(max(test_pos_labels)-min(test_pos_labels))\n",
    "\n",
    "#plt.hist(pos_labels,bins=50,label='Balanced Training Data')\n",
    "plt.hist(pos_labels,bins=100,label='Balanced Training Data')\n",
    "plt.legend()\n",
    "\n",
    "plt.ylabel('Counts',fontsize=12)\n",
    "plt.xlabel('Values of Sequences (from MPAthic)',fontsize=12)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('Write_up/balanced_training_histogram.png')\n",
    "plt.show()\n",
    "# assigned random values to negative examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pD5JWSnpe0uiCfWdKei21bP4s6ZQWnqObpJ9I+kv6+UnatgcwNxVbKenxZh6+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\nBcu7DQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(pos_labels,bins=50,label='Balanced Training Data')\n",
    "plt.hist(test_pos_labels,bins=100,label='Sampled Test Data')\n",
    "plt.legend()\n",
    "\n",
    "plt.ylabel('Counts',fontsize=12)\n",
    "plt.xlabel('Values of Sequences (from MPAthic)',fontsize=12)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('Write_up/balanced_training_histogram.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sequences = np.array(sequences)\n",
    "\n",
    "pos = []\n",
    "#pos = list(sequences[index_random_sample_training_data])\n",
    "pos = list(input_data_raw['seq'])\n",
    "\n",
    "test_pos = []\n",
    "#test_pos = list(sequences[index_random_sample_test_data])\n",
    "test_pos = list(input_data_raw_test['seq'])\n",
    "\n",
    "pos = np.array(pos)\n",
    "test_pos = np.array(test_pos)\n",
    "\n",
    "seq_length = len(pos[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>val</th>\n",
       "      <th>seq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87941</th>\n",
       "      <td>19.8315</td>\n",
       "      <td>TCTATCGCCGGTTGCGATGCTTTGCTGAACGCAGCCTGAGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2829</th>\n",
       "      <td>21.5992</td>\n",
       "      <td>CTGCTACATTGAACGTATTCGACGTAACGGGATTCTGGCAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51415</th>\n",
       "      <td>20.1728</td>\n",
       "      <td>GGCTTCCCGATTAACGAATATAACTCGCTGCGTGCAGGTCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67730</th>\n",
       "      <td>20.0015</td>\n",
       "      <td>TAGGTTCATGCCAGGCCGTTTGCGACCAGCTGGGGTCCGAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65639</th>\n",
       "      <td>20.0203</td>\n",
       "      <td>GCAAGGAACGCTGCATACTCTGCAACTCGGCTATCCGTGCT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           val                                        seq\n",
       "87941  19.8315  TCTATCGCCGGTTGCGATGCTTTGCTGAACGCAGCCTGAGC\n",
       "2829   21.5992  CTGCTACATTGAACGTATTCGACGTAACGGGATTCTGGCAA\n",
       "51415  20.1728  GGCTTCCCGATTAACGAATATAACTCGCTGCGTGCAGGTCT\n",
       "67730  20.0015  TAGGTTCATGCCAGGCCGTTTGCGACCAGCTGGGGTCCGAA\n",
       "65639  20.0203  GCAAGGAACGCTGCATACTCTGCAACTCGGCTATCCGTGCT"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " input_data_raw_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0037178670065857594"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pos_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_pos = []\n",
    "temp_pos_test = []\n",
    "for reshape_index in range(len(pos)):\n",
    "    temp_pos.append(list(pos[reshape_index]))\n",
    "\n",
    "for reshape_test_index in range(len(test_pos)):\n",
    "    temp_pos_test.append(list(test_pos[reshape_test_index]))\n",
    "    \n",
    "pos = temp_pos\n",
    "test_pos = temp_pos_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos = np.array(pos)\n",
    "test_pos = np.array(test_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "riQRzV6CNp33",
    "outputId": "a27104ff-7917-4987-dccc-7780792beac2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape: (84612, 41, 4)\n",
      "Training set label shape: (84612,)\n",
      "Test set shape: (5000, 41, 4)\n",
      "Test set label shape: (5000,)\n"
     ]
    }
   ],
   "source": [
    "pos_tensor = np.zeros(list(pos.shape) + [4])\n",
    "test_pos_tensor = np.zeros(list(test_pos.shape) + [4])\n",
    "\n",
    "base_dict = {'A': 0, 'C': 1, 'G': 2, 'T': 3}\n",
    "\n",
    "#naive one-hot encoding\n",
    "for row in range(num_sample):\n",
    "    for col in range(seq_length):\n",
    "        pos_tensor[row,col,base_dict[pos[row,col]]] = 1\n",
    "        if(row<test_size):\n",
    "            test_pos_tensor[row,col,base_dict[test_pos[row,col]]] = 1\n",
    "\n",
    "#print('Positive sample matrix shape: {}'.format(pos.shape))\n",
    "# this should be a 3D tensor with shape: (samples, steps, input_dim)\n",
    "#print('Positive sample tensor shape: {}'.format(pos_tensor.shape))\n",
    "\n",
    "X = pos_tensor\n",
    "y = pos_labels\n",
    "\n",
    "X_test = test_pos_tensor\n",
    "y_test = test_pos_labels\n",
    "\n",
    "print('Training set shape: {}'.format(X.shape))\n",
    "print('Training set label shape: {}'.format(y.shape))\n",
    "\n",
    "print('Test set shape: {}'.format(X_test.shape))\n",
    "print('Test set label shape: {}'.format(y_test.shape))\n",
    "#print('\\nOne-hot encoding looks like:\\n {}'.format(X[0,0:10,:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define error metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rmse(y_true, y_pred):\n",
    "    from keras import backend\n",
    "    return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))\n",
    "\n",
    "# coefficient of determination (R^2) for regression\n",
    "def r_square(y_true, y_pred):\n",
    "    from keras import backend as K\n",
    "    SS_res =  K.sum(K.square(y_true - y_pred)) \n",
    "    SS_tot = K.sum(K.square(y_true - K.mean(y_true))) \n",
    "    return (1 - SS_res/(SS_tot + K.epsilon()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "bD1LpHUKNtVh",
    "outputId": "ec377700-10d7-44a3-c80b-45c2a7133a11"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#here comes the deep learning part\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv1D, Dense, Flatten, Dropout\n",
    "from keras.activations import relu\n",
    "from keras.layers.pooling import MaxPooling1D,GlobalMaxPool1D\n",
    "from keras.optimizers import SGD, Adam\n",
    "from keras.initializers import TruncatedNormal,glorot_normal\n",
    "\n",
    "#sanity check for dimensions\n",
    "#print('Shape of the output of first layer: {}'.format(model.predict_on_batch(pos_tensor[0:1,:,:]).shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "2CkOlW_EN2gn",
    "outputId": "fbdf111b-fb35-4569-c9e3-34ead250e689",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 67689 samples, validate on 16923 samples\n",
      "Epoch 1/20\n",
      "67689/67689 [==============================] - 6s 86us/step - loss: 0.0253 - mean_squared_error: 0.0253 - val_loss: 0.0192 - val_mean_squared_error: 0.0192\n",
      "Epoch 2/20\n",
      "67689/67689 [==============================] - 6s 84us/step - loss: 0.0198 - mean_squared_error: 0.0198 - val_loss: 0.0187 - val_mean_squared_error: 0.0187\n",
      "Epoch 3/20\n",
      "67689/67689 [==============================] - 6s 92us/step - loss: 0.0188 - mean_squared_error: 0.0188 - val_loss: 0.0177 - val_mean_squared_error: 0.0177\n",
      "Epoch 4/20\n",
      "67689/67689 [==============================] - 6s 91us/step - loss: 0.0174 - mean_squared_error: 0.0174 - val_loss: 0.0157 - val_mean_squared_error: 0.0157\n",
      "Epoch 5/20\n",
      "67689/67689 [==============================] - 6s 89us/step - loss: 0.0149 - mean_squared_error: 0.0149 - val_loss: 0.0123 - val_mean_squared_error: 0.0123\n",
      "Epoch 6/20\n",
      "67689/67689 [==============================] - 6s 90us/step - loss: 0.0113 - mean_squared_error: 0.0113 - val_loss: 0.0081 - val_mean_squared_error: 0.0081\n",
      "Epoch 7/20\n",
      "67689/67689 [==============================] - 6s 89us/step - loss: 0.0071 - mean_squared_error: 0.0071 - val_loss: 0.0038 - val_mean_squared_error: 0.0038\n",
      "Epoch 8/20\n",
      "67689/67689 [==============================] - 6s 87us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
      "Epoch 9/20\n",
      "67689/67689 [==============================] - 6s 87us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
      "Epoch 10/20\n",
      "67689/67689 [==============================] - 6s 89us/step - loss: 0.0021 - mean_squared_error: 0.0021 - val_loss: 7.3484e-04 - val_mean_squared_error: 7.3484e-04\n",
      "Epoch 11/20\n",
      "67689/67689 [==============================] - 7s 97us/step - loss: 0.0017 - mean_squared_error: 0.0017 - val_loss: 5.3729e-04 - val_mean_squared_error: 5.3729e-04\n",
      "Epoch 12/20\n",
      "67689/67689 [==============================] - 6s 89us/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 3.1969e-04 - val_mean_squared_error: 3.1969e-04\n",
      "Epoch 13/20\n",
      "67689/67689 [==============================] - 6s 85us/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 2.7023e-04 - val_mean_squared_error: 2.7023e-04\n",
      "Epoch 14/20\n",
      "67689/67689 [==============================] - 6s 85us/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 2.1122e-04 - val_mean_squared_error: 2.1122e-04\n",
      "Epoch 15/20\n",
      "67689/67689 [==============================] - 6s 95us/step - loss: 0.0010 - mean_squared_error: 0.0010 - val_loss: 1.7817e-04 - val_mean_squared_error: 1.7817e-04\n",
      "Epoch 16/20\n",
      "67689/67689 [==============================] - 6s 83us/step - loss: 9.8698e-04 - mean_squared_error: 9.8698e-04 - val_loss: 1.4819e-04 - val_mean_squared_error: 1.4819e-04\n",
      "Epoch 17/20\n",
      "67689/67689 [==============================] - 7s 102us/step - loss: 9.7263e-04 - mean_squared_error: 9.7263e-04 - val_loss: 1.8890e-04 - val_mean_squared_error: 1.8890e-04\n",
      "Epoch 18/20\n",
      "67689/67689 [==============================] - 6s 91us/step - loss: 9.4251e-04 - mean_squared_error: 9.4251e-04 - val_loss: 8.6844e-05 - val_mean_squared_error: 8.6844e-05\n",
      "Epoch 19/20\n",
      "67689/67689 [==============================] - 7s 107us/step - loss: 9.1423e-04 - mean_squared_error: 9.1423e-04 - val_loss: 1.1700e-04 - val_mean_squared_error: 1.1700e-04\n",
      "Epoch 20/20\n",
      "67689/67689 [==============================] - 7s 109us/step - loss: 8.9539e-04 - mean_squared_error: 8.9539e-04 - val_loss: 1.5288e-04 - val_mean_squared_error: 1.5288e-04\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "# padding same works\n",
    "#model.add(Conv1D(filters = 41, kernel_size=41, padding='same', input_shape=(seq_length, 4), activation='relu'))\n",
    "#model.add(Conv1D(41, 41, input_shape=(seq_length, 4),padding='same', activation='relu',kernel_initializer=glorot_normal(0)))\n",
    "model.add(Conv1D(10, 41, input_shape=(seq_length, 4),padding='same', activation='relu'))\n",
    "#model.add(MaxPooling1D(pool_size=2))\n",
    "#model.add(Conv1D(4, 10, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=4))\n",
    "#model.add(Conv1D(10, 4, input_shape=(seq_length, 4),padding='same', activation='relu'))\n",
    "#model.add(Conv1D(10, 10, activation='relu'))\n",
    "#model.add(MaxPooling1D(pool_size=4))\n",
    "#model.add(Conv1D(30, 15, padding='same', input_shape=(seq_length, 4), activation='relu'))\n",
    "#model.add(MaxPooling1D(pool_size=5))\n",
    "model.add(Flatten())\n",
    "#model.add(Dense(50, activation='relu'))\n",
    "#model.add(Dropout(0.25))\n",
    "model.add(Dense(20, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "#model.add(Dense(1, activation='relu'))\n",
    "\n",
    "sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "\n",
    "#model.compile(loss='mean_squared_error',optimizer='adam', metrics=['mean_squared_error',r_square])\n",
    "#model.compile(loss='mean_squared_error',optimizer=sgd, metrics=[rmse,r_square])\n",
    "#model.compile(loss='mean_squared_error',optimizer=Adam(lr=0.0001), metrics=[rmse,r_square])\n",
    "model.compile(loss='mean_squared_error',optimizer=Adam(lr=0.0001), metrics=['mean_squared_error'])\n",
    "\n",
    "#history = model.fit(X, y, validation_split=0.2, epochs=50)  # starts training\n",
    "history = model.fit(X, y, validation_split=0.2, epochs=20)  # starts training\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "-VpzZqooOAPx",
    "outputId": "30d34bfd-b21e-47c7-9c64-d1cf407b23c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvuXvf49zbSKSMCUaT0XkIG2v9h1ORjHeCkDNmn5P5e23YcaMONcmIgmhUJHS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\nRUREYkahIiIiMaNQERGRmFGoiIhIzPw/x2F1n/nwH8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.rcParams[\"figure.figsize\"] = (5,5)\n",
    "plt.plot(history.history['loss'],color='blue')\n",
    "plt.plot(history.history['val_loss'],color='orange')\n",
    "plt.title('Model loss',fontsize=12)\n",
    "plt.ylabel('loss',fontsize=12)\n",
    "plt.xlabel('epoch',fontsize=12)\n",
    "plt.legend(['train', 'validation'])\n",
    "#plt.savefig('Write_up/model_loss.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_mean_squared_error', 'loss', 'mean_squared_error'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "VeIdsrBEO49U",
    "outputId": "04003ed6-30d1-494a-da95-3672e19fb46d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nplt.rcParams[\"figure.figsize\"] = (5,5)\\nplt.plot(history.history[\\'val_rmse\\'],color=\\'orange\\')\\nplt.plot(history.history[\\'rmse\\'],color=\\'b\\')\\nplt.title(\\'Model RMSE\\',fontsize=12)\\nplt.ylabel(\\'RMSE\\',fontsize=12)\\nplt.xlabel(\\'epoch\\',fontsize=12)\\nplt.legend([\\'train\\', \\'validation\\'])\\nplt.savefig(\\'Write_up/RMSE_loss.png\\')\\nplt.legend([\\'rmse\\', \\'val_rmse\\'], loc=\\'upper right\\')\\n#plt.savefig(\\'Write_up/rmse.png\\')\\nplt.show()\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "plt.rcParams[\"figure.figsize\"] = (5,5)\n",
    "plt.plot(history.history['val_rmse'],color='orange')\n",
    "plt.plot(history.history['rmse'],color='b')\n",
    "plt.title('Model RMSE',fontsize=12)\n",
    "plt.ylabel('RMSE',fontsize=12)\n",
    "plt.xlabel('epoch',fontsize=12)\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.savefig('Write_up/RMSE_loss.png')\n",
    "plt.legend(['rmse', 'val_rmse'], loc='upper right')\n",
    "#plt.savefig('Write_up/rmse.png')\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n#plt.rcParams[\"figure.figsize\"] = (5,5)\\nplt.plot(history.history[\\'val_r_square\\'],color=\\'b\\')\\nplt.plot(history.history[\\'r_square\\'],color=\\'orange\\')\\nplt.title(\\'model R^2\\',fontsize=12)\\nplt.ylabel(\\'R^2\\',fontsize=12)\\nplt.xlabel(\\'epoch\\',fontsize=12)\\nplt.legend([\\'r_sq\\', \\'val_r_sq\\'], loc=\\'lower right\\')\\nplt.tight_layout()\\n#plt.savefig(\\'Write_up/Rsquared.png\\')\\nplt.show()\\n'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "#plt.rcParams[\"figure.figsize\"] = (5,5)\n",
    "plt.plot(history.history['val_r_square'],color='b')\n",
    "plt.plot(history.history['r_square'],color='orange')\n",
    "plt.title('model R^2',fontsize=12)\n",
    "plt.ylabel('R^2',fontsize=12)\n",
    "plt.xlabel('epoch',fontsize=12)\n",
    "plt.legend(['r_sq', 'val_r_sq'], loc='lower right')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('Write_up/Rsquared.png')\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vuJi3zO_OaMi",
    "outputId": "91f9168e-a49f-4c51-8a07-85d32e324e0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution parameter shape: (41, 4, 10)\n"
     ]
    }
   ],
   "source": [
    "#have a look at the filter\n",
    "convlayer = model.layers[0]\n",
    "weights = convlayer.get_weights()[0].squeeze()\n",
    "print('Convolution parameter shape: {}'.format(weights.shape))\n",
    "\n",
    "num2seq = ['A','C','G','T']\n",
    "\n",
    "#''.join([num2seq[np.argmax(weights[i,:])] for i in range(weights.shape[0])])\n",
    "#test_pred = ''.join([num2seq[np.argmax(weights[i,:])] for i in range(weights.shape[0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cf0Q9NUUkVBoRikjwFIQiErx2dWg8tmgFY7bWPRQWEdkXjQhFJHgKQhEJnoJQRIKnIBSR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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(test_pos_labels,bins=50,alpha=0.75,label='Test labels')\n",
    "plt.hist(model_prediction,bins=50,alpha=0.5,label='Model Predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a/TWcpwLvNHVgxrR7oZBrg87Lg+xsFi1fzmpfch42bBiDBg1KYIAm3oJOGvu8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\ni1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_model_prediction = model.predict(test_pos_tensor)\n",
    "#from pylab import rcParams\n",
    "#rcParams['xtick.labelsize'] = 15 \n",
    "#rcParams['ytick.labelsize'] = 15 \n",
    "#rcParams['figure.figsize'] = 6, 6\n",
    "\n",
    "plt.scatter(y_test,pos_model_prediction,s=20,alpha=0.5,color='red')\n",
    "plt.plot([0,1],alpha=0.35,linewidth=3,color='black')\n",
    "plt.ylabel('Neural network predictions of values',fontsize=15)\n",
    "plt.xlabel('Values from test set (from MPAthic)',fontsize=15)\n",
    "plt.title('Convnet Performance. Correlation: '+str(np.corrcoef(y_test,pos_model_prediction.flatten())[1,0]))\n",
    "plt.tight_layout()\n",
    "#plt.rcParams['axes.facecolor']='white'\n",
    "#plt.rcParams['savefig.facecolor']='white'\n",
    "#plt.savefig('prediction_vs_observed.png',facecolor=fig.get_facecolor(), transparent=True)\n",
    "#plt.savefig('prediction_vs_observed.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Saliency and Plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow.keras.backend as K\n",
    "import tensorflow as tf\n",
    "\n",
    "K.set_session(tf.Session(graph=model.output.graph)) \n",
    "init = tf.global_variables_initializer() \n",
    "K.get_session().run(init)\n",
    "\n",
    "def compute_salient_bases(model, x):\n",
    "  input_tensors = [model.input]\n",
    "  gradients = model.optimizer.get_gradients(model.output[0][0], model.input)\n",
    "  \n",
    "  compute_gradients = K.function(inputs = input_tensors, outputs = gradients)\n",
    "  x_value = np.expand_dims(x, axis=0)\n",
    "\n",
    "  gradients = compute_gradients([x_value])[0][0]\n",
    "  sal = np.clip(np.sum(np.multiply(gradients,x), axis=1),a_min=0, a_max=None)\n",
    "  return sal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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8ffv20apVq2jVqlWMGzeu4PHSastiXCdptGWx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\nOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_index = model_prediction.argmax()\n",
    "sequence_index = 3000\n",
    "sal = compute_salient_bases(model, X_test[sequence_index])\n",
    "\n",
    "plt.figure(figsize=[16,5])\n",
    "barlist = plt.bar(np.arange(len(sal)), sal)\n",
    "#[barlist[i].set_color('C1') for i in range(5,17)]\n",
    "plt.xlabel('Bases')\n",
    "plt.ylabel('Magnitude of saliency values')\n",
    "plt.xticks(np.arange(len(sal)), list(sequences[sequence_index]));\n",
    "plt.title('Saliency map');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare prediction of individual sequence against model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: \n",
      "[[0.18899988]]\n",
      "label: \n",
      "[0.19412577]\n"
     ]
    }
   ],
   "source": [
    "predict_single_sequence = 230\n",
    "print('prediction: ')\n",
    "print(model.predict(X_test[predict_single_sequence:predict_single_sequence+1]))\n",
    "\n",
    "label_single_sequence = predict_single_sequence\n",
    "print('label: ')\n",
    "print(test_pos_labels[label_single_sequence:label_single_sequence+1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize convolution filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of conv1d layer\n",
      "(41, 4, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model.get_weights()[0][:,0,0]\n",
    "# values of first filter\n",
    "#model.get_weights()[0][0].T\n",
    "\n",
    "print('shape of conv1d layer')\n",
    "print(np.shape(model.get_weights()[0]))\n",
    "plt.rcParams[\"figure.figsize\"] = (30,5)\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.heatmap(model.get_weights()[0][10],cmap='RdBu_r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 4, 10)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(model.get_weights()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution parameter shape: (41, 4, 10)\n"
     ]
    }
   ],
   "source": [
    "#have a look at the filter\n",
    "convlayer = model.layers[0]\n",
    "weights = convlayer.get_weights()[0].squeeze()\n",
    "print('Convolution parameter shape: {}'.format(weights.shape))\n",
    "\n",
    "#convlayer2 = model.layers[2]\n",
    "#weights2 = convlayer2.get_weights()[0].squeeze()\n",
    "#print('Convolution parameter shape: {}'.format(weights2.shape))\n",
    "\n",
    "num2seq = ['A','C','G','T']\n",
    "\n",
    "#''.join([num2seq[np.argmax(weights[i,:])] for i in range(weights.shape[0])])\n",
    "#test_pred = ''.join([num2seq[np.argmax(weights[i,:])] for i in range(weights.shape[0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#argmax_conv_filter_list = []\n",
    "#for filter_index in range(41):\n",
    "#    argmax_conv_filter_list.append(''.join([num2seq[np.argmax(model.get_weights()[0][filter_index][:,x])] for x in range(41)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#for fa_index in range(len(argmax_conv_filter_list)):\n",
    "#    print('>')\n",
    "#    print(argmax_conv_filter_list[fa_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'/Users/tareen/Desktop/Desktop_Tests/logomaker3')\n",
    "import logomaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<logomaker.Logo.Logo object at 0x139a487f0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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io6MBAE5OTnByclJ5DHNzc7i5uaFOnTql++SIiIiIiEijp0+B9HTAyQmoVKms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AetgkEdzE2ndSB392490333bn1bt246jdjQoTpgddZZR13+fnG02LUQPrkb6tbpQG7Tyo7eV+kd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\nIYQQQgghRIf7/4ozvv75M650AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "learned_filter = pd.DataFrame(weights[:,:,1],columns = ['A','C','G','T'])\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "baseline_param_dict = {\n",
    "                          'baseline_width':1.0,\n",
    "                      }\n",
    "\n",
    "logo = logomaker.make_logo(\n",
    "                            dataframe=learned_filter,\n",
    "                            matrix_type='information',\n",
    "                            remove_flattened_characters=False,\n",
    "                            ytick_format='%0.1f',\n",
    "                            draw_now=True,\n",
    "                            figsize=[12,3],\n",
    "                            baseline_param_dict = baseline_param_dict,\n",
    "                            ylim=[0,0.04]\n",
    "                    )\n",
    "print(logo)\n",
    "plt.xlabel('Position',fontsize=12)\n",
    "plt.title('Convolution Filters visualized as information logo',fontsize=12)\n",
    "plt.ylabel('Information (bits)',fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig('convolution_visualization2.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<logomaker.Logo.Logo object at 0x139a487f0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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pGaXlj/LuFQ2wCCS7YIJzWaYdC9jLjk+788twm3xRVzWyIoY6/wX4bJRtE/ZI\n77qZGaKUnTYzQF60g902My1qSgpf2gkzOvGH/WjzymslbokNrelxVsPFLsg6rGmamnRAlciOCtVB\n2hlwHMh8z1nioNr9H3sZcG3PAzFJ7yY7OZsAUw6qMtenBs1WqflpFykVGTG9aysX3SvltXNv+xur\nzWYXOv8BhIcMfaTs/cgS+5tOZO7rff4+9I2Y/F1r/J09op/H0/onOUW/VXfvVIHk7Iz0F6UiflbF\n3060SvKy2G0zA+TNXScxLSa1DaqlWqqmQLaRbfrcNc20WkVkel5n0V2i7H/T1M43JPa9DIR9hj5S\npgHXNNiEmJULbI9jODSMjiF59YcscUC3S5ruKMFeoPt18fvYzdkEAIObAK+xBz1ZhY9HMX1/40Cm\nKCU7CoVE21+YV94uaFHWdVMGjTZJ0x0lGgA6/mHoI2WLSJbY33Qi09EGmDz3aMg+aenjGW4GhgS/\ng0N7rN1dMxFd/2ZZH5JIf1HKbpsZwFINRyTU57HbZgYA+9829KDa4e2Q0iZ9lKYB8Y43RiF7M5PR\n7UeInjcAn4U6BelBi7GXuih7bBYlBbCNzMAUJE3TpH7nQ9GQdeqopUB2B63GgUZzGxsEe+0X4g8A\n+yTUW7GjswkAul4z9HGyxRRZnfyMQOZ7rs/fB1/IWOfBIdhNgAbkCDPe3cyusRtGr3XJtqydBOi0\nEuS6XgWCxpdwkYKooGS34JlRwl6gb52026W/KLX3UbNHQEPUozrcYs/NTIsYelCVvfm0edsQjspT\njVUi28g23bu093Fzn0+le5XY9WGfPQ/ogBxBTif7R/ZLP1yZ/p3XiexxeoNeDAQGpN6Ti5ZngVjI\nvOdT6RJc64D8bj9G0bXSsEcFI0G0e9ul3tMuB9VgJIg2j7y0fMBkZ9tQA4sEths9q8XTdVV04zaC\nrpVy0pl0Int/s8u+DqTZ3O2UojsRkQL/mpa5c59AeotS/m57CjOAuCjVanxtJmkYaLzKNrZiWgyt\nHuM7DVGQHdlkquEaDQIdL5j3fBG6BQ24zn8CUfsU4T0IA0Wp5sFm6fe0S2SkiihGU41Xu3SZnIhn\nq1i6brDP0P1RKj3/kVdTKwUtnhZokHsotoso1epplT53U6OgW58379kiRIaA/vX06zXNvu85fwcw\nZFwtsrQrRaETX8iH/SNyI4tM29fDQ9KLZhvK/tWsXBCF/vftl+UxHolph+ktSrX/FZC8ORtG9yqx\ng6rBtZmkYqDRreSganYam05kz90T9GDAb1L0RNdrLIzUjoy0M28wFTuvdc92wL/PkEepEJDs4lFN\nq7mPdAD73zLn2TIQcTi1v2CieKxpAAAgAElEQVSvRg7jCXvFDukcqPhu2iV9L+3Ed7uKUoBYFPTg\nZvE6NWZikB2vQphp87QhGLF+J2MVa33voEn7etdr9nWuAqyWWs9q2rXtNnWojzKwUU5tXKS7KGVC\nJzdpjLQCPqIRNNzKlFe74tkK+LsMeVSzp1n6Pe1yUFWyoZk1dztHBgIsWopCxG/P4ojjMch4zWQB\nOq0ipdolFRA2C+paB+xt0wCGrXUVIkrzYDOiMet3hUqr99zQHlYk366IpOs6a10XKta6Bk3JOpJN\nWjmbDC6OrwRqzch0mHvXq1Juk76iVMhDVy2tAtXLIqOYqtmI1tnRiYqXuh1SejRNU2JommK8ajHW\nyc3OUI3XnjeYh8bOGJTCl1bCDCcq5m5a5EjHS+Y8VxbUeisRv33LEYxi0FpXsS7DsbDUDpaqSCtR\nyu6Htd536Ck9Euu0mIJB6bqqvpt2cDipGGOrp9X4urixKNBpcxseoDmIfc2AZ5v0oRiOJOd4+opS\n3atYW1Y7Qz2o2j1yAnCiJxTTO9KLkfCI9PuaIsgNfMC6cdmZ7tfZxsxLWgjQxhRFVbEu7eBN1TQ1\nXl8VUaYpifhZ7QY7Q42C7lltfwG6b60h3XVVpaDYweGkYl2aNu99crzvphELAr1v81/n72YpMXYm\n7DUkY0OVY8gOa13FGGNaDG1euY0SUtL/npque/lTgfnfBk75J/CpVuDCLuDMjcDRvwSmni7/eUO7\nmcjEQ+c/5Y8DAEoXAoffDJy+Griwk/1Z8Saw9KdA2RL5z9v3mhQ7Pn1FKYNbkiqh+3UWBcJDNCSn\nw08qXFlq72/AQTUUDSnxfLZ4WqTfUzaqDtOmzD0dRNjwIBPXeFHpTc2tBGrOAuo/C0z9BJBdpOY5\n/n2stpRiVHznO4c6LV97ome4R4kAbYogt3+N/YUZgOZ0MeI958pWe/9Y2JBOaqoOqnaIjFSxLk3Z\n16MB+wvQAM0eN+L8otqGBwxxLqsSj+zgXFY1RsP39g7Jwow7Fzjsh8B5DcDR9wF15wJF9UDBVKBy\nGTD/GuATK4GPrwJK5st9dt+7fJ+XPffcScAxvwXO3gosvRWYcgpQUMP+TD4ZOPyHwFkfAMc/AeRW\nyHtuqI9ecmgcii0Qk9A0tR6Wgjpg8klAdgn7h9j/lppIjVAfMLgFqDhS/zV9a1nXD9kUzQJmfxWo\nPRsoP4JtaIEeZjS0Pi8//320+HOp5BfGONq97Yjxin46sEP0hKoxmjJ3VWs9rwqoORuY+nEgbwoQ\n8QF97zGDUUW4bfdKYNIx+j8/tEdNx466C4DDvg9UHge4xxmusTDQ8yaw5X+AvnVyn9mzGihfLPee\n44hpMbQMyj9YadDQ5m3D3Mq50u8tC1WGa8tgC2JaDG6Xgb4tVWs9uwiYugKoOQMonM6+64Nb2JpU\nUVS9eyUw7xt816gQpVzZwMzLgGmfAqZ8HMgtY51M+99nqXa7fwWE+uU+s3sVsyMUoWmauugJGxxU\nVezB3qAXg4FBlOeXS793QnreVChAu4BJy4HSBez/DzWwPU1FEwFKKQpVAnTd+cC0z7D3XH41c3p7\ntrMxNvxGvj3RvQpYfLPce05AWVSkDdZ6+ohSEtN0c0qBU/7BxJhUVH8C+OTbwBvnyIvq618PzLhE\n32fDQyzNVRbFc4GP/xsonpn8c+4sYNblQMVRwBtnAv5OOc/vex8oEbOF01OUGmoAhpvl37f+YuDw\nHx0QZVwf/VyLsQ1t043yDdj9b/GJUp2SN7NRxXnR94HsgoN/VzAVmP459mf/28C6r8rtFtK/Qako\npcrD0uXrQiASQH52vpL7yyBtNrOQB+hdK/eeWQXA4p8AC68H3DkH/27GfzHRu/0F4IPvyH3P8Ao9\nslP38qqA5Y8BdefF/707h23iU98BGn4NbLhW3rMpUWIcdPm6EIyqiWhqHmy2tCilak2GY2F0DnVi\nWuk0JfePi/TDmguY+w3giDsO9RrWXwQs+TETojdcK1eI5b2Xr0l+i/UppwLH/AYoO+zgn2flMY/q\n5JNZ6sPm7wOND8t7bv8GefeKd3t/P7xBNZ1YrR4pFYwE0Tkk6YAxgebBZhxZzWGLiiKpcO5B5JQC\ni/8XmHkpkD/54N+Fh4Dmp4AtN8kVYgc2AJERILtQ3+djUfnie8l8ttarJ6QsubKAiqXsz7yrgQ/v\nA7b8SF7pk/6N7Hyk0HGRqel7qtLyAYPnPtzKmlvJIK8K+PhKoOIIvms+sQp47XjAu0N8DH0c4lbX\nSiAWEn8mAJQvZYJU/hSOaxYDn/gP8NpxQNgjPob+9cDMzwvdIj3T92RvZrmTWE7qyc8zgWi8IAWw\nF27VCcDpa1iuqkwGODuPyDyoZhUCp/4LWPKTQwWpiUw+ic2/TGK0g+KDqkoBpdXTquzeMlCZvqcZ\nUB9ojO7X5Xo3i+cC52wDDrvxUEFqFJeLHVjP2ghUckQ2pYL3+y4zdS+/Gjj9jcSC1Hhcbhb+vPxx\nAK5Un9aH4voZKo0sq0dGps3ch1sB705598spZd/5Y3+TPIx90nHAireAGV+Q9+yRdr7oatli3Owr\nWOrCREFqInmVwLEPsdoUshj4QGlqvkrhyOrREyrtDhWRpkmRLczUnAWcsxNYeN2hghQA5JQA874J\nnLebCbay0GJ8kdUDG1iWhCwmnwyc+f6hgtREsvJYhPSJz8lL440MAT5161HTtIxN3xsIDCgT3w2t\nF7mfUHMtHu4c4GMv8AlSo+SUACf9MbHNz8PARv31YSn15uKRXw2c+jKfIDVK6XyW7ieD/vXCt0hP\nUUrmZpZXBXzidZaTmgqXix3Wjn1I3vN52uH69wGDm+U8N7sIOO0VFhmhl4Kp7O+qUJLn3MailNUP\nqqrG5wv50O+XnO6RDJkCdMkCVhSweLa+z+dWMM9ExTI5zx9uAYI6/+6iIXmduHInsXmXHc533ewv\nAYt/LGcMnm1sTopQaWBafa2nzdxl7us55Wzt6gnxBwB3NnD8k8B0nWH5euBxOMkUpeZ+AzjuoYNT\nc5PhcrHaFPMlRUaGPcCwuu+kSlHK6pFSaWPTjHTITZGf9mnglBeBwtrUn82bBJz6EjDlNHnPN2ut\nTzmVOZZzSvVfM/1iYPmj8sag0I7vHu6Gn9rdMAUqIy5lkDbOJllpc4ffwoIjqJQvBg77H/FxRHzA\nsM59gieqKhnLHwEK6+jXz7gEqNXhkE7F4CZhh1P6iVKxiLzDWk4pE1kqlvJdN/dKYOENcsYwuI3V\nt9BDL2eBtWQsfwyY8jH+6/InA8ufkDMGxR5Vld4Aqx9U08Z47X5Dzn3yprAQXj1G63hyy4GTnpdX\nBFyv8erZDkQlGWMnPUdPkx1NZxYlFlZa7Dxtvu8EVIpShob5y9rXXdnAqf9gEVA8uLOA5Q/LK4yq\n97CmafJSlKvPAI7+FS2d5sif8wvXiVB4UFUpHHX5uuAPqzkEy0Dlu8jQYueUOkyJqD2P7XE8URDZ\nRcDH/gYUzZAzBh5RStZaL5rF5pBTzH/trMvkCfAK17rq/cfKKXxps6/3vSd+j4plLMpPlMN+KCeo\nwqMjojsWZVGRosz6MlB7jtg9XC7g6AfEmx+EvSw4RoD0E6W8u4DIsJx7LX8MKCe2Tlx6K1A8R3wM\nsSCbkx5kfMEBYOblwPTP0q+v/gQw41LxcYT65BVgi4PKF6/hoe4cqMxFBww0XsND8mqYnfAU3dNQ\nMod5aWSgNzJS1lqf+w2gegX9enc2cOyDkJLGp1CUShuvIoG0mbusFM+lt7OUFgrZRcAxv5YzDr0H\n1ZF2Oe2yc8pYxx29EVITycpldWlkMCApojsOqtNurJzWkzbiu4Q0EAAs6vmEp2hpObnl8tJaeARo\nWTXXTnhSrLvWUfeyZk6iyMreiIPqyEUrr3WV+7phXYVjETn7+nEPy0m9y8oDFnxH/D56alN5d4pr\nFbkVbJ3KoHimHCFasC5X+olSsgzX+s8C9Z+mX5+VL6++lN7DWr+EuedOAo76hfh9lt3DCkaL4pVc\n3HUcSg04I3OyOekZ7lEW8gwYaLwObgYgIZJu5heBmk+K3WPBdUDpQvGx6BWgZaz1/GrgiDvF71N1\nPFD/GfH7DOmcOwGV69HKhms0FlVaZ8awuYd9cvaCsiXAQkGjs3oFa+4hit76WLJsmiPvAgqqxe4x\n5RQ5hqvsou3jaBwQb0ud9P79au8vgsr3nKGRUjL2NwA45kHWUZJK7dmsG60onu36ov79HXIE6DlX\n0oX3UQpqgMUSnG0KbXjV+4+V03VVzl2DZkxdXBkR/zVnAZVHyRkPAMz5GkvvF0FPpJSMCLF5V4sJ\nzxNZJCHDy+OIUgcjI1Q0uxg46v/E71N7DjCVoyZTIvS81GV5WI68m9XREqWgmrWaFkWR8aqySw1g\n7bBf1aKRYXOXYbjmlAPLJIiwWbly8tH1GnAyIqWOvIt5g2Ww4Hrxe+gV5Aio/E7uG9qHQERV63Ix\nOoY6ENab/k3AMFFKlgB93O/keFRlHNaGdus7qMoQpSYdzw6qMlj0PfF7KDyoqj5Iqha9REiLSCkt\nJie6Zvol4s4mAFj2cwhHAkeGgEBX6s/JsGnyJgNH/Ez8PgAw9yrxaKnhJkBR51vla93CAnRaRITK\nqCe18Lvi9xhPTgkw/7/F7qHHwSo6d3cuMO9bYveYSMURTOQTQdCOd0SpeCy+Raxo2Hjmf1v8HnpS\nlPydQLBH7DmF04FZl4vdYzwLJMxd0UG11dMKTcYhJwFWjp5Ii80MkHNYO+wHtI4V8Zj+OSB/qtg9\n9IiwsbB4+ktBjdyizVUn8NfomYiitR6JRZR6/TRolk3XVS0Qt3paEVJYoH4MGWu9/rPseyqDssOB\nqSm6WaUiMswiI1Ihw9l0+E3y2rJXHi0ehTG0m4kPkglHw8o9/FY+qKoUjgwr/Dy0hxULFiGrQF5a\nS8k88ZotgD47XsZ7bv63WNdMGeSUAnOuELuHFgN8e+SMZwKZLECr3tsNec+JRguVHyG+D8dj3jWA\nO49+vZ6yM6Jzn/lF8cjneCy6Uex6wZI76SVKaTFxUSq7GJj7dTnjAYDac1nBQRH0HFRlbGbzrmY1\nYmRRdhgrrCqCIo+qaq9fl68LI+ERpc+gojxSyihRStSrmJXPQnVlkZXHajSJEOgGQp7kn/HsYLXm\nRJj7TRbdJQuXSzxaaqhByUG13duOqKazRS8Rq4b5q16LMS1mTJi/jAiC+deI32M8UpwuBuztxXNY\nGpJMFlwndn3UD4y0yRnLOFo9rYgpeIeMx6oHVdXR34BBUdAybNmZlzLHiyxkdJ7Us9ZFBWh3DjDn\nKrF7TGT+NRCOFFNkx2eqKBXTYsrteENsGtHucwu/x2xP2RRMFaur7N+X3JaNBoDBLfT7A+JlCBIx\n5TSgdBH9ekeUGoeviVV/F2HmZXztU1PhzmKeCxG8OsL8rXZAH0XUeB1Rc+AxQjixagFkIzqWaAq7\nJgJgL3XBgnqY8QXW/lkmc78hnh7kS2EICRuuufINV4DVlRLpXBILAaF+eeM5gBEHKcuKUukyd1Fn\nU/lS8eieidSeK97MJNVa9+8T7maDef8tL0pqlLoLxDuTDcvf2ynfxapCvnIFe/rVRH2IYoQ4bIjD\nSUa2wzzB9JuJVH9SvGakEZFS9Z+THz1RPBuYJlhXS8FaD0VDaPe2c12TxdldrHmwGdGYWocWhS5f\nF4KKUiJH2TuoeF+P+AHPVvr1BXXAjP+SN56J1F9Mv1aLAoEkteEGNgNahH7/2nPkdcKdiMslNndH\nlBqHjM1MNJc0HrO/CmQV0q+PDKUOZxatMaPigA4ANWeKtdAWNcgTYIRgZNW6UqqLsPsjfnQPdyt9\nBga3she/CLINV4AZhNMFN8pUtSdEDdfplzBPkGzcOeJ1axSsd0PWukXTdXnHlZfFH7KuXJSKBsQ7\nM877lnyPqsstHn2Vaq0LO5sKgdlfEbtHPNzZ4u9PPTV2OOH9LuZn5+OY2mO4rrHqQTVtbBpRO77q\nRKDiSDljGcXlEl/rIylSdf1d4t2mZUeDjt1XMFIsIH9fbxls4S7BcXTt0Vyfj8QiaPPKj+gUhbLn\n8u7tyvf1gQ/EbPgF18mpD5mI6jNY5hSVZKn5/YKpewsl1HVMhkjjokAXILA/ppkoJWjATTlVjfqY\nW87yP0VQbbyq2sxcbrFOReFBdiiRDO9hrTiX/+WULtETlpy76FqvOlFux47xiEYcphJmRCOlFkhI\nRUjEtIvErvfLP6jyrvUZZTPg5owssepa5x3X8mnLlT+Dm8FtYl7FnHJg5hfkjWc8sy4HXAIp76nW\nuuh7btbl8poZTERUfFcgQPOm20wvm45Z5XzlFcKxsCUPqkYI48qfoWnitqwKZxMAzLxc7JCq2tlU\neSxQxf/+1sWUU1m3XioK9nXKvnPK9FO4r7FiDTleG97tcnOL73sH9qrNeBAp9O3OVZPZM57sArF6\nVckEZpG0xdLDWIqdSsqX0rMetKhQfes0E6UEPSzzrpYzjniItkxP9lL3d+srmJqIySfL9yyNR7Sl\nroINjdereMI0/gK5VoyeiGkx7tbOpLmr9qgKG66Su1aMp+oksTatyYzXWESsM1HViaxQsSrKFovV\n0FPgUeVdh7MrZqOuhK/RhVVFKd65n1R/EvczLC9Az/4qkF0kZywTya0ApvAfdMZI6WwSFKBFSwck\no2g6ULGMfr0CUYr3uzijbAZmls/kfo4VD6q8Ns28ynnI4Yw0UL7WR9qBUB/9+rzJ4rZ2InKKWeQ/\nFbs6lgHmXK47n369gn2d97uYm5VLcrpYsa4U775eW1KLOZV8qebeoBf9fvnlFMYQKfRdvUKds+Wg\n56gSpQTmPv1iNXW0xuNyAVM+Qb9eIOIzfUQpUQ9LQY24lz8ZU05V52URNdpVbmYAi0gpqKVfr+Kg\nyimanFh/Iv8zLChK7Rvax90t6+Tp/LVYlM9d5DufP1Wd4Qqw1Jbac+nXJxNhvTtZkWAqqrzIo7hc\ngnM3f61PL5uOGeV89XKaBg2oo8ZJIBLgLny8YNICTCni60ZpbVHKBcz7prShxKXuU/RrUzlcROY+\n9RPq6k6MIuJwskD63szymZhRxl8by4oHVV5Rakb5DNSV8onv6vd1QcfynCtZwxFV1AoIMyrXet4U\nsYwEPQi958wXpepL67mjIinPMQLedTi9bDqml07nfo7SuYtESk37tLxxJENFpFTIo695WSKMmnv1\nCvq1jigF9tITCBnDnKvU5qdm5Yl1okv2UhfZzHJKgboL6dfrweVmBjIVyZFS/jB/zaO5lXNRXcwX\nvmzFzYxSd8Jy0ROxsFjnitlXyO08F48agW5XyURYkcgJd66YYamXqR+nX6tClKIYcGV8BpxyryKB\nlkG+iEjggCDHeUhvHGhUHOYvsL9VfxIomStvLPGoOYt+bbK1Htgv1qFu1pfp1+pFyJMsd61rmsYt\nFmVypFR9aT33e655sFntWhexZV1uuZ2z41F7Dsid6MKDrLhzIkT29hmfVyvGAcyxzlkofAwFAjR3\nWn45ca1bUIA2wtEGKLTjQwOs2zIFl9sYOxZgXeioXTwDCfSI/vX08RTPZql1RiAiyCWauw6ki1LR\naBQ333wzZs2ahYKCAsyZMwe33XbbQRuZpmm45ZZbUFNTg4KCAqxYsQINDcQv6CiiHhbRmk96EBFm\nkr3URTazmrPUH9ABtqFRkRwpRRFmKAdVQ7rQcULxdB5bdyx3mL9Sj6pnB+vURqXeAE+DyPc9mQgr\nFDlxOktBUI1QOpPctU5pk25JryIByhqkGK/eoBcDgQHuZ+lCVIA2Yq2XzGfRlxT8XYk764oe0GvP\noV+vl8pjWedeCpIPqgOBAXiDfN2X0+mgymvXUGyakfAIeoYFnL+pELHjJ5/KUkpVkj8ZKF9Cvz6Q\nwBka7BXrNC2SWqeXnBKggliHM9gLcEbop4KSqltZUImiHL5UbisK0NyOtlJ+ZxOg0KbpExBmqk5m\n69AIRNLYEtVCFokQq7tQfereKIW19I6jAnWgpYtSd911F37729/iV7/6FXbu3Im77roLd999Nx54\n4IGxz9x999345S9/iQcffBDr1q1DUVERzjzzTAQCAgWtRTazkgXqvamA2GFN1UHVKMVZ6JBuvihF\n8SoOhYbQ5xeoj6AA3rlX5FegNK8U00r5it4pPaCLfN8LasTqoOilsI7eLl5VpJRRaz2vCigjGu6S\n1zpv/TTAgl5FIpS6btNKp1nLePXsBGICra9rz5M3lkS4XPT9LRZkERTxEIkQqzpJTTfdiWTlAZP4\naw4CkL7WKd/BGWUzMKVoCvKz+YQ1q4lS/rAf+3x8f5/1pfUk8V2pw0nEjq8zYK0DwGQFNeT6Bead\nUyZ2tuBByLksryMzJSpyetl0uFwubhFaeSQwJ6FoCO3edq5rLGfTiHSfE0krIz2PGDEUS6BniNbS\nMhJqtJSVRKl33nkHF1xwAc4991zMnDkTF198Mc444wy89x77h9A0Dffddx9uuukmXHDBBVi6dCme\nfPJJdHZ24oUXXqA/WEiYMWgzKzucXgA50WYW7AOG+Q9eAFgorkiaEQ/Fc+lhkMFeqUPhNapccKGu\ntI50WDOkhTIHlLBfANwbWru3nbt2lW5EDmu157EoAiOgGq+JBOhYFBjYRB+PUe85gG68RnxSh0FZ\nf5QIAsB6NeR4x1NTXIO87DzS3JUZryL7esUyJg4bgchBNdF6F7JpDIicGIW61kNy93WSKFU+Ay6X\niz9ltd9aB9VWD3+UTX0Zv6MNUGjTBHvF0lWNEKABNY0NBgSzHVSWHhmPSNcviXY8KSrywBqnRAJb\nKTW/1dOKmBbjumZG+QzUl9ZzP2vvoEJnExWR8hCk51GFmQTONC9x7q4s1pTMSKhzF3AkSj+dnXji\niVi1ahV2794NANi8eTPeeustnH02Ez+amprQ1dWFFSs+UvzKysqwfPlyrF27Nu49g8EgvF7vQX8O\nQcTDYtRm5nIDkz9GuzbRC13Im3oikFdJv54Hl4uFV1NItLiJ8EYL1ZTUIDcr11qHNSLNnmauz4/O\nmXfuMS1GMpR1YYe1DtAPa9ERIJ7RMbSL/Y5C2WJ6i1cKU0+jXSd5rVOEovrSemtFCxHhHc+YAG2l\nuQtFTthAmAESC7EiUZF2eM9pMdZNVBK8aTZZrizUlrAGLLzRE0OhIfSOyBXVRDCqJAGgUHwXiRYq\nmQeUzpc3lmRQbXhAjR1v5FqffDLdqScS8ToBqgAN0PY3K0VGUh1tBTkF1mli4ttDuy6rEKg8Tu5Y\nUlE0ndVy4iXeOolFAR/x77TyWJZCayTUvV3A8S9dlPrBD36ASy65BAsXLkROTg6WLVuG6667Dpde\neikAoKuLeQqmTj24BsPUqVPHfjeRO++8E2VlZWN/6usnKL6hAWC4mTbgnFJgMn8hZzJUj2qiF7qI\nN1XE60F6nkCKg0R4japRD4OlDDgivBva2NwpYf4qPKqxKDBIjBZyZRnrZRHxqMbCh/5sYLM5Y6Eg\n+z1HhPc7OKlgEopyi9JCgKYUeAf4PcmARSOlRBqL8FJ2GJBLdPDEq48XGgCGie/PvCn0WhAUJi1n\nTRQomHhQnVY6DdnubAD8ohQA7OknHqwUYFRJAkBhpJSIAF1jQP20UQqqWR05ComcLkK1Ik+jX8tL\nbhlQfiTtWrNFqQNilN0bG1BrRQL8glyrpxXhaBxbVBQf8e9z8snG1ECeSBV/9/W4tRb9HfR6uEZH\niAEsaKV0Ef91bmKdSSgQpf70pz/h6aefxjPPPIONGzfiiSeewD333IMnnniCfM8f/vCH8Hg8Y3/a\n2iaE+Ip4WKpOMi70FaAfDmWHAoqMhfw8a4hSlIKg4/+XByul70ViEbR5+cLjheauQpDzNQKRYdq1\nFcuM9TQUzQQK+UOmAcT/zlM3ckAsvYhC/mQWncVLPDFOAG4Buoz9e5XklaAiny/V2nKiFDVV1yqR\nUppGF2LdeUDlMXLHkwyXW+7e7t1FH8uUU4wrhgoA2QVMmKIgMTKSN9VkvPhq9+gJSq3Iotyisfcd\nD8ocbXYRZgD6Wo+3r0eD9OiJolnGRkADAhHg8tY6tV4iYP+1zjv3ktwSlOWVAeB3OCnJeAgN0lM5\nRaIURSCJUgWH/mxIwJFhp7lnx5m7TqSLUjfccMNYtNSSJUtw2WWX4frrr8edd94JAKiurgYAdHcf\nXPSuu7t77HcTycvLQ2lp6UF/DmJIwIAzOkezYhmQzdf9AUBiddVHNBBcWcCk42nXUildCOQRuiZI\nrt1AjhaiRE+oyskm0OHtQIQzXWLUaLVM5IiQMGPwS93lEogYirPe7TR3gBaBSo24SABVgJ74//XQ\n6mnlXl+q8AQ83B3xRudbnl+Oklw+8VaJ0R7cD0SGaNdOOk59i/SJSF3rAgd/M9Y62cElb2+ndOMa\nJdOiJ0bXemle6diBVdWzdCMixFYZmO0AyF3rwy0grwNnretitF4iYLFIYAKUte464KSwhCAnYsdW\nHCFvHDyQhJk453xq2iIAlNto7lkEjeMA0kWpkZERuN0H3zYrKwuxGKuRMmvWLFRXV2PVqlVjv/d6\nvVi3bh1OOIHYxUXIgDNYlHJn0/6RkaCw3TDxZVlxlDHt4cfjctE8qtSW03EYCvJ3xBs14CYVTEIB\npwJspUgpat2J8f/LgxLjlZrSAthHmAEAV5zoTao3tXgua+9qNCavdYDWOnkUXuM1qkXR5hEo1CsR\nytobNVhdLhdJkJMe5m83YYa61uNFalPXOmDO3KkOLknrPRwNc3v0hUUpC0VP8O7t4yOkeNd6y2CL\nfPFd0+h7e8kC41rEj0JdY/FqRYqs9SlmrHViVKTEvV0kKtLua50qQE/8/3qRLsiJRAuVL5U3Dh7K\nFgPZnFkWRbMO/Rl17rmT6I3CRKHYNcVx5q4T6aLU+eefjzvuuAMvvfQSmpub8be//Q333nsvLrro\nIgDM4L3uuutw++2340Qf9u0AACAASURBVMUXX8TWrVtx+eWXo7a2FhdeeCHtodTNzJ3DiocZzSRC\nobZ4OZrRIDDSQRuD0WLcKJS5S9zMRIQZl8vFfVBt8bQgGotyP1MFIrnoltjMAHsJ0ADt+w7E/85T\njVfqGEQxWZTyhXzcxYgPMuAIddSs4lGlFkMdxRJh/nYToMuPoJUCkLnWs0vMMdyp7xi3nGg2Skeq\n8YdTSvSElQ6q3KJUKV2UimpR7pb0KQkNAGG+bmpjmLGvF82gRf3HW+tUxzJgztwLamhlCQTqzExE\nJCpyStEU5HFG0VoqKpJzbx8/d0uk5lOjhXJKgUJ+m0wK7iygitPxEq/uHHXu5UuNTckfT8l8/nqZ\n1Jp7UCBKPfDAA7j44otx9dVXY9GiRfje976Hr3/967jtttvGPnPjjTfimmuuwVVXXYVjjz0WPp8P\nr7zyCvLziS8tqgFXcbRQ7iMZSveAeC9RkbDfCmKxQlEoB1VJhitALAgq4FWMxCLyDTgivHN3u9xj\n3YmKcoswqWAS1/VKosTI0UKzjfemAkDZEtr3d2IaWzTAiiSSxkCo7SSD0oXMkOAhW170ptHCDGAd\nUYoyjoPmbgnjVeB+ZgixWXm0IsAyD6rlS5gBbTT5U1gNPR5cWSxyXAIUgWj8+q4urkYuZwFdqxxU\n/WE/uoe7U39wHKLRE9L3dqHIQINT94ADUf8U53IcW4A696wCoHge7VpRSA4nOXZ8JBZBy2AL1zXj\n9zO3y829t3cMdcAf9nNdowJfyIf9I/u5rrGcTUONFipbYp4wA/BnOJXEWZvUuZcvoV0nA5eLb+45\nZUBeFflx0kWpkpIS3HfffWhpaYHf70djYyNuv/125OZ+tOG7XC7ceuut6OrqQiAQwMqVKzF/vkA7\nV2r0RMUy+jNFmESIzpLpTQWAssPp14pAmXtB/FpjFESihQBiFzqLdODjHUdtSe1YdyKA33jt8/fB\nGyR6PxNBjZ4w6/uelcv/nskqPHTzpXYXBYByk0Qpl5s/EpXSejcBwmvdKimrBHjHUZhTiMqCj7xh\nlhClqGu9oIZ1jTEDUiRw4aE/o9o0Zr3nAP6Dar68fV2kGxdw4KDK+Z3vHu6GL+Tjfq5sWjx8B3RA\nLFIKUPCeE4mKNM2OlxT1T13rpYvMEaAB2twlrfd2bzuiGl/2wcTvOGV/ozi0ZSPsaLPCvk6tKWVW\n6t4oPMKMK5tFU45H0zJj7iXzhMRD6aKU4YQGgfAg7dqyw+SORS8FNfwdM+KFz5E3chetzaMMciv4\nQ/uK50h7PO/GkpeVh8mFH0XYWCaNjYBIiD9gc49qqZmHNU4DLl7EgR0FaIA/5Ll4rrRHi6TqAhYx\n4IiIFEMd/W9eLBMpZae1DhxqvEZDwAixNpmpohTn3CXu65Tv3sTOc5aIIiBA2WNFor+pz0wKOS3f\nxWpKmQGl9Ec8O576nrOTAJ1dBORPlfJokgA9YW1bouA3AVFHW3l+OYpz+aLRGwcaoclsNjXML6ID\nMF+YmbQcgE6xpXjOoVHAoT4gOkJ7ttlzn8wjSgkEGCEdRCmRGjOlJolSACGCII4BR05lmgVkx/HO\nGgXvhibReKW0iBc9rFml2Dm1RXyi/9b1TJke1dAAEPbQrrXTYa1E4lrPKuRPq5EJ71ovkSdK8X7f\ns93ZqC7+yJtrCWGGiEjdCYB4QJfdadSW0UKca72g5tC9eEQgLd9OB1UTRanq4mrkZx8ctTKzbCb3\nc62Qwicqvpu+rwN0B2vxbHNKcAC0qP+J33lNo6fqmrnWK49mkdB6KZotLfVKNCoSsG+3TdFIKZfL\nxS3IeYNe7k6+CdFiQGAf7VozU9gAILdMfymMeKl7I53EB7vMXesA0ytcOqMy482dA/uLUiJhv2ZF\nSgFyvIp29LAA5opSnC/1idFCljisEQhFQ9y1rQ6Zu9mRI0LRQmaudc7ve1GcFDbyWj+Mz3iUjY0E\n6Gml05A1Lh1iavFU7jozVhClNE3jPqjKSG+QOvdYBBghFk43c62XzGc1FfQSd62L2DQm7u0VR7G0\nBb3EE9+JiBQ+HoVyUN3TL9BJShK8a90FF+pK6sb+2xKiFFmANnGt51Xxp5tP/LxIgXcz13p2EV+t\nShPXOhAnUsqmjQ14153b5UZdad1BPzM1IjTYB8SIXXrNqo06nimn6vtcvDp3fqIoVTLX3CASgD1f\nr2ZRJVbjz/6iFHUzy62UFk5KQkakFLXOjJnpDQDfQTWnVKho2kRED2t2jZRq87RB4/S+W27uIiH+\npQvljYOX4jksbVUv8Qw48gHd5LXOWwDZRFFq4vfb7XIfIsymQkkdNU66h7vhj/AVZZ0495qSGuRw\ndpKTKkqNtAGcdUPGMPM7z1tHLe6+TrVpKqTWaeImu4AvzUBSqq6madwHxngHM7u2im/2NHN9vrq4\nGjlZH63t2pJauDkdF9JtGup33mxblmet508FciakTgk51W1kx0tMy+fdZ8ryylCad3DDFdOdLkR4\nbZq6krqD6sICJs+d2qynaCaLVDKbaRfo+1ztuYf+jDp3s1P3RqnTMffsImDKKUKPsb8oRS58fJi5\nlfwrj+H7fDyPDLkbl8mbWflS/R3JiudI+3ca8A/AE+RL/5p4WKsrqYNLb17xAey4mQFyRCmpUWLU\nXHSz01VdLr6Om/EOqn5iyLMVvEt6jde8KmmGh6ZpwlGRgAWEWAKiIf4AE+SmlfLVPRwMDGLALynM\nn7rWAfP3N54o6LgCtMC+bqZNA/DNXZIA3e/v5xaC4x3M7Bo9Iepoy3Znj3XZ1cs+3z553ci0GH29\nmxkpBXB+3+PY8NS1nlV4aC06ozFhrQP8tmw6CdC8Z4l49oupZQmo0UJmp+6NMuXU1JHQhdPj293U\nuZdZZO7TLkz9meozhLts2l+UogozZtaTAtjhiyd6Y+KGFosAgR7as8022nk6kplc+HjiQTUvO++g\nujN66B7uxkiYWOBOEqS5l1ms0Hmgi3ad2d93QNyAI4tSFpi73s4dFUdJe2S/vx9DoSGua+J9v+1Y\n/JjyfMvNnbrWC2qB3HI5Y6AiutapNTessNZ5oickpfTIqDEDZE6dmYn7OkDb2yld/+IS7ANiQdq1\nZotSolGR5LVuclo+wLnWzYuUirfWJ3aW1kPTQBNiWozrGplQHG1x93VTI6WIwozZEZGjuHOAaRcl\n/8ycK+M7h8iilEXmXrogtUA250rhx6SBKGXjg6reDS2/+tBIj0APSMVQXW5zU5lG0RviJxgKOB4Z\n0UKJfpYKs9vJyoiemFo8lTulp2mwSV7nDqowY4UNTe9BNSsfKJp18M80TcB4tUCkVO05+j5Xc6a0\nR4oW/x37Wan9ip1T3nNxI0dMNV6pa93kQyrAJ0rFG6+doyL17tdlS/hSmpMgo8YMANQU86estnpa\nEY4Sa6RIYDg0jP0j+7musVxEKFWANrOL9CiVR+kXh+JmO9h4rZceBuROSv05dw4wibMLbwK8QS96\nR3q5rom3j2W5s7hT84PRIDq8xCAICfSO9GI4PMx1jeWcTdTIwOJZqT9jFItuRMIufO5cYO5V8X9H\nDaCx0twP+2Hi35XMk2LD21+UEvE0mE3VCfo+Fy/Vj7qRF88xr1vJeGrP0/c5kw+qsryKZh9UeetO\n5GfnY1LBwQaH2+WO+/eRjEAkgC4f1eiceDOqAG2BtT7lFGacpWLyKSyScDxhDxAN8D8zpxQo5EvB\nUkLJXH1tu2vOkvZIWQI0xYCTXgSY9/mch0UXXIcUQwVMFqXsHBVZUKNvHHlVQMURh/7cznMvnq3P\n6VVzhrRHUr5z8aKistxZ3PtbVIvKixgiQHm25cR3qjBTNNMCBYCLgPI4azgeMu14K6x1dxZQe3bq\nz1WdeGgtLSIUITTRHm63KGhpNo0dI6XMTlUdT9kiYMbn4/9u8S2sjmo80mHu0z+X2PF31H1Sojft\nLUppGj1SygoeVb2HsOpPHvozO6fzAEyQS+UpLZ4tNX2PsqHF86ZQXup2qzMzvWw6XHFCUElzl3VI\nt/N3PqcUmPyx1J+rjnNYE4kaMbvGzCh1KUTossVSvd4yIgMT/SwVZgvQvOutpqQmbpdBytyl1d2w\ncwQ0EL/Q6USqPxnfiLPzew7Q53CSKEDLSt8D7JfCJ6MkAWByBz47O5sAfd9lVzYw5bRDf277ta7j\nPWfyWk/03abYsmbWlSIJcnHmWF1czZ26KC0iNB2EGQBYdg9zPo1n6unAoh8kvoYy96xCfdGIRuHO\nApY/zCLCxrPwe/ozIlI9QspdzCLioeWi55Qd+oUyg+JZ+g5i8byK1AixEguk7gGAOzu18TrrS1IP\n1bzRQhX5FSjJKznk53Y8qIoWQ03182SYGz1hcue98egx4OKJN1TDlbddtUrmXImEIc8AMPfrUtd6\nWngViYh2HRzF3JpSNo6ABvSt9Xj7XywKBLr5n5dbkdhDazSpOhQV1gNTPi7tcbzNNBLt6wAws2wm\n9/PNPKiaGf0tz9lkY8cyoO8wVnUCkBPnO0d2OFnEpqk5M3nTIpcbmPlFaY+TlZoO2E+AlmXTUFIX\no1oUrR5iB+jxUEWpQv73k1IKaoAz1gM1ZzM9YfZXgZOeY6JNPKj7etEM6ziWR6k6Afjku6wudF4V\ncPjNwNI7pN3e1qKUO0gt9G2hCIJUG1rZ4fFTX8ghzxZSnBdcn/h3Ljcw+wqpj5NREBQw2YAj4A/7\nsc/H931JtGmZVnsiGgRC/fzXFdaZH+I/Sv1nAFeCTQsAJp/MiglOhHpAL+QzPJRSugCovzj+7/Im\nAzMvk/o43vUWr200kPgdkOrZZhVEjcQiaPO0cV0j05MsL8yfeFAtmSfn+aJUnZh8/eVWAPWfPvTn\nwV5Ai/I/r9BC+3rVScnrZc75WmLjnQB34eMkYiupA5+ZB1UTI0JNryllFQF60vFATormCon2PtLe\n7rJGWj7A3mOzv5r497XnSh2rrPpxQGZESlnO4RQe5L8mb7J1bPjxFNYCp70MXDwILH+ECTSJiAyx\nLqO8WOm8Pp7KZcBZG4FP7weW3npoyREBbC1Kuaib2cRCwmaSSnhJWMk/DQ6qlcsSh/bO/SYTFCSh\naZqp0UJmilIUD4fl5k5d61b6vhfNSJyLDgBzvh7/52QB2mLepcW3xK+rtfwR1o1UIjK61ACsttrU\noqlc9wpFQ9g3RPw3E6TN04Yop6iRqJ4MRZCTFuZPOay585gBawXc2cDC7yb+/czLWVODiVAFaCut\ndZcLOPx/4v8uvxqYd7W0R4WiIe79Ldlh1G6t4nmjv3PcOZhSdGhEnbmRUtRIYIvY8e5sYPZXkvw+\nL360kBajie8FNfrqUxrFohviO9tc2cDiH0t9FK8wkpeVF/f7DtDWuplR0LwRoaV5pSjLj29XmeZw\nCnv5r7GqMMMDZd5AesydE3uLUtRIKat4GQBWNK0uQbh7TnniCIJ0OKQDwBF3HZqfWjIfOPJuqY+h\ndK6QGS20d2CvvC50nFCMR9lzF4YaOWG17/ui7yNuGtvkjyUWrMgCtIUOqgBQvhg47tGDw/3nXwPU\nnS/1MZqmcRcATva9tlO6rkxPcn52PqqLq7nuFdWiaPPyRWodQizMIoZ4KZxmnQhogEUExasHUVDD\nBNp4pMt7ru5T8VP0lj+a3KPMSaunlTsqUbYotad/D/c1suB1tE0rnQZ3nDpm5fnlKM7lK0Y9GBjE\nYIAQ+TARqi1bYCE7fuF3EgtFsy4D8ioP/XmoH9Ai/M+y2lovngUsuO7Qny/+MVB5tNRH8e5v9WX1\ncb/vADEq0kaRUsnsFluJUlYotSMKVZTKT4O5c2JrUcodJORoAtZ7qR/+I8Q9qB7zq/ibGZA+0RMV\nS4FTX/rIUC2eDZz4nPRwTVkt4gGgsqAShTl84/OFfOjz93GPQQYy525epFQaCTOHTSiGmF0MHP9E\n4pSWIF/L7zGs9p4DgFlfBM54Fzj8JuDUl4Gj7pf+iC5fFwIRvm6FyWos2KlLj6y6E3p+lwjhuVNq\nLwDWW+vZRcCxv8Uhe/txjybe19MhVRdg6fcn//mjSGh3Dqs7oadbFwcyRViAHj1hlsNJVvS3y+Xi\nrjMDSErhIztYLSRKFU6Ln8ZWUJfYwZouziYAOOJOYP63P/rvGZceaucIEtNi3PtbMvFlWuk0uJLV\nuYxDv79fjhDLSTTGX9NJ+r7OGal1CNEgEAvxX5dnkVqJIpBFqTSYOye2FqVcwTTxKk46FjjyroN/\nVn8xMOMLia+h1NfJLmZF2axG9Qrg3F3AireAc3awtD7JyDysuVwuWxVAlpmLTtnM2jxtCEUJm9F4\n0iWCAGCHs6PuZ/UYKpYBp/0reSoCOfTXgsYrAFQcCSy9jR1QFUS2SBdmzGyXzonMtQ6Y5FFNp7U+\n/bPM6VI4jYXin/AUUJukG1WQsK8D1jyo5lWyuX/yXeC8xsQpfQLI7LwHALUltchKVvcvDv4If81G\nGfhCPvSO8EUUJkvJNc3hRBFn8qYAWUkKbJvBkXdPiAxyHUhNT9BlmmLDA9Z8z7lzgKPvA87eCpyz\nHTjxKZbWKJF9Q/u47chkaz03Kxd1pfwlQsyoIdcx1IFwjC8tPtncTXG0kYUZvvIJliST586J3LeG\nwbhChBB/wFoellEW3cA6ajQ/xVJ55n4j+YGN8iUvrLdWesN48iYBk09SdntZrZNHmV42HTt7d3Ld\nr2mgCcfVHcc9DlF4604AiY3XwpxCVBVWcRnDGjS0eloxt3Iu9zjGCHto11nRgHO5gAXXsj96iAzx\nPyOrMHXx1TRFujBDMODMqiEnW5AzRZSirvUiC651gImvF+hMaaSsdcC6c3e5garlym4vO1Iq252N\n+rJ6bnuhsb8RtSW13GMRQYVNw4twpFQ0RFvvVrThc0qZY3XbbcBwC7Dg28zhnAjqQdWKNs0o5YuV\n3Vr2WgfY/tbubecex9G1ctMSU5EWzqZMFmYiGTx3TuwdKZVuL/W684GT/gjM/1ZqLwPFeC0w1miy\nErJf6naqM8M790kFk5KmJ5oyd+phzYrGKy9hwtzzp1hXgFZMWqSwEeGde1FOESryE3jyYZJHlbrW\nrVRjhgrVpsnQvZ3yXUuVomeXYucy0/JT/S4RwuI79bBmVRs+Kx844g4WKZRMkAJo+zrAun5lILKj\nIgH71JWSbdNQmpgMBgYx4B/gvm6MTBalMnnunNhblIr6+C9y51inQw8VLQZE+Ip2A8jI/NRReKOF\nXHAl9XzaqQMfr/GaasMyxaNKNeAytUhiJq91yYe1dE7VnVE+A64k4qUpghx5rfMVZbckVEEuHepu\nEOA9IBbmFGJSQZzi8+MgtYo3IaXHCpFSpq31dHA2OWudC8p3LdV3embZTO57mrHWZc+d0lWYOo4x\nMjlayBGldGNrUQoRgiiVlwYRBJR5A/YX4wTgPazVltQiJytx2127iFK+kA/7R/gKZaeaG6XGjvDc\nqcZrOnznKcZrOsybCO93ze1ySxeg9/n2wR/2c18nwkh4BN3DfEXCU83NFEGOfFhLg+885T2Xlc+K\nqmcYmqZxf9dmlCUXYYH0jpSyXE0pqi2bDk4Xqk2TnwbvOQKUQtsp0/cyNFIKMCEKmirMSOzWahqU\nubtzWR3oDMPWopSLJEqlwQs9kw/oBGS3iNfz+3iYET3RMsg3byC16GSb9L3sEusVQ6VAWe8Z6k0F\n5AvQlG6bAO3QKAIpRTnFWqcYrgOBAcEwf+phLQ2+81QB2u6ONgL9/n54g3zGvp7vczqLUrLT95oH\nm8U6D2ayLZvJ4jsBXhvSBRemlSaPqKN22zQa3r09y5WVssad4Q4nqiiVU0p/plWgzD2nNCP3dXuL\nUlFKClsavNCd9pJckFrEp0hho7zQWz2tiMQi3NeJQPGwpJy7GYWfSXWV0mCtazGa8Zqhaz0Si0ht\nnQzYp9umCm9qeX45SvP4jUKh9Z7JhzVq/bgMREWNGb2fmYgZKT28a6w4txhleYm7L9eV1MEFvkNQ\nIBJAl4/YLRPI8LVOseNdQG7y9NN0heJsys3KTfoZylpv80roJs0J71qvK61Ddoq6xLYRpdIhWogy\n93SYNwF7i1KZmtaSyRs5AZJHMUUEQV0pvwEXiUW4O32IItubquf38TAlUiodooUoteOAjF3r7d52\nRLUo1zV6vs92iIyU3cyB5zMTMdx4decAOYkP3LaBFBWZmWtdlShFiZ7o8/dhMDDIfZ0I3LUiS+uT\npi7mZeehupi/LpuQAJ3JKWyktT4JcGfJH4vFGQmPYJ9vH9c1epynlL0tpsUMjYIORALoHOrkukbZ\nvk5IoRzDEaX4SId5E7C1KEWrKZWhmxmQsR5VFdFCuVm5qCnhL6ItXPDbgOclK4YK0DYzSqrFQWRq\npBQ5vSEN8vAJqEhhA2heRaNryFGep8dwNzxyhHRYq0qPUHdKMdh0EN8JqGgRDwDTSqfB7eI3jY2M\nlvIGvej393Ndo2ffpnTlErJpMtnBmqmONgIqnKsAUJBTQCr4beRaJ5Xg0DF3Uj0toX2d6GxKEe1m\nCxxRSje2FaXycgCXRkiFSoeDKnUjz62UOw6boGpDs0Oxc96ug0DqeU0pmpIyLDoehhuvmWq4AkBO\nidxx2ATZxX9HsUWklIL0PcCEMP9MXesAUZDLzH2dUsdJz3c5JysHdSV1hoyHCuWgmsrZBJhg02Ry\nTSlnretGVVQkYELBb05I+7oiR1urpxXhaJj7OgCZLcxQnE3pMndObCtKleQTL8zUzQzI2C952qS1\nEKAUSEwVAeZ2uXUZuBMRS+khfOdzyunPswrkkOfM68YFKBRmLG64Avxr3QWXrsM3ae5CYf4ZutYB\nYkOHzNzXVUVKAcRi5wZGTygT3ymddQ2PlHKlh4PVWeu6USpKUSKBDRSgKXNXZdNEtSjavG3c1wHI\nbFGKVOg8TebOiX1FqQLihWlRd8I5qPJAiRbS5VWkGHBGR0pxGq+1JbUpCyQCJnhUKQZcOrzUHQGa\nC1WiFFWAFupMxYGmadxzT9V1cBTDxfdMXeuxCBDla8gBIGP3dd7vWLY7GzXF+lLurd6Bz0rvOcMj\npXJK0qOuEumQnplrnSJ8KhWgjVzriuZelleGklz+iHry3p7J0UKZLMhxYl9RihoplQ4vdWpKTzrM\nnQDvSz0/Ox9Vhalr8lg9esIT8GAgwNeaXa9RaujcoyEgRuh2kpUG33dnrXOhKiqS4k0dDg+jd6SX\n+zoKlJptutc6Ye4tgy30TqOUg6qz1jOKUDTE7bGfXjYdWTrFDKsfVEmRUlZM3yNFC6XJ9z1T33ME\nKJG3Kvc3I6MiVQnQLpfLWDs+k4WZTJ47J/YVpaiRUumwoVE2M1c24E6DgnGcRGNR7hbxqbrUjEIy\n4AwsdK4qxB8wOEoskw9rTqQUF7zfsaKcIlTkV6T8XG1JLan4sVEitCrDFRAI8/cQw/wz9aDqrHXd\ntHpaEdNiXNfwHD6tflC1Uu08sTozGSzMZGpEKAGV6XsUAdrIKGile7uR9SIzVZjRtMydOwH7ilJO\npBQf2UXp0Z2Ik86hToRjfAaT3hc6xYDrHu7GcGiY+zoKqgokAgan9GTyYY0S8gykx3uOE2rrZD0C\nNLX4sWGiFCXEX6dBWl1cTWpsYOh6T4u1nsHiOycq60kBtINqu7cdwUiQ+zoKVoqUimkxep0ZSgft\ndPm+Z7Igx4GmadzrvSK/AiV5+lLTKE4Xf8SPLl8X93UUePf2srwylOaV6vqs5UWpdBBhI8MACAJm\nOtg0BBxRyo5kqtFOQFXnPZ7PTYQyJqOeo3LuzYPNNO9SJh/WMlmQ44Q3IhLg+x5bubGBSm+q4Y0N\nMjZSiihAZ+BBVWXkBEATpTTw13Wjwru3VxVWoSAndXpBVWEV8rP5jWtyBDjJlk2D73s0BMQIAmY6\nHNI56RnuwUh4hOsaHqGJIswAxqTrDgYGlZXgAAwuw5Gp0ULkGtBpMHcC9hWlqOl76WDAOQUSdUMx\nEvUewCryK1CUw//3apThSjEUdafvEQ7ogUiA5l0iCzNp8J2nCnJZhXLHYQNU1ZMahWLAWXmtW1KQ\niwYBzshWAOmx1jP5PccJJVWO5/BZX1YPF/gjy41I4aPUitRr07hcLmPrSmWqAE3e19Ng7pxQvls8\na70krwSVBfzdHI1Y6yoLvAMGO9oytdskda3n8BehTweUiFIdHR344he/iEmTJqGgoABLlizB+vXr\nx36vaRpuueUW1NTUoKCgACtWrEBDQwPXM8iRUungacjUjZyAymghwwsFckLpOqg6Sow090zuNkmZ\ne1ZBenQn4kRltBBAq6OWDpFSANGjSihOm9FRkeS5p4FNwwnlu8XzHc7NykVtSS33M/b07+G+hheV\ntSIB/QLWeIyNlEqD7/v/s/fm4XVV573/52ieJcuDJM9mCCZhDFMICU0ICUlDkzRpkzQ0c2/uzYUM\nkDS/0iS9zUiaNoWSQAYuJROENjPDBUrBTMZmMBDbGDzIkqzBGiydo6Ph6Iz798e2HFk+w17vXns8\n/j6PnwesvdZey9prrff9ru/7vscJaMuQnKGq9qkoh5wLSiknU3CAbN7RuSjRhBopDggvm0Kw1iXz\nhnDMXQDtpFQ0GuWiiy6iurqa++67j127dvGd73yHJUv+mEz229/+NjfeeCM/+MEPeOqpp2hsbOSy\nyy5jbs56OeTyVkqV6UEugEgppWDA+TnZuUgpZdEgra+uZ3nDcuX+RTeq5eyslWt4gwBOfu9QvuF7\n4GLuiXIOVz3uqFqG0+F74N8KfKKLNgVH9bhSygWUs02jCL+udTfOdj+qv0G43o2sepswrHXJvCEc\ncxegSneH//RP/8SaNWu47bbbjvzdhg0bjvy3YRjccMMNfOlLX+Kd73wnAD/96U/p6Ojgd7/7He9/\n//uP6TOZTJJM/jH+Oh6PC5VSEaiUSqx8hHI9yAVwUikFQvWEREGgCMMwlOfeUN2gJGNe27qWsdkx\npXe466iG4JsvOYpVtgAAIABJREFUV8mzAH5UC/XH+0ln01RXViu3tYqckVNe6801zbTVtVl+3v+k\nVJmudQjH3BUgSXwcIaJ02QTmet/cv1mpjV9JKccv2qSkVLleupTzPqcIp4saQMiUUgrrt7Opk+qK\nauViUPuj+3l116utNzAMGTlTUavexm8wMrJ2YZi7ANqVUnfddRfnnnsuf/mXf8mKFSs4++yzueWW\nW478vKenh+HhYS699NIjf9fa2soFF1zAli1b8vZ53XXX0draeuTPmjVrZEqpsFSgK9eDXAA/qifc\nUEpNJCaYSql9J1YrkS18XhXuKqVC8M0fX+uW4TgBLaxMJUnAroKhqSFS2ZRSGzfW+kRigthcTK3R\n8bWujjIjoScSE8STamHNXc1dyhUk17euV3oe3Mkz41TlvXm4ZtMYhmy9hyHaoZz3OUW4oZSSXDi5\nklPKYVKqIlKhTNaD4Hdi5JTfAUAkBGkopEqpSGBTftuC9lnv37+f73//+5x88sk88MADfPKTn+TT\nn/40P/nJTwAYHjYTHXd0dBzVrqOj48jPFuPaa69lcnLyyJ/+/n6ZUiosG/px9YQlpLNp5VLF7fXt\nNNZY/06kxIyoCp3iO1ShmkvCNeO1nG8Vs2pVZ4BwzFsAyTe/umW15WddzaOmAKcl/mBD5q86tnKu\nQFfOufMU4IaTCrKQnp5YD9mc0AlReIcqnCbfR2ZGlCukkZ0r35Ce4wS0ZTj9vYNsrY/NjjGVFP4e\nLcKVs90NFXQ5EzPiuYeAkBNAe/heLpfj3HPP5Zvf/CYAZ599Njt37uQHP/gBH/7wh0V91tbWUlt7\ntJStvEmp446qFQzEB8gpMvSqG7rEgJtOTXNo9hDLG9VzMlmF06oRyfMgdNDLuVJNTiD9LUPDdX5N\nqaCzqZPaKusS6ZbaFtrq2pTVP46TUi4Y7ZLkx2CGOJzddbb1BuJKNSH45rMJ9TaRSqhQUwAFHZKw\nGYnTKWmTyqYYnBoUE9hW4MfwPTDH9crlr7TeoJzVQpLLJgjH3BWQyqbon1S7WK6rqmNF4wqlNhJi\nBsyz/czOM0VtS8EwDOWzvTJSqVygQaQSU92DpSFsThMz2TmY7gY7FwnVLdCwGioK0CkSGx6cn3tq\nEmYPmIpVCSIVULME6ldqjUDTTkp1dXXxylcefTCdeuqp/PrXvwags7MTgJGREbq6uo48MzIywlln\nnWX5PaLwvTA4qSC8XQqB0a4IN9RCdhIFOklKOR22CLKDfCA+QCqbUgulyFovgHAEFdWgGK7hS0jW\nemWD/nH4HG7cKIL5zfuOlHJh7rVVtXQ1dXFw+qBSO+W5S9Y6hONsl57rYUhJoAC3lFLSs717ottX\npFRFpELJUZUS0Puj+9VIqXJWQIuTH5eXHd8X68NAzWlWDU0HG2s92u0YKTU8PcxcRu08XN2ymkrF\nysv+Vko5SMzs/wm88HlIql1m5kVNO6x8O2z4MHS+6eif+W3uhgHbvwi7r5fbWwvRuA5WvQNO/AS0\nnWa7O+3auIsuuojdu3cf9Xd79uxh3Trzw9+wYQOdnZ089NBDR34ej8d56qmnuPDCCy2/p1ZCp4Xh\nNhWOO6oW4YZaaFXzKiKoOwVOO6p+VUoZGPTF+hQbSb73EBiuIEwO6VxSbb/CDbWQtI04CbCD/UsM\nUVeUkeVcqUZESpXfue5G4mOQK4acTIAcm4sxmZxUarOyeSVVhW7x80BcWVeVHM8JHaIwEDPifa68\n1rtbBPSSuiU01zQrt3Myr5SfbZq+WB8ZFQWQ34iZwXvh6Y/pIaQAUhPQ+zPYdCk8e9XR6iO/hS7u\n+ibsuk4PIQUw0wd7vgv3nw37fmi7O+2zvvrqq9m6dSvf/OY32bdvH3fccQc/+tGPuPLKKwGIRCJ8\n9rOf5etf/zp33XUXO3bs4EMf+hArV67kXe96l+X3VEpGHgZH1TBA8eYAKE9H1QUFQXVltbJcFpxP\ndu7GgSY12pXHVs7OmmTuZRiLLlrrgsqZrlWhU4BbxqvEsXePlArBehet9fI7191yVOuq6uhq6ir9\n4CI46ag6neT8SBtB8mPlfUgaMlOuBDSUnR3v1lqPRCLuhLEpwE31tyqyRlYtrNJPpFTyEDz5fnny\n9VLYe5NJ0szDT3MfewK2f0l/v2CGaD7zv8x32IB2Uuq8887jt7/9Lb/4xS847bTT+NrXvsYNN9zA\nFVdcceSZL3zhC3zqU5/iE5/4BOeddx7T09Pcf//91NVZTxQlIqXK1XCFcCSMU0TvZK9yG4kB51pu\nJQU4nXcCYHnjcmor1cuWqjuqgpjssBhvkrmXISnlhjJQ2sbptS7p3y3j1TVSKgzrXZJ7ogzPdbeU\nUtJ2+6L7RO+yAlFYvoBgckURKl3rYVD9S+eu32XzNdw620CWQ87Js93Pl02gOHdxXiUHvvc934XM\ntP5+F+Klb0M2af63OJ+WA3Pf9S39fS7Gi9+w1Vx7TimAyy+/nMsvv7zgzyORCF/96lf56le/Kn6H\niJSKODJdd+En1tXncOumYW3rWrYMbFFq42RIj2EYrtyozpeT3TehZoSry/wl33xIjDeResLhuecy\nMLnzj4euBFWNZix6tbpkPh/8bMBF56LE5mK01bUpty2FZCbJYHxQqY1qjpl5SEipvklT5m85fKic\nnbXjqsiSSGVTyhV1QZ7IeH3berYObFVq4zellEQRKmmjfK6LCejjdny5QBSaLiWgBXtEGJRSdnLI\nvYk3lX4QbJzrmquUGznovkVvn/mQGDTD+U78G/ncdVdonx2EoXv19pkPB++Hieeg/dWi5oHd3WWk\nVJkaruD8YZaZgcldNjYfzCoGTSeCQH2TD26ohcB/eWZGZ0ZJZNQqOS1rWEZ9tXr1gLWta9VJKTdu\nVMNivPlt7mObYcsHYUbD91tRA51vhnVXwLr320rYLCpq4NJaB9PAVKpCZxEHJg8oJ4Jd2byS6kp1\nZZFk7plchoH4gPVbaL+eb27Ab2sdTMM4/jKk1fIYHYWKWrM6UZ39wh4HJg8oV9RdWr+UxhpZyNf6\n1vXKbbqj3RiGoZxs2Qr8btMozbucCWhp6KLT633mACSG5O0jVVC3Ahr1JPp3K3xP2q4v1kc6mxad\np6XgVq7I2qpaOps6GZ4eVmqn9LuR+t66Q+wmtkFCrViLGC/9M5zwceT7lea5D96lt79iePlf4LV3\niJqWGSlVpoYr4OhB3vNTeO6zkIra76u6BVb+mckwd7xB3E0yk2RoSu1wdVVBEFNUECjALdUI+Dik\nx2kC2jAgvsssqypFZS00rDGNuILv8ZGjOt0Lj79LX3LIXMq8uRm6F/puh9f9WkRIG4bh6/wLYH7z\nTpBSrq51GzJ/y6SUX5216V57zlpFFdR1QMPawuSr3/a52QF4/N0w8Yye/pa9Fta+F076n1BpPVXD\nQkhUSNLvVto2nowznhhnWcMy8XsLwc8pCeLJONG5KO317dYaHCeg1eHUes/OmZdN/b/S01/LqbDm\nL+AVVxW3b4rAMAyREkm63iXhe1kjy4HJA5zYfqLoncXgth2vTErFVEgp4Zq1I3LIh4MP6O2vGKb2\nwPS+8pz70H1mRIXAvz1OSulELg3RP8irioCZjL1pA9QUCPUQS54dmvvIJnjq4/K42cVIx00Hte92\neOXfw5my+FSpgkBCEkkOgqyRVVMQKMCtZKjg49wTTq517c7ahbD2ffmdNb/M3TBgywf0EVKLMXQv\nvPC3cM6Nyk0nEhNMpdTKi9dW1ooqTHU0dVBTWUMqm1Jq51TuCQkZ5+ZNMphzv2TDJdYe9qWz9tfQ\n/2s9/bVsPOysfepYZ80vax0gkzAJ6Ilt+vo89KT5Z9+P4JKHob5DuQs3lRMgc1TBJM8cIaV8nDsP\nzP3IcVLKKVt2HnOjMLUXeehQBdQsgeZXFB6rnwg5w4CnP6GPkAKIvwQvfg32fR/+5D5Yeq5yF9G5\nKPFkXKlNRaSCVc2rlN8F9i5ddJNS6WyaA5MHlNtJVJFgzv2pwaeU2qgppaTEjGa1UPxlvf2VwsEH\noPkUWdsgzz0dM/2jZRcqNy0zUsrBW8WxJ2HLFTDTa7+vimozrGX9B01ndeHNqp8Os/QUbH6vPkJq\nMXZ9E1pfCeuvKP3sIrh5yyBtp6QgUIBbqhFpu4nEBJNzk7TWtVpr4CcFQSYBj/85TDyrr89DW8w/\n3f8XLnnoaGfVL3Mfe8Ico5PovgVe9WXlMB/pWpeE11REKljTskb5BtcxUsrFfa61rpXW2lblkvRK\nc/fT+WYYZjUZXYQUmIbhi183Sye/4f6j8y74iZT6w7V6CamFiO+CzX8Bb3pMOWRXso7snLFiUira\nzQWrLxC/Nx+kilBXw5RjPZyz8hxrD/tN9W8YsP2L8PJ3TBWvXdR1HiagrzTJ6KPe5aN9rvfnZv4b\nJ5A8BI++Hf5sn3L+SMlal4amg5y87o5282beLGpbCP3xfuUw5ba6NlpqW0TvczziQRoRolstNLVH\nb3+lMP40tL5K1lbn3HMZmHYu/1lejD8tIqUCG5ztK6XU7IDpqOogpMBUXA39P3jyr+CJ95j/Pw8/\nHeQ9P3FOOTGPXdeJEr65daNop53EwLSCQMxdxZn2k7O2/Yt6CamFmNwJm9939N/pPpSl2PdD59+R\nnTPL6SrCze9d2tapHHJuklIgu012h5Ry4Hzr+4V5xjmB5JjprKUXVAESzd2BeScnnE8GO/YEHNqs\n3EwpZOQw7CilxI6qA8nOo3NRVxWh1YKKlko2jZ+IGTBVPbuu00NIAcwNw97vwX1nwYFfHv0zv+xz\nhmHmvnESyVHY/+/KzdxUAQOsaFxBXZV6WLETa93Ni2WQ/btNJCaIzcWsPewHpZRhuE9Kxbb7Y+4z\nfc6JRwohtl3UrLxIKaemu+WD5sbrBAZ+C9u/9Mf/94uTCu44qpMvimJhRTeKwhC2tro2mmqalNv5\nyVF1M3wP3DBeHVjrqRh0/0h/vwsx+oh9RZI0L0+x/g7ep7fPQth7k0lOKcBtA07S1imllJsls6Vt\nA6mUcsNZmxs+mvTyy9nedztkZ51/z8vfUW4iCt+zkVOqvrqeFY3qOXGcqMolTXIuVoQKFFaBJaAn\nd8G2z+jvFyCXNEOAJ3f98e/8stbHt8LkDuffs/sGU62hALfXeiQS8U0FPreSnM/DcTveD3mVkmP2\nCnZIEN8lr6Knc+5uk3FwnJSyBCdi0Se2mY6kk9h7s72EyqD/EJzuNZUdbmD3vyo3kSQElW7MkUjE\nV46qm8oRO+VkLcMvBlzfHWaVSafx8vV//G/RYa67YskzkJrQ22chJA+ZKgoFuK4WEhh/vbFesrrJ\nQoJxoxrItT7xDMRecP49u2+wRyI78e81eI/+PvNh4PcwZb1yq2EYrueUAlkIn29IKeH5LG3ruAIa\nnFFK7f43Z5UEuRTsvr70c6Wge727tdZnemHgd0pNgrLWnbDjXT/XbeTTsoSINFOQRlt2aq++vqwi\nl4aM8IJHp1LKi7kLqxyWFynlBPY5rJwAyEzD/lvN/xYfyJod1YP36+2vGEYeUibl3FRKgewwdEIp\nlTNy9E32KbeTJkiU3iSrzV1y06D5ewczGbcbGPi1SfqCbL3rNlxjLtykLsTIw0qPu01KSdqmc2kG\npwbF78yHqeQU44lx5XZuhzONJ8aZnLO6f0tvFXWXT3bJWZveB0OH3+UHAjqTgNFH9fZZEIZSmerx\nxLhy4mOwp54AISnlQEiPVCklheNhyn4hoDOzZl4lp9HzM5gbM//bDyE94J4CGsyoDwVIQnXtnOsg\nO9+6o90YUjVMAQThog0UyHfp955Nytrlg47q8BJk1M8swFRY6oIXc08eEqnE/ELtKENdkIz+sBbD\ncM9R3XOjOX6/HGZCaZ4IRk7ZUA5CnhknblgOTh1UrgxWGamkq6lL/E7H5y65ZdFt8GbnzEqTbsDI\nweDvzf8WkVKa1/q0dTWDFqgqpQJyq6g7h5zEcG2pbbFeYCAPnL9R9YHMH9y9dJlPpO6HtT61R68x\nXAojD1l+VHJeNtU0saRuiXK7hZA4bAenDzKT0quqFe1zLe7aNL2xXoUEzT4IawHzXHcjXDWXhH0/\nMP/bD3Z8LutetAOYl00KTqoXSinJ+TadmmZsdszWexfD7bD8+SImqrB+rkcQee06IxMU00JoQ0Yt\nD+Af2wV87rmkaA6BJaWyor1ZswE3uRMSem+/C2Kmz0yw7Bej3e1M/mOPW350Nj3LyMyI8ivcJqVG\nZ0b1G64CR3V1y2oqbYS2On6j6gtnbS9kE3r7LIZ5AswPhJxCiI0WTO60bLwahiFTBtpQRdqptqkT\nbpNxdto7T0rpTIiac/fSZXST+b37QRXp+rn+hOW1LnVSJTmVFkJagU/3epekJHBbKZXKpjg4ZTFs\nww9rHdwloOcTnvvBjk8MHF1EyWnMDVu+4MrkMhyYPKD8Ci9UkaBfGem2Ukra3vEKfNmAEzMAaaFS\nSicplfNo7oJCaOVFSuk+zKLP6+2vFEY3+ecgd1s9oXCj0xdTd1Lrq+ppr29XbjcPV6rQWYDbEn+Q\n3cb2RHus36j6gZRy3Vl7zIajGvC1nopCYsjSo8PTw8xl1A9cO9+8K3nULMALw1V6Ex2o3BOzg+6q\nhWYHzJwrfljrbhPQ6TjM9lt61O3Ex/MQO6qa80q5nVPKcZvGL6ko3LTjJ3eYIXx+mLvbax0gZs2O\nH4gPkFFMjA4BON8sYCY1w+iMeuEs23N3urKu5JvXSczYvJwQQ5oPWmv+Wo/mnlRXEAaWlMqISKmA\n3yqOPuYPUiqXNpVbbkKBlJI6a3ZuVF2pQudQf17csCSzSYanh6097AcFwZTLaz0VNRMFej13w3B/\nnwPLiRkla31ZwzIaqhuU281DmkdNkiOjGNwO5wGzVHxNZY1yu0CF73nxvU/u9EdOKa/mbgFehPPY\n6UOnesIwjECkJACVilxCAjrodvzYE/6w40O21tvr20VVsBdCSmLrJKAl67yqospWCg6Q7XN9k33W\nycOKOuX+yUyrtymESsH7dUAcvheCuR9XSpWA9ltFlzf1yR3+MNpn+txPUjk7YLmKgSdqoSArpWzc\npoJfy8kGXC0EMPmi96RUasKdioPHvNdaAm8vSFgQFjbQTEBLSC67ypGKSIVM5m91rOXqrMV2ysIb\ntDvoHuxz8d2WHvOMlPKBozqRmGA6pe6keKEIdVwppXOtZ2bMsDI3MSlc66C5TPzxtb4YXU1dVFdU\nK7fTuda9SMEBsn+/TC7DQHzA2sPVLcr9a7U9JaSYDkjnoHPux0kp5yEjpQJ+wzI7AGkhe6qVlOrV\n15cKkg46qjYVBKuaV1ERUV9OoQjp8WPy46CH7wFMvSybu87cV27m0VoIq2vdg+9d2kcYckpJ+7A8\nd6lhHXRSKi5c67ptGi/OdosEtMTxk4beLURTTRPLGpYpt/PaUW2pbaGlVuAIHkZzbbMoSbzzpJRO\nVaT+YjMlIb1sguDb8RadVK9CdSsrKkVErk5VZJDOdVD4XVULCqzoJGaqGvX1pQJpEQWd+bQqPZp7\nOqbcpLxIqTBI3S2GtRwDnTkyvFBOgGXjVZIQ1O6mXl1Zzcrmlcrt/KCU8ir5saPGq3YCWu/vyRKS\nh7yPw/cqOaSPCWiQ3SqOzIxoK2xgGIZnhJxk7r2xXrJWqt/6wVnzYq2nhGtdZ8ls8OZst7DWU9kU\n/ZPWck8thA5HFYSl4jU6ql6c69I+nFVAEwIC+iX53HWexzqdXquwqoAWnG06lFIgI7J1Xjh5dq47\nfbnstVKq+SR9fanAMGRKpTDMXZAXrrxIKZ2Ga3palMTLNlKH8Ly0pptJYBfCQUfVbvgeeK+ekFYs\nsRu+t7xhObWVtcrtHFVPaHfWNMZ3W0VyXFixRGN5a6/Wuo8JaDt9SJzLfBibHWM2rf579oqUsizz\nlzprWi9dhDkg7CA57j0BDfr3TSuwcK73xfowsF5Ofh5eOqpK+VZKwIuwfJDZRcHKH+fFZZONROc6\nz3Yv1npqwtJjXoXvSfs5OH1QdB7ng2TuXl20gcJ4azxWStWvkhFjdmFkoMpjQq71lfr6UoFxnJQq\njlxK3wCkZR7tQmq8aj3MPFJPWHVUA3araFgsiV0Kg/FBsgKDze7cI5GIbO6WlVIel5IFb755Pziq\nEfX8ClrgpFIqCLeKJSCZd2WkUqTmXAxH5y511rSqAz1y1qTnuqbzA/CmdLSFc12ybmoqa+ho6pCM\n6BhISCnpJVE+eEVKSZzdgfgAqawFW1tMQOu044WVsewgOY7Y9Qq6CtriuS4iZjSc6+B9BT6vlFKO\nFzGREDM67fhIBFo8IGeMjPeEXOMJ3uSVOq6UKgGLibItwTMFgdB41eqo2kuoJ4aFA20qOcV4wtrB\ntxBeGXAz6RkOzaong8sHyWHWWN1IW12b7Xf7Tuaf0e2seeGojkOloEqcTgLaqwSJFhxVqdMXiPwL\nJSBZ66taVlElTbC7AI4a7WJSSufZHiACGvTmffPibLdwrkudVEmex3yQ5qbaN6EnmXSQLtoMDGv7\nsh/WuhcEdC4FCNVeOufuxVpPT5Z0VOPJuMgm1hWqK13rOsJ1DcMQXTjpmLu4iElQwvcAWl+ltz8r\nyGW8n3tFJTRv1NefVRxXSpVqFAZSalwYnxoGR7W09FcaHuNV+B7oc1SlhmskIggHzdOPKgbiAyQz\nFtaRyHgyvFujupCagOpm9XZaDzP1sEwtsJAQdSA+4IkyEHxwm+qRQsxOP46SUjrPdkl4vF2kolCh\nfksN6J27F+vdwrnuZTiPnb505ZXyoqIwOFxZ1w9r3SsbIZeWtQtDRa4S611apVZb+J6HKuiJxART\nKfXwcS/PdmcTnU/rvVz2gpQyssLwPc0pQ7yauyLKi5TSuaHrru5lFckJH7CuXpWXLH2jKlEQLGtY\nRkO1QJGyCNLDTFeycy8dVYlBYPlGVeqsBd2AS44LDzONRrtXFUsshEdLvvfqimotIT3t9e2iPUPX\nWvcyvGFN6xoiAuJmf8zCmP2w1j0hYg1/zN2rfa4ELH07i6CTlBKrJzRU4JMWNdCi/nayiIm00mYY\n7Hipox0GFXSJ9S751uur6kUVMvPBy7UutQ90rHWQ7ZnjiXEm5yyEwUr8ViOnlzhe9lp9fanA6/A9\ngOUezV0RgSWlUpL8kaFQC41DlUA9ofUwq9fXlwpS0ZKPeJV7AXyglBIkfQ7E3CVqIdC73r1wVJPj\nsrnnkmCl0pkVVDdBnZ68LEqwEIsuMeBWt6zWEtIjzaPmad4JDclQwczV09XcpdzO0twlZxuEw1mT\nfpda7RoPzvbsLJTIQSRRHOkK57HTlw5H9dDsIc+KGjiqlPKDMtCrtS4IawE0E9D+tOOlFy46FP8A\nq5pXiWwELaSU4KJtSd0SmmuF5+YiSIl8S/aINMm4zm9+6fnQdpa+/qzC63xaAOuu8O6SWQGBJaWm\nJeRp0GXu4A+lVKM+Y08JFg7yIIa1SOXKOvrxfO5WDjPJhg7BN14zU1DVJGur80Br8SIWvTSp5lWe\nlXlIDLj90f1aCht4lQx1HpK5WyIWxIZrwNe6HYT8bDcMw/PwvZbaFpbULVFupyN8T5qSYHXLatvv\n7mruolIQZmftXBeebVrXukd2vDR8T6dN48O1DsJQXY0EdHVlNauaVym307HWvVRAgw3y3dLZLlAL\ngeZ8yBE4/R/19WcVXvvsYKq1TrlGb58OILCk1JQkt2fQZe5gKqUk6gmth9l6+S2XHVhQT3ipFmqt\nbaW5Rv13IwlNyAev8k6AX5VSOsvJ2q9aJoL0ZkOn4d5yqr6+rMIKAe0xMSPpK5FJMDIzYuu92VzW\nswTvdvqyJPMv57UuvejSebZ7QUBD0fU+nhgX5VnR6aiCLKxHBwktOddXNK6gtso+4VJVUcWqFnUH\n3Rop5YO17lUqCmnlO51z92qtl7DjvSagQbbWe2O9ZG0q1CU2jc59zlk7XnjhpLtC5up3wimf1dtn\nKUjmno7rzacFcNqXYcUb9fapGfZL8XiEKcmePl8+WYfMs7rFLFUvleFKkU16z7pWVELzK2Byp74+\nrcDnSqn5kJ4Xx15UaqdDKZXKphiIDyi305lnRgJrsl8fhPS0bIRDT+rrzyok1fcg+Marz9c62JC6\nR3vobOoUv3cgPkBGUGpXaziTDZn/WZ1F5PN+CN/zylmTXnQFfa1DUWWkNOT12aFnGYwPSkd0DAzU\nHYSZ9AwjMyO21rvXitC1rWuVSXBLe3NVI2ZRAcV/V51rvXapvr5UIA7VDfdaB3+QUuva1vH4gceV\n2qRzaQbiA7bOWS/D8sHhAi5SUioxDG2ny9oWwquvh87L4LnPwNQevX3ng0QllkuZoa617frGUVEN\nb3wQ9t8Kf/h7S1Wu3UZgSam4RCll5MxftA7JbkU1NJ0IU7vt96UCIyMz3HUqJ8A80HxISnltwK1r\nW6dMSh2YPEAml7FVrr1/sl9kNOuae11VHR2NHcoqEGvGqw9i0b0y4KRKKa1OuhdKqdI3jkFUSoFp\nwF245kLxe6XJUP0g898f3V+clPJD3onmU/T1pQI/5NjxYq1DUfWElJT62wf/Vjoareie6LZFSnmV\n5NxOX2OzY0ynpmmqKRKiF4mYF04WilocBa0qYI/OdWnlwTDMvYgdnzNyou+9pbaF4elhO6M6Cu11\nMiKgO9ptj5Ty+KJtvoiJqi9hKdpDGr6XGJK1K4WVb4WOHdB3O0y+BMkxa+2iz0Fsu9q7xITckF5S\nCkxRyUmfgDV/Ab0/g+n91vfg4Qchoe+iJx8CS0qJwvfANOB0xZG3nOINKSX5wHUnTfPiQCuhDogm\nokwm1aWeukLYQHZrkTWy9E/2s2HJBvF7pY6qjrwT81jbulaZlApMonPPSCkfKKXaztDXlybMZeYY\nmlI3VvxCzNiBxHBtrW2lpVZoGOWBYzeqflFFeoGyVkrpJ6X8gu5oNxetvUjc3sviLWAvV+bpHSUU\nDlUCUkrC4hmaAAAgAElEQVTnWveKgMYw17tqGJ9OO75miVnEZM5eOLkyiqz1g1MHSZUoepAPn77/\n03z6/k/bGZUWdE90c8mGS0Rtc0aOvsk+5XY6bZr5IiaqtpWzSimHSCmAyho44aNqbbZ/2V1Squ00\nWdtSqG2HUz6j1uaRP3WclApuTilhSHbgDTgjK8wpNae3/K1XN6pF4AcFgaMJv4u1FziqKxpXUFel\nL6eCZO7RuSixuVjxh6QbeigcVWGFHK05djqh8y36+tMASU4l8Mdat5tDzutkqHb6Kzn2ilpThawK\nnQR0fZc8jNAOIoJ5g+a5r/Jm7kWgI4Gwl7A7fq/V347aNBJbVufZVrcCqtv09WcVRlamANce8eAv\nOz7oBLSd8Q9NDYkIOT+c7ZbyaUnt+LmDsnZ+glglFoK5K6L8lFI6N3UvbllyaRvhTLNmiXcdWPk2\n2U2Pg5AYb5WRSrqa1MubF4IdZ016wwLe36aCvRvVs7vOLvyAHxKiNm4wnWVp1Rwp/BC+B/CKq2D4\nv/T2aQPSPGw6v/n50tE5RbLfbg45CYEdnYvymfsUb8WKQFKiHiwY7ZGIud5TE2od667Q03IKTDyr\nr08r8IMqMhKBNe+Gnp/o69MmdBUC8Qr7ovvEbQ3D8LSACThcVVhytus82+bX+vhT+vq0glzaJOSS\no2rtdFfkWvMeGH1Eb582EHRSqjsqJ6CldsEPtv2AO3feKX7vYoxMqyvnMrlM6XxafgvfcxN+VIn5\nFMElpaRcSODVE4a9EAddpFTNEljzXuj9qZ7+NECyqa9qWUVlhTC+Pw8cNeCKtfc4v46d/npiJUgp\nP4TvVVSZVSvcJmakhFx6Wu84Vl4Oa98LB/5Tb79CSL73uqo6Hul9ROs4WmpbSiv9FsF2+J5g7gPx\nAW58+kZb79UBy+G6qqSUbhK2883uk1LS8D3dcz/xE/4ipYLuqNpQSo3NjpHIqN/A+iJ8zzGllAP5\nUd0mpTCEaTg0z339FfDCFyArveXXi8CvdRuklHTuP/2DP3yw/dH9xUmpyjpZcbAwEDPHSSnLCCwp\nJUp0DppzrZzujVrID8lgAc74Ogz+Xn/JTiEkN4orm1eSSOs7kFc0rhC1sxu+J5l7V1OX1rl3NHaI\n2pUk5MQVuTR/7yf+jfuklJSQs5q00SoiETj/FqhZCvt+gHLFJM2QkLhzmTnecec7HBiNGgbiAyQz\nSXHJdh3VOr3CvMy/6EWAKKxF81o/4eOw6zq9fZZCpNJURqrORffcl78W1v0V9P1Cb78CpLIp+if7\nvR6GLXihnghO+J4P1vrSC7whYUUFizTPvWYJnP41eOHzevsVIuiqyO6JbgzDICKo8G7XB/Aa+6P7\neeOGNxZ+IBIx17vqhVMYQtjKOXRREYElpeQ5pTTeNFQ3H85g/3N9fVqB1ElPTQDyZNrHoHENXHg7\nbP2wL0pLSjb1rQNbafimMGxCI7xQT9z87M3c/OzNtt6rAyXnXllj5prJJdU61n2juuqdZtJv1SSH\ndiAN35vTV4nmCKpb4LybYcOHYNunYGIbXpFTQTbgDAz6Jvt4xdJXKLdNpBMcnA6uoZLOpRmcGizu\n6PpBPdF8Iqz/oFmdxk1UNas7nqpGvhWc8z3TGfA4tKcv1ieqKusnHJo9RDwZFxUakFw2VVVU2ar2\ntxitta001zQzlZpSaheI8D2Ade+H565Wty/sQuKoOrHWN14NUy9D9//V37cignzhAjCZnCQ6F6W9\nXr1iWpBtGrCqgm4VkFJDYBgmqRVUlHPooiIcJ6W+9a1vce211/KZz3yGG264AYC5uTk+97nPceed\nd5JMJrnsssu4+eab6eiwrrSwVX1PJ17xaei9HVedM7EU0AFHddXb4fKX4fnPey75lxhwfoGdAymR\nTmgth+s2LMv8k4pGo24FX2UNvOZn8NDF7qkDK6SSZwe/h2WvgcuegWzqcOUeCzmVDj4Az/xPbUMI\nugHXE+0RkVKS6jx+w/7o/uKklMRRdWI9nnMjjG+Fqb36+y6E6hZ1QtkJArq2HS552DzTn/+cM86w\nBQQ9nGce3RPdxUPUC0Bi0xiGwetve71yu2JIZtUJm55YT2nViB8I6JolsOHD0P0jvf2WgmTuTqz1\nSIWpgl73AfOMdnO/W4QwrPfuiW7aVwlIqYATcpZUbhLfNZeCVNQ8k4IKacRDGFRiinCUlHrmmWf4\n4Q9/yBlnHF1S/Oqrr+bee+/ll7/8Ja2trVx11VW8+93vZvPmzZb7Fiul0mq3PSWx9DzY+Dl4+V/0\n9lsM0g/ciQMNoHYZvObHsPHzEN8FcxaTNw7erS0cyjCMQDuqozOjTKemaapRz/kVdEfVcjnZ5CG1\njp0odbzkDHj7S7Dt09D/K/39L0YkcjjHTlStnRuy38oaUy1pBXWysNZCCDIBDXLjOwxG+/7oft6w\n/g2FH5AYrk6s9Zo2eOvzsOMrsPtfzYpZTkNytjtFQEcicMJHYNU7zJw7swesFXpIRWHHP2gZgp3Q\nNz+hO+oeKZU1smwZ2KLcTjemU9OMJ8ZZ1rCs8EOiEDbNNjzAWd+Gg/fBrIuhoqJ9bhRyWdCYB/UI\nOt4Ib9sOY5thps/6Bf4f/k5LWOFsejbQKuB5dEe7OW/Vecrtguy/gEXbpHaprPPEULBJqYpqc72n\n42rtwqASU4RjpNT09DRXXHEFt9xyC1//+teP/P3k5CS33nord9xxB5dcYlYbu+222zj11FPZunUr\nr3nNa47pK5lMklygkojH43KllO5cKwBnXmeWkd7+Zf1KrHwQx6c6rKZpO838YxVzw9pIqUOzh8QV\nofyC3lgvp61Q+Pdb0C7I6I31kjNyVEQqCj8kMV6d+t7ru+B1v4TBe2HoXuvOWjYBY4+rv6+6RZ2U\nclIp5TGmU9McmlUkKH0GKbkU9NtUsDB3qYLACeOtqhHO/raZY6nnJzDbZ12pMbpJvVqnJJ+W0+d6\nbbtZbdcqZvq1kVJhIGFBnuy8d7JX70BcRk+0pzgpJVnr2YRZyENX0R6Amla4dDM8+79h6B59/RaD\nxKYxsmaqDM2XPEdQWQedb1Jrs/OrWkipoNux85Cs9WQmyWB80IHRuAdLe3X9KlnniSE139KPqF+l\nTkrlUqZKWUrmBRCOkVJXXnklb3/727n00kuPIqW2bdtGOp3m0ksvPfJ3GzduZO3atWzZsiUvKXXd\nddfxla985ai/q5GO3AkDrqIKNl4Dq//cjE0/tEW91KsKpDmlQuyoBv2WAcxNXUJKBd1RTWaTHJw6\nyKqWIgeWhIh1+ntf9Xbzj1XM9MNdgqSxfnRUPUTQv3eQJ3QNyz5XFKKqVAlTQSG9sCmF9rPNPyr4\nzQr1SzA/EXI+QGhIKaHiK+h7XU+sp7hqRHzBOqKXlAJT9XvxXWaF2Re/AdP7nK1KZycNh1OklIcI\ny1qXzKNvskxy54lJqeAr6KhfBfGX1NslDh4npezizjvv5LnnnuOZZ5455mfDw8PU1NTQ1tZ21N93\ndHQwPJzfkbr22mu55pprjvx/PB5nzZo1GJFqIobiTaQTMv95NG2Ai39n/ncubRqKpTD4O9j8PrX3\nHM/kfwzCcMsiNUBDMfdYTwlSSuKsObjW3YRk7kkHZf4eIwzEjHSth2HuJR10O5cuTpFSbkGUcyMd\n/JwbBRAWR1VCShmGEf7QfOlanxsxixHoRiQC695n/jEM60rHx/5MXfUvnvswcEbJx4KGoBOw85Cs\n9bDMfX90P2d1nlX4gQYpKTUga+cnSOc+OxB8lZgCisTLyNDf389nPvMZbr/9durq6rT0WVtbS0tL\ny1F/AIwqwU2JW2qhimoz50qpP5Fq9b4r683khKoIs1IqBJu61OEMg6Na8vcnzT2hu4SyF5A4qkbO\nmVBlHyAMa707apaOVkUY5u5I+B6EQx1oy1ENFwzDCA0ptW9in3KbkZkR5jLS5Kn+QMn9ys9rPRKx\nZsNX1sjscb+m4fAIYVnrIlIqBDY8WPgdNqyWdTy5S9bOT5DOPR6CuStAOym1bds2RkdHefWrX01V\nVRVVVVU8+uij3HjjjVRVVdHR0UEqlSIWix3VbmRkhM5OxTK2fsoz4yYikeMhPYsQBrWQ9FAui7nb\nkfkHHWLlSDiVkWH43uPJONE5xTxhhMN4nZf5F0Q5O2vikJ7wrfXxxDhTKQeSWnuA/sl+khm1CnZh\n2OdK7lflTMIer8h1FKQh7X7DYHxQmUwOw2UTWLDjpeF7se2ydn6CeO479I7D59BOSr3pTW9ix44d\nvPDCC0f+nHvuuVxxxRVH/ru6upqHHnroSJvdu3dz4MABLrzwQqV3iZRSYXBSwT/lZH2CMDhrZa2U\ncsp4DYM6sJyd9DwIw/cO6iR0NBElNhcr/WAAUNQIL+u1XsZO+iKERTkBYGAok0xlQUqJv/cQ2PHl\nTMjlQVjWu2Stl41NIyVm4i9DNiVr6xeUMyGnAO05pZqbmznttKPjHxsbG1m6dOmRv//4xz/ONddc\nQ3t7Oy0tLXzqU5/iwgsvzJvkvBgMyaYellwrEkc1MwPpKbkh4GOEwYDbH92PYRhEFBLWhqESGTip\nlAqBASdNchiGuedBWAy4nmgP56481/rzIZk3mOv9zM4z8/+wnNe6neTHIYO0Yp1f0R3t5pRlp1h+\nPgzqib5YH9lclspC9nY5f+/lPPdFCFOoLph718ZlGy0/H5azveTvsK4DIpVmFUkVGBmY2g1tp8sH\n5zWkOaXiu8LBWViEdqWUFVx//fVcfvnlvOc97+Hiiy+ms7OT3/zmN+odSZRSRg6SwXfiqW4r/Uw+\nhMFwX4SckQsFKTWbnmVsVi0PUBjmDU7eqIbge69fKWsXQpm/YRihcNZA/WY4LPOGEnMXh7WEYK0f\nP9ePIExOKqiTbGE429O5NENTQ4UfKGe1UM3xtT6PsdkxZtOzXg9DG1TzSoXlbC+5Z1dUQp1imp55\nBF0xJFVKZefMSqBlAkeq7y3GI488ctT/19XVcdNNN3HTTTfZ6teoFJaEnRuB+g5b7/Yc9cKFnRiG\n5pP1jsVjjEyPkMyq5WvwK3qiPaxotF7uNwyGK5hx+MlMktqq2vwPlPOtopSUCqHxOpGYCE2eGWVS\nKiS3qVBi7pJ8iRCO711qsIdh7osQOlJK0VHtnex1ZiAuoyfWw5rWNfl/WM7he8fX+hGEbq0rENDx\nZJzxxLiDo3EPvbHe4spIMBN+JwbVOw96bqW6FRCpMlVfqojtgBbrKtsgwxOllC6IwvcgHJv6cUf1\nCMrGWcuDsNywGJQof127XNZxGL53sVKqyA11QBGqta6Y2DUsax1KzL2ujNd6g3Sth2DuixCWxMfz\nUCalQnLh5Ej+uDCs9ZolUFHgEq4YwrjWw0ZKKaz1MJ3r6VyawakShFPjBlnnkwEnpSIV0Lhe1jbo\nKjEFBJuUKmepe12XrF0Y5r4IYTHeQN3pDtXcix3O9WX8vUtJqfhuvePwAcrme8/3fJgIuWJOSF0H\nYD2v3hGEYa0fP9ePIHSOqoJ6Imfk6IsVuaQJEIruWxVVMmIqMQyGIR+UHxCJyOyadMwM6QkRwrbW\nVeYTpnMdLMxdGqkTBmJGOvegE3IKCDYpVSsMwQvDTYPUUZ0O1+YP4bppOB7SUwBitVAY1rrQUZ3a\nDbm03rF4jDCt9b7JPjI561LuMK31eZl/XlRUy5SRc4eLmAQZUvXETE/wnfQFSGVT9E/2ez0Mrdgf\n3U/OyFl6dnh6ODwpCUrtW5JUFLkkpCdlA/ITxHZ8eM4CCCcpZXWth8mmAQdJqdkBSEVlbf0CMSF3\nnJQKBHLiW8UQxKNLD7PJXXrH4QOESj1RzkqpYnOXqickset+Q2Ud1LSrt8ulYSpcCRLDRMxkchkG\n4gOWng1LMYd5pLKp4gmQJUSskTWr6wYZkYjsbM/MwOwB/ePxCL2xXgzCQ7IBJLPJ4t/8AoRprZcu\nEy9NRVHGdnw8XHZ82EgplbUeJpsGHCSlAGI75W39AOncp/dDelrvWHyKQJNSYqXUXAiqUknVEyE7\nzCBcm/pxpVQBVFRD7TL1ThNDkI7LB+UXiEnoF/WOw2OE6XsH6+t9eHqYuUwZhWyInbWXZe38BOnZ\nHqK1HjYndR77JqxdEoSJlCqpBJFeLoehuqx4rYfLjg/buQ7Ww3XDNndnSamAh/CJ526E6nwvhmCT\nUtLqFWEIYZMa7bMDkAqB7HkBwmTA9U/2Ww7pic3FiM3FHB6Reygt85cacC/J2vkJxx1VIHxSd6vz\nCdu8oRQpVcbO2nEVdGhJKauOaphsmqGpIZKZIqGI0rVeznZ8iM71MIbqgvU9LGx7Xcn51LSDNB/0\noSdl7fwCO4Rc0OduEYEmpXJSpdTkruDnX5DmnoBQqaWyuSwHJsMTtpA1spYP6DAZrmDB8S5nqftx\n4zV0IWxg3SAN220qlKhQVM5K4OMEdOgctXlYrcoVpn2uZGXdcl7rUpVYiNZ6X6wvdKG6YG2tG4YR\nqrUOFuYdicjJmdFNwfbdG9aaUR8SjG7SOxafItCkFFUtUFmv3i4zFfxcM9LKHRCqG9WhqSHSIUvm\nbNUgD9thFp2LFld+lbMB17BG1i4Mcz+MkemR0CT/nYfVsvflp5QqY7XQcQK67EmpsJHQRfcv8ble\nxmt9ajcoFMnwM8p5rY/OjDKbnnVhNO7h0Owh4skSKTOkpFTioPntBxUVVdB4gqzt6KOhWfPFEGxS\nyhYxE4aQnuPGa9iMN7A+pzA6qkXnVM4kbJPwIJvaA9mU3rF4hFCudavheyGce1Fn5Lijqo54CBTg\nhxFaR7UMw/egxP4lVkqFwIZvEK71XBqmw1HEpJzXehjPdbBg19gJYxsJuGJIOvd0HKLP6x2LDxFs\nUgrkxmsYpL+N62XtQkRKhc14g/JVSoFTyY9DsNabTpK1MzImMRUChJGELefwPUfWenIUkodkbf0C\n6bkekgp8hmGE11G1oJ7IGTn6YkXC3QIIRy6bZnrNbz7IaFgnbxsSOz6MZxtYO9vDaNOAhbm3nSnv\nfORheVs/YEkZz90Cgk9KlbPMv+lEWbswOOmHEcZN3bJSKoSHedE5NayWdTrTF/xyqtK1DseNVx9j\nbHaMqeRUyefCuM8Vnbt0rUPwz/YyX+uHZg8xlSq9JoKI2FyMicRE0WcOTh0MXUqC4kopoQ0Pwa+2\nWd0EdTZy44YAYSWgxxPjTM4VLyoVRpsGLPxOl54v7/zgAxDkNA7tNuY+eLe+cfgU5UtKhYGYkRqv\nIarA1zvZ6/UQtMOqAxpGpVTRuUtD2CD4Uv/6Lln+PAiFowrhJGagtGGazqbpj4evOhEUmXt9l7yQ\nR9CdtfouqKyTtQ3BWg+rkzqPUmE9oTzXi+1x1a02zraAr3WQ2/EhWOsQ7vVeShkZVpum5O+0YZXc\nd89MwchDsrZ+wNLz5G0PPWnm1QoxqrwegG2IQ9gO51+IRLQOx1XYddKXvUbfWDxCGDd1K4e0YRih\nvGUpmvjZzvc+ucveYeA1IhFz/hJDdHKH/vF4gDAS0GDuYWd0nFHw5wcmD5Azci6OyD3sj+7PP/dI\nhfm9S8jkoF84RSqgcYNs7iFw0qVO6jcu+QZXnX+V5tEUxsnfPZnRmVHldt3Rbs5bVfgsCuO5XtRO\ni0RMO75Mv3eaTpCVew/B3A3DsJz8P4jonujm1V2vLvhzq4VOggZL81p6Pgz8TvaC/t/Ayj+VtfUa\n9V1m4aJZyUWjYf6bnfxJ7cPyC4JPSkkd1dQEJMegboXe8bgJuzL/EJBSYbxVHJsdYzo1TVNNU8Fn\nJhITTKcCHpKWB0WN16pGU+o+N6LecdAdVTDXu4SUGtscfAKecBLQUNoJD6OTOo+ic5eSUiFw1mg6\nUTj34KsnpKTUxmUbaalt0TyawtjQtkFGSpWhUmo8MU48GS/8+xET0AFXQIPcjp+vwFcRXDcuOhct\nXaktD9a0rOGNG97owIjy44F9DzAyo253ljzby9SmAcwwNikpNfh7yP0guN/+0vOFpBQmIXeclPIx\n7KongkxKzYf0ZBPqbSeehRM/rn9MLiLUYS3RHk7vOL3wz0PqqPbEesgZOSoiBSKLm06UkVIhcNbE\nxmtyzMy90Xqq3vG4iEwuw4HJ4CdxzodyNVzBAiklQTmv9dh2M99GpTD00QeQklLr29brHYiF9z01\n+JRyu33R4lXTwkhKgbmPndlZIMmvdK2H5bJJglwKYjug/Wy943ER0rPtbSe9jR/+2Q81j6Yw3v+r\n9/MfL/6HcrtiKrAw2zS9sV6yuSyVFZWFH7KTVyp5CIbugdXvkvchxUyv2vP5iLP286H/17L3jz4M\n0z3QtEHWXopcVr2QSkSdYgp+Tik7v5igH2jzIT0SjD2udyweYCA+ENqwllKkU1gN11Q2xcGpIjHT\nYkd1p6ydn9AsrMAHMPaYvnF4gIH4AFkj6/UwHEEpqXtYCWgoRUoJnbW54eBX4GuWOqpJ88IpwJCG\ntGxoc9dIl76vHJVSUGIfaxSe69PdkJ2TtfUL7EQ8BNyODxIBLUExUirMNk0qm2Joaqj4Q+3n2nvJ\nS/9sr70EI5ug9+dqbSLVx/6dHULOyMHu6+Xtpdjzb+oXfhV55l4CwVdK2QnpCYXMX5pn5kVIjkPt\nUv1jcgll66wRfvXEqpZV+X8oNeBm+mB2CBpsVPrxGk02SKnRx+Ck/6lvLC4jzN97qblJ97lzV55L\nTWWNqK0qxmbG2DuxV7mdI0opgENbYNWfydt7DamTDuZaX36RvrG4jFKkTT601LbQVtfmwGgKY8MS\nISlVIodOaEkpJ4qYGDmI74ElhXPy+R529rmxx+CUT+sbi8sICinlBAEttWlWt6xmbetaUVsJtvRv\nwcBQbrc/up81rWsKP1DTCi0b5RU0Dz1ppqZw66zLzMDTf6PeLq9S6hwgAoJ/VwC6b4XT/o97/vvU\nXtj+RfV2AqVU8EkpMDd1CSkVfU7/WNyGrVuWJ2D1O/WNxWUcd1TDiZ5YD69f9/r8P7RlwD0O694n\nb+812mwY3qOPBjqvVNi/92Ihq5J9LkKEzR/b7Bop9cC+B3jr7W9Vbld07nbOttHHg01KSZVScFg9\nca22obiJZCbJQHxAud36tvVEXN7bpI7q0NQQiXSC+upjK85lc9nQhvQU3cPtnOvR54JNStV1mJfr\nmRn1tqOPB/pcDwopJX1ff7yfVDaV9xyW2jR/d9HfceX5V4raSrD+hvX0TfYpt9sf3c+frP+T4g+1\nnycnpcBUS7lFSr3wdzAt+F7zKaWqm6HlVHm0VnYW9t4Mp31Z1l4FuQw89TGZIrUslVJg3ioe2qLe\nbuJZyCSgSliO1g9ofoW87ehjgSalwnqjCOUbvgelblRtOmtBJqXqO80yuokSsuh8SAya+WaWFMjp\n4XNIv/cvvv6LnL6icG42nRidGeXT96vfWs9l5hieHmZlc34Vn8Rw72ruco2QArnRPi/zX92y+tgf\nSivrQuDDWmjcYN4yGhn1toc2m/kfiuXz8Cn6JvtEt/JuO6kgV0qBuaZfteJVx/z90NQQ6VzazrB8\ni+KklI3Qy7En4ISPyNt7jUgEmk6G2AvqbZOjMLUHWk7RPy4XIA3VdV0pJVzrOSNHX6yPk5eefMzP\npJfq69rWidpJsb5NTkqVxNLzofdnglEdxuDvTVKrZaO8Dyvo+Tns/Z6sbSFiZun59lII7fkubPy8\n8/zFC18w91gJypaUkt6y5NIw8QysuFjveNzEksLlRkti5GF94/AA0puG9vp2KiPuGOxzmTmmUlPK\n7ZyoyBUhwrKGZcrtpJhKTTGXUWfXixoqdpVSQUf7OTAoIKXAPMADSkpJ1/oHTv8Ar1z+Ss2jyY9E\nOiEipcBc7/lIqenUNGOzY8r9uW202wkn2B/dn5+Uqmowi3kkiuSYK4SJZyEza/YRRFTWQuurIPYH\n9bbpOES32ctb4RGkygm380mB+c1HiIhItO5od15Sqmwvm+yk4ZA6TH5C+6tlpBSYdnxASSkJMVNb\nWUtHU4cDoykMO+dbd7Q7LykVFEJufdt6Hu17VLmdpfnpOKNe/g6cf4v9fgph4nl45n/I2xeqELj0\nfOj5sbzf5Bj0/tTZtBy9t9vLX1XW4XtSjD0RbFKq7QyIVIIkYV7sBZjph8Yicb8+htSA67+6n4Zq\nd5yVu3ffzTvufIdyu55YD4Zh5A1JMAxDNPeNyzay60r38qj94yP/yFce/Ypyu6KGSl0n1LRDakJ9\nQLEdkIpCzRL1tn5B+7kweLes7eBdcNo/6B2PSxDfKra6d6tYX11PZ1Mnw9PDym17oj28bu3rjvl7\n6R7n5rzB3ty7J7q5eF2BM7j1NBkpZWRg/CnocK9suHa0nyMjpcDcI8qIlPJCKVVTWcOqllWicMNC\nuWZCTUoVsWkAeRqOqd0wNwZ1y+0N0Eu0nwP7/13WdvDuQJaIz+QyIgXOurZ1haszO4S6qjpWNq8s\nnbw7Dwqt9SDYNGAjybuV3IBtZ5pqGjvq0J6fwulfMyMJdCN5CJ74c3vFFCoKVMLVcT6/9C9wwt84\no4qeeF6WQ2shBFWAw0FK2apKFXD1RFX94RvV7bL2Q3fDyf9b75hcgkQ9sbxhuWuEFMg39Nn0LKMz\no3lvhEZmRkQKJC9kvxIUdUwiEVMdOPLfgp4NGH4Q1r5XNC5fYMk58rYT22B2ABryqFJ8DslaX1q/\nlMaaRgdGUxjrWteJiJlC37zUcPXCSdc9d8Bc68MPygZ08P7gk1JiR/UuOONresfjAoJESoGp0BKR\nUgWSnUtJqQ+d+SFObj9WjeEEJhITXL9V/fZ8Nj3L2OwYKxpX5H+g6SRZGg4wL5fX/LmsrR9g51wf\neQjS01DdpG88LmAgPkAmpx6e7NVaX9+2XkRKFTzbBTZNe307zbXNyu3sQEqCWdrLK2uh7SwzYkmK\nXAqe+yy89hd6c6vlMrD5fWaRJDuoac//962nm4RVLinve3qfqWQ69fPyPvJBBxkHhedeBOEgpewk\nAHIfSgMAACAASURBVB7bbLK0gthH32DJOXJSajCYpFQykxQdEG4TM3be1xPryUtKBUU9IX3f0NQQ\nyUyS2qoCLHv7OUJSChi8J9ikVLsN4xWg7079B5gVJMfVnl8g+53LzAVirYNpuD41+JRyu0JSd2nY\nottrHfTPHbD3vQ/eDWf9k7y917ATmh/bbhrTje5/B3ZQqjJdIXhGSi3ZwOMH1C82C81Tut6vec01\nnNnpTmj2dGpaREqBSbIXJKWWnCXPLzP6SLBJKTsRD7mUSdwHbP5iArp1vd6BWMSGtg082f+kcrt8\naz2RTogucLw61yUYmx1jKjlVmkRber49UgrgwH9Ax5vgJBthdgthGPDC3+pJcVNbgJiprIElZ8P4\nVnv9/+FaWP56WHaBvX7mkU3pIeNAREq5q4F0CtUt0Cy8JcpMBT8mvd1mXqm0es4jryGR/YK92HAJ\n7JSqLqSSkKon3J67lBQwMIr/fu1870P/z0wCHFTMJzuXYu/3zBsgN5Gehp2KYZwLSKm+mGyte2HA\n6b5VDEoyVHBIGWmHmIm/BFMyksMXaDvTdFSlGLxL31hcQhCVUhLsm9iX9+/FF04urvemmiba69Wd\nDXBwrQ/dazqSQUVVPbTYyH04+Ht9Y3EJ5bLW85FSQVjn87Dz722JZNcVZv7cp2HyRfv9GIZJ9Oy+\nwX5fUJyY0TF3IwNPvh9SMft9ZefgiXfryzddiJArgnCQUmDvQBu8R984vICd2+RcCnp/rm8sVmEY\n6lLtBWq2oKiFQL+zFpS5r25ZLY79d8x4TY3bv5nwGu3nytvO9LnvrO74PzDbr9amzNZ6QQJaqJzw\nwnB3hJRqOgGqW2UDAhgK8Nlu11HtvtUbR/2QupqAimoMwxA5qm11beKLH7uQOqp9sT6yeS5HJHtd\nc00zrbU21ogA0r216H625CzhaIDpbrMKXZBhx47v/5Uep1QVky+p5/eMmGd7kELT7bx3f3Q/xqJ9\nWHyue6ASc8yOn4cuUio7B0+81yz0IUUuC89dDS9pVFg7TUoBzPTCUx+3d+GenoLH3mkS/LpQtkop\nsHnLEmDDFQ7fqNr4Ve650X3jte8X6mzsAkc1KEkC7byz0MElDulx+ZalprKmYJn7Uij6+2060Z6j\n2v8beVs7kJR3z6eUsBvCt/vf7LVXwcTzsEdw47RAKRWU7x3khuvg1GDePHHS22S3VZEgn/vozCjT\nqen8P5zPISeFZ2vdUF/vutd67A/u58xMx+H5q9XbRaoZT4wX/g6KwCsnFeSl4tO5NINTg0f9XSaX\noT+uSN5zuAqgzlwqFiDdW4ue6zVt5tkuRTlfLmdmYP9t+sZiBYYBz35SPeTwsB0flOpz85Cu9dn0\nLCMzRyfwl57rXtg01ZXV+avjWoCleTafYk/9vxDxXfDfr4fZwdLPLsbcKDxyGezRbB/XLi38sxVv\n0Peegd/A5r8wqw6rIrYDHjgXhv9L33hAVFQqPKSUnZCeqT0QD/AtS1WDvRvV+MvyZLISpKImG62K\nBY5qkOSvukN6gqQccSRJYiRixmJL0XObbOO2iz03qrfJV1LVTlJUgLHHTLLIaaSn4Mm/AiOn3nZB\nGd1AEdA29pfF69owjEAUc5iHLZl/sd+xnbN97DGI7ZS3l6L3dvOcU0G+tW5n7uAuAQ2w/UuyaokV\nVWJHTapW0gE73/zi+Q5NDYkSPwfJpim5n5Xz5bLdy6Y933U3NUHvz2D0UfV2h8/2oIXv6TzfgmTT\ngEMq6HlEIrDy7aL+8yK2Hf7rAjhokWBJHoKX/hnuO9MsGqAbjesL/6xhlb09bzEGfgcPv9Ekmaxg\nqhue/4L576VbadqwWpSrOzyklB0nFaDvDj3jUEVCsXJLPsMVNBxoAodZiuf/FpKj6u0WKqWE6okg\nKQh0KqUqI5V0NXeJxmEHuud+BLZC+KKm0+gmoi/I1lhFPkfV5loH/bdBi2EY8PQnzHLdEmhY60Fy\n1uBYAy5oyhGdcz8Kdo22Pd+1114VqSg8/zn1dvnWul0CevB3ehKWWsGhrbD3JlnbSFXgwnkAVjWv\nolpYJEeXo7q2xX2bxjFSyi4BPXNA3l4KI6euzMhnx9uNeJjp0Rt6UwyJEdkeB7bC92ora/MW/3ED\na1rWaAtjC1JYvp33WiYeV14u6r8gEoOm6mnTW2DoPjOv6ULMp4/Z8iH43Wp44Qswp554viSqmqF2\nefFnVmme+/jTcP9ZsPVjfyzkthC5LAzcBY+8De45CV7+Z8gm9I4BxKrX8JBStUuhwcbhvO+HZtZ5\nN5GcgJ1fVWtTyACy66gO3QtTe+31YQW9v4D9t8ra6lBKBUg9cWDyAOns0RtKzsiJEj+vbllNVT6n\nx2E4Vk7WroJgz3fdC1k1cvDMJ2WKoUie9V7fCfWr7I2p7xemYekU9v0ADtwpb68jfM+Dtd5Y08iy\nhmWittpuUz0g4wDqq+vpaJQ5DEXXu9213vtzddWSHfzhi7YvXY5gyVn2HFUjB3tvlre3iuSEmWxV\nsscBVFQHTjkBUFlRKb7oWjzfQKm/bdg0+XJpHYGdy2UjZ547bmP/j2HSojJhHvnWut2IB3DncjmX\nhS1/bSpMJKioYjo1zdjsmHLTdW3rxMSQXdgJY1tswwTpog3kuaws7+mdb4KKAtW27WD4QXj0T+HX\nS+D+V8NDl5iKqN+ugAdfa6r9ckn9751H+zmmEqwYdBNyYO6FPbfBf7/OnPt/XQgPvRHufaX5/4+/\nEw7er/+9CyHMfRseUgrsGa9zw2ZMppvY/kX1jb2QUkqHBHDP9+z3UQzxPfDMJ+TtbaonGqsbxVVj\n7EDqHOeM3DE5JoamhkgvZr6tjMGjw0yce6LkjapNEnZyh3mz6gZ2/5s8uXohItHu/HMp2C0r610S\nE9vguc/a66Pqj2WEJc6aV2sd5E7y4io9QSLj5uGIMrL5FVDVJBsQQHYWuv9d3l4FI5vkjnG+s72q\nAVpOtTem7lvMnDNOwTDgqY/aUGRFIFIRSFIK5LlmFufUkZJSXqi/pXtMJpdhIF4kOsBuxEP3LWbC\nY7eQnIA//H/q7Zyy40cecj5cedc3YeS/5e0j1YFURYI8VPgYpZRg/k01TSypU8/RowPSf/feWC85\nKxcVVY3QcYnoHZZgZCD6PIxuMsP7pISqKpa9tvQz7edAnYPqv8yM6X+MPmJWJM5MOfeuhbAy9zzQ\nTkpdd911nHfeeTQ3N7NixQre9a53sXv30SEcc3NzXHnllSxdupSmpibe8573MDKi4dbetszfYVJm\nIcafMdVZqiiklLJ7owpmokQ7lQuKIZOAze+FjHo4yhEcnvtsepbRGfWb6HVt61xPCAp6Y9GDpBAD\n+dxjczGiiSLqhqaTzYPMDna7cKs4/rTMaJ1HoZLwOkL4Xv4XOPSU/X4WIhWFJ/7SJL3s4HCCxOnU\nNIdm1Q0Ir9Y66CNmgmi4O0JKRSrsVeYC2Ps95/OtJEbgyQ8AQgWmU46q0+HKu6+3V9Hz8LkuJWG9\nzCll5/2LFc+9k72ifoKk/oYSv+e6FWYuEimSh+DAf8rbq2L738scXKcum8DZcOWRTbDzH+31UVEd\nqOpzCyEloBfONzYXY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w9Xm9OcRHUxWk4t/Uz7OaaKIllCURYkHCalAk3MCA24I0op6dz9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\n0u4bbQ7uKknI/cEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "motif = np.array([[   0,   2, 104, 104,   1,   2, 103, 102,   0,   0,  99, 105,   0,   0, 100, 102,   5,   3],\n",
    "                  [   0,   0,   0,   0,   0,   0,   0,   0,   0,   2,   4,   0,   0,   2,   3,   0,   0,   3],\n",
    "                  [ 105, 103,   1,   1, 104, 102,   2,   3, 104, 103,   2,   0, 105, 103,   0,   2,  97,  97],\n",
    "                  [   0,   0,   0,   0,   0,   1,   0,   0,   1,   0,   0,   0,   0,   0,   2,   1,   3,   2]])\n",
    "\n",
    "motif_df = pd.DataFrame(motif)\n",
    "motif_df = motif_df.T\n",
    "motif_df.columns = ['A','C','G','T']\n",
    "motif_df\n",
    "\n",
    "logo_motif = logomaker.make_logo(\n",
    "                            dataframe=motif_df,\n",
    "                            #matrix_type='information',\n",
    "                            remove_flattened_characters=False,\n",
    "                            ytick_format='%0.1f',\n",
    "                            draw_now=True,\n",
    "                            figsize=[12,3],                        \n",
    "                            #ylim=[0,0.04]\n",
    "                    )\n",
    "print(logo)\n",
    "plt.xlabel('Position',fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HViUkJIT77rvPazHu37+fmJgYAB588EHn96KiIo4ePQrAxIkTyxUUXbduHb1792bBggUe\nFSq9Iq+WVMXf358hQ4awfPlyNm/ezB//+EfatGlzqcMSERERERGRa4A3a2QUFxcDULdu3UrHOPoc\nYy+mrKwsJk+ejM1mo3379s6aHQCHDh1yXjUJDQ1l+fLlJCcns2PHDl555RXq169PYmIi06ZN82jP\nq+pERkWaNm1KVFQUUVFRlzoUERERERERuQb4eFgHwxWTyYTNZnPWhKyII1ngeEnkYsnMzGTs2LFk\nZmYSGBjIwoULqVOnjrM/ICCAsWPHUlRUxIwZM5x9/v7+DBs2jNDQUB599FHi4uL49ttv6datm1v7\nXvWJDBEREREREZGLyZs1Mvz9/SktLeXcuXOVjikpKQFcn9rwtpSUFMaNG0d2djb16tXjrbfeKlcf\no127dkyfPr3SNbp06ULPnj3Zvn07W7ZscTuRcVVeLRERERERERG5VHzqmFw2TzhqY+Tl5VXYb7Va\nyc/PB87XvrgYkpKSGDlyJNnZ2Vx33XVER0e7nYT4NUdx0szMTLfnKJEhIiIiIiIi4kU+JsNl80Tb\ntm0ByMjIqLA/Ozsbq9WKYRi0atWqxrFXJTY2lnHjxlFQUECjRo346KOP6Nq1a6XjbTab88RIRRxX\nZnx9fd2OQYkMERERERERES8y+ZpcNk906tQJgOTk5Ar7Hd9DQ0OpV69ezQKvwpYtW3j22WcpKSmh\nTZs2fPzxx3Ts2LHS8dOnT+eWW25xeb3k4MGDwPlrKO5SIkNERERERETEiwyT4bJ5YsCAAQBs3ryZ\nU6dOletfvXo1AIMHD6554C6kp6czdepULBYLHTp0YNWqVbRs2dLlnA4dOmC1Wtm2bRs5OTnl+g8e\nPEhSUhKGYTh/TncokSEiIiIiIiLiRT6+ZpfNE6GhofTv35+ioiImTZrkTGZYLBbmzJnDjh07CA4O\n5rHHHiszr6ioiLS0NNLS0igtLa3xzzRz5kyKiooICQnhnXfe4frrr69yzsMPP0yDBg34+eefmTJl\nCidOnHD27du3j6ioKGw2G8OHD6dNmzZux6JXS0RERERERES8yMfk3TMDL730EocOHWLPnj307duX\n9u3bk5WVRU5ODr6+vixZsoTAwMAyc/bt28eYMWMAiI+Pr/L0hCvJycl8++23AJjNZv74xz+6HL9q\n1SoA6tevz6JFi4iKimLXrl3069ePG2+8kZKSEn788UcA7rrrLmbMmOFRPEpkiIiIiIiIiHiRTx3v\n/qnduHFj1qxZw7Jly9i0aROpqakEBAQQGRnJhAkTuPnmm72636/t3r3b+d8nT57k5MmTbs/t1q0b\nn3/+OdHR0Wzbto3Dhw9Tt25dunXrxtChQ3nooYcwDM+u2xh2R4lQcamouPhShyAi15j2T3x4qUMQ\nkWtM+B03XeoQROQaEzep16UOoVakTx3psr/t/JUXKZKrk05kiIiIiIiIiHiRTx33nxIVzymRISIi\nIiIiIuJFJg8Leopn9Nt101T/yt/GFRGpDdaHp17qEETkGpP4/geXOgQRudZcpVdLDC8X+5SylMgQ\nERERERER8SJPn1gVz3jlt5uWlkbr1q0xm39Z7j//+Q///Oc/OXHiBK1ateLxxx+ne/fu3thORERE\nRERE5LKlExm1q0a/3f/97388+OCDPPDAAxw5csT5/auvvuL3v/89W7du5fvvvyc2NpYxY8awfPny\nmsYrIiIiIiIiclkz+ZpdNqmZaicycnJyePLJJ0lJScFms3Hs2DEALBYLr732GjabDZPJxB133EGz\nZs2w2+3MnTuXgwcPei14ERERERERkcuNYfJx2aRmqv0b/Oc//0leXh7+/v789a9/dV4b2b59O6dO\nncIwDF555RXeffddNm7cSPfu3bFaraxcqfdyRURERERE5Orl42t22aRmqp3I2LZtG4Zh8PzzzzNi\nxAgCAgIA2Lx5MwBBQUEMHjwYgDp16jBp0iTsdjs7d+70QtgiIiIiIiIilydTHV+XrTry8/OZPXs2\n/fr145ZbbuGOO+7g2Wef5dChQ16JecWKFXTo0IG4uDiX49LT03nuuefo2bMnt9xyC/369eNvf/sb\neXl5lc6x2WysWLGCIUOG0KlTJ7p3786YMWPYsmVLtWKtdiLj+PHjANx5551lvv/73//GMAx69uyJ\nj88vy7dr1w6An376qbpbioiIiIiIiFz2vH21JD8/n5EjR/Lee+9x+vRpwsLCsNlsbNiwgaFDh/Kf\n//ynRvHu37+fuXPnVjnu4MGDPPLII3z55ZfY7XbCwsI4ffo077//PkOGDOHEiRMVzps+fTovv/wy\nKSkp3HjjjQQFBbFz506ioqJYunSpx/FWO5Fx9uxZAK677jrntx9//JGMjAwAevToUWZ8aWkpcD4T\nIyIiIiIiInK18vbVkpkzZ5KamkpERARbt25l7dq1JCYm8vvf/55z584xdepUCgoKqhXrnj17eOqp\npygsLHQ5rqSkhAkTJnD27FmeeOIJEhMTWbt2LVu3bqVnz55kZmYyffr0cvNWrlxJTEwMTZo0Yf36\n9Xz++efExcWxcOFCfH19WbRoEbt37/Yo5monMkJCQgDIyspyfktISHD+d+/evcuM//777wFo1KhR\ndbcUERERERERuex580RGeno6sbGx+Pn5MXfuXIKDgwEwm8288MIL9OjRg9zcXI/rUVosFt577z1G\njx5Nbm5uleNjYmLIzMwkPDycadOmYTafT8gEBwezaNEigoOD2b59O3v37i2zxzvvvAPArFmzCAsL\nc/YNHDiQCRMmYLfbWbZsmUexVzuR0aVLF+D8PRqA4uJiPvnkEwzDIDw8nKZNmzrHFhQUsGTJEgzD\ncM4TERERERERuRr5mH1dNk+sX78em81Gnz59aNy4cbn+4cOHA/D111+7vWZeXh6DBw9m9uzZWK1W\nJk+eTIsWLVzOWbduHQBDhw7FMIwyfddddx333nsvAF999ZXz+65du8jMzCQkJIR+/fqVW3PEiBEA\nJCUluZVMcah2ImPo0KHY7XY+++wzHnjgAe677z7S09OBX36RAEuWLOHBBx/kwIEDADz22GPV3VJE\nRERERETk8udjct08sG/fPgBuu+22CvsdhwUOHDjg9vWS/Px80tLS6NixIytWrGDSpEkux1utVv73\nv/+5Fcc333zj/OY4ndGlS5dyyQ+Ahg0b0rJlSywWC3v27HErdqhBIuOuu+7iiSeewG63c+jQITIz\nM4HzxT8dWRWAtWvXOutm/OEPf6Br167V3VJERERERETksmf4+rpsnjhy5AgAN9xwQ4X9TZo0wWw2\nY7fbOXbsmFtrBgQEMH/+fNatW1dpYuJC2dnZnDt3zmUcjhMdR48edX5z/Hdlcy6c5/g53VGjB2yn\nTZvGXXfdxaZNmygpKaFbt24MGjSoTKYlPDyc1q1b89RTT9GrV6+abCciIiIiIiJy+fPw1IUrZ86c\nAXDWxvg1k8lEYGAgubm5zrFVCQkJ4b777nM7hpycHOde9evXr3BMUFAQcP60h8ViwWw2O+c5amxW\nxPFzuRs71DCRARAREUFERESl/W+88UZNtxARERERERG5Yhge1sFwpbi4GIC6detWOsbR5xjrbY7T\nGH5+flXG4IgjMDDQrdgdazr2cEe1r5ZcasePH3fZ/+mnn3Lw4MGLFI2IiIiIiIjI/2eu47p5wGQ6\nf7rDbrdXOsZmswFUWIfCG3x8qk4dOGK4MI7ait0riYy0tDT+9re/MWzYMHr16sVvfvMbZ9/MmTP5\n8MMPKSkp8cZWZGdn84c//IEBAwbw448/VjimpKSE//u//+Ohhx5iwoQJnDhxwit7i4iIiIiIiFTF\n8PFx2Tzh7+8PuD6x4Ph729XJh5qoV6+e2zHAL6cs3Im9tLS0zBx31CiRYbPZeP3113nggQf44IMP\n+O677zh16lSZ4yzbtm3j9ddfZ+jQoWRlZdVkO9LS0hg6dCibN2/GarWye/fuCscdO3aMwMBA7HY7\nW7duZejQoZUmPURERERERES8yosnMhw1JPLy8irst1qt5OfnA65rUdSEIwar1VrpyyiO51ODgoIw\nm81l5lUWO/xSG+P66693O54aJTJmzZrFhx9+iM1mo2HDhvTt27fcGD8/P+x2Oz/88APjxo1zZls8\nVVJSwsSJEzl9+jQtWrRg3rx5DB48uMKx7dq1Y/v27SxYsICmTZty6tQpJk+eXO29RURERERERNxl\nmEwumyfatm0L4HwN9Neys7OxWq0YhkGrVq1qHHtFmjZt6jyVUVmZB0d8F8ZQVewX9rVu3drteKqd\nyEhKSmLNmjUATJ06lS1btjBnzpxy477++mumTp0KwA8//MAnn3xSrf0+++wzjhw5QseOHVm3bh2D\nBg3C18WzNYZhMHDgQFavXk3Lli354YcfWL9+fbX2FhEREREREXGb2dd180CnTp0ASE5OrrDf8T00\nNNSZbKgNVcWxZ88eAG699Vbnt86dO7ucc/LkSTIyMjAMw7m+O6qdyFi1ahUAI0aMYPz48ZjN5gqL\nc5hMJsaPH8/IkSOx2+18/fXX1dpv48aNGIbBn//8Z6677jq35zVq1IhZs2bVaG8RERERERERdxk+\nJpfNEwMGDABg8+bNnDp1qlz/6tWrASq9seAtjjg+/fTTcsU78/Pz2bhxIwAPPvig83vXrl1p2LAh\nJ0+eZMuWLeXWdMTeu3fvi3O1ZM+ePRiGwaOPPurW+GHDhgFw6NChau2XkpJCSEgI3bt393junXfe\nSUhICAcOHKjW3iIiIiIiIiLuMsy+LpsnQkND6d+/P0VFRUyaNMmZzLBYLMyZM4cdO3YQHBzMY489\nVmZeUVERaWlppKWleaXMwkMPPUTTpk3Zv38/L7/8srO4Z25uLs888wx5eXn07NnTeQoDzr92Mm7c\nOABmzJjBd9995+yLjY1l2bJlGIbB+PHjPYrFXN0fwlGQo3nz5m6Nb9asGQCFhYXV2q+goIAOHTpU\nay5Ay5YtlcgQERERERGR2udhsqIqL730EocOHWLPnj307duX9u3bk5WVRU5ODr6+vixZsoTAwMAy\nc/bt28eYMWMAiI+Pp2XLljWKoV69esydO5enn36alStX8tVXX9GiRQvS09MpKiqiRYsWFZabGDVq\nFDt37iQ+Pp5hw4YRGhpKcXExR48eBeC5556jW7duHsVS7RMZ9evXB87faXGHo4BHUFBQtfarV68e\np0+frtZcOJ8l8uQ5FxEREREREZHq8GaxT4DGjRuzZs0annzySRo3bkxqaio2m43IyEg++eSTat1c\nqI7u3buzdu1a7r//fsxmMykpKc7TIKtXr6Zhw4bl5phMJhYvXsysWbMIDw/n2LFjnDx5kq5du7Jw\n4ULniQ1PVPtERnh4OP/5z3/46quvmDx5cpXjHUU+w8PDq7Vf27Zt2bdvHzk5OR7dnQE4ffo0mZmZ\nzoqpIiIiIiIiIrXGwydW3VG/fn2mTZvGtGnT3BofERFBSkqK2+tv3rzZrXFt27Zl3rx5bq8L55MZ\njz/+OI8//rhH8ypT7RMZDzzwAHa7nbfffptt27a5HLtixQo++eQTDMNg0KBB1dqvd+/e2Gw23n33\nXY/nfvDBB1itVo+Pq4iIiIiIiIh4yvDxcdmkZqp9IuPBBx9k9erV/Pe//2X8+PH06tWrzGmLmJgY\njh8/zubNm521KcLDw8tUMPXEiBEjiI6O5oMPPuDmm2/mvvvuc2teXFwc0dHRGIbBI488Uq29RURE\nRERERNxm8m6NDCnLsP/63RQP5ObmEhUVRXJycoVPrzrY7XZCQ0OJjo6mSZMm1d2OtWvX8qc//QmT\nycTgwYN56qmnaN++fYVjDx06xHvvvcf69eux2WyMHDmSWbNmVXvvKKNNteeKiFRHzMNTL3UIInKN\nOfPj/ksdgohcY87tfudSh1ArbOnfuuz3aavbAjVR7RMZAMHBwaxYsYKPP/6Yjz/+uMKnVW+44QaG\nDRvGmDFjqFu3bk224+GHH+bIkSO8/fbbxMTEEBMTQ4MGDejQoQPBwcFYrVbOnDlDWlqaszCo3W7n\noYceYsaMGTXaW0RERERERMQddp8a/aktVajRiYxfO3PmDMePH6egoAB/f3+aNWtWoxMYldm+fTt/\n+ctfOHbsmPObYRj8+kfp0KEDf/jDH7jnnntqvKdOZIjIxaYTGSJyselEhohcbFfriQzrkb0u+02t\nb71IkVydvJomCgkJISQkxJvoVWfWAAAgAElEQVRLVuiOO+7gX//6F7t27WLHjh2kp6eTn59P3bp1\nadSoETfeeCN33303rVu3rvVYRERERERERC5kN+lERm26Yn+7hmEQERFBRETEpQ5FRERERERE5Be6\nWlKrqv3bffrpp6s1zzAM3n777epuKyIiIiIiInJ5M/TEam2qdiIjMTHR5UslFbHb7R7PERERERER\nEbmSqNhn7ar2b7dz584ukxLFxcXk5uZy4sQJAIKCgnj00UeVyBAREREREZGrWy383Zufn8/SpUv5\n17/+xYkTJwgKCiIiIoIJEyYQGhrq8Xo2m41Vq1bx6aefkpaWRt26dQkPD+eJJ57g7rvvLjP2+PHj\n9OvXz+214+PjadmypfPfI0aMIDk5udLxDRs2ZPv27W6vX+1ExurVq90al5WVxcKFC4mJiSEjI4O5\nc+dWd0sRERERERGRy563T2Tk5+czcuRIUlNT8ff3JywsjKysLDZs2EBcXBxvvvkmPXv29GjN6dOn\nExMTg4+PD6GhoRQWFrJz50527tzJlClTmDhxonOsn58ft912m8v10tLSyMvLIzg4mKCgIOd3u91O\namoqQKVrBAcHexR7rZ93adasGX/729/Iy8tjw4YN3H333QwaNKi2txURERERERG5NHy8WyNj5syZ\npKamEhERwaJFiwgODsZisTB//nyio6OZOnUq8fHxBAYGurXeypUriYmJoUmTJkRHRxMWFgbAxo0b\nef7551m0aBERERF07doVgEaNGrFq1apK10tJSeGRRx7Bx8eHBQsWcN111zn7jh07RmFhIU2bNnW5\nhicuWgWSp59+Grvdzscff3yxthQRERERERG5+HzMrpsH0tPTiY2Nxc/Pj7lz5zpPL5jNZl544QV6\n9OhBbm4uK1eudGs9i8XCO++8A8CsWbOcSQyAgQMHMmHCBOx2O8uWLXNrvZKSEp5//nlKSkp48skn\n6dGjR5n+lJQUgGpdf6nMRUtk3HjjjcAvP4SIiIiIiIjI1cjuY3bZPLF+/XpsNht9+vShcePG5fqH\nDx8OwNdff+3Wert27SIzM5OQkJAK616MGDECgKSkJHJzc6tc79133yU1NZVWrVrxzDPPlOu/ohMZ\nx48fB6C0tPRibSkiIiIiIiJy8Rk+rpsH9u3bB1ReX6JLly4AHDhwgIKCgirX27t3r3NeRY9xNGzY\nkJYtW2KxWNizZ4/LtU6fPu083TFt2jT8/PzKjXHUx/BmIuOivAlTWlrKggULAGjTps3F2FJERERE\nRETk0vAxeW2pI0eOAHDDDTdU2N+kSRPMZjMWi4Vjx44RHh7ucr2jR4+6XA+gRYsWHD9+3Ll3Zd54\n4w3Onj3LbbfdRv/+/Ssc4ziR0axZMz788EN27dpFfn4+zZo1IzIy0qPXUByqnchwZF1cKS0tJScn\nh82bN5OVlYVhGNx7773V3fKS+kfhwUsdgohcY97rObHqQSIiXtR9xKhLHYKIyFXB7uGpC1fOnDkD\nVP6yh8lkIjAwkNzcXOdYV3JycgAICQmpdIxjL1fr5ebm8tlnnwEwfvz4CscUFRU5EycTJ06ksLCw\nTP+6devo3bs3CxYscLtQKdQgkTFv3rwKj6FUxG63A9CxY0d+97vfVXdLERERERERkcue3YsnMoqL\niwGoW7dupWMcfY6xNV3PcUXk3LlzlY5Zs2YNxcXFhIWF0adPnwrHHDp0CJvNBpy/WjJ16lS6dOlC\ncXExcXFx/P3vfycxMZFp06bxxhtvVBm7Q42uljgSFC43MJtp06YNkZGRPPXUUxXemRERERERERG5\nWrjxp7LbTCYTNpvN5d/fjmSBO4cNTKbzSZaarGe3252vpIwdO7bScQEBAYwdO5aioiJmzJhBnTp1\nAPD392fYsGGEhoby6KOPEhcXx7fffku3bt2qjB9qkMhwFBxxxcfHB7P5opThEBEREREREbksWL2Y\nyfD396e0tNTl6YiSkhLA9SmLC9cD16ctHI90VHYQYf/+/WRkZODn58fAgQMrXaddu3ZMnz690v4u\nXbrQs2dPtm/fzpYtW9xOZFT74k6dOnWqbEpiiIiIiIiIyLXGZnfdPOGoV5GXl1dhv9VqJT8/H3Bd\n98Ld9eCX2hjXX399hf1btmwB4M477yQgIKDKPV1xFCfNzMx0e85Fe35VRERERERE5FpgtdldNk+0\nbdsWgIyMjAr7s7OzsVqtGIZBq1atarzehX2tW7eusH/r1q0Abj3mYbPZnCdGKuK44uLr61vlWg5u\nHZlw5xqJJzp37uzV9UREREREREQuF1Yv1sjo1KkTW7duJTk5mTFjxpTrT05OBs4X06xXr16V6zn+\nHnfM+7WTJ0+SkZGBYRh06tSpXH9RUREHD55/1TMiIsLlXtOnT2f9+vXce++9zJs3r8IxjrXatWtX\nZewObiUyhg8f7vYLJVUxDIPvv//eK2uJiIiIiIiIXG7ceRjDXQMGDGDx4sVs3ryZU6dO0bBhwzL9\nq1evBmDw4MFurde1a1caNmzIyZMn2bJlC3fffXeF6/Xu3bvCqyUHDx7EarXSpEmTcrH8WocOHbBa\nrWzbto2cnJxy6x08eJCkpCQMw2DAgAFuxQ8eXC2x2+1eayIiIiIiIiJXK6vddfNEaGgo/fv3p6io\niEmTJnHq1CkALBYLc+bMYceOHQQHB/PYY4+VmVdUVERaWhppaWnO4p1w/lGOcePGATBjxgy+++47\nZ19sbCzLli3DMAzGjx9fYTwHDhxwxlWVhx9+mAYNGvDzzz8zZcoUTpw44ezbt28fUVFR2Gw2hg8f\nTps2bdz7heDmiYx33nnH7QVFRERERERErmWeFvSsyksvvcShQ4fYs2cPffv2pX379mRlZZGTk4Ov\nry9LliwhMDCwzJx9+/Y5r6LEx8fTsmVLZ9+oUaPYuXMn8fHxzmdQi4uLOXr0KADPPfdcpS+InDx5\nEoCgoKAq465fvz6LFi0iKiqKXbt20a9fP2688UZKSkr48ccfAbjrrruYMWOGR78PtxIZvXv39mhR\nERERERERkWuVN59fBWjcuDFr1qxh2bJlbNq0idTUVAICAoiMjGTChAncfPPNHq1nMplYvHgxH3/8\nMWvWrOHw4cPA+WsnY8aMcfmkquNFk+uuu86tvbp168bnn39OdHQ027Zt4/Dhw9StW5du3boxdOhQ\nHnroIY9LWRh23fVwS2FR8aUOQUSuMcE9J17qEETkGtN9xKhLHYKIXGO2v9j3UodQK46cLnDZ37pB\noMt+ce2iPb9aWFjIgQMHWLhw4cXaUkREREREROSis9rtLpvUjFtXSypz6tQpli5dSmJiIjk5OVit\n1nLFPG02GxaLpcy3KVOm1GRbERERERERkcuW1XapI7i6VTuRkZ+fz4gRI8jMzPToJZK6detWd0sR\nERERERGRy54NnbqoTdVOZHz00UdkZGQA559d6dq1K1lZWSQkJNC1a1c6d+5Mbm4uu3btIiMjA8Mw\n+N3vfsekSZO8FvyvffPNNxV+b9iwITfeeGOt7SsiIiIiIiLioBMZtavaiYyEhAQMw+COO+7g7bff\nxsfHh4MHD5KQkICfnx/Tpk0Dzl8tmTdvHu+++y4xMTE8/fTT5Z6F8dTatWtZvXo1zzzzDD179nR+\nHz16dIXVToODg/nqq68ICQmp0b4iIiIiIiIiVVEZjNpV7WKfjudZfv/73+Pjc36ZDh064O/vz549\ne7DZzqegfHx8eOGFF+jVqxd5eXmsWrWq2sEWFBQwcuRI/vznP5OcnMzmzZvLjfHx8SEwMNDZTCYT\nubm5zJ8/v9r7ioiIiIiIiLhLxT5rV7UTGWfPngWgbdu2zm+GYdC2bVuKi4udiQ6H0aNHY7fbSUhI\nqO6WTJkyhf/+978EBgYydepUnn766XJjunTpwjfffONsixcvxm63ExMTw+nTp6u9t4iIiIiIiIg7\nbHa7yyY1U+1ERkBAAEC5qxytW7cGIC0trcz3jh07AnD06NFq7bd9+3a2b99OkyZNWLt2LePHj6dJ\nkyZVzrv77ru56667KC0t5YsvvqjW3iIiIiIiIiLuKrXaXTapmWonMhxJhCNHjpT5fsMNNwCQmppa\n4TzHSQ5PbdiwAcMweOGFF5x7uOt3v/sddrudpKSkau0tIiIiIiIi4i5dLald1U5kdO/eHbvdTnR0\nNFar1fm9Xbt22O12EhMTy4zfvXs3APXq1avWfrt378bf358BAwZ4PDciIoLAwMBKkysiIiIiIiIi\n3lIbJzLy8/OZPXs2/fr145ZbbuGOO+7g2Wef5dChQ9Vaz2azsWLFCoYMGUKnTp3o3r07Y8aMYcuW\nLZXOSUxMpEOHDi5bRXUxS0pKWLZsGffeey+33HILt99+O1FRUc48gaeq/WrJ8OHDWblyJQkJCQwf\nPpwJEybQv39/evTogclkYt++fcybN4/hw4eTlpbG66+/jmEYzismnjp58iQ33HADvr6+Hs/18fGh\nRYsW5U6PiIiIiIiIiHhbqc2776/m5+czcuRIUlNT8ff3JywsjKysLDZs2EBcXBxvvvlmmRc93TF9\n+nRiYmLw8fEhNDSUwsJCdu7cyc6dO5kyZQoTJ04sNyclJQWA5s2b07Rp0wrXbdSoUZl/l5aWMm7c\nOJKSkvD19SUsLIxTp06xZcsWtm3bxquvvspDDz3kUezVTmR06NCBp59+mrfffpvvv/+euLg4+vfv\nT6NGjRgyZAifffYZ0dHRREdHA2C32zEMg0cffbRa+5WWlmIymVyO+e9//1vpGLvdjl1HeERERERE\nRKSW2bz8p+fMmTNJTU0lIiKCRYsWERwcjMViYf78+URHRzN16lTi4+MJDAx0a72VK1cSExNDkyZN\niI6OJiwsDICNGzfy/PPPs2jRIiIiIujatWuZeY5ExsSJExk2bJhbey1YsICkpCTCwsJ46623aN68\nOXa7nY8++ohXXnmFWbNm0a1bN49KSFT7agnAs88+y+zZswkNDaVly5bO7zNnzqRXr17O5IEjgfC7\n3/2O++67r1p7hYSE8NNPP7kcU69ePfz8/Mp9t9vtZGZmlssMiYiIiIiIiHhbqdXmsnkiPT2d2NhY\n/Pz8mDt3LsHBwQCYzWZeeOEFevToQW5uLitXrnRrPYvFwjvvvAPArFmznEkMgIEDBzJhwgTsdjvL\nli0rN9eRyLhwjisXxjV79myaN28OnH80ZPTo0QwdOpSSkhLnAQh3uZXIOHHiRKV9Dz74IJ9//jlR\nUVHOb3Xr1iU6Oprly5czZcoUnn32WdauXcuLL77oUXAX6tChA6dPn+bgwYMez92xYwcFBQXcfPPN\n1d5fRERERERExB1Wu+vmifXr12Oz2ejTpw+NGzcu1z98+HAAvv76a7fW27VrF5mZmYSEhNCvX79y\n/SNGjAAgKSmJ3Nxc53eLxUJ6ejqGYdCuXTu39tq0aROFhYXcfPPN3HTTTeX6HTc2YmNjy9TerIpb\niYy+ffsSFRVFXFxcpYubzeVvqdx+++1MmDCBcePGVRi0J+666y7sdjtLly71eO7bb7+NYRhERkbW\nKAYRERERERGRqnjzRMa+ffsAuO222yrs79KlCwAHDhygoKCgyvX27t3rnGcYRrn+hg0b0rJlSywW\nC3v27HF+T09Pp7S0lObNm7t9hcWxV2Wx33TTTfj6+nLmzBnS0tLcWhPcTGRYrVYSEhKYPHkyffr0\nYe7cuRw+fNjtTbxhyJAhBAUFsWnTJpYsWeL2vPnz55OUlESzZs0YOHBgLUYoIiIiIiIiAja73WXz\nhOPRispqSDRp0gSz2YzdbufYsWNVrnf06FGX6wG0aNGizN7wy7WS0NBQdu/ezV//+lfGjh3L+PHj\nWbJkSYWlIBzzLyxFcSGz2UyTJk3KxOUOtxIZjz/+OEFBQdjtdk6dOsW7777Lfffdx+OPP05MTAzF\nxcVub1hdAQEB/PnPf8Zut/PGG28wduxYduzYUekJkW+//ZYnnniCt99+G5PJxF/+8pcKT42IiIiI\niIiIeFOpze6yeeLMmTMAztoYv2YymZwnJBxjXcnJyQHO16GsjGOvC9dzJDK++eYbRo4cycqVK0lK\nSmLr1q0sXryYAQMGsGnTpgpjd7VXUFCQ27E7uPWX/cyZM3nxxRdJSEggJiaGrVu3YrFY2L17N//9\n73955ZVXuP/++xk6dCidOnVye3NPDR48mJycHP7+9787n4UJDAykY8eONGzYELvdzunTpzl8+DCn\nT5/GbrdjMpl46aWXuPPOO2stLhERERERERGHUk8LYbjgODhQt27dSsc4+tw5ZODOeo5HNM6dO+f8\nlpqaCkBJSQnPPPMMDz/8MA0aNCAtLY3FixcTHx/P1KlTWblyJZ07d66V2B3cPqLg6+tL//796d+/\nP7m5uXz55ZfExMSwf/9+CgoK+OSTT/jkk08ICwtj+PDhPPDAA9SvX9/tQNw1duxYunTpwpw5c9i9\nezf5+fl88803zrs9Fz6xeuuttzJ9+nTnnSERERERERGR2ubp9RFXTCYTNputzN+65fazna+7UVHN\ni4rWAzxer0+fPjRo0IDIyEj69u3r/B4eHs4bb7zB+PHjSUhI4B//+AfLly+v0V5VqdZdi+DgYEaN\nGsWoUaNIS0tj3bp1fPnll2RnZ5OSksIrr7zC3//+dyIjIxk6dCg9evSozjaV6tKlCytWrCAtLY3E\nxEQOHjzIyZMnnbF16NCB3r17Ex4e7tV9RURERERERKriaUFPV/z9/SktLS1zOuLXSkpKANcnHy5c\nD3C5XmlpKfDLyQw4X3KiMoZhEBUVRUJCArt27eLs2bMEBAS4tZcj9gv3qkqNi0a0a9eO559/nuee\ne46kpCTWrVtHXFwcRUVFbNiwgQ0bNtCyZUseeeQRhgwZ4izk4Q3t2rVz+9kXERERERERkYvBizdL\nCA4O5ueffyYvL6/ivaxW8vPzAde1KC5cD6h0PfilXsX111/vdpyOl0qtVivZ2dm0a9fOrb0cT7x6\nspdbxT7dYRgGPXv2ZM6cOWzfvp3XX3+d3/72t/j4+HDs2DEWLFhAv379iIqK8taWIiIiIiIiIped\nUpvNZfNE27ZtAcjIyKiwPzs7G6vVimEYtGrVqsbrXdjXunXrMt9d1bG48PpInTp13NrLYrE4Xzv5\n9V6ueC2RcaF69erx0EMP8cEHH5CYmMhf//pXwsPDsVgsJCQk1MaWIiIiIiIiIpcFq83usnnC8aBG\ncnJyhf2O76GhodSrV6/K9RyFOCtb7+TJk2RkZGAYhnPv9PR0fvOb33Drrbdy+PDhCucdOHAAOJ8P\naNasmVt77d+/n9LSUurXr8+NN95YZewOtZLIcMjOzubLL7/kyy+/dD7VIiIiIiIiInI1K7HYXDZP\nDBgwAIDNmzdz6tSpcv2rV68Gzr/y6Y6uXbvSsGFDTp48yZYtWypdr3fv3s7rHq1atXLWsIiJialw\nXUeBz379+mE2n69icffdd+Pr68vevXs5ePBgpXsNGjTIWRjUHV5PZOTl5fHJJ58watQo+vbty+uv\nv84333yD1Wrltttu49VXX/X2liIiIiIiIiKXDW+eyAgNDaV///4UFRUxadIkZzLDYrEwZ84cduzY\nQXBwMI899liZeUVFRaSlpZGWluYs3gng4+PDuHHjAJgxYwbfffedsy82NpZly5ZhGAbjx493fjeb\nzYwdOxaA6Oho1q5d6+wrLi7mtddeIzY2Fn9/f5555hlnX1BQECNHjsRut/PHP/6RI0eOAOevoXz0\n0Ud89tln1KlThyeffNKj34lhd/UOiptKSkqIj4/niy++IDExEYvF4rwf07BhQ4YMGcLQoUM9Oipy\nuSkscv9NWxERbwjuOfFShyAi15juI0Zd6hBE5Bqz/cW+VQ+6Ar28yfWNhFmRHTxa76effmLUqFEc\nOXIEPz8/2rdvT1ZWFjk5Ofj6+vL+++/TvXv3MnN27tzJmDFjAIiPj6dly5bOPqvVyuTJk4mPj8cw\nDEJDQykuLubo0aMAPPfcc85kx4Vznn32WTZu3AhAgwYNaNasGYcPH+bs2bP4+/uzZMkSevXqVWbe\n2bNnefLJJ0lOTsZsNhMWFkZOTg7Z2dkYhsG8efMYNGiQR7+Par9aYrfbSUpK4osvvmDTpk2cPXvW\n+d1sNtOnTx8eeeQR7rzzTo+OiIiIiIiIiIhcyTy9PlKVxo0bs2bNGpYtW8amTZtITU0lICCAyMhI\nJkyYwM033+zReiaTicWLF/Pxxx+zZs0aZ92Lrl27MmbMGAYOHFjhnIULF/L555/z6aefcuDAAVJS\nUmjcuDH3338/48aNK5MscQgICODDDz/k3Xff5csvv+SHH36gTp069OrVi9///vf06NHD49+Hxycy\n9u/fzxdffMGGDRs4ffo08Et10nbt2vHwww8zZMgQGjRo4HEwlzOdyBCRi00nMkTkYtOJDBG52K7W\nExnTN3zvsv/1QTddpEiuTm6dyDh69ChffPEFX3zxRZk7LXA+u3LvvffyyCOP0KVLl9qLVERERERE\nROQKcM7LJzKkLLcSGffccw+GYQC/JDC6du3K0KFDuffee/H396+9CC8TJ2Y+falDEJFrTI9RT13q\nEETkGpN7svBShyAiclXwtKCneMbtGhl2u51GjRo5C3e2adOmFsMSERERERERuTKVWHUioza5lcjo\n378/Q4cOpU+fPvj4eP3FVhEREREREZGrhk5k1C63EhlLliyp7ThERERERERErgrefrVEyqr286si\nIiIiIiIiUl6JxXqpQ7iqKZEhIiIiIiIi4kW6WlK7lMgQERERERER8SI9v1q7lMgQERERERER8SKd\nyKhdSmSIiIiIiIiIeFFtFPvMz89n6dKl/Otf/+LEiRMEBQURERHBhAkTCA0N9Xg9m83GqlWr+PTT\nT0lLS6Nu3bqEh4fzxBNPcPfdd1c6LykpiY8++ojk5GTy8vIIDAykc+fOjB49mt69e1c4Z8SIESQn\nJ1e6ZsOGDdm+fbvbsSuRISIiIiIiIuJF3j6RkZ+fz8iRI0lNTcXf35+wsDCysrLYsGEDcXFxvPnm\nm/Ts2dOjNadPn05MTAw+Pj6EhoZSWFjIzp072blzJ1OmTGHixInl5ixevNj5qmlgYCDt27cnIyOD\nhIQEEhISmDhxIlOmTCkzx263k5qaCsBtt91WYSzBwcEexa5EhoiIiIiIiIgXWb18ImPmzJmkpqYS\nERHBokWLCA4OxmKxMH/+fKKjo5k6dSrx8fEEBga6td7KlSuJiYmhSZMmREdHExYWBsDGjRt5/vnn\nWbRoEREREXTt2tU5JzExkSVLlmAymXjxxRcZPXo0hmFgt9tZsWIFr776KkuXLqVTp0707dvXOe/Y\nsWMUFhbStGlTVq1a5ZXfh49XVhERERERERER4PwpBFfNE+np6cTGxuLn58fcuXOdpxfMZjMvvPAC\nPXr0IDc3l5UrV7q1nsVi4Z133gFg1qxZziQGwMCBA5kwYQJ2u51ly5aVmbd8+XLg/DWRMWPGYBgG\nAIZhMGrUKB5//PEy4xxSUlIAqnX9pTJKZIiIiIiIiIh4kdVic9k8sX79emw2G3369KFx48bl+ocP\nHw7A119/7dZ6u3btIjMzk5CQEPr161euf8SIEcD5Whi5ubnA+cTM7t27gfPJjoo41vrf//5X5vs1\nm8iIiYkhMTHxUochIiIiIiIiUiWb1e6yeWLfvn1A5fUlunTpAsCBAwcoKCiocr29e/c65zlOVVyo\nYcOGtGzZEovFwp49e87/PDYbCxcu5OWXXyY8PLzCdc+ePesceyFHfQxvJjKuiBoZL774It26dau0\nAqqIiIiIiIjI5cLmxWKfR44cAeCGG26osL9JkyaYzWYsFgvHjh2rNNHgcPToUZfrAbRo0YLjx487\n9zaZTPTp08fluvHx8QC0b9++zHfHiYxmzZrx4YcfsmvXLvLz82nWrBmRkZEVngqpyhWRyAA8vkck\nIiIiIiIicinYvFjs88yZM0DlL3uYTCYCAwPJzc11jnUlJycHgJCQkErHOPZyZz2A/fv3ExMTA8CD\nDz7o/F5UVORMnEycOJHCwsIy89atW0fv3r1ZsGCB24VK4Qq5WiIiIiIiIiJypbDZ7S6bJ4qLiwGo\nW7dupWMcfY6xNV3Pz88PgHPnzlW5XlZWFpMnT8Zms9G+fXtnzQ6AQ4cOOa+ahIaGsnz5cpKTk9mx\nYwevvPIK9evXJzExkWnTplW5z4WumBMZIiIiIiIiIlcCb57IMJlM2Gw2l7cUHMmCimpeVLQeuL71\n4O56mZmZjB07lszMTAIDA1m4cCF16tRx9gcEBDB27FiKioqYMWOGs8/f359hw4YRGhrKo48+Slxc\nHN9++y3dunWrMn5QIkNERERERETEq7xZI8Pf35/S0lKXpyNKSkoA16csLlwPXJ+2KC0tBX45mVGR\nlJQUxo0bR3Z2NvXq1eOtt94qVx+jXbt2TJ8+vdI1unTpQs+ePdm+fTtbtmy5+hIZR44ccfkLqIph\nGLz22mtejEhERERERESkPE9fJnElODiYn3/+mby8vAr7rVYr+fn5gOu6FxeuB1S6HvxSG+P666+v\nsD8pKYlJkyZRUFDAddddx1tvvUXXrl2r3Lsi4eHhbN++nczMTLfnXDGJjNOnT7Nu3TqP5xmGgd1u\nVyJDRERERERELgqrF6+WtG3blqNHj5KRkVFhf3Z2NlarFcMwaNWqlVvrAZWud2Ff69aty/XFxsby\n/PPPU1JSQqNGjYiOjqZjx46VrmWz2bBYLGWunFzIccXF19e3ytgdrphERoMGDfT8qoiIiIiIiFz2\n7F68WtKpUye2bt1KcnIyY8aMKdefnJwMnC+mWa9evSrX69y5c5l5v3by5EkyMjIwDINOnTqV6duy\nZQvPPvssFouFNm3a8O6779KyZctK95o+fTrr16/n3nvvZd68/9fenUfXfCf+H3/dZkEWEsEombZE\nBHEYy2AUVWvnO6qWEhvpn70AABVwSURBVKOEolVLLVFTDimhnaiOdpQ5tKqVOpbk5NjToZ1IYynR\ninwpIkSkmpJJJGQlsvz+8Ms98s0i0Xtze2+ej3OcE/f9ft+8bv64J/eVz/v9WVPhnPj4eEkPtqFU\nl9UUGU8//bSCg4MtHQMAAAAAgCoVFZnuioyhQ4dq3bp1Onz4sNLT09WkSZMy42FhYZKk4cOHV+v5\nunXrpiZNmigtLU1RUVF6/vnnK3y+vn37ltlacvXqVc2fP1+FhYXy8fHRli1bKt16UsrHx0dFRUU6\ncuSIMjIyys2Pj4/XiRMnZDAYNHTo0Grll7j9KgAAAAAAJlVSXFLlv5rw9vbWoEGDlJ+fr9mzZys9\nPV2SVFhYqA8++EAnT56Um5ub/vrXv5ZZl5+fr8TERCUmJhoP75SkJ554Qq+//rokaenSpTp37pxx\n7NChQ9qwYYMMBoOmT59e5vkCAwOVn58vd3d3bdq06ZElhiSNGjVKHh4eysrK0ty5c5WammocO3v2\nrN544w0VFxdr7NixeuaZZ6r9M7GaKzIAAAAAALAGprwiQ5KWLVumy5cv68yZMxowYIDatGmjGzdu\nKCMjQw4ODlq/fr1cXFzKrDl79qxxK0pkZGSZLSATJkxQTEyMIiMjjbdBvXv3rn766SdJ0oIFC8rc\nQSQuLk4//PCDJMne3l7z5s2rMu+OHTskSQ0bNtTHH3+sN954Q6dOndLAgQPVqlUrFRQU6Nq1a5Kk\n/v37a+nSpTX6eVBkAAAAAABgQqY8I0OSmjVrpvDwcG3YsEHffPONEhIS5OzsrMGDB2vGjBny9fWt\n0fPZ2dlp3bp12rlzp8LDw5WUlCTpwbYTf39/vfDCC2Xmnz592vh1Wlqa0tLSqv29unfvrn379umz\nzz7TkSNHlJSUpPr166t79+4aPXq0Ro4cKYPBUKP8hpLSI0J/w9q1a6du3bpp27ZtFsuQ9NZEi31v\nAHXT5CenWjoCgDrmdlqepSMAqGP+d9X/WDqCWTwzLbTK8Wuf+dVSEttkFVdkBAcHy8PDw9IxAAAA\nAAB4pJLiIktHsGlWUWSMHDnS0hEAAAAAAKiW4vsFlo5g06yiyAAAAAAAwFoUF1JkmBNFBgAAAAAA\nJsTWEvOiyAAAAAAAwIS4IsO8KDIAAAAAADChYq7IMCuKDAAAAAAATIgrMsyLIgMAAAAAABMqKeKK\nDHOiyAAAAAAAwIS4IsO8KDIAAAAAADCh4sL7lo5g0ygyAAAAAAAwIW6/al4UGQAAAAAAmFARW0vM\niiIDAAAAAAAT4rBP86LIAAAAAADAhDjs07woMgAAAAAAMCHOyDAvQ0lJSYmlQwAAAAAAAFTHE5YO\nAAAAAAAAUF0UGQAAAAAAwGpQZAAAAAAAAKtBkQEAAAAAAKwGRQYAAAAAALAaFBkAAAAAAMBqUGQA\nAAAAAACrQZEBAAAAAACsBkUGYAbXrl1TYGCgBg4cqI4dO6pr167y8/PTtm3bVFhYaOl4AGzI3Llz\n5ePjo6ysrGqvmTNnjnx8fLRlyxbzBQNgs6rzvnP9+nUtWbJE/fv3V8eOHdW7d28FBAToypUrtZgU\ngK2iyABMLDo6WiNGjFBYWJj++9//qlWrVnJxcVFcXJxWrFih1157TQUFBZaOCcAGhIaG6uDBgzVa\n8+9//1uHDh0yUyIAtq467zvHjh3TsGHDFB4erry8PLVp00Z5eXmKiIjQ6NGjFRMTU0tpAdgqigzA\nhDIyMhQQEKD8/HyNHDlSJ06c0P79+3XkyBFt2bJFHh4e+u677/Thhx9aOioAK7dz504tX768Rmsy\nMzO1cuVK8wQCYPOq876TmpqqOXPm6O7du5o8ebKOHTumPXv26Pjx4xo6dKju3r2rhQsX8kcdAL8K\nRQZgQuHh4crJyVHbtm313nvvycXFxTj2pz/9SUFBQZIe/CJw7949S8UEYMVu376txYsXa9myZSou\nLq7R2nfffVe3bt1S/fr1zZQOgC2qyfvO2rVrlZubq8GDB2vx4sVydHSUJDk7O2vVqlVq1KiRUlNT\ndfTo0dqIDsBGUWQAJnTq1ClJ0pAhQ2RnZ1du/LnnnpOdnZ3y8/OVmJhY2/EAWLnY2FgNHTpUu3bt\nkqurqwIDA6u99j//+Y8OHDig559/Xp06dTJjSgC2pCbvO/fu3TNuXQsICCg37uTkpCVLlmjx4sXy\n9PQ0W2YAto8iAzChN998U6tWrdKQIUMqHL93757xLxlFRUW1GQ2ADbh27Zpu376tQYMG6cCBA+rf\nv3+11t25c0fLly+Xi4tLjbejAKjbavK+c+7cOeX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ArAZFBgAAAAAAsBoUGQAAAAAAwGpQ\nZAAAAAAAAKtBkQEAAAAAAKwGRQYAAAAAALAaFBkAAAAAAMBqUGQAAAAAAACrQZEBAAAAAACsBkUG\nAAAAAACwGhQZAAAAAADAalBkAAAAAAAAq0GRAQAAAAAArMb/AwhH+msxAi5CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "plt.rcParams[\"figure.figsize\"] = (12,3)\n",
    "sns.set(font_scale = 1.75)\n",
    "sns.heatmap(logo.df[(logo.df > 0).all(1)].T,cmap='RdBu_r')\n",
    "plt.xlabel('Position',fontsize=20)\n",
    "plt.ylabel('Values',fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.savefig('convolution_filter_hm.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "#model.save('train_90k_test_5k_10_filters_1Convlayer.h5')  # creates a HDF5 file 'my_model.h5'\n",
    "#del model  # deletes the existing model\n",
    "\n",
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "#model = load_model('train_50k_test_2k_10_filters_1Convlayer.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.shape(weights2[:,0,:])\n",
    "#np.shape(weights[:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nlearned_filter2 = pd.DataFrame(weights2[:,9,:],columns = [\\'A\\',\\'C\\',\\'G\\',\\'T\\'])\\n\\nimport warnings\\nwarnings.filterwarnings(\"ignore\")\\n\\nlogo = logomaker.make_logo(\\n                            dataframe=learned_filter2,\\n                            matrix_type=\\'information\\',\\n                            remove_flattened_characters=False,\\n                            ytick_format=\\'%0.1f\\',\\n                            draw_now=True,\\n                            figsize=[7,3],\\n                            ylim=[0,0.04]\\n                    )\\nprint(logo)\\nplt.show()\\n'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "learned_filter2 = pd.DataFrame(weights2[:,9,:],columns = ['A','C','G','T'])\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "logo = logomaker.make_logo(\n",
    "                            dataframe=learned_filter2,\n",
    "                            matrix_type='information',\n",
    "                            remove_flattened_characters=False,\n",
    "                            ytick_format='%0.1f',\n",
    "                            draw_now=True,\n",
    "                            figsize=[7,3],\n",
    "                            ylim=[0,0.04]\n",
    "                    )\n",
    "print(logo)\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fig, axs = plt.subplots(5,2,figsize=[12,10])\n",
    "learned_filter = pd.DataFrame(weights[:,:,0],columns = ['A','C','G','T'])\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "filter_index = 0\n",
    "for i in range(5):\n",
    "    for j in range(2):\n",
    "        learned_filter = pd.DataFrame(weights[:,:,filter_index],columns = ['A','C','G','T'])\n",
    "\n",
    "        \n",
    "        logo = logomaker.make_logo(\n",
    "                                    dataframe=learned_filter,\n",
    "                                    matrix_type='information',\n",
    "                                    remove_flattened_characters=False,\n",
    "                                    #ytick_format='%0.1f',\n",
    "                                    ylim=[0,0.05],\n",
    "                                    draw_now=False,\n",
    "                                )\n",
    "        axs[i,j].set_title(filter_index)\n",
    "        filter_index+=1\n",
    "        logo.draw(axs[i,j])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/tareen/Desktop/Desktop_Tests/MPathic3/mpathic/data/rnap_convnet'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiIiIiIiISA2jhJCIiIiIiIiISA2jhJCIiIiIiIiISA2jhJCIiIiIiIjIr+T1RjoCkfAo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2rU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aCyGDBp0qQqfZTLluXrr78mPj4eq9VKSkpKtfbnnnuOpUuXAtC9e3dMJhM//PAD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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_pos_tensor[0].T)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "motif_learn_convnet.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}