{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import keras\n", "from keras.models import Sequential, Model, load_model\n", "\n", "import os\n", "import pickle\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import scipy.sparse as sp\n", "import scipy.io as spio\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from scrambler.models import *\n", "from scrambler.utils import OneHotEncoder, get_sequence_masks\n", "from scrambler.visualizations import plot_dna_logo, plot_dna_importance_scores\n", "\n", "from apa_utils import load_apa_data, load_apa_predictor_cleavage_logodds, animate_apa_examples\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len(data_df) = 34748 (loaded)\n", "x_train.shape = (32992, 1, 205, 4)\n", "x_test.shape = (1728, 1, 205, 4)\n", "y_train.shape = (32992, 1)\n", "y_test.shape = (1728, 1)\n" ] } ], "source": [ "#Load APA data and predictor\n", "\n", "encoder = OneHotEncoder(seq_length=205, channel_map={'A' : 0, 'C' : 1, 'G' : 2, 'T' : 3})\n", "\n", "data_path = 'apa_doubledope_cached_set.csv'\n", "\n", "x_train, y_train, x_test, y_test = load_apa_data(data_path, encoder)\n", "\n", "predictor_path = 'saved_models/aparent_plasmid_iso_cut_distalpas_all_libs_no_sampleweights_sgd.h5'\n", "\n", "predictor = load_apa_predictor_cleavage_logodds(predictor_path)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predictor.inputs = [, , ]\n", "predictor.outputs = []\n" ] } ], "source": [ "#Print predictor input/output details\n", "\n", "print(\"predictor.inputs = \" + str(predictor.inputs))\n", "print(\"predictor.outputs = \" + str(predictor.outputs))\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#Define sequence template and background\n", "\n", "sequence_template = 'CTTCCGATCT$$$$$$$$$$$$$$$$$$$$CATTACTCGCATCCA$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$CAGCCAATTAAGCC$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$CTAC'\n", "\n", "pseudo_count = 1.0\n", "\n", "onehot_template = encoder(sequence_template)[None, ...]\n", "sequence_mask = get_sequence_masks([sequence_template])[0]\n", "\n", "x_mean = (np.sum(x_train, axis=(0, 1)) + pseudo_count) / (x_train.shape[0] + 4. * pseudo_count)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Visualize background sequence distribution\n", "\n", "plot_dna_logo(np.copy(x_mean), sequence_template=sequence_template, figsize=(14, 0.65), logo_height=1.0, plot_start=0, plot_end=205)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean KL Div against background (bits) = 1.8729476983107292\n" ] } ], "source": [ "#Calculate mean training set kl-divergence against background\n", "\n", "x_train_clipped = np.clip(np.copy(x_train[:, 0, :, :]), 1e-8, 1. - 1e-8)\n", "\n", "kl_divs = np.sum(x_train_clipped * np.log(x_train_clipped / np.tile(np.expand_dims(x_mean, axis=0), (x_train_clipped.shape[0], 1, 1))), axis=-1) / np.log(2.0)\n", "\n", "x_mean_kl_divs = np.sum(kl_divs * sequence_mask, axis=-1) / np.sum(sequence_mask)\n", "x_mean_kl_div = np.mean(x_mean_kl_divs)\n", "\n", "print(\"Mean KL Div against background (bits) = \" + str(x_mean_kl_div))\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(32992, 205, 4)\n", "(1728, 205, 4)\n" ] } ], "source": [ "#For the sake of the example, lets transform x to a 1d shape\n", "\n", "x_train = x_train[:, 0, ...]\n", "x_test = x_test[:, 0, ...]\n", "\n", "print(x_train.shape)\n", "print(x_test.shape)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(32992, 13)\n", "(32992, 1)\n" ] } ], "source": [ "#Create extra inputs that the predictor model expects\n", "\n", "feat_1_train = np.zeros((x_train.shape[0], 13))\n", "feat_1_test = np.zeros((x_test.shape[0], 13))\n", "feat_1_train[:, 4] = 1.\n", "feat_1_test[:, 4] = 1.\n", "\n", "feat_2_train = np.ones((x_train.shape[0], 1))\n", "feat_2_test = np.ones((x_test.shape[0], 1))\n", "\n", "print(feat_1_train.shape)\n", "print(feat_2_train.shape)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "\n", "#Scrambler network configuration\n", "network_config = {\n", " 'n_groups' : 5,\n", " 'n_resblocks_per_group' : 4,\n", " 'n_channels' : 32,\n", " 'window_size' : 3,\n", " 'dilation_rates' : [1, 2, 4, 2, 1],\n", " 'drop_rate' : 0.0,\n", " 'norm_mode' : 'instance',\n", " 'mask_smoothing' : True,\n", " 'mask_smoothing_window_size' : 5,\n", " 'mask_smoothing_std' : 1.,\n", " 'mask_drop_scales' : [1, 5],\n", " 'mask_min_drop_rate' : 0.0,\n", " 'mask_max_drop_rate' : 0.5,\n", " 'label_input' : False\n", "}\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training scrambler for cleavage position = 0.\n", "Train on 32992 samples, validate on 1728 samples\n", "Epoch 1/5\n", "32992/32992 [==============================] - 180s 5ms/step - loss: 3.4686 - nll_loss: 3.3073 - entropy_loss: 0.1613 - val_loss: 3.2598 - val_nll_loss: 3.1952 - val_entropy_loss: 0.0646\n", "Epoch 2/5\n", "32992/32992 [==============================] - 163s 5ms/step - loss: 3.2132 - nll_loss: 3.1495 - entropy_loss: 0.0636 - val_loss: 3.1753 - val_nll_loss: 3.0945 - val_entropy_loss: 0.0808\n", "Epoch 3/5\n", "32992/32992 [==============================] - 163s 5ms/step - loss: 3.1444 - nll_loss: 3.0763 - entropy_loss: 0.0681 - val_loss: 3.1162 - val_nll_loss: 3.0327 - val_entropy_loss: 0.0835\n", "Epoch 4/5\n", "32992/32992 [==============================] - 172s 5ms/step - loss: 3.0962 - nll_loss: 3.0251 - entropy_loss: 0.0710 - val_loss: 3.0844 - val_nll_loss: 3.0077 - val_entropy_loss: 0.0767\n", "Epoch 5/5\n", "32992/32992 [==============================] - 162s 5ms/step - loss: 3.0751 - nll_loss: 3.0051 - entropy_loss: 0.0700 - val_loss: 3.0701 - val_nll_loss: 2.9821 - val_entropy_loss: 0.0880\n", "Saved scrambler model at saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_0.h5 \n", "Training scrambler for cleavage position = 1.\n", "Train on 32992 samples, validate on 1728 samples\n", "Epoch 1/5\n", "32992/32992 [==============================] - 186s 6ms/step - loss: 1.4403 - nll_loss: 1.4013 - entropy_loss: 0.0390 - val_loss: 1.4186 - val_nll_loss: 1.3926 - val_entropy_loss: 0.0261\n", "Epoch 2/5\n", "32992/32992 [==============================] - 164s 5ms/step - loss: 1.4065 - nll_loss: 1.3860 - entropy_loss: 0.0204 - val_loss: 1.3974 - val_nll_loss: 1.3818 - val_entropy_loss: 0.0156\n", "Epoch 3/5\n", "32992/32992 [==============================] - 164s 5ms/step - loss: 1.3897 - nll_loss: 1.3760 - entropy_loss: 0.0137 - val_loss: 1.3815 - val_nll_loss: 1.3690 - val_entropy_loss: 0.0125\n", "Epoch 4/5\n", "32992/32992 [==============================] - 164s 5ms/step - loss: 1.3767 - nll_loss: 1.3612 - entropy_loss: 0.0155 - val_loss: 1.3690 - val_nll_loss: 1.3515 - val_entropy_loss: 0.0175\n", "Epoch 5/5\n", "32992/32992 [==============================] - 164s 5ms/step - loss: 1.3444 - nll_loss: 1.3233 - entropy_loss: 0.0211 - val_loss: 1.3080 - val_nll_loss: 1.2800 - val_entropy_loss: 0.0280\n", "Saved scrambler model at saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_1.h5 \n", "Training scrambler for cleavage position = 2.\n", "Train on 32992 samples, validate on 1728 samples\n", "Epoch 1/5\n", "32992/32992 [==============================] - 196s 6ms/step - loss: 3.6210 - nll_loss: 3.4691 - entropy_loss: 0.1519 - val_loss: 3.3546 - val_nll_loss: 3.2851 - val_entropy_loss: 0.0696\n", "Epoch 2/5\n", "32992/32992 [==============================] - 166s 5ms/step - loss: 3.2659 - nll_loss: 3.2138 - entropy_loss: 0.0521 - val_loss: 3.1926 - val_nll_loss: 3.1233 - val_entropy_loss: 0.0692\n", "Epoch 3/5\n", "32992/32992 [==============================] - 178s 5ms/step - loss: 3.1582 - nll_loss: 3.0994 - entropy_loss: 0.0588 - val_loss: 3.1292 - val_nll_loss: 3.0730 - val_entropy_loss: 0.0562\n", "Epoch 4/5\n", "32992/32992 [==============================] - 165s 5ms/step - loss: 3.1201 - nll_loss: 3.0595 - entropy_loss: 0.0607 - val_loss: 3.1054 - val_nll_loss: 3.0619 - val_entropy_loss: 0.0436\n", "Epoch 5/5\n", "32992/32992 [==============================] - 165s 5ms/step - loss: 3.1010 - nll_loss: 3.0396 - entropy_loss: 0.0614 - val_loss: 3.0931 - val_nll_loss: 3.0397 - val_entropy_loss: 0.0533\n", "Saved scrambler model at saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_2.h5 \n", "Training scrambler for cleavage position = 3.\n", "Train on 32992 samples, validate on 1728 samples\n", "Epoch 1/5\n", "32992/32992 [==============================] - 199s 6ms/step - loss: 4.2247 - nll_loss: 4.1326 - entropy_loss: 0.0921 - val_loss: 4.0715 - val_nll_loss: 3.9446 - val_entropy_loss: 0.1269\n", "Epoch 2/5\n", "32992/32992 [==============================] - 167s 5ms/step - loss: 3.9482 - nll_loss: 3.8660 - entropy_loss: 0.0822 - val_loss: 3.8639 - val_nll_loss: 3.7782 - val_entropy_loss: 0.0856\n", "Epoch 3/5\n", "32992/32992 [==============================] - 177s 5ms/step - loss: 3.8204 - nll_loss: 3.7241 - entropy_loss: 0.0964 - val_loss: 3.7941 - val_nll_loss: 3.6414 - val_entropy_loss: 0.1527\n", "Epoch 4/5\n", "32992/32992 [==============================] - 166s 5ms/step - loss: 3.7544 - nll_loss: 3.6487 - entropy_loss: 0.1058 - val_loss: 3.7285 - val_nll_loss: 3.6036 - val_entropy_loss: 0.1249\n", "Epoch 5/5\n", "32992/32992 [==============================] - 166s 5ms/step - loss: 3.7147 - nll_loss: 3.6035 - entropy_loss: 0.1112 - val_loss: 3.7091 - val_nll_loss: 3.6212 - val_entropy_loss: 0.0879\n", "Saved scrambler model at saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_3.h5 \n" ] } ], "source": [ "#Train scrambler(s) to maximize cleavage logodds at different positions\n", "\n", "save_dir = 'saved_models'\n", "\n", "for cut_pos in [0, 1, 2, 3] :\n", "\n", " print(\"Training scrambler for cleavage position = \" + str(cut_pos) + \".\")\n", " \n", " #Initialize scrambler\n", " scrambler = Scrambler(\n", " scrambler_mode='inclusion',\n", " input_size_x=None,\n", " input_size_y=205,\n", " n_out_channels=4,\n", " n_classes=4,\n", " input_templates=[onehot_template],\n", " input_backgrounds=[x_mean],\n", " batch_size=32,\n", " n_samples=32,\n", " sample_mode='gumbel',\n", " zeropad_input=False,\n", " mask_dropout=False,\n", " network_config=network_config\n", " )\n", "\n", " #y_pred_scrambled.shape = (batch_size, n_samples, n_classes)\n", " def maximize_cleavage_logodds(y_pred_non_scrambled, y_pred_scrambled, cut_pos=cut_pos) :\n", "\n", " return -K.mean(y_pred_scrambled[..., cut_pos], axis=-1)\n", "\n", " n_epochs = 5\n", "\n", " _ = scrambler.train(\n", " predictor,\n", " x_train,\n", " y_train,\n", " x_test,\n", " y_test,\n", " n_epochs,\n", " extra_input_train=[feat_1_train, feat_2_train],\n", " extra_input_test=[feat_1_test, feat_2_test],\n", " monitor_test_indices=None,\n", " custom_loss_func=maximize_cleavage_logodds,\n", " entropy_mode='target',\n", " entropy_bits=0.1,\n", " entropy_weight=20.\n", " )\n", " \n", " #Save scrambler checkpoint\n", "\n", " model_name = 'apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_' + str(cut_pos)\n", "\n", " if not os.path.isdir(save_dir):\n", " os.makedirs(save_dir)\n", "\n", " model_path = os.path.join(save_dir, model_name + '.h5')\n", "\n", " scrambler.save_model(model_path)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interpreting for cleavage position = 0.\n", "Loaded scrambler model from saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_0.h5 \n", "1728/1728 [==============================] - 3s 2ms/step\n", "Interpreting for cleavage position = 1.\n", "Loaded scrambler model from saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_1.h5 \n", "1728/1728 [==============================] - 1s 800us/step\n", "Interpreting for cleavage position = 2.\n", "Loaded scrambler model from saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_2.h5 \n", "1728/1728 [==============================] - 1s 805us/step\n", "Interpreting for cleavage position = 3.\n", "Loaded scrambler model from saved_models/apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_3.h5 \n", "1728/1728 [==============================] - 1s 795us/step\n" ] } ], "source": [ "#Load models and interpret test patterns for all cleavage positions\n", "\n", "save_dir = 'saved_models'\n", "\n", "pwm_test = []\n", "sample_test = []\n", "importance_scores_test = []\n", "\n", "for cut_pos in [0, 1, 2, 3] :\n", "\n", " print(\"Interpreting for cleavage position = \" + str(cut_pos) + \".\")\n", "\n", " model_name = 'apa_inclusion_scrambler_smooth_target_bits_01_epochs_10_deeper_cut_pos_' + str(cut_pos)\n", " model_path = os.path.join(save_dir, model_name + '.h5')\n", "\n", " scrambler.load_model(model_path)\n", " \n", " #Interpret the test set using the trained scrambler\n", " pwm_t, sample_t, importance_scores_t = scrambler.interpret(x_test)\n", "\n", " pwm_test.append(pwm_t[None, ...])\n", " sample_test.append(sample_t[None, ...])\n", " importance_scores_test.append(importance_scores_t[None, ...])\n", "\n", "pwm_test = np.concatenate(pwm_test, axis=0)\n", "sample_test = np.concatenate(sample_test, axis=0)\n", "importance_scores_test = np.concatenate(importance_scores_test, axis=0)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test sequence 3:\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAAnCAYAAACc9WIYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAG3hJREFUeJztnXmYXEW1wH8nCdlmspAQMz0hIcEkJIQEkCQQkEVlEzc2ERtkFRRFVPxA0SeLCMr3QDF+IKgIPJiriOBDFh+gAgoIyJ4ECFuAECBAwpYMkITU++NUza25c3tyezJJembO7/v6676nb9U93ffcqjpVp6rEOYdhGIZhGIZhGIZhGBuWXhtaAcMwDMMwDMMwDMMwzEE3DMMwDMMwDMMwjJrAHHTDMAzDMAzDMAzDqAHMQTcMwzAMwzAMwzCMGsAcdMMwDMMwDMMwDMOoAcxBNwzDMAzDMAzDMIwawBx0wzAMwzAMwzAMw6gBzEE3DMMwDMMwDMMwjBrAHHTDMAzDMAzDMAzDqAGqdtBFZLqIPCciX/XHLn61I+svIreLyJ9FZCMvGyciT4rIGVH+ZRFZKCLHRbJTROTk6Dgv/+Ei8pCI/FqUNucYGxYRGSAid4jItZENTBeR34nIGH9c1J5ERH4rIvtH+Q8XkYdF5KL2bEBEjhaRZ0VkJ3/cS0SaRGSuiEwtoOvoSnp5+Q9E5AkRmeyPe4vINd7+B3nZ5j6vaevyPzdqBBFB5OeI7FPxlGPExa8o7RGInJLJ73REvhgdD0XkfERmrQPtNwyJ7EEit5HIJzegDnUkchOJ/A+JDIzkQ0lk7Tu4E9mYRK4jkTNa8ktkNxJ5jES+HZ13AIlcTyIfWetr9gBEZISIPCoiF4iIeNnevswdEJ33Pd+e+Zw/FhG5WETuF5GRXjZQRO4UkatFpI+Xbe/zGhXlVRaRC6PjonXZBJ/XVlHajupa53X9Y9DV6GGIDEdkNiI7RLLpiPwSbydetj8iZ28IFQ2jKyIi9SJyt4j83rfrXYUy/VARmZ1Je6aIzBOR8YUv6Jwr/AJ6A08CDngfGOA/t7z8eXmyn0SyL3jZ7ZFsM2A00OyPVwOj/HlPAHMiPfLyvyKS7Zh3jr027Av4aXRPPu9lX/XHu7dzb/Nkw/zxxVH+TdF5syqkGwu852X/9rLjovPu8LJz2tH1E+3oNdPbrgP+6GXfiM77tpd92h8fvqHvi73WwwuGO3AOLqh0Dl/Gxa8o7T8cvBodi4N3HVwXybbx+Z+ywX9rZ7yaGOmaWOqacK6Jd10Tw9fDNXu7Jq50TdzvmtjKy37tdXCuidO87EB/fJdrom4tr3lNlP+hrolBronFkWyqa2Kia6LZH7/smui1we9Pjb+Aq6Iyd7qXhTJ9c3+8Q1RWL0MHLA6M0p3jzzsvku3rZaFM3zW6ZuJl/fxx0bpsX398SJRXEV0/n6Prz7K62qubv2CRi9pBDmb4uuCkSHaCl300kiUOVjvos8F/g73s1QVewOyofP10tjyPyvQ/+vK6jz/eJTrn8qLXq3YEYAdggv/cFyjUQ+t7sA+LRHUishmwayQbCByEOv0A4mUAJf+qlP9AtLJqyb+IXsb6w9vA4ZEo3KNS5r0ordKJSD3auMrmn+UgoF/mnCOj7wfl2WsVuh6K2m6crqN5Gd2HtbnfJWAEPpIDGAL0z+TVpe1JhCkiPC7CmV50MLCx/9wfGOzPO0WEe0SYtA7UOBE4BNgOuMCPmH8p+n44iQwCLvXHOwKf6fDVEhkF7B9JSmil/6FINgT4Mmm92EBavhg5iMhgWv+vlcrcw2hbVreqo6qsC8LnhipVbi+v9nTNtqkq6Wp0V0T6A43oAFegiD2FzwLEo+pHIHJQ5ytqGF0bEelF6/ZAe+VreLZCXV40XSuqddC38++/Ap6qIl0DWojcDfwpk1cC3O8/T0d7GH4MPA8tzvdgYLiI9K2Q/1ao03UT8Pcq9DLWH42oHdwNXBPJGzLvRck2hqagnUY3Av9oJ912wCrgTGCZD1/fBngWHbX4INL1royuRZyg6cA7wFnASp//1sBjwCXReUHvLulQrXMSOZFEXiSRU0mkOzgka+ugQ9qQKtoA6xKI0Au4FpgE/JcImwHb+68vBd7w5+0NnO2/+/E6UCXuqBsITEU7B+4B/urlewL1sfprcb2Z/v1fwEP+8wz/fhHwtP88Cy2XrgBWrMX1egrT0MGDv6BReoFsmTsdjdg7DW9jXvYCcL4/3gzYBL1H/5uTV8MaZEXIc+yzsvZ0/UWk63Dgnxldje5LsJPGHFkRBz0rOwM4qdO0M4zuw+bAULRO+csazs0+W9PRcvsnaF1eiGrnKE1FHY2vo40GnHMS4u6dcxLLwrGI7OnTH4s69juhQ/5LgKOAUV6XacAVzrkfisif0RCBEuq0C1pZvZCT/9Go03WYTzMx+q7lPGODMtW/fxl4Bh19Ar2/4T6Td9/ybCybzue/Ch0BWQ1MqGADU4HLnHOnishdwBbARsDxzrm/isjnI12PQR33Qrr6EYytgHOdcz8Skb2i/L8F/A3Yo4L+RiCRXdGwUtAGwxXAgoJpe6ORFM9RdveuE/06Rtv7LdIbdfaW49wq9xtvT8eIC5/ROahDorQvRnk1INIL51bn5t91mAlMjI5Dp9ZVlN1RJPJ7L49HN/vRmejI+GS0g/ditK6ahlamBwCvoqP62wIrgd1JOxE6yjTgLXTUXIB9gL2Av1N2x5HIBDSKYBowm7I7kUTuXMtr9gSmoh0Zh6PtirFe3vKMiD57WwE/9WX1PNS5bQAOc85d4dstoS44Cn32ts/mFV23Tf2wprZRkbwiXX/inDuzgq57Rboe7XXdAaO705h5B7WL1bTt8GnXXn19NIrOLlsNo3sQ1wWvADPyfAzvB8Tldx+0bfFD59x/+7K6ENWOoE8C7nTK3ehcqKLp3gAec86tQHsgJgH3Oufed849izptW/jvcM49iDpHDcBrPn2lnulJPu8lzrk3gPuq/F3GumcSsMQ597i3gTu8vATMp3rHosGna/ChJ5OAeZEN/CebwDd0JqKjIQC3oA8O6KgDaIRHrOv7pKMwLdesoFMjMCiTfwjFvctPTrnVH4ffXe1oS08gLH42F3WQqulguxL4A3APSU2F6pXQzskS0hIR8AngTdQBbC8daPkY98o+hTofw9vk3/WYgd7nGcB3SkNf6o3WBTcDUHa3oiOFM4Hb0JHCxztZh639+4mU3dXAd4AtgXmU3UuU3So02mtb4P8ou38C56IRQR1lCnA3Zfc2ZfcWOm9tS0IEUNk9BbyMRpCFEfxL0IrfqMwk4FHn3JvOudeBB7w8LnPHoNMGbvffXYvaHKTl960+r1ecc087594jrSda1VuSdqStTV1WqiAromuoa2Jd78Do7jQCrwPDEQmOdV5d0OBl2t5II1Pj8xrQdaZGRnkZhqFMAl50zi1wzr1LWvZmqUcj8EJdMxbt9IrL6kJU66APJQptD7Phi6YL5/v3VnmhP6g3+qPi/EvAYv+qVPF1VC9j/VHpHpWAR+nYHPRHSZ2UIjZQh9rYU9E5Q4CFzrnlkayjug7173H+Q9GHujknr0fayasnMwsNIZoG7NciTaSPH+nMR1e4PhiNpHDAuHWqZXWE+92XdG71hMx7pXSvk46cB9lT6GKHsUztSbrclIDtgDud437g/NMPOH05+pw+EU6QQ9wQNMTsl87xAhrq3pmMR//PeQCU3aPofWrRgbJbjXYOPOyPHWX3/FpccxhxR0PZfYCWF/Ojc4KtPNFyTtmXIYmMJ5GZnbKafPeiTfntp8cNJy2/h/iv54dzfLoVwMKMrEhdEN7n0vG6LDj7na2r0X1pRMt9yKsLUrKy8D4n+jwGHQhbDWzaklJkhHfoDaMnU7R8DaPnj/nPeX5BIaqt2OvR0exqqUcbme3Jwry+bP4lNJzgFSpXfHn5G7VFm3vkR75H0nEHfT7aSClRzDbzbKyIbca6tudUh/yzdp1nmx3tmOjeJNIXDee8yjtA1wMLSGRrdAeJt0nksgqp9wCWoyOTU9DRx1qhhDpjq0jv+US0I2FipUTkd1CGMjErewQdaRvcmYqvBxrRZwHnWH3sx38TOhgWZ84B7xw7x5udrMMg4BnvJAfqMjqAPs/PdOgKifQnkRNI5DsksonPa3H0fW/0/r2auR7o/Y7zOgq1p3vRkX0jJa/MDZFKocytVFYvda6VDeTVBX2AEbR10JeikR4dqcvieqWSrkuq1dXo9jSiNreEtIwMbYvBiAz0i4vm2esSWnf8jvZ5veI/47dle5XO7xA1jK5G0fK1hPoYi0jLb4fWD1VR7Rz0OuDdai/i0zWvQRYqoWYROQKdi/cz0gbqRlSu+Oqorca40ZY8G9gEtcGOjCSX0HDD4KTUoQ/EmnSA1jZcH/QSkYv88Tsd1LUOWrYgjGWt8vJzVBrQCrNORAY5595Zg+49hSFox+GcFknZORK5GJ0f9yBaNuBHincGXsC559D5oTdTdk8CkEg8ErmhKaHh2a/6z/NQx/w/dMxBfzZHNg+1vxI6t7mrMIjWz254TptJ5GBg5yN2+d3dl/3zSFjzM95R6oG3AUhkCmqD9V6HkeiidPO9bBmJNKIdSQt8KHr76EKHt6D2Cjo/OOQ/Ed22K9h8M4l8Bp36MA9YSdmtJJFj0Y6nXwA/R/+L+wiNaSOQV9eU0Cl5C9CdPOqA951zH4jId9GFQh/KSZeX10h02s0j6FSIkH94TgvvMOCnXYWO3yE+VL6E1kFB1/q10NXo3jSiURSv0NZBD59De+QR4JuRPNjrLC8bjZYpK0jLlGmZd8PoqRQtX+Nna4ZP925HIpqqHUHvhy6QUy156bKyMOdlJRBCVTelWIh7R/Uy1h9596iEzj19HN3erJptYbJ2UcQGYhvL02s/0m3Y8nRdhYaaDpb8kK9+wMrMg5iX1zA01Dk0yG0UPSWM/r5BIluTyL4kMh11vr9E2W1HOmd7HDrP8ix/PII03C+EJNcKeeXYBHSe9ZpC3PMc9FSWLkqyCB0V6Wr2NBiNfAj09+8r0K04v7Zx3RubAyudW2ermNeTdtzdgTq+/dFndzC6uOU+pJ3Uu6P37oiC+c9EnfMfoCvBz/X5r0DruePRNQnwsmnoImfjSFdu/yxwAtpBNRjYk7I7iNZbaxmVy+9K9cUuaHtjSE66Snm9j4YsDhOdr1uknZLHJuh0jrguaE/XXb2ugwvqanRvGkkjTBvRSL8G1Gl/k9Se3kN3hQjbdebZa3DQF5E66FPRwa9pXXDqlGF0JkXL1474JrlUO4LeTMdWeGxGnZKsrH/mmIwM9Ac+ho6gzyKfbF5G7dFM29DbBnRE8ZXouGj4aPwQNFDMNoON9cvIsraTp2spo2spR9dmoK+I9HKuxTnMy78BHel8CZ3zVULDt3skIvRDF+t7zTW1inI4FvgauiXee4Stg8ouLMw1C20oh3JhKOrYTwa+jy7gdRHwKdTBXwg0UXaxM7i+aD1VR+eZjkNHVX+IyHCcW7LGdPmyoahNr2kqUK3Sm9Zbj7R5Tld90Cd7Tmez0usRk1emrEDromrZE3iCstNQ0UT+hnY0xfmH3xeXFytzdBgDPNkSKVJ2HQu5775UKnNDfTECLU+y5+TJKtVbIS/QEfBYVs3Cn6GTehE6at5A67ptbXU1ujfBQX/Zfx6OtuvjKVDvR8eg9ppXr4xGR96zI+jXojs3NWCRqkbPpaifmXXQO+yfVjuCvowqNlnPpMuOOGZlYUX4gWhDPMzDK6GhraEnsGj+Rm2Rd49KaGXwMdK55GvEj7QPQkNM+5OGL67JNoON1WVkQa+L1lLXkP+ACvnHeS1HR8LerZBXj0CE8Wjo8I3AnPNuPDEsihX/h0OAxZTdChI5mURmk0g9GiZ8NTDOz5XrhXZ8jAQORbdz/B5wFTpK+UV0QbL1i8gg1OYmoZETJXTBM0FD3JdROcy9hC4UNprWDvo49BmIy8Vt6Jpbrb1D62cydKDUAdcDSzeue6MZ6C/SxonuTB3Ccxp2AVnudVhCuu9peJ6f96+ilIA5JNKbRD4LfDzKfwG6wmtY3LAOnQ7xLNq47uPXZkj8ORsTGsqJXEIiV1b5W7s7lcrcPugzIminRx+/INtvUSe5OSddpbxWohERH5A+g/XoMz7Sh64XIdRdn/TXD3nFuvb1um7UAV2N7k0jaivv+8+h7N+O1ra5Eu2kXh3J6tDFMRv8yPtodE76i7QeQb/Hy8I2U4bREylavpbQjtIt0E6tZUC/KuqEFqp10JejznK1LCdeFTJfFhplo5xz/yCdjF9C580cT+WGZ17+Rm2Rd49K6KrIN5A6LkUI512EhoUGh3dNttliYxlZOL56DbqOXYOuIf+sXY+W1uFhoTF3A60r1Z7IaegUhzHAmFHDFgWnZ2N03u9zqNMSOBidrzsAHTm/FQ013QEN6RuMLnQTRtlPAk6g7I6k7D7G2m2L1VHC/f0dqb1ORDt6zkMbUu056Eeg+8GPRDunBgPnoP9F7KBfj454dDV7ehsN9Q2Ejq5Gyu4m4PXhg5aE8PPhrBuWkY58nhLJGim7paTbnOkoZ9ndQXXbWPVHG9F9gevQsivkHxx0UMer0UeJPEc6f7SRskvQhvYq0tH+fWl/m76eSKXyeya6DSOkHUKjnHN/Rv/Xd4FN/DzwNeX1YfR56036DH4KON/LNqEYJbQD8gbSkc0S6kwFXUOjcJRz7lr0/hfV1eiuiNSjdUETcDSt2xI3oFs2BnuagJZhvSLZZ4HZpDvhjAFORevXMehiiFuiEayPYQ660bMpWr6GZ+tcfOeql1ftO1froL+KXwBFRDbyPbpFWAyM96ufhj1DWxZT8c7LarTBMjlK1xsN8QpU6plutTBLptIyaoPFwIRw/6LFcGKqddDj4xbbjPLP0ow+ZJOicxYDw0UktrOO6hpCyLL5D8Xb8Vr+7u7IVOBm5/jAOZYdPOuql9CyYApl9zLqoLwNjPSjiGUA7mUAunf1QNKFbpYCkym7Z9G9pPugjv4jJHIkidwP7L9ef52Sd78noKN4X0cb6JXmocdpQ4Mp+31Xt6cl6OJnAJxxzanN6H1veZ7Hj3w6LKI4BWAdjKS/DozObOP3Kq3ro3Be9h4oSbtzNF8jjcI5rZ38W5VjaIcAGdmbaGMa4HPtXLOnktceyD4Toe0S/69L0BHrCVG6xcBEv4tHe+V3Z9ZllXSNbWVpQV2N7kvWTvI6+yvZZlY2Bu0gGuPzGY2OrvdHO6I+ii0UZ/RsFgNbhMG2dsrX7LMV2gWxX1CIah30ucAeIjIYXZimaFz9XLQx+kkRaQQ+42UzRWRTdH7eWHTF2gP8HyCk+8cFKvVMzwXGisi2IjIN2LGqX2WsD8KiSHuLyCjg03Ruo2YusLmIbC0iW5OzXoFfvG0esL+3sa/4dAAHkj5IHdLVOfcGOq98P5//sVH++/nnplwkrx6EOkaBsluB/mdf8A6PoCOJ/dFF/LQT5AamoQ7rr9DGww7AA8A+JDLMp1uFOjgT0ZF2R+sOv/VF3v2eiEYOXIpuHdZ2BD3dHiemOzroDwO7iDBChN1Ov/aMzdARmwOCDew59ZYlqAO1vwhC8cXZihJWPd6f1uXAFiSyVSSbA+xNInVeJn7KxdXAGSRylre/vPx3Qhu/D0b570Qipcw19yWRPl72DvqMHBg9D/OBMSSyE6kDn4/IKYjcg8hBRf+IbsBc1FGdIiLboSvpZp+JAfj/NWpvLEDLiP186PuRPq96YHcRGYOGoq9vBz1P12czuh5VQVej+9KYc9xRB32rzPFQ9LlZjdqUYCPoRs9mLjqY8jERGUfYTagt2WerD7rW1H6+8/TYohes1kGfgzrI9wAXVpFunn+/FN23tR79sX2B20lDi+cCu6FzACejo18OnWsZFj/Jq/iCE3Q98Heq/13GuifYwGWo/dSh9/JktAFyEdU1au7z6fYkzO9UbqB9G5iDzv98AG3UvIA2cs8F/lBQ14vb0XUuuvryg163hegI8LleFirH2T6vU9vJqydwMfA1EX4qwiV+Tvo9aOhu2IrsdTQ0/Uq0/IDXmeE/fwTtLJzBe9yBLkb5FPAtn/+F6LZUJ1Dd4k2dSYnUjkIDfyJwOc4dhXYy5IW4j/Tv40nnQk9EbaoOHdEYgE4T+ZOXHUPXs6f70fL9XvT5BX1O9wT+DUzo0/uDcN7x/rt91+qKIoLIXojs5ztCnkGjay5AQ0aDDqChoT/wnx9Gw9zuQZ35QWhI6PeBu4DptB0VB62bVqG7DPwyyn8AGir/lUg2Ff0vdo5kR6P/RX+0jHkRLeeui37TDoicjcjhLccarn8FcBHaOd4TCO2Bv6KLMIZdDr6A/t83k3bqHoXWJf3QqI2n0Xv5MNoZFvK6Ev3/wwj6if7zJahjNAIdGBjg01RTl/3Mp/sRaf3Qnq59c3SdXEFXo/vSiHbUDELLnSFoZ/cf0LrgOFJ7+raXXYqG2m6C2msd2hk6CY0w3Ii0rb0jcBPODUGnZm3pw94NoycSytcErevbLEotIv1Rv3V7tPydT1p+fwXdHnPnbLpKVOvI3unfJ6OVRCH8Hs8Po/NcQgz/fWi434fRAgbSeXjT/fvGwGvOuWU+j7DidZYn0dDAUPAYNYZz7i10FGkTUhsoAYucc++hI8/VNGpe8ukWomHOL6NhpJvS/jzVYMPber2clw3Ej062o+uLBXQNNrxNJv961NazeS1qJ69uj2sS9429Zv9402ELG3ebfFvvh87eph4tACF1dJaivY4L0EibCxnMSqZzO028xTksYDC38E3mowXnMLTh8QjagP0uWsb8grQTZn1SAl7CuWa0Q6ge3af8Af/9I6jt5qVz6D7vzWj0wFjgZX8c5uuPBRZ52UJq2Z5E+iJyIiInIXIuIg3o8/EWuvBdmBscnqPto9TX+/eWcHg5Rlz8KqhDf7ShegI6b/hGDmEYOi2iDi0HHNpwXYqWAWH+2G3+fSt/7ltoVMzJ6Aj5UjQEvTVl9zbq0C9F5zufjjr5H6BhymHUPfzuj5DOXcv+Fw74EtqxWALOQ2Qf4HL0/n8VkbOo5xuM4QLG8xZDuJx+HI3ITohch8gliJxM8WlqXYlw30aj/2svtLPrBV/mvoz+b9n2Bl7WF1/2ON1Z4XHUAY/3mY7rgrFoJ8BCL6tmJ4W8uqAzdTW6L41ovbIMddQhvy4oAQu97CW0QxfgeS97BV20dBHOrULb2m+iDsaN/tzHUHv70Dr/VYZRgzjnFqODP2GtkDzCINDzmbrgX2gdUdU0Ealm73SRgg0gwzAMwzAMwzAMwzAAcM61t15NC1U56IZhGIZhGIZhGIZhrBtsrrZhGIZhGIZhGIZh1ADmoBuGYRiGYRiGYRhGDWAOumEYhmEYhmEYhmHUAOagG4ZhGIZhGIZhGEYNYA66YRiGYRiGYRiGYdQA5qAbhmEYhmEYhmEYRg1gDrphGIZhGIZhGIZh1ADmoBuGYRiGYRiGYRhGDWAOumEYhmEYhmEYhmHUAOagG4ZhGIZhGIZhGEYN8P98FdAW+nd+9wAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Test sequence 5:\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAAnCAYAAACc9WIYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztnXm0XUWV/z+VOXkZyUBuwiQQSF4gYQizzDMIyKRwwRBGFdFWAQVFUVHAXwsttAMtiii8JwpttzaToIIig+JE8gjIIBKmJBAChMzD/v2xq+6pU7fOuee+4K+z1q++a2W9vHpV+3zPrl27dg2nyogICQkJCQkJCQkJCQkJCQkJ/7vo879NICEhISEhISEhISEhISEhIQ3QExISEhISEhISEhISEhI2CKQBekJCQkJCQkJCQkJCQkLCBoA0QE9ISEhISEhISEhISEhI2ACQBugJCQkJCQkJCQkJCQkJCRsA0gA9ISEhISEhISEhISEhIWEDQBqgJyQkJCQkJCQkJCQkJCRsAEgD9ISEhISEhISEhISEhISEDQBpgJ6QkJCQkJCQkJCQkJCQsAGgX7sFjDEzgNuAK0XkOmOM+H8XEVOQNgi4G1gMvE9EVhtj3gX8AviRiFxq5deBrwKXi8i3bdrFwFoR+T/295j80cAvgUeBDwLrwjxalpOBi4GzRPiDOTuQdb0YYxgA/BR4A5jFWWZ1qAeb70DgWuACEe4qkGWA7wPjgBNFWIoxmwMXAP+BSI9V7L7ACcBFiCy1abOAzRD5EgDdxgDXATsAp1GXJ+k2w4CfA28CM6nLW8awKfAz4BYRrM5ac6Xb9Ad+AowATqEurxRw3Q843nGNytJ8XwSeRuRm/dUMRm1gEfB+awMzgHOBL4jIvDbsyQDXA3eKyE+t/NHAr4BHgA9TaAPmTOCzwAdE5EFjTB/gJmA6cLKIzGnB9VIReSHGy8r/LPAB4FgRecIY09fqdTRwlIgsMcZsCVwCfF1EZhfqsNt8xD7zQupyp9Xrl4CnELk5LNco22067TPvAj5FXQRjjgXejcj5Lq8xfBKo6zvybIENjwTuAB4GLhRBMGZ/4Djg04gss7JOAT4NnCHCH9vgOhX4MXAn8GlzX1BvmT19EliFyDesbvoDtwLDUHudjzFbAOcD30ZkboltngFMQOTLANF8xowELgNuQeRB+8xqXLvNZcBRwAepy+9Re70G+AUid1gOveJq0y4DnkSky/7uuP4IkYds2o7AGcAViLxs044D9kTkgsL37jYDrF47gFPNfbxCAGsXO6Lt5koRmupXrhdTIH848D9o2zqNuiwxhs1Rn3WTCFdZHb4P+CJwOXW5SelzAXAS8D4R/l6Fa4y7lfUB4ELgdBH+ZP3r9cBUYKa5j6cK3jvHtajeQq7G0Af4ATASeD9nmaUler0ZuFyErhK7+DIwF5Fuq68RVq+vArPMfbxVVRcYcwGwHJFv2t/7oPZ6FyJ3lrTd/YFvAddTl6tt2TOB8Yh8xfIywHeBTrSPehpjRgFfAroRedjm2x64xb7DxdRFjOFQ4GrgEyLc43Fdhsi3IG7DxpixaF/wAHCeaKdxGPA+4CMislxFmYuADwH/IiI/s/3KdcDOwJEissAYMwS4B3gF7R/WGGN2Q+OMz4nIS1ZWHXi3iJxrf6/al01CY5Krxfax68G1A42pXgbqIrIGY3YGZgFfQWS+yuIw4Crg4yLcW+D/nA3cichdto42QeONPwHnUpfVxrAz8EPgKyI4W/wX4GzgfOryC/vMqy3X4zjLvEYA+8wmrnSbk4BLgcuoq60bw6eAE9G29VzMPgvkHwi8F7gAkZUlbfffgJ0qcv0yIgss15OBzzuulX2iMVsBHwe+iciTKImDgGMcV8vrNOCTwCwR/kK36QPcAGyDtq1n0DjoC0AXIo9YWTOA03JcjTkR2AWRTzXIGHMh8DY29kZjl68DdyByt31HF1/+EfiIuY9VBTqcBXwCOE2Evxb0BSPR9j4fOJ26vG0M7wL+G7hBhGtK/N+FwBJEroMiP8Bo4HbgfhEutuUOBo4GzkdkVZU6isHK3xL4r1ZcjeEiNF46UYTn6TYDgf8EBgCnUpeFlusdwK9F+IwttwtwI3CZCLdY/mcDYxC5ovEgYy4HZiNyiy03GK2jF4CzRVhHt5kOdFvdXmJjwhOBGYh8OhOV52oMfdH+aDBwkggr6DabWfl/AD5CXdZU0at9wBXAXxH5cUm/0sTVGI4E/g/wURF+bWV9GnizYQPKtQsYVMbVGHZFx2VfEuHHtuwZwL8AM0V4zI7dvg5MA44TYbExZijaFzwPnAqsaeKvPv1UYFcR+VimV3OZ1esxIvJMWC6GtlbQ7UCjG9gcuMYOYqriUmBf1DkeZ9O+D0wCPm+M2dwYsynakW8CfNMYM9HmOw0d8JTh6+jA9Wxgjzh/NrHytwf+rUTWJcCRwCmog4zJGorqohPtJItwNsr/cPt/bJnzgBlevgNt2iZe2imo03Y4HTgH2BX4tk27GtjP8rzIpt0A7AhcaQybtMH1M2j97I9OkoAGq+ehHWvIdSJF0ODBBagOlwL7AMfa54Dq4HS0g2kHo4AzgUO9tGvRQfYHgd0LaG0BfBN4F/A1m/xBdJA6FfiGTftCb7gaY3ZFB0rbooML0AH2caj9n2XTOq2sHQvfsNvsgAZIncCP6Tb9PL2+v6RcH3TQMtXmPcL+5b3AJ7Ht1hh2Av4VrdsvRyQ5XAPsiQ4mnc3mbMAYNkMHN1nbqs71J5brhR7XGD6E1rnDJajdHwBcadO2o9leYzgVdcZl2MLK2reA6+HRUt3mCMttOhq4AmwEfBT1Kw694xrXa56rYneb5tvre4Hz0QnTIlyCdrQHAlfEMtiO8BZUF98xhv4t3sHHNWRt60KbdiOqr381hpoNAr8PTAZuoNuMM4YZqF/aGW1jAJ9rxbWA/+bAf6D2erVNPhu1r93RQWcR8lzj8ndBg4md0YEoqP2eCrwHbfuxcn3RCaBOyvSqNnAhOohzuAbYG/U1F5Twj+HD6GSOw2jUdorbo04O34LW0VV2gA3aV3/My3mOlb076ntB/e95qB1Ad2MSsxOd5DvUGEaggWEn+foIucbwDbRuzyVrX/ujeq8BGGN2By5H45kuO1F7vOW7MzoQArW1vdAJ9PfYtF2trK29Z74H+LAxZmALbiGmWlnTvbQqXE8o4Xqix3UPVNeTVFahXkOMteV8P/dDtM86C5jp+QFnr33pNjPQNjUV+AndxhjDe9HB2j4e1xj29LnSbTZH45nJwI10mzHGsBvq72eQ9bFVcYiVH223AMZwLBp39Ybr9zyuo9vgNc3K2iHCdbzl9S40fptGFrt8CI0v9wD+3aZtacvtXchVcRxwIcb4PuYj5H1TmQ2cTUFcbgeujutV8VcGy/ndqC07Xf/QlrvaGMaVlA25xvAN1O9cZAzTbFpOr+sJn+vYWAZjcG13FzJ7/SIaCxxMFnt9E9gNuNgYphlDP7K29V07wQta3+d5D+iD9gUneI+9zMo+AziMbtMPjQk70Tj/QJvveNQG+lmue3hcv2DznIfG8ccAM23aTaitnoP2aaCx+HnAxgW6chM+Idc8lOttHtcDjGEUqutOsj4E1AZmeb9/FI2LCrnaPjWnV2PYCvWFfts6Hu3H9kN9F6hu9rD6iMd/iqOB80xDr2YfNK7qRGOWSmh3i/vuZA18ABVX4O1M70wvqcPoyqwfTA5Bgw036Dc2DdSZljhUMwTtkBryC7Ke6MkcHpeFId/oo/nQyhnXIg8FsmrBz7K0UV4wfbb3t5F0m8FkjQNgpF09P8j+boChlbjqKkdefntcQwxDde0CDIM6FgdXR1VkxZArZ2e2/EZfZAPvAwYGefw6Ghaz1za4norq3S/XW1mzgL72/0PR9jocbSNl5XZHHYFDUV3OJPMBRe2hg/xAsMiG/bY73PvZiuseBVxjyPyA2utZ3t/K7LVI1hiMGdAij/+zKtczI3l6247c332uI9AZ4ir+oygtHpwU6zXEHmQD/8FkdlqObjMUONmXbwPP/ezvBm0jJ5H56n42LW+vyjWm6yp4P832GvN/ORRwjeEDZH7Aya/Sr+xJ1scOobiPHoX2wa49NOm1oFwz1N+FfWwV2zwKcgH0CK/MWBf0Ua1f2Qsd2Pj5jgTG2N+Hl3DNwRgznGwRAIp97kyafXWuj2qzLyhvW8Wo0nZjXMOYqirX96ATMFAeu+TLdZut0IkDh5Ho4MpNUnSg9no6zf1KFduPcT2ZrJ32R9uEr4syWVXkxzDL+387XOs0c10fXmHaSajf93mtT8zm/q8Dqip+oNtsTeb//GeGOIksziqKOYeTjy1GGsMk1KZAbSiuw0p+gOHkY8Ky+LttGMM2qN8q56o+JbNXnejP9Vt20uz4gOs+6GQLaNtyMmrAxnawC+oj+9GIhelD3o85Wf7kjG8rhsyPx9pWvu12m21wE6vNsvyfMYxBY4WyPPuQn/gciQ54N8rxUhsYH8iqwnUfdIIYNK4G9TOhvc7yZdlJUX9Cqqjvh2a9Vi2XQ7sDdDcT/W3g6TbKjQcmAA+hMyO+rG6w22F1RlTQGaXnoTH4Hg6MNsXB9Haocu9Et7UVYRd02/NVwLKCPBujq9h/RLeblMkCnaFr2gKl3OmHzjQ+DXbrl6L9YFpnlXYE5qErxevQ9x4EPIhul4FshfOnwJyqXNE6mgj0oDPW7XINEeaZYJ/xkMfVPdf/WRVhMDQVDVjvALv9JY6d0W0plwFvG5093gH4O7o6t9bj6uvVf2bZe88AlgBfAVZb+dOBuejsuoPjXSZrF2Cl5fVmGxxcffttK1Z2F2ApuuK9tkCWa1v3ovook7WWfNtaH6556ATMUGCcDfwn2H9z0JXWIl5FyAcn5Xn8dwTdQvWnknK7AK+juxNWlvDqLdd2/Uc7z5xo/zYbXSkugtPFDahPqortUXt6AN0aCJnPug143JMv6Gz2fC/tbXS31No2uBbxd/a63G6V3wH4B+on1xWUi3Etkt/gaj+b2gH4G9itisXlQPX6Qkm+sB6nof7vt+i2wHbgJtL8oK+dtvst8vGAC042tnqdTqZX8fL4P52sm4E/B2nXou2piGuIaWjA+nPgfi899LkzUF91KfrpnUubh9Yb6Ir1GNRefb3G+q13qi+LpZVxvcbjOppmGyjSta/XMl5hOV+vLu17wIte2gp0lXuJl/YyusuqDLFnOj+wwEtbgtZRUTutKj+GXYCX3iGu68MrJn8N6rNW0G0Gof707+iqYlHbqprmJv3G21XZIg6gW4Z/U/I+ea5xTEcnMu5HbQoy//oT4IkS+T5XU5BnOuoH7gZ+76W/IwN0Mq4/ppzrLsBbaDtdR+ZT/oKu7vpc7/K4Ol1/F7VHf2KiL9nkZfg+Tv7DYD8LymT9EPirxy1Wvw2udqv89mj8emsg679RX1MkK4Z2+hWfq0u7Bv30GNTX9cfagOW6XQWuLu16nF41bTVqryu9tBdR/YNOlowkb69FCN9zBuq3r6A41m6GiFT+Z1/ocbTz3RMYZtNFReXyivf/Q2weN4jaH+1oXkODtS3R1ZjHgR/YMjsBW9l/62z5zQrkn4kqdzTacHeL55M5IDfa/x/ZSD8rl+dgEAGZBjII5MCCfHeB/DImy+UDmWxlHQHSB+QIEUHgGwJrBW5t6Az+ZNM+aX8fKFp4rcAe0sUU6UKki6NFBOmiLl2cIV2slS5q0kUf6eJkkM+DLLLct7H/WnKVLg6x8ndqyFce37QcfuJx/bNN+0RUlubZz+ZZK+pMDrN1OMXawH62fn5uDfbqIvuJ2Rg6I7UWeMH+flZgA7sW2Mpc4Hr7/0PRBi3A4TbtxIDrQGB/+7f/KeNq28VbwOcj8g+2fz/E/u1bVtZPcrKy+jDSxVvSxeX29xOkiwEC+1udrhHoG7NN6eJ70sWT0kVf6WK0dHGYrZPXbNkTQAzImyBfbNEezgRZAzIWZAzI7lbWX6ysj9t8j4PckJO1PlzDfDDJyhKBCdLFYdZedwjs9ds23y1RvWqewV7b2jWaR/N91uZ50D7jhoDroZF6G2l5fcj+foqVNdPKet6T3zuucID9fbVAn4Dr77yyt9u0f/XSFtm04wr8wOGW//ScXpvt4gaQHuvXDgQZFPUDzfV9jnSxRrrY2OrxJJAvgSwEGQgyBWRLq+cbbZm93rh++JYgS0AubdhYxnWabS+nFNZlM/8nQL7rydreyjqixXuHXLcK81md5LiCbG/7gkPs3w8vkP99208ZkINABkb1CgfZelwlYKSLD0oXq6WLcU6vVXUhMNlrW+Nt2iyb9lxJXf5SurjX/n+SdLGrQIdnrzNs3Yh0qX/12unnbJ7f2vQbpYu5th8bI10cAvJLkLsCn+Jz3TiuQz6MBlkj0UB1hk2fY33uR9E+aZnnq49Ht/MKejYJaNxylE3bGp0M39f+7S4r66vec9+wae/10iTWlwW/X2/LdXlpPTbtPI/r50q4Hhrhup/V2V1WZ1daXf4K5M6cXuM2doYt96yto69IF69KF4OkiyHSxbEgPwB5zNrrIbP2uWGgdLFUuviiLXNyx8Alo63tz4o9M2ebcLfPVbp4Wrr4nv3/3q9/Z+TmIEtBLmkpKy6/x8r/SOy9QXrD9YoI132ki02jeo3L+q6VdZOX9rhNO9dyeRrkOs9n7Wjb1qFB2/qCLXe/J+sXOa6a9pZNO9r+PtVrW2Nt2pk27Rn7jMuli4W+DRT4sTzXuP84V7pYZdt7X+ni/SCXg7wCMsD6zM0LfJbPdUwBh3NBVoKMAhkPsrMtO9eW/XCh7VewJ5ArAq6bhflA+oIsB/mMV29H23qb4votj+tIxxXkJpC/2LZ1GEhfgRGef93Bvs9h9vflAgbkaGvDk0A6QPaXLm6WLmZb/zpOujjIll1iy74nxhVkJytrf/v3w6SLK6WLBdLFQOmiQ7qsv4MnrKwPlfiUw22eZQKmwC66PK4bSxcHgfwG5Oc5e4LtPRsY7XHdz3I93HKd73MFuRnkz1avh9v++FmQb3nvPc7K+oDNdyT6SZ6gq++DgX0yW8j1PwZd+FqL7lbqh05SXWj/3ogbW/1rdwV9MhoAiuhBRG+3UW4xMFdEVqEzEJOB34vIShH5O/As+t3u/fZt/4zODI5HD71ZTPHM9GQre5GILEYPA8jBbvuYTDaLcmeBrCloRztHhBUUr8ZOqSgL4LcirEM7ddBZladonjH309zPZ+z/3dba++xP9w3FXOryCnVZh87kTQH+IMIKEZ5CVzaqcO1EV2n/4sl3PJ4mP+MVSwvh8vRBg4nJwCIRecLagJt9raGrSu3OZo635cbbrSeTgcc9G3g0LGDPUNgGXQ0BnV1s1JH9eVvAdSXZakHjmQWcJqBb+335btvmg9Zj32t/d+9dJGuileXs7z/RCQinV38GNUQn8Bvqspa6LALusdujR5PZ2AR0NaqKDT8uwqsivEY2u9uwV/sd4rZkdXpnkKcv2ZbK1lzjGI+uSrxl5XaiPuEx+/cye43JAvU5VWzY5e8E7ve43hsp4+zJ1duP7M+sfWcz/jE/EKKGdgrPevlcuX5kei3zKfqO+m3sRrm0ZnSiq2qz7e9Fq71TgAesX/s12axzK3QCPdRlAXVZi66UTAF+L8JKEZ54/KudL6Db8VSHdXlws4/NW4PuoPDttRNYRF1mUxch03Up7M6mSeTt1fnX++3PsvducEX7qBATI1ydXTid3V0i/wGNP/gV5A9g8uBssz9qA06vCz29VsV49AC0t2m2sbIVqk6yOnoa3QFTQyfUnb12ovbrdO30Gtqra1vrqMtr6IGvsX5rPLoSu5RiG54MzBaRN0TkNbLdLr7P3QwNsu63f/sp6sMg89/3WlnzReQZEVnh8cn1W/Y8nhGsX18W9rEurQpX19f4XP0+1m/zVeIBx0F9qdpAJ/AQdVlBXZahq6i+vd573RkfmoBu9XX+78crVg8K+9gqzxxvD9LaiszGHtj8Y8+7rcRVZMXQyue2wzXTazPX35KtzlVBaZxlDE5+6P9ibSvst5rT9DDBYTTHnC8By4n5AUUn8GBgAzl4XMN4IEQn8Bh1ec36rFtR/T8iwioR5lC8O2s8urq5guL2NgWYLcJiEeaT7cypEiNUwRTgYY9rbMfT5uhkWVhv86nLE16/NQV4TIQ3PK5T0LGDoAc/rrOc16A7ksI6GoTGdFOA+SI8LcJS1G/4/nUh8GtvV6LzDTGuU+xzHxRhreXhbGAldVlKtprczrhgMMWfj3QC91muC9A2VdQXvIDGHuNtnrXAQ5br3QVcfb3ejS4YbkneXv2xm9i0ycCLIvKc6MGdzveGGIr6KNfXbEG2axCKY9wmtDtAH4m3lc0OOCqXc/ntz5ws9IX6oi/ly6+hgfkCiiu+Cq8ONKB9SvNQxH2UckUq5Ksi6xURncjw8tXQgYULnPui21ezNP25GHVQNfuOL1MX3TKmA/KRePqyaQ1e3jOrcB0JPGkdhpNVlWsMNTRIW+bxj9WR26LarrN05dwgpaoN9HX5bJ4R6Cr8Ui+tt1zdtzi+/JFoo14WkVWmQydL67cuYuumhk7aLG9RNrQLtz3a8R9lf69iw3l70i3m4zz+Q1G9hrKqcn0y4BpD6Aeq2WuxrEVoB98qn8rSADWm19j7rAGeK+A1gEz3veVaQ21sRZDmc43Jdx16KxuO6TVEzqeU2E9MfqnP6tzkiWFo39TI99byEa49+DZWpT5iGEazvY4E5tmgs9J7e2VjeUKuo4AXRFheUi4nv4Ve3QBxFVl76I0unKywj3W2M4js2/IQsWfW0J1xL3u8YnoNbTPmB2L9Vq/iAZNNUDrbd+/0N5fHlluFDbTb7Avcz54SXkXIyfoncfV1XSUe8Mu5YLplHQ3svyrst9atXddvlOX6fBvPrNlnGjwbW7JieMwPVEM2QVnmc9eXa5W+rJWsGFcnP/RZ/6AuK4LnxfqVor5gDnkbnk/cD3RgzDCq+ZkY1xiqxq8xxLiGiMUug2x6q363CnrbF/QqbrdpNWAhOqHq19FcdHBaK5AVa7s1dIKnxysXch0F/ENEJ4oLZekE5UiqxTM+1xhK9RP0Bb4NFHFtJcv51/C9V9Dwr5T519g7rrPv6fpAyI8LKqHdAfpQdDW7XQyl+dvnMM19rB/Kd5Uwn+IKjckP4T7MX1ghX2kee5Bcy3wlebIOWYPpsWhdhB2+H4gMjciKpeWe2QbXmCzHww/8Y1xjcPxdvTXVkV353riCrCL5foBaxTZjNlbFNn2uZQ7IyQ/tOmabrQb7RfYa6rWobKzcEnTmtVYiv4qscWgH7Pi34lrWiRbZXYgq7cHlq9JJtNKhyzebLECt2o5epy7h1Yw1tFNbQ76NV21HYdAUS5uNTnQO8ybSQp/yJtmkXxH/KvVRRRdVy4VpMXuKpVXlGuPQW1lV/X4ov6q+quYLbbi39eHLCu3pCYoCKT11fXDkme2009nAUG8lx++3qsqPIeZzw8mpIl/9uoisDdLCvqAf2g+Gbet1yttWEYoGTyHXRe1yJeBq9TqI6jb2ONnKXct4g8z2Xw3SFtlVrWI067XMD/QmDo0NSkNU5do/4OrqaH3aYJkNtONTXNsajjFDIlxdnkXoinmrfuVxaAwOq/jJqrFFFXsqQhU/EJNVZZK6KtrpC0I/U+W9i+K4WB3Nt3mL+oLYM2vkJ1NjXKvKqtK23DNf8bjGEI5h3IHkrXRRlWvVeOM1u9vNl9VqnOl4vUrWtoai7afsvI8o2r0HvQNdDWsXHTQfyhamOQe3zOj93+7+U1cJ/Smv0Fb33zbkV8jXKs9Asu/CWsnK6ys75CELpvX3VegMzik2Z2h8yxvP6zbXWtkdwIt0m7HowSRPgYT8q3LV+tDD6L4HvMYpXEDWcYRc/4aeWlqEGrrq5zeekIM7fbI3s5k1dMuLL7/VljLXEP06aejLGHOd/X1JL7l2oA1xZZCWk2Wy0ydno6fvDhORJeQxFA2MVtFtPo4eEPgF++x5tA5Ql9Ft9kSvkLvDcnL2NM3yUvnlGEp2+I9DDZ1dfAY9/dK1rdA31NAJgVadqM/1dupyaySfaw8d+Pakd6F/F1jIKXwK1etjuMBfJPYZTpXO3eXrIR+cLKPb7IVe2RHjmtW33oW+GfrNaw39PEU7JmPmktnAMIzpwO7iqMC1hm6tLguk3qR80m9qwTs7vQ5Av41dSF0ujOSr4idjcDocjx5++GTEZ2W+utucBhxw0h7dD93y8MlrRPAnPkKuC6h7d/qWc4C8vfr19k1gIHU5i2ZUee+hQMi1qr6q5quh7c8fCPt6fYK6XFlSPpS1gOYdGfeigYYbrPtwJxYvo9scjX4X/QOa7XU+mV6/BfSnLmeTtS33LFeX7wbOfvqqre6ddP4z0KwLJ99NzMYQ62tq6Bb+59AbJzqAlSKy1uhdujugn3e1ilNAJ74M6mfOD3gtIH8afSlMfkfaCLtV3k2mOq5D3wGus9FrrFwfWNXG7iazAVdHU4FPAb8raLsrqctaus35wLRtxj/Z89T8bas8z9frx8n7gdOB/Y7b5baHf/ro8StbDqCL38dNUh9fkKdq+/N3pH0CX6+WK3AVdZkdKZtH3gbcrT0h11j8mnHtNt8G+lKXc8jiS+z/XTzyGNl1nb697hGkuQkZl3YHOjDxbWB79CrHB6iLO0TL5wWtxwpO1gT08KyeiD0VwXF1/48hJquGfib3PPmbHnqDKlw7gOXBQM+992D0KrqX7XuH+oqllU3O5/uCZh7L6Db7olev/Te6m8/J2tU9LzIoLZLlbOC3aP/wJqrX91KMkGsMri/bFzjjr5dPu3eHzzxGhIfTRR/vvUN9Oa7TUF/924iuY/Za/N6t4dfRLjRsoPrKuUO7K+gDgXBlqLflwjR3xP1q9IC4k9DT1KsE01V4+fLb5fpOyhpp0/3gpKjR+WmDPFnvQw9Jc2lD0esRDo08sypXJ8td1XJcG1xjCPPFdFFDV2ieQK83q3z9QEX5IWK68MsdS3YNW4zrGnQVdLi9XSAmf3XQEGOyNkKdoztlP6bHgcAqu9V4f7RONqK99rClLbdTpJzl2nKLYJEuQlmUQmKbAAAXJ0lEQVRUyBeDszufawxF7aEPmb1W0Ws1XtlE2stkwUlMr0XvA3pAyExbLnymO4G0N1zzaRnXl9BVEddOV6MTaSPJAv+q9eH0emxBvvXtC4ZZ+YdEZPn2tDswc/TQRRMiz3Nc+7bgGuPg5IeyQK/+OYU4qvqZVv1dO2VjKPJ/w8n0WhVV+p8Q7rqn1eiE30z0fI+yfkv1mk1QvoiuKvj5trKydvTkt+IaoqrPcnn2QeONEZFyRbJWolsWN7L3nlftF0O4a4d8P1DGdV/LdXgvuI6ewaMuGO2Njbk6qqF1tFfkmdpvKQ4EZg4bvGRkG89zXMfwZmPQuxodRM4cPXSR82u9wTsVSzpZbpJ6DG/h63UPVD8TK/LydyVCdnVUqz7W53oyULenr7tvc98gsyfHdaxdUa/a5lvZwJ6R92k35hxhZR1M7/1fDFX8wPqgt32Be+/+6HsfU5CvKv8qsbB75tb2mdMrliuTNYHMBno7LojBPXMSMLNvn7U72PR2fHoR1z0i+Vq1rbK0GHozNomi3QH6MrKXabfcoBZpbmYizFelQmPyY3li8v/ZskJ9uXcIg2m3jd+fQfW39sdkFaVV0WuMa0z3gg5SXCDleC3wuMbg53P8w7zj0RXF+d7vVeHbxXiq2abTxcAgrZVtuuf5XGO2uAwYYLIrSopkjSfT6+ISWYPsXc8hj1bfXhXZXZM92U8gytBK1ugteM45n1bPrCo/hhj/WB2tQ9uWC07KZJXp0N01HtpwFRuL5Qn5O64vVuTq6zBMcxNpofwFZDuLxhfIivFv5SvayRcrV9VnNfINHfT2SmBQYK/r0x/l5Lchq2r/EOP6Tuo15l97owtfltpFNuFTZisxHTbLivNyE2mF+UZ1LHY+JdbGe2PD7pDJBeiAaEUkTyytlSzQ1U8/rd1+bC3qs5bQPDhbX66+vvgylwyz6eU2lreBsj481nZzeQb2WxnjGoP/PHissUujyQ9UkFUmfz56XWfsmr5229/8Iq5t8lqN2oA7/NDXxbjhvOmuKgt1HT5vNLrTz6+3vF7VXlv3K3E/0Ns4qyhfq76gCL31A74uyq5qrILKfUEkrWosXyWmqhIbVY0Jq/iU9dFr2/FrG31Bb/W6PuOCGMIxa29jpbYH6G/TxiXrQblwxTFMc1tRh6DbL9z3ADWy+26LKjQmP5bHyW+Xaw526+KqXsqqoU74ALLDs2royvU0m8d10qPQkxVr5HV/vSe/Ax3k/aeX1nhmm1zdtmd3D3pVrjHU0OsIRnj8Y7pYha4Ql21ZzMFkJ5Buhxp+qJ8iOBvoCNIcr+siae1wdfIHF8j3ZS1FdzwUHaDmtjsPRreOulUJp9fhBeXcMzvQbZD+CcaD0VWusV6HP7i5eJOsGP8+WBs4l2+5v8fy9YZrDM72xpGv77Vk96A7vR6EOsWyQe8odPt5WR7QlTy35c89888lXH07/A4Av6bDpk1BBybuXapy3SjgGuo1xtXZ615kKx41tI62IbtPvoj/GsrvFq/ic4vKdaCTk//lpcX6gg6b57WRQ95YgfqdgUG+KlxjHIg8M/SvRWWr+NIY1yr6qpqvhu7kcJ8dOf6v0f496DV0hWGs/b+7a7yTzF5DLEcnGTvQU3bdafbOXjcNeEGmVydvJ5rb1p+Av4zqWLzGyo/5FJ9rDEU+qx+6PdzVTT97INt3UT+yLFKuSNZqYG+yb/Rr6E62ycDGpnrg7979cDI/EHIdYLm6z3l6w3UPYO3ePDCKuF5DuAnKTrLPC10dPUN2onz4zKVAf/vp0Q3A2sEDlse4xuC47g6s44mG3XSg8c3rIwa/uQLoZwz9K8iLye+Lxg190L4kRDvtr4zr4qKCBbLeRleQ/QF6g+ssbnQBfpHP+o4nC2Bn8ra5GtiNfP/Qga6ounvPXazt2pa7a3wqzTbwNMX3oLcTa7vvgX/upVXV/0R0B0q7fsC3gbEVnlWEKlyXAgPs2Q9+uQ50t8hNXlrV9jwat7s4m0TZCvU/RbG2e+ajZDffOJ+1NTC+L2veLuBaJOsp8rda9EP1Gr9hKM41hhzXscNfdbFvTBe+DVTlWjb2pCBPUVoMrg/dFh0fvQ0MbKNPaKDdAfpSqm/bCctt0iLNDUgmisivyT7Gr6HfzZxHcYXG5MfyQGv+Vd+xSr6lwCbBSopzjHeTbeGvoRfZ3+blqaHb2L8EjGMNy7zn3ZLjUJc3yK7tifGqynWCd92F4zHUyvYnSWYEefLITsq8HN2O5wYjYR3V0AmI2ykOBGNw+a5Dt9E5+VXekSCfX+5WLy3GdYsWXJ380K43Nfmripxeb0eDzTJZE6jLT4FVvMxAdLX0CjK9xuDq8nHy36Idgd6/bt7D7a7Dr2TDQVoN/bbmJwAHc6+7LiPLl107dGUFrhMt1zkFedwzLyDzA+4d15BvN8NQey3Sq8t3GvpNf9Fsryv7M3Qiwn9mGdelwEb2+zJtp481vlf8Hpm99pZrB+r8v4puGfYH6D/3uLoB3N1kwVUNOBL4Bhr4xwLUmF6L3rO3fcEE6vI6emVLTJZv+/cAr4/qWLzSpjW3XT2Qr4xrjAM0t9PQvxaVXV8/06pseb5sgvKr6Cc5vm36eq2KGnAh8BHy9nQDOiHZbJt6Gq72SXV5CHvqtc17OnApMB6J6tXJ+x80oMv8d116gJ4+fQTiuqih3z+fG+WlKPJZuwI329/dwGaiiPwXOoBZDoyx34G3krWV5d+XfNv6OuVXYIaooX7ydrKVzRo6mHJcXVA4UUR+ik5ItcN1a/SK175DWO52m7WyRafbG9HvqX0b+wfwiPfMorZ7G7CmY+DS5cBYe/1Wq2c6rn2Y1zhdeQJ1+QWweFTHYjex3BvfU0MnKn7k/R5iaRtcJzW4vtDgOtFxbZPXKNRXuwlox7Ub4HDucjsfQl1PsP8P29bt6OSKk5VxzdKOBq4luwmnBnwa+BB5P3Aj+mmF306fI7OBEDH/WpRvYnBlaVU/WQMu8rgWy28utyfQ5f3eW6xfX1CXleiViS6t1RgJlO9ZwCVkvmMQcBV6VkFRLOyeORs9XdzJOgb1Wf335gE36dWq33J+4Dny1+7uSeazYnr1uR5XkMeX/xgwd0C/1e5cp1hdXoyeXdTqvf/ucS0ae4Zp4+wBdX5aK5t2vI5GzwVzB9wR4dYS7Q7QF2IPQDHG9LczulWwANjann7q7gxd4Mky6MzeUrL750A7On+Gq2hmuiHLkx9iOTqTYZ9ZuHK4EJhk78ptla+KrJHuHWy+0ChrFdL68CKr0MDf10eDQwEvY59Zles29nRen0cVriHCVfUaWkeTXP15h+G0khVDrFxOFwU2sAy1sclengXAaGNyeu0tV7frI5Tv2UDvZAEwp+ku8SJ9xewil/dUbnZOo4pdeHpttuEpPDEK1e3kojxtcs0ju3bIYTxS2V5j8NOLgumWNlaAhejgd5tGyqLGAN2X1Ruu/cju624lq7dtdyGwLd2mVd/g+5Rw1r1SuQJZZvrFf12NbuFt5Ntu05437H99G6vKNcTbaH8QyhpHtxlVVjDkWtBuAj/QkD/WGDby0lrJH1ig197aZjOy63F8WevTdv28/ZnPcmDjQK9V+ef7rRjX+B3tsXggfKaLXfxnLkLb7iSv3AJgG/fZUon/fif7siKufmz0+npwrRIP9KaOBr7+9ih3unoj38SNXgq4Vnzmy4xAdwE1ZHVuMvdNX36JrNbyi/1f+1xfZHjIdT155dL25KFhvnzPp0yg2wwPypbKKkhzu3FalaviZ96KcI2hSvzavDU4m6DMeMX9QMvYJfJ7O2jNtbgv2Db4fDHG1Zc/yJjG5IrDIPw4Q+HqaFu3MBjKCvI2cApdrh9tsjFj9M7yqrIiv1fNQ4H8sC8YRn4nrHvvicaobZRwzekV/cRwNc3v3RediHVpC4Bt3WJbwRgj9l6unv1xQSW0G9j0AAcbY9xBNFX31fegW8oON8ZMAI6yabsaYzZBD7TZwqYdbxXg7h32URRM9wBbGGN2NMZMI3JwhT0M63HgWGu455RwHQQcagwT0IOeivIdZQfyZ5fkwT5zGHrqeRVnOYFwdeu+xhH9x5NVeA+wFd1mhyBtN8v9EHSF2uda9t6D0a12/j3KrbhWaoh9M70eZoyZiOr1nQxqeoAtjTHTjTHTyU4mbcAe3vY4cJy1sQ+S1dEJ5HXYNlcReR39rvxYK/8ccjZghlNsA3nU5TXUIZxgHbnhxSbbL+qYeoBDbccdrcvDuXsI+u1OlfawlTFsbwzT0S19OVkDWTWe5rbV/I7FXA+h2wzzuIYIJ3z68w9eQB30oUXvGPndv8qnPF+xjR3aguuT6GRj1k6X9nKAnl2P4+OfPUDvoVmvMfQAhxjDUHTFtOpkbQ8anGxPvr3tYQzjgcNnz5s+AZ3lb9j+Xts89BbaaTobO9vjekgLrjnYU2rnkrdX1059/1rE33E9gsisuAivFnDFpo1Ad5UUyT/EGDpQvcY+Q2iux3X0AJPpNtu14B8ibFsD0VXMsuf5XI+xN38YltEXgknEBxpXgx1HeTvtAQ6j2wwlbxdH20mKcwq4hjGCK7eNMWaqMWZndLdP+MzB6C69E7x44zl0+/exduv76VbWUOAgY8xmaP/4/3qAHuP694DrGW1wDfValVcP8G57U4BfR4cawxDgzL2/9MCr6ORBo+2ec8B3PK4MAGZVeuYqNkYPkG3I2m/K/YvRiZRj7YClKPaq+k4h5lqu7+0F1yeB4xv99frxyqUNZ4nThTsIs8hn9bYvCPuVIehnVGE5ZwPuxP0miLDGcnWneZfFFlPpNlPI29NexjAOXYWMfUIZch9CfsDuy+80hm2NYQbZYbllstpBD/BuYxhruYb9POgZMIvJ22sP6rcOIP/eIdce4DA7ODzrfvZ1uxJ9xOKBHptvP2PYHO3Le4DD6TYdFNjKLG7sV8AVtD2MQnds9QD7BDbwTg7Qe4AjAq49wDEer7Ds0Bk86j6zCrnuTbcZF8hyej0bjdeeJG+vcz1ZA1E/4PS6vzHmXaheYwi5eXo1fShuD01od4A+Bx0gP4Jula2Kx+3P76PbDIaiLzsA/ZbJbS3uQbdT/QGdLXbfSw1Dt3VCcVAJuuXsVxS/1xy0UTyKbhOJwcm6EX3PoQX55qCN41F0O18ML6JXD3wN/bbObRP/BhpYfo7MWc60abejAVIf9BsG/Ubn1xh0ZvIqsu1MbpvtHR6HOWjg8hsyvfpcP9/ivX+AbnvC8vp3y+HzHtcP2LQ7KG6IL9k8U4EBX86uQLsR1au7LutTaAByXYGsGGqojQxGg/MamS5up5oN/AkNauahh/N8jUyvzl6LuP5HCdcedEvyny23F9B6+5pNczq81spyei3iehrwMNDB64xBbaoD3RpaFqBuDDwEnMgajP19P1v2r2Q6O93qoujUZ2cXdwC/JNsid6qVdSdZp3AQamNn2DTHdXt0wiPsXNw7jrfveEIBhxr6Pd0w3ADg1saE1U3ANV6+a+0zv0Bcr65T2ZrsW+giG77N5jnLe8dyrnVZjn6fdzGqL1jJxmR25ILmqlwJuG4DjcmJaagNbe5xdZ2Xs9cO9LtVN+m3r00rugfWtaOb0K1vRXD19giZv6gCZ093odv0nKzB6DdiP/byHWzlT/LynYna68Ee15tbcC3icQhqr7PQwdlSK6eruFiOa9lWeJ/rQeh1SUuBfwP+SPHZHe4e2UfI7DpEDd1S3oGexjuEeTxn/+brtQpqZDeBuAnASajv6EC3bZf5p2lon/5unm/00Vvizka5o/G99NfJb3+81eZx2xPdez9Mdv2Rk/8o2p58rmM9WSF8G7sHbe819JOQwegnAK49n4H2JQPRbZTPAJ9BfWSnJ+tmy82twn3S/v97ZN/E72nTekp0FqKGXic7GP2czbXdMq4DIlynlHA9H9X198l0PR3V60UlvH5nyx3llRsCPEAWZM6x7/974OtzX5rq0s5CbXjADps/tgJ4FvgsetZIOKjwn+mugnNcdeJGZW3Zr+9aJ/9stG0dUCCrSP77rfx7iE6ys8xyvaQXXOeg/v0Rmge3rXhdZWV9hcwG3mfT7vXkH2EMf0T732fRnUbXAj/0ZN1iy33Yk+Wugvs+2Xe7e9q0uWibX4m2LdfvbENmA0d7HDpQGyibHOkBjvS4xuD89y/Q9u3SnPwiP1zzuI730srk/4LMD5xon/HLgnJV4bj+roirXRicg/q5P6ExmON1C1rvkLVdn2sPWle/B67ejh7HdTP73CVovT1rf58BjDiYe562+X6E+vFB9pmboH7hGJbTFz0rZA9b9sl+rHX167jui/oYZ2OPon7Or6Mz7bNqaEzUgcbfRTFVjivx1eSeHFfFHHTS4o9oXFNDd8E1vmX/GccsR+3i320+x3Wo5XpWIP/3qO91aUdZe62LNK65/Dwat29LVkfdwINEDkE02ee9u6H+929kfuyDqE/ZO/LOUbQ7QP+d/ekOO6oEe8fzX8kONwDtaFahWwjc7NcD9ucM+3MU8KqIvG1lFJ14/RR2ewPl3345+TsXc+Ut9BCFMehBN61k7UCBHm3j/B1qIC7I1IGLyDJ0AOMc6PM27WV0NwFe2gLWNTrMIWR3GP8NvaN0AtlKzh/IrlMI9VrI1X7HPgdttG4FpQrXooY43+Z5HuAi1cFsVK+bePleEpEVJbJiqAEv23IvoDp5BdXFJoSrOHk4G94RGqvqTq+dNu3NAq4vVuDq69qXPxS7XSaQ9VIFWbsBsIQxBHotKOvKTQU6WMhgdPeJqzd36qXLt2OziAaeRFdvNgXG9GUNaKcY2kDYtmrAK21zjaMGLETkbXSXwls8Rgc6kbIReb2+ENhrTJYA8xptqzjfyzbPC8E7lnF179QPp9fVbOzJmofawsRect2CZr1uAbwUcK155V5CB/F9gH8ENpCHfsM8F21DWzX9Pf+OTheV+wIr+3X0/Z3PegT9rnYS2YSok79r5Jk7tsk1hry96jfVD6L1WhSUg3bqIdcy+dbPsA71A8NoXqGOlduOYr02+Veu5g20f9yE7LvUKnBtaykii9Dt/1uQt9fhxK+VdFx3AvrZLcnr8O11DeNRvQ4l06vfr4Rtazuyb659HbqJwQWW62tkB2qFcDa2Keof+qCDjnnW574SPHOGV/YBVO9TAER18gQa7Dm9hn3BFmhA/YJNi7etOGJ9wTvN1fcDod8vigF9/zcPGMFj/AWdKN0ajc0cB9B66x+khW13IOVty/ffIddQlsH2sZWgn6uNI+u3nF5jqMp1XkEfuGthqWJZYV8Q4xr6rLXoACxsW7G+4AWP6+Y2rx8PbIFrWxpHLSffr8wDRjGXP6NtfBKZDcTQMtZGBy1vkh0oCTooW4dODpTFA/Mt14XoALJoou4N9H03GsnidmygCqpwhdBe67IAd5VgNpHjdLE56rNcOdAFjv6DWV5D+56XyuKBezi0Hzom2pjMD/iyBvNSo+8K44EcVxGcjY0g62MfwreB1RjU57TSa7sxoXLNp+1INtHi7HUBsHICr4zzuG4ZcN2GbDHLl98vSPPt9QGygzIRadSbOyskBjdh9HzQFzi9TisoF4Vp5+50Y0zbF60nJCQkJCQkJCQkJCQkJPz/DBGp9AlMWwP0hISEhISEhISEhISEhISEfw7a3eKekJCQkJCQkJCQkJCQkJDwT0AaoCckJCQkJCQkJCQkJCQkbABIA/SEhISEhISEhISEhISEhA0AaYCekJCQkJCQkJCQkJCQkLABIA3QExISEhISEhISEhISEhI2AKQBekJCQkJCQkJCQkJCQkLCBoA0QE9ISEhISEhISEhISEhI2ACQBugJCQkJCQkJCQkJCQkJCRsA0gA9ISEhISEhISEhISEhIWEDQBqgJyQkJCQkJCQkJCQkJCRsAPi/FgejbTkuQnkAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAAnCAYAAACc9WIYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAvhJREFUeJzt3bGKXVUUBuB/xUCcJEWagGgIBBJEULCwkLSmkoj4AGm0sLcR8Q0E30ItrGwsrMREJYVdRDAWCXZGYqVRFN0WZw8Ol3EyFu6zufN9zeb8rIHV/nMP+1RrLQAAAMC6jq29AAAAAKCgAwAAwBQUdAAAAJiAgg4AAAATUNABAABgAgo6AAAATEBBBwAAgAko6AAAADABBR0AAAAmoKADAMNU1cmqul5VH1bV8Z5drKrbVfX2nrmXqupOVV1db1sAGKtaa2vvAAAcEVX1bpI3+uMrrbWPquqLJJeTtCTnkjxIcifJmSR3W2sXVlkWAAY7vvYCAMDRUFWV5Nqe6HRVXcxSzpOkkuwkeSFLOU+SU+M2BIB1KegAwCjnk5xN8nmS+z17rp/vJ3l6I3sniVfcATgyFHQAYJTdAv5aku+TPJ/kSpIfkrya5EKSv/rcx621N6vq0zUWBYA1uCQOABjlqST3Wmu3W2u/JfmsZzdba7+31r5NcrdnN/rffLLKpgCwAgUdABjlTJLvdh/aclPtgVlzmy0AR4iCDgCMcirJjwdlVfVIloviNucAYOsp6ADAKKezfELtoOxkPzfnAGDrKegAwCgnkvzxkOxEPzfnAGDrKegAwCgPkjz6kGz3l/PNOQDYego6ADDKz/nnFfZ/y35N0vaZA4Ctp6ADAKP8kuTcQVm/tX2/OQDYego6ADDKvSSXqupYklTVTs+e7M+1T7az0q4AMJyCDgCM8nWWW9uvVNX5JC/27HJVPZbkapLHe/Zy/+Ta62stCwCjKegAwCi3+vleki+zfO/8Vj+vJ/lgz9wzSb5K8tbgHQFgNQo6ADBEa+2nJN8kOZvkiR7fTPJnkktZfl1Pkhv9fDZJjdwRANZUy10shxyuOvwwAAAAkNbaof7h/J8KOgAAAPD/8Io7AAAATEBBBwAAgAko6AAAADABBR0AAAAmoKADAADABBR0AAAAmICCDgAAABNQ0AEAAGACCjoAAABMQEEHAACACfwN832ByLTNQDAAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Test sequence 6:\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAAnCAYAAACc9WIYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAvRJREFUeJzt3T2LXGUYBuD7CetGs4sEbYJaCeJXKhFBIlhFrdNYhoSAhbVgYWtnk0LsxD/gD9BOYsAiRVBRQ0JESBULG5MQEn0t5gQOw7C7p3DPe+C6mjPPPe+Bpxtu5jBTrbUAAAAA8zoy9wIAAACAgg4AAABdUNABAACgAwo6AAAAdEBBBwAAgA4o6AAAANABBR0AAAA6oKADAABABxR0AAAA6ICCDsBkVfVGVd2sqguj7EJV3aiqN4e5qurzqrpSVSfm2xYAYBmqtTb3DgAsSFVtJbmW5Pkk95M8meTZJL8mOZrkcmvtrao6k+Tr4bbPWmsfzbEvAMBS+AYdgKlOZVXOk1UhP5Lk/eF1kuwM17Oje3YCAMCeFHQApnp9uH6R5PdR9jDJp0nujLJbSS4e6nYAAAu1NfcCACzOySQ/Jvkwyduj7MvW2idV9V5VPZXkmSTnWmtfVdW7M+0KALAYvkEHYKqXk3zfVj9i8l2Sf5K8kOTS8P43SV4aXj/Kvj3UDQEAFkhBB2Cq40muJ8lQ0nez+jwZZ8eTPEjyxygDAGAPCjoAU+0k+XNtzobsr9baw0PbCgBg4RR0AKbaTXJ3bc6GbDwDALAPBR2AqY5m9fj6eM6GbDwDALAPBR2Aqe4meXxtzoZsPAMAsA8FHYCp/k5ybG3Ohmw8AwCwDwUdgKnuJHlubc6G7OmqUtIBAA5IQQdgqttJXkySqtrO6nH2e6PsieFMsvp/9EcZAAB7UNABmOrnJO9U1W6S80m2kvyS5ExVVZIPkvyW5N8heyzJubmWBQBYCgUdgKl+SnIiyQ9JLo6y00muJDnbWruX5HqSj5NcTXJyhj0BABZFQQdgqkvD9dUk22vZa2vntpO8ckh7AQAsWrXWDn646uCHAQAAgLTW6iDnJhV0AAAA4P/hEXcAAADogIIOAAAAHVDQAQAAoAMKOgAAAHRAQQcAAIAOKOgAAADQAQUdAAAAOqCgAwAAQAcUdAAAAOiAgg4AAAAd+A/7j3oS+NWX7AAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAAnCAYAAACc9WIYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFzxJREFUeJztnXu0XFV9xz+/EPIgNySQxGQIIZeQ8DIvJDzEKqKAiFgFRLuuFBUFdbVd0i6roGittkuxaitdUhCrWMyoYKE+WWKlyEsFBQ0JII8gBJAAiShJkCSw+8dv78y+J2fmnrlJ7kzu/X7WOuucs2fv3/xmzu/svX/7aSEEhBBCCCGEEEII0VlGdVoBIYQQQgghhBBCyEEXQgghhBBCCCG6AjnoQgghhBBCCCFEFyAHXQghhBBCCCGE6ALkoAshhBBCCCGEEF2AHHQhhBBCCCGEEKILkIMuhBBCCCGEEEJ0AXLQhRBCCCGEEEKILkAOuhBCCCGEEEII0QW07aCb2RIz+62ZvSfeh/xoETbOzK43s6vNbNcYtq+Z3Wtm/5jJ7zOzVWb23izsPDP7QHZfJn+Kmd1hZl80Z6s4orOY2Xgz+4mZXZXZwBIz+7KZ7RPvq9qTmdmXzOyUTP4UM/uVmV3cygbM7J1mttLMXhbvR5nZUjNbbmYLKug6q5leMfzDZnaPmR0U73cxs/+O9j8xhs2JshbuyP9cdAlmhtnnMXtdFjYTsw9jth+AnWUhP7J4Z2J2fkHexzF7a3Y/GbMLMTtqR/+UrqNuli6L/+GW/7Fur6BuK6jbOR3TUwwZZjbNzJaZ2RfM3D7M7ISY547P4p0b6zNviPdmZpeY2S/MbHoM283MbjKzK81sdAw7IsqamcnqM7OLsvuqZdm8KGt+lnawuk6Iul6RdBVCCLHtmFmPmd1iZl+P9frQJE8/3cwuLKT9hJmtMLO5lb8whFD5AHYB7gUC8BwwPl5vOWK8srBPZmFviWHXZ2GzgVnAhnj/AjAzxrsHuDPTo0z+5VnYUWVxdHT2AD6VPZPTYth74v2xLZ5tWdie8f6STP7SLN5Lm6TrBf4Uw34aw96bxftJDLugha6vbqHX4dF2A3BFDPubLN7fxrCT4v3bOv1cdAzBAVMChAAXZWEnxrC3hhDgXYT8yOJdF+DJ7N4CPBvgO1nY4ijrvI7/1qE8ljI5LOVXYSn3hqUcVPwPeRchLKUnLOXxsJQQjwUd11vHDj2Ab2Z57pIYlvL0OfH+yCyvXod3WLwpS3dBjPfZLOyNMSzl6Udn31mPYWPjfdWy7I3x/q2ZrCq6nlai6+eKuurQoUOHjm0/gAuz/PWkYn6e5elXxPx6dLx/RRbnq1W/r90W1iOBefF6DFRLH1uwz8iCJpjZbODoLGw34ETc6QewGAZQAza1kL8bXlhtkV9FLzF0RBt4WxaUnlGtcK5Kv3Rm1oNXroryi7wZGFuI847s84ll9tqGrqfjtpunG6wsMXwoe95VbaAGTMVsDCFsBCYB4wYpa7jxOWBRvP7HJnFOAqZn95N3qEYV6TdKIhIuDVYWV1THzHYHTsmCyvLclXi+XMyr+5VRbZYF6XoG8FAbKg+UNzTTtVinaqarEEKIbcDMRgF/mQW1yl9reH79IuCxNtL1o10H/dB4/g/g2DbSzQD2Am7Blc1l1YH94/USvIXhn4k/KDrfu8frMcErqEXm407XD2g4X6KDlFQ+98bt4Bbgd1n4jMK5KnllCODFeKPR92k08pRxKLAZH9FxbBy+vhivBF0JvBq31RnAzcDjJd/ZyglaAjyDt7TNj/IXAXcBP83iJb1HmkM1UtlWBx3cyVy1jbKGD3UbjTe4JXZtEvOweL4Ef7+3yp+6yjGu28HA+4Dv0he+12l1dkIW4nWb7xDrDpFinrsEH7F3AXBOFvYwcBVuT7OBqcCNwJoSWTOahA3GQZ/RIqyVrlfjv3c2MAW4AVjbxvcLIYQoYvZ2YCUh3ADMwRv3rwf+OEDKvD72GJ5X/x64GNiv6te366AvwB2Nv8KHEBNCsDTuPgSv5KSwdG9mx8f0ZwP3AS/Du/zXAGcCM6MuC4HLQwgfMbOr8SECNdxpN7ywerhE/jtxp+uMmGb/7LMt8URHWRDP7wIewKchgD/f9Jwpe25lNlZMF+VvxntAXgDmNbGBBcBlIYSPmtnNwAF4ReyvQwjXmNlpma5n4Y57JV1jD8Z84DMhhI+b2Wsy+ecA/wsc10R/MbxJz7tYCQ8ke4pOop1lYYvD6HNQJ9GwlVWZrOmYjSKEF5rIH+4cgLdGX4I3zr659D+s24+A6/CpLHPx6THdSd2mAv+Ht7yfTd0W0hfu7LBWOxsLgI14WTAan9YEWZ5rZrvgefWnYl69AnduZwBnhBAuj/WWVBacCTwCHFGUlX3vVuXDQHWjKrIyXT8ZQvhEE11fk+n6zqjrke39bUIIIQDwNTy+BHwbb/TMy4LHgcPKfIzoB+T592jgIOAjIYR/iXl1Jdp10A8EboqD7G9Ji69UTPd74K7oyVyPO+s/DyE8B6yMhdABwGcAQgi3R/lHAU/iTs4MvMW4TP5dIYQ1AGZ2a5u/S+x4DgTWhBDuBjCzn8TwGvAb2ndUZ8R08+LQkwOBFZkN3FZMEG1sf3wuPMC1NIbF3xDP38J7r3Jdry98ZzMnaC9gIt7bkuSfGq9vjrb/o3iffvdIcqhGMjV8/Y45mFmc0FQD7qe17Scn/gH69wDei9v8FDx/TGEjqcFnMV4InktfeJq63dMk3sHAF+gLAbiPunVm95K6jQU+hI/y+myTxoT34c55YtJQq1nGTjYc/0BgWQjhaQAzSz3feZ67Dz7S6vr42VV4xwE08u8fAe8HHg8h3B9l3VCQlaZYpYa0uxl8WVZrElZF12tLdE1lrBjpeCfWJcDhhHB7p9URYidgJr7u2ux4fyDwSAjhQQAzu7FJuh58evbdeD7ei5f5eV5diXYrKpPxHnCgMRu+aroUP577ycJ/0C54oZTLrwGr49Gs4BusXmIHkSpv4dJg8brZM6oByxjcHPRleCPTlBbycybgNnZfFmcSsCqEsD4LG6yuaW5rLn8y/lJvKJH16xayxPAiPe8xwB6FsIEc9DV4j1g+bOo+fLHDPMxlVW843dmZAzxAnzti9IV7m8SbTFau0BdeyJ3LIXQ0vwZ8FPgg8O9N4vwZ8CAwDW/EFu2zVf5tZmPwciLl36nh4zcpTky3ER+l0m5ZkM7LGXxZlpz97a2rEPvjdZ95A0Xsh5lhdjS+ZpQQI4newrlq/pp6z++K12V+QSXaddB78N6adukBnhogrCeei/Jr+HCCx2le8JXJF93FVs8o9nxPZ/AO+m/wSkqNarZZZmNVbDPXtZVDleQX7brMNgfbMCF2Tmr4bhSbKXOqW6crNlA2C1uGN3RO3J6KdzE9+MiC5nhv+W7AE0OhUAs9avhonV/ju000my9/BPBV+sJT9IVLga1GAg0ZdZsah9znDa39rruUsjw3jT5JeW6zvHptCOH5VrLikMVpbO2gr8VH+A2mLMvzgWa65nPgK+kqhjnuQN+G2aezsDmY3YjZqVnMuYUzmL0bs8v7NeiafR+zy7N0s/CRG+fuAO2F6GZ68RGOU/DtkavmrzXcx3iURv4dGMS6IO066BOAZ9v9kphuwwBhqRDaYGZvj/vMHUa1HvQy+aK7KHtGU/Ee8MH0JKeGm2QXVWwzrZ6Yx+tJepnvn/61bdB1AmzZgjAP6ycrTt2YgVfAJljcG10Ma5K9PkGjlzs51bvji2E2S1fVQV9BNqd9BDAReIa67ULdXkrdDimJk/7XDdTt9dTtK9TtVUOoY+I4vDHxNfSF04Hzm8SbDtyx5a4vPNck3o6lbu/HF/N8krp9rEU8i458Z6YNlFOWf9fw7ckepFFePBdCeN7MPmhmX8eHkg9UTwF/Rkb/sqBKPWUr4rSr1PA7KQ6Vr+ELjSZde7ZBVzG8mY0vQJXPa12Cj8R5dRY2Dx9ym/egnx6PfQAwGxflnJg57QsKZyFGCr3AL/H6/Gyq56/FsmAC8OxgRjS1W6iOpcV2Z22mK4al1dc3AS8B/gJf+btKwTdYvcTQUfaMasDzeMEx0cza2RamaBdVbCC3sTK9TqaxDVuZrpvxXtDdrdyhGgtsKryIZbL2xIc6p8WfRopDNZIp2useuA0syz6vkm7rsIaz/yjeyzZS7CkVmGPw3SGuKIkzLp434YuQvh1f62So2R/4BX1hNQB9vr5FP+o2Dv8tT1C36dRtUerBHlLqtg/waXxdjs/jUwnK4s0Cfk7qLahbe8NndxzN8u9m5cUr8PrGpJJ0zWQ9hw9Z3NPMxpbIr8pUfOhxXha00vXoqOvuFXUVw5tFwB+Ag/CpESlsHWn7SS8f5uKL1M6NYaPi58/ia3mAr9XxJ7xsmhnD5gO/BeaPoKlTQoA76A/h0wt7qZ6/DsY3KaVdB30Dg9vGbAONilKzsNQyUYxXpeArky+6i7JnNAPvUXw8u69KbhczqGabycbGFsIGss30fbmuZba4ARgTh8O3kjUD7+l8DF88caQ4VCOZ4lSdvXAbeASvYO1VMV1Z2GTcpgeaCjTc2MjAC52WvfOdYE/gkdjbfyJ1e2VJnC2jyPDdLn4FnDZE+uWciud1r6UvnIPv2lLGJ/C87Ex82P6UoVFvQJrluam8mIY7IsU4ZWEDyQLvAc/D2i3Hnscb156JafOybVt1FcObRfhihpvwlaLBHe7/ARZFR3wvfJHBH9PoQd8XH110DQ0HfRHeYPwAybn3nvOr8carvXfkDxGiy+jFpyw9HK+r5q9Fn3XQ+XK7Dvo62thkvZCu2ONYDFsXz7vhmUuaM1jDW/NSy3JV+aK7KHtGNbySfQyNueQDEnvaJ+Ktu+NoDF8cyDaTjU0ohCW9Lt5GXZP88YWwMlnr8eFkzzaRJYYLPoVhAl6BGkMjL1sHvBy3hVY96HviwxBzB31fvBctzxcPYWRt3Zfe+ReAnzWJ8yzeEDIB375s5dCothVpCs4Y4PvApSVx0tSbTjcm9OIjEjZTt9n030vcqZsBpwDn0xe+Ql94P96b3g00y3NH486I4f/x6Lgg25dwJ3lDSbpmsjbh7+7zNN7BHnyl3+lx6HoVUj7w2vj9SVau65io666D0FUMb5JTfRcNp3oRcCVeD5mD95qvxafOvAiz3XHbuhf4BQ0HfSG+yOHyeA1ex7oN70mcv4N/ixDdRC/9HfSq+WsNLzMPwBtc1wFj2ygTttDuNmvraQx9aTddsfWtGLY+nmeGEH5sZmkyfg3f9gq8x7GZfK0y2d2U2UANf27fy+6rkOIlh3ot3hM9UAvvFhvDh6qnsGTTVwLntdC1dwBdk/y9aawavR6YlTa/zdL2DCBLDB/S8/3PeH4qhk3E9+fO45SlTfMLN+ONU7sDF8Swm7O03+kny4cgH4D3zN1BX9i4Tb+i+1gHTKcvPEfd/gn4t61i9IVA3fwd7wtLqdtvh1jHxNP4gksv4PPayrZP24A7X3sA/4U7wJ1gEv7fjsOHt96HD9HPmRiPFdTtdcAb8F70btjaq1n+fTi+kj40GmlnhhCuNrNNeAPJVDMbH0JIjSXNZO0HfDe7rwEnAK+LYVNp9LC3oob/33lZUMMXC0x7rqdK4cwQwlVmtrkNXcXwZhH+3s3Be8yn4vWZ2/AFrhbj9vU03ngUcNtdTMMZPzuT9W181MYivEHoIHwK4t14b/o1Q/KrhOgkvhDoLOAM/H1ZiTdoVclfa/j6D38e75OfPZPybcKb0m4P+hP4S46Z7RpbdKuwGpgbVz9Ne4auzmQZXnFZR2OYDvjcrGnZfbOW6S2yMvmiu1iN71m+C2x5RkWnpF0HPb/fYpuZ/CIb8ErMgVmc1cAUM8vtbLC6pgpZUf5koh1v4+8WOydlz7uqDeTho/F5ggPLqtu5uPN/KO40HFVZ252Hp4CDY29uK/rlDR1iLTAvLvr2sdIYvk/7GuDF9IVVeKW6EzyJ51cbab4dXFq8bizuqJ9K/7K7k5TVB4rvSKq75HaxBu+xnpelWw3sn6Yttci/t2dZ1kzX/P9dW1FXMVzxkVn7Af+KOwOLaPSiX4c7GItwG5kD/JCGzSzG1174JNCL2eQY9++Ad8TrufjojeuAV6EedDFySHugvwkfmdiL568HRH+1Vf5azL9T/ST3CyrRroO+HDjOfIjMGVQfV78cL8hfa2Z7Aa+PYYeb2d7A8fgfsAI4Nf4Bae/snF3wluky+b1mdoiZLWR4VkZ3dpbj9nKCmc0ETmL7VmqWA3PMbJGZLQJeWkwUe7BXAKdEG3t3TAf+IqYXaVC6hhB+j4/yODnKPzuTf3J8b/qqyBLDisFV6L0BdFohtJqD7hWt8/GFgcbRf9rFcGEZ3jv0KhrvbhnLgTdQt9EDxNuR/BKYH1eab6XD7cBpHdZ1BV55nwpcWxrDGxoeBE6gL3wDX4W8W1iOO6ovNrNDgbQbTM54vIHnTVl940G8h/HkOPT9HVFWD3Csme2DD0Ufage9TNeVBV3PbKJrV2BnWciPTuszTFiA28BueN04Oegb8VE4m2k42jlzY/hE3IkPeI/fnvg7vwc+YuYleMfZqChLK7mLkUJvyf1yvL5xjJntS/+dE3KK+fdofITvybHx9Oytk5TTroN+J/4C/wy4qI10K+L5K/g8tR78x47B91i8Mn6+HHglcCveWrwHnnlMpDEPrqzgS07Qd/GFMLppy5cRQYWCN9nAZbj9TMCf5QfwCsjFtFepuTWmOz7ep1Vwv0drG7gTr9D/Eq/UPIwPAf4M8I2Kul7SQtflwNvwivbxwCrgj1H+7TQqYBdGWR9tIUsMD2o07ChV8JMN9OA9qmU2MD2e58a0a/CK06p4vxC3odnAt2LYWVHW7fiw4/V4z8fLt/uv6jzJKfwmZcPbG9yJVy5/Tuf+h+vxXudb8HnEzbgFb1y8C1+xuxPOzdV4xXwZ3jvXjEuAD1O3a/GRGt1Cqg9cgzcwpF0O3oK/Lz+k0ah7Jl6WjMWfz/3Ah/AF+g7OZH0N+GlMnxrAxuPTVvbCG9KOimHLaa8s+1xM93EaeUMrXceU6HpQE127gnBpsHRO12KbWQTcQQjTcFudiucdXySEGfiOFYvxHvOz8TrRRXh5MAtYSAiT8LLpOOApQpiAd4y9gK+38wNCmAwci68U3+60WCG6GjPMjMPMeIsZJ5jxMtwhvwcvFxYCU97sDbgAdXxq4VZrxZhvVbgHPj1pPD7VNeXf78bXgahcB2nXkb0pntNiR5UIITyDFyJTaIzhvxVv6dsPd8ABboznJfG8B/BkCGFdlNFsxet78WGMMynvYRfbwICVwnz7jSbTHkIIf8ArfFNp2EANeDSE8Ce857mdSs1jMd0qvAXZ9+x12a1WE042fEjUK8Sw3Yi9ky10faSCrsmGFxfk9+C2XpT1aAtZYnhQAx4jhA14g1APnletIoT1+EruZTZQwxsoH45pV+MFx+/i/UMxXi/waAxbRaOCfzvw9/j8wW/tkF82xPRzUvvCWhrlyn74f1VGeidfQvvrrmwf+sI64B/w0QwvolnPtDcSbsIr1aOA1eGYRjkdjmFshSH9lTFjHzNuMuNUM44wYz594Q/4qKFH8GH25zVJ/lm80TLgDaxl29x1grvwIeCz8F7BUXhj18Mxz/0d/o4U6xvEsDHE4eQhhDX4+zONxk4LxbKgF28EWBXD2tlJoaws2J66dhwzesw4Ml7PMmPPTus0TFiHdyKA28lluA2ltUhuw1d4nwvcj9dFVuJ5y1dpOByX4bsCfBmAEDbijYi70sin7o7yUqOx2I4UG2E1ymRImYZ3Eic/4lnSFmv+LqwE+KZ/fh/+DjTL39MOHg8VyoIb8TJiYZN0pVg7e6ebyWiEEEIIIYQQQoh2CKHaKKK2HHQhhBBCCCGEEELsGDRXWwghhBBCCCGE6ALkoAshhBBCCCGEEF2AHHQhhBBCCCGEEKILkIMuhBBCCCGEEEJ0AXLQhRBCCCGEEEKILkAOuhBCCCGEEEII0QXIQRdCCCGEEEIIIboAOehCCCGEEEIIIUQXIAddCCGEEEIIIYToAuSgCyGEEEIIIYQQXcD/A1LytJMj4ARTAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAAnCAYAAACc9WIYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAvtJREFUeJzt3D2rXGUUhuFnhRA9SYpADBqNgpAQv0oLSa2I2MXez58gCOJP0NpeRAQR7dPFKAYsLCIRo4WKCB4xlUZRkmUxWwxDkJkBz7wbrqvZs9e8xeqGm9kz1d0BAAAAtmvfthcAAAAABDoAAAAMQaADAADAAAQ6AAAADECgAwAAwAAEOgAAAAxAoAMAAMAABDoAAAAMQKADAADAAAQ6AMxILbxZVZ9V1Z3T7GBVna+q96pq/zQ7WVVXquq17W4MAKyqunvbOwAAK6qqZ5K8P92+3t2vVNUbSV6eZme7+8Oq+iTJmSSd5ER3/7iFdQGANfgGHQDm5bmbXh+qqkry7NLsZBZxniSVZGevlgMANrd/2wsAAGt5NMkPST7I4nP8viTHknyc5JebziTJO0ke2esFAYDNCHQAmImqOprkeJLnu/utqnoy/wb4S0m+T/JYkseT/JTkxST3J7mxhXUBgDV5xB0A5uOB6Xphup5L8mCS3e6+0t1/JDk/zS5295/d/VWSb/d8UwBgbQIdAObjSJK/knyXJL34p9cjSb7+58B/zACAwQl0AJiPQ0mudvf1pdnPtzi3PAMABifQAWA+Die5tuEMABicQAeA+bgti0fcN5kBAIMT6AAwH9eS3L7hDAAYnEAHgPn4NcnBDWcAwOAEOgDMx29J7qiqnaXZiVucW54BAIMT6AAwH7vT9VSSTKG+m+RUVe1bmp2e7msp6AGAQQl0AJiPL5PcSHK2qg4keSHJF1n8a/sTVXVvkqem2ZmquivJ00nu3tK+AMAaBDoAzER3/57kmySvJvk8ycNJLk1vv53k0yx+e34pyU6Sj5K8u/ebAgCbEOgAMC8XkhxI8lCSdPfVJJeTHEtyz3TmYpLrWTwKf3gLOwIAG6juXv1w1eqHAQAAgHR3rXJurUAHAAAA/h8ecQcAAIABCHQAAAAYgEAHAACAAQh0AAAAGIBABwAAgAEIdAAAABiAQAcAAIABCHQAAAAYgEAHAACAAQh0AAAAGMDfp4mJ806OSpUAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Visualize a few reconstructed sequence patterns\n", "\n", "importance_scores_test *= sequence_mask[None, None, :, None]\n", "\n", "plot_examples = [3, 5, 6]\n", "save_examples = []\n", "\n", "cuts = [76 + 5, 76 + 15, 76 + 25, 76 + 35]\n", "\n", "for test_ix in plot_examples :\n", " \n", " print(\"Test sequence \" + str(test_ix) + \":\")\n", " \n", " save_figs = False\n", " if save_examples is not None and test_ix in save_examples :\n", " save_figs = True\n", " \n", " plot_dna_logo(x_test[test_ix, :, :], sequence_template=sequence_template, figsize=(14, 0.65), plot_start=0, plot_end=205, plot_sequence_template=True, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_orig_sequence\")\n", " \n", " #Plot interpretation PWM for each cleavage position\n", " for cut_ix, cut_pos in enumerate([0, 1, 2, 3]) :\n", " #Mark the position where we are maximizing cleavage\n", " cut_template = 'N' * 205\n", " cut_template = cut_template[:cuts[cut_ix]] + 'CCC' + cut_template[cuts[cut_ix]+1:]\n", " plot_dna_logo(np.zeros((205, 4)), sequence_template=cut_template, figsize=(14, 0.65), plot_start=0, plot_end=205, plot_sequence_template=True, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scrambld_pwm\")\n", " #Plot Scrambler interpretation (PWM)\n", " plot_dna_logo(pwm_test[cut_ix, test_ix, :, :], sequence_template=sequence_template, figsize=(14, 0.65), plot_start=0, plot_end=205, plot_sequence_template=True, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scrambld_pwm\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Environment (conda_tensorflow_p36)", "language": "python", "name": "conda_tensorflow_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }