Parametric_bootstrap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5a85207b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/noTF/bin/python3.8\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.executable) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00778ed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To run a demo, execute\n",
      "\n",
      "\t>>> mavenn.run_demo(name)\n",
      "\n",
      "where 'name' is one of the following strings:\n",
      "\n",
      "\t1. \"gb1_ge_evaluation\"\n",
      "\t2. \"mpsa_ge_training\"\n",
      "\t3. \"sortseq_mpa_visualization\"\n",
      "\n",
      "Python code for each demo is located in\n",
      "\n",
      "\t/opt/homebrew/Caskroom/miniforge/base/envs/noTF/lib/python3.8/site-packages/mavenn/examples/demos/\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['gb1_ge_evaluation', 'mpsa_ge_training', 'sortseq_mpa_visualization']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import pickle\n",
    "import scipy.stats\n",
    "\n",
    "# Import MAVE-NN\n",
    "import mavenn\n",
    "\n",
    "# Import Logomaker for visualization\n",
    "import logomaker\n",
    "\n",
    "# Get list of demos\n",
    "mavenn.run_demo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "18d6646f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set figure size\n",
    "sns.set(rc={\"figure.dpi\":300, 'savefig.dpi':300}, font_scale = 1)\n",
    "sns.set_theme(style = \"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9bff4a9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-16 16:04:13.884856: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_0.pickle\n",
      "\tall_promoters_additive_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_1.pickle\n",
      "\tall_promoters_additive_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_2.pickle\n",
      "\tall_promoters_additive_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_3.pickle\n",
      "\tall_promoters_additive_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_4.pickle\n",
      "\tall_promoters_additive_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_5.pickle\n",
      "\tall_promoters_additive_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_6.pickle\n",
      "\tall_promoters_additive_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_7.pickle\n",
      "\tall_promoters_additive_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_8.pickle\n",
      "\tall_promoters_additive_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_9.pickle\n",
      "\tall_promoters_additive_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_10.pickle\n",
      "\tall_promoters_additive_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_11.pickle\n",
      "\tall_promoters_additive_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_12.pickle\n",
      "\tall_promoters_additive_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_13.pickle\n",
      "\tall_promoters_additive_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_14.pickle\n",
      "\tall_promoters_additive_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_15.pickle\n",
      "\tall_promoters_additive_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_16.pickle\n",
      "\tall_promoters_additive_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_17.pickle\n",
      "\tall_promoters_additive_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_18.pickle\n",
      "\tall_promoters_additive_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_19.pickle\n",
      "\tall_promoters_additive_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_20.pickle\n",
      "\tall_promoters_additive_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_21.pickle\n",
      "\tall_promoters_additive_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_22.pickle\n",
      "\tall_promoters_additive_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_23.pickle\n",
      "\tall_promoters_additive_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_additive_ge_24.pickle\n",
      "\tall_promoters_additive_ge_24.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_0.pickle\n",
      "\tall_promoters_neighbor_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_1.pickle\n",
      "\tall_promoters_neighbor_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_2.pickle\n",
      "\tall_promoters_neighbor_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_3.pickle\n",
      "\tall_promoters_neighbor_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_4.pickle\n",
      "\tall_promoters_neighbor_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_5.pickle\n",
      "\tall_promoters_neighbor_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_6.pickle\n",
      "\tall_promoters_neighbor_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_7.pickle\n",
      "\tall_promoters_neighbor_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_8.pickle\n",
      "\tall_promoters_neighbor_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_9.pickle\n",
      "\tall_promoters_neighbor_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_10.pickle\n",
      "\tall_promoters_neighbor_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_11.pickle\n",
      "\tall_promoters_neighbor_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_12.pickle\n",
      "\tall_promoters_neighbor_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_13.pickle\n",
      "\tall_promoters_neighbor_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_14.pickle\n",
      "\tall_promoters_neighbor_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_15.pickle\n",
      "\tall_promoters_neighbor_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_16.pickle\n",
      "\tall_promoters_neighbor_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_17.pickle\n",
      "\tall_promoters_neighbor_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_18.pickle\n",
      "\tall_promoters_neighbor_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_19.pickle\n",
      "\tall_promoters_neighbor_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_20.pickle\n",
      "\tall_promoters_neighbor_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_21.pickle\n",
      "\tall_promoters_neighbor_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_22.pickle\n",
      "\tall_promoters_neighbor_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_23.pickle\n",
      "\tall_promoters_neighbor_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_neighbor_ge_24.pickle\n",
      "\tall_promoters_neighbor_ge_24.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_0.pickle\n",
      "\tall_promoters_pairwise_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_1.pickle\n",
      "\tall_promoters_pairwise_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_2.pickle\n",
      "\tall_promoters_pairwise_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_3.pickle\n",
      "\tall_promoters_pairwise_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_4.pickle\n",
      "\tall_promoters_pairwise_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_5.pickle\n",
      "\tall_promoters_pairwise_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_6.pickle\n",
      "\tall_promoters_pairwise_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_7.pickle\n",
      "\tall_promoters_pairwise_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_8.pickle\n",
      "\tall_promoters_pairwise_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_9.pickle\n",
      "\tall_promoters_pairwise_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_10.pickle\n",
      "\tall_promoters_pairwise_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_11.pickle\n",
      "\tall_promoters_pairwise_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_12.pickle\n",
      "\tall_promoters_pairwise_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_13.pickle\n",
      "\tall_promoters_pairwise_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_14.pickle\n",
      "\tall_promoters_pairwise_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_15.pickle\n",
      "\tall_promoters_pairwise_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_16.pickle\n",
      "\tall_promoters_pairwise_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_17.pickle\n",
      "\tall_promoters_pairwise_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_18.pickle\n",
      "\tall_promoters_pairwise_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_19.pickle\n",
      "\tall_promoters_pairwise_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_20.pickle\n",
      "\tall_promoters_pairwise_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_21.pickle\n",
      "\tall_promoters_pairwise_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_22.pickle\n",
      "\tall_promoters_pairwise_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_23.pickle\n",
      "\tall_promoters_pairwise_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_pairwise_ge_24.pickle\n",
      "\tall_promoters_pairwise_ge_24.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_0.pickle\n",
      "\tall_promoters_blackbox_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_1.pickle\n",
      "\tall_promoters_blackbox_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_2.pickle\n",
      "\tall_promoters_blackbox_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_3.pickle\n",
      "\tall_promoters_blackbox_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_4.pickle\n",
      "\tall_promoters_blackbox_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_5.pickle\n",
      "\tall_promoters_blackbox_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_6.pickle\n",
      "\tall_promoters_blackbox_ge_6.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_7.pickle\n",
      "\tall_promoters_blackbox_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_8.pickle\n",
      "\tall_promoters_blackbox_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_9.pickle\n",
      "\tall_promoters_blackbox_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_10.pickle\n",
      "\tall_promoters_blackbox_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_11.pickle\n",
      "\tall_promoters_blackbox_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_12.pickle\n",
      "\tall_promoters_blackbox_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_13.pickle\n",
      "\tall_promoters_blackbox_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_14.pickle\n",
      "\tall_promoters_blackbox_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_15.pickle\n",
      "\tall_promoters_blackbox_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_16.pickle\n",
      "\tall_promoters_blackbox_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_17.pickle\n",
      "\tall_promoters_blackbox_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_18.pickle\n",
      "\tall_promoters_blackbox_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_19.pickle\n",
      "\tall_promoters_blackbox_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_20.pickle\n",
      "\tall_promoters_blackbox_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_21.pickle\n",
      "\tall_promoters_blackbox_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_22.pickle\n",
      "\tall_promoters_blackbox_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_23.pickle\n",
      "\tall_promoters_blackbox_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tall_promoters_blackbox_ge_24.pickle\n",
      "\tall_promoters_blackbox_ge_24.h5\n"
     ]
    }
   ],
   "source": [
    "# Iterate over types of G-P maps\n",
    "gpmap_types = ['additive','neighbor','pairwise','blackbox']\n",
    "\n",
    "# Create list of model names\n",
    "model_names = [f'all_promoters_{gpmap_type}_ge_{i}' for gpmap_type in gpmap_types for i in range(25)]\n",
    "\n",
    "# Load models into a dictionary indexed by model name\n",
    "model_dict = {name:mavenn.load(name) for name in model_names}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a11c3a3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>277</th>\n",
       "      <td>XXXnH</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2582 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         x         set\n",
       "0    OOOOP    training\n",
       "1    OOOcP  validation\n",
       "2    OOOnP    training\n",
       "3    OOONP    training\n",
       "4    OOOrP    training\n",
       "..     ...         ...\n",
       "273  xRnRH        test\n",
       "274  XRXXH        test\n",
       "275  XxCNH        test\n",
       "276  xxNxH        test\n",
       "277  XXXnH        test\n",
       "\n",
       "[2582 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data from Jamie's library in CRX wt retina\n",
    "\n",
    "Data_file = open(\"CRX_NRL_trainval.pkl\", \"rb\")\n",
    "trainval_df = pickle.load(Data_file)\n",
    "Data_file.close()\n",
    "\n",
    "Data_file = open(\"CRX_NRL_test.pkl\", \"rb\")\n",
    "test_df = pickle.load(Data_file)\n",
    "Data_file.close()\n",
    "\n",
    "Data_df = pd.DataFrame()\n",
    "Data_df['x'] = pd.concat([trainval_df['Sequence'], test_df['Sequence']])\n",
    "assignments = []\n",
    "for validation in trainval_df['validation']:\n",
    "    if validation:\n",
    "        assignments.append('validation')\n",
    "    else:\n",
    "        assignments.append('training')\n",
    "assignments = assignments + ['test']*len(test_df)\n",
    "Data_df['set'] = assignments\n",
    "Data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89cba37c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 0 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.163 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "980687d224204d5b9be0c1bb60d0f9fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 31.5 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 1 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.325 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32f0fef1d3884c2783c52e8d841e3067",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 38.7 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 2 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.144 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a3fda1cbef44c24ae13932d538d52b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 36.6 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 3 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0927 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76f3aff5c71a47a1ab169de4582e952f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 94.1 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 4 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.139 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "097909d6a4f64663bed16684207cc9c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 49.3 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 5 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0931 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cbc932f757943979522a6f9e73fde8a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 31.0 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 6 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.248 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18d831f2d10948f5aee6ad949c962862",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 25.0 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 7 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.193 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bef886277e8a4ec4b1bf82cf5dfb12c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 27.0 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 8 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.106 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f41b8bdfa7ee4c85afa8111004ffdda0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 29.4 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 9 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.104 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d080fcfde24e47159fc5e464988bac20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 85.2 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 10 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.121 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adbfa77cb979451e91232f05ccca3e9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 24.8 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 11 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.124 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffbf75bfc0a54d6291d2bc286008bd73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 28.2 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 12 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.149 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b45721c8f2b748d0a55a21bda0b3b7b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 38.8 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 13 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.21 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1370ba45122f4503a8a587f66863316b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 29.6 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 14 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0878 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5366e5ef45d8423287f775e21d956a9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 24.5 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 15 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.087 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7dcd40067eb48d3998d8cc7c40fc584",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 25.1 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 16 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0891 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff03ade3a8734551936f8ffe2c3d4753",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 37.5 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 17 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0874 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa2361e20fc04a7997ec932017692d49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 21.1 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 18 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0967 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1206dd7f1d4a439fb354e8ba6c97ab9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 29.0 seconds\n",
      "Training set   :    2,064 observations (  79.94%)\n",
      "Validation set :      240 observations (   9.30%)\n",
      "Test set       :      278 observations (  10.77%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    2,582 observations ( 100.00%)\n",
      "\n",
      "training model 19 ...\n",
      "N = 2,304 observations set as training data.\n",
      "Using 10.4% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.157 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df1ee46a9b384e7e8a96abf6a4f14a04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 47.0 seconds\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_0.pickle\n",
      "\tCRX_NRL_bootstrap_0.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_1.pickle\n",
      "\tCRX_NRL_bootstrap_1.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_2.pickle\n",
      "\tCRX_NRL_bootstrap_2.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_3.pickle\n",
      "\tCRX_NRL_bootstrap_3.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_4.pickle\n",
      "\tCRX_NRL_bootstrap_4.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_5.pickle\n",
      "\tCRX_NRL_bootstrap_5.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_6.pickle\n",
      "\tCRX_NRL_bootstrap_6.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_7.pickle\n",
      "\tCRX_NRL_bootstrap_7.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_8.pickle\n",
      "\tCRX_NRL_bootstrap_8.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_9.pickle\n",
      "\tCRX_NRL_bootstrap_9.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_10.pickle\n",
      "\tCRX_NRL_bootstrap_10.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_11.pickle\n",
      "\tCRX_NRL_bootstrap_11.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_12.pickle\n",
      "\tCRX_NRL_bootstrap_12.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_13.pickle\n",
      "\tCRX_NRL_bootstrap_13.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_14.pickle\n",
      "\tCRX_NRL_bootstrap_14.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_15.pickle\n",
      "\tCRX_NRL_bootstrap_15.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_16.pickle\n",
      "\tCRX_NRL_bootstrap_16.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_17.pickle\n",
      "\tCRX_NRL_bootstrap_17.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_18.pickle\n",
      "\tCRX_NRL_bootstrap_18.h5\n",
      "Model saved to these files:\n",
      "\tCRX_NRL_bootstrap_19.pickle\n",
      "\tCRX_NRL_bootstrap_19.h5\n"
     ]
    }
   ],
   "source": [
    "# Fit bootstrap models\n",
    "\n",
    "name = 'all_promoters_pairwise_ge_14'\n",
    "\n",
    "# Get model\n",
    "model = model_dict[name]\n",
    "\n",
    "bootstrap_models = model.bootstrap(Data_df, num_models = 20)\n",
    "for i, model in enumerate(bootstrap_models):\n",
    "    model_name = \"CRX_NRL_bootstrap_\" + str(i)\n",
    "    model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "28007008",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_0.pickle\n",
      "\tCRX_NRL_bootstrap_0.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_1.pickle\n",
      "\tCRX_NRL_bootstrap_1.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_2.pickle\n",
      "\tCRX_NRL_bootstrap_2.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_3.pickle\n",
      "\tCRX_NRL_bootstrap_3.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_4.pickle\n",
      "\tCRX_NRL_bootstrap_4.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_5.pickle\n",
      "\tCRX_NRL_bootstrap_5.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_6.pickle\n",
      "\tCRX_NRL_bootstrap_6.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_7.pickle\n",
      "\tCRX_NRL_bootstrap_7.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_8.pickle\n",
      "\tCRX_NRL_bootstrap_8.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_9.pickle\n",
      "\tCRX_NRL_bootstrap_9.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_10.pickle\n",
      "\tCRX_NRL_bootstrap_10.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_11.pickle\n",
      "\tCRX_NRL_bootstrap_11.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_12.pickle\n",
      "\tCRX_NRL_bootstrap_12.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_13.pickle\n",
      "\tCRX_NRL_bootstrap_13.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_14.pickle\n",
      "\tCRX_NRL_bootstrap_14.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_15.pickle\n",
      "\tCRX_NRL_bootstrap_15.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_16.pickle\n",
      "\tCRX_NRL_bootstrap_16.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_17.pickle\n",
      "\tCRX_NRL_bootstrap_17.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_18.pickle\n",
      "\tCRX_NRL_bootstrap_18.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_NRL_bootstrap_19.pickle\n",
      "\tCRX_NRL_bootstrap_19.h5\n"
     ]
    }
   ],
   "source": [
    "# Load bootstrap models\n",
    "model_names = [\"CRX_NRL_bootstrap_\" + str(i) for i in range(20)]\n",
    "bootstrap_models = [mavenn.load(name) for name in model_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c61f3a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot additive contributions of CRX, NRL, and Rho in CRX wt retina\n",
    "\n",
    "All_contributions = []\n",
    "for model in bootstrap_models:\n",
    "    # Retrieve G-P map parameter dict and view dict keys\n",
    "    theta_dict = model.get_theta(gauge=\"user\", x_wt = \"OOOOH\")\n",
    "    logo_df = theta_dict['logomaker_df']\n",
    "    TF_additive = ((logo_df.iloc[0][0:8] + logo_df.iloc[1][0:8] + logo_df.iloc[2][0:8] + logo_df.iloc[3][0:8])/4).tolist()\n",
    "    TF_additive.append(logo_df.iloc[4][8])\n",
    "    All_contributions.append(TF_additive)\n",
    "        \n",
    "        \n",
    "plt.boxplot(np.asarray(All_contributions))\n",
    "x = np.linspace(1, 9, 9)\n",
    "plt.xticks(x, [\"NRL\\n forward\", \"NRL\\n reverse\", \"High\\n affinity\\n CRX\\n forward\", \"High\\n affinity\\n CRX\\n reverse\",\n",
    "              \"Medium\\n affinity\\n CRX\\n forward\", \"Medium\\n affinity\\n CRX\\n reverse\", \n",
    "              \"Low\\n affinity\\n CRX\\n forward\", \"Low\\n affinity\\n CRX\\n reverse\", \"Rho\"], fontsize = 7)\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No interaction\")\n",
    "plt.legend()\n",
    "plt.title(\"Additive contributions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "12dc5a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot interactions between high-affinity CRX sites and CRX, NRL, and Rho in CRX wt retina\n",
    "\n",
    "All_CRX_high_interactions = []\n",
    "for model in bootstrap_models:\n",
    "    # Retrieve G-P map parameter dict and view dict keys\n",
    "    theta_dict = model.get_theta(gauge=\"user\", x_wt = \"OOOOH\")\n",
    "    TF_TF_interactions = np.transpose(theta_dict['theta_lclc'], axes = (1, 3, 0, 2))\n",
    "    All_CRX_high_interactions.append([])\n",
    "    for i in range(8):\n",
    "        interactions = TF_TF_interactions[2:4, i, 0:5, 0:5].flatten() + TF_TF_interactions[i, 2:4, 0:5, 0:5].flatten()\n",
    "        All_CRX_high_interactions[-1].append(np.mean(interactions[np.logical_not(np.isnan(interactions))]))\n",
    "    All_CRX_high_interactions[-1].append(np.mean(TF_TF_interactions[2:4, 8, 0:4, 4]))\n",
    "        \n",
    "plt.boxplot(np.asarray(All_CRX_high_interactions))\n",
    "x = np.linspace(1, 9, 9)\n",
    "plt.xticks(x, [\"NRL\\n forward\", \"NRL\\n reverse\", \"High\\n affinity\\n CRX\\n forward\", \"High\\n affinity\\n CRX\\n reverse\",\n",
    "              \"Medium\\n affinity\\n CRX\\n forward\", \"Medium\\n affinity\\n CRX\\n reverse\", \n",
    "              \"Low\\n affinity\\n CRX\\n forward\", \"Low\\n affinity\\n CRX\\n reverse\", \"Rho\"], fontsize = 7)\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No interaction\")\n",
    "plt.legend()\n",
    "plt.title(\"Interactions with High-affinity CRX sites\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3d7dc48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i0qRJSUcAAAAAgB6xnBQAAAAAAJBKSgyAIaiioiLpCEWjrKwsysvLk44BAAAAMCQpMQCGoEwmEzU1NUnHGPLKyspi5syZMXy41RkBAAAA+oOfugAMUffdd1+0t7fHypUrk46yS7lcLqqrq/PmGhoaIpvNJpSoe8rLyxUYAAAAAP3IT14AhrBMJhOVlZVJx+iVbDY7aLOTToOl1NtaRUVFZDKZpGMAAABAYpQYAMCQN2fOnKirq4vGxsako/RIVVVVzJw50/JwAAAAFC17YgAAQ15tbe2gKzAiIhobG6O2tjbpGAAAAJAYJQYAAAAAAJBKSgwAYMirr6+PqqqqpGP0WFVVVdTX1ycdAwAAABJjTwwAYMirqamJ6dOnF3Rj71wuF9XV1XlzDQ0Nkc1mC3YNG3sDAABQ7JQYAEBRyGQyUVlZ2a/XyGaz/X4NAAAAKCaWkwIAAAAAAFJJiQEAAAAAAKSSEgMAAAAAAEglJQYAAAAAAJBKSgwAAAAAACCVlBgAAAAAAEAqKTEAAAAAAIBUUmIAAAAAAACpNDzpAAAAAAAw1A0ridh9dGlERGxavyba15YmnGjw27R+TVSM2fx9XLW+NTZ1JBwI6BdKDAAAAADoRx+ctE9c++HDIjt2VERErL3n8libcKah4rkZH4qIiFzLhrj2f36bcBqgPygxAAAAAKAfXX/W1Bg/2pMX/Sk7dlRcf9bUpGMA/cCeGAAAAAAAQCopMQAAAACgH814YH7kWjYkHWNIy7VsiBkPzE86BtAPLCcFAAAAAP3opwuXxCMNSzo39m54+eXITpiQcKrBL7diRVQfckhE2NgbhjIlBgAAAAD0s00dESvXtUZExLDR4yKzW1nCiQa/YetaO7+nwNBlOSkAAAAAACCVlBgAAAAAAEAqKTEAAAAAAIBUUmIAAAAAAACppMQAAAAAAABSSYkBAAAAAACkkhIDAAAAAABIpeFJBwAAAAAAIH3a2tqiqakp6Ri7lMvlujWXNuXl5TF8uB/R74rvEAAAAAAAeWbPnh11dXXR3NycdJReqa6uTjrCLpWVlcXMmTPjwgsvTDpKqikx/uyll16KBx54IObNmxdLliyJlpaWKC0tjX322ScOO+ywOO200+K9733vgGZ67rnn4vHHH48XXnghlixZEs3NzdHR0RG777577L333nHEEUfEiSeeGEcdddSA5gIAAAAAhq62trZBXWAMFs3NzVFXVxfnnXeeJzJ2oui/M42NjXHNNdfE448/3uVrbW1t8dprr8Vrr70WP/jBD+Loo4+OG2+8MfbZZ59+zfSb3/wmvvrVr8Yrr7yy3a8vW7Ysli1bFvPnz4//9//+X0ydOjW+8pWvxOTJk/s1FwAAAAAw9DU1NSkwBkhzc3M0NTVFZWVl0lFSq6g39l60aFHU1NRst8DYnrlz58YZZ5wR8+bN67dM9fX1cdFFF+2wwNie+fPnx/nnnx8//OEP+y0XAAAAAAAMtKJ9EqOpqSlqa2vjrbfe6pzLZDJx0kknxXHHHRfZbDZyuVz88pe/jMceeyza2toiIqKlpSUuueSSmDNnTuy7774FzfS9730vbr755ry54cOHxwc+8IF4z3veE3vssUds2rQplixZEk899VQ8/fTT0dHRERERra2t8ZWvfCXGjRsXJ598ckFzAQAAAADFraGhIbLZbNIxBr1cLjco9utIk6ItMa677rpYsmRJ57iysjJuv/32OOyww/Je95GPfCQWLFgQl156aSxdujQiNhcgM2bMiHvuuadgeRYvXhw33nhj3tzkyZPjG9/4xnbLkr/927+NBQsWxD/+4z92/jk2bdoUM2bMiKOOOioqKioKlg0AAAAAKG7ZbNaSRySiKEuMF154IR566KHO8YgRI2LWrFk7bMAOPfTQuOuuu+K8886LVatWRcTmpaWefPLJeN/73leQTDNnzoy33367c/xXf/VXcffdd8duu+22w2MOPfTQuPvuu6OmpiZWrlwZERFr1qyJWbNmxRVXXFGQXAAAAFAM2tvbO/9tPVhUVFREJpNJOgYA9KuiLDG++93v5o0vuuiiXT7Cs//++8fll18eV111Vefct7/97YKUGBs2bIhHHnkkb+7qq6/eaYGxxcSJE+PSSy+Na665pnPu4YcfVmIAAABAN82ZMyfq6uqisbEx6Sg9UlVVFTNnzoyampqkowBAvym6jb1bWlri5z//ed7cBRdc0K1jzzzzzCgvL+8cz58/P958880+Z5o7d26sW7euczxx4sSYNm1at48//fTTY9iwv/y/8s0338xbKgsAAADYsdra2kFXYERENDY2Rm1tbdIxAKBfFV2J8atf/So2btzYOT7kkEO6vUH3iBEj4vjjj+8cd3R0xMMPP9znTK+99lre+PDDD+/R8ePGjeuyB8by5cv7GgsAAAAAABJVdCXGvHnz8sZHHnlkj44/4ogj8sZz587tc6YjjzwyvvCFL8SFF14YJ598chx66KE9PsfWT3JERN6TGQAAAMCO1dfXR1VVVdIxeqyqqirq6+uTjgEA/aro9sR49dVX88YHHXRQj44/8MAD88avvPJKnzMdfvjhPX76YmuvvvpqlxJjjz326GMqAAAAKA41NTUxffr0gm7sncvluuy/2dDQENlstmDXsLE3AMWg6EqMP/7xj3njd7zjHT06fuLEiXnjt956K1pbW6O0tLTP2Xrrhz/8Yd54n332UWIAAABAD2QymaisrOzXa2Sz2X6/BgAMNUW35tC2e0X09HHRCRMmRElJSed406ZNsWLFioJk640//elP8V//9V95cyeddFJCaQAAAAAAoHCKqsRYv359l2WXxo8f36NzjBgxIkaPHp0319zc3OdsvbFx48b48pe/HBs2bOicGz58eHz0ox9NJA8AAAAAABRS0ZUY2xozZkyPz7PtMdsWIwPlmmuuieeffz5v7uMf/3iPl8gCAAAAAIA0KqoSY+PGjV3mRowY0ePzbLtpVltbW68z9dY///M/d9kLo7q6Oi699NIBzwIAAAAAAP2hqDb2bm9v7zI3bFjPe5xtS4xNmzb1OlNPbdq0Kb761a/G9773vbz5ysrKuOOOOxLdYBwAABhchpVE7D56878hNq1fE+1r/XuirzatXxMVYzZ/H1etb41NHQkHAgAY5IqqxBg+vOsft729fbvzO7PtEx0DVRy8/fbbcfnll8cjjzySN7/77rvHd77zndhrr70GJAcAADD4fXDSPnHthw+L7NhRERGx9p7LY23CmYaK52Z8KCIici0b4tr/+W3CaQAABreiKjFGjRrVZa61tTVGjhzZo/MkUWKsWLEi6urqYt68eXnz2Ww27rrrrvirv/qrfs8AAAAMHdefNTXGj/bkRX/Kjh0V1581NekYAACDWlGVGOPGjYtMJpO3rFRLS0uMGzeuR+dZuzb/95PGjx9fkHw78sorr8SnP/3pWLp0ad78xIkT484774z999+/X68PAEDPLF++POkIQ0Iul0s6AgAAkLCiKjFKSkqivLw87x9DK1eu7NEyTKtXr+7yJEY2my1Yxm09+uij8eUvfznWrVuXN19dXR2zZs2KysrKfrs2AAC9M2nSpKQjwC7NeGB+3nJSFN6W5aT+49KkkwAADF5FVWJEROy77755JcayZct69I/MZcuW5Y3LyspizJgxBcu3tdtvvz3+7d/+LTo68neC+z//5//ELbfcErvttlu/XBcAABj6frpwSTzSsKRzY++Gl1+O7IQJCaca/HIrVkT1IYdEhI29AQAKoehKjP333z/mz5/fOX7jjTd6dPwf/vCHvPGBBx5YiFh52tra4uqrr47777+/y9c+9rGPxZVXXhnDhg0r+HUBAOi5ioqKpCMUjbKysigvL086xpCyqSNi5brWiIgYNnpcZHYrSzjR4DdsXWvn9xQAgL4rup+ET5kyJW+8cOHCHh3/4osv5o2rq6v7nGlrra2tcemll3YpMIYPHx7XXnttXHXVVQoMAIAUyWQyUVNTk3SMIa+srCxmzpwZw4cX3e9hAQBAUSu6fwFMmzYtb/zrX/86Ojo6oqSkpFvHP/PMM3njY445pmDZ2tvb4wtf+EL8/Oc/z5vfbbfd4pvf/GYcd9xxBbsWAACFc99990V7e3usXLky6Sg7lcvluvwSTkNDQ7/u8VYo5eXlCgwAAChCRfevgAMOOCD222+/zmWkli9fHk8//XS3CoLf//73eU9i7LbbbvGe97ynYNluvvnm+NnPfpY3V1lZGfX19XHIn9dUBQAgnTKZTFRWViYdo8ey2eygzA0AABSHoisxIiJqamripptu6hzfeuutMW3atCgtLd3pcTfddFPeJttnnHFGwTb1fuKJJ+I73/lO3lxlZWXMnj079ttvv4JcAwAAAIDkLV++POkIQ0Iul0s6AjAAirLEOO+88+LOO++MpqamiNi8L8aVV14ZX//61yOTyWz3mJtvvjkef/zxzvHIkSOjtrZ2h9e4//7748orr8ybu/vuu7ssZxURsW7durjqqqvyCpLRo0dHfX29AgMAAABgiJk0aVLSEQAGjaIsMcaOHRtf+tKX8kqGn/zkJ7FkyZK44oor4vDDD++cf/311+Nf//Vf47HHHss7x2c+85nYZ599CpJn9uzZsWLFiry5U089NZqamrrswdFdBx54YFRVVRUiHgAAAAAAJKIoS4yIiOnTp8fLL78cd999d+fc/Pnz49xzz43KysrYY489oqmpKZYsWdLl2JNPPjn+4R/+oSA5Nm3aFP/xH//RZf5HP/pR/OhHP+r1eW+44YaYPn16X6IBAAAA0EcVFRVJRygaZWVlUV5ennQMoMCGJR0gSVdddVVcfPHFMWxY/rdh+fLl8dJLL223wDjnnHPi1ltvjZKSkoJkaGhosH4fAAAAwBCVyWSipqYm6RhDXllZWcycOTOGDy/a39mGIavo/6v+/Oc/H6ecckp84xvfiGeeeSY2bty43ddNnTo1Pv3pT8cJJ5xQ0OsvXry4oOc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kxAAAAAAAAFJJiQEAAAAAAKSSEgMAAAAAAEglJQYAAAAAAJBKSgwAAAAAACCVlBgAAAAAAEAqKTEAAAAAAIBUUmIAAAAAAACppMQAAAAAAABSSYkBAAAAAACkkhIDAAAAAABIJSUGAAAAAACQSkoMAAAAAAAglZQYAAAAAABAKikxAAAAAACAVBqedACKT3t7+3bnly9fPsBJAAAAAADYkR39zHZHP+PtD0oMBtzKlSu3O3/OOecMcBIAAAAAAHpq5cqVsffeew/ItSwnBQAAAAAApJISAwAAAAAASCUlBgAAAAAAkEpKDAAAAAAAIJVKOjo6OpIOQXFpbW2NV199tct8RUVFZDKZBBIBAAAAALCt9vb2WLlyZZf5gw46KEpLSwckgxIDAAAAAABIJctJAQAAAAAAqaTEAAAAAAAAUkmJAQAAAAAApJISAwAAAAAASCUlBgAAAAAAkEpKDAAAAAAAIJWUGAAAAAAAQCopMQAAAAAAgFRSYgAAAAAAAKmkxAAAAAAAAFJJiQEAAAAAAKSSEgMAAAAAAEglJQYAAAAAAJBKSgwAAAAAACCVlBgAAAAAAEAqKTEAAAAAAIBUUmIAAAAAAACppMQAAAAAAABSSYkBAAAAAACkkhIDAAAAAABIJSUGAAAAAACQSkoMAAAAAAAglZQYAAAAAABAKikxAAAAAACAVFJiAAAAAAAAqaTEAAAAAAAAUkmJAQAAAAAApJISAwAAAAAASCUlBgAAAAAAkEpKDAAAAAAAIJWUGAAAAAAAQCopMQAAAAAAgFRSYgAAAAAAAKmkxAAAAAAAAFJJiQEAAAAAAKSSEgMG0EUXXRQHH3xwHHzwwXHfffd1+7jjjz8+Dj744Ljooovy5q+44orO8+3s/yZPnhzHHHNMnHvuuXHTTTfFsmXLdnitrc+5ePHiXv9ZGVq8dxlsevt+2HLMtu/ZLfOXXXZZQXMuXry489y33nprQc/N4OS9S3868cQT8/6enT17do+OP+200/KOb2tr66ekO7blz3DFFVfkzd9///2duZ555pkBz0X/8b6FHdvy3jrxxBOTjgLd5n1LbygxICE33XRTNDY2Dsi1Nm7cGCtWrIgXXngh7rzzzjjttNPiF7/4xYBcm6HHexcAhoaf/exn3X7tokWLYtGiRf2YBrrH+xYAis/wpANAsVq9enX88z//c9x2220FOd+sWbOiqqpqu19bu3ZtvP7663HffffFwoULo6WlJb7whS/Ej370o3jHO95RkOtTPLx3AWBo+M1vfhOrVq2K3XfffZev/elPf9r/gaAbvG8BoPgoMSBBjzzySPz85z+PD3zgA30+1wEHHBATJ07c4dePOuqoqKmpicsuuyweeuihaGlpibvuuiuuvfbaPl+b4uO9S7H53e9+l3QE6BXvXbZnwoQJsWLFimhra4snnngizj777F0e88gjj0RERGlpabS2tvZ3xB6bPn16TJ8+PekY9CPvWwAoXpaTggSMGDGi83//3//7f6OlpWVArpvJZOLqq6/uvL7fTKKnvHcBYPB75zvfGQcccEBERDz66KO7fP0bb7wRr776amQymXjPe97T3/Fgu7xvAaB4KTEgARUVFfGRj3wkIiIaGxvjpptuGtBrH3TQQRERsWrVqlizZs2AXZvBz3sXAIaGk046KSIi/vd//zfWrVu309c+/PDDEbH56cgJEyb0ezbYEe9bAChOSgxIyJe+9KWorKyMiIjvf//78Zvf/GbArr1p06bO/71x48YBuy5Dg/cuxejggw+Ogw8+OC677LLtfr2trS3uvffeOO+88+KII46Iww47LD70oQ/FHXfcERs2bIgHH3yw8xw7s3Hjxrj77rujpqYmjjjiiDjiiCPizDPPjG9961u7/GENbI/3LjtyyimnRETE22+/HU899dROX7tlSZ4tx+xMW1tbzJkzJ/7u7/4ujjnmmJg8eXIce+yxcfHFF8dDDz0UHR0dOz1+7dq1cdddd8U555wTRx11VLz73e+Oj3/84/HEE0/s9Lj777+/8736zDPP5H3txBNPjIMPPjjOP//8HR7/zDPPdB5///33533tiiuuiIMPPjg+/vGPR0TE/Pnz43Of+1wce+yxceihh8ZJJ50U//Iv/xKrVq3qPOaFF16Iurq6eO973xtTpkyJU045JW699Vb/PfSR920+71sKYctyveeff34cffTRMXny5DjhhBPi0ksvjV/84hddXr/1++7HP/7xds953333db7mW9/61nZf88QTT3S+xvKX9JT3bfGxJwYkpKysLK666qr4x3/8x+jo6Ih/+qd/igceeCBKS0v79bpNTU2xaNGiiIjYfffdo7y8vF+vx9DjvQv5Wlpa4tOf/nTMnTs3b/61116Lb37zm/GTn/wk/uZv/maX52lqaopzzz03Fi5cmDf/yiuvxCuvvBL//d//HbNnz46KioqC5qd4ee8Wt+rq6pg4cWIsXrw4fvazn+3wB71/+tOfoqGhIYYNGxYnn3xyvPjiizs855tvvhmf+tSnuvyjfvny5fHEE0/EE088Effee2/cdttt2/17/I033ohPfOITsWTJkrz5Z599Np599tn45Cc/2Ys/aWF997vfjX/5l3/J+8WKP/7xj3HXXXfFk08+GXPmzIkf//jH8bWvfS3a29s7X/PGG2/Et771rfj1r38d99xzT94SnXSf923veN+yI/PmzYvPf/7z8dZbb+XNv/XWW/Hwww/Hww8/HCeffHJ8/etfj9GjR0fE5qebxowZE+vWrYtf/epXceaZZ3Y5769+9avO//3cc8/FxRdf3OU1W4rIvffee5e/LAFb874tTkoMSNAHP/jB+O///u94/PHHY9GiRfHv//7vcemll/brNW+55ZbOTe1OOeWUKCkp6dfrMTR578JffPazn+38IfC0adPiwgsvjD322CNef/31uPPOO2PRokVx88037/I83//+9yNi8wfs888/P/bee+9444034o477og//elPsWjRorjhhhsGdBk3hjbvXU4++eTOH2Ju3Lhxuz+g3HpJni1PYm7PmjVr4sILL4zFixfH8OHD45xzzokTTzwxKioqYtmyZfHQQw/FQw89FHPnzo2LL744Zs+enXe9lpaWuOiii6KxsTFKSkpi+vTpcfrpp8fo0aPj+eefj/r6+rjzzjtj2LDkFhN4+eWX41e/+lVMmDAhPvnJT8aUKVNi+fLlcccdd8Srr74av//97+Nzn/tcPPvss7H33nvHpz71qTj44INj6dKlna+ZP39+PPDAA1FTU5PYn2Ow877tGe9bduTVV1+N2traaGlpiWHDhsVZZ50Vp556auy+++7x+9//Pu6+++5oaGiIRx99NNatWxf19fUxbNiwKC0tjb/+67+Oxx57LJ599tntnnvrX5CYN29etLW1xfDh+T+CfPrppyMi4oT/r727j6+xfvw4/prdz6Y1DREltSFmaBSySl/3FBHl7tuNqExR4ivJ14SQftK3SO42U7mXmyx3kdzN/f19GGqImRl2tp3fH3ucqx3n7NbYObyfj0ePzjnX57quz7H3Pjvn+lyfzyc8/Na9SbnjKLd3L3ViiBSxTz75hM2bN5OcnMykSZNo3rw5jz76aKEdPzU1lUuXLrFv3z5iYmKMYXUlS5YkIiKi0M4jdx9lV5zF0aNHb9kaKsuWLTOmf3jxxRf59NNPjQ62GjVq0Lx5c15//XW2bt2ap+N17NiRIUOGGMeoVasWjRo1onnz5pw/f55ly5bx6aef3vKRT+IYlF251SwXgy9fvszGjRt56qmnbMrkdUqe0aNHc+rUKTw8PJg0aZLNQsr/+te/eOKJJxg8eDA7duwgJibGmOYG4Ouvv+bs2bMADBkyhI4dOxrbatWqRZMmTejYsSN///13Qd/uTUtMTCQgIIBZs2ZRrlw54/XatWvz7LPPYjKZ+P333ylfvjxz5szB398fyPydqlu3Ls899xxXrlxh9erVuhh8E5Tb/FFuJTtDhgwhOTkZFxcXxo0bR+PGjY1tNWrUoFWrVvTt25fY2FjWrVvH7Nmz6dChA5B5AXflypX8+eefHD9+nIceesjY9/Dhw5w7dw53d3dMJhMpKSns27ePkJAQo0x8fDwnTpwA4Omnn74t71fuDMrt3UudGCJFrEyZMvTt25ehQ4diMpn4+OOPmTlzZr7v1mnUqFGeyz700EN89dVXOd6VJJIbZVecxZtvvnnLjh0dHQ1AYGAggwcPthkh5O3tzZgxY2jcuHGu67j4+/szYMAAm2Pcc889NGvWjOjoaFJTU4mPj6dSpUqF+0bEISm7cquFhoZSunRpEhISWL58uc3F4DNnzrB7926KFStmLKhsT2JiIgsWLADg5ZdftrkQbNGhQwcWLFjAtm3bmDlzpnExOCMjg7lz5wKZF1azXgi2qFChAu+//z4DBw4swDstPK+++qrVhWCAUqVKERoaSlxcHABvv/22cSHYIiAggBo1arB+/Xri4+NvV3XvSMpt/im3cqNdu3YZNyq0b9/e6kKwhZubG8OHD2fz5s1cvHiRKVOmWF0Mtti4caPVxWDLlDw1a9bk9OnTnD59mri4OKuLwWvXrgXAy8sr2989kRspt3c3Lewt4gBeeeUVatWqBWQuuDZz5sxCP4e/vz/h4eGMGDGCRYsWFeod83L3Unblbnbp0iW2b98OZE6x5uXlZbdc2bJladCgQa7HCwsLM+ZsvVHWD9iXLl3Kf2VFslB2xcLFxYXnnnsOgFWrVlnNlw//3M1es2ZNSpcune1xNm/ezPXr1wF48sknczynJVMnTpww5rLevXu3sbhwixYtst23RYsWeHp65nj8Wy27ixalSpUyHtetW9duGct6CleuXCn8it1FlNv8U27lRlmn08lphI2vry8tW7YEMtdIOXXqFJB5Q1tQUJDNsQA2bdoEQJ06dahevTqA0VlmYVlX4Iknnsj2c4jIjZTbu5tGYog4ABcXF4YNG8bzzz+PyWRi7NixNGrUiPvvvz/Px/j222+ND6Fms5nLly+zZs0a4+7HwMBAevbsaVxwFikMyq44g5UrV/LAAw/kqWx+Fmc7cuSIsfjlY489lmPZ6tWrs3r16hzLlClTJtttWS9+pKWl5bmO4tyUXbkdGjduTExMDOfOnWPHjh1Wf28tF4ObNm2a4zH2799vPLa3CGZ2Tp06RZkyZTh27JjxWk5Z9vLy4pFHHrFZRP52yu53MutUaffdd1+uZeTmKLf5o9zKjQ4fPgxk3rVepUqVHMtaLuhC5mcIS57Cw8M5dOgQmzZtwmw24+LiQkZGhnHht06dOvj5+bFs2TK2bt1KRkYGxYoVIzU11bhgrCl5JD+U27ubRmKIOIhKlSoZH56vXLnCf//733zvX6VKFapUqULVqlWpW7cuH374IdOmTcPb25vDhw/TtWtXYxEikcKi7MrdKuv81pa7FLNTsmTJXI9XvHjxPJ3XbDbnqZxIdpRdySosLMzIwfLly43XExIS2LFjBy4uLrmuK2C5Gz2/kpKSAOtM3jidzY3ykslbKS95d3V1vQ01ubspt/mj3MqNLPn38/OzWqzenqz5zfp7Y5ma5+LFixw8eBDI7BxMTEzEw8OD0NBQwsLCgMzfG0uZrVu3kpKSAuhisOSPcnt300gMEQfy5ptv8vPPP3PkyBFWr17N0qVLad68+U0ds3bt2gwdOpR+/fphMpmIiIhg9uzZPPLII4VUaxFlV+5OqampxuPcLs7q4q04EmVXsnJ1daVRo0bMmTOH5cuX079/fyDzbnaz2ZzrlDyAMbIHYNq0able0LXI60ijrNzcbs1X2BunJMqOLvQ6BuU2k3IrBWX5+37jelb2ZP1dyVq+Zs2alChRgqSkJDZs2EDlypWNdQVq1KiBp6cnVapUMcrExcVRpUoV4+a04ODgfI3gF1Fu724aiSHiQDw8PIiMjDQWRv70008LZf7o1q1bG/MBpqSkGBeFRQqLsit3o6x3sF+4cCHHshcvXrzV1RHJM2VXbmRZGDM+Pp4DBw4A/9zdntuUPJC5iLtF8eLFjRGWuf3n5+cHWE9jk1smLXfBF0ROnXLJyckFPq4UDeVWuZWCs3TaJSUl5fr9Kmu+s3b2ubm5Ua9ePeCf9QU2b94MZE7JA1CsWDFq164N/LO+gOVisO5ml/xSbu9u6sQQcTC1atXi5ZdfBuD8+fOMHDmyUI770UcfGQ33vn37+O677wrluCIWyq7cbbLOw7pv374cy2add1ukqCm7cqMnn3zSuDC7YsUKLly4wNatW/M0JQ9kTg1pYbkQkJ3ffvuN6dOns2LFCmOh4KyjLPfs2ZPtvhkZGcZ82PlhuQvesoizPZbFmsV5KLfKrRScZXHjtLS0XP/W79q1y3hcsWJFq22WqXni4uJITU1l27ZtAMZ0PPDPovFxcXEkJCQYnY66GCz5pdze3dSJIeKA+vbtayySOW/evFzv7MmLgIAABgwYYDyfMGECp06duunjimSl7MrdJCAgwFgwLjY2NtuLDBcuXGDt2rW3s2oiOVJ25UYeHh7Gl/JVq1axatUq0tPTCQkJydOUCU8++aQxXc2sWbOspizLKj09nf/+978MHz6cvn37Gq9Xq1bNOM+CBQuynSJnzZo1BRrpabnQ/ddff2V77PXr1+f7uFK0lFvlVgruySefNB7PmTMn23LJycksXboUgLJly1KhQgWr7Q0bNsTFxYWUlBRmz55NUlIS7u7u1KxZ0yhjuRh88eJFpk6dCmTeGR8aGlpYb0fuEsrt3U2dGCIOyNfXl08++cR4XljT57zwwgvGkLhr164xbNiwQjmuiIWyK3ebLl26AHD27FlGjBhhsz0tLY1BgwZx7dq12101kRwpu3Ijy53re/fu5fvvvwfyNiUPQOnSpY2pfU6cOEFkZKTdKXDGjBlDfHw8kPm3Petiw506dQLg4MGDjB8/3mbfc+fO8emnn+bjHf3DcufmhQsX+Pnnn222x8bGsmbNmgIdW4qWcqvcSsGEhIQYF2xnz57NihUrbMqkpaUxcOBAY1Hkbt262ZS57777qFq1KgDffvstkLmugJeXl1GmcuXKxvRtM2fOBDIvIlumIhbJK+X27qaFvUUc1LPPPkuzZs3sfmAtKBcXFwYPHkzbtm1JT09n9erVrFy5kkaNGtktP3HiRHx9fXM9bqNGjXj88ccLrZ7i3JRduZu0bt2auXPnsmnTJr7//ntOnjxJx44dKVOmDCdOnGDatGk5TjEhUlSUXbnRU089hbe3N1evXjV+9nmZksdi4MCBbN68mb///ptZs2Zx5MgRXnnlFSpUqEBCQgLz5s1j9erVAAQGBvLee+9Z7f/qq6+yZMkS9u/fz9dff83Bgwdp3749AQEB7Nmzh4kTJ5KQkGDUMT9atmzJvHnzgMxpKo8fP07dunVJSUnhl19+Ye7cuZQrV44///wzzwsli2NQbpVbyXTp0iU+++yzPJX997//TenSpRk2bBjt2rXj6tWrRERE0LZtWxo3boy/vz9//PEHUVFR7N27F8i8K71r1652jxceHs7evXuN6c2yWXpK/AAARaNJREFUTskD/6wvsGrVKmP0p6bkEVBuJX/UiSHiwAYNGsT69esLZYFki8qVK9OpUyeioqKAzAWY69Wrh7e3t03ZWbNm5emYpUuX1oVgsaLsyt3CxcWF8ePH8/rrr7N7925+//13fv/9d6sy1atXp2zZssTGxuLh4VFENRWxpuzKjby8vGjYsCGxsbFA5s+/XLlyed6/VKlSREdH89Zbb3HixAm2bdtmzDGd1QMPPMDEiRMJCAiwet3NzY0pU6bQo0cPdu3axcqVK1m5cqVVmXbt2nH+/Hl+/fXXfL23+vXr06lTJ2JiYrh69Spffvml1fYyZcrw7bff0rp163wdV4qecqvcSqbk5GSmTJmSp7KtW7emdOnSPPLII0yZMoWIiAjOnz/PnDlz7E7R06JFCyIjI7O9Az08PJyvv/7aeG6ZhierunXrsmrVKiDz9+app57KU13lzqbcSn5oDIyIA7vvvvv48MMPC/247777LoGBgQCcPn2ab775ptDPIXc3ZVfuJvfccw8//PADgwcPpmbNmpQoUQJPT0+CgoLo168fM2fOxMfHB8D4v4gjUHblRpapdSB/d7NbVKpUicWLFzNkyBDq1atHyZIlcXNzw8/Pj5o1a9K/f38WLVpktSByVgEBAcTExDB8+HBq1aqFv78/Pj4+hISEMGLEiAJPywMwePBgvvnmGxo2bIi/vz+enp5UrFiRN998k4ULF2ZbJ3F8yq1IwdWqVYvY2Fjef/99QkNDueeee/D09OTBBx+kZcuWREVFMXbsWKtp1G4UEhJidPDduK6ARdYLxJbPHCIFpdzenVzM9iZ9FBEREZFC07NnT1avXk1QUBCLFi0q6uqI5JmyKyIiIiIiRU3TSYmIiIgUUL9+/XBzc6NevXq0atXKbpnr16+zc+dO4J9FOkWKmrIrIiIiIiLOQp0YIiIiIgV04cIF1q1bx+rVq6lfv77NXNkAX331FRcuXADIdjF6kdtN2RUREREREWeh6aRERERECmjZsmW8++67AJQvX54uXboQHByMh4cHZ86c4aeffmLNmjUAPPHEE0ybNg0XF5eirLIIoOyKiIiIiIjzUCeGiIiIyE34/PPPmTRpEjl9pKpfvz6ff/459957722smUjOlF0REREREXEG6sQQERERuUm7du3ihx9+YOvWrfz1118AlCpViuDgYJ5//nkaNWpEsWLFiriWIraUXRERERERcXTqxBAREREREREREREREYek26pERERERERERERERMQhqRNDREREREREREREREQckjoxRERERERERERERETEIakTQ6QAUlNTGT9+PE2aNKFGjRrUqVOHFi1akJCQUNRVu2OcOnWK4OBggoODmTdvXlFXR0TuQGazmenTp9OqVStq1qxJ7dq1adq0KXv27AEgISGBjz76iPDwcKpVq0b9+vV54403rNqnL774olDrNG/ePOPY69evt1vmzJkzJCcnF+p5xXkot+KslF1xRsqtOCtlV5yRcis5cSvqCog4o759+7J8+XLj+bVr17h+/TqBgYFFWCsREcmPkSNHMm3aNKvXkpOTKVWqFMnJybz88sucPn3a2Hb+/HnMZvNtruU/MjIymDFjBl988QWLFi3C19e3yOoiRUe5FWel7IozUm7FWSm74oyUW8mJOjFE8unw4cNGB0ZwcDDvvvsu9913H9euXaNYMQ1uEhFxBpcvXyY6OhqAcuXK8cEHH1CuXDmSkpIoVaoUMTExxgfkJk2a8PLLL+Ph4UHx4sWLrM6LFi3i008/LbLzS9FTbsVZKbvijJRbcVbKrjgj5VZyo04MkXw6duyY8bh///7Ur1+/CGsjIiIFceLECdLT0wHo2bMnzZs3t9puaevd3d0ZOXIkPj4+VtsPHjx4S+rVtm1b2rZta3ebpb5y91JuxVkpu+KMlFtxVsquOCPlVnKj28ZF8unq1avG4wceeKAIayIiIgWVW1uekpICQEBAgM0HZJGiotyKs1J2xRkpt+KslF1xRsqt5EadGCL5lJGRYTx2dXUtwpqIiEhB5daWW+ZWdXPToFVxHMqtOCtlV5yRcivOStkVZ6TcSm5czEW5AoqIExkwYADz58/PdvvKlSuteouTk5OZNWsWy5cv5+jRo6SkpFCyZElCQ0Np27Yt4eHhOZ6nTp06TJw4kZEjR7Js2TJSU1MpV64c169fJz4+HoBff/2V+++/3+YY77//PosXLwbg+++/p1atWjZlPvvsM6ZMmcK9997L+vXrrdbzOHPmDD/++CMbN24kPj6epKQkPD09KVWqFGFhYbzyyitUrlw533Xv3r07L7zwgtV5pk6dyvr16zl9+jS+vr7UrVuXHj164OPjQ6NGjQAYMWJEtsP3JG+Cg4OBzH/LoKAghg0bxr59+/D29iYoKIhRo0ZZZSkhIYHp06fz22+/cfr0adLT0ylTpgxPPPEEXbt2pVKlSlbHj42NpXfv3kBmtrL+nG80bdo0RowYAcD8+fOpWrWq1fZjx44RHR3N+vXrSUhIwMXFhQceeIAGDRrQrVs3ypQpY3PMU6dOGXmJiorCZDIxZswYjh49ip+fH1WrVmX8+PF4e3sDsGbNGubOncv27du5ePEi3t7elC1blnr16vHKK69Qvnz5bOtvNptZtmwZCxcuZM+ePSQmJuLr60vlypVp3rw5bdq0wd3dPdv9pXBt376dBQsWsHXrVs6ePcuVK1fw9fU1MtOlSxfuu+8+o/z48eP56quvsj1euXLlrBaLu3HbqlWrrPLWs2dP+vTpY5TJ2g5GR0ezb98+pk+fzqZNmzh//jz33HMPtWvXpnPnztSpU8fmHPPmzeM///kPAFOnTqVevXps2rSJrl272q1TnTp1+O6776hfvz6XL18mNDSUH3/8Mdv3l5SURL169TCZTDZ1l9tHuVVunZWyq+w6I+VWuXVWyq6y64yUW+X2VlH3lcgtsG3bNvr06cNff/1l9fpff/3FsmXLWLZsGY0bN2bUqFHGRdUbZWRkEBERwbp164zXjhw5QkREBOPHjwdg48aNtGnTxmbfTZs2GY/j4uLsdmL89ttvADRs2NCqA2PGjBmMHDkSk8lkVd5kMpGcnMyxY8eYPXs2kZGRtGvXLl91DwwMNJ6vWrWKPn36cO3aNeO1q1evsnjxYn755Rfef/99u8eWm3Ps2DEiIyONoZiWTrHSpUsbZRYvXsygQYOshnMCHD9+nOPHjzN79mz69u3LG2+8YWx75plnKFGiBElJSSxdujTHTowlS5YAUKlSJZsOjClTpvD555+TlpZm9fqhQ4c4dOgQM2fOJDIyktatW2d7/Li4OL7++mtjfsq///6b5ORkvL29MZvNDBw4kHnz5lntYzKZSEpK4sCBA0RHRxMZGWn3dyspKYmIiAg2btxo9frFixfZsGEDGzZsIDo6mm+++UbTzd1iJpOJjz/+2G7ncmJiIomJiezZs4cffviBKVOm8Nhjj932Os6ePZv//ve/Vu3p+fPniY2NJTY2lj59+tCzZ8+bPo+npydNmjRhzpw57Nixg/j4+Gw74mJjY4365PR7JLeGcvsP5da5KLv/UHadh3L7D+XWuSi7/1B2nYdy+w/l9tZQJ4ZIHvXu3Ztu3bqxcuVKoxPh22+/pVSpUgDG/w8dOkT37t1JTk6mWLFivPDCCzRt2hR/f3+OHTtGVFQU+/bt45dffiElJYVJkyZZdSJYbNu2jYyMDJ555hm6detGamoqa9eu5e233yY6OprExEQ2bNhgc6H16NGjnDt3zngeFxdHjx49rMokJCRw+PBhAJ5++mnj9dWrVxMZGQlk9mh37dqVoKAgvLy8OHPmDAsXLmTt2rVkZGQwbNgwnn32WQICAvJc9yeffBKAHTt20KtXL9LT0/Hx8eH111/nySefJDU1leXLl/PDDz/w2Wef5evnI3kzefJkXF1def/996ldu7axOJYlgytWrOCDDz7AbDZz//3306VLF2rUqIGrqyv79+8nOjqaY8eOMXr0aDw9PenSpQsAHh4eNGnShNmzZ7N+/XoSExPx9/e3OX98fDy7du0CbP9AR0VFGT/3SpUq0alTJ6pWrUp6ejo7d+5k+vTpJCQk8OGHH1K8eHHjTosb/e9//8PX15f33nuPypUrs3fvXmOUydy5c40OjHr16tGhQwfKli1LcnIyW7ZsYerUqaSkpPDxxx8TFhZm1RFhMpl444032LlzJwDNmzenefPmlC5dmosXL7JixQrmzZvHoUOHePXVV5k7dy4lSpQo0M9Jcjd27FjjA3LNmjXp2LEjDzzwAGlpaRw/fpzo6GiOHDlCYmIin3zyCXPmzAGgY8eOPPfcc+zZs4dBgwYBMGzYMKpVqwaAi4sLZrOZcePGsXr1agIDA5k0aRJAvkbYHD58mE8++QQ/Pz+6detGnTp1SE9PZ8WKFcyYMYOMjAzGjRtHo0aNePTRR3M8VrVq1ViwYIHdvz+W+WBfeOEF4z0uXbrUpt23+OmnnwB47LHHbEZUya2n3Cq3zkrZVXadkXKr3DorZVfZdUbKrXJ7q6kTQySPypYtS9myZdm/f7/xWqVKlWzuth4yZAjJycm4uLgwbtw4GjdubGyrUaMGrVq1om/fvsTGxrJu3Tpmz55Nhw4dbM6XkZFBSEgI//vf/4z5AC1TUDVo0IDFixezYcMGm/0sd4i7u7tjMpnYtm0b6enpVnMKrl27FsicS7BBgwbG65bG19/fn5kzZ1pN21OrVi1atmxJZGQkM2bM4OrVq/z22288//zz+ao7wKeffkp6ejre3t7ExMRY3Y3/5JNPUq9ePXr16mVzXLl5GRkZfPTRR3Tu3BmA2rVrG9uuXLnCRx99hNlspnr16kydOhU/Pz9je82aNWnbti09evRg48aNjB49mqZNmxojbFq3bs3s2bMxmUwsX76c9u3b25x/6dKlQOYHkVatWhmvnz59mtGjRwOZozq+/PJLPDw8jO2PP/44L774Ip07dzY+fDRo0ABPT0+773HUqFE888wzNu9x4cKFAAQFBTFp0iSr+TTr1atHjRo1ePPNNzGZTCxatIi33nrL2D516lR27tyJi4sLo0ePtqo/ZGb8X//6F2+++SYnT55k/PjxfPTRR/Z/EHJTLl68SHR0NJDZNkVFRVl9gH3iiSdo164d7du3Z9++fezevZu//vqLMmXKEBgYSGBgIElJSUb5ChUqUKVKFatzWDrhPDw8bLbltY4lS5Zk1qxZVn8n6tatS9myZRk5ciQZGRn89NNPuY48K168OFWqVMnx78/jjz9uDLVesmSJ3Q/JCQkJbNmyBdBdPkVBuVVunZWyq+w6I+VWuXVWyq6y64yUW+X2dtDC3iKFaNeuXWzduhWA9u3bW3VgWLi5uTF8+HDuvfdeIHP6nOy89NJLdhc0atiwIQBnz5417qS3sHRitGzZEsi8ML1v3z6rMpappGrVqmXcKX758mUyMjLw8/Ojbdu2dtcdgMy7zy3+/PPPfNf9wIEDxp34//73v22mEwJ47rnn7HaOyM1zd3fPdhqwBQsWkJiYCGTe+ZC1A8PCy8uLoUOHAplTUVnuLAAICwujbNmyAPz88892z2GZSqp27dqUK1fOeD0mJobU1FTc3d0ZPny4VQeGhb+/PwMHDgTg3LlzxMbG2j1HmTJljA6MG50/f94oY29BsPDwcLp06UJERASPP/648Xp6ejpRUVEANGrUyKYDw6Jhw4bG796cOXNITU21W05uzuHDhylfvjyenp68+eabdu/AcXNzs2qDz5w5czurCMAbb7xhd1qxdu3a4eLiAmCMirtZLi4uxgffgwcPcvToUZsyixcvJiMjA1dXVyOncvsot7aUW+eg7NpSdh2fcmtLuXUOyq4tZdfxKbe2lNvCp04MkUKUdWSEvbvQLXx9fY0G6vjx45w6dcpuuerVq9t9/amnnjKm/8l6TrPZzObNmwFo06YNJUuWBDKnlLJIT083OjqyTiXl5+fHggUL2LJlC/369cu27lnXtbh+/Xq25bKru2UUCECLFi2y3d/eegRy8x599FG8vLzsbrN0bvn7+9tduN3iwQcfNOZztOQNMv9IW3K9ceNGLly4YLXf0aNHOXjwIIBNJ4Dl3EFBQXanKLMICwszRl9kPXdW2WUPoGLFisb5xo0bZ1NHgEGDBtGrVy/CwsKM1w4cOGBM02aZFi07ltFNKSkp7N69O8eyUjB16tTh559/ZufOnVbt2I2yLhiXU3t1q9SrV8/u635+ftxzzz1AZkdzYcm6Fs3ixYttti9atMioV9Z/G7k9lFv7lFvHp+zap+w6NuXWPuXW8Sm79im7jk25tU+5LVyaTkqkEFl6bN3c3HId3pb1QuuRI0fs9gZnXWw5q4CAAKpXr87OnTvZuHEjnTp1AjIvtCYmJuLh4UFoaCg1atRg1apVxMXF8dprrwGwc+dOLl26BJDtHxdLB8nly5c5efIkJ06c4OjRo+zZs8cY6gaZ0/ZkJ7u6//HHH0DmiICHH3442/0t8x9K4cru5wKZ+YHMRbeCg4PzdLz4+Hir588//zzffvst6enpxMbG8vLLLxvbLKMw3N3dadasmfF6WloaR44cAWDv3r0FPrdFTu/x1Vdf5ddffyU9PZ2vv/6aCRMmEBISQoMGDWjYsCEhISHGHRhZZR0mGhkZaawdk5c6Zp3OSgpX1p/VuXPnjPbq0KFD7Nq1yxj1BZmdvLdb1tFGN/Lx8SExMdFYgL4wPPTQQ4SGhrJjxw6WLl3Ku+++a2w7cuSIkWONdCtayq015dZ5KLvWlF3noNxaU26dh7JrTdl1DsqtNeW2cKkTQ6QQWabi8fPzy3WBIcsoiaz73cjX1zfb/Rs2bMjOnTvZvHkzGRkZFCtWzBhhUaNGDTw9PalTpw6rVq1i69atmM1mXFxcjDvey5cvb3fRoPj4eL777jtWr15NQkKCzXZ7i5Dnp+5///03kPlvZG+6qaz7e3l5ce3atTydT/KmePHi2W7LLoc5uXz5stXzRx55xJgbcunSpVadGJb1MMLDw427HACSkpJy7BDL67ktcvq9CQsLY+zYsQwZMoSLFy+SkZHBjh072LFjB1999RWBgYE0a9aM119/3WpKtYL82+RURykcmzdvJioqik2bNlnNoWqR1/bqVvH29s52m+UDfmF/eG/dujU7duzg+PHj7Nmzx+gQttzl4+Pjw3PPPVeo55T8UW5tKbfOQdm1pew6PuXWlnLrHJRdW8qu41NubSm3hUedGCKFyNLY2buT+0ZZe3ezK5/TccLDwxk/fjyJiYns37+fxx57zOjEqFOnjtX/L126xMGDB6lcuTLr1q0D7I/CWLlyJX369LEa1ufv70+lSpWoXLkytWrVomLFirRt2zbX95eXf4Pc2FuzQG5OTj+XtLQ0IHOtlMGDB+fpePY+hLRu3Zr9+/ezZcsWzp07R2BgIPv27TNG4dy4YJXlvABNmzalZ8+eeTq3vUW986Jp06aEh4ezcuVKVqxYwfr1643RSefOnSMqKoo5c+YwYcIE6tatC1j/vo4aNYqgoKA8nSunUSFyc7744gsmTJhg9dr9999PpUqVeOyxx3j88cc5c+YMn3zySRHVsGi0aNGCESNGYDKZWLJkifEh2TJ8uXHjxjl+eJdbS7m1T7l1fMqufcquY1Nu7VNuHZ+ya5+y69iUW/uU28KjK4Qihcjf3x/IvLPcZDLlOBoj61z8lv3yo1q1atx3332cP3+eDRs2ULlyZWOqJ0vnRZUqVShRogRJSUnExcVRunRp9uzZA9h2YiQkJNCvXz+uX7+Or68vvXv35rnnnrMZbnfo0KF81zUryzx/ly9fJi0tLduOirS0NFJSUm7qXJI//v7+nDt3jqSkpFynQ8tJy5YtGT16NBkZGcTGxtK5c2djFIafn5/NottZR2VcvXr1ps6dV97e3rRs2ZKWLVuSkZHBnj17WLduHUuXLuXw4cOkpKTwn//8h19++QU3NzerOnp4eNyWOkr2Vq1aZXxArlixIu+++y716tWz+jkBzJgxoyiqV6T8/f15+umnWb58ObGxsfTv35/du3cbay/d2Ikot49ymz3l1rEpu9lTdh2Xcps95daxKbvZU3Ydl3KbPeW28Ghhb5FCZLk7Oy0tzWoOfXuyzgVoWWw4P1xcXHjqqaeAzMW9Dxw4QHJyMu7u7oSGhgKZd8lb5uPfvHkz69evJyMjAx8fH6Ojw2LRokXGAkaDBw+mW7duducL/PPPP/Nd16weeeQRAEwmk7EGgz2HDh0q0BRDUnCW6cWOHj1qTPuVncmTJzN79my2bdtms61UqVI88cQTQOaHGYBly5YB0KRJEzw8PKzKe3p6GguFb9++HZPJlO15U1NT+frrr5k/f36uv2P2JCUlsWPHDs6ePWu8VqxYMUJCQnj77bf56aefjA6+06dPc/ToUeCf3EL2C4pb7Ny5kylTphAbG2t34XC5eT/88AMArq6uTJ48mWbNmtl8QIabb6+clWVO1dOnT3PgwAF+/fVXAAIDA3NdmF5uHeU2Z8qt41J2c6bsOiblNmfKreNSdnOm7Dom5TZnym3hUCeGSCHK2vjMmTMn23LJycnGnelly5alQoUKBTpfw4YNAdi6dSsbNmwAMtfD8PLyMspYLibHxcWxdu1aAOrVq2dzIfnkyZPG46pVq2Z7Tku9gQIteJR1rr/58+dnW84yP6DcPvXr1wcyp0XL6Q6JjRs3MmrUKAYNGsT3339vt4zlboLNmzezZcsWYxHu7O4yqFevHpDZybBw4cJsz71o0SLGjRvHgAEDiI2Nzf1NZXH06FHCwsLo0KEDP/74o90yxYoVM+oCGFOrhYSE4OfnB2QO+8xpjYzRo0fz2Wef0bt3b5KTk/NVR8kbS3tVokSJbBdnS01NZcWKFcbzrNOWOau8ziEbHh5ujPBbsWKF8SG5VatWRT4P7d1Muc2Zcuu4lN2cKbuOSbnNmXLruJTdnCm7jkm5zZlyWzj0LyVSiEJCQqhZsyYAs2fPtmqgLdLS0hg4cKBxEbRbt24FPt9TTz2Fm5sbV69eJTo6GsBmhIXl+cWLF/n5558B++th3HvvvcZjy+LfN/rxxx9ZsGCB8TynO+azU6FCBaPz5fvvvzfW6Mhq69atxvuR26ddu3bGXIyTJk0yOsayunTpktUclp06dbJ7LMu8jiaTieHDhwNQpkwZm3xadO7c2fjjPWrUKA4ePGhTJj4+ns8//xwAd3d3XnrppXy8O3j44Yd54IEHgMxhrKdPn7Ypk56ebnSOeHt78/DDDwOZU0h16NAByOxo+fDDD0lNTbXZPyYmhri4OAAaNGhQ4A5KyZnlA+DFixeNKfKySk1NZfDgwRw/ftx4rSDtlaPJ2vmc03R7Hh4eNG/eHICFCxeyd+9eQEOVi5pyq9w6K2VX2XVGyq1y66yUXWXXGSm3yu3toE4MkUI2bNgwvL29ycjIICIigo8++og1a9awc+dOFixYwEsvvWRcJK1bty5du3Yt8Ln8/PyMqaP++usvwLYTo3LlysYwvuvXr+Pi4kJ4eLjNsf71r38Zj8eOHcuoUaP4/fff2bZtGwsWLODVV1+1Wey5oHeZDx48mOLFi5Oenk7Pnj0ZPnw4mzZtYsuWLYwdO5ZXX30VFxcXXF1dC3R8KZiAgAA++ugjIPMDRffu3Rk6dCi///47W7duJSYmhjZt2hgfPF566SUjfzcqXrw4zz77LIDxB7ply5bZLiweFBREjx49gMyOko4dOzJ27Fg2bdrE5s2bmTRpEu3btzemuerVqxdly5bN1/tzcXHhnXfeASAxMZF27doxceJEI+cLFy6kU6dObN26FcjsWPH19TX2f+edd4xOjTVr1vDiiy8ye/Zsdu7cyapVq/jwww+JjIwEwMfHx/i3lMLXpEkT43GPHj2YNm0aW7ZsYePGjUybNo3nn3/eZqTXnTAqpmTJksbjiRMnsmvXLvbt22e3rGXI8smTJzGbzQQFBWktlyKm3Cq3zkrZVXadkXKr3DorZVfZdUbKrXJ7O2hhb5FC9sgjjzBlyhQiIiI4f/48c+bMsTu1VIsWLYiMjLzpoWPh4eHGgt7u7u7GSBALy7oYlrUJqlatSqlSpWyOU61aNd566y2++eYbTCYTkydPZvLkyTbHeuutt4iNjeXIkSPGegH5Vb58eaZPn86bb77JhQsXmD59OtOnTze2u7q68umnnzJ06FAt7n2btW/fntTUVEaMGIHJZCImJoaYmBibcq1atbLp1LpR69atWbJkidXznLz77ruYzWa+/fZbUlJSmDhxIhMnTrQq4+Liwuuvv07Pnj3z8a7+0bZtWw4fPsyUKVO4cOECY8eOtVuuVatW9OnTx+o1Hx8fpk2bxjvvvMPu3bs5dOgQgwYNstk3ICCAL7/80ujwkMLXqVMnfv31VzZu3Mj58+cZMWKETZkSJUrw4YcfGj+jY8eO3e5qFrrq1asTGBjIuXPnWLx4MYsXL6Z06dLGVIFZhYaG8tBDDxmdjq1atbrNtZUbKbfKrbNSdpVdZ6TcKrfOStlVdp2Rcqvc3g4aiSFyC9SqVYvY2Fjef/99QkNDueeee/D09OTBBx+kZcuWREVFMXbsWIoXL37T58o6qiIkJMRqPQyLunXrGo/tTSVl8d577/HNN9/QoEED/P39cXV1pXjx4jz66KN06NCB+fPn07t3b2Ptj127dtmdkicvqlevztKlS4mIiKBy5cr4+Pjg7+/P008/TVRUFG3atCnQceXmderUiWXLlvHqq68SFBSEr68v7u7ulC5dmiZNmvDdd98xZswY3N3dczxOgwYNjDsTgoODCQ4OzrG8i4sLffr0YcGCBXTs2JGKFSvi4+ODu7s75cqV4/nnn+eHH36gX79+N/X++vfvz4wZM2jVqhXly5fH09MTT09PypUrR8uWLZkyZQpjxoyxOxKodOnSzJo1izFjxvDMM88QGBiIu7s7Pj4+PPbYY/Tq1YslS5YQFhZ2U3WUnHl4eDB58mQGDhxISEgIPj4+uLm54e/vT0hICO+88w5Lly6lffv2Ru6WLVuG2Wwu4prfHB8fHyZPnkyDBg3w8/PD09MTDw+PbDt7mzZtCmR2QGuoctFTbpVbZ6XsKrvOSLlVbp2VsqvsOiPlVrm9HVzMzp4YEREREbHx5ptvsmbNGp544gmr0W4ijky5FWel7IozUm7FWSm74oyU25ujkRgiIiIid5jz58/z+++/A2hkmzgN5VaclbIrzki5FWel7IozUm5vnjoxRERERO4gZrOZ0aNHk5aWhr+/vzFsWcSRKbfirJRdcUbKrTgrZVeckXJbOLSwt4iIiIiTu3btGr169aJMmTLs37+fPXv2APDaa6/ZXStJxBEot+KslF1xRsqtOCtlV5yRclv4tCaGiIiIyB2gVq1aXLlyxXhevXp1vv/+e9zd3YuwViI5U27FWSm74oyUW3FWyq44I+W2cGk6KREREZE7QP369fHy8qJkyZJ06NCBqVOn6gOyODzlVpyVsivOSLkVZ6XsijNSbguXRmKIiIiIiIiIiIiIiIhD0kgMERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBAREREREREREREREYfkVtQVEBEREbnddu7cyeLFi9m6dSsnT57k6tWrFC9enAoVKlCnTh3atWvHww8/nO3+48eP56uvvsr1PO7u7hQvXpxy5cpRs2ZNOnfuTMWKFW3Kbd++nU6dOpGeno6Pjw8//fQT5cuXz/X4q1evpmfPngA0aNCA7777DhcXl1z3E+ek3IqzUnbFGSm34qyUXXFGyq3kxsVsNpuLuhIiIiIit8Px48cZMmQIGzZsyLGcq6srnTt3pl+/fri7u9tsz+uH5Bt5eHgwePBg2rdvb7NtzJgxTJo0CYA6deoQFRWV4wfec+fO0bp1ay5cuMC9997LokWLCAwMzHedxPEpt+KslF1xRsqtOCtlV5yRcit5pZEYIiIiclfYsGED77zzDleuXAEgJCSEFi1aEBwcjK+vLxcvXiQuLo5Zs2aRmJjI9OnTOXr0KBMnTsTNLfuPTMOGDaNatWp2t129epUTJ06wYMECNm7cSGpqKkOGDOGRRx6hZs2aVmV79+7Nr7/+yuHDh9m8eTMxMTF07tzZ7nHNZjP9+/fnwoULRh30AfnOpNyKs1J2xRkpt+KslF1xRsqt5ItZRERE5A538OBBc2hoqDkoKMhcpUoV85w5c7Ite/78eXP79u3NQUFB5qCgIPNnn31mU+bLL780tm/cuDFPdRg5cqSxzxtvvGG3zJ49e8xVq1Y1BwUFmUNDQ80nTpywW+67774zjvXxxx/n6fzifJRbcVbKrjgj5VaclbIrzki5lfzSwt4iIiJyR8vIyOA///kPKSkpAHz22We8+OKL2ZYvWbIk33zzDQEBAQBERUVx8uTJm65Hnz59jLtx1q1bR3Jysk2Zxx57jB49egCQkpLCwIEDMd8w8+e+ffv44osvAHjooYcYMGDATddNHI9yK85K2RVnpNyKs1J2xRkpt1IQ6sQQERGRO9rKlSvZs2cPAE8//TStWrXKdZ+SJUvSvXt3AEwmE7Nmzbrpenh4eBhDlDMyMjh16pTdcm+99RZVq1YFIC4ujqioKGPb1atX6du3LyaTCXd3d8aMGYOPj89N100cj3IrzkrZFWek3IqzUnbFGSm3UhDqxBAREZE72uzZs43Hlg++efHCCy/w/PPPM2zYMDp27FgodcnIyDAem0wmu2Xc3d0ZOXKksWDd//3f/xEfHw/A559/zh9//AFAREQE1atXL5R6ieNRbsVZKbvijJRbcVbKrjgj5VYKQp0YIiIicsdKTU1l06ZNAPj5+VGrVq087xsQEMCoUaNo3749DzzwwE3XxWQysXPnTgCKFStGhQoVsi0bHBxMREQEkDlseciQIWzZsoWYmBgAHn/88Xx94BfnotyKs1J2xRkpt+KslF1xRsqtFJQ6MUREROSOdezYMa5duwZAaGgoxYoV3Uef7777jnPnzgFQr1497rnnnhzLv/HGG9SoUQPInKP1nXfeISMjAz8/P0aNGlWk70VuLeVWnJWyK85IuRVnpeyKM1JupaDciroCIiIiIrfK6dOnjcdly5a9redOS0sjKSmJgwcPMn/+fBYuXAiAl5cX/fv3z3V/V1dXRowYQZs2bbh+/TqJiYkAfPLJJ5QrV+5WVl2KmHIrzkrZFWek3IqzUnbFGSm3UlDqxBAREZE71pUrV4zH/v7+t+QcXbt2zXPZwMBAvvjiC4KCgvJUvlKlSrzwwgv8+OOPAJQqVYpmzZoVqJ7iPJRbcVbKrjgj5VaclbIrzki5lYLSOBcRERG5Y2Ud0pvdQm23mq+vL0888QQff/wxsbGxhIWF5Xnf48ePs2jRIuP52bNnmTBhwq2opjgQ5VaclbIrzki5FWel7IozUm6loDQSQ0RERO5YWec1tQz3LWzDhg2jWrVqAJjNZlJSUoiLi2Py5MlcvnyZEiVK0K1bN5599tl8HTctLY0PPviAlJQUXF1d8fT0JCUlhQkTJvDMM8/w2GOP3Yq3Iw5AuRVnpeyKM1JuxVkpu+KMlFspKI3EEBERkTvWgw8+aDw+e/bsLTlHhQoVqFKlClWqVKFq1ao8/vjjvPXWW8yaNYuSJUty5swZ3n77bebOnZuv444fP57du3cD0LFjR959910g846l/v37k5qaWujvRRyDcivOStkVZ6TcirNSdsUZKbdSUOrEEBERkTtW+fLluffeewHYvn07aWlp+dp/3LhxTJs2jYMHD+b73A8//DDjxo2jWLFimM1mBg8ezIYNG/K077Zt25g0aRIApUuXpk+fPnTp0sW4o+jw4cP83//9X77rJM5BuRVnpeyKM1JuxVkpu+KMlFspKHViiIiIyB3LxcWFBg0aAJmLyO3atSvP+6akpDBlyhRGjBhB165d8/0BGyAsLIzu3bsDmcOPBwwYQFJSUo77JCcn069fP9LT0wEYPnw4fn5+uLq6MnLkSDw8PACYOnUq27Zty3edxPEpt+KslF1xRsqtOCtlV5yRcisFpU4MERERuaO1adPGeDxz5sw87zd//nyuXbsGQJMmTXBzK9hSYr169eKhhx4C4K+//mLUqFE5lo+MjOTUqVMAvPzyy8aHfIBHH32UXr16AZCRkcGAAQO4evVqgeoljk25FWel7IozUm7FWSm74oyUWykIdWKIiIjIHa1+/fpUr14dgMWLF/Prr7/mus/Zs2f58ssvAXB1deW1114r8Pk9PDwYOnSo8XzOnDns2LHDbtlly5axYMECIHMu1w8//NCmzBtvvEFISAgAJ06cYPTo0QWumzgu5VaclbIrzki5FWel7IozUm6lINSJISIiIne8oUOH4u7ujtls5r333mPRokXZlj1x4gSvvvoqiYmJAHTv3t24U6eg6tatS8uWLQEwm80MGTLEGI5skZCQwCeffAJAsWLFGDlyJD4+PjbHunHY8syZM/M8l6s4F+VWnJWyK85IuRVnpeyKM1JuJb/UiSEiIiJ3vKpVqzJixAhcXV25evUqH3zwAe3btycqKootW7awa9cufvnlFz7++GNatWrFkSNHAGjcuDERERGFUocBAwbg6+sLwP79+4mJiTG2mc1m+vfvb3wwf+2116hdu3a2x6pUqRK9e/c29h04cCDJycmFUk9xHMqtOCtlV5yRcivOStkVZ6TcSn4VbPIwERERESfTqlUrSpYsyaBBgzh9+jS7du3KdiE5Nzc3unfvTq9evQo81+qNAgMDiYiIYMSIEQCMGzeOZs2aERgYyLRp04y7dYKCgnj33XdzPd7rr7/OypUr2b59O2fOnOHTTz81ji13DuVWnJWyK85IuRVnpeyKM1JuJT80EkNERETuGvXq1WPZsmWMGTOGZs2a8dBDD+Hj44Orqyv33HMPoaGhvPXWW/zyyy+89957hfYB2aJLly4EBwcDkJyczMiRIzl48CBjx44FwN3dnc8++8wYipyTYsWKMXz4cLy8vACYN28eq1atKtT6imNQbsVZKbvijJRbcVbKrjgj5VbyysVsNpuLuhIiIiIiIiIiIiIiIiI30kgMERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBAREREREREREREREYekTgwREREREREREREREXFI6sQQERERERERERERERGHpE4MERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBARERGHlZqayvjx42nSpAk1atSgTp06tGjRgoSEhKKu2h3j1KlTBAcHExwczLx584q6OiJShNTm3npqc0XEQm3urac2V+TO4VbUFRARERHJTt++fVm+fLnx/Nq1a1y/fp3AwMAirJWIyJ1Jba6IyO2jNldEJO/UiSEiIiIO6fDhw8YXu+DgYN59913uu+8+rl27RrFiGkwqIlKY1OaKiNw+anNFRPJHnRgiIiLikI4dO2Y87t+/P/Xr1y/C2oiI3NnU5oqI3D5qc0VE8kfduyIiIuKQrl69ajx+4IEHirAmIiJ3PrW5IiK3j9pcEZH8USeGiIiIOKSMjAzjsauraxHWRETkzqc2V0Tk9lGbKyKSP5pOSkRERBzKgAEDmD9/vtVrjRo1Mh6vXLnS6o615ORkZs2axfLlyzl69CgpKSmULFmS0NBQ2rZtS3h4eI7nqVOnDhMnTmTkyJEsW7aM1NRUypUrx/Xr14mPjwfg119/5f7777c5xvvvv8/ixYsB+P7776lVq5ZNmc8++4wpU6Zw7733sn79eqt5js+cOcOPP/7Ixo0biY+PJykpCU9PT0qVKkVYWBivvPIKlStXznfdu3fvzgsvvGB1nqlTp7J+/XpOnz6Nr68vdevWpUePHvj4+Nj995GCCw4OBmDEiBEEBQUxbNgw9u3bh7e3N0FBQYwaNcoqTwkJCUyfPp3ffvuN06dPk56eTpkyZXjiiSfo2rUrlSpVsjp+bGwsvXv3BjLzlfVnfaNp06YxYsQIAObPn0/VqlWtth87dozo6GjWr19PQkICLi4uPPDAAzRo0IBu3bpRpkwZm2OeOnXK+J2MiorCZDIxZswYjh49ip+fH1WrVmX8+PF4e3sDsGbNGubOncv27du5ePEi3t7elC1blnr16vHKK69Qvnz5bOtvNptZtmwZCxcuZM+ePSQmJuLr60vlypVp3rw5bdq0wd3dPdv9JXdqc9XmOjO1t2pvnY3aXLW5zkxtrtrcoqRODBEREXFa27Zto0+fPvz1119Wr//1118sW7aMZcuW0bhxY0aNGmV82LxRRkYGERERrFu3znjtyJEjREREMH78eAA2btxImzZtbPbdtGmT8TguLs7ul7vffvsNgIYNG1p9sZsxYwYjR47EZDJZlTeZTCQnJ3Ps2DFmz55NZGQk7dq1y1fdAwMDjeerVq2iT58+XLt2zXjt6tWrLF68mF9++YX333/f7rHl5h07dozIyEhSUlIAjAsGpUuXNsosXryYQYMGWU0rAXD8+HGOHz/O7Nmz6du3L2+88Yax7ZlnnqFEiRIkJSWxdOnSHL/gLVmyBIBKlSrZfLmbMmUKn3/+OWlpaVavHzp0iEOHDjFz5kwiIyNp3bp1tsePi4vj66+/Jj09HYC///6b5ORkvL29MZvNDBw4kHnz5lntYzKZSEpK4sCBA0RHRxMZGWn39yspKYmIiAg2btxo9frFixfZsGEDGzZsIDo6mm+++UZTcdwmanPV5joqtbdqb+9EanPV5joqtblqc4uCOjFERETEofTu3Ztu3bqxcuVK48vVt99+S6lSpQCM/x86dIju3buTnJxMsWLFeOGFF2jatCn+/v4cO3aMqKgo9u3bxy+//EJKSgqTJk2y+nJlsW3bNjIyMnjmmWfo1q0bqamprF27lrfffpvo6GgSExPZsGGDzQfQo0ePcu7cOeN5XFwcPXr0sCqTkJDA4cOHAXj66aeN11evXk1kZCQA5cqVo2vXrgQFBeHl5cWZM2dYuHAha9euJSMjg2HDhvHss88SEBCQ57o/+eSTAOzYsYNevXqRnp6Oj48Pr7/+Ok8++SSpqaksX76cH374gc8++yxfPx/Ju8mTJ+Pq6sr7779P7dq1jUU8LTlcsWIFH3zwAWazmfvvv58uXbpQo0YNXF1d2b9/P9HR0Rw7dozRo0fj6elJly5dAPDw8KBJkybMnj2b9evXk5iYiL+/v8354+Pj2bVrF4DNl7SoqCjjZ1+pUiU6depE1apVSU9PZ+fOnUyfPp2EhAQ+/PBDihcvbnWXaFb/+9//8PX15b333qNy5crs3bvXuANv7ty5xpe7evXq0aFDB8qWLUtycjJbtmxh6tSppKSk8PHHHxMWFmb1Jc1kMvHGG2+wc+dOAJo3b07z5s0pXbo0Fy9eZMWKFcybN49Dhw7x6quvMnfuXEqUKFGgn9PdTm2u2tw7gdpbtbfOQm2u2tw7gdpctblFwiwiIiLigObOnWsOCgoyBwUFmePj4222v/zyy+agoCBzcHCwOTY21ma7yWQyR0REGMf44YcfrLb379/f2NauXTtzWlqazTH69u1rDgoKMjdo0MBm24wZM8xBQUHmxx57zBwUFGSuWbOmzTFmzZplDgoKMletWtV86dIl4/U2bdqYg4KCzHXq1DH/+eefdt//0KFDjfotWLAg33U3m83mdu3amYOCgsw1atQw792712b78uXLzcHBwcax5s6da/c4kj+Wf8+goCBzdHS03TLJycnmOnXqmIOCgswvvviiOSkpyabM1atXzV27djUHBQWZq1evbj579qyxbdOmTcY5Zs2aZfccEyZMMH5HTp06Zbx+6tQpc7Vq1cxBQUHmHj16mK9fv26z78WLF80tWrQwBwUFmevXr2++du2asS0+Pt7qPa5atcru+Tt37mwOCgoyt2zZ0mwymWy2//rrr8Yxvv76a6ttEydONOr+008/2T3+mjVrjPwOGzbMbhnJO7W5anOdkdrbTGpvnY/aXLW5zkhtbia1uUVDC3uLiIiI09m1axdbt24FoH379jRu3NimjJubG8OHD+fee+8FMocVZ+ell16yu6hiw4YNATh79qxxh5GFZfhvy5YtAbhy5Qr79u2zKmMZYl+rVi3jDprLly+TkZGBn58fbdu2tTsfK2TelWPx559/5rvuBw4cMO5Q+ve//20zzBrgueee4/nnn8/22HJz3N3ds50iYcGCBSQmJgIwbNgw/Pz8bMp4eXkxdOhQIHOY/pw5c4xtYWFhlC1bFoCff/7Z7jksw+xr165NuXLljNdjYmJITU3F3d2d4cOH4+HhYbOvv78/AwcOBODcuXPExsbaPUeZMmV45pln7G47f/68UcbNzXYAeHh4OF26dCEiIoLHH3/ceD09PZ2oqCggc57wVq1a2T1+w4YNjd+/OXPmkJqaarec3Dy1ubnXXW1u0VJ7q/b2TqI2N/e6q80tWmpz1eYWBXViiIiIiNPZsGGD8bh9+/bZlvP19TU+AB4/fpxTp07ZLVe9enW7rz/11FPGsOis5zSbzWzevBmANm3aULJkSSBzqL1Fenq68QUw6xB7Pz8/FixYwJYtW+jXr1+2dc863+/169ezLZdd3deuXWs8btGiRbb725unVQrHo48+ipeXl91tli/+/v7+dhe1tHjwwQeNRQEtmQNwcXExsr1x40YuXLhgtd/Ro0c5ePAggM0XJMu5g4KC7E7fYBEWFoanp6fNubPKLn8AFStWNM43btw4mzoCDBo0iF69ehEWFma8duDAAWMKC8uUEdlp0KABACkpKezevTvHslJwanNzr7va3KKl9lbt7Z1EbW7udVebW7TU5qrNLQpaE0NEREScjmX+XTc3N6pUqZJj2awfQI8cOWJ3cbSsi9BlFRAQQPXq1dm5cycbN26kU6dOQOYH0MTERDw8PAgNDaVGjRqsWrWKuLg4XnvtNQB27tzJpUuXAOsvd1lZvjhevnyZkydPcuLECY4ePcqePXvYsmWLUS4jIyPb95dd3f/44w8g806phx9+ONv9q1Wrlu02uTnZ/WwgM0MAiYmJBAcH5+l48fHxVs+ff/55vv32W9LT04mNjeXll182tlnuUHN3d6dZs2bG62lpaRw5cgSAvXv3FvjcFjm9x1dffZVff/2V9PR0vv76ayZMmEBISAgNGjSgYcOGhISE4OLiYrPf/v37jceRkZHGvNp5qWPt2rXzVFbyR21u7nVXm1u01N6qvb2TqM3Nve5qc4uW2ly1uUVBnRgiIiLidCxDlP38/HB3d8+xrOXusaz73cjX1zfb/Rs2bMjOnTvZvHkzGRkZFCtWzLjzrEaNGnh6elKnTh1WrVrF1q1bMZvNuLi4GHcClS9fnkqVKtkcNz4+nu+++47Vq1eTkJBgs93e4oz5qfvff/8NZP4b2RuGn3V/Ly8vrl27lqfzSd4VL148223ZZTEnly9ftnr+yCOPUKVKFfbv38/SpUutvuAtXboUyBzOfs899xivJyUl5XixIK/ntsjpdycsLIyxY8cyZMgQLl68SEZGBjt27GDHjh189dVXBAYG0qxZM15//XWr6SYK8m+TUx3l5qnNzb3uanOLltpbtbd3ErW5udddbW7RUpurNrcoqBNDREREnI7ZbAawe4fLjdLT043H2ZXP6Tjh4eGMHz+exMRE9u/fz2OPPWZ8uatTp47V/y9dusTBgwepXLky69atA+zfnbZy5Ur69OljNXze39+fSpUqUblyZWrVqkXFihVp27Ztru8vL/8GubE3l6vcvJx+NmlpaUDmPNKDBw/O0/HsfeFv3bo1+/fvZ8uWLZw7d47AwED27dtn3KHYunVru+cFaNq0KT179szTuS1D7vOradOmhIeHs3LlSlasWMH69euNOzfPnTtHVFQUc+bMYcKECdStWxew/p0dNWoUQUFBeTpXTnfMyc1Rm5u3uueV2tzCp/ZW7e2dRG1u3uqeV2pzC5/aXLW5RUG/ySIiIuJ0/P39gcw7bkwmU453qWWdo9SyX35Uq1aN++67j/Pnz7NhwwYqV65sDIG3fKmrUqUKJUqUICkpibi4OEqXLs2ePXsA2y93CQkJ9OvXj+vXr+Pr60vv3r157rnnrBalAzh06FC+65rVfffdB2TeuZOWlpbtF7i0tDRSUlJu6lySf/7+/pw7d46kpKRcp4rIScuWLRk9ejQZGRnExsbSuXNn4w41Pz8/mwUJs96xdvXq1Zs6d155e3vTsmVLWrZsSUZGBnv27GHdunUsXbqUw4cPk5KSwn/+8x9++eUX3NzcrOro4eFxW+ooOVObmzu1uY5L7a3aW2ejNjd3anMdl9pctbm3ihb2FhEREadjuWslLS3Nam5Re3bt2mU8tizClh8uLi489dRTQOaihwcOHCA5ORl3d3dCQ0OBzLuHLPOUbt68mfXr15ORkYGPj4/xBdBi0aJFXLlyBYDBgwfTrVs3my92AH/++We+65rVI488AoDJZDLmprXn0KFDBRp6LTfHMvXC0aNHjSkRsjN58mRmz57Ntm3bbLaVKlWKJ554AoBVq1YBsGzZMgCaNGmCh4eHVXlPT09jEcXt27djMpmyPW9qaipff/018+fPz/X3zJ6kpCR27NjB2bNnjdeKFStGSEgIb7/9Nj/99JNx8eP06dMcPXoU+Ce7kP1iixY7d+5kypQpxMbG2l1UUQqH2tzcqc11XGpv1d46G7W5uVOb67jU5qrNvVXUiSEiIiJO58knnzQez5kzJ9tyycnJxh07ZcuWpUKFCgU6X8OGDQHYunUrGzZsADLnCfby8jLKWD5kx8XFsXbtWgDq1atn8wH75MmTxuOqVatme05LvcF66HFePffcc8bj+fPnZ1tu0aJF+T623Lz69esDmVNGzJgxI9tyGzduZNSoUQwaNIjvv//ebhnLcPrNmzezZcsWY4HCG4fZW9SrVw/I/AK2cOHCbM+9aNEixo0bx4ABA4iNjc39TWVx9OhRwsLC6NChAz/++KPdMsWKFTPqAhjTToSEhODn5wfA4sWLc5w/ePTo0Xz22Wf07t2b5OTkfNVR8k5tbu7U5joutbdqb52N2tzcqc11XGpz1ebeKurEEBEREacTEhJCzZo1AZg9ezYrVqywKZOWlsbAgQOND4fdunUr8Pmeeuop3NzcuHr1KtHR0QA2d55Znl+8eJGff/4ZsD9P8L333ms8tiyKeKMff/yRBQsWGM9zupMoOxUqVDC+lH7//ffG3MVZbd261Xg/cnu1a9cOb29vACZNmmRcNMjq0qVLfPLJJ8bzTp062T1W48aN8fb2xmQyMXz4cADKlCljk1GLzp07G3MPjxo1ioMHD9qUiY+P5/PPPwfA3d2dl156KR/vDh5++GEeeOABAGbMmMHp06dtyqSnpxtfHL29vXn44YeBzOH1HTp0ADK/hH744Yekpqba7B8TE0NcXBwADRo0KPDFG8md2tzcqc11XGpv1d46G7W5uVOb67jU5qrNvVW0JoaIiIg4pWHDhtGuXTuuXr1KREQEbdu2pXHjxvj7+/PHH38QFRXF3r17Aahbty5du3Yt8Ln8/PwIDQ1ly5Yt/PXXX4Dtl7vKlStzzz33cOnSJa5fv46Liwvh4eE2x/rXv/7FhAkTABg7diznz5+nfv36eHt7c/LkSRYuXMj69eut9ino3TeDBw/m+eef58qVK/Ts2ZNXXnmFRo0a4erqytq1a5k2bRouLi64uroW6C44KbiAgAA++ugjBg0ahMlkonv37rz00ks0atQILy8vDhw4wOTJk40vRi+99JIxrcONihcvzrPPPsuSJUuMzLds2TLbRReDgoLo0aMH33zzDZcuXaJjx4506dKF+vXr4+Liws6dO5k8eTIXL14EoFevXpQtWzZf78/FxYV33nmH//znPyQmJtKuXTv+/e9/U61aNby9vYmPj+f7779n+/btQOaXTl9fX2P/d955h1WrVnHs2DHWrFnDiy++SNeuXQkKCuLvv/9m2bJl/PTTTwD4+Pjw0Ucf5at+kn9qc3OnNtcxqb1Ve+uM1ObmTm2uY1Kbqzb3VlEnhoiIiDilRx55hClTphAREcH58+eZM2eO3SH3LVq0IDIy0rgrp6DCw8ONhQ7d3d2NO+QsLPMFW+ZsrVq1KqVKlbI5TrVq1Xjrrbf45ptvMJlMTJ48mcmTJ9sc66233iI2NpYjR44Y86jmV/ny5Zk+fTpvvvkmFy5cYPr06UyfPt3Y7urqyqeffsrQoUO16GERaN++PampqYwYMQKTyURMTAwxMTE25Vq1asXgwYNzPFbr1q1ZsmSJ1fOcvPvuu5jNZr799ltSUlKYOHEiEydOtCrj4uLC66+/Ts+ePfPxrv7Rtm1bDh8+zJQpU7hw4QJjx461W65Vq1b06dPH6jUfHx+mTZvGO++8w+7duzl06BCDBg2y2TcgIIAvv/zSuMNNbh21ublTm+u41N5mUnvrPNTm5k5truNSm5tJbW7hUieGiIiIOK1atWoRGxvLzJkzWblyJX/88QfXrl2jTJkyVK9enZdeeom6desWyrnCw8ONocchISFW8wRb1K1b1/hyZ2+IvcV7771HSEgIMTEx7Nmzh8uXL+Pl5UXZsmWpVasWr7zyCpUrVyYpKYkjR46wa9cuTp8+bXdhxNxUr16dpUuXEhMTw/Llyzl58iQeHh6EhobSvXt3Hn/8cYYOHZrv40rh6NSpE+Hh4cyYMYPff/+dM2fOcP36dQICAggNDaV9+/bGgps5adCgASVLluTvv/8mODiY4ODgHMu7uLjQp08fmjdvzsyZM9m0aRMJCQmYTCZKlSrF448/ziuvvJLtnXF51b9/f5599ll+/PFHqwUQ77vvPmrWrEnbtm2NuZNvVLp0aWbNmsWSJUtYsmQJe/bsITExEXd3dypWrMgzzzxDp06dCAgIuKk6St6pzc2d2lzHpfZW7a2zUZubO7W5jkttrtrcwuZiNpvNRV0JERERERERERERERGRG2lhbxERERERERERERERcUjqxBAREREREREREREREYekTgwREREREREREREREXFI6sQQERERERERERERERGHpE4MERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBAREREREREREREREYekTgwREREREREREREREXFI6sQQERERERERERERERGHpE4MERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBAREREREREREREREYekTgwREREREREREREREXFI6sQQERERERERERERERGHpE4MERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBAREREREREREREREYekTgwREREREREREREREXFI6sQQERERERERERERERGHpE4MERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBAREREREREREREREYekTgwREREREREREREREXFI6sQQERERERERERERERGHpE4MERERERERERERERFxSOrEEBERERERERERERERh6RODBERERERERERERERcUjqxBAREREREREREREREYekTgwREREREREREREREXFI6sQQERERERERERERERGHpE4MERERERERERERERFxSP8Po96CdzTakQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot interactions between medium-affinity reverse orientation CRX sites and CRX, NRL, and Rho in CRX wt retina\n",
    "\n",
    "All_CRX_medium_interactions = []\n",
    "for model in bootstrap_models:\n",
    "    # Retrieve G-P map parameter dict and view dict keys\n",
    "    theta_dict = model.get_theta(gauge=\"user\", x_wt = \"OOOOH\")\n",
    "    TF_TF_interactions = np.transpose(theta_dict['theta_lclc'], axes = (1, 3, 0, 2))\n",
    "    All_CRX_medium_interactions.append([])\n",
    "    for i in range(8):\n",
    "        interactions = TF_TF_interactions[i, 5, 0:5, 0:5].flatten() # + TF_TF_interactions[5, i, 0:5, 0:5].flatten()\n",
    "        All_CRX_medium_interactions[-1].append(np.mean(interactions[np.logical_not(np.isnan(interactions))]))\n",
    "    #All_CRX_medium_interactions[-1].append(np.mean(TF_TF_interactions[5, 8, 0:4, 4]))\n",
    "        \n",
    "plt.boxplot(np.asarray(All_CRX_medium_interactions))\n",
    "x = np.linspace(1, 8, 8)\n",
    "plt.xticks(x, [\"NRL\\n forward\", \"NRL\\n reverse\", \"High\\n affinity\\n CRX\\n forward\", \"High\\n affinity\\n CRX\\n reverse\",\n",
    "              \"Medium\\n affinity\\n CRX\\n forward\", \"Medium\\n affinity\\n CRX\\n reverse\", \n",
    "              \"Low\\n affinity\\n CRX\\n forward\", \"Low\\n affinity\\n CRX\\n reverse\"], fontsize = 7) #, \"Rho\"\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No interaction\")\n",
    "plt.legend()\n",
    "plt.title(\"Interactions with Medium-affinity reverse orientation CRX sites\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f3e1768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_0.pickle\n",
      "\tcrxnull_additive_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_1.pickle\n",
      "\tcrxnull_additive_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_2.pickle\n",
      "\tcrxnull_additive_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_3.pickle\n",
      "\tcrxnull_additive_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_4.pickle\n",
      "\tcrxnull_additive_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_5.pickle\n",
      "\tcrxnull_additive_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_6.pickle\n",
      "\tcrxnull_additive_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_7.pickle\n",
      "\tcrxnull_additive_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_8.pickle\n",
      "\tcrxnull_additive_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_9.pickle\n",
      "\tcrxnull_additive_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_10.pickle\n",
      "\tcrxnull_additive_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_11.pickle\n",
      "\tcrxnull_additive_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_12.pickle\n",
      "\tcrxnull_additive_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_13.pickle\n",
      "\tcrxnull_additive_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_14.pickle\n",
      "\tcrxnull_additive_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_15.pickle\n",
      "\tcrxnull_additive_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_16.pickle\n",
      "\tcrxnull_additive_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_17.pickle\n",
      "\tcrxnull_additive_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_18.pickle\n",
      "\tcrxnull_additive_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_additive_ge_19.pickle\n",
      "\tcrxnull_additive_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_0.pickle\n",
      "\tcrxnull_neighbor_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_1.pickle\n",
      "\tcrxnull_neighbor_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_2.pickle\n",
      "\tcrxnull_neighbor_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_3.pickle\n",
      "\tcrxnull_neighbor_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_4.pickle\n",
      "\tcrxnull_neighbor_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_5.pickle\n",
      "\tcrxnull_neighbor_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_6.pickle\n",
      "\tcrxnull_neighbor_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_7.pickle\n",
      "\tcrxnull_neighbor_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_8.pickle\n",
      "\tcrxnull_neighbor_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_9.pickle\n",
      "\tcrxnull_neighbor_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_10.pickle\n",
      "\tcrxnull_neighbor_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_11.pickle\n",
      "\tcrxnull_neighbor_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_12.pickle\n",
      "\tcrxnull_neighbor_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_13.pickle\n",
      "\tcrxnull_neighbor_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_14.pickle\n",
      "\tcrxnull_neighbor_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_15.pickle\n",
      "\tcrxnull_neighbor_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_16.pickle\n",
      "\tcrxnull_neighbor_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_17.pickle\n",
      "\tcrxnull_neighbor_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_18.pickle\n",
      "\tcrxnull_neighbor_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_neighbor_ge_19.pickle\n",
      "\tcrxnull_neighbor_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_0.pickle\n",
      "\tcrxnull_pairwise_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_1.pickle\n",
      "\tcrxnull_pairwise_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_2.pickle\n",
      "\tcrxnull_pairwise_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_3.pickle\n",
      "\tcrxnull_pairwise_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_4.pickle\n",
      "\tcrxnull_pairwise_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_5.pickle\n",
      "\tcrxnull_pairwise_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_6.pickle\n",
      "\tcrxnull_pairwise_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_7.pickle\n",
      "\tcrxnull_pairwise_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_8.pickle\n",
      "\tcrxnull_pairwise_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_9.pickle\n",
      "\tcrxnull_pairwise_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_10.pickle\n",
      "\tcrxnull_pairwise_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_11.pickle\n",
      "\tcrxnull_pairwise_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_12.pickle\n",
      "\tcrxnull_pairwise_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_13.pickle\n",
      "\tcrxnull_pairwise_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_14.pickle\n",
      "\tcrxnull_pairwise_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_15.pickle\n",
      "\tcrxnull_pairwise_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_16.pickle\n",
      "\tcrxnull_pairwise_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_17.pickle\n",
      "\tcrxnull_pairwise_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_18.pickle\n",
      "\tcrxnull_pairwise_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_pairwise_ge_19.pickle\n",
      "\tcrxnull_pairwise_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_0.pickle\n",
      "\tcrxnull_blackbox_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_1.pickle\n",
      "\tcrxnull_blackbox_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_2.pickle\n",
      "\tcrxnull_blackbox_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_3.pickle\n",
      "\tcrxnull_blackbox_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_4.pickle\n",
      "\tcrxnull_blackbox_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_5.pickle\n",
      "\tcrxnull_blackbox_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_6.pickle\n",
      "\tcrxnull_blackbox_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_7.pickle\n",
      "\tcrxnull_blackbox_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_8.pickle\n",
      "\tcrxnull_blackbox_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_9.pickle\n",
      "\tcrxnull_blackbox_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_10.pickle\n",
      "\tcrxnull_blackbox_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_11.pickle\n",
      "\tcrxnull_blackbox_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_12.pickle\n",
      "\tcrxnull_blackbox_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_13.pickle\n",
      "\tcrxnull_blackbox_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_14.pickle\n",
      "\tcrxnull_blackbox_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_15.pickle\n",
      "\tcrxnull_blackbox_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_16.pickle\n",
      "\tcrxnull_blackbox_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_17.pickle\n",
      "\tcrxnull_blackbox_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_18.pickle\n",
      "\tcrxnull_blackbox_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tcrxnull_blackbox_ge_19.pickle\n",
      "\tcrxnull_blackbox_ge_19.h5\n"
     ]
    }
   ],
   "source": [
    "# Iterate over types of G-P maps\n",
    "gpmap_types = ['additive','neighbor','pairwise','blackbox']\n",
    "\n",
    "# Create list of model names\n",
    "model_names = [f'crxnull_{gpmap_type}_ge_{i}' for gpmap_type in gpmap_types for i in range(20)]\n",
    "\n",
    "# Load models into a dictionary indexed by model name\n",
    "model_dict = {name:mavenn.load(name) for name in model_names}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f062df1f",
   "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>x</th>\n",
       "      <th>set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OOOO</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OOOc</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OOOC</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OOOn</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OOON</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>XxNX</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>xXRc</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>xxRr</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>xxXc</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>XXXx</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        x       set\n",
       "0    OOOO  training\n",
       "1    OOOc  training\n",
       "2    OOOC  training\n",
       "3    OOOn  training\n",
       "4    OOON  training\n",
       "..    ...       ...\n",
       "148  XxNX      test\n",
       "149  xXRc      test\n",
       "150  xxRr      test\n",
       "151  xxXc      test\n",
       "152  XXXx      test\n",
       "\n",
       "[1291 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data from Jamie's library in CRX null retina\n",
    "\n",
    "Data_file = open(\"CRX_null_trainval.pkl\", \"rb\")\n",
    "trainval_df = pickle.load(Data_file)\n",
    "Data_file.close()\n",
    "\n",
    "Data_file = open(\"CRX_null_test.pkl\", \"rb\")\n",
    "test_df = pickle.load(Data_file)\n",
    "Data_file.close()\n",
    "\n",
    "Data_df = pd.DataFrame()\n",
    "Data_df['x'] = pd.concat([trainval_df['synthetic_promoter'], test_df['synthetic_promoter']])\n",
    "assignments = []\n",
    "for validation in trainval_df['validation']:\n",
    "    if validation:\n",
    "        assignments.append('validation')\n",
    "    else:\n",
    "        assignments.append('training')\n",
    "assignments = assignments + ['test']*len(test_df)\n",
    "Data_df['set'] = assignments\n",
    "Data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19824e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 0 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0547 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "240f1703b5b84063aba757635c75fe6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 19.0 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 1 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.16 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef50919848784e2f986b98ae8a929c84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 19.2 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 2 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0566 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e9fbc39125c4673a0414afef68b183d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 19.6 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 3 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0897 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d62f2de7f4144a7da39d5a646b2d84a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 21.8 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 4 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0549 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9bed23d58104d8399c58b9e9e3cdaa9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 23.2 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 5 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0518 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee8dbeab2c344a2382fa724dea59558f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 20.1 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 6 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.102 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "169d3f35d2c44fbeb2c00f0acaa45002",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 20.5 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 7 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0727 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "948f5506d319496e9a4ff80a42b551e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 18.8 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 8 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0621 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9462db11a236473dad0eb5e5fd0ec919",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 17.3 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 9 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0581 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d6d0ce813f34a4b9061589e49150c5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 16.6 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 10 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0509 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c480564ecd44ecbbfbcef0e48ed75ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 15.9 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 11 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0491 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1d47c8ca38147f383e6267275124402",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 15.9 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 12 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0573 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "988015edfbb046ebb143e1fc42c8e86e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 16.9 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 13 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0539 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5db9d4d5582e49e7a1e42ba9403ffe00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 13.4 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 14 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0451 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "464e7887a4ac43709ebbeba4dbd909a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 18.8 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 15 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.062 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f06f9d278332444e8cf9db5b3064ae63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 19.3 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 16 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0615 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe714420cdc243df88fe515339bf1213",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 26.8 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 17 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0461 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c39b19cf2e1e4bc5a2c5e00a59547378",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 17.2 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 18 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0686 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b77b6cf374564280a39fe8c8607bb199",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 15.2 seconds\n",
      "Training set   :    1,024 observations (  79.32%)\n",
      "Validation set :      114 observations (   8.83%)\n",
      "Test set       :      153 observations (  11.85%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    1,291 observations ( 100.00%)\n",
      "\n",
      "training model 19 ...\n",
      "N = 1,138 observations set as training data.\n",
      "Using 10.0% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.0533 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "342631015af54b68a02a5e22d213d492",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 17.2 seconds\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_0.pickle\n",
      "\tCRX_null_bootstrap_0.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_1.pickle\n",
      "\tCRX_null_bootstrap_1.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_2.pickle\n",
      "\tCRX_null_bootstrap_2.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_3.pickle\n",
      "\tCRX_null_bootstrap_3.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_4.pickle\n",
      "\tCRX_null_bootstrap_4.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_5.pickle\n",
      "\tCRX_null_bootstrap_5.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_6.pickle\n",
      "\tCRX_null_bootstrap_6.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_7.pickle\n",
      "\tCRX_null_bootstrap_7.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_8.pickle\n",
      "\tCRX_null_bootstrap_8.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_9.pickle\n",
      "\tCRX_null_bootstrap_9.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_10.pickle\n",
      "\tCRX_null_bootstrap_10.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_11.pickle\n",
      "\tCRX_null_bootstrap_11.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_12.pickle\n",
      "\tCRX_null_bootstrap_12.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_13.pickle\n",
      "\tCRX_null_bootstrap_13.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_14.pickle\n",
      "\tCRX_null_bootstrap_14.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_15.pickle\n",
      "\tCRX_null_bootstrap_15.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_16.pickle\n",
      "\tCRX_null_bootstrap_16.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_17.pickle\n",
      "\tCRX_null_bootstrap_17.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_18.pickle\n",
      "\tCRX_null_bootstrap_18.h5\n",
      "Model saved to these files:\n",
      "\tCRX_null_bootstrap_19.pickle\n",
      "\tCRX_null_bootstrap_19.h5\n"
     ]
    }
   ],
   "source": [
    "# Fit bootstrap models\n",
    "\n",
    "name = 'crxnull_pairwise_ge_12'\n",
    "\n",
    "# Get model\n",
    "model = model_dict[name]\n",
    "\n",
    "bootstrap_models = model.bootstrap(Data_df, num_models = 20)\n",
    "for i, model in enumerate(bootstrap_models):\n",
    "    model_name = \"CRX_null_bootstrap_\" + str(i)\n",
    "    model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a6b42b11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_0.pickle\n",
      "\tCRX_null_bootstrap_0.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_1.pickle\n",
      "\tCRX_null_bootstrap_1.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_2.pickle\n",
      "\tCRX_null_bootstrap_2.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_3.pickle\n",
      "\tCRX_null_bootstrap_3.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_4.pickle\n",
      "\tCRX_null_bootstrap_4.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_5.pickle\n",
      "\tCRX_null_bootstrap_5.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_6.pickle\n",
      "\tCRX_null_bootstrap_6.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_7.pickle\n",
      "\tCRX_null_bootstrap_7.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_8.pickle\n",
      "\tCRX_null_bootstrap_8.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_9.pickle\n",
      "\tCRX_null_bootstrap_9.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_10.pickle\n",
      "\tCRX_null_bootstrap_10.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_11.pickle\n",
      "\tCRX_null_bootstrap_11.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_12.pickle\n",
      "\tCRX_null_bootstrap_12.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_13.pickle\n",
      "\tCRX_null_bootstrap_13.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_14.pickle\n",
      "\tCRX_null_bootstrap_14.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_15.pickle\n",
      "\tCRX_null_bootstrap_15.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_16.pickle\n",
      "\tCRX_null_bootstrap_16.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_17.pickle\n",
      "\tCRX_null_bootstrap_17.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_18.pickle\n",
      "\tCRX_null_bootstrap_18.h5\n",
      "Model loaded from these files:\n",
      "\tCRX_null_bootstrap_19.pickle\n",
      "\tCRX_null_bootstrap_19.h5\n"
     ]
    }
   ],
   "source": [
    "# Load bootstrap models\n",
    "model_names = [\"CRX_null_bootstrap_\" + str(i) for i in range(20)]\n",
    "CRX_null_bootstrap_models = [mavenn.load(name) for name in model_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0a40dfed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAAAAAAAUJSUGAAAAAABQlJQYAAAAAABAUVJiAAAAAAAARUmJAQAAAAAAFCUlBgAAAAAAUJSUGAAAAAAAQFFSYgAAAAAAAEXpfwH9fqHB/Xe3sQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot additive contributions in CRX null retina\n",
    "\n",
    "Additive_contributions = []\n",
    "for model in CRX_null_bootstrap_models:\n",
    "    # Retrieve G-P map parameter dict and view dict keys\n",
    "    theta_dict = model.get_theta(gauge=\"user\", x_wt = \"OOOO\")\n",
    "    \n",
    "    # Get logo dataframe\n",
    "    logo_df = theta_dict['logomaker_df']\n",
    "    \n",
    "    TF_additive = (logo_df.iloc[0] + logo_df.iloc[1] + logo_df.iloc[2] + logo_df.iloc[3])/4\n",
    "    Additive_contributions.append(TF_additive[0:8])\n",
    "    \n",
    "plt.boxplot(np.asarray(Additive_contributions))\n",
    "x = np.linspace(1, 8, 8)\n",
    "plt.xticks(x, [\"N\", \"n\", \"C\", \"c\", \"R\", \"r\", \"X\", \"x\"], fontsize = 7)\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No contribution\")\n",
    "plt.legend()\n",
    "plt.title(\"Additive contributions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dc67b8e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot interactions involving high-affinity CRX sites in CRX null retina\n",
    "\n",
    "Interactions = []\n",
    "for model in CRX_null_bootstrap_models:\n",
    "    # Retrieve G-P map parameter dict and view dict keys\n",
    "    theta_dict = model.get_theta(gauge=\"user\", x_wt = \"OOOO\")\n",
    "    \n",
    "    TF_TF_interactions = np.transpose(theta_dict['theta_lclc'], axes = (1, 3, 0, 2))\n",
    "    Interactions.append([])\n",
    "    for i in range(8):\n",
    "        Interactions_flattened = np.concatenate([TF_TF_interactions[2:3, i], TF_TF_interactions[i, 2:3]]).flatten()\n",
    "        Interactions[-1].append(np.mean(Interactions_flattened[np.logical_not(np.isnan(Interactions_flattened))]))\n",
    "\n",
    "plt.boxplot(np.asarray(Interactions))\n",
    "x = np.linspace(1, 8, 8)\n",
    "plt.xticks(x, [\"N\", \"n\", \"C\", \"c\", \"R\", \"r\", \"X\", \"x\"], fontsize = 7)\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No interaction\")\n",
    "plt.legend()\n",
    "plt.title(\"Interaction with high-affinity CRX sites\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "94cf30b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot interactions involving medium affinity reverse orientation CRX sites in CRX null retina\n",
    "\n",
    "All_CRX_medium_interactions = []\n",
    "for model in CRX_null_bootstrap_models:\n",
    "    # Retrieve G-P map parameter dict and view dict keys\n",
    "    theta_dict = model.get_theta(gauge=\"user\", x_wt = \"OOOO\")\n",
    "    TF_TF_interactions = np.transpose(theta_dict['theta_lclc'], axes = (1, 3, 0, 2))\n",
    "    All_CRX_medium_interactions.append([])\n",
    "    for i in range(8):\n",
    "        interactions = TF_TF_interactions[i, 5, 0:5, 0:5].flatten() # + TF_TF_interactions[5, i, 0:5, 0:5].flatten()\n",
    "        All_CRX_medium_interactions[-1].append(np.mean(interactions[np.logical_not(np.isnan(interactions))]))\n",
    "    #All_CRX_medium_interactions[-1].append(np.mean(TF_TF_interactions[5, 8, 0:4, 4]))\n",
    "        \n",
    "plt.boxplot(np.asarray(All_CRX_medium_interactions))\n",
    "x = np.linspace(1, 8, 8)\n",
    "plt.xticks(x, [\"NRL\\n forward\", \"NRL\\n reverse\", \"High\\n affinity\\n CRX\\n forward\", \"High\\n affinity\\n CRX\\n reverse\",\n",
    "              \"Medium\\n affinity\\n CRX\\n forward\", \"Medium\\n affinity\\n CRX\\n reverse\", \n",
    "              \"Low\\n affinity\\n CRX\\n forward\", \"Low\\n affinity\\n CRX\\n reverse\"], fontsize = 7) #, \"Rho\"\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No interaction\")\n",
    "plt.legend()\n",
    "plt.title(\"Interactions with Medium-affinity reverse orientation CRX sites\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d125ce5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_0.pickle\n",
      "\tCDNRM_additive_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_1.pickle\n",
      "\tCDNRM_additive_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_2.pickle\n",
      "\tCDNRM_additive_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_3.pickle\n",
      "\tCDNRM_additive_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_4.pickle\n",
      "\tCDNRM_additive_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_5.pickle\n",
      "\tCDNRM_additive_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_6.pickle\n",
      "\tCDNRM_additive_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_7.pickle\n",
      "\tCDNRM_additive_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_8.pickle\n",
      "\tCDNRM_additive_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_9.pickle\n",
      "\tCDNRM_additive_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_10.pickle\n",
      "\tCDNRM_additive_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_11.pickle\n",
      "\tCDNRM_additive_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_12.pickle\n",
      "\tCDNRM_additive_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_13.pickle\n",
      "\tCDNRM_additive_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_14.pickle\n",
      "\tCDNRM_additive_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_15.pickle\n",
      "\tCDNRM_additive_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_16.pickle\n",
      "\tCDNRM_additive_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_17.pickle\n",
      "\tCDNRM_additive_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_18.pickle\n",
      "\tCDNRM_additive_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_19.pickle\n",
      "\tCDNRM_additive_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_20.pickle\n",
      "\tCDNRM_additive_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_21.pickle\n",
      "\tCDNRM_additive_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_22.pickle\n",
      "\tCDNRM_additive_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_23.pickle\n",
      "\tCDNRM_additive_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_24.pickle\n",
      "\tCDNRM_additive_ge_24.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_25.pickle\n",
      "\tCDNRM_additive_ge_25.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_26.pickle\n",
      "\tCDNRM_additive_ge_26.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_27.pickle\n",
      "\tCDNRM_additive_ge_27.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_28.pickle\n",
      "\tCDNRM_additive_ge_28.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_29.pickle\n",
      "\tCDNRM_additive_ge_29.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_30.pickle\n",
      "\tCDNRM_additive_ge_30.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_31.pickle\n",
      "\tCDNRM_additive_ge_31.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_32.pickle\n",
      "\tCDNRM_additive_ge_32.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_33.pickle\n",
      "\tCDNRM_additive_ge_33.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_34.pickle\n",
      "\tCDNRM_additive_ge_34.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_35.pickle\n",
      "\tCDNRM_additive_ge_35.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_36.pickle\n",
      "\tCDNRM_additive_ge_36.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_37.pickle\n",
      "\tCDNRM_additive_ge_37.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_38.pickle\n",
      "\tCDNRM_additive_ge_38.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_39.pickle\n",
      "\tCDNRM_additive_ge_39.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_40.pickle\n",
      "\tCDNRM_additive_ge_40.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_41.pickle\n",
      "\tCDNRM_additive_ge_41.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_42.pickle\n",
      "\tCDNRM_additive_ge_42.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_43.pickle\n",
      "\tCDNRM_additive_ge_43.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_44.pickle\n",
      "\tCDNRM_additive_ge_44.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_45.pickle\n",
      "\tCDNRM_additive_ge_45.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_46.pickle\n",
      "\tCDNRM_additive_ge_46.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_47.pickle\n",
      "\tCDNRM_additive_ge_47.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_48.pickle\n",
      "\tCDNRM_additive_ge_48.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_additive_ge_49.pickle\n",
      "\tCDNRM_additive_ge_49.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_0.pickle\n",
      "\tCDNRM_neighbor_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_1.pickle\n",
      "\tCDNRM_neighbor_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_2.pickle\n",
      "\tCDNRM_neighbor_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_3.pickle\n",
      "\tCDNRM_neighbor_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_4.pickle\n",
      "\tCDNRM_neighbor_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_5.pickle\n",
      "\tCDNRM_neighbor_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_6.pickle\n",
      "\tCDNRM_neighbor_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_7.pickle\n",
      "\tCDNRM_neighbor_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_8.pickle\n",
      "\tCDNRM_neighbor_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_9.pickle\n",
      "\tCDNRM_neighbor_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_10.pickle\n",
      "\tCDNRM_neighbor_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_11.pickle\n",
      "\tCDNRM_neighbor_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_12.pickle\n",
      "\tCDNRM_neighbor_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_13.pickle\n",
      "\tCDNRM_neighbor_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_14.pickle\n",
      "\tCDNRM_neighbor_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_15.pickle\n",
      "\tCDNRM_neighbor_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_16.pickle\n",
      "\tCDNRM_neighbor_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_17.pickle\n",
      "\tCDNRM_neighbor_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_18.pickle\n",
      "\tCDNRM_neighbor_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_19.pickle\n",
      "\tCDNRM_neighbor_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_20.pickle\n",
      "\tCDNRM_neighbor_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_21.pickle\n",
      "\tCDNRM_neighbor_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_22.pickle\n",
      "\tCDNRM_neighbor_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_23.pickle\n",
      "\tCDNRM_neighbor_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_24.pickle\n",
      "\tCDNRM_neighbor_ge_24.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_25.pickle\n",
      "\tCDNRM_neighbor_ge_25.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_26.pickle\n",
      "\tCDNRM_neighbor_ge_26.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_27.pickle\n",
      "\tCDNRM_neighbor_ge_27.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_28.pickle\n",
      "\tCDNRM_neighbor_ge_28.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_29.pickle\n",
      "\tCDNRM_neighbor_ge_29.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_30.pickle\n",
      "\tCDNRM_neighbor_ge_30.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_31.pickle\n",
      "\tCDNRM_neighbor_ge_31.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_32.pickle\n",
      "\tCDNRM_neighbor_ge_32.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_33.pickle\n",
      "\tCDNRM_neighbor_ge_33.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_34.pickle\n",
      "\tCDNRM_neighbor_ge_34.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_35.pickle\n",
      "\tCDNRM_neighbor_ge_35.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_36.pickle\n",
      "\tCDNRM_neighbor_ge_36.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_37.pickle\n",
      "\tCDNRM_neighbor_ge_37.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_38.pickle\n",
      "\tCDNRM_neighbor_ge_38.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_39.pickle\n",
      "\tCDNRM_neighbor_ge_39.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_40.pickle\n",
      "\tCDNRM_neighbor_ge_40.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_41.pickle\n",
      "\tCDNRM_neighbor_ge_41.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_42.pickle\n",
      "\tCDNRM_neighbor_ge_42.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_43.pickle\n",
      "\tCDNRM_neighbor_ge_43.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_44.pickle\n",
      "\tCDNRM_neighbor_ge_44.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_45.pickle\n",
      "\tCDNRM_neighbor_ge_45.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_46.pickle\n",
      "\tCDNRM_neighbor_ge_46.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_47.pickle\n",
      "\tCDNRM_neighbor_ge_47.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_48.pickle\n",
      "\tCDNRM_neighbor_ge_48.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_neighbor_ge_49.pickle\n",
      "\tCDNRM_neighbor_ge_49.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_0.pickle\n",
      "\tCDNRM_pairwise_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_1.pickle\n",
      "\tCDNRM_pairwise_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_2.pickle\n",
      "\tCDNRM_pairwise_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_3.pickle\n",
      "\tCDNRM_pairwise_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_4.pickle\n",
      "\tCDNRM_pairwise_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_5.pickle\n",
      "\tCDNRM_pairwise_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_6.pickle\n",
      "\tCDNRM_pairwise_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_7.pickle\n",
      "\tCDNRM_pairwise_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_8.pickle\n",
      "\tCDNRM_pairwise_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_9.pickle\n",
      "\tCDNRM_pairwise_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_10.pickle\n",
      "\tCDNRM_pairwise_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_11.pickle\n",
      "\tCDNRM_pairwise_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_12.pickle\n",
      "\tCDNRM_pairwise_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_13.pickle\n",
      "\tCDNRM_pairwise_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_14.pickle\n",
      "\tCDNRM_pairwise_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_15.pickle\n",
      "\tCDNRM_pairwise_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_16.pickle\n",
      "\tCDNRM_pairwise_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_17.pickle\n",
      "\tCDNRM_pairwise_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_18.pickle\n",
      "\tCDNRM_pairwise_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_19.pickle\n",
      "\tCDNRM_pairwise_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_20.pickle\n",
      "\tCDNRM_pairwise_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_21.pickle\n",
      "\tCDNRM_pairwise_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_22.pickle\n",
      "\tCDNRM_pairwise_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_23.pickle\n",
      "\tCDNRM_pairwise_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_24.pickle\n",
      "\tCDNRM_pairwise_ge_24.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_25.pickle\n",
      "\tCDNRM_pairwise_ge_25.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_26.pickle\n",
      "\tCDNRM_pairwise_ge_26.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_27.pickle\n",
      "\tCDNRM_pairwise_ge_27.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_28.pickle\n",
      "\tCDNRM_pairwise_ge_28.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_29.pickle\n",
      "\tCDNRM_pairwise_ge_29.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_30.pickle\n",
      "\tCDNRM_pairwise_ge_30.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_31.pickle\n",
      "\tCDNRM_pairwise_ge_31.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_32.pickle\n",
      "\tCDNRM_pairwise_ge_32.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_33.pickle\n",
      "\tCDNRM_pairwise_ge_33.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_34.pickle\n",
      "\tCDNRM_pairwise_ge_34.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_35.pickle\n",
      "\tCDNRM_pairwise_ge_35.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_36.pickle\n",
      "\tCDNRM_pairwise_ge_36.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_37.pickle\n",
      "\tCDNRM_pairwise_ge_37.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_38.pickle\n",
      "\tCDNRM_pairwise_ge_38.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_39.pickle\n",
      "\tCDNRM_pairwise_ge_39.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_40.pickle\n",
      "\tCDNRM_pairwise_ge_40.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_41.pickle\n",
      "\tCDNRM_pairwise_ge_41.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_42.pickle\n",
      "\tCDNRM_pairwise_ge_42.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_43.pickle\n",
      "\tCDNRM_pairwise_ge_43.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_44.pickle\n",
      "\tCDNRM_pairwise_ge_44.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_45.pickle\n",
      "\tCDNRM_pairwise_ge_45.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_46.pickle\n",
      "\tCDNRM_pairwise_ge_46.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_47.pickle\n",
      "\tCDNRM_pairwise_ge_47.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_48.pickle\n",
      "\tCDNRM_pairwise_ge_48.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_pairwise_ge_49.pickle\n",
      "\tCDNRM_pairwise_ge_49.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_0.pickle\n",
      "\tCDNRM_blackbox_ge_0.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_1.pickle\n",
      "\tCDNRM_blackbox_ge_1.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_2.pickle\n",
      "\tCDNRM_blackbox_ge_2.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_3.pickle\n",
      "\tCDNRM_blackbox_ge_3.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_4.pickle\n",
      "\tCDNRM_blackbox_ge_4.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_5.pickle\n",
      "\tCDNRM_blackbox_ge_5.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_6.pickle\n",
      "\tCDNRM_blackbox_ge_6.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_7.pickle\n",
      "\tCDNRM_blackbox_ge_7.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_8.pickle\n",
      "\tCDNRM_blackbox_ge_8.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_9.pickle\n",
      "\tCDNRM_blackbox_ge_9.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_10.pickle\n",
      "\tCDNRM_blackbox_ge_10.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_11.pickle\n",
      "\tCDNRM_blackbox_ge_11.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_12.pickle\n",
      "\tCDNRM_blackbox_ge_12.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_13.pickle\n",
      "\tCDNRM_blackbox_ge_13.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_14.pickle\n",
      "\tCDNRM_blackbox_ge_14.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_15.pickle\n",
      "\tCDNRM_blackbox_ge_15.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_16.pickle\n",
      "\tCDNRM_blackbox_ge_16.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_17.pickle\n",
      "\tCDNRM_blackbox_ge_17.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_18.pickle\n",
      "\tCDNRM_blackbox_ge_18.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_19.pickle\n",
      "\tCDNRM_blackbox_ge_19.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_20.pickle\n",
      "\tCDNRM_blackbox_ge_20.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_21.pickle\n",
      "\tCDNRM_blackbox_ge_21.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_22.pickle\n",
      "\tCDNRM_blackbox_ge_22.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_23.pickle\n",
      "\tCDNRM_blackbox_ge_23.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_24.pickle\n",
      "\tCDNRM_blackbox_ge_24.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_25.pickle\n",
      "\tCDNRM_blackbox_ge_25.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_26.pickle\n",
      "\tCDNRM_blackbox_ge_26.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_27.pickle\n",
      "\tCDNRM_blackbox_ge_27.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_28.pickle\n",
      "\tCDNRM_blackbox_ge_28.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_29.pickle\n",
      "\tCDNRM_blackbox_ge_29.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_30.pickle\n",
      "\tCDNRM_blackbox_ge_30.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_31.pickle\n",
      "\tCDNRM_blackbox_ge_31.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_32.pickle\n",
      "\tCDNRM_blackbox_ge_32.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_33.pickle\n",
      "\tCDNRM_blackbox_ge_33.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_34.pickle\n",
      "\tCDNRM_blackbox_ge_34.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_35.pickle\n",
      "\tCDNRM_blackbox_ge_35.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_36.pickle\n",
      "\tCDNRM_blackbox_ge_36.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_37.pickle\n",
      "\tCDNRM_blackbox_ge_37.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_38.pickle\n",
      "\tCDNRM_blackbox_ge_38.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_39.pickle\n",
      "\tCDNRM_blackbox_ge_39.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_40.pickle\n",
      "\tCDNRM_blackbox_ge_40.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_41.pickle\n",
      "\tCDNRM_blackbox_ge_41.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_42.pickle\n",
      "\tCDNRM_blackbox_ge_42.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_43.pickle\n",
      "\tCDNRM_blackbox_ge_43.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_44.pickle\n",
      "\tCDNRM_blackbox_ge_44.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_45.pickle\n",
      "\tCDNRM_blackbox_ge_45.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_46.pickle\n",
      "\tCDNRM_blackbox_ge_46.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_47.pickle\n",
      "\tCDNRM_blackbox_ge_47.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_48.pickle\n",
      "\tCDNRM_blackbox_ge_48.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_blackbox_ge_49.pickle\n",
      "\tCDNRM_blackbox_ge_49.h5\n"
     ]
    }
   ],
   "source": [
    "# Iterate over types of G-P maps\n",
    "gpmap_types = ['additive','neighbor','pairwise','blackbox']\n",
    "\n",
    "# Create list of model names\n",
    "model_names = [f'CDNRM_{gpmap_type}_ge_{i}' for gpmap_type in gpmap_types for i in range(50)]\n",
    "\n",
    "# Load models into a dictionary indexed by model name\n",
    "model_dict = {name:mavenn.load(name) for name in model_names}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4819678",
   "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>x</th>\n",
       "      <th>set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCCDZ</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CCCEA</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCCEN</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCCAE</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCCAN</td>\n",
       "      <td>training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>BERCX</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>BARXC</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>BZRCX</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>BNXRC</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>BNRCX</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6598 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         x       set\n",
       "0    CCCDZ  training\n",
       "1    CCCEA  training\n",
       "2    CCCEN  training\n",
       "3    CCCAE  training\n",
       "4    CCCAN  training\n",
       "..     ...       ...\n",
       "661  BERCX      test\n",
       "662  BARXC      test\n",
       "663  BZRCX      test\n",
       "664  BNXRC      test\n",
       "665  BNRCX      test\n",
       "\n",
       "[6598 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data from Ryan's library\n",
    "\n",
    "Data_file = open(\"Ryan_trainval.pkl\", \"rb\")\n",
    "trainval_df = pickle.load(Data_file)\n",
    "Data_file.close()\n",
    "\n",
    "Data_file = open(\"Ryan_test.pkl\", \"rb\")\n",
    "test_df = pickle.load(Data_file)\n",
    "Data_file.close()\n",
    "\n",
    "Data_df = pd.DataFrame()\n",
    "Data_df['x'] = pd.concat([trainval_df['Sequence'], test_df['Sequence']])\n",
    "assignments = []\n",
    "for validation in trainval_df['validation']:\n",
    "    if validation:\n",
    "        assignments.append('validation')\n",
    "    else:\n",
    "        assignments.append('training')\n",
    "assignments = assignments + ['test']*len(test_df)\n",
    "Data_df['set'] = assignments\n",
    "Data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8dfa9788",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 0 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.219 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5bb95973a704580baee45c5d0718fbd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 65.1 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 1 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.221 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "217f4527e01444f8abb3048802a590fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 89.9 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 2 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.231 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d757ff82f9bd422fb7bbb0df8d90ac46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 102.1 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 3 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.251 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8712bd232651450da6f8b480a799ad42",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 63.4 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 4 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.249 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ee0a459521a4289aab848831b25806e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 87.3 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 5 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.365 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d71722d43d84d12b4e4f99d1466383d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 81.4 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 6 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.195 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b98d5d38cee4b4f8d8aef92a1e37ad7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 82.9 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 7 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.438 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4f9f295fe114d52a32044ffa121e145",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 71.2 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 8 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.214 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4936f76aa99340abbb70eeaea2c3ea99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 60.2 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 9 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.407 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed2b8d7d449f44dda115ef28aab6f0bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 85.0 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 10 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.623 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6013d3faaf544fda63d5a3925a065ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 84.7 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 11 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.211 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e51cc281398240a3a7de7c517582be07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 67.1 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 12 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.184 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcfaa623aa8140c89065191293a73b80",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 71.7 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 13 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.188 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50d8c3c85981484eacfdf67a55525278",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 62.6 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 14 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.186 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "173f08a1dad4401d948e77df372deac7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 50.8 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 15 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.244 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2788e7bed7f7464596282dca709d67c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 85.2 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 16 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.285 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6433f7ef1114eaab58bdefd4cb34d9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 67.6 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 17 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.286 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee80d29ecbef407799e24fc5318bd967",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 62.9 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 18 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.297 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5fd6be4f0184a84964a0718b1020c6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 72.6 seconds\n",
      "Training set   :    5,274 observations (  79.93%)\n",
      "Validation set :      658 observations (   9.97%)\n",
      "Test set       :      666 observations (  10.09%)\n",
      "-------------------------------------------------\n",
      "Total dataset  :    6,598 observations ( 100.00%)\n",
      "\n",
      "training model 19 ...\n",
      "N = 5,932 observations set as training data.\n",
      "Using 11.1% for validation.\n",
      "Data shuffled.\n",
      "Time to set data: 0.382 sec.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "617197af594948beab61cfe5bde0ded8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0epoch [00:00, ?epoch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 58.3 seconds\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_0.pickle\n",
      "\tCDNRM_bootstrap_0.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_1.pickle\n",
      "\tCDNRM_bootstrap_1.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_2.pickle\n",
      "\tCDNRM_bootstrap_2.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_3.pickle\n",
      "\tCDNRM_bootstrap_3.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_4.pickle\n",
      "\tCDNRM_bootstrap_4.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_5.pickle\n",
      "\tCDNRM_bootstrap_5.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_6.pickle\n",
      "\tCDNRM_bootstrap_6.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_7.pickle\n",
      "\tCDNRM_bootstrap_7.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_8.pickle\n",
      "\tCDNRM_bootstrap_8.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_9.pickle\n",
      "\tCDNRM_bootstrap_9.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_10.pickle\n",
      "\tCDNRM_bootstrap_10.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_11.pickle\n",
      "\tCDNRM_bootstrap_11.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_12.pickle\n",
      "\tCDNRM_bootstrap_12.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_13.pickle\n",
      "\tCDNRM_bootstrap_13.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_14.pickle\n",
      "\tCDNRM_bootstrap_14.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_15.pickle\n",
      "\tCDNRM_bootstrap_15.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_16.pickle\n",
      "\tCDNRM_bootstrap_16.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_17.pickle\n",
      "\tCDNRM_bootstrap_17.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_18.pickle\n",
      "\tCDNRM_bootstrap_18.h5\n",
      "Model saved to these files:\n",
      "\tCDNRM_bootstrap_19.pickle\n",
      "\tCDNRM_bootstrap_19.h5\n"
     ]
    }
   ],
   "source": [
    "# Fit bootstrap models\n",
    "\n",
    "name = \"CDNRM_pairwise_ge_39\"\n",
    "\n",
    "# Get model\n",
    "model = model_dict[name]\n",
    "\n",
    "bootstrap_models = model.bootstrap(Data_df, num_models = 20)\n",
    "for i, model in enumerate(bootstrap_models):\n",
    "    model_name = \"CDNRM_bootstrap_\" + str(i)\n",
    "    model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a7d60c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_0.pickle\n",
      "\tCDNRM_bootstrap_0.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_1.pickle\n",
      "\tCDNRM_bootstrap_1.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_2.pickle\n",
      "\tCDNRM_bootstrap_2.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_3.pickle\n",
      "\tCDNRM_bootstrap_3.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_4.pickle\n",
      "\tCDNRM_bootstrap_4.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_5.pickle\n",
      "\tCDNRM_bootstrap_5.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_6.pickle\n",
      "\tCDNRM_bootstrap_6.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_7.pickle\n",
      "\tCDNRM_bootstrap_7.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_8.pickle\n",
      "\tCDNRM_bootstrap_8.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_9.pickle\n",
      "\tCDNRM_bootstrap_9.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_10.pickle\n",
      "\tCDNRM_bootstrap_10.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_11.pickle\n",
      "\tCDNRM_bootstrap_11.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_12.pickle\n",
      "\tCDNRM_bootstrap_12.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_13.pickle\n",
      "\tCDNRM_bootstrap_13.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_14.pickle\n",
      "\tCDNRM_bootstrap_14.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_15.pickle\n",
      "\tCDNRM_bootstrap_15.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_16.pickle\n",
      "\tCDNRM_bootstrap_16.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_17.pickle\n",
      "\tCDNRM_bootstrap_17.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_18.pickle\n",
      "\tCDNRM_bootstrap_18.h5\n",
      "Model loaded from these files:\n",
      "\tCDNRM_bootstrap_19.pickle\n",
      "\tCDNRM_bootstrap_19.h5\n"
     ]
    }
   ],
   "source": [
    "# Load bootstrap models\n",
    "model_names = [\"CDNRM_bootstrap_\" + str(i) for i in range(20)]\n",
    "CDNRM_bootstrap_models = [mavenn.load(name) for name in model_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9cd54250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.26189754, 0.15277357, 0.08714762, 0.08275235, 0.04183086,\n",
       "        0.08275235, 0.04183086, 0.08260079, 0.04183086, 0.08275235,\n",
       "        0.04183086],\n",
       "       [0.26159442, 0.15277357, 0.08729918, 0.08275235, 0.04183086,\n",
       "        0.08275235, 0.04183086, 0.08275235, 0.04183086, 0.08275235,\n",
       "        0.04183086],\n",
       "       [0.26189754, 0.15277357, 0.08729918, 0.08275235, 0.04183086,\n",
       "        0.08275235, 0.0416793 , 0.08260079, 0.04183086, 0.08275235,\n",
       "        0.04183086],\n",
       "       [0.26189754, 0.15262201, 0.08729918, 0.08260079, 0.04183086,\n",
       "        0.08275235, 0.04183086, 0.08275235, 0.04183086, 0.08275235,\n",
       "        0.04183086],\n",
       "       [0.26189754, 0.15262201, 0.08729918, 0.08260079, 0.04183086,\n",
       "        0.08275235, 0.04183086, 0.08275235, 0.04183086, 0.08275235,\n",
       "        0.04183086]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get site frequencies in Ryan's library (used to set gauge)\n",
    "\n",
    "site_letters = ['C', 'R', 'X', 'D', 'E', 'N', 'L', 'O', 'B', 'A', 'Z']\n",
    "\n",
    "Counts = [[0 for TF in site_letters] for i in range(5)]\n",
    "for sequence in trainval_df[\"Sequence\"]:\n",
    "    for i, site in enumerate(sequence):\n",
    "        for j, TF in enumerate(site_letters):\n",
    "            if site == TF:\n",
    "                Counts[i][j] = Counts[i][j] + 1\n",
    "for sequence in test_df[\"Sequence\"]:\n",
    "    for i, site in enumerate(sequence):\n",
    "        for j, TF in enumerate(site_letters):\n",
    "            if site == TF:\n",
    "                Counts[i][j] = Counts[i][j] + 1\n",
    "    \n",
    "Frequencies = np.asarray([np.asarray(position)/sum(position) for position in Counts])\n",
    "Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "57628391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot additive contributions in Ryan's library\n",
    "\n",
    "All_contributions = []\n",
    "for model in CDNRM_bootstrap_models:\n",
    "    theta_dict = model.get_theta(gauge=\"user\", p_lc = Frequencies)\n",
    "    # Get logo dataframe\n",
    "    logo_df = theta_dict['logomaker_df']\n",
    "    TF_additive = (logo_df.iloc[0] + logo_df.iloc[1] + logo_df.iloc[2] + logo_df.iloc[3] + logo_df.iloc[4])/5\n",
    "    All_contributions.append(TF_additive)\n",
    "\n",
    "#np.asarray(All_contributions).shape\n",
    "plt.boxplot(np.asarray(All_contributions))\n",
    "x = np.linspace(1, 11, 11)\n",
    "plt.xticks(x, [\"CRX\\nhigh\", \"CRX\\nmedium\", \"CRX\\nlow\", \"NeuroD1\\nhigh\", \"NeuroD1\\nmedium\", \"NRL\\nhigh\", \"NRL\\nmedium\", \"RORB\\nhigh\", \"RORB\\nmedium\", \"MAZ\\nhigh\", \"MAZ\\nmedium\"], fontsize = 7)\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No contribution\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5bdfd003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Snnv99dfjs88+i4iIPn36xHHHHVeiY5555pnRpUuXiFg3+WumQqmuXbumHv/vf/8r17E2nIB6+vTp5TrWtkrbZ2/bR0QceeSRERHx3nvvxbJly4rd9uWXX46IdXfONWzYsETHXz+c0I9//OPYfffd4+ijj46Idef3Df9OK6unnnoq9XdmnTp14s477yx2omn+j7bffm2b33DAFvDUU09FREROTk6prmDu27dvnHjiiXHbbbdl5Jb0tWvXph5vavzpunXrxk033ZRaHjhwYGpSoeuuuy41puf1118fTZs2LXdN2zttn720fTJdfvnlqdtsn3zyyfjwww/LfKwVK1bEI488Ev369YuuXbtGmzZt4uCDD47f/va38e67725yv9NOOy1atmwZ3bt33+Q206dPj5YtW0bLli3jnnvuSXvunnvuiZYtW8Zhhx0Wa9asiTvvvDMOPPDAaNeuXRx11FHx8MMPp22/atWqePLJJ+PMM8+M/fbbL9q0aRPdunWL8847L1588cWMXOm3oZNPPjn1+I033ij0/HfffRdz5syJiIgePXrECy+8EO3bt89oDUXR9tnb9hUhk++30irJeZ8tQ7tnn/X/72vWrElb//jjj6cen3POOaU65h/+8IfU45EjR5ajuv+z4fl+41pLa8OLWXbYYYdyHWtbpu2zt+179OgREevmDFw/9M+mrB9OaP0+m/Pf//43dSHI+r8FDz300NT/d3knef7vf/+buns2IuLGG28sdthC0mn77ZcQAyJi1qxZMXXq1IiI+MlPfhI/+tGPSrzvXnvtFTfddFMcf/zxJRrbrjiLFi1K3TK24447Rv369Te57UEHHRR9+/aNiHVXYd9+++3xwgsvpL6QOO6440p8RUk20/bZS9snV7169eLqq6+OiHV/1F133XVlujX3iy++iGOOOSb+/Oc/x8SJE2Px4sWxevXq+O677+Lll1+Os88+O37/+9/HihUrMv0jpLn++uvjwQcfjPnz58fKlStj+vTpUbdu3dTz06ZNi169esV1110X48ePj0WLFsXq1atj3rx5MXbs2Lj00kvjlFNOSQVVmXDAAQekHr///vuFns/JyYlOnTrFY489FoMHDy7xlUnlpe2zt+0rQqbeb6VVmvM+mafds8tXX32Vuuhjjz32SK1fvnx5TJw4MSIiWrZsWezcBkVp165dNGnSJCIiPvroo9RrlMeGgdqGtZbF559/nnq84ZX52UTbZ2/bR0S0atUq9eXva6+9tsntZsyYEVOmTIlKlSrFUUcdVaJjbzi5/M9+9rOIWDdfyWGHHRYR6+52/fbbb8tU96pVq+LSSy9N3UHQt2/f1JX+lIy2334JMSAipkyZknrcsWPHCqvjjjvuSP0R1aNHj8jJySl2+yuuuCJ22WWXiFh3RfkNN9wQERG77LJLXH/99Vu22O2Ets9e2j7Zjj766NSHwWnTpsX9999fqv2//fbbOP300+Pbb7+NGjVqxFlnnRVDhw6Np556Ku68887o1q1bRKwb0/Tyyy/PeP3rzZ49O55++uno0KFDDBkyJB599NE49dRT45hjjomIiDlz5sRZZ52VCtSOPPLI+Otf/xpPPfVU3HXXXanhBT766KP45S9/mbEv3evVqxc77bRTRER8+eWXhZ7v3LlzPPHEEyUeGzaTtH32tn1FKO/7rSxKe94n87R7dli7dm3ccsstqeUNvwz69NNPU1e8l/WL3vXjoOfn58ekSZPKUWnE/PnzY8iQIanl8nxxtWbNmrj33nsjYt2Xa7179y5XbdsibZ+9bb+h9V9Mjx07dpN3v204nFBJLk4rKCiIF154ISLWXQi39957p55bfzHZ2rVr45lnnilTzXfeeWdMnjw5ItZdOLe5iaIpmrbfPgkxINZdkb3e1hyGZdWqVTFv3rx466234le/+lVqaJuGDRvGRRddtNn9a9WqFTfffHPk5OTE2rVr4/vvv49KlSrFrbfeGnXq1NnS5W8XtH320vbJd/3110ft2rUjIuKhhx6Kr7/+usT7XnfddbFkyZLYcccdY9SoUXHFFVekhvQ59thj429/+1ucd955EbHuNuJMjF9alLVr10bTpk1j6NChcfDBB8f+++8f1157bernuv322+O7776LiIgrr7wy7r333jjiiCOiXbt2ccwxx8Tjjz8ep59+ekSsu7vgvvvuy1htjRs3joh1k1NufBVyRc+pou2zt+0rQnnebyVR3vM+W4Z23z6tXLkyvvvuu3jllVeif//+qeED99xzzzj11FNT2214pexuu+1WptdaP1dZRNnmMVi6dGl888038fe//z369OmTGsrv5z//ebHD+C1dujQ+//zztH9TpkyJDz/8MEaNGhV9+vSJ8ePHR5UqVeLWW2+NBg0alLq2bZG2z96235T1X2T/8MMP8e9//7vIbUo7nNCHH36Yeg9tfAf8QQcdlLrL7tlnn00bQrAk3n333Xj00UcjYt1QYHfeeWdWDwlWHtp++2RmEIh1HwbWWz/5VyYdfvjhJd52jz32iHvvvbfEQ9R07do1DjzwwNSHtNatW0fnzp3LVGc20vbZS9sn38477xyXXHJJ3HDDDbF69eq49tpr44knntjsl6xTp05N/d9ccMEFqYlUN3bRRRfFiy++GN9++22MGDEiI/ObFOX444+PmjVrFlo/b968GD16dESsu6LvzDPPLHL/K664IsaNGxdTp06NESNGxG9+85uoXr16ueuqUaNG6vGSJUvKPTRaJmn7dbKx7StCWd9vG9uS530yT7tv21q2bFnibdu2bRt//etf087HS5YsST0u60UgGw63t6mJ2idMmFCqWnv37h1//OMfi91m8uTJccIJJxS7TdWqVeOhhx6K/fffv8Svva3Q9icUu8323Pal1b59+2jSpEnMmTMnXnvttTjooIPSnp81a1ZMmjQpKlWqlJoMenPWDyeUk5MTxx57bNpzVatWjaOPPjqeeOKJmD17drz33nuFXnNTFi5cGAMGDEjNj3L11VfHT37ykxLtS2HafvuUfZdaQRE2/GOlIibZ23HHHePggw+Om2++OV544YVSnbA+/PDDGDduXGp50qRJ8dxzz22JMrdL2j57afttQ//+/VPDfX388cfxxBNPbHafDSdwK+4PuKpVq6aGzJk4ceIWex+0a9euyPXvv/9+5OfnR0TEL37xi03uX6VKldTzeXl58cknn2Skrg2vwE/i1ffaPnvbviKU5f1WWuU577NlaPftV6NGjaJHjx4xePDgeOqpp1JzGKy34RB91apVK9NrVK5cOfV4w4mZSyMnJyf22GOP6Nu3b4waNSpuvvnmMtezodWrV8fFF1+cNkxRttD22dv2G8vJyUldqPLGG28Uujp+/ZX4HTp0KPQ+KcrKlStT+3Ts2LHICZc3vEJ//R14JTFgwICYN29eRKwbUuykk04q8b4Upu23T+7EgFg3PvR6ixcvzvjxhwwZkhq6oaCgIH744Yd46623YtiwYbFq1apo1KhRnH/++aUelz8vLy8uv/zyWLt2bdSsWTNWrlwZ+fn5MWjQoNhvv/1KNVFxttL22UvbbxtycnLipptuip///OexevXquPPOO+Pwww8v9ufccELDkk50vmLFipg/f/4W+f/b1AfjDYcuadu2bbHH2PD5qVOnpuZLKI8NrxxM4lBk2r7w89nS9hWhLO+3jW2p8z5bjnbfdm04ueratWtjyZIl8fLLL8dTTz0Va9eujWbNmsVvf/vbaN68eZH7b3ju29SV9Juz4YTO64cm21jr1q1j0KBBqeU1a9bE7NmzY+TIkakLUjp06BB/+MMfSnw+7tKlSwwbNixtXUFBQSxdujQWLFgQEydOjAcffDD++9//xh133JG6ynd7oe2zt+3L4qijjooRI0bEvHnz4pNPPkk7F6//Urpnz54lOtbrr7+ees+sn9R5Yx07doxdd901ZsyYEW+88UYsXLhws8N6PfbYY/HWW29FRESzZs3ixhtvLFE9FE/bb39cegURsfvuu6cez507N+PHb968eeyzzz6xzz77RKtWraJr165x+eWXx6OPPho1atSIr7/+Ok4//fTUEBglNXDgwNSYfBdeeGH0798/ItZ9GLv66qvLfFVINtH22UvbbzuaN28e559/fkSsGwZsc7falzWU2vAP0kza1B+3G9a54bAERdnw+UyEbgUFBbFgwYKIWHeVcCau/NsStH32tn1FKO37raj9t8R5ny1Lu2+b1v+f77PPPtG6des44IAD4oYbboi77rorKlWqFB9//HH07ds3vvjiiyL333AuhA3nSSuNDedC2PBz5YZq1aqVVmvbtm3jqKOOiqFDh8bpp58eBQUF8dxzz8Upp5xS5i/UI9YFcrVr147dd989evXqFaNGjYof//jHERHxyCOPZOxOviTQ9umyqe3L4qc//WlqroLXXnsttX7OnDnxySefRE5OTonnRPjnP/+ZevzHP/4xWrZsWeS/GTNmRMS6u2L+8Y9/FHvML774Im6//faIWHen8F133eUCkwzR9tsfIQZERJs2bVLDKXz44Yel3v+GG26IkSNHxvTp00u1X6dOneKGG26IiHUnuYsuuiimTp1aon3HjBkTL7zwQkREtGrVKs4444z43e9+l5qg+L333tsit8Rvb7R99tL225Zf/epX0aJFi4iIePPNN2PMmDGb3Hb9MD3VqlWL559/vsT/NvVH6KaUdMK2nJycItdvGDhtapuiXmtz25bE//73v9QfzK1bty738bYkbZ+9bV8RSvN+K6nynPfZOrT79qNnz55x8cUXR8S6CzzOOeecmD9/fqHt2rVrl/ocOHHixDK91qeffhoR64blK+35NCcnJ6688sro1q1bRER8+eWXcfHFF5d6MthNqVu3bvzqV79KLW/4Bdz2Stuvk41tX5zKlSun5i7a8IvsV155JQoKClJzJ2zOwoUL04YtLamnn356k8+tWLEiLrnkktQwn7/73e82ORQppafttz9CDIh1t5S2b98+IiKmTZsWc+bMKfG+M2bMiBEjRsTAgQNTH5pK4/jjj0/djrZs2bL4wx/+sNmxub/77rsYOHBgRKz7ouaWW26JKlWqRO3ateOmm25KbXf77benXSVCYdo+e2n7bUu1atXixhtvTP3ROWjQoLSJGTe0fqiwVatWRZMmTdKugivu3w477FDoWMXd2ZKXl1eun2nDCeXXXxm/KRs+v+FQaGU1YcKE1ONOnTqV+3hbkrbP3ravCKV5v5VGWc77bD3affty3nnnpc5v8+bNi2uuuabQNvXr109tM3ny5Jg2bVqpXmPKlCmpoQE7dOiQutq3NCpVqhQ333xz6nfCuHHj4pFHHin1cTZlwy/XS3vRzbZK26+TjW1fnKOOOioi1v0Nt/4OnfVfapd0OKEXX3wx1qxZExERZ511Vtx9993F/lvfBlOnTo2PP/64yGP+6U9/Sr3/DjrooDj77LPL/kNSJG2/fRFiwP/Xq1ev1OPSXMk8cuTI1ONjjz22TK999dVXpz7ATJkyJR5++OFNbltQUBCXX3556g+riy++OPbaa6/U8wceeGD07ds3Itb9sTRgwICMXdWxvdL22Uvbb1s6duwY/fr1i4iI+fPnx5///Ocit9twDOT333+/2GOOGTMmRowYEWPHjk37YqlKlXXThq1cuXKT+5Ym+CrKhm04adKkYrfNzc1NPd5zzz3L9boREaNGjUo93tS4rkmi7dfJxravCCV9v5VWac77bH3afftRqVKluPHGG1PD5b355ptFXo2+fkjOiIh77723VK9x3333pR6vf9+URePGjePyyy9PLd99990Zuxhlw8+CNWvWzMgxk07br5ONbV+c/fffPzVMz7/+9a9YuHBhfPTRR6UaTmj90EDVqlWLCy64IHr27Fnsvw0nZy7qivzXXnstnnzyyYhYNxn9LbfckpE7bkmn7bcvQgz4/0444YTYeeedIyJi6NChMWXKlM3u8+WXX6Ym1qpdu3acfPLJZXrtBg0apE249cADD8TMmTOL3PZvf/tb6ouZjh07FpnYXn755bHLLrtERMRHH32U0as6tkfaPntp+23PJZdckmqzZ599NhYuXFhomwMPPDD1eMSIEZs8Vl5eXlxzzTVxww03xMCBA1NfXkf836SP33///Savun/vvffK9DOs16VLl9RrFne78Zo1a+LZZ5+NiIgddtgh9t1333K97gsvvJD6YvzQQw8t9VBKFUXbZ2/bV4SSvN9KqzTnfSqGdt9+NG/ePM4777zU8q233lpo3oGjjz462rZtGxHrgu0NQ97iPPHEE6kreffZZ58yX9CyXp8+faJLly4RsS5Az9TErhuG+T/5yU8ycsxtgbbP3rbflGrVqsUhhxwSERFvvPFGvPHGG5Gfnx/t2rWLH/3oR5vdf9q0afHZZ59FxLqr5ksyb8HRRx8dVatWjYh177GlS5emnvvuu+9SdwlVqlQpbrvtts3OkUbZaPvtixAD/r9q1aqlhmpZtWpVnH322cV+STFp0qQ455xzUmPYXXHFFVG3bt0yv/4JJ5yQuq11xYoVacPDrPfFF1/EX/7yl4hYd0XFLbfckrrtfUO1a9eOQYMGpdLcu+++2xi8xdD22Uvbb3tq164d119/fWq5qGE52rdvnxpT9IMPPki7Ym69goKCuOaaa1IfKvv37592Bcz6K+ULCgqKvEtn4sSJ8dRTT5XrZ2ncuHHqCqB33303hg8fXuR2t956a+p245NOOqlcV9SNHz8+9Z7fYYcd4g9/+EOZj7W1afvsbfuKUJL3W1mU5LxPxdHu25dzzz03NYnzvHnz4u677057PicnJ26//fbUl1LXXXddPPDAA6lhQza2Zs2auP/++1NzndSsWTNuvfXWIj+Xldb111+f+tLr7bffjldffbVcx5s1a1YMGTIkItZ9UZZtd95p++xt+01Z/7lr8uTJqbvqSzqc0IYTNJc0uKpXr14ceuihEbHuTvn1cy2tXbs2/vCHP8TixYsjIuKCCy6I/fffv0THpGy0/fajyuY3gexx6KGHxqWXXhp33HFHLFy4MM4666w44IAD4uijj44WLVpE5cqVY+bMmfH666/HK6+8kvqQc9ppp6XdMlYWOTk5cd1110Xv3r0jPz8/3nzzzXj99ddTExGtXLkyLrvsstQfU3/4wx9SH8yKsv/++8fJJ58cI0eOjJUrV8YVV1wRTz75ZNrVpvwfbZ+9tP2257DDDoujjz46XnrppU1uc9NNN8VJJ50UK1asiLvvvjs+/fTT6NOnTzRu3DhmzpwZI0aMSE3muNdee8UZZ5yRtv/RRx8df/3rX2Pt2rXxl7/8JRYtWhSHHHJI5OfnxzvvvBMjRoxIfaFcnnHTr7zyypgwYULMmzcvbrzxxpgwYUIcf/zxqTr//ve/p66m23PPPeOSSy7Z5LEWL14cn3/+edq61atXR15eXvznP/+Jt956K95+++2IWPdH7Z/+9Ke04Ze2Bdq+aNnQ9hWhJO+30trceX9jDz74YNSuXXuzxz388MOjc+fOGaszm2n37Uf16tXjmmuuSU1y/MQTT0Tv3r2jVatWqW322GOPGDp0aJx77rmxePHiuOuuu+K5556L448/Ptq1axf16tWLxYsXR25ubrzwwgvxzTffRMS6u/buu+++tOEBy6NFixZx+umnx9/+9reIWDdeerdu3YoMr5cuXVronB8RkZ+fHz/88EN8+OGHMWLEiFi0aFFERJxyyikZGY5wW6Lts7ftN+Wggw6KGjVqxPLly1NX1pdkOKGCgoJ44YUXIiKiRo0aqS+nS+LnP/95KpR6+umn48QTT4zHHnssNT/ZXnvtFYcffniRbbopVatWjRYtWpR4e7T99mT7+lYDMuBXv/pVNG3aNAYNGhQLFy6McePGxbhx44rctkaNGnHppZfGaaedlpHX3nvvveOUU06Jxx9/PCLWTSp4wAEHRI0aNeK2225LTSDWrVu3tHE8N+Xyyy+Pd955J2bOnBmfffZZPPDAA3HhhRdmpNbtkbbPXtp+23PNNdfEuHHjNvklcsuWLWPo0KFx8cUXx/z582Ps2LExduzYQtu1atUqHnjggahevXra+ubNm8ell14at912W+Tn58fQoUNj6NChqefr1q0b9957b1x99dXl+iK7UaNG8fjjj8evf/3r+Oabb+KVV16JV155pdB2++23X9xxxx1Ro0aNTR7rzTffjDfffHOzr9m4ceO44YYbSvVBPEm0fWHZ0vYVYXPvt7Io7ry/sZIOcdKkSRNfZmeQdt9+HHzwwXH44YfH66+/Hvn5+TFw4MB48skn0+7Aa9euXfzzn/+MgQMHxhtvvBHffPNNDB48uNhjXnfdddGsWbOM1vqb3/wmRo8eHd99913Mnj077r333rQ5E9abPHlynHDCCSU6Zu/evdOGM8sm2j57274oO+ywQ3Tv3j31Watt27apoXiLM2HChJg1a1ZErLv4rTR3xXbv3j123HHHWLx4cXzyyScxderU+PLLL1PPf/XVV2lzNJbELrvsEm+88Uap9sl22n77YTgpKMLPfvazeO211+KGG26Iww8/PJo1axY1atSIKlWqRIMGDaJLly5xySWXxOuvv56xLzLX++1vfxuNGjWKiIhvv/027r///rShJurWrRuDBg0q0bFq1qwZN998c+qD2gMPPBCTJ0/OaL3bG22fvbT9tmWnnXYq8o+7DXXq1CleeeWVuOyyy6JTp06x4447RpUqVWLHHXeM/fbbL2688cYYNWpUNGnSpMj9zznnnBgxYkT06NEjGjZsGFWrVo1mzZrFKaecEv/85z9TYxiX15577hkvvPBCXHfdddGlS5fYcccdo2rVqrHLLrvE4YcfHvfff3888sgjsdNOO5X62JUqVYpatWrFHnvsET179ow///nP8eqrr27TX2Jr+5LZHtu+IpTk/VYWRZ33SQ7tvn25+uqrU2HRp59+WmRI1KRJk7j//vvj2WefjTPOOCNat24dderUicqVK0edOnWiTZs2cdZZZ8Vzzz0XQ4YMyfiX2BERtWrVSvvS+fHHH09dzFISVatWjR133DFat24dp5xySowaNSpuvvnm7e6O3NLQ9tnb9kU56qijUo9LO6lzRMmHE1qvWrVqcfTRR6eWyzscKWWn7bcPOQUFBQUVXQQAAAAAAMDG3IkBAAAAAAAkkhADAAAAAABIJCEGAAAAAACQSEIMAAAAAAAgkYQYAAAAAABAIgkxAAAAAACARBJiAAAAAAAAiSTEAAAAAAAAEkmIAQAAAAAAJJIQA4owc+bMaNmyZbRs2TLuuuuuUu9/2GGHRcuWLaNfv34Zr219XZdddlnGj83Wsf79MWDAgLT1zz77bKp9x40bV0HVsaHyngtIPud7tjTn/OTR79mS9Pnk0efZ0vT75NHv2dL0+61PiAEAAAAAACSSEAMAAAAAAEikKhVdAGyP3njjjYougW1Q7969o3fv3hVdBlAKzveUlXP+tku/pyz0+W2XPk9Z6ffbLv2estLvtxx3YgAAAAAAAIkkxAAAAAAAABJJiAElsHr16nj88cfjxBNPjI4dO0bHjh3j5z//eTzwwAOxbNmyQtsfdthh0bJly+jXr1+RxysoKIjRo0fHmWeeGV26dIm2bdvGUUcdFbfeemssWbIkPvnkk2jZsmW0bNkyZs6cucm6CgoK4rnnnotTTz01fvrTn0b79u3j6KOPjjvuuCMWLlyYsZ8/KWbOnJn6f/nwww9jxYoV8eCDD8Zxxx0X7du3j/322y/OOOOMeOutt1L7rFy5Mh544IE47rjjYt99942f/vSncfbZZ8cHH3ywyddZs2ZNPPXUU/HLX/4yDjjggGjTpk1069Ytzj///BgzZkwUFBQUW+fSpUtj6NCh0adPn+jcuXP89Kc/jTPPPDPefPPNYvd79tlnUz/fuHHj0p7b3HsqImLcuHGp/Z999tm05wYMGBAtW7aMM888MyIiPv7447j44oujW7du0a5duzjyyCPjlltuicWLF6f2+eSTT+LCCy+MAw88MNq2bRs9evSIu+66q8j3fLZau3ZtjBkzJs4///zo1q1btGnTJvbff/8444wz4u9//3usXr06bftly5ZF27Zto2XLlnH11VcXecxvvvkm1Y5nnXVWkdv88MMP0apVq2jZsmUMGzYs4z9XNnO+Tw7nfOf8rUW/TwZ9Xp/fWvT55NDv9futRb9PDv1evy8rc2LAZixatCj69u0bkydPTlv/xRdfxBdffBH//Oc/Y/jw4dGgQYMSHW/16tVx+eWXx5gxY9LWT58+Pf72t7/FCy+8EL/73e82e5wVK1bEueeeG++8807a+v/85z8xZMiQ+Mc//hGPPvpo7LnnniWqa1szf/786NOnT0ydOjW1bvny5fHvf/873n///fjjH/8YRx55ZPzyl7+ML774IrXNihUr4t13343x48fH4MGD44gjjkg77uzZs+O8886LL7/8Mm39vHnz4s0334w333wzRo4cGYMHD4769esXquubb76Js846K7799tu09ePHj4/x48fHOeeck4kfv1weffTRuOWWW2Lt2rWpdf/73/9i6NChMXbs2HjqqafiH//4RwwaNCjy8/NT23zzzTfxwAMPxPvvvx/Dhg2LqlWrVkT5iTF37tz47W9/GxMnTkxbv3Dhwvj3v/8d//73v2PYsGHxwAMPxK677hoRETVr1oyf/vSn8d5778X48eOLPO6///3v1OOPP/441qxZE1WqpP+6fu+991Jtc8ghh2Twp8puzvfJ5Zxfds75xdPvk0mfLzt9vnj6fHLp92Wn3xdPv08u/b7ssrHfCzFgM5588smIiOjcuXP069cvmjZtGt98803cd999MWPGjJg2bVrcfPPNcdttt5XoeAMHDkz9smvVqlX88pe/jN133z1mzpwZjz32WHz66adx/fXXb/Y4r732WkRE7L333nHaaafFnnvuGbNmzYqhQ4fG5MmTY86cOXHNNdfEE088UcafPNkGDhwYixYtiiOPPDJ69+4dtWvXjnfffTceeuihWLt2bdx2223x7LPPxpdffhm/+MUv4phjjokddtgh3njjjRg6dGjk5+fHoEGD4tBDD43KlStHxLqr20899dSYOXNmVKlSJfr06ROHHXZYNGjQIObMmRNjxoyJMWPGxIQJE+L888+P4cOHp53w8/Ly4rTTTou5c+dGTk5O9O7dO4499tioUaNGfPTRR/HQQw/Fww8/HJUqVdxNcJ9//nn8+9//joYNG8Y555wTbdu2jXnz5sV9990XX331VfznP/+Jiy++OMaPHx9NmzaN8847L1q2bBmzZs1KbfPxxx/H888/HyeeeGKF/RwVbenSpXHuueemPkh16dIlTj755GjWrFnMmzcvnn/++Xjttddi6tSpccopp8Tzzz+f+lDcvXv3eO+99+Lbb7+NGTNmpAKO9TYMMZYtWxaTJ0+OfffdN22bd999NyIimjdvXmh/ys75Prmc88vGOX/z9Ptk0ufLRp/fPH0+ufT7stHvN0+/Ty79vmyytd8LMaAETj755Bg4cGDk5ORERETHjh3j8MMPj2OOOSbmz58fL7/8cgwaNCiqVatW7HFyc3PjmWeeiYiIbt26xf3335/aZ999942ePXvGJZdcEi+//HKJ6jr00ENj8ODBqWN07NgxjjrqqOjTp0989dVX8dFHH8W3334bu+yyS1l/9MRatGhRnH322XH55Zen1nXp0iWWLl0aw4cPjx9++CE++eSTuOqqq+KMM85IbdOpU6eoVKlSDBkyJGbNmhVfffVV7LPPPhERcdttt8XMmTOjWrVq8dBDD8V+++2X9ppHHnlk7LfffnHdddfFJ598EiNGjEjdxhcRcd9998XcuXMjYt0v45NPPjn1XMeOHaNHjx5x8sknx4IFC7bEf0mJLF68OBo0aBCjRo1Ke1906tQpDjvssFi9enW89957seuuu8bTTz8dO+64Y0Sse3927do1jjjiiFi6dGm8+eab29Qvu0x76KGHUgHG6aefXmhoqCOOOCIeffTRuPnmm2POnDkxaNCguOOOOyJi3Z0TN998c0Ssu5JjwxCioKAgJkyYEBERVatWjdWrV8eECRM2GWIcfPDBW+YHzGLO98nknF82zvklo98njz5fNvp8yejzyaTfl41+XzL6fTLp92WTrf3enBiwGTvuuGMMGDAg9ctuvXr16sXRRx8dERGrVq2KGTNmbPZYw4cPj4KCgqhWrVr8+c9/LvQLsnLlyjFo0KAib2fbWKVKleKPf/xjoWNUq1Yt+vTpk1re8La87Um9evXi4osvLrT+yCOPTD3eZZdd4vTTTy+0zYbD7/zvf/+LiHW/BJ5//vmIiOjXr1+hX3Tr9e3bNzp27BgRkXZFxNq1a1MfZjp16pT2i2693XbbLS699NLN/GRb3i9/+ctCH4IaN24c7du3Ty1fcMEFqV906zVo0CD1ZXpJ3u/bqzVr1sTw4cMjYt2dEFdccUWR25155pnRrVu3iIh46aWXYvbs2RERsccee8Tuu+8eEel3XUREfPXVV7FgwYKoVq1a6r384Ycfpm3z9ddfp4516KGHZuinIsL5Psmc88vOOb94+n0y6fNlp88XT59PLv2+7PT74un3yaXfl1029nshBmzGT3/606hRo0aRz+2xxx6px0uWLCn2OGvXro233347IiIOOuigaNSoUZHb1a5dO4499tjN1rX33ntHkyZNinxu/RekERHff//9Zo+1LWrfvn3ssMMOhdY3btw49bhr166FPqhERNoHivWTGU2YMCFWrlwZERH7779/sa+9/ovp6dOnx3fffRcREZMmTUpNnlRc+x177LFRvXr1Yo+/pW3qF/nG/3dFWf9/t3Tp0swXto2YNGlS/PDDDxER0atXr0LzVWyob9++ERGRn5+fNgdG9+7dI2JdiLHhhGLvv/9+RKx7f3fu3DkiIj766KO0cS7X34VRt27d1AcvMsP5Prmc88vOOb94+n0y6fNlp88XT59PLv2+7PT74un3yaXfl1029nvDScFm7Lzzzpt8bsOT1po1a4o9zrx582LRokUREdG6detit23btu1m69rUL7vS1rWt2tTtlBtexdCwYcPNbrP+C+TPP/88te78888vcR0zZ86MnXfeOf7zn/+k1rVs2XKT2++www7RokWLQpOKbU3NmjUrcv2G/y877bTTZrfJVhteCbO5vtquXbvU46+//jr1uHv37jFs2LBYsGBBfPXVV6n3zPo7M7p06RIdOnSIiHVjen7xxRfRqlWriIjUxG/dunUrNkCh9Jzvk8s5v+yc84un3yeTPl92+nzx9Pnk0u/LTr8vnn6fXPp92WVjv3cnBmxGrVq1SrTdhldTF2X+/Pmpx5u7tXBTJ+ktUde2qiQ///qJnUpifdpeWuuvithwPMSNb9fbWEnad0vK9P9dtln/wTVi8225fjLviPQre7p27Zq6Gmh9cLF27dr44IMPImLd1UJ777131K1bNyIiNU/GihUrUsNLmQ8j85zvk8s5v+yc84un3yeTPl92+nzx9Pnk0u/LTr8vnn6fXPp92WVjv3cJJ2wlq1atSj3e3C+hkvySKup2umyS6ZNxfn5+6vGjjz662V9Y620q/S7Olrp6fsMhh4qzvf0iq0ib64cbtsmG21avXj26du0aY8eOjfHjx8cZZ5wRkydPju+//z6qVasWHTp0iEqVKkWnTp3izTffjA8//DDOPPPMeP/992PlypVRqVKl1JBUJI/zfeY55xfmnJ8s+n1m6fOF6fPJos9nnn5fmH6fLPp95un3hen3mybEgK1kw6R+4cKFxW674ZXebB316tVLPa5Vq1bss88+pdp/w9v0Nte+5RnTsrgPQ3l5eWU+LiW34XtlwYIF0aJFi01uu+GVHBvuF7FuSKmxY8fGBx98EPn5+am7Lfbdd9/U7cNdu3aNN998Mz744IMoKChIzYex7777pt3lQbI43yefcz6Zpt8nmz5Ppunzyaffk2n6ffLp99s3w0nBVrLbbrulbveaMmVKsdtuOI4fW0fz5s1Tj9d/mbwp77zzTjz22GPxr3/9KzUR0oZfZH/22Web3Hft2rVpcyOU1PqUf/0kVUVZPxkVW9Zee+2Vejxp0qRit83NzU09/vGPf5z23CGHHBIR6z6k5Obmpg0ltV6XLl0iYt1tsV999VVqPoz1+5JMzvfJ55xPpun3yabPk2n6fPLp92Safp98+v32TYgBW0mlSpXioIMOioiI9957L+0K7Q2tWrUqXnrppa1ZGhGx//77p27HGzVqVNqtohvKz8+PP/7xj/GnP/0pLrnkktT6Nm3axI9+9KOIiHj++ec3eQvgW2+9lTY3QknVqVMnItb9QtvUsceNG1fq41J6rVu3Tl3h8dxzz6Xdsrqxp556KvV4v/32S3tul112SX3IGjduXHz88ccR8X/BRUTEPvvsk/Za//3vfyNCiJF0zvfJ55xPpun3yabPk2n6fPLp92Safp98+v32TYgBW9Fpp50WEetS16uvvjrWrFlTaJs///nPktcK0KRJkzjqqKMiImL69Olx4403FnmL3+233x4zZsyIiIgTTjghbTKlU045JSIivvzyy7jnnnsK7Ttv3rwYNGhQmepbf/X/woULi/xA9Morr8Rbb71VpmNTOtWqVYu+fftGRMTUqVPj9ttvL3K7Rx99NN57772IiDj88MNj1113LbTN+sm5R40aFYsXL46qVatGhw4dUs+vnxcjIuKJJ56IiIidd9459t5778z9QGwRzvfJ5pzPlqDfJ5c+z5agzyebfs+WoN8nm36/fTMnBmxFnTt3jt69e8ezzz4bb775ZvTt2zfOPPPM2H333eO7776Lv//976kvPdcz2dPWc9VVV8WECRNiwYIFMWrUqJg6dWr0798/dtttt5gzZ06q3SIiGjVqFL/73e/S9v/lL38Zo0ePjs8//zzuu++++PLLL+PEE0+MBg0axGeffRYPPvhgzJkzJ2rUqBHLly8vVW0/+9nP4tlnn42IiKuvvjq++eab6Nq1ayxbtixeffXVeOaZZ2KXXXaJ2bNnl3giKMruggsuiDfeeCOmTp0aQ4cOjSlTpkTfvn2jWbNmMW/evHj++efj1VdfjYiIhg0bxg033FDkcbp37x5Dhw5Nfcht165d7LDDDmnbdO3aNd54443ULafrgw+Szfk++ZzzyTT9Ptn0eTJNn08+/Z5M0++TT7/ffgkxYCv74x//GAsXLoyxY8fGZ599Fpdddlna87vttlvsv//+8eSTT0bEuqu+2ToaN24cw4YNi1//+tcxffr0mDhxYkycOLHQds2aNYsHH3yw0MTKVapUiaFDh8Z5550Xubm58frrr8frr7+ets0vfvGLmD9/fowdO7ZUtR144IFxyimnxIgRI2L58uUxePDgtOd33nnnGDJkSBx//PGlOi5lU6NGjXjkkUfiN7/5TeTm5sa///3v+Pe//11ou7333jsGDx6cNkHYhjp37hy1atVKjcG54VBS62287tBDD83AT8DW4HyfbM75bAn6fXLp82wJ+nyy6fdsCfp9sun32y8hBmxl1apViwcffDD++c9/xrPPPhtffPFFLF26NJo2bRo9e/aMc889Nx555JHU9jVr1qzAarNP8+bN48UXX4xnnnkmXn311fjyyy9jyZIlUaNGjWjRokUcddRRcfLJJ2+yXRo0aBAjRoyIF154IZ5++un4z3/+E6tWrYoWLVpEv379onfv3nHeeeeVqbbrrrsuunXrFiNHjozc3NxYvnx5NG3aNI488sg4++yzY8cddyzHT05pNW7cOJ588sl44YUX4sUXX4wpU6bEkiVLomHDhtGiRYs44YQTokePHsV+aK1atWoceOCBqbs2unbtWmibvffeO+rVqxdLliyJ6tWrF5pbg+Ryvk8+53wyTb9PNn2eTNPnk0+/J9P0++TT77dPOQVFDQ4GVKibbrophg0bFjVr1kxN9gvA9sf5HrKPfg/ZRZ+H7KPfQ+a5EwO2oj//+c+xYMGC2HfffePUU0/d5Hbvv/9+RES0bNlya5UGQAY530P20e8hu+jzkH30e6g4lSq6AMgmy5Yti3/+859x8803x/Tp04vc5u9//3t89dVXERFx2GGHbc3yAMgQ53vIPvo9ZBd9HrKPfg8Vx3BSsBXl5ubGSSedFAUFBbHTTjvFaaedFm3bto1atWrFnDlz4tVXX40xY8bE2rVro3nz5vHcc89F9erVK7psAErJ+R6yj34P2UWfh+yj30PFEWLAVjZixIj405/+FGvWrNnkNq1bt47BgwdHs2bNtmJlAGSS8z1kH/0esos+D9lHv4eKIcSACjBt2rR44okn4v3334+ZM2dGfn5+NGrUKPbcc8/42c9+Fsccc0xUq1atossEoJyc7yH76PeQXfR5yD76PWx9QgwAAAAAACCRTOwNAAAAAAAkkhADAAAAAABIJCEGAAAAAACQSEIMAAAAAAAgkYQYAAAAAABAIgkxAAAAAACARBJiAAAAAAAAiSTEAAAAAAAAEkmIAQAAAAAAJJIQAwAAAAAASCQhBgAAAAAAkEhCDAAAAAAAIJGEGAAAAAAAQCIJMQAAAAAAgEQSYgAAAAAAAIkkxAAAAAAAABJJiAEAAAAAACSSEAMAAAAAAEgkIQYAAAAAAJBIQgwAAAAAACCRhBgAAAAAAEAiCTEAAAAAAIBEEmIAAAAAAACJJMQAAAAAAAASSYjB/2vPjgUAAAAABvlbT2NHaQQAAAAAAEsSAwAAAAAAWJIYAAAAAADAksQAAAAAAACWAhX4p6Djm8aKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot interactions involving high affinity CRX sites in Ryan's library\n",
    "\n",
    "TF_TF_interactions_flattened = []\n",
    "for model in CDNRM_bootstrap_models:\n",
    "    theta_dict = model.get_theta(gauge=\"user\", p_lc = Frequencies)\n",
    "    TF_TF_interactions = np.transpose(theta_dict['theta_lclc'], axes = (1, 3, 0, 2))\n",
    "    TF_TF_interactions_flattened.append([])\n",
    "    for i in range(len(TF_TF_interactions)):\n",
    "        interactions = np.concatenate([TF_TF_interactions[i, 0].flatten(), TF_TF_interactions[0, i].flatten()])\n",
    "        TF_TF_interactions_flattened[-1].append(np.mean(interactions[np.logical_not(np.isnan(interactions))]))\n",
    "\n",
    "TF_TF_interactions_flattened = np.asarray(TF_TF_interactions_flattened)\n",
    "plt.boxplot(TF_TF_interactions_flattened)\n",
    "plt.xticks(x, [\"CRX\\nhigh\", \"CRX\\nmedium\", \"CRX\\nlow\", \"NeuroD1\\nhigh\", \"NeuroD1\\nmedium\", \"NRL\\nhigh\", \"NRL\\nmedium\", \"RORB\\nhigh\", \"RORB\\nmedium\", \"MAZ\\nhigh\", \"MAZ\\nmedium\"], fontsize = 7)\n",
    "plt.axhline(0, linestyle = \"--\", label = \"No interaction\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b86caf80",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}