{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4aaae2b",
   "metadata": {},
   "source": [
    "# Comparison of *apo* and *holo* CDR loops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5040c318",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The following notebook aims to quantify the movement of CDR domains in TCR variable regions between unbound (*apo*) and bound (*holo*) conformations. The *holo* conforomations in this case are TCRs bound to class I pMHC complexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e019f48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:13.471271Z",
     "iopub.status.busy": "2024-05-16T15:31:13.470840Z",
     "iopub.status.idle": "2024-05-16T15:31:37.943428Z",
     "shell.execute_reply": "2024-05-16T15:31:37.941998Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import itertools\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "427502e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.174923Z",
     "iopub.status.busy": "2024-05-16T15:31:38.174547Z",
     "iopub.status.idle": "2024-05-16T15:31:38.178669Z",
     "shell.execute_reply": "2024-05-16T15:31:38.177886Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_DIR = '../data/processed/apo-holo-tcr-pmhc-class-I-comparisons'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21142499",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.182703Z",
     "iopub.status.busy": "2024-05-16T15:31:38.182173Z",
     "iopub.status.idle": "2024-05-16T15:31:38.185901Z",
     "shell.execute_reply": "2024-05-16T15:31:38.185140Z"
    }
   },
   "outputs": [],
   "source": [
    "NOISE_LEVEL = 0.5 # Å"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1dabb90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.189577Z",
     "iopub.status.busy": "2024-05-16T15:31:38.189253Z",
     "iopub.status.idle": "2024-05-16T15:31:38.283301Z",
     "shell.execute_reply": "2024-05-16T15:31:38.282439Z"
    }
   },
   "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>file_name</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>structure_type</th>\n",
       "      <th>state</th>\n",
       "      <th>alpha_chain</th>\n",
       "      <th>beta_chain</th>\n",
       "      <th>antigen_chain</th>\n",
       "      <th>mhc_chain1</th>\n",
       "      <th>mhc_chain2</th>\n",
       "      <th>cdr_sequences_collated</th>\n",
       "      <th>peptide_sequence</th>\n",
       "      <th>mhc_slug</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1ao7_D-E-C-A-B_tcr_pmhc</th>\n",
       "      <td>1ao7_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>1ao7</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>DRGSQS-IYSNGD-AVTTDSWGKLQ-MNHEY-SVGAGI-ASRPGLA...</td>\n",
       "      <td>LLFGYPVYV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1b0g_C-A-B_pmhc</th>\n",
       "      <td>1b0g_C-A-B_pmhc.pdb</td>\n",
       "      <td>1b0g</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ALWGFFPVL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1b0g_F-D-E_pmhc</th>\n",
       "      <td>1b0g_F-D-E_pmhc.pdb</td>\n",
       "      <td>1b0g</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ALWGFFPVL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1bd2_D-E-C-A-B_tcr_pmhc</th>\n",
       "      <td>1bd2_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>1bd2</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NSMFDY-ISSIKDK-AAMEGAQKLV-MNHEY-SVGAGI-ASSYPGG...</td>\n",
       "      <td>LLFGYPVYV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1bii_P-A-B_pmhc</th>\n",
       "      <td>1bii_P-A-B_pmhc.pdb</td>\n",
       "      <td>1bii</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RGPGRAFVTI</td>\n",
       "      <td>h2_dd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7rtd_C-A-B_pmhc</th>\n",
       "      <td>7rtd_C-A-B_pmhc.pdb</td>\n",
       "      <td>7rtd</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YLQPRTFLL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7rtr_D-E-C-A-B_tcr_pmhc</th>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>7rtr</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY</td>\n",
       "      <td>YLQPRTFLL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8gvb_A-B-P-H-L_tcr_pmhc</th>\n",
       "      <td>8gvb_A-B-P-H-L_tcr_pmhc.pdb</td>\n",
       "      <td>8gvb</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>P</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...</td>\n",
       "      <td>RYPLTFGW</td>\n",
       "      <td>hla_a_24_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8gvg_A-B-P-H-L_tcr_pmhc</th>\n",
       "      <td>8gvg_A-B-P-H-L_tcr_pmhc.pdb</td>\n",
       "      <td>8gvg</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>P</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...</td>\n",
       "      <td>RFPLTFGW</td>\n",
       "      <td>hla_a_24_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8gvi_A-B-P-H-L_tcr_pmhc</th>\n",
       "      <td>8gvi_A-B-P-H-L_tcr_pmhc.pdb</td>\n",
       "      <td>8gvi</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>P</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>YGATPY-YFSGDTLV-AVVFTGGGNKLT-SEHNR-FQNEAQ-ASSL...</td>\n",
       "      <td>RYPLTFGW</td>\n",
       "      <td>hla_a_24_02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>391 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           file_name pdb_id structure_type  \\\n",
       "id                                                                           \n",
       "1ao7_D-E-C-A-B_tcr_pmhc  1ao7_D-E-C-A-B_tcr_pmhc.pdb   1ao7       tcr_pmhc   \n",
       "1b0g_C-A-B_pmhc                  1b0g_C-A-B_pmhc.pdb   1b0g           pmhc   \n",
       "1b0g_F-D-E_pmhc                  1b0g_F-D-E_pmhc.pdb   1b0g           pmhc   \n",
       "1bd2_D-E-C-A-B_tcr_pmhc  1bd2_D-E-C-A-B_tcr_pmhc.pdb   1bd2       tcr_pmhc   \n",
       "1bii_P-A-B_pmhc                  1bii_P-A-B_pmhc.pdb   1bii           pmhc   \n",
       "...                                              ...    ...            ...   \n",
       "7rtd_C-A-B_pmhc                  7rtd_C-A-B_pmhc.pdb   7rtd           pmhc   \n",
       "7rtr_D-E-C-A-B_tcr_pmhc  7rtr_D-E-C-A-B_tcr_pmhc.pdb   7rtr       tcr_pmhc   \n",
       "8gvb_A-B-P-H-L_tcr_pmhc  8gvb_A-B-P-H-L_tcr_pmhc.pdb   8gvb       tcr_pmhc   \n",
       "8gvg_A-B-P-H-L_tcr_pmhc  8gvg_A-B-P-H-L_tcr_pmhc.pdb   8gvg       tcr_pmhc   \n",
       "8gvi_A-B-P-H-L_tcr_pmhc  8gvi_A-B-P-H-L_tcr_pmhc.pdb   8gvi       tcr_pmhc   \n",
       "\n",
       "                        state alpha_chain beta_chain antigen_chain mhc_chain1  \\\n",
       "id                                                                              \n",
       "1ao7_D-E-C-A-B_tcr_pmhc  holo           D          E             C          A   \n",
       "1b0g_C-A-B_pmhc           apo         NaN        NaN             C          A   \n",
       "1b0g_F-D-E_pmhc           apo         NaN        NaN             F          D   \n",
       "1bd2_D-E-C-A-B_tcr_pmhc  holo           D          E             C          A   \n",
       "1bii_P-A-B_pmhc           apo         NaN        NaN             P          A   \n",
       "...                       ...         ...        ...           ...        ...   \n",
       "7rtd_C-A-B_pmhc           apo         NaN        NaN             C          A   \n",
       "7rtr_D-E-C-A-B_tcr_pmhc  holo           D          E             C          A   \n",
       "8gvb_A-B-P-H-L_tcr_pmhc  holo           A          B             P          H   \n",
       "8gvg_A-B-P-H-L_tcr_pmhc  holo           A          B             P          H   \n",
       "8gvi_A-B-P-H-L_tcr_pmhc  holo           A          B             P          H   \n",
       "\n",
       "                        mhc_chain2  \\\n",
       "id                                   \n",
       "1ao7_D-E-C-A-B_tcr_pmhc          B   \n",
       "1b0g_C-A-B_pmhc                  B   \n",
       "1b0g_F-D-E_pmhc                  E   \n",
       "1bd2_D-E-C-A-B_tcr_pmhc          B   \n",
       "1bii_P-A-B_pmhc                  B   \n",
       "...                            ...   \n",
       "7rtd_C-A-B_pmhc                  B   \n",
       "7rtr_D-E-C-A-B_tcr_pmhc          B   \n",
       "8gvb_A-B-P-H-L_tcr_pmhc          L   \n",
       "8gvg_A-B-P-H-L_tcr_pmhc          L   \n",
       "8gvi_A-B-P-H-L_tcr_pmhc          L   \n",
       "\n",
       "                                                    cdr_sequences_collated  \\\n",
       "id                                                                           \n",
       "1ao7_D-E-C-A-B_tcr_pmhc  DRGSQS-IYSNGD-AVTTDSWGKLQ-MNHEY-SVGAGI-ASRPGLA...   \n",
       "1b0g_C-A-B_pmhc                                                        NaN   \n",
       "1b0g_F-D-E_pmhc                                                        NaN   \n",
       "1bd2_D-E-C-A-B_tcr_pmhc  NSMFDY-ISSIKDK-AAMEGAQKLV-MNHEY-SVGAGI-ASSYPGG...   \n",
       "1bii_P-A-B_pmhc                                                        NaN   \n",
       "...                                                                    ...   \n",
       "7rtd_C-A-B_pmhc                                                        NaN   \n",
       "7rtr_D-E-C-A-B_tcr_pmhc     DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY   \n",
       "8gvb_A-B-P-H-L_tcr_pmhc  YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...   \n",
       "8gvg_A-B-P-H-L_tcr_pmhc  YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...   \n",
       "8gvi_A-B-P-H-L_tcr_pmhc  YGATPY-YFSGDTLV-AVVFTGGGNKLT-SEHNR-FQNEAQ-ASSL...   \n",
       "\n",
       "                        peptide_sequence     mhc_slug  \n",
       "id                                                     \n",
       "1ao7_D-E-C-A-B_tcr_pmhc        LLFGYPVYV  hla_a_02_01  \n",
       "1b0g_C-A-B_pmhc                ALWGFFPVL  hla_a_02_01  \n",
       "1b0g_F-D-E_pmhc                ALWGFFPVL  hla_a_02_01  \n",
       "1bd2_D-E-C-A-B_tcr_pmhc        LLFGYPVYV  hla_a_02_01  \n",
       "1bii_P-A-B_pmhc               RGPGRAFVTI        h2_dd  \n",
       "...                                  ...          ...  \n",
       "7rtd_C-A-B_pmhc                YLQPRTFLL  hla_a_02_01  \n",
       "7rtr_D-E-C-A-B_tcr_pmhc        YLQPRTFLL  hla_a_02_01  \n",
       "8gvb_A-B-P-H-L_tcr_pmhc         RYPLTFGW  hla_a_24_02  \n",
       "8gvg_A-B-P-H-L_tcr_pmhc         RFPLTFGW  hla_a_24_02  \n",
       "8gvi_A-B-P-H-L_tcr_pmhc         RYPLTFGW  hla_a_24_02  \n",
       "\n",
       "[391 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I/apo_holo_summary.csv')\n",
    "\n",
    "ids = summary_df['file_name'].str.replace('.pdb', '', regex=True)\n",
    "ids.name = 'id'\n",
    "summary_df = summary_df.set_index(ids)\n",
    "\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb58b66",
   "metadata": {},
   "source": [
    "## Calculating RMSD between conformations\n",
    "\n",
    "RMSD is used to compute the difference between all states of the same TCR.\n",
    "Comparions between the different *apo* forms and the difference*holo* forms are also done so that these can be used as a control for the differences in *apo* to *holo* comparisons.\n",
    "This process is done two different ways.\n",
    "The first, termed **framework alignment**, computes the comparison between TCRs after aligning on the framework region of the proteins.\n",
    "This method captures the \"global\" conformational changes that these loops undergo including any combined rotations and deformations.\n",
    "The second method, termed **loop alignment**, does the same calculations as the first method, but after aligning the two loops being compared on their backbones.\n",
    "This method captures the deformation aspects of loops regardless of any hinging from the framework region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffca5716",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.289406Z",
     "iopub.status.busy": "2024-05-16T15:31:38.289042Z",
     "iopub.status.idle": "2024-05-16T15:31:38.295940Z",
     "shell.execute_reply": "2024-05-16T15:31:38.295145Z"
    }
   },
   "outputs": [],
   "source": [
    "def categorize_movement(rmsd: float) -> str:\n",
    "    if rmsd < NOISE_LEVEL:\n",
    "        return f'Little Movement (<{NOISE_LEVEL} Å)'\n",
    "\n",
    "    if NOISE_LEVEL <= rmsd < 1.0:\n",
    "        return f'Some Movement ({NOISE_LEVEL} to 1.0 Å)'\n",
    "\n",
    "    if 1.0 <= rmsd < 2.0:\n",
    "        return 'Movement (1.0 to 2.0 Å)'\n",
    "\n",
    "    if 2.0 <= rmsd < 4.0:\n",
    "        return 'Large Movement (2.0 to 4.0 Å)'\n",
    "\n",
    "    if 4.0 <= rmsd:\n",
    "        return 'Significant Movement (>4.0 Å)'\n",
    "\n",
    "\n",
    "movement_order = pd.CategoricalDtype(categories=[f'Little Movement (<{NOISE_LEVEL} Å)',\n",
    "                                                 f'Some Movement ({NOISE_LEVEL} to 1.0 Å)',\n",
    "                                                 'Movement (1.0 to 2.0 Å)',\n",
    "                                                 'Large Movement (2.0 to 4.0 Å)',\n",
    "                                                 'Significant Movement (>4.0 Å)'], ordered=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579bea54-4fe0-4215-9ce9-560bb734147f",
   "metadata": {},
   "source": [
    "### Framework Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6af0dcf7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.299953Z",
     "iopub.status.busy": "2024-05-16T15:31:38.299446Z",
     "iopub.status.idle": "2024-05-16T15:31:38.325084Z",
     "shell.execute_reply": "2024-05-16T15:31:38.324252Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework = pd.read_csv(os.path.join(DATA_DIR, 'rmsd_cdr_fw_align_results.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd52a987",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.329742Z",
     "iopub.status.busy": "2024-05-16T15:31:38.329136Z",
     "iopub.status.idle": "2024-05-16T15:31:38.339657Z",
     "shell.execute_reply": "2024-05-16T15:31:38.338775Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework = results_framework.merge(\n",
    "    summary_df[['cdr_sequences_collated', 'peptide_sequence', 'mhc_slug']],\n",
    "    how='left',\n",
    "    left_on='complex_id',\n",
    "    right_index=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "973ecfd5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.343446Z",
     "iopub.status.busy": "2024-05-16T15:31:38.343110Z",
     "iopub.status.idle": "2024-05-16T15:31:38.389399Z",
     "shell.execute_reply": "2024-05-16T15:31:38.388393Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework_holo_holo = pd.read_csv(os.path.join(DATA_DIR, 'rmsd_cdr_fw_align_holo.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "345662a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.393768Z",
     "iopub.status.busy": "2024-05-16T15:31:38.393398Z",
     "iopub.status.idle": "2024-05-16T15:31:38.399598Z",
     "shell.execute_reply": "2024-05-16T15:31:38.398842Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework_holo_holo['cdr_sequences_collated'] = None\n",
    "\n",
    "results_framework_holo_holo['mhc_slug'] = None\n",
    "results_framework_holo_holo['peptide_sequence'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad7f4501",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.403201Z",
     "iopub.status.busy": "2024-05-16T15:31:38.402868Z",
     "iopub.status.idle": "2024-05-16T15:31:38.421425Z",
     "shell.execute_reply": "2024-05-16T15:31:38.420558Z"
    }
   },
   "outputs": [],
   "source": [
    "cdr_pattern = r'[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+'\n",
    "cdr_complex_ids = results_framework_holo_holo['complex_id'].str.contains(cdr_pattern)\n",
    "\n",
    "holo_cdr_sequences_collated = results_framework_holo_holo[cdr_complex_ids]['complex_id']\n",
    "results_framework_holo_holo.loc[cdr_complex_ids, 'cdr_sequences_collated'] = holo_cdr_sequences_collated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f98e32b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.425077Z",
     "iopub.status.busy": "2024-05-16T15:31:38.424726Z",
     "iopub.status.idle": "2024-05-16T15:31:38.432197Z",
     "shell.execute_reply": "2024-05-16T15:31:38.431468Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework_holo_holo = results_framework_holo_holo.query('cdr_sequences_collated.notnull()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d025fcc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.435793Z",
     "iopub.status.busy": "2024-05-16T15:31:38.435464Z",
     "iopub.status.idle": "2024-05-16T15:31:38.466857Z",
     "shell.execute_reply": "2024-05-16T15:31:38.466013Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework = pd.concat([results_framework, results_framework_holo_holo])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22fb04d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.470705Z",
     "iopub.status.busy": "2024-05-16T15:31:38.470367Z",
     "iopub.status.idle": "2024-05-16T15:31:38.490994Z",
     "shell.execute_reply": "2024-05-16T15:31:38.490182Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework = results_framework.merge(\n",
    "    summary_df[['file_name','pdb_id', 'structure_type', 'state', 'alpha_chain', 'beta_chain', 'antigen_chain', 'mhc_chain1', 'mhc_chain2']],\n",
    "    how='left',\n",
    "    left_on='structure_x_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    summary_df[['file_name', 'pdb_id', 'structure_type', 'state', 'alpha_chain', 'beta_chain', 'antigen_chain', 'mhc_chain1', 'mhc_chain2']],\n",
    "    how='left',\n",
    "    left_on='structure_y_name',\n",
    "    right_on='file_name',\n",
    "    suffixes=('_x', '_y')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f4c436a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.495248Z",
     "iopub.status.busy": "2024-05-16T15:31:38.494883Z",
     "iopub.status.idle": "2024-05-16T15:31:38.505401Z",
     "shell.execute_reply": "2024-05-16T15:31:38.504645Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework['comparison'] = results_framework['state_x'] + '-' + results_framework['state_y']\n",
    "results_framework['comparison'] = results_framework['comparison'].map(lambda entry: 'apo-holo' if entry == 'holo-apo' else entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47e09fa0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.509094Z",
     "iopub.status.busy": "2024-05-16T15:31:38.508744Z",
     "iopub.status.idle": "2024-05-16T15:31:38.712477Z",
     "shell.execute_reply": "2024-05-16T15:31:38.711491Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework['structure_comparison'] = results_framework.apply(\n",
    "    lambda row: '-'.join(sorted([row.structure_x_name, row.structure_y_name])),\n",
    "    axis='columns',\n",
    ")\n",
    "results_framework = results_framework.drop_duplicates(['structure_comparison', 'chain_type', 'cdr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be44bfab-70b4-4a67-b69f-1196e985754d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.717290Z",
     "iopub.status.busy": "2024-05-16T15:31:38.716907Z",
     "iopub.status.idle": "2024-05-16T15:31:38.728611Z",
     "shell.execute_reply": "2024-05-16T15:31:38.727699Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework = results_framework.groupby(['cdr_sequences_collated', 'comparison', 'cdr', 'chain_type'])['rmsd'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "94bde298",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.732303Z",
     "iopub.status.busy": "2024-05-16T15:31:38.731963Z",
     "iopub.status.idle": "2024-05-16T15:31:38.738062Z",
     "shell.execute_reply": "2024-05-16T15:31:38.737330Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework['movement'] = results_framework['rmsd'].map(categorize_movement).astype(movement_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d6ea898-bec5-45b0-9200-7fd199900991",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.741617Z",
     "iopub.status.busy": "2024-05-16T15:31:38.741281Z",
     "iopub.status.idle": "2024-05-16T15:31:38.758672Z",
     "shell.execute_reply": "2024-05-16T15:31:38.757961Z"
    }
   },
   "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>cdr_sequences_collated</th>\n",
       "      <th>comparison</th>\n",
       "      <th>cdr</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>rmsd</th>\n",
       "      <th>movement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1.267393</td>\n",
       "      <td>Movement (1.0 to 2.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>1</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.372470</td>\n",
       "      <td>Little Movement (&lt;0.5 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>0.736941</td>\n",
       "      <td>Some Movement (0.5 to 1.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.909569</td>\n",
       "      <td>Some Movement (0.5 to 1.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>3</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2.747458</td>\n",
       "      <td>Large Movement (2.0 to 4.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>1</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.646786</td>\n",
       "      <td>Some Movement (0.5 to 1.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2.650761</td>\n",
       "      <td>Large Movement (2.0 to 4.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.786869</td>\n",
       "      <td>Some Movement (0.5 to 1.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>3</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1.956881</td>\n",
       "      <td>Movement (1.0 to 2.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>3</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3.343748</td>\n",
       "      <td>Large Movement (2.0 to 4.0 Å)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                cdr_sequences_collated comparison  cdr  \\\n",
       "0    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    1   \n",
       "1    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    1   \n",
       "2    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    2   \n",
       "3    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    2   \n",
       "4    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    3   \n",
       "..                                                 ...        ...  ...   \n",
       "366  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    1   \n",
       "367  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    2   \n",
       "368  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    2   \n",
       "369  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    3   \n",
       "370  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    3   \n",
       "\n",
       "      chain_type      rmsd                       movement  \n",
       "0    alpha_chain  1.267393        Movement (1.0 to 2.0 Å)  \n",
       "1     beta_chain  0.372470       Little Movement (<0.5 Å)  \n",
       "2    alpha_chain  0.736941   Some Movement (0.5 to 1.0 Å)  \n",
       "3     beta_chain  0.909569   Some Movement (0.5 to 1.0 Å)  \n",
       "4    alpha_chain  2.747458  Large Movement (2.0 to 4.0 Å)  \n",
       "..           ...       ...                            ...  \n",
       "366   beta_chain  0.646786   Some Movement (0.5 to 1.0 Å)  \n",
       "367  alpha_chain  2.650761  Large Movement (2.0 to 4.0 Å)  \n",
       "368   beta_chain  0.786869   Some Movement (0.5 to 1.0 Å)  \n",
       "369  alpha_chain  1.956881        Movement (1.0 to 2.0 Å)  \n",
       "370   beta_chain  3.343748  Large Movement (2.0 to 4.0 Å)  \n",
       "\n",
       "[371 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_framework"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d479bcba",
   "metadata": {},
   "source": [
    "### Loop Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "52e78d76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.762389Z",
     "iopub.status.busy": "2024-05-16T15:31:38.762055Z",
     "iopub.status.idle": "2024-05-16T15:31:38.801865Z",
     "shell.execute_reply": "2024-05-16T15:31:38.800800Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop = pd.read_csv(os.path.join(DATA_DIR, 'rmsd_cdr_loop_align_results.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3f0454e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.806506Z",
     "iopub.status.busy": "2024-05-16T15:31:38.806122Z",
     "iopub.status.idle": "2024-05-16T15:31:38.814578Z",
     "shell.execute_reply": "2024-05-16T15:31:38.813682Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop= results_loop.merge(\n",
    "    summary_df[['cdr_sequences_collated', 'peptide_sequence', 'mhc_slug']],\n",
    "    how='left',\n",
    "    left_on='complex_id',\n",
    "    right_index=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2021fad8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.818875Z",
     "iopub.status.busy": "2024-05-16T15:31:38.818456Z",
     "iopub.status.idle": "2024-05-16T15:31:38.872698Z",
     "shell.execute_reply": "2024-05-16T15:31:38.871850Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop_holo_holo = pd.read_csv(os.path.join(DATA_DIR, 'rmsd_cdr_loop_align_holo.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "404e7adf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.877525Z",
     "iopub.status.busy": "2024-05-16T15:31:38.877137Z",
     "iopub.status.idle": "2024-05-16T15:31:38.883073Z",
     "shell.execute_reply": "2024-05-16T15:31:38.882232Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop_holo_holo['cdr_sequences_collated'] = None\n",
    "\n",
    "results_loop_holo_holo['mhc_slug'] = None\n",
    "results_loop_holo_holo['peptide_sequence'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3594e58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.887219Z",
     "iopub.status.busy": "2024-05-16T15:31:38.886530Z",
     "iopub.status.idle": "2024-05-16T15:31:38.904403Z",
     "shell.execute_reply": "2024-05-16T15:31:38.903622Z"
    }
   },
   "outputs": [],
   "source": [
    "cdr_pattern = r'[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+'\n",
    "cdr_complex_ids = results_loop_holo_holo['complex_id'].str.contains(cdr_pattern)\n",
    "\n",
    "holo_cdr_sequences_collated = results_loop_holo_holo[cdr_complex_ids]['complex_id']\n",
    "results_loop_holo_holo.loc[cdr_complex_ids, 'cdr_sequences_collated'] = holo_cdr_sequences_collated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8afdde56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.908435Z",
     "iopub.status.busy": "2024-05-16T15:31:38.908063Z",
     "iopub.status.idle": "2024-05-16T15:31:38.915403Z",
     "shell.execute_reply": "2024-05-16T15:31:38.914667Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop_holo_holo = results_loop_holo_holo.query('cdr_sequences_collated.notnull()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b145a088",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.918987Z",
     "iopub.status.busy": "2024-05-16T15:31:38.918631Z",
     "iopub.status.idle": "2024-05-16T15:31:38.950210Z",
     "shell.execute_reply": "2024-05-16T15:31:38.949427Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop = pd.concat([results_loop, results_loop_holo_holo])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d75b3ffc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.954476Z",
     "iopub.status.busy": "2024-05-16T15:31:38.954098Z",
     "iopub.status.idle": "2024-05-16T15:31:38.974747Z",
     "shell.execute_reply": "2024-05-16T15:31:38.973932Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop = results_loop.merge(\n",
    "    summary_df[['file_name', 'pdb_id', 'structure_type', 'state', 'alpha_chain', 'beta_chain', 'antigen_chain', 'mhc_chain1', 'mhc_chain2']],\n",
    "    how='left',\n",
    "    left_on='structure_x_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    summary_df[['file_name', 'pdb_id', 'structure_type', 'state', 'alpha_chain', 'beta_chain', 'antigen_chain', 'mhc_chain1', 'mhc_chain2']],\n",
    "    how='left',\n",
    "    left_on='structure_y_name',\n",
    "    right_on='file_name',\n",
    "    suffixes=('_x', '_y')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe62bbc1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.979028Z",
     "iopub.status.busy": "2024-05-16T15:31:38.978661Z",
     "iopub.status.idle": "2024-05-16T15:31:38.988955Z",
     "shell.execute_reply": "2024-05-16T15:31:38.988175Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop['comparison'] = results_loop['state_x'] + '-' + results_loop['state_y']\n",
    "results_loop['comparison'] = results_loop['comparison'].map(lambda entry: 'apo-holo' if entry == 'holo-apo' else entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc2d4034",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:38.993081Z",
     "iopub.status.busy": "2024-05-16T15:31:38.992779Z",
     "iopub.status.idle": "2024-05-16T15:31:39.195761Z",
     "shell.execute_reply": "2024-05-16T15:31:39.194848Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop['structure_comparison'] = results_loop.apply(\n",
    "    lambda row: '-'.join(sorted([row.structure_x_name, row.structure_y_name])),\n",
    "    axis='columns',\n",
    ")\n",
    "results_loop = results_loop.drop_duplicates(['structure_comparison', 'chain_type', 'cdr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a756948",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:39.200069Z",
     "iopub.status.busy": "2024-05-16T15:31:39.199699Z",
     "iopub.status.idle": "2024-05-16T15:31:39.210486Z",
     "shell.execute_reply": "2024-05-16T15:31:39.209580Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop = results_loop.groupby(['cdr_sequences_collated', 'comparison', 'cdr', 'chain_type'])['rmsd'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7292ca60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:39.214225Z",
     "iopub.status.busy": "2024-05-16T15:31:39.213945Z",
     "iopub.status.idle": "2024-05-16T15:31:39.219637Z",
     "shell.execute_reply": "2024-05-16T15:31:39.218924Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop['movement'] = results_loop['rmsd'].map(categorize_movement).astype(movement_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa5b6fd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:39.223121Z",
     "iopub.status.busy": "2024-05-16T15:31:39.222800Z",
     "iopub.status.idle": "2024-05-16T15:31:39.239421Z",
     "shell.execute_reply": "2024-05-16T15:31:39.238520Z"
    }
   },
   "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>cdr_sequences_collated</th>\n",
       "      <th>comparison</th>\n",
       "      <th>cdr</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>rmsd</th>\n",
       "      <th>movement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>0.773921</td>\n",
       "      <td>Some Movement (0.5 to 1.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>1</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.233623</td>\n",
       "      <td>Little Movement (&lt;0.5 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>0.438770</td>\n",
       "      <td>Little Movement (&lt;0.5 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.777213</td>\n",
       "      <td>Some Movement (0.5 to 1.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>3</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1.235649</td>\n",
       "      <td>Movement (1.0 to 2.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>1</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.113593</td>\n",
       "      <td>Little Movement (&lt;0.5 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1.082823</td>\n",
       "      <td>Movement (1.0 to 2.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>2</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>0.201656</td>\n",
       "      <td>Little Movement (&lt;0.5 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>3</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1.475462</td>\n",
       "      <td>Movement (1.0 to 2.0 Å)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>3</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2.503307</td>\n",
       "      <td>Large Movement (2.0 to 4.0 Å)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                cdr_sequences_collated comparison  cdr  \\\n",
       "0    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    1   \n",
       "1    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    1   \n",
       "2    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    2   \n",
       "3    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    2   \n",
       "4    ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo    3   \n",
       "..                                                 ...        ...  ...   \n",
       "366  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    1   \n",
       "367  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    2   \n",
       "368  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    2   \n",
       "369  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    3   \n",
       "370  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo    3   \n",
       "\n",
       "      chain_type      rmsd                       movement  \n",
       "0    alpha_chain  0.773921   Some Movement (0.5 to 1.0 Å)  \n",
       "1     beta_chain  0.233623       Little Movement (<0.5 Å)  \n",
       "2    alpha_chain  0.438770       Little Movement (<0.5 Å)  \n",
       "3     beta_chain  0.777213   Some Movement (0.5 to 1.0 Å)  \n",
       "4    alpha_chain  1.235649        Movement (1.0 to 2.0 Å)  \n",
       "..           ...       ...                            ...  \n",
       "366   beta_chain  0.113593       Little Movement (<0.5 Å)  \n",
       "367  alpha_chain  1.082823        Movement (1.0 to 2.0 Å)  \n",
       "368   beta_chain  0.201656       Little Movement (<0.5 Å)  \n",
       "369  alpha_chain  1.475462        Movement (1.0 to 2.0 Å)  \n",
       "370   beta_chain  2.503307  Large Movement (2.0 to 4.0 Å)  \n",
       "\n",
       "[371 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_loop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b15d924",
   "metadata": {},
   "source": [
    "## Visualizing and analysing the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3f2426",
   "metadata": {},
   "source": [
    "### Measuring the distance between apo and holo confomations\n",
    "\n",
    "Since RMSD was calculated for all pairings of the same TCR, the apo-holo (or holo-apo) comparisons were selected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466a5e86",
   "metadata": {},
   "source": [
    "#### Framework Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3593114b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:39.243509Z",
     "iopub.status.busy": "2024-05-16T15:31:39.243180Z",
     "iopub.status.idle": "2024-05-16T15:31:39.249012Z",
     "shell.execute_reply": "2024-05-16T15:31:39.248269Z"
    }
   },
   "outputs": [],
   "source": [
    "results_framework['chain_type_formatted'] = results_framework['chain_type'].str.replace('_', ' ').str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f171cb7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:39.252505Z",
     "iopub.status.busy": "2024-05-16T15:31:39.252170Z",
     "iopub.status.idle": "2024-05-16T15:31:39.865423Z",
     "shell.execute_reply": "2024-05-16T15:31:39.864544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chain_type</th>\n",
       "      <th>cdr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">alpha_chain</th>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.688538</td>\n",
       "      <td>1.118687</td>\n",
       "      <td>0.516638</td>\n",
       "      <td>0.892867</td>\n",
       "      <td>1.567689</td>\n",
       "      <td>2.080515</td>\n",
       "      <td>5.527264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.327793</td>\n",
       "      <td>0.721653</td>\n",
       "      <td>0.496448</td>\n",
       "      <td>0.861526</td>\n",
       "      <td>1.062559</td>\n",
       "      <td>1.427239</td>\n",
       "      <td>3.075190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2.378904</td>\n",
       "      <td>1.668149</td>\n",
       "      <td>0.545553</td>\n",
       "      <td>1.420477</td>\n",
       "      <td>1.851111</td>\n",
       "      <td>2.925171</td>\n",
       "      <td>7.818020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">beta_chain</th>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.823919</td>\n",
       "      <td>0.448008</td>\n",
       "      <td>0.322518</td>\n",
       "      <td>0.473089</td>\n",
       "      <td>0.746551</td>\n",
       "      <td>0.988184</td>\n",
       "      <td>2.121011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.911582</td>\n",
       "      <td>0.769147</td>\n",
       "      <td>0.311457</td>\n",
       "      <td>0.452917</td>\n",
       "      <td>0.779960</td>\n",
       "      <td>0.959763</td>\n",
       "      <td>3.928769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.498268</td>\n",
       "      <td>0.976070</td>\n",
       "      <td>0.451473</td>\n",
       "      <td>0.894738</td>\n",
       "      <td>1.051407</td>\n",
       "      <td>1.986540</td>\n",
       "      <td>3.849010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count      mean       std       min       25%       50%  \\\n",
       "chain_type  cdr                                                            \n",
       "alpha_chain 1     22.0  1.688538  1.118687  0.516638  0.892867  1.567689   \n",
       "            2     21.0  1.327793  0.721653  0.496448  0.861526  1.062559   \n",
       "            3     22.0  2.378904  1.668149  0.545553  1.420477  1.851111   \n",
       "beta_chain  1     22.0  0.823919  0.448008  0.322518  0.473089  0.746551   \n",
       "            2     22.0  0.911582  0.769147  0.311457  0.452917  0.779960   \n",
       "            3     22.0  1.498268  0.976070  0.451473  0.894738  1.051407   \n",
       "\n",
       "                      75%       max  \n",
       "chain_type  cdr                      \n",
       "alpha_chain 1    2.080515  5.527264  \n",
       "            2    1.427239  3.075190  \n",
       "            3    2.925171  7.818020  \n",
       "beta_chain  1    0.988184  2.121011  \n",
       "            2    0.959763  3.928769  \n",
       "            3    1.986540  3.849010  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.boxplot(data=results_framework.query(\"comparison == 'apo-holo'\"), y='rmsd', x='cdr', hue='chain_type_formatted')\n",
    "\n",
    "x = np.linspace(-0.75, 2.75)\n",
    "y = np.repeat(NOISE_LEVEL, len(x))\n",
    "\n",
    "plt.plot(x, y, '--r', label=f'Noise Level ({NOISE_LEVEL: .2f} Å)')\n",
    "plt.xlim(-0.75, 2.75)\n",
    "\n",
    "plt.legend(title='Chain Type')\n",
    "plt.xlabel('CDR')\n",
    "plt.ylabel('RMSD (Å)')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "results_framework.query(\"comparison == 'apo-holo'\").groupby(['chain_type', 'cdr'])['rmsd'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d46c0966",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:39.869430Z",
     "iopub.status.busy": "2024-05-16T15:31:39.869036Z",
     "iopub.status.idle": "2024-05-16T15:31:42.327884Z",
     "shell.execute_reply": "2024-05-16T15:31:42.327040Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7f4ebb42e0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(results_framework.query(\"comparison == 'apo-holo'\"),\n",
    "            x='rmsd', col='cdr', row='chain_type_formatted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c222f064-c425-4221-8bef-dc393de40cf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:42.333560Z",
     "iopub.status.busy": "2024-05-16T15:31:42.332952Z",
     "iopub.status.idle": "2024-05-16T15:31:42.345774Z",
     "shell.execute_reply": "2024-05-16T15:31:42.344922Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=35.4925691934713, pvalue=1.1997168153031063e-06)\n"
     ]
    }
   ],
   "source": [
    "treatments = [(group, df['rmsd'].to_numpy()) for group, df in results_framework.query(\"comparison == 'apo-holo'\").groupby(['chain_type', 'cdr'])]\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abe4ab6d-70b1-4fc2-bd9e-38b14682242a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:42.349575Z",
     "iopub.status.busy": "2024-05-16T15:31:42.349219Z",
     "iopub.status.idle": "2024-05-16T15:31:42.374936Z",
     "shell.execute_reply": "2024-05-16T15:31:42.374079Z"
    }
   },
   "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>chain_type_x</th>\n",
       "      <th>cdr_x</th>\n",
       "      <th>chain_type_y</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>3.427003</td>\n",
       "      <td>0.000610</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>2.672612</td>\n",
       "      <td>0.007526</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>2.206427</td>\n",
       "      <td>0.027354</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chain_type_x  cdr_x chain_type_y  cdr_y  statistic     p_val  significant\n",
       "0  alpha_chain      1   beta_chain      1   3.427003  0.000610         True\n",
       "1  alpha_chain      2   beta_chain      2   2.672612  0.007526         True\n",
       "2  alpha_chain      3   beta_chain      3   2.206427  0.027354        False"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = [(((chain_x, cdr_x), sample_x), ((chain_y, cdr_y), sample_y))\n",
    "          for ((chain_x, cdr_x), sample_x), ((chain_y, cdr_y), sample_y) in itertools.combinations(treatments, 2)\n",
    "          if cdr_x == cdr_y]\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "\n",
    "framework_stats = {\n",
    "    'chain_type_x': [],\n",
    "    'cdr_x': [],\n",
    "    'chain_type_y': [],\n",
    "    'cdr_y': [],\n",
    "    'statistic': [],\n",
    "    'p_val': [],\n",
    "}\n",
    "\n",
    "for ((chain_x, cdr_x), sample_x), ((chain_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "    \n",
    "    framework_stats['chain_type_x'].append(chain_x)\n",
    "    framework_stats['cdr_x'].append(cdr_x)\n",
    "    framework_stats['chain_type_y'].append(chain_y)\n",
    "    framework_stats['cdr_y'].append(cdr_y)\n",
    "\n",
    "    framework_stats['statistic'].append(stat)\n",
    "    framework_stats['p_val'].append(p_val)\n",
    "\n",
    "framework_stats = pd.DataFrame(framework_stats)\n",
    "framework_stats['significant'] = framework_stats['p_val'] < significance_level\n",
    "\n",
    "framework_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b09f4fa-2d2a-40dc-8ed2-8c256214396d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:42.378953Z",
     "iopub.status.busy": "2024-05-16T15:31:42.378393Z",
     "iopub.status.idle": "2024-05-16T15:31:42.402375Z",
     "shell.execute_reply": "2024-05-16T15:31:42.401503Z"
    }
   },
   "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>cdr_x</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.517867</td>\n",
       "      <td>0.604551</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.854099</td>\n",
       "      <td>0.004316</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.540175</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cdr_x  cdr_y  statistic     p_val  significant\n",
       "0      1      2   0.517867  0.604551        False\n",
       "1      1      3  -2.854099  0.004316         True\n",
       "2      2      3  -3.540175  0.000400         True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = list(itertools.combinations([(cdr, df['rmsd'].to_numpy())\n",
    "                                       for cdr, df in results_framework.query(\"comparison == 'apo-holo'\").groupby('cdr')], 2))\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "\n",
    "framework_stats = {\n",
    "    'cdr_x': [],\n",
    "    'cdr_y': [],\n",
    "    'statistic': [],\n",
    "    'p_val': [],\n",
    "}\n",
    "\n",
    "for (cdr_x, sample_x), (cdr_y, sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "\n",
    "    framework_stats['cdr_x'].append(cdr_x)\n",
    "    framework_stats['cdr_y'].append(cdr_y)\n",
    "\n",
    "    framework_stats['statistic'].append(stat)\n",
    "    framework_stats['p_val'].append(p_val)\n",
    "\n",
    "framework_stats = pd.DataFrame(framework_stats)\n",
    "framework_stats['significant'] =  framework_stats['p_val'] < significance_level\n",
    "\n",
    "framework_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6e97f4",
   "metadata": {},
   "source": [
    "The figure above shows there appears to be an increase in the movement of CDR3 compared to CDR1 and CDR2 and that the alpha chain has more movement than the beta chain of the TCR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1eb0f178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movement_counts = results_framework.query(\"comparison == 'apo-holo'\")['movement'].dropna().value_counts()\n",
    "movement_counts = movement_counts[movement_counts > 0]\n",
    "movement_counts = movement_counts.sort_index()\n",
    "\n",
    "ax = movement_counts.plot.pie(title='CDR Movement from Framework Alignment',\n",
    "                            autopct='%1.2f%%',\n",
    "                            legend=True,\n",
    "                            ylabel='',\n",
    "                            labeldistance=None)\n",
    "ax.legend(bbox_to_anchor=(1, 1), loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8a75c236",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:42.406897Z",
     "iopub.status.busy": "2024-05-16T15:31:42.406244Z",
     "iopub.status.idle": "2024-05-16T15:31:42.844913Z",
     "shell.execute_reply": "2024-05-16T15:31:42.843607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))\n",
    "\n",
    "lines = []\n",
    "\n",
    "for i, chain_type in enumerate(('alpha_chain', 'beta_chain')):\n",
    "    for j, cdr in enumerate((1, 2, 3)):\n",
    "        movement_counts = results_framework.query(\n",
    "            \"comparison == 'apo-holo' and cdr == @cdr and chain_type == @chain_type\"\n",
    "        )['movement'].dropna().value_counts()\n",
    "\n",
    "        movement_counts = movement_counts[movement_counts > 0]\n",
    "        movement_counts = movement_counts.sort_index()\n",
    "\n",
    "        ax = movement_counts.plot.pie(\n",
    "            title='CDR' + str(cdr) + (r'$\\alpha$' if chain_type == 'alpha_chain' else r'$\\beta$'),\n",
    "            autopct='%1.2f%%',\n",
    "            legend=False,\n",
    "            ylabel='',\n",
    "            labeldistance=None,\n",
    "            ax=axes[i, j],\n",
    "        )\n",
    "        lines.append(ax)\n",
    "\n",
    "lines_labels = [fig.axes[0].get_legend_handles_labels()]\n",
    "lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n",
    "fig.legend(lines, labels, bbox_to_anchor=(1.05, 1))\n",
    "\n",
    "fig.suptitle('CDR Movement from Framework Alignment', fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7d3826",
   "metadata": {},
   "source": [
    "#### Loop Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8eae33b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:42.849756Z",
     "iopub.status.busy": "2024-05-16T15:31:42.849358Z",
     "iopub.status.idle": "2024-05-16T15:31:42.858883Z",
     "shell.execute_reply": "2024-05-16T15:31:42.857864Z"
    }
   },
   "outputs": [],
   "source": [
    "results_loop['chain_type_formatted'] = results_loop['chain_type'].str.replace('_', ' ').str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "78e764ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:42.862785Z",
     "iopub.status.busy": "2024-05-16T15:31:42.862402Z",
     "iopub.status.idle": "2024-05-16T15:31:43.360380Z",
     "shell.execute_reply": "2024-05-16T15:31:43.359412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chain_type</th>\n",
       "      <th>cdr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">alpha_chain</th>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.498767</td>\n",
       "      <td>0.346897</td>\n",
       "      <td>0.022408</td>\n",
       "      <td>0.268565</td>\n",
       "      <td>0.360193</td>\n",
       "      <td>0.751915</td>\n",
       "      <td>1.317612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.592797</td>\n",
       "      <td>0.434249</td>\n",
       "      <td>0.215395</td>\n",
       "      <td>0.277901</td>\n",
       "      <td>0.405613</td>\n",
       "      <td>0.879081</td>\n",
       "      <td>1.748959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.202180</td>\n",
       "      <td>0.624488</td>\n",
       "      <td>0.414519</td>\n",
       "      <td>0.700162</td>\n",
       "      <td>1.159450</td>\n",
       "      <td>1.462535</td>\n",
       "      <td>2.495455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">beta_chain</th>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.270704</td>\n",
       "      <td>0.164392</td>\n",
       "      <td>0.113593</td>\n",
       "      <td>0.150641</td>\n",
       "      <td>0.203997</td>\n",
       "      <td>0.361489</td>\n",
       "      <td>0.613358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.346270</td>\n",
       "      <td>0.239926</td>\n",
       "      <td>0.140385</td>\n",
       "      <td>0.193959</td>\n",
       "      <td>0.242435</td>\n",
       "      <td>0.413456</td>\n",
       "      <td>1.005001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.968526</td>\n",
       "      <td>0.689371</td>\n",
       "      <td>0.215410</td>\n",
       "      <td>0.473122</td>\n",
       "      <td>0.745160</td>\n",
       "      <td>1.355077</td>\n",
       "      <td>2.503307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count      mean       std       min       25%       50%  \\\n",
       "chain_type  cdr                                                            \n",
       "alpha_chain 1     22.0  0.498767  0.346897  0.022408  0.268565  0.360193   \n",
       "            2     21.0  0.592797  0.434249  0.215395  0.277901  0.405613   \n",
       "            3     22.0  1.202180  0.624488  0.414519  0.700162  1.159450   \n",
       "beta_chain  1     22.0  0.270704  0.164392  0.113593  0.150641  0.203997   \n",
       "            2     22.0  0.346270  0.239926  0.140385  0.193959  0.242435   \n",
       "            3     22.0  0.968526  0.689371  0.215410  0.473122  0.745160   \n",
       "\n",
       "                      75%       max  \n",
       "chain_type  cdr                      \n",
       "alpha_chain 1    0.751915  1.317612  \n",
       "            2    0.879081  1.748959  \n",
       "            3    1.462535  2.495455  \n",
       "beta_chain  1    0.361489  0.613358  \n",
       "            2    0.413456  1.005001  \n",
       "            3    1.355077  2.503307  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.boxplot(data=results_loop.query(\"comparison == 'apo-holo'\"), y='rmsd', x='cdr', hue='chain_type_formatted')\n",
    "\n",
    "x = np.linspace(-0.75, 2.75)\n",
    "y = np.repeat(NOISE_LEVEL, len(x))\n",
    "\n",
    "plt.plot(x, y, '--r', label=f'Noise Level ({NOISE_LEVEL: .2f} Å)')\n",
    "plt.xlim(-0.75, 2.75)\n",
    "\n",
    "plt.legend(title='Chain Type')\n",
    "plt.xlabel('CDR')\n",
    "plt.ylabel('RMSD (Å)')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "results_loop.query(\"comparison == 'apo-holo'\").groupby(['chain_type', 'cdr'])['rmsd'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "99dd7187",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:43.364668Z",
     "iopub.status.busy": "2024-05-16T15:31:43.364319Z",
     "iopub.status.idle": "2024-05-16T15:31:45.555323Z",
     "shell.execute_reply": "2024-05-16T15:31:45.554286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7f4b860f10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(results_loop.query(\"comparison == 'apo-holo'\"),\n",
    "            x='rmsd', col='cdr', row='chain_type_formatted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aea35cc8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:45.560855Z",
     "iopub.status.busy": "2024-05-16T15:31:45.560441Z",
     "iopub.status.idle": "2024-05-16T15:31:45.577168Z",
     "shell.execute_reply": "2024-05-16T15:31:45.576395Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=54.747881311378876, pvalue=1.4709296709056658e-10)\n"
     ]
    }
   ],
   "source": [
    "treatments = [(group, df['rmsd'].to_numpy()) for group, df in results_loop.query(\"comparison == 'apo-holo'\").groupby(['chain_type', 'cdr'])]\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "438662ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:45.581260Z",
     "iopub.status.busy": "2024-05-16T15:31:45.580748Z",
     "iopub.status.idle": "2024-05-16T15:31:45.606350Z",
     "shell.execute_reply": "2024-05-16T15:31:45.605590Z"
    }
   },
   "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>chain_x</th>\n",
       "      <th>cdr_x</th>\n",
       "      <th>chain_y</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>2.769770</td>\n",
       "      <td>0.005610</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>2.769798</td>\n",
       "      <td>0.005609</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>1.502248</td>\n",
       "      <td>0.133033</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       chain_x  cdr_x     chain_y  cdr_y  statistic     p_val  significant\n",
       "0  alpha_chain      1  beta_chain      1   2.769770  0.005610         True\n",
       "1  alpha_chain      2  beta_chain      2   2.769798  0.005609         True\n",
       "2  alpha_chain      3  beta_chain      3   1.502248  0.133033        False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = [(((chain_x, cdr_x), sample_x), ((chain_y, cdr_y), sample_y))\n",
    "          for ((chain_x, cdr_x), sample_x), ((chain_y, cdr_y), sample_y) in itertools.combinations(treatments, 2)\n",
    "          if cdr_x == cdr_y]\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "\n",
    "loop_stats = {\n",
    "    'chain_x': [],\n",
    "    'cdr_x': [],\n",
    "    'chain_y': [],\n",
    "    'cdr_y': [],\n",
    "    'statistic': [],\n",
    "    'p_val': [],\n",
    "}\n",
    "\n",
    "for ((chain_x, cdr_x), sample_x), ((chain_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "    loop_stats['chain_x'].append(chain_x)\n",
    "    loop_stats['cdr_x'].append(cdr_x)\n",
    "    loop_stats['chain_y'].append(chain_y)\n",
    "    loop_stats['cdr_y'].append(cdr_y)\n",
    "\n",
    "    loop_stats['statistic'].append(stat)\n",
    "    loop_stats['p_val'].append(p_val)\n",
    "\n",
    "loop_stats = pd.DataFrame(loop_stats)\n",
    "\n",
    "loop_stats['significant'] = loop_stats['p_val'] < significance_level\n",
    "\n",
    "loop_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4303f949",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:45.610990Z",
     "iopub.status.busy": "2024-05-16T15:31:45.610337Z",
     "iopub.status.idle": "2024-05-16T15:31:45.635243Z",
     "shell.execute_reply": "2024-05-16T15:31:45.634404Z"
    }
   },
   "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>cdr_x</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.230996</td>\n",
       "      <td>2.183243e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-5.816687</td>\n",
       "      <td>6.002526e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-5.127736</td>\n",
       "      <td>2.932472e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cdr_x  cdr_y  statistic         p_val  significant\n",
       "0      1      2  -1.230996  2.183243e-01        False\n",
       "1      1      3  -5.816687  6.002526e-09         True\n",
       "2      2      3  -5.127736  2.932472e-07         True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = list(itertools.combinations([(cdr, df['rmsd'].to_numpy())\n",
    "                                      for cdr, df in results_loop.query(\"comparison == 'apo-holo'\").groupby('cdr')], 2))\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "\n",
    "loop_stats = {\n",
    "    'cdr_x': [],\n",
    "    'cdr_y': [],\n",
    "    'statistic': [],\n",
    "    'p_val': [],\n",
    "}\n",
    "\n",
    "for (cdr_x, sample_x), (cdr_y, sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "    loop_stats['cdr_x'].append(cdr_x)\n",
    "    loop_stats['cdr_y'].append(cdr_y)\n",
    "\n",
    "    loop_stats['statistic'].append(stat)\n",
    "    loop_stats['p_val'].append(p_val)\n",
    "\n",
    "loop_stats = pd.DataFrame(loop_stats)\n",
    "\n",
    "loop_stats['significant'] = loop_stats['p_val'] < significance_level\n",
    "\n",
    "loop_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26356dc0",
   "metadata": {},
   "source": [
    "The figure above shows there appears to be an increase in the movement of CDR3 compared to CDR1 and CDR2 and that the alpha chain has more movement than the beta chain of the TCR.\n",
    "In this alignment procedure, the CDR-1 and CDR-2 loops median drop below the noise level line, indicating that these show different effects to the CDR3 loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "68e60cc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:45.639584Z",
     "iopub.status.busy": "2024-05-16T15:31:45.639199Z",
     "iopub.status.idle": "2024-05-16T15:31:45.762103Z",
     "shell.execute_reply": "2024-05-16T15:31:45.761116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movement_counts = results_loop.query(\"comparison == 'apo-holo'\")['movement'].dropna().value_counts()\n",
    "movement_counts = movement_counts[movement_counts > 0]\n",
    "\n",
    "ax = movement_counts.plot.pie(title='CDR Movement from Loop Alignment',\n",
    "                              autopct='%1.2f%%',\n",
    "                              legend=True,\n",
    "                              ylabel='',\n",
    "                              labeldistance=None)\n",
    "ax.legend(bbox_to_anchor=(1, 1), loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6c185f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Little Movement (<0.5 Å)         74\n",
       "Some Movement (0.5 to 1.0 Å)     29\n",
       "Movement (1.0 to 2.0 Å)          22\n",
       "Large Movement (2.0 to 4.0 Å)     6\n",
       "Significant Movement (>4.0 Å)     0\n",
       "Name: movement, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_loop.query(\"comparison == 'apo-holo'\")['movement'].dropna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "462b6d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))\n",
    "\n",
    "lines = []\n",
    "\n",
    "for i, chain_type in enumerate(('alpha_chain', 'beta_chain')):\n",
    "    for j, cdr in enumerate((1, 2, 3)):\n",
    "        movement_counts = results_loop.query(\n",
    "            \"comparison == 'apo-holo' and cdr == @cdr and chain_type == @chain_type\"\n",
    "        )['movement'].dropna().value_counts()\n",
    "\n",
    "        movement_counts = movement_counts[movement_counts > 0]\n",
    "        movement_counts = movement_counts.sort_index()\n",
    "\n",
    "        ax = movement_counts.plot.pie(\n",
    "            title='CDR' + str(cdr) + (r'$\\alpha$' if chain_type == 'alpha_chain' else r'$\\beta$'),\n",
    "            autopct='%1.2f%%',\n",
    "            legend=False,\n",
    "            ylabel='',\n",
    "            labeldistance=None,\n",
    "            ax=axes[i, j],\n",
    "        )\n",
    "        lines.append(ax)\n",
    "\n",
    "lines_labels = [fig.axes[0].get_legend_handles_labels()]\n",
    "lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n",
    "fig.legend(lines, labels, bbox_to_anchor=(1.05, 1))\n",
    "\n",
    "fig.suptitle('CDR Movement from Loop Alignment', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a96d3b0",
   "metadata": {},
   "source": [
    "### Comparison of *apo*-*holo*, *apo*-*apo* and *holo*-*holo* conformational changes\n",
    "\n",
    "The following analysis aims to ascertain whether there is notable movement in the CDR domains between the *apo* and *holo* conformations, using *apo*-*apo* and *holo*-*holo* differences as controls."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156d8879",
   "metadata": {},
   "source": [
    "#### Framework Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ed0144e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:45.766809Z",
     "iopub.status.busy": "2024-05-16T15:31:45.766426Z",
     "iopub.status.idle": "2024-05-16T15:31:48.235467Z",
     "shell.execute_reply": "2024-05-16T15:31:48.234692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7f4c13a890>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1511.11x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(results_framework.sort_values(['comparison', 'chain_type', 'cdr']),\n",
    "            x='comparison',\n",
    "            y='rmsd',\n",
    "            col='cdr',\n",
    "            row='chain_type',\n",
    "            kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b2e0f9a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:48.241034Z",
     "iopub.status.busy": "2024-05-16T15:31:48.240440Z",
     "iopub.status.idle": "2024-05-16T15:31:51.016987Z",
     "shell.execute_reply": "2024-05-16T15:31:51.015593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7f48d3d630>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1614.38x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(results_framework.sort_values(['comparison', 'chain_type', 'cdr']),\n",
    "            hue='comparison',\n",
    "            x='rmsd',\n",
    "            col='cdr',\n",
    "            row='chain_type',\n",
    "            kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0a2c4ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "comparison  chain_type   cdr\n",
       "apo-apo     alpha_chain  1      10\n",
       "                         2      10\n",
       "                         3      10\n",
       "            beta_chain   1      10\n",
       "                         2      10\n",
       "                         3      10\n",
       "apo-holo    alpha_chain  1      22\n",
       "                         2      21\n",
       "                         3      22\n",
       "            beta_chain   1      22\n",
       "                         2      22\n",
       "                         3      22\n",
       "holo-holo   alpha_chain  1      30\n",
       "                         2      30\n",
       "                         3      30\n",
       "            beta_chain   1      30\n",
       "                         2      30\n",
       "                         3      30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_framework.groupby(['comparison', 'chain_type', 'cdr']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0b11255",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:51.023479Z",
     "iopub.status.busy": "2024-05-16T15:31:51.022570Z",
     "iopub.status.idle": "2024-05-16T15:31:51.040367Z",
     "shell.execute_reply": "2024-05-16T15:31:51.039398Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=136.04243752942966, pvalue=1.2772577873614328e-20)\n"
     ]
    }
   ],
   "source": [
    "treatment_options = ['comparison', 'chain_type', 'cdr']\n",
    "treatments = [(group, df['rmsd'].to_numpy()) for group, df in results_framework.groupby(treatment_options)]\n",
    "treatments\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d299e9d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:51.045071Z",
     "iopub.status.busy": "2024-05-16T15:31:51.044246Z",
     "iopub.status.idle": "2024-05-16T15:31:51.102081Z",
     "shell.execute_reply": "2024-05-16T15:31:51.101058Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002777777777777778\n"
     ]
    },
    {
     "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>comparison_x</th>\n",
       "      <th>chain_type_x</th>\n",
       "      <th>cdr_x</th>\n",
       "      <th>comparison_y</th>\n",
       "      <th>chain_type_y</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.130495</td>\n",
       "      <td>1.75e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>0.156174</td>\n",
       "      <td>8.76e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.577720</td>\n",
       "      <td>9.95e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>0.124939</td>\n",
       "      <td>9.01e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.601970</td>\n",
       "      <td>9.27e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>1.811616</td>\n",
       "      <td>7.00e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.300985</td>\n",
       "      <td>1.93e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>1.061982</td>\n",
       "      <td>2.88e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.813116</td>\n",
       "      <td>4.16e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>1.749146</td>\n",
       "      <td>8.03e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.707543</td>\n",
       "      <td>8.77e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>1.874085</td>\n",
       "      <td>6.09e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>4.834162</td>\n",
       "      <td>1.34e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>3.636405</td>\n",
       "      <td>2.76e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>5.223118</td>\n",
       "      <td>1.76e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>3.759904</td>\n",
       "      <td>1.70e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>3.148688</td>\n",
       "      <td>1.64e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>4.723032</td>\n",
       "      <td>2.32e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   comparison_x chain_type_x  cdr_x comparison_y chain_type_y  cdr_y  \\\n",
       "0       apo-apo  alpha_chain      1     apo-holo  alpha_chain      1   \n",
       "1       apo-apo  alpha_chain      1    holo-holo  alpha_chain      1   \n",
       "2       apo-apo  alpha_chain      2     apo-holo  alpha_chain      2   \n",
       "3       apo-apo  alpha_chain      2    holo-holo  alpha_chain      2   \n",
       "4       apo-apo  alpha_chain      3     apo-holo  alpha_chain      3   \n",
       "5       apo-apo  alpha_chain      3    holo-holo  alpha_chain      3   \n",
       "6       apo-apo   beta_chain      1     apo-holo   beta_chain      1   \n",
       "7       apo-apo   beta_chain      1    holo-holo   beta_chain      1   \n",
       "8       apo-apo   beta_chain      2     apo-holo   beta_chain      2   \n",
       "9       apo-apo   beta_chain      2    holo-holo   beta_chain      2   \n",
       "10      apo-apo   beta_chain      3     apo-holo   beta_chain      3   \n",
       "11      apo-apo   beta_chain      3    holo-holo   beta_chain      3   \n",
       "12     apo-holo  alpha_chain      1    holo-holo  alpha_chain      1   \n",
       "13     apo-holo  alpha_chain      2    holo-holo  alpha_chain      2   \n",
       "14     apo-holo  alpha_chain      3    holo-holo  alpha_chain      3   \n",
       "15     apo-holo   beta_chain      1    holo-holo   beta_chain      1   \n",
       "16     apo-holo   beta_chain      2    holo-holo   beta_chain      2   \n",
       "17     apo-holo   beta_chain      3    holo-holo   beta_chain      3   \n",
       "\n",
       "    statistic     p_val  significant  \n",
       "0   -3.130495  1.75e-03         True  \n",
       "1    0.156174  8.76e-01        False  \n",
       "2   -2.577720  9.95e-03        False  \n",
       "3    0.124939  9.01e-01        False  \n",
       "4   -2.601970  9.27e-03        False  \n",
       "5    1.811616  7.00e-02        False  \n",
       "6   -1.300985  1.93e-01        False  \n",
       "7    1.061982  2.88e-01        False  \n",
       "8   -0.813116  4.16e-01        False  \n",
       "9    1.749146  8.03e-02        False  \n",
       "10  -1.707543  8.77e-02        False  \n",
       "11   1.874085  6.09e-02        False  \n",
       "12   4.834162  1.34e-06         True  \n",
       "13   3.636405  2.76e-04         True  \n",
       "14   5.223118  1.76e-07         True  \n",
       "15   3.759904  1.70e-04         True  \n",
       "16   3.148688  1.64e-03         True  \n",
       "17   4.723032  2.32e-06         True  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = []\n",
    "for pairing in list(itertools.combinations(treatments, 2)):\n",
    "    chain_type_x = pairing[0][0][1]\n",
    "    cdr_x = pairing[0][0][2]\n",
    "    chain_type_y = pairing[1][0][1]\n",
    "    cdr_y = pairing[1][0][2]\n",
    "\n",
    "    if (chain_type_x, cdr_x) == (chain_type_y, cdr_y):\n",
    "        combos.append(pairing)\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "print(significance_level)\n",
    "statistics = []\n",
    "p_vals = []\n",
    "\n",
    "for ((comparison_x, chain_type_x, cdr_x), sample_x), ((comparison_y, chain_type_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "momvement_statistics_fw = pd.DataFrame({\n",
    "    'comparison_x': [name for ((name, _, _), _), _ in combos],\n",
    "    'chain_type_x': [name for ((_, name, _), _), _ in combos],\n",
    "    'cdr_x': [name for ((_, _, name), _), _ in combos],\n",
    "    'comparison_y': [name for _, ((name, _, _), _) in combos],\n",
    "    'chain_type_y': [name for _, ((_, name, _), _) in combos],\n",
    "    'cdr_y': [name for _, ((_, _, name), _) in combos],\n",
    "    'statistic': statistics,\n",
    "    'p_val': p_vals,\n",
    "    'significant': [p_val < significance_level for p_val in p_vals],\n",
    "})\n",
    "\n",
    "momvement_statistics_fw['p_val'] = momvement_statistics_fw['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "momvement_statistics_fw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "853cafb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:51.106570Z",
     "iopub.status.busy": "2024-05-16T15:31:51.105764Z",
     "iopub.status.idle": "2024-05-16T15:31:51.277799Z",
     "shell.execute_reply": "2024-05-16T15:31:51.276754Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>comparison_x</th>\n",
       "      <th>chain_type_x</th>\n",
       "      <th>cdr_x</th>\n",
       "      <th>comparison_y</th>\n",
       "      <th>chain_type_y</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.302372</td>\n",
       "      <td>0.762369</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.360672</td>\n",
       "      <td>0.173617</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.226779</td>\n",
       "      <td>0.820596</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.604743</td>\n",
       "      <td>0.545350</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.982708</td>\n",
       "      <td>0.325751</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>0.650515</td>\n",
       "      <td>0.515360</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>1.153185</td>\n",
       "      <td>0.248834</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.005341</td>\n",
       "      <td>0.314733</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.266120</td>\n",
       "      <td>0.790147</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>0.591377</td>\n",
       "      <td>0.554268</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    comparison_x chain_type_x  cdr_x comparison_y chain_type_y  cdr_y  \\\n",
       "0        apo-apo  alpha_chain      1      apo-apo  alpha_chain      2   \n",
       "1        apo-apo  alpha_chain      1      apo-apo  alpha_chain      3   \n",
       "2        apo-apo  alpha_chain      1      apo-apo   beta_chain      1   \n",
       "3        apo-apo  alpha_chain      1      apo-apo   beta_chain      2   \n",
       "4        apo-apo  alpha_chain      1      apo-apo   beta_chain      3   \n",
       "..           ...          ...    ...          ...          ...    ...   \n",
       "148    holo-holo  alpha_chain      3    holo-holo   beta_chain      2   \n",
       "149    holo-holo  alpha_chain      3    holo-holo   beta_chain      3   \n",
       "150    holo-holo   beta_chain      1    holo-holo   beta_chain      2   \n",
       "151    holo-holo   beta_chain      1    holo-holo   beta_chain      3   \n",
       "152    holo-holo   beta_chain      2    holo-holo   beta_chain      3   \n",
       "\n",
       "     statistic     p_val  significant  \n",
       "0    -0.302372  0.762369        False  \n",
       "1    -1.360672  0.173617        False  \n",
       "2    -0.226779  0.820596        False  \n",
       "3    -0.604743  0.545350        False  \n",
       "4    -0.982708  0.325751        False  \n",
       "..         ...       ...          ...  \n",
       "148   0.650515  0.515360        False  \n",
       "149   1.153185  0.248834        False  \n",
       "150  -1.005341  0.314733        False  \n",
       "151  -0.266120  0.790147        False  \n",
       "152   0.591377  0.554268        False  \n",
       "\n",
       "[153 rows x 9 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = list(itertools.combinations(treatments, 2))\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "\n",
    "statistics = []\n",
    "p_vals = []\n",
    "\n",
    "for ((comparison_x, chain_type_x, cdr_x), sample_x), ((comparison_y, chain_type_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "pd.DataFrame({\n",
    "    'comparison_x': [name for ((name, _, _), _), _ in combos],\n",
    "    'chain_type_x': [name for ((_, name, _), _), _ in combos],\n",
    "    'cdr_x': [name for ((_, _, name), _), _ in combos],\n",
    "    'comparison_y': [name for _, ((name, _, _), _) in combos],\n",
    "    'chain_type_y': [name for _, ((_, name, _), _) in combos],\n",
    "    'cdr_y': [name for _, ((_, _, name), _) in combos],\n",
    "    'statistic': statistics,\n",
    "    'p_val': p_vals,\n",
    "    'significant': [p_val < significance_level for p_val in p_vals],\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e842048",
   "metadata": {},
   "source": [
    "The analysis of the plots and statistical tests shows that there is a statistically significant (p-value: 0.05) difference between the target and controls (as seen by the results of the Kruskal-Wallis test) but it also shows that there is a significant difference between the *apo*-*holo* comparisons and the *holo*-*holo* baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cc1393",
   "metadata": {},
   "source": [
    "#### Loop Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eabd7ce0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:51.282346Z",
     "iopub.status.busy": "2024-05-16T15:31:51.281896Z",
     "iopub.status.idle": "2024-05-16T15:31:53.563158Z",
     "shell.execute_reply": "2024-05-16T15:31:53.562110Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7f4b710d00>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1511.11x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(results_loop.sort_values(['comparison', 'chain_type', 'cdr']),\n",
    "            x='comparison',\n",
    "            y='rmsd',\n",
    "            col='cdr',\n",
    "            row='chain_type',\n",
    "            kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3d9be509",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:53.568485Z",
     "iopub.status.busy": "2024-05-16T15:31:53.568045Z",
     "iopub.status.idle": "2024-05-16T15:31:56.523442Z",
     "shell.execute_reply": "2024-05-16T15:31:56.522395Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7f487adf00>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z+ZSbm9vu67m5uSoqKgrrOZuamlRdXd3uA7HHMAw1eBskha/DpK+nryQ6TADEH+6t8amkplGSlJNmdgJbHSZGCAMTl8OlIWlDJLHHBEBs496K47EWts/oN6Nbrz9vyHmSGMsFAIDUC5a+9wYPPPCA0tPTAx+DBg2KdEkIAa/fqxajRVL4O0wITADEG+6t8ccwDJW0dpjkpFodJm0juXKTco/72p4KjOVijwmAGMa9FR2paqrSF+VfSJKm5U3r1jkm5UxStiebsVwAACiCgUlWVpYcDoeKi9uPTiguLj7uQvdQnfPuu+9WVVVV4GPfvn3duj56t3pvfeCxx+kJyzXZYQIgXnFvjT/VjS1qavFLkrJTrQ4Ta+l7aAOT4X2GS5K2V24P2TUAINK4t6Ijq4pWyZCh/LR85SZ3717rsDs0d4g52vzdPe8GszwAAKJOxAKThIQETZkyRYsWLQp8ze/3a9GiRZo5c2ZYz5mYmKi0tLR2H4g91v6SREeinHZnWK6Z6c6UxA4TAPGHe2v8KW0dx5XmdsrtMuenD7BGcvlS1CchO2TXHtFnhCRGcgGIbdxb0ZGVxSslSdP7Te/Reaw9Jh/v/1g+v6/HdQEAEK3C81vj45g/f75uuOEGTZ06VdOmTdPjjz+uuro63XTTTZKk66+/XgMGDNADDzwgyVzq/sUXXwQeHzhwQOvWrVNKSooKCgo6dU7ErzpvnaTw7S+R2gITOkwAALHOGsdldZdIUqLLJ9m8kuGS3Z8RsmsX9DF/DtxVuUt+wy+7jamzAID4sL5kvSRpcs7kHp1nYs5EpbpSdbjpsDaWb9SE7AnBKA8AgKgT0cDkmmuuUWlpqe69914VFRVp4sSJWrhwYWBpe2Fhoez2tr/wHjx4UJMmTQr8+eGHH9bDDz+sOXPmaPHixZ06J+KX1WESrv0lUttIrvqWejW2NMrtdIft2gAAhFNJTfv9JZJUWl8qm7NahrevGhpdIbv2oNRBSrAnqNHXqAM1BzQojbn+AIDY19jSqC0VWyRJE3J6FnC47C7N7D9T7+19Tx/v/5jABAAQtyIamEjS7bffrttvv73D56wQxJKfny/DMHp0TsQva4dJOAOTFFeKnHanWvwtOtx4WP1S+oXt2gAAhFNxtTmSKyetrcOkqL5INmetDG9flbYGKqHgsDs0rM8wbanYou2V2wlMAABxYXPFZrUYLerr7qv+yf17fL4vDfyS3tv7npbsX6LbJ/E7FQBAfGJeAeJGoMMkjCO5bDabMhPNsVwVTRVhuy4AAOFWVts6kivliMCkrkh2Z42ktg6UULHGcu2s3BnS6wAA0FtY47gmZE+QzWbr8flOH3C6JDOIKa0v7fH5AACIRgQmiBuBDpMwBiaSlOlhjwkAIPYdrvdKkjKSEwJfK64vls1ZLUkqaV0KHyrD+wyXJG2v3B7S6wAA0FtsKNsgSRqfPT4o58vyZOnUvqdKkj458ElQzgkAQLQhMEHciMRILknKSDT3mBCYAABiWaUVmCQdEZjUFctmdZhUh7bDZESfEZLoMAEAxAfDMNp1mATL7IGzJUlL9i8J2jkBAIgmBCaIG9ZIrmRXclivay1+r2hkJBcAIHZV1jdLkvoktS13L64vDt9IrgxzJNfuqt3y+r0hvRYAAJFWXF+skoYSOWwOnZJ1StDO+6WBX5IkLTu0TF4f91MAQPwhMEHcsAITj9MT1utmult3mBCYAABiWGWD+UuVowOTtpFcoQ1M+iX3k8fpkdfv1b7qfSG9FgAAkbaudJ0kaWTGyKD+HXds37HKSMxQnbdOn5d9HrTzAgAQLQhMEDciNZLLCkwYyQUAiGWBDhNPxyO5SkMcmNht9sDi9x2VO0J6LQAAIm1Dqbm/JJjjuCTzfjq933RJ0vJDy4N6bgAAogGBCeKG1WES7qXvjOQCAMQ6wzDadpgkmx0mzb5mlTeWBwKT8romtfj8Ia3DWvxOYAIAiHWBwCQnuIGJJM3oN0OStOzgsqCfGwCA3o7ABHGjzlsnKXKBCR0mAIBYVdvUoha/Iamtw6SkvkSS5E5okd0mGYZUXtcc0jroMAEAxAOf36etFVslSaf0Dd7+EsuM/mZg8nnZ56ptrg36+QEA6M0ITBA3GrwNksI/kquvu68kOkwAALHL6i5JdNrlSXBIMveXSFJeco6yUhIlhX4s14g+IyQRmAAAYtvemr1q9DXK4/RocOrgoJ9/QMoADU4dLJ/h06riVUE/PwAAvRmBCeKGNZIr2ZUc1usGOkya6DABAMSmwDiupPb7SyQpNzm3LTCpDW1gYo3kKqwuVLMvtN0sAABEitVdMjJjpBx2R0iuYY3lYo8JACDeEJggbgSWvkdoJFedt05NvtD+oggAgEiobGhd+J7kCnzN6jDJTcpVVqoZmJSFuMMkJylHqQmp8hk+7a7aHdJrAQAQKVsqtkiSRmeODtk1rLFc7DEBAMQbAhPEjcDS9zCP5Ep1pcppd0pijwkAIDYdbu0wOW5gkmJ2npTVhrbrw2azMZYLABDzrMBkVOaokF1jWt402WTTrqpdga5RAADiAYEJ4kaklr7bbDZlJmZKYo8JACA2VdW3dph4Oh7Jld06kqssxCO5pLaxXAQmAIBYZBhGW4dJRug6TNIT0zW271hJ0oqiFSG7DgAAvQ2BCeJGYCRXmDtMpCP2mNBhAgCIQVaHSUZyW4dJUV2RJKvDJHyBSUGfAkkEJgCA2FTWUKaKxgrZbXYVZBSE9Foz+8+UJC0/yB4TAED8IDBBXDAMQ3UtZodJiisl7Ne3AhM6TAAAsehwa4dJ+pEdJvVHLH1PtUZyhT4wGZHROpLrMIEJACD2WN0l+Wn58jg9Ib3WkYvfDcMI6bUAAOgtCEwQFxpaGuQ3/JKkZFdy2K+f6WYkFwAgdlVZHSatO0y8fq/KGsokSXlJeW0dJjWh3WEitY3k2l+7P9BdCgBArNh6eKuk0O4vsUzMmahER6JKG0q1s3JnyK8HAEBvQGCCuGDtL7HJFvJ34XQky5MlSYFfHgEAEEuqGszAJN1jBiZl9WUyZMhldynDnRHWkVyZ7szAGxV2V+0O+fUAAAgnq8NkTOaYkF8r0ZGoyTmTJUnLDi0L+fUAAOgNCEwQF6zAJNmVLJvNFvbrZ3uyJUmlDaVhvzYAAKFW09QiSUp1m4GJNY4rJylHdps9EJhU1DerxecPeT0j+phjubZXbg/5tQAACKetFeHrMJGkGf3NsVwrDrH4HQAQHwhMEBeODEwioa+nryQ6TAAAsam20QxMUtxOSVJRfdvCd0nKTE6Q3SYZhhmahJo1lovxIQCAWFLvrdfe6r2SpFEZ4QlMpvebLklaVbxKLf6WsFwTAIBIIjBBXIh0YJKdZHaYlDeUR+T6AACEUm1rh0lKohmYFNe1LXyXJIfdpszk1sXvYdhjUpBRIIkOEwBAbNlZuVOGDPV19w28KS/URmeMVnpiuuq8ddpYtjEs1wQAIJIITBAXar21kqQUV0pErp/lNneYMJILABCLagMjuVo7TOrMDpO8pLzAMeHcY1LQxwxMdhzeEfJrAQAQLjsqzfua9caAcHDYHZqWN02StPzQ8rBdFwCASCEwQVywOkySXEkRub7VYVLVVKVmX+jfWQsAQLgYhqGaRnPpuxWYWDtMrA4TKbyBiTWSq7i+WDXNNSG/HgAA4RAITPqELzCRpBn92GMCAIgfBCaIC1ZgEqkOk7SENLns5iJcxnIBAGJJU4tfXp8h6YiRXK2BSfsOk9aRXGEITNIS0gL7U7YfZiwXACA2WLu5rDcGhIu1x2R96XrVe+vDem0AAMKNwARxIdI7TGw2m7I85lguFr8DAGKJNY5LkpITOt5hIkl9Ax0m4em0HJVpLsPdenhrWK4HAECoRarDZHDqYOUl58nr92ptydqwXhsAgHAjMEFciHRgIikQmLDHBAAQS2ob2xa+2+02tfhbAm8OsLo8pCNGctWEvsNEkkZltAYmFQQmAIDoV9NcE+jgDHeHic1mYywXACBuEJggLvSmwIQOEwBALLE6TKxxXOUN5fIZPjltTmW6MwPHWSO5SsMwkkuSRmaOlCRtO7wtLNcDACCUrHFcOZ4cpSWkhf361lguFr8DAGIdgQniQq23VhKBCQAAwVZjdZgctfA9OylbDrsjcFxWaphHcrV2mGw/vF0+vy8s1wQAIFQitb/EYnWYbKnYosrGyojUAABAOBCYIC5Yi+kitfRdkrI92ZIITAAAseXoDpOiuiJJ7cdxSVJ2YIdJeDpMBqcOltvhVqOvUYU1hWG5JgAAoWLtL4lUYJLlyVJBnwIZMvRZ0WcRqQEAgHAgMEFcsDpMklxJEauhr6evJHaYAABiS22TV5KUelSHyZEL36W2HSYVdc3y+42Q1+WwOzQiY4QkFr8DAKKf1WES7oXvR7K6TBjLBQCIZQQmiAu9qcOkvKE8YjUAABBs1kiuQGBS1xqYHNVh0rd1h4nPb+hwfXjGco3MaN1jUsEeEwBAdIt0h4nUtseExe8AgFhGYIK40Jt2mNBhAgCIJYEdJontO0zykvPaHedy2NUnySUpjHtMMs09JnSYAACiWVVTVeDvkZEMTKbmTpXD5lBhTaEO1h6MWB0AAIQSgQniQp23TpKUnBC5wCQ7qW2HiWGEfhQJAADh0LbDxAxDAiO5juowkdrGcoVrj4m1+H1rBYEJACB6WeO48pLzlJqQGrE6UhJSdGrWqZLoMgEAxC4CE8SFQGDijFxg0tdt7jBp8beoqqkqYnUAABBMtVaHydEjuZI7CkzMsVzhCkyskVzF9cXcewEAUWtnlRmYDE+PXHeJxRrLxR4TAECsIjBBzDMMIxCYpCREboeJy+FSn8Q+kswuEwAAYoHVYZKa6JTf8KukvkTSiTtMSmvCE5ikJKRoQMoASXSZAACi1+6q3ZKkoelDI1xJ2+L3FYdWMDkBABCTCEwQ8xpaGmTI/EEuyZkU0VrYYwIAiDU1R3SYlDeUq8Vokd1mD9zzjtQ2kis8O0ykI8ZysccEABCl9lTtkdQ7ApMJ2RPkdrhV3lgeWEQPAEAsITBBzLO6S+w2uzxOT+gu1NIkeRtPeIj1yyM6TAAAsaK2ySvJXPpu7S/J8mTJaXcec2x2anh3mEhHLH6nwwQAEKV6U4dJgiNBk3MnS2KPCQAgNhGYIObVemslmftLbDZb8C9Qsll68UrpgYHSL/Kk386Rtr3X4aEEJgCAWBNY+u52BvaX5CXldXhsuHeYSG0dJtsObwvbNQEACJYmX5MO1h2U1DsCE6ltLBd7TAAAsYjABDGv3lsvSUpOCMHC923vmQHJjvclX7MkQzq0TnrpKmnxQ9JRM12zk7IlKTDfHQCAaGeN5EpzO1VUXySp44Xv0pEjucIXmIzuO1qStL1yu5p94RsFBgBAMBRWF8pv+JXqSlVfd99IlyOpbfH7quJV8vq9Ea4GAIDgIjBBzDuywySo9q+SXv665GuShp8jfe8zaf4Wadq3zOcX3y999vt2L7EW4FojSwAAiHa11g6TRFfg/tbRwnfpiMCkJnzBRf/k/kpPTFeLv0XbD28P23UBAAgGaxxXfnp+aCYmdMPozNFKT0xXnbdOm8o2RbocAACCisAEMc/aYRLUDpPGaumVm8yuklEXSl97WcoeJaX1ky78pXTOfeZxC/9L2r868DJrRIk1sgQAgGhX09FIruTjjORq3WFSXtck46guzFCx2WwamzlWkrSpnF/qAACiy57qPZJ6zzguydwPOi1vmiTGcgEAYg+BCWJeIDAJZofJRw9JlYVSn8HSZc9IDlf758/4T+mUyyTDJ735fclntilbv0AqqisKXi0AAERIU4tPzS1+Se2Xvh+vw6RvsrnDxOszVNUQvhEeY/uagckX5V+E7ZoAAARDb1r4fiRrjwmL3wEAsYbABDHPCkxSElKCc8KyHdKKZ8zHX35Mcqcfe4zNJl34sOTJlEo2Sauek9Q20720oZRZrwCAqFfX5As8Tkl0Bt4QcLwdJm6XQ6lup6Tw7jEhMAEARKvASK60/MgWchQrMFlXui6wNxQAgFhAYIKYZ+0wSXImBeeES34l+VukEedJI+Ye/7jkLOnse8zHH/1SaqpRpjtTTrtThgyV1ZcFpx4AACLE2l+SlOCQzWaopL5E0vE7TCQpu3WPSWkY95hYgQmL3wEA0cQwjF45kkuSBqUOUv/k/mrxt2h18eqTvwAAgChBYIKYZ73bJSgdJhW7pc//bj4+679Pfvzk66XMYVJ9mbTyWdltdha/AwBiRk2T2S2ZkujU4cbD8vq9ssmm7KTs477GWvxeXhe+DpMBKQOUlpBmLn6vZPE7ACA6lDaUqs5bJ7vNrkGpgyJdTjs2m00z+8+UJC09uDTC1QAAEDwEJoh5VodJsisIO0xWPGPuJRl+jtR/0smPd7ik2Xeaj5c/LbU0BQIT9pgAAKKd1WGS4m7bX5LlyZLL7jrua7JSzT0mZTXhC0xsNptO6XuKJMZyAQCix56qPZKkgSkDleBIiGwxHThjwBmSpE8OfBLhSgAACB4CE8S8wNL3ngYmTbXSupfMxzO/2/nXjbtKSu0n1RZJn78SmOtOhwkAINrVtAYmqW6XiutOvPDdYnWYlNWGdzQWe0wAANGmty58t8zoN0NOm1N7q/dqX/W+SJcDAEBQEJgg5gWWvrt6OJJr4ytSU7U5YmvY2Z1/nTNBmvZN8/Hq55WXnCeJDhMAQPSrbWoNTBLbOkyOt/Dd0haYhK/DRCIwAQBEn93VvXPhuyUlIUUTcyZKkj4+8HFkiwEAIEgITBDzrMAkydXDpe9r/2x+nnKTZO/i/zoTvy7ZndL+lRrZ7JNEhwkAIPrVtAYmKYnOwBsBOt9hEpnAZNvhbSx+BwBEBWskV2/tMJEYywUAiD0EJoh5QekwKdsh7f9Mstml8Vd3/fWpudLI8yVJ4/etlUSHCQAg+nW0w+TkHSbmDPbSMI/kYvE7ACDa9PaRXFJbYLKyaKWafOF9MwQAAKFAYIKYF5QdJhteNj8PP0dKzeveOabcKEnqv/NjJfiNwKx3AACiVW2TV5LZYRIITE7WYZLa2mESxqXvkrn4nbFcAIBo0dDSoEN1hyRJ+en5kS3mBEZmjFROUo4afY1aVbQq0uUAANBjBCaIebXeWkk9DEw2v2l+Hndl988x/GwpfZCcTdWaW1+v0oZSef3e7p8PAIAIqw0sfXd2eul79hEjuQzDCG2BRzml7ymSpE1lm8J6XQAAuqqwulCGDKUnpisjMSPS5RyXzWZjLBcAIKYQmCDm1XvrJfUgMCndJpVuMXeQtI7V6ha7w9xlIuni2noZMlRWX9b98wEAEGHWDpPkbix9b2rxB5bGh8v47PGSpPWl68N6XQAAusoax5Wfli+bzRbhak6MwAQAEEsITBDT/Ia/5yO5tvzT/Dx0juTp07OCxl0lSZre0Kh0n09F9ewxAQBEL6vDxOnwBuaWn6zDxJPgUHKCQ5JUGuaxXFZgsrNyp2qba8N6bQAAumJ3de/fX2KZ0W+GnDan9lTv0b6afZEuBwCAHiEwQUyr89bJkDnuIzUhtXsn2dwamIy9uOcFZRVIeePklKG5dQ0sfgcARDWrQ8RnM8OHTHemEhwJJ31dvz4eSdKhqsbQFdeBLE+WBqQMkCFDG8s3hvXaAAB0RTQsfLekJqRqQs4ESXSZAACiH4EJYpr17tEEe4ISHYldP0FloXRwrSSbNOrLwSnqlMslSefX1bH4HQAQ1WpaO0ya/FWSTt5dYhmYYQYm+w/Xh6awEwiM5SphLBcAoPfaU7VHkjmSKxpYY7k+3v9xhCsBAKBnCEwQ06qbqyVJKQkp3TvB5rfMz0NmSSnZwSnqVDMwOa2xSdWHdwbnnAAARIDVYdJgHJZ08v0llrbApCE0hZ3AhGzzHbAbyjaE/doAAHSG3/BrT/UeSdHRYSJJcwbOkSStOLQisEcUAIBoRGCCmFbrNTtMuj2Oa+u/zM9jLgpSRZIy8lWemS+HpLy9y4N3XgAAwszqMKltKZPUlQ6TJEmRCUzGZ5kdJhtKN8gwjLBfHwCAkympL1FDS4OcNqcGpg6MdDmdUtCnQANTBqrZ36xPD34a6XIAAOg2AhPENGskV6qrG4FJU61U2BpojDgviFVJVSPmSpLGFm0L6nkBAAin2iavJKm6pUSSlJec16nXRXIk1+jM0UqwJ6iyqZLFtACAXsnaXzIwdaBcdleEq+kcm82mswafJUn6cN+HEa4GAIDuIzBBTKvx1kjq5kiuPR9Lfq+UkS/1HR7UulynXCZJGlNbKaOuPKjnBgAgHLw+vxq9fklSpfeQpOjoMHE5XBrTd4wkaX0pe0wAAL1PNC18P9JZg8zA5KP9H6nF3xLhagAA6B4CE8S0mmYzMOnWSK4dH5ifC+YGsSJTzoDTtCXBJYekui9eC/r5AQAItbqmtl+ElDcelNT1DpOi6kY1t/iDX9xJWIvfN5SyxwQA0PtYgUl+en5kC+miSTmTlJ6YrqqmKq0tWRvpcgAA6BYCE8Q0ayRXiquLHSaGIW1/33wcgsAk0ZGoz9KzJEm+zW8G/fwAAISatb/E7bKrpLFrHSZ9kxPkdtllGNKhqgjsMbECExa/AwB6ocDC97To6jBx2p2B5e+M5QIARCsCE8Q0ayRXlztMKnZJlXslu0vKnx2CyqQduaMkScmFKyRv+H9ZBABAT9S2dpikJDrU0GLex3KScjr1WpvNFhjLta8i/PfACVkTJEnbKrYFagcAoLeI1pFcUttYrg8LP5RhGBGuBgCAriMwQUyzRnJ1eYeJNY5ryEwpsRv7TzrBl3uKDjkccrY0Sbs+Csk1AAAIlUCHSYL55z6JfeR2ujv9+iGZZmCyu7zu5Af7/VJzJ47rpLzkPOV4ctRitGhj2cagnRcAgJ6q99aruL5YUnQGJrP6z1KCPUH7a/drR+WOSJcDAECXEZggplkjuVJdXewwCeH+EsvA1EH6MNmc4a4tb4XsOgAAhEJtk1eS5HL6JHV+HJdleI75hoSdJbUdH+DzSiuflX53lvSzLOn+/tIjo6W3/lMq69kvYGw2myblTpIkrSle06NzAQAQTNY4rkx3ptIT0yNbTDckuZI0vd90SYzlAgBEJwITxLRujeTyeaW9n5qPh50VgqpMA1IH6N9J5rtrtW2h5PeF7FoAAASb1WHicJjBSW5y1wKTgmwzMNnRUWBStt0MSt6eLx1cIxmt98iaQ9Kq56TfzJA+eczsPOmmKblTJEmri1d3+xwAAARbYOF7Wn5kC+mBswa3jeUCACDaEJggpnVrJNfBtVJzreTJlHJPDVFl0sCUgVrtTlSN3SHVlUr7V4XsWgAABJu1w0T2RklSv+R+XXq91WFyTGCyf5X07LlS8eeSJ0Oad7/0Hxuku/ZI33hdGn6O5PdKH/xUeuPb5hsdumFyzmRJ0rrSdWrxt3TrHAAABFtg4XsUjuOynDnwTEnSxvKNKq4rjmwxAAB0EYEJYlq3RnLtXmJ+zj9Dsofuf5EBKQPUYrNpSVLrvHfGcgEAokhta4eJz2buFslLzuvS6wtaA5Oi6kbVNLaGHmXbpRevkBoOSwOmSN/7TJr5PSljiBmeDD9buu5V6SuPSzaHtOFl6fVvd6vTZETGCKUmpKqhpUFbKrZ0+fUAAIRCNC98t2QnZWti9kRJ0geFH0S2GAAAuojABDHNCky61GFiBSZDvxSCitpkJ2UrwZ6gfye17jHZ+q+QXg8AgGCyOky8htnN2dXAJN3jUk5qoiRpW3GN1FgtvXS11FgpDZgqXf+mlJJz7AttNmnqTdJX/yzZndLGV6TFD3S5frvNHugyYSwXAKC3iIWRXJJ0Xv55kqT39rwX4UoAAOgaAhPEtC7vMGlpkvatMB/nzw5RVSa7za7+Kf31icctv90lle8w31kLAEAUsHaYNPorJUl5SV0LTCRp3ABzme3n+6ukhf8lVeyS0gdJ1/5FSjzJmx1GXSBd9IT5eMkvpe1dfwcre0wAAL2J3/Brb/VeSdHdYSJJ5w45V5K0tmStSupLIlwNAACdR2CCmOX1e9XQ0iCpCyO59q+SWhql5Bwpe1QIqzMNTB2oertdJbmt16LLBAAQJawOk3p/uaSud5hI0riBZmCy4YsvpHV/lmx26fLfddxZ0pFJ10mn3WY+fv1bUm3XfiEzOdfsMFlbslZ+o/sL5AEACIZDdYfU5GuSy+5S/5T+kS6nR/KS8zQxe6IMGXp/7/uRLgcAgE4jMEHMqmuuCzxOTkju3IsC47hmmyM/QmxAygBJ0uasfPMLWwhMAADRwdo74rPVySabcpNyu3yO8a2Byed7WhfCzvq+NGRW105y3s+lnFOk+jKzS6ULxmaOlcfpUWVTpXZV7uradQEACDJrHNeQtCFy2p0RrqbnGMsFAIhGBCaIWdY4Lo/TI5fd1bkX7fnY/Bzi/SWWQamDJElLU9LML+xbIdWVheXaAAD0hNVhYrM3KsuTJZejk/faI4wf2EeStMOXraqUAmnOXV0vxOWWLn3K7E7Z+Kq07d3Ov9Th0vjs8ZKkNSVrun5tAACCKBYWvh+JsVwAgGhEYIKYVdPcur+ks+O4vI3S/pXm4xDvL7FYgcmG5nIpb7wko0u/6AEAIFJqG63ApEn9kvt16xxZqtIwW5EM2bXi1HulznaEHq3/JGnGd83H7/635PN2+qVTcsw9JquKV3Xv2gAABMmuKrPbMVYCk7zkPE3InsBYLgBAVCEwQcyqba6VJKUknGRprOXAasnXLKXkSpnDQlhZm/z0fEnS3uq9MkZdaH6RPSYAgChQ09phIkeTcpO7Po5LkvTxI5pl/1yS9GnT8J4VNOcuKSlLKt8hrV7Q6ZcFFr8XrZZhGD2rAQCAHoi1DhNJOm8IY7kAANGFwAQxyxrJ1enApPBT8/OQWWHZXyJJg1IGyWFzqL6lXhVDZphf3Plvs9sFAIBerK3DpLFbC99VUyytek6n2zdKkj7ZWd6zgtxp0pmtO0wWPyA1VnXqZeOzxyvBnqCShhLtrt7dsxoAAOiBmAxMWveYrC1Zq9L60ghXAwDAyRGYIGYFRnIldHIk197WwGRwF5fN9oDL4Qosft/pTpLSBkjeemn3R2GrAQCA7mjbYdLNkVyf/U7yNWvWQLecdpt2lNRqZ2ltz4qacqPUd4RUXy598ninXuJ2ujUpZ5IkacWhFT27PgAA3VTVVKWKxgpJ0tC02AlMjhzL9d5eukwAAL0fgQliVnVTtSQpLSHt5Af7WqR9n5mPh8wMYVXHssZy7anZK426wPwiY7kAAL2Yz2+ovtln/qE7HSbNddKqZyVJ6bO/qVkFWZKkhRuLelaYwyWd+7/m4+W/kaoPdupl0/tNl0RgAgCIHKu7JC85T0mupAhXE1zz8udJkt7Z/U6EKwEA4OQITBCzqprNURzpCeknP7j4c6m5VkpMl3LGhriy9vLT8iW1/oAcCEwWSn5/WOsAAKCzrO4SSbI5mpSX1MXAZN1LUsNhKWOoNPrLuvBU8/X/+vxQz4sbdYE0aIbU0igtfbJTL7ECk8+KPpPP7+t5DQAAdFFgHFcMdZdYzs8/X3abXetL12tfzb5IlwMAwAkRmCBmBTpMEjvRYbJ3mfl58HTJ7ghhVccakjbELKF6r5Q/W0pIkWqLpENrw1oHAACdVdPoNR/YvLLZfOqX0oWRXH6ftOzX5uOZ35PsDp13Sp4cdps2HazWrp6O5bLZpDk/Nh+vfl6qLTnpS8b2HasUV4pqmmu0pWJLz64PAEA3xOL+Ekt2Uram5U2TRJcJAKD3IzBBzOpSh8mRC9/DzPqBeE/1HsmZKBWcYz6xlR8kAQC9U9v+kkY57U5lujM7/+Itb0mH90ieTGni1yVJmckJmjW8ryTpn+uD0GUy/GxpwBSzy+TT/zvp4U67U1PzpkqSlh9a3vPrAwDQRbEcmEjSl4d9WZL09q63ZRhGhKsBAOD4ekVg8tRTTyk/P19ut1vTp0/XZ599dsLj//73v2v06NFyu90aN26c/vWv9vsebrzxRtlstnYf559/fii/BfRC1c2d7DAxjCM6TMIfmFgdJgdqD6jZ1yyNutB8Ygt7TAAAvVNtY+tILrs5jstu68KPlCv/YH6eerOU0Daj/ZKJAyRJb64/0PNfpNhs0pdau0xWPivVlZ/0JTP6zZDEHhMAQGTsqtolSRqWPizClYTG3MFzlehI1K6qXXRzAgB6tYgHJi+//LLmz5+v++67T2vWrNGECRM0b948lZR0PD7h008/1bXXXqtbbrlFa9eu1aWXXqpLL71UGzdubHfc+eefr0OHDgU+/vKXv4Tj20EvYo3kOmmHSdl2qb5Mcrql/pPCUFl72Z5spbhS5Df8ZpfJiPMkm0Mq2SRV7A57PQAAnEyN1WHi6OLC94pd0u4lkmzSlBvbPTXvlFwlOO3aWVqnLw5V97zIkfOkvPGSt85cAH8S0/PMPSZrS9aab2AAACBMmn3N2l+7X1LsdpikJKRozsA5kswuEwAAequIByaPPvqobrvtNt10000aO3asnnnmGSUlJem5557r8PgnnnhC559/vn70ox9pzJgx+tnPfqbJkyfr17/+dbvjEhMTlZeXF/jIyMgIx7eDXqTTHSbWOK4BUyVnQoirOpbNZlNBnwJJ0s7KnVJSppR/uvnk5jfDXg8AACdjdZjY7E1dC0zWvmh+LjhH6jOo3VOpbpfOHpUjSXpz/cGeF2mzSV/6kfl45R+kphPvRhneZ7iyPFlq9DVqfen6nl8fAIBOKqwulN/wK8WVoixPVqTLCRlrLNc7u9+Rz++LcDUAAHQsooFJc3OzVq9erblz5wa+ZrfbNXfuXC1btqzD1yxbtqzd8ZI0b968Y45fvHixcnJyNGrUKH3nO99RefnJRzEgtlQ1dXKHiTWOa8jMEFd0fMP7DJckbT+83fzC2EvMz18QmAAAep+2HSZN6pfcyYXvvhZp7Z/Nx5Ov7/CQiyf2lyS9tf6Q/P4gzDcf/WUpc5jUWCmt+/MJD7XZbIGFtMsOdvxzKAAAobC72pwsMCx9mGw2W4SrCZ3ZA2YrLSFNJQ0lWlW8KtLlAADQoYgGJmVlZfL5fMrNzW339dzcXBUVFXX4mqKiopMef/755+uFF17QokWL9NBDD+mjjz7SBRdcIJ+v43cwNDU1qbq6ut0Hopvf8He+w2Rv5Ba+W0ZkjJDU2mEiSaMvkmSTDqySKvdFrC4A6C7urbGtptFrPujKSK4d70u1RVJSljTygg4POXt0jlISnTpQ2aC1+w73vFC7Q5rxXfPxsqekk7ybdVZ/82eBpQeX9vzaABBk3Ftj165Kc39Jfnp+ZAsJMZfDpfPyz5PEWC4AQO8V8ZFcofDVr35VF198scaNG6dLL71Ub731llauXKnFixd3ePwDDzyg9PT0wMegQYM6PA7Ro85bJ7/hlySlJZwgMKnaL1UVmjtDBk4LU3XHsjpMdlTuML+QmisNbu142fzPCFUFAN3HvTW21QRGcnUhMFnzgvl54rXHHYHpdjl09mhzLNeHW0p7XKd5va9Lnkypcu9J76mnDzBHYn5R/oXKGsqCc30ACBLurbHL6jCJ1f0lR7pw6IWSpPf3vq/GlsYIVwMAwLEiGphkZWXJ4XCouLi43deLi4uVl9fxX77z8vK6dLwkDRs2TFlZWdqxY0eHz999992qqqoKfOzbxzv6o53VXZLoSJTb6T7+gdY4rn7jpcSUMFTWMWuHyb6afWpoaTC/GBjL9Y8IVQUA3ce9NbZ1OTCpKZK2vWs+ntTxOC7Ll0ZmS5I+3h6kwCQhSTrtFvPxsl+f8NAsT5bGZI4xD2UsF4Behntr7NpdFT+ByZTcKRqQMkC13lotKlwU6XIAADhGRAOThIQETZkyRYsWtd0k/X6/Fi1apJkzO94nMXPmzHbHS9L7779/3OMlaf/+/SovL1e/fh3P2E5MTFRaWlq7D0S3Tu8vsRa+D47cOC5J6uvuq4zEDBkyAj8sa8xF5ud9y6XqICy/BYAw4t4a2yobWt8R2tmRXBtflQyf2c2ZPfKEh84eYS673XCgSofrmntaqum02yRHgrR/pVS44oSHnjHgDEnSxwc+Ds61ASBIuLfGJsNo+zvgsPRhEa4m9Ow2uy4efrEk6R87eHMgAKD3ifhIrvnz5+v3v/+9/vjHP2rz5s36zne+o7q6Ot10002SpOuvv15333134Pj/+I//0MKFC/XII49oy5Yt+ulPf6pVq1bp9ttvlyTV1tbqRz/6kZYvX649e/Zo0aJFuuSSS1RQUKB58+ZF5HtE+HV9f0nkFr5L5qLZY8ZypQ+QBk03H296PUKVAQBwrNLaWkmSJ+Ekoy8tG/5mfh5/9UkPzU1za2RuigxDWrozSGOxUnOl8deYjz998oSHWoHJpwc/le8kO08AAOip4vpiNbQ0yGlzamDqwEiXExZWYLL80HIdqj0U4WoAAGgv4oHJNddco4cfflj33nuvJk6cqHXr1mnhwoWBxe6FhYU6dKjtBjpr1iy99NJL+t3vfqcJEybolVde0RtvvKFTTz1VkuRwOLRhwwZdfPHFGjlypG655RZNmTJFH3/8sRITEyPyPSL8rA6TE/4Sp75CKt1iPh4c2cBEalv8vrVia9sXx11lft7wcgQqAgCgYxX15vjIvknJJz+4bLt0aJ25L+yUyzp1/tMLzC6TFbsqulvisWaab67Rlrel8p3HPWx89nilulJV1VSlTeWbgnd9AAA6sKvKXPg+KG2QXHZXhKsJj4GpA3Va3mkyZOjNnW9GuhwAANqJeGAiSbfffrv27t2rpqYmrVixQtOnTw88t3jxYi1YsKDd8VdddZW2bt2qpqYmbdy4URdeeGHgOY/Ho3fffVclJSVqbm7Wnj179Lvf/S4QwCA+dKrDpLB1NnnWSCk5KwxVnZg1M31LxZa2L55yuWR3SofWSyVbjvNKAADCq7rRHJWVk5x68oM//7v5ueCcTt9vpwzJkCSt3Xe4W/V1KGe0VHCuJENa/pvjHua0OzWj/wxJ0icHPgne9QEA6EBgf0la7O8vOdKlBZdKkv6x8x8yDCOyxQAAcIReEZgAwdapDpPAOC5zf4nPb2jVngq9vLJQb64/qENVDaEus53RmaMlSZsrNrf9wJjct/WXO5I+/1tY6wEA4HhqG81RVXlpfU58oGG0jeOyuiY7YfJgMzDZfKhG9c0t3SmxY7O+b35e+2ez0/Q4Zg+YLUlaemBp8K4NAEAHdlWaHSbD+sT+/pIjzR08V8muZO2r2afVxasjXQ4AAAEEJohJVodJeuIJlr5bHSaDZ2nR5mKd88hiXfnMMt316uf6wV/WataD/9b3Xlqj4urGMFQsFfQpkNPuVE1zjQ7WHbHk3Zr3vuHvkt8flloAADiRRq/5eXD6STpGDqyWDu+WXEnSqAtPfOwR+vfxKC/NLZ/f0Ib9VT2o9ChDvyTljZNaGqSVzx73sNMHnC5J+rzscx1uDGKXCwAAR7F2WFo7LeNFkitJ8/LNPbNv7HgjssUAAHAEAhPEpOqm1pFcx+swaaqVDq6TYUiPFQ7TLX9cpT3l9UpzOzVnZLYmDOojw5De3nBIX37yY60tDP0vS1wOlwr6FEiStpQfMX5r1AVSQqpUVdgW8gAAEEHNXockaXB6zokPtLpLRn9ZSkzp0jUmDe4jSVpbWNnF6k7AZpNm/cB8/NnvJG/Hb4rIScrRqIxRMmTo4wMfB+/6AAAcwTCMQGBi/V0wnlwy/BJJ0nt731O9tz7C1QAAYCIwQUw6aYfJ/pWS4dNzrq/qiaUlkqSbTs/XsrvP0R9vnqZ/fO90/esHszWmX5rKapt1w3OfafOh6pDXfeRYrgCXRzrlUvPxmj+GvAYAAE6k0euTYZiBybDMfsc/0O+XvnjDfNyFcVwWKzBZF8w9JpK5eD5tgFRX0rZfpQNnDjpTkvRh4YfBvT4AAK3KGspU3Vwtu82uoenxtcNEkiblTNKQtCFqaGnQe3vfi3Q5AABIIjBBjDrpDpPCZVrsG69f1F4kSbr7gtG676JTlJzoDBwytn+aXv3OTE0ZkqHqxhZ949nPQr7XxApM2i1+l6SpN5mfN70u1ZWHtAYAAE6kpLam9ZFfwzMGHv/Afcul2mIpMV0adlaXr3Nqf/NND5sP1ZzkyC5yuKTp3zYfL/u1uWelA2cNNmteenCpmnxNwa0BAAC1jeMalDpIiY7ECFcTfjabLdBlwlguAEBvQWCCmHS4yXw3akZiRofPV+xco//0fk9+2XTN1EH65pc6XrCXlODUczeeptF5qSqrbdJ/vrxOPn/Hv1gJhrF9x0qSNpVvalv8Lkn9J0v9Jki+Zmndn0N2fQAATmZnxQFJks3erHT3cd6YIElf/MP8PPpCyZnQ5euM6Weeu7CiXjXW0pRgmXKDOe6ydIu044MODxmbOVa5SblqaGnQikMrgnt9AAAk7azcKSk+x3FZLhp+kew2u1YXr9a+6n2RLgcAAAITxKbKxkpJUh93n2OfbGnWL/aM1GGlanRWgn526amy2WzHPVe6x6Wnr5uipASHlu+q0O+W7ApN0TI7TJw2p8oaylRUV9T2hM0mTb3FfLz6eZa/AwAiZm9lsSTJ5fQd/yC/X/riTfPx2Eu6dZ2M5AT1S3dLkrYUBbnLxJ1uhiaS9OmTHR5is9kCY7n+Xfjv4F4fAADF78L3I+Ul52lmv5mSpNd3vB7hagAAIDBBDDIMI9BhkunOPOb5ZatW6tWW02WTX/dfNUUJzpP/bzA0K1k/vegUSdJj72/TnrK64BbdyuP0aGTmSEnS+rL17Z889QopMU2q2CXtXhyS6wMAcDKFVaWSpMSEE3Rc7l8p1Rw0uziGn93ta41t7TL54mAI9ohN/7Zkc0i7l0iHNnR4yNmDzNo/2v+R/AZvVgAABFc8L3w/0mUjLpNkBiZef5C7SgEA6CICE8ScWm+tWvwtkqQ+iX3aPWcYhu7/0Ozc+HrfHZo85NhA5XiumjpQXxqZrWafXz9764ug1Xu0cVnjJEkbSo/65U1iijThq+bjT38dsusDAHAih6orJUnJiSf4MdIaxzXqAsnZ/ZnsY/ubgcnmQyEITPoMkk651Hy8rOP76ml5pynFlaKyhjJtLNsY/BoAAHHLMIzASK547jCRzDcoZLozVdZQpo/2fRTpcgAAcY7ABDHHGsflcXrkdrrbPffB5hJ9XuVWkhr1n1O7Nk/dZrPpvovGyuWwadGWEv17S3GwSm5nQvYESR0EJpI047vmu2F3LpIOrg3J9QEAOJHiWjO8SHO7Oj7A728LTLo5jsti7TEJSWAiSTNvNz9vfFWqOnDM0y6HS2cMOEOS9OG+D0NTAwAgLhXXF6vWWyuHzaH8tPxIlxNRLodLlxWYXSZ/3/b3CFcDAIh3BCaIOcdb+G4Yhh57f6sk6UbHu+o7YkaXzz08O0U3nz5UkvTAv7aEZAH8+OzxkqTN5Zvl9R3Vjpw51BzNJUkfPxr0awMAcDJldbWSpD4ed8cHHFwjVe+XElKkgnN6dK2RuSmSpB0ltTKM4N9zNWCyNOQMyd8irXi6w0POGnSWJPaYAACCy+ouGZw2WAmOrr2ZLxZdMdL8e+6nBz/VvhqWvwMAIofABDHncKMZmBy98H3R5hJ9cahGKarXbZ5/S/3Gd+v83z2rQGlup7aX1Oqf6w/2tNxjDE4drPTEdDX7m7W5YvOxB5zxn+bnzf+USrcF/foAAByPYRg6XN8gScpOSe34oC/eMD+PnCe5PD263pC+yXLabapr9ulQVWOPznVcs75vfl71vFRfcczTswfOlsvu0q6qXdp+eHtoagAAxB32l7Q3KHWQZvWfJUl6ddurEa4GABDPCEwQcwIdJu72HSbPfrJbkvR1xyJlDD5FchxnlMhJpHtc+tYcc8bs4x9sk9cX3CWwNptNk7InSZJWF68+9oDcsdKoL0sypI8eDOq1AQA4kcqmSjV6zR8f81LTjz3AMKRN1jiuS3t8PZfDrvysZElml0lIjJwn5Z4qNddKK5455unUhNTAWK6FexaGpgYAQNxhf8mxrhp5laTW5e9HT1sAACBMCEwQc6wOkyNHcm0+VK1lu8rlkF/XO9+TBs/q0TVunJWvrJQE7Smv1xtrj5153lNT86ZKklYVr+r4gDPvkmQzZ64Xrgj69QEA6EhhTaHkM0dxZXg6WOZ+cK1UVSi5kqSCuUG5ZkG2OZZre6gCE5tN+tKd5uPlz0iNVccccn7++ZKkd/e8G5rRYACAuGN1mBCYtJkzaI6yPFmqaKzQon2LIl0OACBOEZgg5nTUYfL8UrO75PzEDRpgK5eGzOzRNZITnbpt9jBJ0m+X7JI/yLtMrMBkTfEa+fy+Yw/oN0GadJ35eOF/mQt2AQAIsX01+2T4zaAkJdF57AHWsvcR50kJSUG55ogj9piEzJhLpKxRUlOV9Nnvjnn6zEFnyu1wa2/1Xm2p2BK6OgAAccHn9wXGPI7MGBnhanoPl71t+fsrW1+JcDUAgHhFYIKYU9lYKamtw6SyvllvrDN3jdxsvC7ZXdKAqT2+zrXTBys10akdJbX695aSHp/vSKMzRivFlaJab622Ht7a8UHn3CslpJrLdTf8NajXBwCgI/uq98nwmx0mqe6jRlsaRtv+klMuDdo1C3KswKQmaOc8ht3e1mWy7DdSU/twJsmVpNkDZ0uS3tnzTujqAADEhcKaQjX6GuV2uDUkdUiky+lVrhx5pew2u1YUrQiMLQMAIJwITBBzAiO5WjtM/rHuoJpb/Brdx6fJtu1S/4lBeddrmtulr80YLEn67ZLg/iDnsDs0OXeyJGll0cqOD0rJafvlzrv/LVUHfwE9AABH2lezT4bPCkyO6jAp2iAd3iM5PWaHSZAMzw5Dh4kknXK5lDlMaqiQVjx9zNPWWK739rzHWC4AQI9Yb4or6FMgh90R4Wp6l/4p/XXmwDMlSS9tfimyxQAA4hKBCWJOYCRXa4fJ31btkyRdk75ZNpukIacH7Vo3nz5UCQ67Vu45rNV7K4J2Xkk6Lfc0SdKKQyfYUTLju+Z4robD0hvfYTQXACCkCmsKpdYOk5SjA5NNb5ifR5wrJSQH7ZrDs1Nks0mH670qr20K2nmP4XBKZ95tPl76pFRX3u7p2QNny+P06EDtAX1e9nno6gAAxLxtFdskSaMyR0W4kt7p62O+Lkn6565/qqrp2N1iAACEEoEJYo7VYdLH3UcbD1Rp08FqJTjsurSudQbq0C8F7Vq5aW5dNmmAJOmZj3YF7bySNLO/uWdlVfEqNfuaOz7ImSBd/gfz3by7FkvLfxPUGgAAOJLZYeKRJKV7jhjJdeQ4rrGXBPWangSHBmaY1wx5l8mpV0p546SmaunjR9rX4fTozEFnSpLe2c1YLgBA91kdJgQmHTst7zQV9ClQQ0uD3tjxRqTLAQDEGQITxJwjl76/snq/JOncgmRl1G4z95cMnhHU6932JXP5+webi1VYXh+0847MGKlsT7YaWhq0pmTN8Q/MHinN+4X5+P17pW3vBq0GAAAsdd46lTdUyPB3EJgUb5IqdkmORGnkvKBfe2iWOZZrT3ld0M/djt0uzf0f8/HK30uH97Z7+ivDviJJenvX2/L6vKGtBQAQs7ZWtAYmGQQmHbHZbLpuzHWSpL9s+Yt8fl/3T2YYUm2JVLhCWv+ytORX0jt3Sa/eKr1wqfSHc6Xfny39do70zGzpuQukv1wrvf5t6b17pFXPS3s+kWqKzXMBAGKe8+SHANHD6/eqptlcCptkT9fra3dIkq7O2SftlTRwalDHhEjmMtovjczWkm2l+tPyPfp/Xx4blPPabDadPuB0vbHjDS09sFQz+p0g6Jl6s3RgtbTuz9Lfb5RufEsaMCUodQAAIJndJTJckmH++NguMLG6S0acKyWmBv3aQ/smaYmk3WXBe2PCcQ0/Wxo6R9r9kfThL6TLfxd4alb/WcryZKmsoUwfH/hYZw8+O/T1AABiSlVTlYrriyWZb5JDxy4cdqEeW/OYDtQe0Ef7P+rcPdfnlYo3SgfWmG/msD5af0fQYyl5Uv7p5pjv/NlS1giZc78BALGEwAQxxZpvarfZ9dnOBlU1eNUv3a0z6v9tHhDEcVxHumHmEC3ZVqqXV+7T/HNHyZMQnMV9p/c3A5NPDnyiH0794fEPtNmki56QaoqknYukF6+QvvZ3adBpQakDAIDC6sLAOC6H3aaUxNYfIw2jbX9JkMdxWfKzzDc77C4L8Uguybynnvs/0u/OlDa8LE29RRo8XZLktDt10bCL9Pym5/XGjjcITAAAXWZ1lwxIGaCUhJQIV9N7eZweXTHiCj238Tm9tPmlju+59RXS/pXSvhXSvs/MNxF6O3pzhU1KHyhl5EsZQ6TkHCmpr/mRmCLZnZLNIdnsZrjSUCk1Vpl/vy7fIZVvlyoLpdoiaeOr5ockZY2Sxl0pnXqF1Hd4CP9pAADCicAEMaW8wVzQ2iexj/6x7pAk6YrJA+TYsMQ8IH92SK575qgcDc5MUmFFvf6x7oC+Om1wUM47s/9M2W127ajcof01+zUwdeDxD3a4pKv/aLYVH1gl/fEi6aoF0qjzg1ILACC+Hb2/xGa9o7Jks/mLBEeCNDI09xwrMNkTjg4TSeo/SZp0nbT2Rent+dI3PzKXwku6ePjFen7T8/p4/8cqbyhXX0/f8NQEAIgJgf0ljOM6qa+O+qoWbFqgFUUrtO3wNo1MzTfDkV0fSjv/LR1cJ+moMVnudHPaQt44KXeclHuKGWY4E3tWjLfBDGT2fGJ+7FshlW01u1E//IU0YKo04zvmm0ccrpOfDwDQa7HDBDElEJg4+2vJ9lJJ0iVDvFJdieR0SwND03HhsNv0jRlDJEkLPt0jI0izTdMT0zU1d6okaVHhopO/IDFVuuFNqeBcqaVB+stXpQ/vl3wtQakHABC/CmsKZfiTJB1nHNfwcyR3WkiuPcwKTMrr5PeHaX743P+VPBnmaI8VzwS+XJBRoFP7nqoWo0X/2PmP8NQCAIgZgf0lLHw/qX4p/XRZ7gxdVV0j/4tXSg/lS3/8ivTxI9LBtZIMqe8IaeJ10kVPSt9dIf14j/SN16Vz/1caf5WUO7bnYYkkuTxS/hnSmf9ljsD+0Q7p0qelgrlmd8qBVdKrt0hPTJSWPik1Vvf8mgCAiCAwQUwpbTBDkpbaU+X1GRqdl6oR1cvNJwdNk1zukF376qmD5HbZtaWoRiv3HA7aeecOmStJen/v+517QUKydO1fpCk3SjKkjx4yu03KdwatJgBA/NldtTvQYZJmBSaGIW163Xx8ymUhu/aAPh457TY1tfh1qLoxZNdpJ7lv2wL4xQ9IVQcCT1016ipJ0t+2/k1+wx+eegAAMWHb4W2S6DA5obId0iePSX+Yq58u+6vuLT+s0eV7JW+dlJwtjbtauvQZaf4W6furpEufkqbcIOWMluxh+jWXO12a+DXpulelH26Vzvxvs7bq/dL7P5GenCgtf0ZqaQ5PPQCAoCEwQUwpayiTJJWX5kuSLprQX9rdOo4rRPtLLOlJLl02aYAk6Y/L9gTtvGcPMme1ri9dr5L6ks69yOEyd5pc8ayUkCIVfir9Zqa05Ff8wAYA6JbdVbulI0ZySTLHcZVtkxyJ0qgLQnZtp8OuwZlmd8uesrqQXecYk74hDZwmNddKb35f8pvhyAVDL1BqQqoO1B7Q0gNLw1cPACCqef1e7ajcIYkOk2NUHzI7M34zS/r1FOmDn5r7SSTtTMnU4xnp+sPpN0k/3CZd8Xtp4rVSWr/I1mxJyZbOvEu6Y6N08a+lvgVSfbm08C7p11Olz18x32QCAIgKBCaIKWUNZfK3pKi4LF2SdNG4PGnPx+aT+aENTCTp+pn5kqSFG4tUVBWcd8DmJudqQvYESZ0cy3WkcVdK3/5EGnaW5GuS/v1z6bdfkgpXBKU2AEB8ONx4WJVNle12mEhq6y4pCN04Lkvb4vcwBiZ2u3Txk+ZYz52LAqO5PE6PLi24VJL08taXw1cPACCq7azcKa/fq1RXqgakDIh0OZHXXCdt+Jv0p8ukx8aanRklm8wl7MPPlr78iDR/i0q//hc92yddzxR/ovKm4E1zCDqXW5r8Dem7y6WvPCal5EqVe81RXS9czNQHAIgSBCaIKWUNZWqpPlWGbJowqI8Gt+yWGg5LrmRpwOSQX39MvzRNG5opn9/QSyv2Bu285w45V5L0wd4Puv7izKHmDNfLfy8lZUmlm6XnzpPe/qHUVBO0GgEAsWt31W5JUoojW5KU7nGGbRyXZWhg8XsYAxNJyhkjnfdz8/EH90mHNkiSrh55tSRpyf4lKqwuDG9NAICotLFsoyRpbNZY2Wy2CFcTQSWbpbfmSw+PlF67zVzgbvilQTOkrzxu7gf5xuvSabdKaf00PW+6Tul7ipp8Tfrz5j9HuvqTc7ikqTdLP1hrjupyus3JF0x9AICoQGCCmFLaUKqWarMb46Lx/drGcQ2Zaf7QEgbXzzSXv7/02T41twRnrvk5g8+RJK0qXhUYO9YlNps0/mrp9pXSpOvMr638g/kD2/ZuhDAAgLhiBSbJjhxJrR0mJV9I5dvNcVwjzw95DRHpMLGcdqs06kLJ12y+S7SxWvnp+Zo9YLYMGVqwaUH4awIARJ1N5ZskSaf2PTXClUSA3yd98ab0/Jel38yQVj1rjrzMyJfOvNsMF255V5p6k+TJaPdSm82mW8bdIkn669a/6v+zd9/hUZRfG8e/u+m9kpBCCRB6ryIgKCiKIBZEERUUK6IgFuzYfopdfEVEVLCAIFakqoiAIL1DCJ1ASCO9t533jyGBSFBKkg3h/lzXXmR3ZmfOTkKeyZx5zskqyLLDBzgHzh5mqa6Rf5et+jD1ckjYae/oRETkNJQwkRolLi2P4twIAK5tHQL7lpoLKrl/ycn6tqhNkJcLx7LyWbQjvkK2Ge4VTqvAVtgMG/P3zz/3Dbn7w8BJcOfP4FsP0g/DjJtg7iNQkFMhsYqISM1TkjBxsZgXMHzdnE8qx9Wn0stxAUQEHE+YJNshYWKxmDXJvULMni1zhkNxYenFm5/2/kRSTlLVxyUiIheUHcfMhEmLwBZ2jqQKFRXAxi9hUmf49g449BdYHKDZALhzLjyyGXo9Bf4N/nUzvev2pr53fTILMpmze07VxF5R/BucVPUhABK2wye9YPXk0v5oIiJSfShhIjXK0YTaALSu406IuwUO/mUuaNi7ymJwcrByW5e6AHy56mCFbbekVvpPe3/CON+GcQ16mXe5XDISsMDGL2DqFZAUfb5hiohIDXQgw0yYOBiewPGSXFVYjgugfqDZ9P1wSg5FxXa4uOARALfOBCd3s5/J/LG0r9WOtrXaUmgr5Ouor6s+JhERuWDkF+ezJ3UPcJHMMCkqgLVTYWIbmPswJO8FVx/oPhbGbIVbvoYGPc2bEs6A1WLl7pZ3AzB9x3RyCi+wG/5Kqj6MXA2RV5mzTRY9BTMGQVaivaMTEZGTKGEiNUZOYQ7ZqU0BuK5NKMT8DUW54Fkbgqv2Dp7bOtfF0Wph/aFUdh7NqJBt9q3fF2erM3vT9hKVEnX+G3T2gKtfhzt/Ao8gs7fJ1N6w+9fz37aIiNQoJTNMiotcAfDOjzcvfDi4QJPKL8cFEOrjhrOjlcJig9i03CrZ5ynC2sOgz8FihY1fYvnzde5ucRcAs3bNOreymSIiclGITommyCjC39Wf2h617R1O5bEVw+aZ8GEHWPA4ZB41Z2he9So8ugP6jAef8HPadP+G/Qn3DCclL4WZu2ZWcOBVxDMIbvsW+r1t9jbZtwSmXAaH19o7MhEROU4JE6kxtsYdxZZXB7AxsE092Hu8N0ej3md810pFCfJ25eqW5knwV6sPVsg2fVx8uKLuFYA5y6TCNOgFD66Eet2hIBO+ucWcGiwiIoJ5R2xsViwABYWOAPjErTQXRl4JLl5VEofVaqF+gDnLxC59TEo0uQauedP8evmb9Nr5Ky39m5NTlMMnWz+xX1wiIlKtlTR8bxHQouY2fN/3B0zuBj89CGkx5s2L/d6G0Vvg0ofP+5zByerEyLYjAZi2fRqZBZkVEXXVs1ig871w3zIIbAKZcTCtnzkj53yrSYiIyHlTwkRqjF+2HAXA3fsItbxczZM1gIZX2CWeO7vWB+DHTbGk5xRWyDZLynItOLCAguKCCtkmYN7lcseP0P5OMGzm1OClr+lkTUREiMmIwWbY8HLyIivfLIXlc/g3c2EVleMqUf94H5OD9kyYgHmR4+oJAFjWTOaDjCIcDYM50XOIyYixb2wiIlItlTZ8D6yB5bhSD8KsofDVDWblAldf6POS2ci9873g6FJhu+oX0Y8GPg3IKMjgy51fVth27SKoKdz7BzS/HmyF5oycH+9Xf1ERETtTwkRqjD+jsgAIqx0HGUchcSdgsVvCpFN9P5rW9iKv0MacDYcrZJuXhFxCkHsQ6fnp/HqogktnOTrDgA/giufM58vegMXPKmkiInKRKynHVc+7Phm55g0APum7zDISjftWaSwRgccTJsnV4ELCJQ/CDZ+AxYFae37np2PZhBXk8s76d+wdmYiIVEOlDd8DalDD96IC+PMN+LAz7JpnNnPv8iCM3gzdx4Cze4Xv0sHqwENtHwLgyx1fXvjlMF084ebpcNX/zOO3dTZ8diWk7Ld3ZCIiFy0lTKRG2JOQSWyKARTRODwX9i4xF4S2A3d/u8RksVhKZ5l8vfoQNtv5Jx4crA7c3PhmAL6J+ua8t3cKiwUuewKufsN8vnoSzBsDNjs01xURkWphT5rZoLaeZxMKi82xzI8saHx1lZXjKlGSMNlv7xkmJdrcYtYhd/OjXlYK38bGE7DjZ34/sNjekYmISDWSXZjN/nTzAniLwBqSMIndAJ/0hD9fMxuYR1xmlnq+ZgK4+VXqrvvU60OLgBbkFOUwafOkSt1XlbBY4NJRMGwueNSChO3wyeVwYLm9IxMRuSgpYSI1wi9b4wBw8NxDiLeP2TgNoFEfO0YF17cLxcvVkYPJOazYWzF3vgxqPAgnqxNbj20trYNb4S55AK770Gxqu2E6LH5aM01ERC5Su1N2AxDqFgmAKwW4W/Kh9eAqj6X+8YTJgWNZVb7v04rsAw+ugvo9cDcMXkhOJWLWMLJ2zbN3ZCIiUk3sTN6JgUGwezCBboH2Duf8FOaalQg+7WNWdXAPhJs+gzvnQlCzKgnBarHyRKcnAPhhzw9Ep0RXyX4rXf3ucP9yCOsIeWlmibMN0+0dlYjIRUcJE7ngGYbBvK1m/xIn762EugWemGESeaUdIwN3Z0cGdQgH4MtVBytkm4FugfStb5ZAmRk1s0K2Wa72d8D1x5u/r/nY7GkiIiIXnd2pZsIkwLme+S/pZm3yRlU/xjaoZSZMYlNzyS8qrvL9n5Z3KNz5M0VXvUKW1YGG+bl4zhqK8Wkf2P49FFdMLzMREbkwbUnaAkCrwFZ2juQ8xW+DT3rB3x+avS9bDYaH1kKrQeYsiSrUIbgDV9W7Cpth4631b2HUlBv8vENh+DxoOQhsRfDLaFj0DNiq0XmPiEgNp4SJXPCi4jLZn5SN1VKMo+dOmmemQX6GOZU1rKO9w+OOS8wLTH9EJ3I4pWJqrg9tNhSARQcXVW7N1ja3Qr+3za+Xvwmr/q/y9iUiItVOdmE2R7KOAOBprQ2AvyUTWlxv9r6qYrU8XfB0ccRmQEx16GNyMqsDjpc+Qsxdc5np40MhYDmyDr67G95rAQuehJjVKnMpInIR2pS4CYD2we3tHMk5stng70kw9QpI2gUeQTBkNtw0FTwC7BbWox0excnqxJq4NSyJWWK3OCqckxvc9Cn0esZ8vnoSfDME8jPtG5eIyEVCCRO54JXMLnH3OYjFIZ8GCcen4zbuC1b7/4g3qOVJj8hADAO+XnOoQrbZMrAlrWu1ptBWyIyoGRWyzdPqfC9c8bz59a/PaUqwiMhFZE+q2b8kyC2I/DzzzlF/S4Z5R6kdWCyW6tfH5B+a1+mO87XvcmWdMCb7+pDv6g1ZCbB2CnzeF95vCYuehoMrdbeoiMhFwGbYTiRMgi7AhEl2MswYBIufgeICaHwNjPwbmlxt78gI9wpneIvhALy+5nWyCqpRyc7zZbFAr3EwaBo4usKexfBZX0iLsXdkIiI1nv2vJoucB7Mcl9m/xPBcD4aB/6E15sIm/ewYWVklzd9nrztMXmHFXBwZ0XIEALN2zSKjIKNCtnlaPR6DbqPNr38ZY5YXERGRGq+kHFdj/8akHjL7ZgU4F0PdrnaLKaK0j0n1TJiA2W/syhZD+cjPh661fVnVawy0vhWcvSAjFlZ/BNP7wduR8PNDEL0ICvPsHbaIiFSCvWl7ySzIxM3RjSb+TewdztmJ3Wg2dt+3BBzd4Np3Ycg34FF9+rDc1/o+6nrVJTE3kYkbJ9o7nIrX8kYYvgA8gyFxhznLJ2aNvaMSEanRlDCRC9q22HRiUnJwcbRi8dhB46JiHDNizTswGvSyd3ilrmgaRJivG2k5hfyy5WiFbLNXnV408m1EVmEWs3bNqpBtnpbFAn1ego53Awb8cD/s/b1y9ykiInZXmjDxa0zKYXMGp19QmF1ncJb0MdmfVL3vIn26y9MMbjyYQgvcf+gHXgoJJXfsNrh1JrQZYvaByUmGTV/DN7fAmw3g2zth6xyV3BARqUE2JZizS9rUaoOj1dHO0ZyFDV+YMyPTD4N/A7h3CXQaUeW9Sv6Lq6MrL3R9AYDZ0bPZnLjZvgFVhvAOcO9SqN0KspPgi/6wZba9oxIRqbGUMJEL2vzjs0s6NnDBYi3g2oLjP9INeoGzh/0C+wcHq4Xbj/cy+fLvQxXSkM5qsXJPq3sA+Hrn1+QUVnItd4vF7GfS4kawFcLsO3Rni4hIDVdSkquVaxDJKWbPLP+6ze0Z0gUxwwTMcfq5S57j3lb3YsHCd7u/47p5g/nWkk1O/3fhib1w51zofB94h0FhNuz8GX64B95pCvMeNZvriojIBW1j4kbgAirHVZgHP4+CXx4xS3A1uRbu+xOCW9g7stPqEtKF6xpeh4HBcyufq/y/je3BJwzuXgxN+5vflx/vg99fVG80EZFKoISJXLBOLsfVsn4+AD2zj99t2tj+9VT/6ZZOdXB2tLItNp0Nh1IrZJt96/eljlcdUvNT+WbXNxWyzX9ldYAbpkCjPlCYAzNvhvjtlb9fERGpcoZhlM4waRO3ixTDE4CAoDB7hkWDQDOO6p4wAbPnyiPtH2HKlVMIdg8mPjueV1a/Qs/ZPXn4z7FMztjJvKY9WX7zZKJumkxihzvJ960DBVmw/nP4uDv5n/QkbeN08oty7f1xRETkHFxQDd/Tj5izSjZ9BRYr9H4BbvkaXH3sHdl/erLTkwS7B3Mo4xBvrnvT3uFUDmcPGPyVWTIb4K/3YPbtkF+9Z92KiFxolDCRC9amw2nEpuXi4exArYBEQguLaJidBliqVf+SEv4eztzYzrzINGX5/grZpqPVkQfbPAjAZ9s+Iz0/vUK2++87dTZP0upcAnnp8NUNkFIxn0dERKqP2KxYsgqzcLQ4ELhrIcmGN2COZ/YUcbwk17GsAtJzC+0ay5nqGtqV+TfO56nOTxHuGU5ecR5/HvmTj7Z8xNMrnuahP0YxeOPr9E75k46+cHftIBZ5uFMIuBzdjO/c0Rx5ow5vfNaRh357kE+3fcrO5J0VMmNVREQqT1xWHHHZcThaHGkV2Mre4fy7uK3waR+I2wzuAXD7D+aFeTuW4TwbPi4+vNb9NSxY+H7P9/x+qIaWkLYeT2Td8Ak4uED0fPj8akg7bO/IRERqjAtj5BMpx7wt5uySPs2DOZYfx5U5x6fd1u8OXsF2jOz07r2sARYL/LYzgb2JFXMXSL+IfjT2a0xmYSafbvu0Qrb5n5zd4bbZENwSshPhy+shI65q9i0iIlVie7I5g3CAczCWY3tIxUyYBHjaN2Hi6eJIkJcLcGHMMinh4uDC0GZDWXDjAuYMmMPjHR/nuobX0bl2Z5oHNKeedz1CPEIIcg9mn18or4Q3YGCDSKb6+ZNhtdKwsJBxh/fw4LrvWLbqTW6ZdwsDfx7IFzu+qJmlR0REaoCSclzNAprh7uRu52j+xd7fYdo1kBkHtZqZJbgaXm7vqM5a55DO3NXyLgBeWPkCB9MP2jegytTmFhg+DzxqQcI2mHo5HF5r76hERGqEC6jjmMgJNpvBgm3mBfr+rUOZFx/HiOzjFwtaXG+/wP5Dw1qeXNksmF93JjB1+X7eGNT6vLfpYHVgTPsxjFwykplRM7mt6W2EeIZUQLT/wc3XvOvo876QesBsPHfnXLO2qoiIXPC2J5kJkxuyzKREisUPDPBzt2/CBMw+JomZ+Rw4lkXbOr72DuesWCwWmvo3pal/0zN+j5GbRvaq93FdPYWWBTl8FZfAr55evF1YxNvpb/Pptk8Z0XIEQ5sPxcnqVInRi4jI2diQsAGAtkFt7RvIv9n4JfwyBoxiiLjMrCbg5mvvqM7ZqLaj2Jiwkc1Jmxm9dDQz+s3A09nT3mFVjjqdzWbw3wwxkybTr4Xr/g/a3GrvyERELmiaYSIXpNUHkonPyMPL1ZHLGgdSlLKf1vkFGBYrNLvO3uH9q/t7NgTgx02xJGTkVcg2u4d1p1PtThTYCnhj3RsVss0z4hUMd/4MPnUheS9M7wdpMVW3fxERqTTbjm3DzWajVfwe8g1HsmzmhfgADxc7RwYNjpflOpB04cwwOR8WN188er+Iw+gt0H4YYOGqrEwWxB3jkXxHMvJSeWfDOwz+ZTDbktQoXkSkOjAMg1VHVwHQpXYXO0dTDsOAJa/A3IfNZEmbITD0+ws6WQLg5ODEe5e/R5B7EPvT9/P0iqcpthXbO6zK41sH7l50UjP4+2HhOCgqsHdkIiIXLCVM5IL0w8ZYAPq3DsHF0YHmCWZT2tyw9uAZZM/Q/lOHen50qu9HQbGNaSsPVsg2LRYLT3V+CkeLI0tilrAkZkmFbPeM+NWDu+aDXwSkHoRp/dTTRETkAldkKyIqJYp+WTk4FuWR4tsSAEerBW83+09QLmn8vu8CKslVITyD4LoP4P7lUK87jsUF3Ht0P0uznGmDK3vT9nLnwjv5YscX6m8iImJnhzMPE5sVi6PVkU61O9k7nLKKjl9YX/G2+bznOLh+stmvsgYIdAvkg8s/wMXBhT+P/Mkrq1+p2eOii+fxZvCPm8/XfGzONkmPtW9cIiIXKCVM5IKTW1DMwuPluG5sH056fjo901MAcGx1sz1DO2P3X2bOMpmx+hCZeRXTsLaxX2OGtRgGwGtrXiOroGJ6pJwR37pw1wIIiIT0w/DZVaqfKiJyAduXto/cwhyGZpoJiWNNbgPMhu8Wi8WeoQFmSS64eGaYnCKktVm3fMBEcPbC/9g+voo5yASCsNkKeXv924xbPo6CYt1dKiJiLyuPrgSgfVD76tW/JDcVvr4Rts4GqyNc9yFc/gxUg/G9IrUIbMHrPV7HarHy/Z7veW/je/YOqXJZrdD7ebj1G3DxgSNrYUoP2PeHvSMTEbngKGEiF5xfd8aTXVBMHX83OtbzI/7gMloWFFAMOLe4yd7hnZErmgbRKMiTzPwiZqypuBJWD7R5gDpedUjMSeTNdW9W2HbPiHeomTSp3Qqyk2B6f9g6p2pjEBGRCrHt2DY65OUTWZAPTu4khPYBINjb1c6RmSJKSnIdy67Zd4z+G4sFOgyHh1ZD5FVYigu49sB6fst2JbwYFh5cyL2/3ktGQYa9IxURuSitijXLcXUN7WrnSE6SFgOf9YWDK8DZC277FtrfYe+oKs2V9a5kfNfxAEzbPo0PNn5Q888bmvaD+5dB7daQkwxf3QjL3gSbzd6RiYhcMOxfU0HkLH1/vBzXDe3CsVgsOGydDcBWvxDaedayZ2hnzGq18EDPhjw+ZwufLN/P7ZfUw9Pl/P87ujq68tKlLzFi8Qh+3PsjnUM6079B/wqI+Ax5BsFdi8zp3bvmwQ/3QNIu6PU0OOjXjYjIhWL7se0MyTw+U7H1YBLzzf4lwd72718CUMfPHQerhdzCYhIy8qntUz0SOXbhE25e8No8ExY+SVDSHn5x9mR8gC9zEzfy4G8PMuXKKTW34a2ISDVUWFzI2nhzxn230G52jua4o5tg5i2QlQBeoTB0DtRuSbHNIDEzj6NpecSn55GcnU9WfhHZ+UVk5xdjGAZWqwUHiwVnRyu+7k74ujnj4+5EbW9Xwv3cqs0M1PLcGHkjWQVZvLX+LaZum0pGQQbPdHkGq6UG3z/sHwEjfoOFT8DGL2Hp/+DgX2bZNZ8we0cnIlLt6QqmXFASM/L4a08SADe2C4PiImrvXQbA7nqdaGfP4M7S9W1DmbR0LweOZfPFqoM8dHmjCtlup9qdeKDNA0zeMpmXVr1EXa+6tK7VukK2fUZK6qcueQlWvm/WxT20Cm78xGxIJyIi1V7s0XX0zs4xn3S+j4SteQAEVZMZJs6OVur6u3PgWDb7k7Iu7oQJmLNN2g2FupfA9/fgeHQj/4vLoquPHy/bNjNyyUg+7vNx9SoJIyJSg21O2kxOUQ7+rv408W9i73Bg92KYcxdpBbDReyA7mo0m+o8CdicsY39SNkW285t14e7sQF1/d5rU9qJZiDfNQrxpFeaDv0f16IlyZ4s7cXV05dXVrzI7ejYpeSm82u3Vmj0uOrnCdf8HdS6B+Y/BgWUwuStc+y60GmTv6EREqjUlTOSC8vPmo9gMs3F6/UAP2P0rnvmZpFitFDTsZe/wzoqjg5XRvSMZM3sznyzfzx1d6+Ht6lQh276/9f1sP7adFbErGLVkFJ/1/YxIv8gK2fYZsVrhypcguCXMexRiVsHkbtBnPHS4y1wuIiLVUnp+Oh2PbMMRKKjTGefgFiRmbgMgyKt6zDABs4/JgWPZ7EvK4tJGgfYOp3oIaAgjfoU/X4cV79I/PZXWOdk8UbiGUX+M4uM+H+PsUD0uXomI1GSrjp4ox2XPmQxpOQWsWDSHlRs2sd42nr1GOCQBSfFl1nOwWqjt7UqIjyuBni54ujri6eKIu7MDVosFm2FQbBjkF9rIyC0kLbeQ1JwC4tLyiM/II6egmF3xmeyKz+TnzUdLt9sg0IMO9fzoVN+fbpGBhPm6VfEROGFwk8F4Onny7Mpn+e3QbxzKOMQHV3xAmGcNn3HRbijU6Qw/3AdHN8L3I2DXfOj3Fnjo/ElEpDxKmMgFwzAMZq0z+33c2P74Sc3mGQDM9/Sgjm9De4V2zga0CeXDpXvZm5jF538dYEyfxhWyXQerA2/3fJu7Ft/FzuSd3LX4Lj684kPaBrWtkO2fsdY3Q3gH+P5eiF0P88fCllkw4H0IblG1sYiIyBnZFLuKQRmZADhf8hBgzvCE6tPDBCAy2JM/diWyOyHL3qFULw5O0PsFaHA5/Hg/dTNi+fpoPBPzlvCi2wv8r8fr1bZsiohITbEy1mz4bo9yXPuSsli4LY4/diWyOSYVG77A5aXLGwR60KaOL01qe9GktheNg72o7e2Kg/Xcxoa8wmJi03I5kJRNdEImUXEZ7IzLYH9SNvuPmY85G44AEBnkSc/GtejZpBad6vvj6uRQAZ/4zPVr0I/aHrV59M9H2Z26m8G/DObFS1/kynpXVmkcVS4w0ryhYsU7Zj+THT/A/qVw5cvQ9nbd0Cgi8g8Wo8Z3vDp7GRkZ+Pj4kJ6ejre3t73DkeNW70/m1k9W4+HswJpn++BZlIbxbjMsxQUMCq3Nm7cspIFPA3uHedbmbT3KqJmb8HJxZPmTl+NXgdOW0/PTGblkJFuTtuJoceShdg8xrPkwnBwqZibLGbMVw7pPYcnLUJAFFgdodzv0espsFi8iNZ7G1gvH798Oos/O30h188Hv8f3g4Ej//1vB9tgMPhvWkd7Ngu0dIgA/bjrCo7O30DnCn2/vr0YNdauTnBT45RGI+gWAVa6u7L38Me7s8qSdAxORiqCxtXo6knmEa364BqvFypKblxDoVvl38R9KzubnzUdZsC2OXfGZZZY1thzmsnpudOnRlw71/ausTFZaTgEbY1JZfzCV1fuT2Xw4jZMrf7k5OdA9MpBrW4XQu1kQXhVUbeFMxGfH8+jSR9mevB2AmyJv4vGOj18c/b5iN8Dc0ZBgzh6m7vEyXcHN7RuXiEg1ohkmcsGYscacXTKwXZjZIH3NdCzFBWx3dmaPqyt1PC/M/hj9WobQPGQfO+MyeO/33bw8sGWFbdvHxYepV05l/KrxLDq4iIkbJ/LT3p8Y3mI4/SL6VV3NVqsDdLkfmvaHRePMCzcbv4Ct38IlD0C30eDmVzWxiIjI6RUV0GbPCgCOtrkZPwfzVDExIx+oXjNMGgd7AbA7IRPDMDRrojzu/mZfsY1fULTgCS7Ny6Pp4tdZX1BIxx7P2js6EZEa6fdDvwPQIbhDpSZLcgqKWLgtnjkbDrN6f0rp645WC91cD9K34Dd6Ou4g7IaXoc2tlRbH6fi6O3NF02CuaGreaJGeU8hfe4+xbHciy3YnkZCRz287E/htZwLODlYuaxxIv1Yh9G4WjI9b5SZPanvU5strvmTS5kl8vv1zvt/zPSuOrODpLk/Tu27vmn1OEdYB7vsT1kyGpa9BzN/wcTdoexv0ekZN4UVE0AyTculOnernWFY+XV9fQmGxwbyHu9OytjtMbAMZsTwdGMCW0KYsuHGBvcM8Z6v2HeO2qWtwsFpYOLpH6UWgimIYBnP3zeXdDe+SkmeeTDtZnegQ3IEOwR2I9Isk0jeSMM8wHKxVMC360N/w+3g4vMZ87uINXR+CSx4EV5/K37+IVDmNrReGnLWf4L7gCRIdHLCO2UagVxhFxTYaP7cQmwFrn+ldbRq/5xUW0/yFRdgMWPNM72qVzKmWkqJJ/GoAQRkJAKS2vRW/ayeaTWFF5IKksbV6um3+bWw7to1nuzzLrU0rPlGx9Uga36w9zC9bjpKVXwSAxQI9ImsxoKETV25+BN/UreDiA7d8BQ16VngM58swDHbGZbBoezzzt8WxPym7dJmzo5U+zYK4sV04PZvUwsmhcstFrYtfx4urXiQm07xBs1edXjzd+WlCPS+CSghph2Hx06UzUXF0hS4PwKWPgEeAfWMTEbEjJUzKoRPP6mfyn/t4Y9Eu2tbx5aeHusGOH2HOcHJdvOgW4suldXrxYe8P7R3mebn/q/Us3pFAj8hAvry7c6Xc1ZJVkMX3e75nzu45HMo4dMpyR6sj4Z7h1PWuS12vumX+DfEIwdFagZPSDAOiF8Ifr0LiDvM1V1/o9gh0vh9cLoLp0CIXEY2tFwBbMdnvt8Qj4yjTQhty130bATiSmkP3N5bi7GBl1ytXYz3HGueV4Yp3/mR/UjZf3t2ZyxrXsnc41V5xQTZLp/WiT9xu83lQMxwGTYegpvYNTETOicbW6udo1lH6ft8XCxb+GPxHhc0wMQyDP3cnMWXZvjKzSer6u3Nzh3Bu6hBOaMZWmHUb5BwD73C4/TsIalYh+69MhmGwOyGL+dviWLAtjr2JJ3qT+Xs4c12bUG5oF0brcJ9Km/mRV5THJ1s/Ydr2aRQZRThbnbmj+R3c0+qei6NM1+G18NsL5mwTAEc3s1l814fA/8Irey4icr5UkkuqPZvNYOZa8+L+0C51zRfXTAFgdVgzCo14mvpf+H/oP9OvGUt3JbFizzH+2JVYKTXiPZ09GdZiGHc2v5ODGQdZGbuSqJQo9qTuYX/6fvKL8zmYcZCDGQdPea+jxZEwrzDaBbWjV3gvLg27FDdHt3MPxmKBpv2g8dWw8yf483U4ttvsc/L3R9B9DHQcAc5VVDZMRORit/MnPDKOkm61ktR8QOnLR1JzAQj1da1WyRKAprW92J+Uze6ETCVMzoCDswcdhv3Ks99cxdjDewlIjMKYchmWy5+BrqPAQX8aiIicj98O/QZUXDmuwmIb87fG8fGyfaW9SRytFq5tHcKtnerSJcLfHJu3fQc/jYTifAhpA0Nmg3fIee+/KlgsltIG9I/2iWRnXAY/bIzl581HOZaVz/RVB5m+6iANa3lwY/twrm8XRpjvefwdWg5XR1ceaf8I/SL68dra11gXv47Ptn/Gj3t/5ME2D3JT45twslZxH9CqVKcz3LXQvKFx2QSI22L2IF3/OTTpZ/YfbdQHqroXqoiInWiGSTl0p071siQqgRFfrMfL1ZG1z/TBLWkLTL0crI7c36Ibq7IO8F6v9+hTr4+9Qz1vry+MYsqy/YT7ubF4zGV4uFTdhYtiWzGJOYkcyjxETEYMhzMPcyjjEIczD3M48zD5xfll1vd29uamxjdxd4u78XX1Pf8AbMXmif6yCZCy33zNMxguewI63KWLOCIXOI2t1VxRAcZHXbCk7GeSrw9Nbvi8dFz9fsMRHpuzhW6NAphxzyV2DrSs93/fzfu/7+HmDuG8dXMbe4dzwdiWtI1H593G+MREeuTmmS+GtoOBH6npq8gFRGNr9TN0/lC2HtvKM12eYUjTIee8nZyCImavO8ynKw4Qm2beuODh7MCQznW5u3sEoSUJA8OA5W/D0lfN502uhZumgrPH+X4UuysqtrFi7zF+3BjL4h3x5BfZSpd1ifDnxvZhXNMqBO8KbhZvGAbLjizjnfXvlN5IWN+7Pg+1e4ir6l2F1VK5JcLszjDgwHJY9QHs/f3E6x61oNVgaHG92QelKkp5i4jYiRIm5dCJZ/UyeMrfrD2Qwv2XNeDpfs1g5q2weyG21rfQIXsdRbYiFt64kHCvcHuHet6y8ovo+95yYtNyGX5pfV68roW9QwLAZthIzElkb9peVsau5I+YPziafRQALycvRrcfzeAmgytminRxEWz5Bpa9CelmHVmCW8I1b0L9bue/fRGxC42t1dyaT2DhExxzsHJD3fosvm0l7k7mDL+Jv+/hvd93c0vHOrwxqLWdAy1r4bY4HpyxkVZhPvzycHd7h3NBmb1rNq+ufoUbsnN5IT0Px4IssDpBj7HQ/VFwqti7d0Wk4mlsrV4qohxXclY+X/x9iC//PkhaTiEAAR7O3NWtPndcUh8f95OSA0UF8Mto2DLTfN51FFz5co28kJ2ZV8jCbfH8sOlImZJkLo5W+jQP5qb2YfSIrNh+J4W2Qr7f/T0fbf6I1PxUABr7NWZU21H0qtOrZjeGL5GwEzZ9Ddu+heykE6+7+UOj3uaskzqdwS/CrCAhIlJDKGFSDp14Vh+bD6dx/aSVOFot/DXuCmrnRMOUy8BiZf/tsxn412N4OnmyasiqGnPCsmJPEnd8thaAOQ90pVN9fztHdKpiWzHLjyxn0uZJRKdGA9A9rDsTekzAx6WCmrYXFcCG6bD0f5CXZr7WchBc8wZ4VEwtYBGpOhpbq7G8DPigLeQk83KAH/HN+vFRn49KFz/53Ra+XX+EsVc25pHekfaLsxyHkrPp+dafODtY2f5SX5wda/hdnxXIMAye+esZ5u2fR1NHH742gnEpuZPUpy70fRWaXacLICLVmMbW6mXy5sl8tOUjutTuwqd9Pz2r9x5OyWHqiv18u/4weYXmTIp6Ae7c26MBgzqE4+r0jyRIVhLMGQaHVoLFAfq9CZ3uqaiPUq0dSc3h581H+WHjEfad1Cw+wMOZAZXQ7ySrIIuvdn7Flzu/JKvQ7K/SMqAlo9qN4tLQS2vMdYh/VVwIe5eYiZO9v0Neetnl7gHmrJParSAgEgIaQUBDcPPTeYSIXJCUMCmHTjyrj4dmbGT+tjhubB/Gu4PbwqyhsGsetBrMT22u5fmVz9MhuAPTr55u71Ar1LjvtjJ7/WEaBHow/5EeuDlXz7uEbIaNb3Z9w3sb3iO/OJ/63vWZ3Gdyxc72yUmBP16B9dMAwzwZ6/c2tLhBJ18iFxCNrdXYkldgxdvEunrQv7Y/z176IoMaDypdfNvU1azal8y7g9twY/vqNZvTMAzavfIbaTmF/DKqO63CKyhpf5HIKcxh6IKh7E3bS+fgTnwSfi0Ov74AGUfMFSJ6Qt/XoHZL+wYqIuXS2Fp92Awb13x/DUezjzKhxwSubXDtGb1ve2w6U5bvZ/7Wo9iOX5lpFebDAz0bcnXL2jiU1zvsyAb49g7IiAVnLxg83bzT/yJjGAbbYtP5YWMsv2w5SnJ2QemyhrU8uKFdGNe3CyPcr2J6YqbnpzN9x3RmRM0gt8gsk9Y+qD0PtX2ITrU7XRyJEzArQhxZC7sXw8EVEL8NigvKX9fR1Syz7Rls3vTo7GmWi3PxNH92XTzNdRxdwdHFnN3q6HL8+UlfO520jqOreqmISKVTwqQcOvGsHvYkZHLV+8sxDFg0pgdNjQPm7BIs8NAa3jjwE19Hfc3tzW5nXOdx9g63QqXnFnLVe8tIyMjn1k51mHBT9SqB8k+7Unbx8B8PE58dT4hHCJ/1/Yw6XnUqdidHN8HPoyBhu/m82QDoPxE8Aip2PyJSKTS2VlOpB2HSJVCUy+igQJZ6eJxSRuSyN5cSk5LD7PsuoUuD6vc7947P1rBizzFevb4lt19Sz97hXHD2p+3n1vm3kluUy72t7uWRliPgr/dh5USzeTBA84HQcxwEV49SoSJi0thafayKXcX9v9+Pl5MXfwz+A1dH19OuaxgGq/Yl8/GyfazYc6z09csa1+KByxrQtWHA6S++b/wS5j9mXqAOiIRbZ0CtJhX9cS44hcU2VuxJ4sdNR/n1H/1OOtTz45qWtbm6Ze0KSZ4k5ybz+fbPmbVrFgU2M1HQulZr7ml5Dz3r9Kz5PU7+qSjfTJrEboCkXZC8F47thcyjlbdPiwO4+Z5IxngGg2cQeIWAfwNzdotvPXB0rrwYRKRGU8KkHDrxrB4emrmR+Vvj6NsimCm3d4AvBph3MLQcBIM+Y+iCoWxN2sqr3V5lYKOB9g63wq3ae4yhn63BMOC9W9pwQ7vqdVfvPyXmJDJi8QgOZhyktkdtpl89nTDPsIrdSVEBrHgHVrwNtiLwCjWbGtZX3XqR6k5jazVkGPD1jbDvDxJrNaa3Ry6tg9owo9+M0lXyCotp/sIibAasfbY3QV6nvwBkL28vjubDpXsZ3DGcNwep8fu5WLB/AeNWmDeffNT7I3qE9zCTab+/BDt+BI7/udD8erO/SWhbO0UqIifT2Fp9PLTkIZYfWc5tTW/j6S5Pl7tOsc1g4fY4pizbz7ZYs6SRg9VC/9Yh3HdZA1qE/sssyaJ8WDgONkwznze5Fm74GFz1ff+nzLxCFm6P58eNsaw+kMzJV7zahPtwdcsQrm5Zm4hAj/PaT0J2Ap9u+5Qf9vxQmjhp5NuIu1vezdURV+NkvchnQRTmQlYCZCZAVrxZOaIgGwqyID/T/Lcg21yvKB+K8o7/e9LzwrwTX5fcxHGmLA5m4qR2K6jdGkJaQ1hH/Z8RkTOihEk5dOJpf7viM7j6/RUALBzdg2apS+HbO83pl6PWkeMRSLdvulFkFLHopkUVf2G+mnj/9928//seXJ2szL6vK23q+No7pH+VlJPE3Yvv5mDGQRr4NODLa76suJ4mJ4vbAt+NgOQ9YLGad71e9kSNbHAoUlNobK2GtsyGH+8DBxfGNr+E37IO8HjHxxnWYljpKtHxmfR9fzlero5sHX9VtSw3sXhHPPd/tYGmtb1YNOYye4dzwXp19avMjp6Nj4sPc/rPIcQzxFyQsBOWvQE7fzqxcngn6HyfOfPE0cUu8YqIxtbq4lDGIfr/2B+AeTfMo5532dmOeYXFzNlwhKnL9xOTkgOAq5OVWzrW4Z4eDajj/x+zHtKPwJzhcGQdYIHLn4Uej4H1IpvJcA7i0nNZvD2eBdvjWXcwpUzypH6AO72aBNGzSS26Ngg4tU/MGTqWe4yvdn7F7OjZZBeaPVWC3IIY1HgQNzW+iSD3oIr4KGKzmUmTkkRKboqZkMlKhMx489/0w5ByAFL2QWHOqduwWM3kSb1uUK8r1L1UFStEpFxKmJRDJ572N2L6OpbsSuTaViFMGtwcJnWCtBjzovgVz5VOeQ7xCOHXQb/aO9xKU2wzuOeLdSyNTqKWlws/jry0wmqwVpaE7ASGLhhKQk4CnWp3YkqfKThVRo3RgmxY8ARsPn4ndL3u5mwT79CK35eInDeNrdVM9jH4sBPkpnDs0oe4PO4XHC2O/H7z7wS4nfjDccG2OEbO2EjbOr789FA3OwZ8evHpeVzy+hKsFtjx0tXVtu9XdVdQXMCdC+9kR/IOWge2ZvrV08uO3wk74K/3YMdPYCs0X3MPhFY3Q8ubILyjeouJVDGNrdXDK3+/wre7v6V7WHcm95lc+npaTgFfrz7E9FUHOZZlzkDwdXdiWNf6DLu0Pv4eZ1AuaOdcmPsw5KWBqw/c9BlEXllJn6RmS8rM59ed8SzcFs+aA8kUFp+4FObiaOWSBgFc0iCAzhH+tArzwdnx7BJSGQUZzNo1ixlRM0jJSwHAweJArzq9uCbiGi4Lvww3R7cK/UxyGoYBGUchMQrit5qPo5vM2bP/FNwKGveFJtdAaHslIkUEUMKkXDrxtK/lu5O48/O1OFotLBpzGY22T4Tlb5r1KEetBxdPJm6cyKfbPuW6htfxv+7/s3fIlSorv4hBk1exKz6T+gHuzLqvK7V9ql9JlJNFp0QzbNEwsguzub7R9bx86cuVd1fy1m9h3qPmlF73QDNp0vCKytmXiJwzja3ViGHAnGGw82cIbsnb7fvzxa6Z9K7bm/cvf7/Mqv+3ZA/v/Labm9qH887g6lvuqvP/ficxM59v7+9K5wh/e4dzwYrNimXwL4PJKMhgaLOhPNX5qVNXykyAjV/A+mll65P71IWWN0DjqyG8Mzg4Vl3gIhcpja32l5iTyNXfX02hrZDP+35Op9qdOJKaw7SVB/lmbQw5BcUAhPm6cW+PCAZ3qoO78xn8fizIhkVPm79vwbyQO+gzsz+DnLes/CJW7j3Gn9FJ/BmdSFx6Xpnlrk5W2tbxpXNEAG3r+NAi1IcgL5cz+pu2oLiA3w/9zuzo2WxM3Fj6upujG73Ce9EtrBuda3c+MZNTqk7GUTi0Cg6tNP9N2lV2uUctiLzKPJdp1Aecq/fNqiJSeZQwKYdOPO2nqNjGNRNXsCcxi7u7RfBC+zz4tA8YxTBoGrS8EYA7FtzB5qTNvHzpy9wQeYOdo658R9NyGTzlb46k5hIR6MHX93QhzLd6352yMnYlI5eMxGbYeKLjE9zZ4s7K21nyPvPiX/w2wAI9nzTLdKlEl0i1obG1GlkzBRY+CVYniu5awBUrHyc1P5UPr/iQnnV6lll1zKxN/LT5KOOubsqDvRraKeD/9tCMjczfFsfYKxvzSO9Ie4dzQVt2eBmj/hgFwIQeE7i2wbXlr1hcCHt/h+3fw64FcLwMCQAuPtCgp3kDQ71uEBip2ScilUBjq/29sfYNvo76mnZB7RjTfCKfrTzAwm1x2I5fZWkW4s0DPRvQr1UITg5neOd6zBr4+SGz/DAW6D7GLMNVGbP2BcMw2J2QxYo9Saw7mMK6g6mkZBecsl6gpzPNQ31oHuJNsxAv6gd4UD/QAx+3039folOiWXBgAYsPLiY2K7bMsjpedWhTqw2RfpFE+kbS0LchtdxrqfdJVcpKgn1LYPci2LsE8jNOLHPygKb9oMWN0Ki3SpCKXGSUMCmHTjzt5+Nl+5iwcBd+7k78OaYrPl/1MbP+LW6Em80Gd9mF2XT/pjtFRhELb1xIuFf1boZeUQ6n5HDrJ6uJTcsl0NOFz4Z1rPY9Tb7e+TVvrHsDq8XKpN6T6B5Wic3ZC/Ng0VMnGiFG9ISbPgVP1YwVqQ40tlYTR9bD51ebJZWufoNfaoXxzF/PUMutFr8O+hVHa9m7Xvu+t5zohEw+vbMjfZoH2yno//bV6kM8/9N2ujYI4Jv7LrF3OBe8kpm8zlZnPuv7GW2D2v77GwpyYM9iiPoF9i0164qfzM0f6nSBupeYj5C24FS9Z8uKXAg0ttrX4czDDPhxIHnpjalrG87uuOLSZT0iAxnRPYKejWud+Uz7gmxY8gqs+RgwzAoLN0wxE9BSZQzDYF9SFmsPpLL+YArbj6azNzGrNAn2T37uTtQP9KCevzu1fdwI8nIh2NuVYG8Xgrxc8fNwwt3JgR0p21l6eClr49ayI3kHxUbxKduyYCHALYBg92CC3IMIcg+illst81/3WqVf+7r4Vsu+che04kKI+RuiF5nnM+kxJ5a5+ECz/uYNvBE9lbwUuQgoYVIOnXjax97ELPp9sIKCIhtvDmrN4ISJsG4qeATBQ2vA3SyxsWD/AsatGEddr7rMu2HeRXWiEJuWy4jp69gVn4mzg5Un+jZhRPcIrNbKOQZ5hcUYhlnG08lqPev9GIbBi3+/yA97fsDTyZMZ186ggU8lTyPf+i38Msa809UzGAZ9DvUrMVEjImdEY2s1kJUEUy83G2I2H0jxTZ9zwy83ciD9AKPbj+aeVveUWT2noIiW4xdjM2DNM70J9q6+F7j3JWXR+51lODta2Tr+qnNu3CqmYlsxj/75KEsPL8Xf1Z8Z/Wac+Q0qtmI4utmcfXJgOcRugKLcsutYnaB2K7N5fJ3OZv8T33qahSJyljS22k9GXiF3zPqQrfsCMArN3l9ODhauaxPGPT0iaBZylt+PvUtg/tgTPRba3g59XwU3v4oNXM5JXmExu+Iz2XE0nR1HM9ibmMXBY9kkZuaf0fstFvByccTbzQkvVyc8XCzYLNnk2zLILk4nqzCVzKIUDArAUoTFWgSWIrAUln5tsRSBtRAshTg5GIR5BRHhG04j/3q0rNWYTiFt8XPT74EKYRjm+cv272HHj5AZd2KZewA0H2j2cKtziXqeiNRQSpiUQyeeVa+w2MYtU/5mY0walzWuxRdtdmH55WFz4W1zoPFVpeuOWTqGJTFLuLfVvTzS/hE7RWw/mXmFPDp7C79HJQDQJtyHp65pRteGAf/xzvLZbAaHUnLYHptuPo6mE5OSQ3JWQWnNXTD/AAj1dSPcz40WoT50rOdHx/r+/9mssLC4kHt+vYeNiRup512PGf1m4OPic06xnrGkaPh2GCRFgcUKVzwH3R7VyYyIHWlstbO8DPiiP8RtMeuf3/cni+L+5onlT+Dt7M3imxbj6exZ5i3rDqZw88d/E+ztwppn+tgp8DNjGAaXvL6EhIx8Zt7ThUsbBdo7pAteTmEOwxcNJyoligY+Dfji6i/wdfU9+w0VFZjNVmP+hpjVcHgNZCedup5HLTOBUvIIbQcunqeuJyKlNLZWvW1H0pmx5hA/bjpMfpH5mqerlWFdIxjWtT5BZ3tzQcoBWPwsRM83n3uHw4CJEFm9x10xZecXcTA5m4PHcohJySEhI4+kzHwSMvJIyMwjMSOf/CJbFUZkw9nRwMfNmUBPd3zcnPB2dcLHzXz4ujsR5OVKLW8Xgr3MWTB+7s6VdgNmjWCzmecwO36AHT9BzrETy7zDzVknrW42bwTRjR8iNYYSJuXQiWfVe21BFJ8s34+niyOLB3sS9v1As1zI5c+a/SiOyy7MpufsnuQX5/PdgO9o4t/EjlHbj2EYfLP2MP+bv5Ps40mN1uE+3NwhnJ6Ng6gbUH5zsrzCYvYnZROdkMH22Ay2xaaz82gGWSVn+2fJYoHO9f3p1yqEfq1CqOVVfl3P5NxkhswfQlx2HJeEXMLkPpNPKftS4QqyYf5jsOUb83mjK+HGT0pnKolI1dLYakeFeTBjEBxcAe6BcPdiiv0jGPTLIPam7WVkm5E82PbBU9722V8HeGXeTvo0C+bTYR3tEPjZGfvtZn7YGMs93SN4rn9ze4dTIyRkJ3DbgttIzEmkmX8zPu37Kd7O5/n/1zAg7ZBZHu7IOji81kyo2P5xLmKxQlALc/ZJnc5mEiWgkS5GiJxEY2vVyM4vYv7WOGasOcSWI+mlr1udE+jWrJgpN911Zo3cT5aXDis/gFX/B8X5YHGAzvfB5c+Aq76XNYVhGOQX2cjIKyQjt4iMvEIy84rIyC0kI6+Q3IJi8otsxx/F5Bee9HWRjYLjy/IKi8kvLCav0EZuYRG5hUXkFBSSX2RQVHx+46KTg4Vani4E+7hSx8+duv7u1A0w/60X4E6wl6sSKiWKi+DAMnPmSdQvZXueBDY2Eyctb4KA6tv3T0TOjBIm5dCJZ9VatD2OB77eCMBH1/jT7+9bzBPIZgPg5i/LzAqYt38eT694mnre9fjl+l8uqnJc5UnKzOeDJXuYtS6GwuIT/5V93Z2o5++Ol6tZWzMtt4DU7ELi0nPLrb3q4milWYg3LcO8aRnqQ8MgT2p5uuDv6YzVYqHYZpCRW0hsWi4xyTlsOpzGuoMp7E3MKt2Gk4OFvi1qM7RLPS5p4H/K9yY6JZo7Ft5BblEutzW9jae7PF05B+VkhgGbvoIFT0BRHniFwnX/pzu2ROxAY6udFOTAnOFmfwlnLxg+D0LbMiNqBhPWTsDLyYuFNy0sd+bfyBkbWLAtnseubMzDF0Aj9cU74rn/qw2E+brx17jLL/pzhIqyP30/dy26i5S8FFoHtmbKlVNOmY103gpzIW6rmUApeWTEnrqeRxA06HXi4RNWsXGIXGA0tlaeYpvBqn3H+GFjLIt3xJfOvHdysBAWnESC03eEB+bz48Afzu53YkE2rP0EVk6E3FTztYiecM2bENS0Ej6J1HQlSZmY9DjWHt3EqiMbWH10K7n5YBS7YdjcCHCuQyPvVgQ41yU120ZCRj6JGXkkl9Pc/p+cHa3U8XOjYS1PGgd7ERnsSWSQFw1qeVzcJVAL82DPr7D9O7PvSfFJ5dlC2x9PntwIXrXtF6OInDMlTMqhE8+qs+FQCkM/XUNeoY0R7bx4/uCd5oljnUvg9u/LlGIwDIPb5t/G9uTtPNjmQUa2HWnHyKuX5Kx8vt94hN93JrIxJpWi03WkA3zcnGgc7EmLUB9ahvnQKsyHhrU8cHQ4+3JVR1JzWLQ9nl+2xrHlcFrp65FBngzvVp8b24Xj5nziJGrJoSWM+XMMAOO7jmdQ40Fnvc9zEr8d5gyD5L3m8/Z3wlX/091bIlVIY6sdZCXBN7eYNZgdXOD27yDiMuKz4xn400ByinJ4rstz3NL0llPeWmwzaP/Kb6TnFvL9g5fSoV71r6GeV1hM+1d+I6egmLmjutE63NfeIdUYu1N3c/fiu0nPT6d1YGs+7P0hfq6V/DORHguxx2ehHFkPRzeZNz+cLCASGl5uJk/q99C4Lhcdja0VyzAMdsZl8PPmo/y8OZaEjBMXQOsHuHNr57q4+G7i3c0vYcHC530/p2PtM5yBmZsGG78wZ5SUlCUMbAxXPG/eKKgkv1Sg/OJ8VsWuYtHBRSyJWUL+8Yv5bo5uXF3/agY3GUzLwJYUFNk4lmWWEItPzyMmJaf0cSg5h9i0XIpPc23BaoF6AR5EBnkSGWwmUxoHe9GwlifOjhdZKey8DNg1D7Z9B/v/BKOktLkFInpAy0HQ/Dr1JBK5gChhUg6deFaNHUfTuW3qGtJzC7k83MInGQ/iVJAGYR3hjh9P+aN3Xfw67l58Ny4OLiy+aTEBbufWs6Omyy0o5sCxbA6n5pBbUIzNMPB1d8LX3Zk6fu4EejpXyl23O46m8/XqGH7eHFt6B5aPmxNDOtflzq71CPV1A2DKlil8uPlDHC2OTOo9iUvDLq3wWMpVkANLXoY1k83n3mHQ93/Q/Hr9gSJSBTS2VrGEHTBrKKQeMP84GzIL6l5Csa2Yh/54iJWxK2lbqy1fXPMFVsupf9RuPpzG9ZNW4uXqyKbnrzynpLo9PDRzI/O3xqksVyXYmbyTe3+9l4yCDOp71+ejPh9Rx6tO1QVQlG+W79r/p/k4uhGMk+rCWx0hvDM0vAIaXQEhbcF6Ed/5KhcFja3nr9hmsOFQKot3xLN4RzxHUnNLl/m6O9G/dQg3tAunfV1f/o77m4eWPESRrYhRbUdxf5v7/3sHqQdh9cfmrPeC47Pz/epDr6fNO9D1e0oqWXp+OvP2z2NO9Bz2pe8rfb1lQEtuaXoLV9e/GlfH8nvvFBXbOJqWx8HkbPYmZrEnMZM9CVnsTsgkI6/8st6OVgsNannQpLY3TWt70STYiya1vQj3c7s4Zv9mJcHOn2DbHLNvWwmrk3mO0uQaaHw1eIfYLUQR+W9KmJRDJ56Vb/X+ZO79Yj2Z+UW098tjRu5I3MiDupfCkG/AzbfM+oZhcP9v9/N33N8MbjyY57s+b5/A5T9l5hUyZ/0Rpq86SExKDgAOVgvXtKzN3d0jaFfHl3HLx7Hw4EKcrc5MvGIi3cO6V12AB/+Cnx8y/3gB847Ua96A4BZVF4PIRUhjaxWx2WD1R7DkJSguAN965ozNQLOk1rsb3mXa9mk4W52Z1X8WkX7ll9p6e3E0Hy7dS98WwUy5o/r3LymxJCqBEV+sx9fdidVP9764S0VUgv1p+3ng9weIy47D29mbCT0m0CO8h32CyU0zx/T9S2HfUkjZV3a5mx80uNy8ONHwCpXvkhpJY+u5Sc0uYOW+Y6zYfYzfoxLKlCVydbLSq3EQN7QP4/ImQaV3yq+LX8dDSx4ityiXvvX78tZlb53+4m9hntnEfdMM83dUSXK3VjPo+hC0uRUcnCr7Y4qUYRgGW5K2MDt6NosPLqbQVgiAj4sPNzS6gcGNB1PH+8xuhDAMg6TMfHYnHE+iJGaxOz6T6IRMMk+TSPF0caRxsCdNanvT5Pi/TWt74efhXGGfsdpJPWT2O9n2HSTuKLsstD006QdNrobglrqJU6SaUcKkHDrxrDyGYfD16kO8Mi+KgmIbnd1imWobj48lB9reDv3fA8dTB8xFBxbxxPIncLQ68vPAn6nrXdcO0cvZKLYZLIlKYNrKg/y9P7n09TZ1fBnWNZw/Ut5lWewfOFmdeK37a1wdcXXVBVeQY9YNXvm+Wd7DYjWbs/V4DIKaVV0cIhcRja1V4OhmWPwMHFppPm98NVz3IXjWAmD2rtm8uuZVAN7o8Qb9GvQrdzM2m0H3N/7gaHoeH97Wjv6tQ6si+gpRbDPo+dZSjqTm8ur1Lbn9knr2DqnGSchO4NE/H2XbsW1YsDC02VAeaf8Ibo5u9g0s9SDs+8N87F8O+elll9dqCg17m8mTepeCs7tdwhSpSBpbz0xOQRGbYtJYsecYf+1NYsfRDE6+CuLt6kifZsFc1aI2PRvXKlNSGODnvT/z8t8vU2Ar4NLQS/m/K/4PZ4d//M1aVAAHV5iNoHf8CHlpJ5Y1vAK6jjL/1UVRqQZS8lL4cc+PfBv9LUezj5a+3i2sG7c2uZUeYT1wOIfZT4ZhEJeeR3R8JrviM4mOz2BXfCb7krLK9Fw9WZCXC01qe5mzUY4nURoFeda8m14So2DXfIheaJYcPZlHLYi4zCwxGtET/HT+KmJvSpiUQyeelSM2LZcX5+7gt50JAFzjsJ73HP8PV2dnuOoV6Hh3uSeQSTlJDPplECl5KYxsM5IH2z5Y1aHLedp5NINpKw/w8+ajFBSbd1gFe7sQGLyDQ8zE4pjDXS3u4pH2j+Boday6wNJi4NfnYOfPJ15rNgC6PAD1uukPGpEKpLG1EiXthuVvmlP/AZzcoe9r0GE4WCwYhsHHWz7moy0fAXBvq3t5pP0jp93cH7sSuHv6erxdHVn7bJ8L7g/WaSsP8NIvOwn2duHPxy8/5cKXnL+C4gLeXPcms6NnAxDuGc5jHR+jd93e1aPcRnGR2btn3xIzgRK7oWz5LgcXqNf1+OyT3hDUHKwXRtk5kZNpbD2VYRgcSs5hY0wqm2LS2BiTyq74zFP6MDQJ9qJ7ZCCXNwmiSwN/nMopPZlTmMP/1vyPufvmAnB5nct5q+dbuDi4mCtkxsOB5bDnN9i9uGyi1jsM2t5mPvwbVNrnFTkfxbZi/or9i1nRs1gZuxID8/9JqEcoAxoO4JqIa2jo2/C891NYbOPAsezSJEpJQuXkEngns1qgfqDH8ZJe3qUJlbr+7lit1eA843xlxpu/M6IXmL9DCnPKLverb1ZfCe8A4Z0gqAU4VOF1EhGpHgmTSZMm8dZbbxEfH0+bNm34v//7Pzp37nza9efMmcPzzz/PwYMHiYyM5I033qBfvxN3SRqGwfjx45k6dSppaWl069aNyZMnExlZftmJf9KJZ8VKyS7gs7/28/lf+8ktNHCkmKccZzLCYSGWiB5w3f+Bf0S5703PT+euxXexJ3UPjXwb8W3/b3HS9OULVlJmPjPXxPDV6kMcyzIbzzlYbVjcd+HovY0WdW281P0pWga2rNrAjm6GFe9A1NwTr/k3gLZDzZknp/n5FJEzp7G1ghXmmhdo1n9m9nMo0foWuOI58DVnYsZlxfHS3y+x8qg56+S+1vcxqu2o017UttkMBnz4FzuOZnBvjwievfbC6wOSX1RM73eWcSQ1l7u61Wf8AJVcrCx/xf7Fi6teJCHHvBmmdWBr7m55N73q9DqnO1MrTU6KeUFi3xLY+wdkHCm73NUXwo5flAjvaH7t7m+XUEXOxsU+tmbmFbI7oeRO9kx2xWWyKz6j3N4KIT6udG0YQI/IQLo1DCTIu/yeDQCFtkJ+3PMjk7dM5ljuMawWKw+1eZARIT1xOLrZvDv8wAo4Fl32jR5B0LQfNB9o3iVenX4PivyHwxmH+Xb3t/y490fST0r+RfpFcmW9K+ke2p3mAc0rdHzPyi8i+vj/35LZKNEJmaTlFJa7vquTlbr+7tT196BegDv1Atyp6+9OvQAPwv3cyk18VntF+XBkvXk+f2CZ+XVp0/jjnNzN3myh7SC4uVkVo1ZTcPawR8Qip1W/fn3GjBnDmDFj7B3KebN7wmT27NnceeedfPzxx3Tp0oX333+fOXPmEB0dTVBQ0Cnrr1q1issuu4zXX3+d/v37M3PmTN544w02btxIy5bmRdY33niD119/nS+++IKIiAief/55tm3bxs6dO3F1Pf2JUYmL/cSzIhQU2fgrOoEfVu/kt3055NvMgauTZRevOn1Ok9re0OdFiLzqtHfx7zi2gyeWP8HhzMPUcqvFl9d8SbhXeBV+Cqks+UXFzN8ax+crD7A9NuOkJUU4uB+iSaiV2zt05qZWHXBxrMI/NBKjzNr/23840ZQRILCx+bPaoJcuooicI42t58kwIHkfxKyCvb/Dnt+hMNtcZrFC42ug55MQ2haAo1lH+WrnV3y/53tyi3JxtjozrvM4BjcZ/K+7+WDJHt79bTeeLo4sf/Jy/C/QutJ/RicyfNo6ACbc2IpbO6uUZ2XJLszm8+2f8+WOL8krzgMgyC2IqyOu5pqIa2ge0ByrpRpdwDAMOLbnePmuJWYflH/e2Qlm/5+SCxJBzSGoqXk+4GTn0mMiJ6npY2t2fhGJmfkkZOQRl57LoeSc449sDiXnlOk9cjJnBystw7xpX9ePdnX9aF/PlxCf//6/G5MRw9y9P7Fm13d4pR8lorCQloYz3V2C8EqNgfyMf7zDAiFtoEFPsxdBeCclSeSCl1eUx5KYJSw6sIi/jv5Fke1EAtLXxZdLQi6hbVBbWge2pql/0wq/obWkP0ppIjQ+k+iEDPYkZJFfZDvt+6wWCPJypbaPKyE+J/4N8XEjxMeVQE8X/D2d8XJxrB6zYU8nPxMO/Q1H1pnJ2SMbTi0zWsKvvtkbyT/C/Nqvvnn+4ltXpUfFLpKSkvDw8MDd/cL/+bN7wqRLly506tSJDz/8EACbzUadOnV4+OGHeeqpp05Z/5ZbbiE7O5t58+aVvnbJJZfQtm1bPv74YwzDIDQ0lMcee4zHH38cgPT0dIKDg5k+fTq33nrrf8ZU0088K1puViZH4mLZeziOXYeTWB9XyIZ0D/KMEwNnS8sBHnb6iasa+2Hpcr9ZAqGc0gf5xfmsil3F3H1z+T3mdwBCPEKY1HvSaRvTyoXLMAyiEzJZsC2eeVuPsD/pH1NyLTYCvAtpWtuT9uG1aRIcSIiPG6G+rgR5ueJQWdNxC7LNMl2bZ0LM32D7x11qfhHmH0eBkeDf0JyN4hVs3lWmExORcmlsPQOGAbmp5jT9zDgzQXIsGpKiIXEn5CSXXd87DFoNgo4jSHb1ZPux7Ww7to2/j/7N1mNbS1drF9SOFy99kQY+py8JkltQzMQle/h4mdk4+81BrRnc8cwaf1ZXbyzaxeQ/zc8zpHNdHr6iEaG+uthdWY7lHmNm1ExmR88mo+DERUUvZy86BHWgda3WNPRtSAOfBoR4hpwoaWNvxYWQsN28o/PIevMCxT8byJ/Moxb41DEvRvjWMb92DwCPQHAPPP5vgBo6S5W4EMZWwzDIL7KRV1hMTkEx2flFpOcWnvJIyykkLaeAhIx8EjLzSMzIJyu//ObRJ6vt7VparqdpiFm+p2GQR9mbroryITfN7CuSm4qRm0pWxhFSUvaSm7KPwvQYLBlH8S3IIbiomNP+73V0M29MCOsAdbpA/e66kUpqtPT8dP6I+YPlR5azOm41WYVZZZY7WZ1o5NuI+j71ifCOoJ53PWq51yLIPYhabrVwd6q4v42LbQaHU3I4lJJDTHI2B48nUGNSsolJySGv8PTJlJM5O1jx93DG38OZAE9nAjyc8XFzwsvVCS9XRzxdHUu/9nZ1xNPF/NrNyQFXJwdcHK1VWxbMZoPkveb5Sfw282+CxCjITvz397kHgGfwiUfJ9Qo3X3D1MWfXuvqceLh4qzypnLOCggKcnS/MG+1Ox64Jk4KCAtzd3fnuu++4/vrrS18fNmwYaWlp/Pzzz6e8p27duowdO7bM9J7x48fz008/sWXLFvbv30/Dhg3ZtGkTbdu2LV2nZ8+etG3blokTJ/5nXBfCiWeVid0AW781LyAX5rA3w5G3jrbiWJELxwpdOVbsQTblz9oJJI0Bzhu5sW4uLdt0xNKsP3ieOmuohM2wceWcK0nMPfGL/5qIa3i2y7P4uPhU+EeT6md/UhY/btvB3O1RxCS6YhR5nXZdi8XAy9VKgIc73q6OeLuZJzLuzo64OFpxcXSgR2OzLvF5yUs370Ld8xscXmOerPwbZy+zwbKLFzh7mtNkSx6ObubrfcafX0wiFyCNrf8ifht8MwSyEqC4/LtlAXBwNi/S1OsGTa81p+VbLEzZMoUPN39YZlULFjqHdObuFnfTNbTrv95JV1Bk48r3lnEo2bzL/qHLG/JE36YV8tHsyTAM3lwcXZo0AWgZ5s1dl0ZwUwfNWK0sBcUFrIhdwcIDC1l+ZDm5ReXXJ/dy9qKWWy0C3ALwcPLA08kTDycPnur8VNX2MytPTsqJCxIlj6QoM6F5phxdTzoP8AQXzxPnAg5O5v9nB6cTX1uPf+1Xz+zrJ3IGKnpszS8qZuLveyiyGRQVGxTZbBTZDIqLDQptNopPfr3YMNc76evCYhu5BcXkFhaXJkhyC4s5nysO7s4O1PZ2JcjbhXr+HtQNcKd+gAf19nxJ3cL9eJMFRXlmUqQw1/y3KBcK88zXC7LN52fBZnWEgEistZqYM8tqNTn+aKY+AnLRKrQVsi1pG2vi1rD12Fa2HdtWpnRXeTycPPBx9sHdyb10rC/52sXBBSerE7Xca3F3y/Mb90pmpcSl5xGXnkd8ei5xGXnEp+cRl5ZHXEYuyVkF5BQU//fGzoCzgxUXJ/Oag6uTtTSR4urkgLODFUcHC45WCw5WKw5WcLRacbCWvGbB0eH4v1YrVsuJ51YLWC0WLIDFYuGOrvUI9DzNDSbZx46fn+yC1IPmI+0QpB4qZzbcGXJ0AyfXf/nX1TxXsToefzic9LXTief1e0Bkn3M8uhcXm83G22+/zSeffMLhw4cJDg7m/vvv59lnn2Xbtm2MHj2av//+G3d3d2666SbeffddPD09ARg+fDhpaWl07tyZiRMnkp+fz9ixY3nmmWd4+umn+eyzz3B3d+eVV17hrrvuAuDgwYNERETwzTff8MEHH7Bx40YaNWrEpEmT6NmzJwDFxcXcd999/PHHH8THx1O3bl1GjhzJ6NGjS+Mu2XenTp2YNGkSLi4uHDhwoExJLsMweOmll/j8889JSEggICCAQYMG8cEHHwCQmprK6NGj+eWXX8jPz6dnz5588MEHpe00pk+fzpgxY5g9ezZjxozh8OHDdO/enWnTphESElLp3xu7jvbHjh2juLiY4ODgMq8HBweza9euct8THx9f7vrx8fGly0teO906/5Sfn09+fn7p8/R085d+RsY5/pKpSWJ2wPLJpU9TbOEsLLjypBVsQA7u5NHA6RgR7gW0CbTQqb4vDZq0wVLrWrBaySxZ9T+OaUvPlmzJ3ULPOj25vuH1NPRrCPmQca6/8OWCEugC93Zsxr0dm5GWl8bifWtZsmcnexOyScl0xij0xlbsi1HoBTiQlgdpadmn3Z5DUQM6hPx3Gb5/Z4E6vc0HmBdLjm4x7/pO2W8+0g5DdhIU55snJ5n/8vPq4gOdHz3PmESqjpeX1zlNW9fYehbybJAYc+K5q695B5hffQhoBIGNICDSLA/kdNLvtMxMAIKsQRTnFlPfuz7NA5rTMrAll4VdRqB74PHVMv8zhCsaeLEgO4sn+zbhyha1a8z36cFLQ+kU4sqkP/ey7mAqW/fncKyZLxkZStpVpk6+nejUrhNFbYrYnbqbTQmb2J22m4PpB4nJiCG3OJe03DTS0tPYw57S9zlYHHi42cPVoFSGI/i3Nh8lucOSGWAZsZB+5Pgj1nyemwLZKZCbfDypYkB+LmTnAklnt+uwTtB4UAV/HqmOzmV8reyxNbegmA8Xb6uQbZXHycGKm5MVH3cnvF2d8HZzKr3xycfVCR93R4K8XKnl6Uotb2dqebni6XKaSxaLZ0PKXs78k1vMO6jdfNmdm0gqNgpdPCn2CMTqHU6t4FbUC+uCm2998A4tv7RWdjnl+0QuIo3cGtGoQSOGNhiKYRjEZsayP30/hzIPEZMRw+HMwyTnJXMs5xi5xblk5GaQ8R//S+t61WVQ3fMf91yBCB8rET7uQPkzW/IKi0nJLiAlO5+UnEJSsgpIzcknM7eYrPxCMvOKyCooIiuviMx889+svEKyC4opLD6R+c07/qhsvRp44Gw73U2kzhDQxnycrOR8JSsBMhPM6xTZiZCVaCZZ8jMg7/gjP928QbT4+LiSnwNUwO+5Swog+PR9qSvTuf7tai9PP/00U6dO5b333qN79+7ExcWxa9cusrOz6du3L127dmXdunUkJiZyzz33MGrUKKZPn176/j/++IPw8HCWL1/OypUrGTFiRGkrizVr1jB79mzuv/9+rrzySsLDT9ww9sQTT/D+++/TvHlz3n33XQYMGMCBAwcICAjAZrMRHh7OnDlzCAgIYNWqVdx3332EhIQwePCJ8s5LlizB29ub3377rdzP9v333/Pee+8xa9YsWrRoQXx8PFu2bCldPnz4cPbs2cPcuXPx9vZm3Lhx9OvXj507d+LkZM71zMnJ4e233+arr77CarVy++238/jjjzNjxowK/k6Uw7Cj2NhYAzBWrVpV5vUnnnjC6Ny5c7nvcXJyMmbOnFnmtUmTJhlBQUGGYRjGypUrDcA4evRomXVuvvlmY/DgweVuc/z48Qaghx566KGHHnr845Genn5OY7zGVj300EMPPfQ4/eNcxleNrXrooYceeuhx+se5/u1qDxkZGYaLi4sxderUU5Z98sknhp+fn5GVlVX62vz58w2r1WrEx8cbhmEYw4YNM+rVq2cUFxeXrtOkSROjR48epc+LiooMDw8P45tvvjEMwzAOHDhgAMaECRNK1yksLDTCw8ONN95447SxPvTQQ8ZNN91U+nzYsGFGcHCwkZ+fX2a9evXqGe+9955hGIbxzjvvGI0bNzYKCgpO2d7u3bsNwFi5cmXpa8eOHTPc3NyMb7/91jAMw5g2bZoBGHv37i1dZ9KkSUZwcPBp46xIdp1hEhgYiIODAwkJCWVeT0hIoHbt2uW+p3bt2v+6fsm/CQkJZaboJCQklCnRdbKnn36asWPHlj632WykpKQQEBBQ6ZnJjIwM6tSpw+HDh1WipBLo+FYeHdvKpeNbeXRsz46X1+lL4/0be46toO9zZdKxrTw6tpVLx7fy6NievXMZXzW21lw6tpVLx7fy6NhWHh3bs3euf7vaQ1RUFPn5+fTu3bvcZW3atMHDw6P0tW7dumGz2YiOji6tqtSiRQusJ/WeCQ4OpmXLlqXPHRwcCAgIIDGxbM+brl27ln7t6OhIx44diYqKKn1t0qRJfP7558TExJCbm0tBQcEp19RbtWr1r31Lbr75Zt5//30aNGjA1VdfTb9+/RgwYACOjo5ERUXh6OhIly5dStcPCAigSZMmZeJwd3enYcOGpc9DQkJO+SyVxa4JE2dnZzp06MCSJUtKe5jYbDaWLFnCqFGjyn1P165dWbJkSZkeJr/99lvpNzsiIoLatWuzZMmS0m9mRkYGa9as4cEHHyx3my4uLri4lK0L6Ovre16f7Wx5e3vrF2Al0vGtPDq2lUvHt/Lo2Fau6jC2gr7PlUnHtvLo2FYuHd/Ko2NbuTS21nw6tpVLx7fy6NhWHh3bmsnNze28t1FSuqqExWIp9zWbzXbG25w1axaPP/4477zzDl27dsXLy4u33nqLNWvWlFnv5GROeerUqUN0dDS///47v/32GyNHjuStt95i2bJlZxxLeZ/FqKJW7Nb/XqVyjR07lqlTp/LFF18QFRXFgw8+SHZ2dmlDmjvvvJOnn366dP3Ro0ezaNEi3nnnHXbt2sWLL77I+vXrSxMsFouFMWPG8OqrrzJ37ly2bdvGnXfeSWhoaJnG8iIiIiIiIiIiIiIiVSkyMhI3NzeWLFlyyrJmzZqxZcsWsrNP9OxduXIlVquVJk2anPe+V69eXfp1UVERGzZsoFmzZqX7ufTSSxk5ciTt2rWjUaNG7Nu375z24+bmxoABA/jggw/4888/+fvvv9m2bRvNmjWjqKioTBImOTmZ6Ohomjdvfn4froLYdYYJwC233EJSUhIvvPAC8fHxtG3blkWLFpVOL4qJiSkzvejSSy9l5syZPPfcczzzzDNERkby008/lZly9OSTT5Kdnc19991HWloa3bt3Z9GiRbi6nm/zZxERERERERERERGRc+Pq6sq4ceN48skncXZ2plu3biQlJbFjxw6GDh3K+PHjGTZsGC+++CJJSUk8/PDD3HHHHaXXy8/HpEmTiIyMpFmzZrz33nukpqZy9913A2Yi58svv2Tx4sVERETw1VdfsW7dOiIiIs5qH9OnT6e4uJguXbrg7u7O119/jZubG/Xq1SMgIICBAwdy7733MmXKFLy8vHjqqacICwtj4MCB5/35KoLdEyYAo0aNOm0Jrj///POU126++WZuvvnm027PYrHw8ssv8/LLL1dUiJXGxcWF8ePHnzK1WiqGjm/l0bGtXDq+lUfH9uKg73Pl0bGtPDq2lUvHt/Lo2F4c9H2uPDq2lUvHt/Lo2FYeHdua7/nnn8fR0ZEXXniBo0ePEhISwgMPPIC7uzuLFy9m9OjRdOrUCXd3d2666SbefffdCtnvhAkTmDBhAps3b6ZRo0bMnTuXwMBAAO6//342bdrELbfcgsViYciQIYwcOZKFCxee1T58fX2ZMGECY8eOpbi4mFatWvHLL78QEBAAwLRp0xg9ejT9+/enoKCAyy67jAULFpxShsteLEZVFf8SEREREREREREREZEqdfDgQSIiIti0adMpTdylLLv3MBEREREREREREREREbE3JUxEREREREREREREROSip5JcIiIiIiIiIiIiIiJy0dMMExERERERERERERERuegpYSIiIiIiIiIiIiIiIhc9JUxEREREREREREREROSip4SJiIiIiIiIiIiIiIhc9JQwERERERERERERERGRi54SJiIiIiIiIiIiIiIictFTwkRERERERERERERERC56SpiIiIiIiIiIiIiIiMhFTwkTERERERERERERERG56ClhIiIiIiIiIiIiIiJSQy1atIju3bvj6+tLQEAA/fv3Z9++fQAcPHgQi8XCrFmzuPTSS3F1daVly5YsW7aszDaWLVtG586dcXFxISQkhKeeeoqioqJ/3e+6deu48sorCQwMxMfHh549e7Jx48Yy61gsFiZPnsw111yDm5sbDRo04LvvviuzzrZt27jiiitwc3MjICCA++67j6ysrAo4MqdSwkRERERERERERERE5CwZhkFOQVGVPwzDOKs4s7OzGTt2LOvXr2fJkiVYrVZuuOEGbDZb6TpPPPEEjz32GJs2baJr164MGDCA5ORkAGJjY+nXrx+dOnViy5YtTJ48mc8++4xXX331X/ebmZnJsGHD+Ouvv1i9ejWRkZH069ePzMzMMus9//zz3HTTTWzZsoWhQ4dy6623EhUVVRp737598fPzY926dcyZM4fff/+dUaNGndUxOFMW42yProiIiIiIiIiIiIjIRS6noIjmLyyu8v3ufLkv7s6O5/z+Y8eOUatWLbZt24anpycRERFMmDCBcePGAVBUVERERAQPP/wwTz75JM8++yzff/89UVFRWCwWAD766CPGjRtHeno6VuuZzcuw2Wz4+voyc+ZM+vfvD5gzTB544AEmT55cut4ll1xC+/bt+eijj5g6dSrjxo3j8OHDeHh4ALBgwQIGDBjA0aNHCQ4OPufjUB7NMBERERERERERERERqaH27NnDkCFDaNCgAd7e3tSvXx+AmJiY0nW6du1a+rWjoyMdO3YsneURFRVF165dS5MlAN26dSMrK4sjR44QExODp6dn6eO1114DICEhgXvvvZfIyEh8fHzw9vYmKyurzH7/ue+S5yfvu02bNqXJkpJ922w2oqOjK+DolHXuaSgRERERERERERERkYuUm5MDO1/ua5f9no0BAwZQr149pk6dSmhoKDabjZYtW1JQUFAh8YSGhrJ58+bS5/7+/gAMGzaM5ORkJk6cSL169XBxcaFr164Vtt/KoBkmUuOVNC46+T/tuejVqxdjxoypkJhqmuHDh3P99dfbO4yz9uKLL9K2bdvz2kZF/XwNHz6cF1988by28U8V8flERMqjsbXyaWzV2CoiFx+Nr5VP46vGV5GKZrFYcHd2rPLHyTM9/ktycjLR0dE899xz9O7dm2bNmpGamnrKeqtXry79uqioiA0bNtCsWTMAmjVrxt9//12md8rKlSvx8vIiPDwcR0dHGjVqVPooSZisXLmSRx55hH79+tGiRQtcXFw4duzYv+675PnJ+96yZQvZ2dll9m21WmnSpMkZH4czpYSJyBn64YcfeOWVVypkWxV1olKT/Pnnn1gsFtLS0uwdylmpU6cOcXFxtGzZ0t6hVBtxcXHcdtttNG7cGKvVqj/WROS0NLZWLo2tNccPP/zAlVdeSa1atfD29qZr164sXlz1tbJF5MKg8bVyaXytOf766y+6detGQEAAbm5uNG3alPfee8/eYYlUOD8/PwICAvjkk0/Yu3cvf/zxB2PHjj1lvUmTJvHjjz+ya9cuHnroIVJTU7n77rsBGDlyJIcPH+bhhx9m165d/Pzzz4wfP56xY8f+a/+SyMhIvvrqK6KiolizZg1Dhw7Fzc3tlPXmzJnD559/zu7duxk/fjxr164tbeo+dOhQXF1dGTZsGNu3b2fp0qU8/PDD3HHHHRXevwSUMBE5Y/7+/nh5edk7DKlmHBwcqF27No6OF16Fw8qa/pifn0+tWrV47rnnaNOmTaXsQ0RqBo2tUh6Nradavnw5V155JQsWLGDDhg1cfvnlDBgwgE2bNlXK/kTkwqbxVcqj8fVUHh4ejBo1iuXLlxMVFcVzzz3Hc889xyeffFIp+xOxF6vVyqxZs9iwYQMtW7bk0Ucf5a233jplvQkTJjBhwgTatGnDX3/9xdy5cwkMDAQgLCyMBQsWsHbtWtq0acMDDzzAiBEjeO655/5135999hmpqam0b9+eO+64g0ceeYSgoKBT1nvppZeYNWsWrVu35ssvv+Sbb76hefPmALi7u7N48WJSUlLo1KkTgwYNonfv3nz44YcVcHROpYSJ1Ag2m40333yTRo0a4eLiQt26dfnf//5XZp39+/dz+eWX4+7uTps2bfj7779LlyUnJzNkyBDCwsJwd3enVatWfPPNN2Xe/89pzfXr1+e1117j7rvvxsvLi7p1657xoBoREQFAu3btsFgs9OrVi+XLl+Pk5ER8fHyZdceMGUOPHj0AmD59Or6+vvz0009ERkbi6upK3759OXz4cJn3/Pzzz7Rv3x5XV1caNGjASy+9RFFR0RnFdj5eeuml0jsfH3jggTInNTabjddff52IiAjc3Nxo06YN3333HWDetXT55ZcDZtbbYrEwfPhwABYtWkT37t3x9fUlICCA/v37s2/fvjOO6ciRIwwZMgR/f388PDzo2LEja9asKbPOV199Rf369fHx8eHWW28lMzOzdNl/7f+fd1yV3G20ZMkSOnbsiLu7O5deemmFNKH6r88yYcIEgoOD8fLyYsSIEeTl5ZV5f8n08//973+EhoZWyrRFMP9vTJw4kTvvvBMfH59K2YeIVD6NrRpbT0dj6wlVNba+//77PPnkk3Tq1InIyEhee+01IiMj+eWXXyplfyJSeTS+anw9HY2vJ1TV+NquXTuGDBlCixYtqF+/Prfffjt9+/ZlxYoVlbI/EXvq06cPO3fuJC8vjy1bttCzZ08MwyhTprBZs2asWbOG/Px8duzYUfr7rkTPnj1Zu3Yt+fn5xMXFMWHChP9MwrZr145169aRm5vL7t27GTRoEAcPHjylGkloaCi//voreXl5HDhwgMGDB5dZ3qpVK/744w9yc3NJTk7mk08+wdPT87yOyekoYSI1wtNPP82ECRN4/vnn2blzJzNnzjxlStazzz7L448/zubNm2ncuDFDhgwpPRHLy8ujQ4cOzJ8/n+3bt3Pfffdxxx13sHbt2n/d7zvvvEPHjh3ZtGkTI0eO5MEHHzyjk4uS7f7+++/ExcXxww8/cNlll9GgQQO++uqr0vUKCwuZMWNG6fQ3gJycHP73v//x5ZdfsnLlStLS0rj11ltLl69YsYI777yT0aNHs3PnTqZMmcL06dNPOQk/2YwZM/D09PzXx3+dMCxZsoSoqCj+/PNPvvnmG3744Qdeeuml0uWvv/46X375JR9//DE7duzg0Ucf5fbbb2fZsmXUqVOH77//HoDo6Gji4uKYOHEiANnZ2YwdO5b169ezZMkSrFYrN9xwAzab7T+Pc1ZWFj179iQ2Npa5c+eyZcsWnnzyyTLv3bdvHz/99BPz5s1j3rx5LFu2jAkTJpQuP9f9P/vss7zzzjusX78eR0fHMt/Dc/Ffn+Xbb7/lxRdf5LXXXmP9+vWEhITw0UcfnbKdJUuWEB0dzW+//ca8efPK3deKFSv+8+dhxowZ5/V5RKT609iqsbU8Glurx9hqs9nIzMwsrQ0tIhcOja8aX8uj8bV6jK+bNm1i1apV9OzZ89w+vIjUDIbIBS4jI8NwcXExpk6dWu7yAwcOGIDx6aeflr62Y8cOAzCioqJOu91rr73WeOyxx0qf9+zZ0xg9enTp83r16hm333576XObzWYEBQUZkydP/s+YS2LatGlTmdffeOMNo1mzZqXPv//+e8PT09PIysoyDMMwpk2bZgDG6tWrS9eJiooyAGPNmjWGYRhG7969jddee63Mdr/66isjJCTktPFkZGQYe/bs+ddHTk7Oad8/bNgww9/f38jOzi59bfLkyYanp6dRXFxs5OXlGe7u7saqVavKvG/EiBHGkCFDDMMwjKVLlxqAkZqaetr9GIZhJCUlGYCxbdu2f13PMAxjypQphpeXl5GcnFzu8vHjxxvu7u5GRkZG6WtPPPGE0aVLlzPe/z+/lyWf4/fffy99z/z58w3AyM3NPe12hw0bZowfP/6cP0vXrl2NkSNHlnmtS5cuRps2bcrsIzg42MjPzz/tfgzDMHJycv7z5+HkY/Zv/vn/RkQuDBpbNbaejsZW+4+thmH+XPv5+RkJCQln/B4RsT+NrxpfT0fjq33H17CwMMPZ2dmwWq3Gyy+//J/ri9Q0p/tdX1UA48cff7TLvstz4RUuFPmHqKgo8vPz6d2797+u17p169KvQ0JCAEhMTKRp06YUFxfz2muv8e233xIbG0tBQQH5+fm4u7uf8TYtFgu1a9cmMTHxnD/L8OHDee6551i9ejWXXHIJ06dPZ/DgwXh4eJSu4+joSKdOnUqfN23aFF9fX6KioujcuTNbtmxh5cqVZe7KKS4uJi8vj5ycnHI/k5eX13nXuG3Tpk2ZbXft2pWsrCwOHz5MVlYWOTk5XHnllWXeU1BQQLt27f51u3v27OGFF15gzZo1HDt2rPSulJiYmP9sVrd582batWv3r3df1q9fv8xnDwkJKfM9PNf9n+7nrW7duv8a87l+lqioKB544IEyr3Xt2pWlS5eWea1Vq1Y4Ozv/677c3Nxo1KjROcUpIjWDxlaNraejsdX+Y+vMmTN56aWX+Pnnn8ut/ywi1ZfGV42vp6Px1b7j64oVK8jKymL16tU89dRTNGrUiCFDhpz3dkUuFPXr18cwDLvt3577Lo8SJnLBc3NzO6P1nJycSr+2WCwApScQb731FhMnTuT999+nVatWeHh4MGbMmP9sLHbyNku2eybTbU8nKCiIAQMGMG3aNCIiIli4cCF//vnnWW0jKyuLl156iRtvvPGUZa6uruW+Z8aMGdx///3/ut2FCxeW1qM9W1lZWQDMnz+fsLCwMstcXFz+9b0DBgygXr16TJ06ldDQUGw2Gy1btjyjpm9n8rPxX9/Dc93/v/28nYsz/Tn/Lyf/AXM6K1as4JprrvnXdaZMmcLQoUMrJCYRqX40tpalsfUEja2nqsqxddasWdxzzz3MmTOHPn36nFWcImJ/Gl/L0vh6gsbXU1Xl+FrSq6dVq1YkJCTw4osvKmEichFTwkQueJGRkbi5ubFkyRLuueeec9rGypUrGThwILfffjtgnhzs3r2b5s2bV2SopUrukiguLj5l2T333MOQIUMIDw+nYcOGdOvWrczyoqIi1q9fT+fOnQGzbmpaWhrNmjUDoH379kRHR5/VXRbXXXcdXbp0+dd1/nmy+E9btmwhNze39ORo9erVeHp6UqdOHfz9/XFxcSEmJua0tUDLOybJyclER0czderU0hPev/7664w/V+vWrfn0009JSUk5pxrf57v/ivRfn6WkMdedd95Z+trq1avPaV8dO3YsbQR4Ov+ssywiNYvGVo2tp6Ox1X5j6zfffMPdd9/NrFmzuPbaa88pDhGxL42vGl9PR+Nr9fnb1WazkZ+ff07xiEjNoISJXPBcXV0ZN24cTz75JM7OznTr1o2kpCR27NjBiBEjzmgbkZGRfPfdd6xatQo/Pz/effddEhISKu2kMygoCDc3NxYtWkR4eDiurq74+PgA0LdvX7y9vXn11Vd5+eWXT3mvk5MTDz/8MB988AGOjo6MGjWKSy65pPQk9IUXXqB///7UrVuXQYMGYbVa2bJlC9u3b+fVV18tN56KmNZcUFDAiBEjeO655zh48CDjx49n1KhRWK1WvLy8ePzxx3n00Uex2Wx0796d9PR0Vq5cibe3N8OGDaNevXpYLBbmzZtHv379cHNzw8/Pj4CAAD755BNCQkKIiYnhqaeeOuOYhgwZwmuvvcb111/P66+/TkhICJs2bSI0NJSuXbv+5/vPd/8V6b8+y+jRoxk+fDgdO3akW7duzJgxgx07dtCgQYOz3ldFTGsuOWnNysoiKSmJzZs34+zsXGn/p0SkYmls1dh6Ohpb7TO2zpw5k2HDhjFx4kS6dOlCfHx86XZLfs5FpPrT+Krx9XQ0vtpnfJ00aRJ169aladOmACxfvpy3336bRx555Jy3KSIXPqu9AxCpCM8//zyPPfYYL7zwAs2aNeOWW245q3qszz33HO3bt6dv37706tWL2rVrc/3111davI6OjnzwwQdMmTKF0NBQBg4cWLrMarUyfPhwiouLy9xxUcLd3Z1x48Zx22230a1bNzw9PZk9e3bp8r59+zJv3jx+/fVXOnXqxCWXXMJ7771HvXr1Ku3zAPTu3ZvIyEguu+wybrnlFq677jpefPHF0uWvvPIKzz//PK+//jrNmjXj6quvZv78+aVTX8PCwnjppZd46qmnCA4OLj1hnTVrFhs2bKBly5Y8+uijvPXWW2cck7OzM7/++itBQUH069ePVq1aMWHCBBwcHM7o/ee7/4r0X5/llltu4fnnn+fJJ5+kQ4cOHDp0iAcffNAusQK0a9eOdu3asWHDBmbOnEm7du3o16+f3eIRkbOnsVVja3k0ttpnbP3kk08oKirioYceIiQkpPQxevRou8QjIudO46vG1/JofLXP+Gqz2Xj66adp27YtHTt2ZNKkSbzxxhvlJgBF5OJhMapbVxURYcSIESQlJTF37twyr0+fPp0xY8aQlpZmn8Ck0gwfPpz69euXOVEXEZGKo7H14qOxVUSk8ml8vfhofBWRmk4luUSqkfT0dLZt28bMmTNPOeEUERGRs6exVUREpOJpfBURkTMxfPhw0tLS+Omnn855G1WdhFdJLpFK8Nprr+Hp6Vnu45prrjnt+wYOHMhVV13FAw88wJVXXlmFEV+YzvU4i4jIhUdja9XQ2CoicnHR+Fo1NL6KiFw4VJJLpBKkpKSQkpJS7jI3NzfCwsKqOKKaqSYd559++glfX1969epl71BERKqlmvQ7vzqrScdZY6uIyH+rSb/3q7OadJw1vorI2bgQZ5goYSIiIiIiIiIiIiIiUkMtWrSIV199le3bt+Pg4EDXrl2ZOHEiDRs25ODBg0RERPDNN9/wwQcfsHHjRho1asSkSZPo2bNn6TaWLVvGE088wZYtW/D392fYsGG8+uqrODqevutHScKke/fuvPPOOxQUFHDrrbfy/vvv4+TkBEBqaiqjR4/ml19+IT8/n549e/LBBx8QGRkJlJ8wmTx5Mm+//TaHDx8mIiKC5557jjvuuKNCjpVKcomIiIiIiIiIiIiI1FDZ2dmMHTuW9evXs2TJEqxWKzfccAM2m610nSeeeILHHnuMTZs20bVrVwYMGEBycjIAsbGx9OvXj06dOrFlyxYmT57MZ599xquvvvqf+166dCn79u1j6dKlfPHFF0yfPp3p06eXLh8+fDjr169n7ty5/P333xiGQb9+/SgsLCx3ez/++COjR4/mscceY/v27dx///3cddddLF269PwO0nGaYVIOwzDIzMzEy8sLi8Vi73BEREQueBpbRUREKpbGVhERkWrAMKAwp+r36+QO5zH+Hzt2jFq1arFt2zY8PT2JiIhgwoQJjBs3DoCioiIiIiJ4+OGHefLJJ3n22Wf5/vvviYqKKj3v+Oijjxg3bhzp6elYreXPyxg+fDh//vkn+/btw8HBAYDBgwdjtVqZNWsWe/bsoXHjxqxcuZJLL70UgOTkZOrUqcMXX3zBzTfffMoMk27dutGiRQs++eST0v0MHjyY7Oxs5s+ff87HpMTp58tcxDIzM/Hx8SE9PR1vb297hyMiInLB09gqIiJSsTS2ioiIVAOFOfBaaNXv95mj4Oxxxqvv2bOHF154gTVr1nDs2LHSmSUxMTE0b94cgK5du5au7+joSMeOHYmKigIgKiqKrl27lrlJo1u3bmRlZXHkyBGA0u0APPPMMzzzzDMAtGjRojRZAhASEsK2bdtKt+vo6EiXLl1KlwcEBNCkSZPSff9TVFQU9913X5nXunXrxsSJE8/4ePwbJUxERERERERERERERGqoAQMGUK9ePaZOnUpoaCg2m42WLVtSUFBQIdsPDQ1l8+bNpc/9/f1Lvy7pVVLCYrGUKQVW3ShhIiIiIiIiIiIiIiJytpzczdke9tjvGUpOTiY6OpqpU6fSo0cPAP76669T1lu9ejWXXXYZYJbk2rBhA6NGjQKgWbNmfP/99xiGUTrLZOXKlXh5eREeHo7VaqVRo0Zn/TGaNWtGUVERa9asKVOSKzo6usyMlX++Z+XKlQwbNqz0tZUrV552/bOlhImIiIiIiIiIiIiIyNmyWM6qNJY9+Pn5ERAQwCeffEJISAgxMTE89dRTp6w3adIkIiMjadasGe+99x6pqancfffdAIwcOZL333+fhx9+mFGjRhEdHc348eMZO3bsafuXnInIyEgGDhzIvffey5QpU/Dy8uKpp54iLCyMgQMHlvueJ554gsGDB9OuXTv69OnDL7/8wg8//MDvv/9+znGc7Nw/jYiIiIiIiIiIiIiIVFslDdY3bNhAy5YtefTRR3nrrbdOWW/ChAlMmDCBNm3a8NdffzF37lwCAwMBCAsLY8GCBaxdu5Y2bdrwwAMPMGLECJ577rnzjm/atGl06NCB/v3707VrVwzDYMGCBaeU8ipx/fXXM3HiRN5++21atGjBlClTmDZtGr169TrvWAAshmEYFbKlGiQjI0PN80RERCqQxlYREZGKpbFVREREKsLBgweJiIhg06ZNtG3b1t7h2J1mmIiIiIiIiIiIiIiIyEVPCRMREREREREREREREbnoqem7iIiIiIiIiIiIiMhFqH79+qhrxwmaYSIiIiIiIiIiIiIiIhc9JUxEREREREREREREROSip4SJiIiIiIiIiIiIiIhc9JQwERERERERERERERGRi54SJiIiIiIiIiIiIiIictFTwkRERERERERERERERC56SpiIiIiIiIiIiIiIiNRQvXr1YsyYMef8/hdffJG2bduedxwWi4WffvrpvLZxvp/lvyhhIiIiIiIiIiIiIiIiFz0lTERERERERERERERE5KKnhImIiIiIiIiIiIiISA1ms9l48skn8ff3p3bt2rz44ouly2JiYhg4cCCenp54e3szePBgEhIS/nVbL7/8MuHh4bi4uNC2bVsWLVp0RnEcO3aMG264AXd3dyIjI5k7d26Z5cuWLaNz5864uLgQEhLCU089RVFR0Wm3l5qayp133omfnx/u7u5cc8017Nmz54xiKY8SJiIiIiIiIiIiIiIiNdgXX3yBh4cHa9as4c033+Tll1/mt99+w2azMXDgQFJSUli2bBm//fYb+/fv55ZbbjnttiZOnMg777zD22+/zdatW+nbty/XXXfdGSUqXnrpJQYPHszWrVvp168fQ4cOJSUlBYDY2Fj69etHp06d2LJlC5MnT+azzz7j1VdfPe32hg8fzvr165k7dy5///03hmHQr18/CgsLz/4gAY7n9C6RGm5z4mbG/jmWjrU78mq3V3F2cLZ3SCIiIiIiIiIiIlKNGIZBblFule/XzdENi8VyVu9p3bo148ePByAyMpIPP/yQJUuWALBt2zYOHDhAnTp1APjyyy9p0aIF69ato1OnTqds6+2332bcuHHceuutALzxxhssXbqU999/n0mTJv1rHMOHD2fIkCEAvPbaa3zwwQesXbuWq6++mo8++og6derw4YcfYrFYaNq0KUePHmXcuHG88MILWK1l53/s2bOHuXPnsnLlSi699FIAZsyYQZ06dfjpp5+4+eabz+oYgRImIqcwDIPX1rxGUm4SCw8spF1QO4Y0HWLvsERERERERERERKQayS3KpcvMLlW+3zW3rcHdyf2s3tO6desyz0NCQkhMTCQqKoo6deqUJksAmjdvjq+vL1FRUackTDIyMjh69CjdunUr83q3bt3YsmULYCZCXnvttdJlO3fupG7duqfE4eHhgbe3N4mJiQBERUXRtWvXMsmgbt26kZWVxZEjR0q3USIqKgpHR0e6dDnxPQgICKBJkyZERUWd+cE5iUpyifzD/vT9RKWc+A/126Hf7BiNiIiIiIiIiIiIyPlxcnIq89xisWCz2SplXw888ACbN28ufYSGhtoljnOhGSYi/7AxcSMAYZ5hxGbFsiFhAzmFOWedtRUREREREREREZGay83RjTW3rbHLfitKs2bNOHz4MIcPHy6dZbJz507S0tJo3rz5Ket7e3sTGhrKypUr6dmzZ+nrK1eupHPnzgD4+/vj7+9/TrF8//33GIZROstk5cqVeHl5ER4eXu76RUVFrFmzprQkV3JyMtHR0eXGfiaUMBH5h82JmwHo36A/P+z5gaTcJHan7qZtUFu7xiUiIiIiIiIiIiLVh8ViueBvsu7Tpw+tWrVi6NChvP/++xQVFTFy5Eh69uxJx44dy33PE088wfjx42nYsCFt27Zl2rRpbN68mRkzZpxXLCNHjuT999/n4YcfZtSoUURHRzN+/HjGjh17Sv8SMHuxDBw4kHvvvZcpU6bg5eXFU089RVhYGAMHDjynGFSSS+QfdqXsAqBVYCsa+zcGYHfqbnuGJCIiIiIiIiIiIlLhLBYLP//8M35+flx22WX06dOHBg0aMHv27NO+55FHHmHs2LE89thjtGrVikWLFjF37lwiIyPPK5awsDAWLFjA2rVradOmDQ888AAjRozgueeeO+17pk2bRocOHejfvz9du3bFMAwWLFhwSumvM2UxDMM41w9QU2VkZODj40N6ejre3t72DkeqkM2w0WVGF/KK85h/w3y+3/M9n2//nMGNB/N81+ftHZ6IyAVLY6uIiEjF0tgqIiIiUvE0w0TkJIk5ieQV5+FocSTUM5RGvo0AOJBxwM6RiYiIiIiIiIiIiEhlUsJE5CQH0s3ESLhXOI5WR+p4mY2OYjNj7RmWiIiIiIiIiIiIiFQyJUxEThKTEQNAfe/6AIR5hgEQnxNPoa3QXmGJiIiIiIiIiIiISCVTwkTkJHHZcQCEeoYCEOgWiIuDCzbDRnx2vD1DExEREREREREREZFKpISJyEkSchIACPYIBsBisZQmT2KzVJZLREREREREREREpKZSwkTkJKUJE/fg0tdKEiZHs47aJSYRERERERERERERqXxKmIicpKTsVm2P2qWvlSRPknKS7BKTiIiIiIiIiIiIiFQ+JUxEjjMMg4TsU2eY1HKrBUBSrhImIiIiIiIiIiIiIjWVEiYix6Xlp1FgKwAgyD2o9PWSrxNzEu0Sl4iIiIiIiIiIiIhUPiVMRI47lnsMAF8XX5wdnEtfD3Q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",
      "text/plain": [
       "<Figure size 1614.38x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(results_loop.sort_values(['comparison', 'chain_type', 'cdr']),\n",
    "            x='rmsd',\n",
    "            hue='comparison',\n",
    "            col='cdr',\n",
    "            row='chain_type',\n",
    "            kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0675146e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "comparison  chain_type   cdr\n",
       "apo-apo     alpha_chain  1      10\n",
       "                         2      10\n",
       "                         3      10\n",
       "            beta_chain   1      10\n",
       "                         2      10\n",
       "                         3      10\n",
       "apo-holo    alpha_chain  1      22\n",
       "                         2      21\n",
       "                         3      22\n",
       "            beta_chain   1      22\n",
       "                         2      22\n",
       "                         3      22\n",
       "holo-holo   alpha_chain  1      30\n",
       "                         2      30\n",
       "                         3      30\n",
       "            beta_chain   1      30\n",
       "                         2      30\n",
       "                         3      30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_loop.groupby(['comparison', 'chain_type', 'cdr']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03cceaf8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:56.529239Z",
     "iopub.status.busy": "2024-05-16T15:31:56.528830Z",
     "iopub.status.idle": "2024-05-16T15:31:56.543936Z",
     "shell.execute_reply": "2024-05-16T15:31:56.543077Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=123.30531506577063, pvalue=3.608293657679283e-18)\n"
     ]
    }
   ],
   "source": [
    "treatment_options = ['comparison', 'chain_type', 'cdr']\n",
    "treatments = [(group, df['rmsd'].to_numpy()) for group, df in results_loop.groupby(treatment_options)]\n",
    "treatments\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ef04b7ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:56.547836Z",
     "iopub.status.busy": "2024-05-16T15:31:56.547462Z",
     "iopub.status.idle": "2024-05-16T15:31:56.610714Z",
     "shell.execute_reply": "2024-05-16T15:31:56.609592Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002777777777777778\n"
     ]
    },
    {
     "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>comparison_x</th>\n",
       "      <th>chain_type_x</th>\n",
       "      <th>cdr_x</th>\n",
       "      <th>comparison_y</th>\n",
       "      <th>chain_type_y</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.317380</td>\n",
       "      <td>2.05e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.311860</td>\n",
       "      <td>1.90e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.718679</td>\n",
       "      <td>2.00e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.310884</td>\n",
       "      <td>9.30e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.951478</td>\n",
       "      <td>5.10e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>1.093216</td>\n",
       "      <td>2.74e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.666887</td>\n",
       "      <td>9.55e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468033</td>\n",
       "      <td>1.42e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.894427</td>\n",
       "      <td>3.71e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>0.374817</td>\n",
       "      <td>7.08e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.829510</td>\n",
       "      <td>6.73e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>0.562226</td>\n",
       "      <td>5.74e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>2.778254</td>\n",
       "      <td>5.47e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>1.588535</td>\n",
       "      <td>1.12e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>4.611902</td>\n",
       "      <td>3.99e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>0.685303</td>\n",
       "      <td>4.93e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>1.648431</td>\n",
       "      <td>9.93e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>4.278511</td>\n",
       "      <td>1.88e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   comparison_x chain_type_x  cdr_x comparison_y chain_type_y  cdr_y  \\\n",
       "0       apo-apo  alpha_chain      1     apo-holo  alpha_chain      1   \n",
       "1       apo-apo  alpha_chain      1    holo-holo  alpha_chain      1   \n",
       "2       apo-apo  alpha_chain      2     apo-holo  alpha_chain      2   \n",
       "3       apo-apo  alpha_chain      2    holo-holo  alpha_chain      2   \n",
       "4       apo-apo  alpha_chain      3     apo-holo  alpha_chain      3   \n",
       "5       apo-apo  alpha_chain      3    holo-holo  alpha_chain      3   \n",
       "6       apo-apo   beta_chain      1     apo-holo   beta_chain      1   \n",
       "7       apo-apo   beta_chain      1    holo-holo   beta_chain      1   \n",
       "8       apo-apo   beta_chain      2     apo-holo   beta_chain      2   \n",
       "9       apo-apo   beta_chain      2    holo-holo   beta_chain      2   \n",
       "10      apo-apo   beta_chain      3     apo-holo   beta_chain      3   \n",
       "11      apo-apo   beta_chain      3    holo-holo   beta_chain      3   \n",
       "12     apo-holo  alpha_chain      1    holo-holo  alpha_chain      1   \n",
       "13     apo-holo  alpha_chain      2    holo-holo  alpha_chain      2   \n",
       "14     apo-holo  alpha_chain      3    holo-holo  alpha_chain      3   \n",
       "15     apo-holo   beta_chain      1    holo-holo   beta_chain      1   \n",
       "16     apo-holo   beta_chain      2    holo-holo   beta_chain      2   \n",
       "17     apo-holo   beta_chain      3    holo-holo   beta_chain      3   \n",
       "\n",
       "    statistic     p_val  significant  \n",
       "0   -2.317380  2.05e-02        False  \n",
       "1   -1.311860  1.90e-01        False  \n",
       "2   -3.718679  2.00e-04         True  \n",
       "3   -3.310884  9.30e-04         True  \n",
       "4   -1.951478  5.10e-02        False  \n",
       "5    1.093216  2.74e-01        False  \n",
       "6   -1.666887  9.55e-02        False  \n",
       "7   -1.468033  1.42e-01        False  \n",
       "8   -0.894427  3.71e-01        False  \n",
       "9    0.374817  7.08e-01        False  \n",
       "10  -1.829510  6.73e-02        False  \n",
       "11   0.562226  5.74e-01        False  \n",
       "12   2.778254  5.47e-03        False  \n",
       "13   1.588535  1.12e-01        False  \n",
       "14   4.611902  3.99e-06         True  \n",
       "15   0.685303  4.93e-01        False  \n",
       "16   1.648431  9.93e-02        False  \n",
       "17   4.278511  1.88e-05         True  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = []\n",
    "for pairing in list(itertools.combinations(treatments, 2)):\n",
    "    chain_type_x = pairing[0][0][1]\n",
    "    cdr_x = pairing[0][0][2]\n",
    "    chain_type_y = pairing[1][0][1]\n",
    "    cdr_y = pairing[1][0][2]\n",
    "\n",
    "    if (chain_type_x, cdr_x) == (chain_type_y, cdr_y):\n",
    "        combos.append(pairing)\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "print(significance_level)\n",
    "statistics = []\n",
    "p_vals = []\n",
    "\n",
    "for ((comparison_x, chain_type_x, cdr_x), sample_x), ((comparison_y, chain_type_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "momvement_statistics_loop = pd.DataFrame({\n",
    "    'comparison_x': [name for ((name, _, _), _), _ in combos],\n",
    "    'chain_type_x': [name for ((_, name, _), _), _ in combos],\n",
    "    'cdr_x': [name for ((_, _, name), _), _ in combos],\n",
    "    'comparison_y': [name for _, ((name, _, _), _) in combos],\n",
    "    'chain_type_y': [name for _, ((_, name, _), _) in combos],\n",
    "    'cdr_y': [name for _, ((_, _, name), _) in combos],\n",
    "    'statistic': statistics,\n",
    "    'p_val': p_vals,\n",
    "    'significant': [p_val < significance_level for p_val in p_vals],\n",
    "})\n",
    "\n",
    "momvement_statistics_loop['p_val'] = momvement_statistics_loop['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "momvement_statistics_loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bbc93310",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-16T15:31:56.615107Z",
     "iopub.status.busy": "2024-05-16T15:31:56.614705Z",
     "iopub.status.idle": "2024-05-16T15:31:56.789091Z",
     "shell.execute_reply": "2024-05-16T15:31:56.788144Z"
    }
   },
   "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>comparison_x</th>\n",
       "      <th>chain_type_x</th>\n",
       "      <th>cdr_x</th>\n",
       "      <th>comparison_y</th>\n",
       "      <th>chain_type_y</th>\n",
       "      <th>cdr_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>0.545350</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.116601</td>\n",
       "      <td>0.034294</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>0.680336</td>\n",
       "      <td>0.496292</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.755929</td>\n",
       "      <td>0.449692</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.360672</td>\n",
       "      <td>0.173617</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>2.690765</td>\n",
       "      <td>0.007129</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>1.626287</td>\n",
       "      <td>0.103889</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.931419</td>\n",
       "      <td>0.351637</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.877622</td>\n",
       "      <td>0.060433</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.887066</td>\n",
       "      <td>0.375044</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    comparison_x chain_type_x  cdr_x comparison_y chain_type_y  cdr_y  \\\n",
       "0        apo-apo  alpha_chain      1      apo-apo  alpha_chain      2   \n",
       "1        apo-apo  alpha_chain      1      apo-apo  alpha_chain      3   \n",
       "2        apo-apo  alpha_chain      1      apo-apo   beta_chain      1   \n",
       "3        apo-apo  alpha_chain      1      apo-apo   beta_chain      2   \n",
       "4        apo-apo  alpha_chain      1      apo-apo   beta_chain      3   \n",
       "..           ...          ...    ...          ...          ...    ...   \n",
       "148    holo-holo  alpha_chain      3    holo-holo   beta_chain      2   \n",
       "149    holo-holo  alpha_chain      3    holo-holo   beta_chain      3   \n",
       "150    holo-holo   beta_chain      1    holo-holo   beta_chain      2   \n",
       "151    holo-holo   beta_chain      1    holo-holo   beta_chain      3   \n",
       "152    holo-holo   beta_chain      2    holo-holo   beta_chain      3   \n",
       "\n",
       "     statistic     p_val  significant  \n",
       "0     0.604743  0.545350        False  \n",
       "1    -2.116601  0.034294        False  \n",
       "2     0.680336  0.496292        False  \n",
       "3    -0.755929  0.449692        False  \n",
       "4    -1.360672  0.173617        False  \n",
       "..         ...       ...          ...  \n",
       "148   2.690765  0.007129        False  \n",
       "149   1.626287  0.103889        False  \n",
       "150  -0.931419  0.351637        False  \n",
       "151  -1.877622  0.060433        False  \n",
       "152  -0.887066  0.375044        False  \n",
       "\n",
       "[153 rows x 9 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = list(itertools.combinations(treatments, 2))\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "\n",
    "statistics = []\n",
    "p_vals = []\n",
    "\n",
    "for ((comparison_x, chain_type_x, cdr_x), sample_x), ((comparison_y, chain_type_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "pd.DataFrame({\n",
    "    'comparison_x': [name for ((name, _, _), _), _ in combos],\n",
    "    'chain_type_x': [name for ((_, name, _), _), _ in combos],\n",
    "    'cdr_x': [name for ((_, _, name), _), _ in combos],\n",
    "    'comparison_y': [name for _, ((name, _, _), _) in combos],\n",
    "    'chain_type_y': [name for _, ((_, name, _), _) in combos],\n",
    "    'cdr_y': [name for _, ((_, _, name), _) in combos],\n",
    "    'statistic': statistics,\n",
    "    'p_val': p_vals,\n",
    "    'significant': [p_val < significance_level for p_val in p_vals],\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a2e44cb",
   "metadata": {},
   "source": [
    "Similarily here, there is a significant difference between the comparison types but in this case the post-hoc testing shows a difference between the comparisons of *apo*-*holo* and *holo*-*holo* structures for CDR3 loops only."
   ]
  },
  {
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   "source": [
    "## Conclusion\n",
    "\n",
    "Overall, this analysis has showed that all six CDR loops undergo bulk conformational changes between *apo* and *holo* states, but that only the CDR3 loops (both $\\alpha$- and $\\beta$-chain) deform when the loops are aligned to each other."
   ]
  }
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