{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare D-scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook, we analyze the *apo*-*holo* conformational changes of TCRs and pMHCs using the D-score derived from $\\phi$- and $\\psi$-angles.\n",
    "We aim to confirm are previous results that all CDR loops undergo conformational changes but only CDR3 loops are flexible, and further probe the underlying sources of the conformational changes.\n",
    "\n",
    "### D-score\n",
    "\n",
    "Described in Mardia & Jupp, 2000 (Directional Statistics), the D-score measures the changes in backbone dihedral angles.\n",
    "\n",
    "(1) $D(\\theta_1, \\theta_2) = 2(1 - \\cos{(\\theta_1 - \\theta_2)})$\n",
    "\n",
    "(2) $\\text{D-score}(A, B) = \\sum_{i}^{n}(D(\\phi_{i}^{A}, \\phi_{i}^{B}) + D(\\psi_{i}^{A}, \\psi_{i}^{B}))$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import itertools\n",
    "\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "\n",
    "from tcr_pmhc_interface_analysis.imgt_numbering import IMGT_CDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = '../data/processed/apo-holo-tcr-pmhc-class-I-comparisons'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_summary = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I/apo_holo_summary.csv')\n",
    "\n",
    "apo_holo_summary['id'] = apo_holo_summary['file_name'].str.replace('.pdb', '', regex=False)\n",
    "apo_holo_summary = apo_holo_summary.set_index('id')\n",
    "\n",
    "apo_holo_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of TCR D-scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load TCR data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df = pd.read_csv(os.path.join(DATA_DIR, 'tcr_per_res_apo_holo_d_score.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df = tcr_d_score_df.merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_x_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_y_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    apo_holo_summary[['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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_score_df_holo = pd.read_csv(os.path.join(DATA_DIR, 'tcr_per_res_holo_holo_d_score.csv'))\n",
    "\n",
    "d_score_df_holo['cdr_sequences_collated'] = None\n",
    "\n",
    "cdr_pattern = r'^[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+-[A-Z]+'\n",
    "tcr_aligned_complex_ids = d_score_df_holo['complex_id'].str.contains(cdr_pattern)\n",
    "\n",
    "holo_cdr_sequences_collated = d_score_df_holo[tcr_aligned_complex_ids]['complex_id']\n",
    "d_score_df_holo.loc[tcr_aligned_complex_ids, 'cdr_sequences_collated'] = holo_cdr_sequences_collated\n",
    "\n",
    "d_score_df_holo = d_score_df_holo.merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_x_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_y_name',\n",
    "    right_on='file_name',\n",
    ")\n",
    "\n",
    "d_score_df_holo_tcr = d_score_df_holo[tcr_aligned_complex_ids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df = pd.concat([tcr_d_score_df, d_score_df_holo_tcr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df['comparison'] = tcr_d_score_df['state_x'] + '-' + tcr_d_score_df['state_y']\n",
    "tcr_d_score_df['comparison'] = tcr_d_score_df['comparison'].map(\n",
    "    lambda entry: 'apo-holo' if entry == 'holo-apo' else entry\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df['structure_comparison'] = tcr_d_score_df.apply(\n",
    "    lambda row: '-'.join(sorted([row.structure_x_name, row.structure_y_name])),\n",
    "    axis='columns',\n",
    ")\n",
    "tcr_d_score_df = tcr_d_score_df.drop_duplicates(['structure_comparison', 'chain_type', 'cdr',\n",
    "                                         'residue_name', 'residue_seq_id', 'residue_insert_code'])\n",
    "tcr_d_score_df = tcr_d_score_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df = tcr_d_score_df[~tcr_d_score_df['d_score'].isnull()].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df = tcr_d_score_df.groupby(['cdr_sequences_collated',\n",
    "                                         'comparison',\n",
    "                                         'chain_type',\n",
    "                                         'cdr',\n",
    "                                         'residue_name',\n",
    "                                         'residue_seq_id',\n",
    "                                         'residue_insert_code'], dropna=False)['d_score'].apply('mean').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df['anchor'] = tcr_d_score_df['residue_seq_id'].map(lambda seq_id: seq_id not in IMGT_CDR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cdr_sequences_collated</th>\n",
       "      <th>comparison</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>cdr</th>\n",
       "      <th>residue_name</th>\n",
       "      <th>residue_seq_id</th>\n",
       "      <th>residue_insert_code</th>\n",
       "      <th>d_score</th>\n",
       "      <th>anchor</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>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>ALA</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.138574</td>\n",
       "      <td>False</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>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>ASN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.076004</td>\n",
       "      <td>True</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>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>CYS</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009379</td>\n",
       "      <td>True</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>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>GLY</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.322146</td>\n",
       "      <td>False</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>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>LEU</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073606</td>\n",
       "      <td>True</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>6526</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>THR</td>\n",
       "      <td>115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6527</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>THR</td>\n",
       "      <td>122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.192570</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6528</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>TYR</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003136</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6529</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>TYR</td>\n",
       "      <td>117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032772</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6530</th>\n",
       "      <td>YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>VAL</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009630</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6531 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 cdr_sequences_collated comparison  \\\n",
       "0     ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo   \n",
       "1     ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo   \n",
       "2     ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo   \n",
       "3     ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo   \n",
       "4     ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...   apo-holo   \n",
       "...                                                 ...        ...   \n",
       "6526  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo   \n",
       "6527  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo   \n",
       "6528  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo   \n",
       "6529  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo   \n",
       "6530  YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...   apo-holo   \n",
       "\n",
       "       chain_type  cdr residue_name  residue_seq_id residue_insert_code  \\\n",
       "0     alpha_chain    1          ALA              27                 NaN   \n",
       "1     alpha_chain    1          ASN              22                 NaN   \n",
       "2     alpha_chain    1          CYS              23                 NaN   \n",
       "3     alpha_chain    1          GLY              29                 NaN   \n",
       "4     alpha_chain    1          LEU              39                 NaN   \n",
       "...           ...  ...          ...             ...                 ...   \n",
       "6526   beta_chain    3          THR             115                 NaN   \n",
       "6527   beta_chain    3          THR             122                 NaN   \n",
       "6528   beta_chain    3          TYR             102                 NaN   \n",
       "6529   beta_chain    3          TYR             117                 NaN   \n",
       "6530   beta_chain    3          VAL             101                 NaN   \n",
       "\n",
       "       d_score  anchor  \n",
       "0     0.138574   False  \n",
       "1     0.076004    True  \n",
       "2     0.009379    True  \n",
       "3     3.322146   False  \n",
       "4     0.073606    True  \n",
       "...        ...     ...  \n",
       "6526  0.000685   False  \n",
       "6527  0.192570    True  \n",
       "6528  0.003136    True  \n",
       "6529  0.032772   False  \n",
       "6530  0.009630    True  \n",
       "\n",
       "[6531 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcr_d_score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing *apo*-*apo*, *apo*-*holo*, and *holo*-*holo* D-scores for the CDR Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdr_d_scores = (tcr_d_score_df.query('not anchor')\n",
    "                              .groupby(['cdr_sequences_collated', 'comparison', 'chain_type', 'cdr'],\n",
    "                                       dropna=False)['d_score']\n",
    "                              .apply('sum')\n",
    "                              .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdr_d_scores['similar'] = cdr_d_scores['d_score'] <= 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe420341030>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yS5xwwgmN+zp16hRjxoyJDRs2xOjRoz/y2O7du8f3v//9+NrXvhaHHXZYFBUVxS9/+cvG/SNGjIhZs2bF7373uzjooIPi0EMPjeuvvz769+/fas/n5z//eaxatSoOOOCA+PrXvx7nn39+9OnT52Mfd80118Q111wT++23X/zhD3+I3/zmN7HTTjtt9XV/8pOfxA477BCf/exnY9SoUTFixIg44IADtvrxt9xyS3zlK1+Jc889NwYNGhTf/OY3o76+PrHrt6QtPZfy8vK4//7746GHHor99tsvbr311rj66qtTqTPig9Ev+++/f9x2223x8ssvx/777x/7779/vP7666nVBDSfHqwH68Htowffd9998dZbb8Xdd98d/fr1a7x9eE5moH3Rg/VgPbh99OBu3brFT3/60/jc5z4XgwcPjgsuuCC+9KUvxaxZs1Kph/anIJfL5dIuAjq6sWPHxltvvfWRr1fNmDEjJk6cGKtXr06nsBby97//PXbfffd4/vnn4zOf+Uza5bR5M2bMiBkzZnzieRQB+OT0YD5MDwZIjh7Mh+nB8L9MWQOtqLa2Nv785z/HzJkzP/ImBABoPXowAKRDDwbYMlPWwFa4+uqro6ioaJO3kSNHbvZxJ5xwQhx33HFxzjnnxLHHHptgxR3PvHnzNvs7+LjV0QFov/Tg9OnBAPlJD06fHgwdkylrYCv861//in/961+b3NetW7ctrtROy3jvvffiH//4x2b3b+vCpElatGhRLFq0KMaMGZN2KQBtnh6cPj0YID/pwenTg6FjEsgDAAAAAEACTFkDAAAAAAAJ6PCBfC6Xi7q6uvBFAABIlh4MAOnQgwGg7erwgfw777wTxcXF8c4776RdCgDkFT0YANKhBwNA29XhA3kAAAAAAGgLBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACOqddAAAAAAC0trVr18aKFSvSLiM15eXlUVhYmHYZkPcE8gAAAAB0eCtWrIhx48alXUZqpk+fHnvttVfaZUDeE8gDAAAA0OGVl5fH9OnTU7n28uXLY8qUKXHxxRdH//79U6mhvLw8lesCTQnkAQAAAOjwCgsLUx8h3r9//9RrANJlUVcAAAAAAEiAQB4AAAAAABKQaiB/yy23xL777hu9evWKXr16xbBhw2L27NmN+9euXRvjx4+PHXfcMYqKiuLkk0+OmpqaFCsGAAAAAIDmSTWQLy0tjWuuuSYWLlwYCxYsiKOOOipOOOGEeOmllyIi4oILLojf/va38etf/zrmzp0br7/+epx00klplgwAAAAAAM2S6qKuo0aNanJ/ypQpccstt8T8+fOjtLQ0fv7zn8fMmTPjqKOOioiIO+64IwYPHhzz58+PQw89NI2SAQAAAACgWVIN5D9sw4YN8etf/zrq6+tj2LBhsXDhwli/fn0cc8wxjccMGjQoysvL49lnn91sIJ/NZiObzTber6ura/XaAQA9GADSogcDQPuR+qKuf/7zn6OoqCgymUycc8458eCDD8bee+8d1dXV0bVr19h+++2bHN+3b9+orq7e7PmmTp0axcXFjbeysrJWfgYAQIQeDABp0YMBoP1IPZD/1Kc+FYsWLYrnnnsuvv3tb8eZZ54Zf/nLX5p9vsmTJ0dtbW3j7bXXXmvBagGAzdGDASAdejAAtB+pT1nTtWvXGDhwYEREHHjggfHHP/4xbrzxxjj11FNj3bp1sXr16iaj5GtqaqKkpGSz58tkMpHJZFq7bADg3+jBAJAOPRgA2o/UR8j/u4aGhshms3HggQdGly5dYs6cOY37lixZEitWrIhhw4alWCEAAAAAAHxyqY6Qnzx5cowcOTLKy8vjnXfeiZkzZ8aTTz4Zjz32WBQXF8fYsWPjwgsvjN69e0evXr1iwoQJMWzYsM0u6AoAAAAAAG1VqoH8m2++GaNHj4433ngjiouLY999943HHnssjj322IiIuP7666NTp05x8sknRzabjREjRsTNN9+cZskAAAAAANAsqQbyP//5z7e4v7CwMG666aa46aabEqoIAAAAAABaR5ubQx4AAAAAADoigTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAJSDeSnTp0aBx10UPTs2TP69OkTJ554YixZsqTJMcOHD4+CgoImt3POOSeligEAAAAAoHlSDeTnzp0b48ePj/nz58fvf//7WL9+fRx33HFRX1/f5LhvfvOb8cYbbzTerrvuupQqBgAAAACA5umc5sUfffTRJvdnzJgRffr0iYULF8bhhx/euL179+5RUlKSdHkAAAAAANBi2tQc8rW1tRER0bt37ybb77nnnthpp51in332icmTJ8e777672XNks9moq6trcgMAWp8eDADp0IMBoP1oM4F8Q0NDTJw4MQ477LDYZ599Grd/7Wtfi7vvvjueeOKJmDx5ctx1111xxhlnbPY8U6dOjeLi4sZbWVlZEuUDQN7TgwEgHXowALQfBblcLpd2ERER3/72t2P27Nnxhz/8IUpLSzd73H//93/H0UcfHUuXLo0BAwZ8ZH82m41sNtt4v66uLsrKyqK2tjZ69erVKrUDAHowAKRFD4a27+WXX45x48bF9OnTY6+99kq7HCBFqc4hv9F5550Xs2bNiqeeemqLYXxExCGHHBIRsdlAPpPJRCaTaZU6AYDN04MBIB16MAC0H6kG8rlcLiZMmBAPPvhgPPnkk7H77rt/7GMWLVoUERH9+vVr5eoAAAAAAKDlpBrIjx8/PmbOnBkPP/xw9OzZM6qrqyMiori4OLp16xbLli2LmTNnRkVFRey4447xpz/9KS644II4/PDDY999902zdAAAAAAA+ERSDeRvueWWiIgYPnx4k+133HFHjBkzJrp27RqPP/543HDDDVFfXx9lZWVx8sknxyWXXJJCtQAAAAAA0HypT1mzJWVlZTF37tyEqgEAAAAAgNbTKe0CAAAAAAAgHwjkAQAAAAAgAQJ5AAAAAABIgEAeAAAAAAASIJAHAAAAAIAECOQBAAAAACABAnkAAAAAAEiAQB4AAAAAABIgkAcAAAAAgAQI5AEAAAAAIAECeQAAAAAASIBAHgAAAAAAEiCQBwAAAACABAjkAQAAAAAgAQJ5AAAAAABIgEAeAAAAAAASIJAHAAAAAIAECOQBAAAAACABAnkAAAAAAEiAQB4AAAAAABIgkAcAAAAAgAQI5AEAAAAAIAECeQAAAAAASIBAHgAAAAAAEiCQBwAAAACABAjkAQAAAAAgAQJ5AAAAAABIgEAeAAAAAAAS0DntAgAAAADILzU1NVFbW5t2GYlZvnx5k//mi+Li4ujbt2/aZUCbIpAHAAAAIDE1NTVxxtdHx/p12bRLSdyUKVPSLiFRXbpm4u677hTKw4cI5AEAAABITG1tbaxfl4339jgiGgqL0y6HVtJpbW3E3+ZGbW2tQB4+RCAPAAAAQOIaCoujocdOaZcBkCiLugIAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACUg3kp06dGgcddFD07Nkz+vTpEyeeeGIsWbKkyTFr166N8ePHx4477hhFRUVx8sknR01NTUoVAwAAAABA86QayM+dOzfGjx8f8+fPj9///vexfv36OO6446K+vr7xmAsuuCB++9vfxq9//euYO3duvP7663HSSSelWDUAAAAAAHxyndO8+KOPPtrk/owZM6JPnz6xcOHCOPzww6O2tjZ+/vOfx8yZM+Ooo46KiIg77rgjBg8eHPPnz49DDz30I+fMZrORzWYb79fV1bXukwAAIkIPBoC06MEA0H60qTnka2trIyKid+/eERGxcOHCWL9+fRxzzDGNxwwaNCjKy8vj2Wef3eQ5pk6dGsXFxY23srKy1i8cANCDASAlejAAtB9tJpBvaGiIiRMnxmGHHRb77LNPRERUV1dH165dY/vtt29ybN++faO6unqT55k8eXLU1tY23l577bXWLh0ACD0YANKiBwNA+5HqlDUfNn78+HjxxRfjD3/4wzadJ5PJRCaTaaGqAICtpQcDQDr0YABoP9rECPnzzjsvZs2aFU888USUlpY2bi8pKYl169bF6tWrmxxfU1MTJSUlCVcJAAAAAADNl2ogn8vl4rzzzosHH3ww/vu//zt23333JvsPPPDA6NKlS8yZM6dx25IlS2LFihUxbNiwpMsFAAAAAIBmS3XKmvHjx8fMmTPj4Ycfjp49ezbOC19cXBzdunWL4uLiGDt2bFx44YXRu3fv6NWrV0yYMCGGDRsWhx56aJqlAwAAAADAJ5JqIH/LLbdERMTw4cObbL/jjjtizJgxERFx/fXXR6dOneLkk0+ObDYbI0aMiJtvvjnhSgEAAAAAYNukGsjncrmPPaawsDBuuummuOmmmxKoCAAAAEjD2rVrY8WKFWmXkYry8vIoLCxMuwwAEpBqIA8AAAAQEbFixYoYN25c2mWkYvr06bHXXnulXQYACRDIAwAAAKkrLy+P6dOnJ37d5cuXx5QpU+Liiy+O/v37J379iA+eOwD5QSAPAAAApK6wsDDVUeL9+/c3Sh2AVtcp7QIAAAAAACAfGCEPAAAAANACLFBtgeqPI5AHAAAAAGgBFqg29dfHEcgDAAAAALSAtBaojkh/kWoLVG8dgTwAAAAAQAtIe4HqCItUt3UWdQUAAAAAgAQI5AEAAAAAIAGmrAEAAKBFrFy5MqqqqqK6ujpKSkqioqIiSktL0y4LAKDNMEIeAACAbTZ79uwYPXp0zJo1K1atWhWzZs2K0aNHx+zZs9MuDQCgzTBCHgAAgG2ycuXKqKysjIqKipgwYUJkMpnIZrMxbdq0qKysjCFDhhgpDwAQRsgDAACwjaqqqqKoqKgxjI+IyGQycf7550ePHj2iqqoq5QoBANoGgTwAAADbpLq6OgYMGNAYxm+UyWRi4MCBUV1dnVJlAABti0AeAACAbVJSUhLLli2LbDbbZHs2m42lS5dGSUlJSpUBALQtAnkAAAC2SUVFRaxZsyamTZvWGMpvnEO+vr4+KioqUq4QAKBtsKgrAAAA26S0tDQmTZoUlZWVMW/evBgwYEAsXbo06uvrY9KkSRZ0BQD4fwTyAAAAbLORI0fGkCFDoqqqKqqrq2PUqFFRUVEhjAcA+BCBPAAAAC2itLQ0xo0bl3YZAABtljnkAQAAAAAgAQJ5AAAAAABIgEAeAAAAAAASIJAHAAAAAIAENDuQf//99+Pxxx+P2267Ld55552IiHj99ddjzZo1LVYcAAAAAAB0FJ2b86Dly5fH8ccfHytWrIhsNhvHHnts9OzZM6699trIZrNx6623tnSdAAAAAADQrjVrhPx3vvOdGDp0aKxatSq6devWuP3LX/5yzJkzp8WKAwAAAACAjqJZI+TnzZsXzzzzTHTt2rXJ9t122y3+8Y9/tEhhAAAAAADQkTRrhHxDQ0Ns2LDhI9tXrlwZPXv23OaiAAAAAACgo2lWIH/cccfFDTfc0Hi/oKAg1qxZE5dffnlUVFS0VG0AAAAAANBhNGvKmv/4j/+I448/Pvbee+9Yu3ZtfO1rX4tXXnkldtppp/jFL37R0jUCAAAAAEC716xAvqysLF544YX45S9/GS+88EKsWbMmxo4dG6effnqTRV4BAAAAAIAPfOJAfv369TFo0KCYNWtWnH766XH66ae3Rl0AAAAAANChfOI55Lt06RJr165tjVoAAAAAAKDDatairuPHj49rr7023n///ZauBwAAAAAAOqRmzSH/xz/+MebMmRO/+93vYsiQIdGjR48m+x944IEWKQ4AAAAAADqKZgXy22+/fZx88sktXQsAAAAAAHRYzQrk77jjjpauAwAAAAAAOrRmBfIbvfXWW7FkyZKIiPjUpz4VO++8c4sUBQAAAAAAHU2zFnWtr6+Ps846K/r16xeHH354HH744bHLLrvE2LFj4913323pGgEAAAAAoN1rViB/4YUXxty5c+O3v/1trF69OlavXh0PP/xwzJ07N7773e+2dI0AAAAAANDuNWvKmvvvvz/uu+++GD58eOO2ioqK6NatW5xyyilxyy23tFR9AAAAAADQITRrhPy7774bffv2/cj2Pn36mLIGAAAAAAA2oVmB/LBhw+Lyyy+PtWvXNm5777334sorr4xhw4a1WHEAAAAAANBRNGvKmhtvvDFGjBgRpaWlsd9++0VExAsvvBCFhYXx2GOPtWiBAAAAAADQETQrkN9nn33ilVdeiXvuuSf++te/RkTEaaedFqeffnp069atRQsEAAAAAICOoFmBfERE9+7d45vf/GZL1gIAAAAAAB1Ws+aQnzp1atx+++0f2X777bfHtddeu81FAQAAAABAR9OsQP62226LQYMGfWT7pz/96bj11lu3uSgAAAAAAOhomhXIV1dXR79+/T6yfeedd4433nhjm4sCAAAAAICOplmBfFlZWTz99NMf2f7000/HLrvsss1FAQAAAABAR9OsRV2/+c1vxsSJE2P9+vVx1FFHRUTEnDlz4qKLLorvfve7LVogAAAAAAB0BM0aIT9p0qQYO3ZsnHvuubHHHnvEHnvsERMmTIjzzz8/Jk+evNXneeqpp2LUqFGxyy67REFBQTz00ENN9o8ZMyYKCgqa3I4//vjmlAwAAAAAAKlq1gj5goKCuPbaa+PSSy+NxYsXR7du3WLPPfeMTCbzic5TX18f++23X5x11llx0kknbfKY448/Pu64447G+5/0GgAAAAAA0BY0K5DfqKioKA466KCoq6uL2bNnx6c+9akYPHjwVj9+5MiRMXLkyC0ek8lkoqSkZKvPmc1mI5vNNt6vq6vb6scCAM2nBwNAOvRgAGg/mjVlzSmnnBL/5//8n4iIeO+992Lo0KFxyimnxL777hv3339/ixb45JNPRp8+feJTn/pUfPvb34633357i8dPnTo1iouLG29lZWUtWg8AsGl6MACkQw8GgPajWYH8U089FZ///OcjIuLBBx+MXC4Xq1evjmnTpsVVV13VYsUdf/zxceedd8acOXPi2muvjblz58bIkSNjw4YNm33M5MmTo7a2tvH22muvtVg9AMDm6cEAkA49GADaj2ZNWVNbWxu9e/eOiIhHH300Tj755OjevXt84QtfiEmTJrVYcV/96lcb/z1kyJDYd999Y8CAAfHkk0/G0UcfvcnHZDIZ88wDQAr0YABIhx4MAO1Hs0bIl5WVxbPPPhv19fXx6KOPxnHHHRcREatWrYrCwsIWLfDD9thjj9hpp51i6dKlrXYNAAAAAABoDc0aIT9x4sQ4/fTTo6ioKPr37x/Dhw+PiA+mshkyZEhL1tfEypUr4+23345+/fq12jUAAKC1rV27NlasWJF2GakoLy9v1UE8AADQljUrkD/33HPjkEMOiRUrVsSxxx4bnTp9MNB+jz32+ERzyK9Zs6bJaPdXX301Fi1aFL17947evXvHlVdeGSeffHKUlJTEsmXL4qKLLoqBAwfGiBEjmlM2AAC0CStWrIhx48alXUYqpk+fHnvttVfaZQAAQCqaFchHRBx44IFx4IEHNtn2hS98ocn9Xr16xaJFi2KPPfbY5DkWLFgQRx55ZOP9Cy+8MCIizjzzzLjlllviT3/6U/zXf/1XrF69OnbZZZc47rjj4kc/+pG58QAAaNfKy8tj+vTpiV93+fLlMWXKlLj44oujf//+iV8/4oPnDgAA+arZgfzWyOVyW9w/fPjwLR7z2GOPtXRJAACQusLCwlRHiffv398odQAASEGzFnUFAAAAAAA+GYE8AAAAAAAkQCAPAAAAAAAJaNVAvqCgoDVPDwAAAAAA7UarBvIft6grAAAAAADki85be+CFF1641Sf9yU9+EhERs2fPjl133fWTVwUAAAAAAB3MVgfyzz//fJP7//M//xPvv/9+fOpTn4qIiJdffjm22267OPDAAxuP+dznPtdCZQIAAAAAQPu21YH8E0880fjvn/zkJ9GzZ8/4r//6r9hhhx0iImLVqlXxjW98Iz7/+c+3fJUAAAAAANDONWsO+R//+McxderUxjA+ImKHHXaIq666Kn784x+3WHEAAAAAANBRNCuQr6uri7feeusj299666145513trkoAAAAAADoaJoVyH/5y1+Ob3zjG/HAAw/EypUrY+XKlXH//ffH2LFj46STTmrpGgEAAAAAoN3b6jnkP+zWW2+N733ve/G1r30t1q9f/8GJOneOsWPHRmVlZYsWCAAAAAAAHUGzAvnu3bvHzTffHJWVlbFs2bKIiBgwYED06NGjRYsDAAAAAICOolmB/EY9evSIfffdt6VqAQAAAACADqtZc8gDAAAAAACfjEAeAAAAAAASIJAHAAAAAIAECOQBAAAAACABAnkAAAAAAEhA57QLAICWtnLlyqiqqorq6uooKSmJioqKKC0tTbssAAAAIM8ZIQ9AhzJ79uwYPXp0zJo1K1atWhWzZs2K0aNHx+zZs9MuDQAAAMhzRsgD0GGsXLkyKisro6KiIiZMmBCZTCay2WxMmzYtKisrY8iQIUbKAwAAAKkxQh6ADqOqqiqKiooaw/iIiEwmE+eff3706NEjqqqqUq4QAAAAyGcCeQA6jOrq6hgwYEBjGL9RJpOJgQMHRnV1dUqVAQAAAAjkAehASkpKYtmyZZHNZptsz2azsXTp0igpKUmpMgAAAACBPAAdSEVFRaxZsyamTZvWGMpvnEO+vr4+KioqUq4QAAAAyGcWdQWgwygtLY1JkyZFZWVlzJs3LwYMGBBLly6N+vr6mDRpkgVdAQAAgFQJ5AHoUEaOHBlDhgyJqqqqqK6ujlGjRkVFRYUwHgAAAEidQB5I1MqVKxuD0pKSEkEpraK0tDTGjRuXdhkAAAAATQjkgcTMnj07rrvuuujSpUt079493n333fjFL34RF110UYwcOTLt8gAAAACgVVnUFUjEypUr47rrrouIiEwmE7vvvntkMpmIiLjuuuti5cqVaZYHAAAAAK1OIA8k4t57741cLhfHH3983HfffXH99dfHfffdFyNGjIhcLhe//OUv0y4RAAAAAFqVQB5IxJ///OfYbrvtYuLEiY0j4zOZTFxwwQWx3XbbxZ/+9KeUKwQAAACA1iWQBwAAAACABFjUFUjEvvvuG8uXL4/rr78+LrjggshkMpHNZuP666+PDRs2xL777pt2iQAAACSo03ur0y6BVuT3C5smkAcSceqpp8asWbPi0UcfjaeffjoGDhwYr7zySqxZsyYKCgri1FNPTbtEAAAAEtTt1afSLgEgcQJ5IBGlpaVx0UUXRWVlZWSz2Xj11Vdj3bp10alTp5g0aVKUlpamXSIAAAAJem/3w6Oh2/Zpl0Er6fTeah+6wCYI5IHEjBw5MoYMGRJVVVVRXV0dJSUlUVFRIYwHAADIQw3dto+GHjulXQZAogTyQKJKS0tj3LhxaZcBAAAAAInrlHYBAAAAAACQDwTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACOqddAG3LypUro6qqKqqrq6OkpCQqKiqitLQ07bIAAAAAANo9I+RpNHv27Bg9enTMmjUrVq1aFbNmzYrRo0fH7Nmz0y4NAAAAAKDdM0KeiPhgZHxlZWVUVFTEhAkTIpPJRDabjWnTpkVlZWUMGTLESHkAAAAAgG1ghDwREVFVVRVFRUWNYXxERCaTifPPPz969OgRVVVVKVcIAAAAANC+CeSJiIjq6uoYMGBAYxi/USaTiYEDB0Z1dXVKlQEAAAAAdAwCeSIioqSkJJYtWxbZbLbJ9mw2G0uXLo2SkpKUKgMAAAAA6BgE8kREREVFRaxZsyamTZvWGMpvnEO+vr4+KioqUq4QAABo6xYsWBDnnntunHLKKXHuuefGggUL0i4JAKBNsagrERFRWloakyZNisrKypg3b14MGDAgli5dGvX19TFp0iQLugIAAFt07bXXxuzZs6OgoCB69OgRixcvju9973tRUVERF110UdrlAQC0CamOkH/qqadi1KhRscsuu0RBQUE89NBDTfbncrm47LLLol+/ftGtW7c45phj4pVXXkmn2DwwcuTIuPPOO+OLX/xi7LDDDjFq1Ki48847Y+TIkWmXBgAAtGELFiyI2bNnR3l5eTz88MMxa9asePjhh6OsrCyqqqqMlAcA+H9SDeTr6+tjv/32i5tuummT+6+77rqYNm1a3HrrrfHcc89Fjx49YsSIEbF27dqEK80fpaWlMW7cuLjsssti3LhxRsYDAAAf6/bbb4+CgoK46aabolevXhER0atXr8a/9W6//fY0ywMAaDNSnbJm5MiRmx19ncvl4oYbbohLLrkkTjjhhIiIuPPOO6Nv377x0EMPxVe/+tVNPi6bzTZZmLSurq7lCwcAPkIPBshf//znP6NHjx7Rs2fPJtt79eoVRUVF8c9//jOlyvKDHgwA7UebXdT11Vdfjerq6jjmmGMatxUXF8chhxwSzz777GYfN3Xq1CguLm68lZWVJVEuAOQ9PRggf+20005RX1//kSC4rq4u1qxZEzvttFNKleUHPRgA2o82G8hXV1dHRETfvn2bbO/bt2/jvk2ZPHly1NbWNt5ee+21Vq0TAPiAHgyQv84666zI5XIxfvz4xlC+rq4uxo8f37if1qMHA0D7keqUNa0hk8lEJpNJuwwAyDt6MED+Gjp0aFRUVERVVVWccMIJ0aNHj1izZk1ERFRUVMTQoUNTrrBj04MBoP1os4F8SUlJRETU1NREv379GrfX1NTEZz7zmZSqAgAAYFMuuuiiOOqoo+L222+Pf/7zn1FeXh5nnXWWMB4A4EPabCC/++67R0lJScyZM6cxgK+rq4vnnnsuvv3tb6dbHAAAAB8xdOhQATwAwBakGsivWbMmli5d2nj/1VdfjUWLFkXv3r2jvLw8Jk6cGFdddVXsueeesfvuu8ell14au+yyS5x44onpFQ0AAAAdWE1NTdTW1qZdRmKWL1/e5L/5pLi4+CNr9wHQulIN5BcsWBBHHnlk4/0LL7wwIiLOPPPMmDFjRlx00UVRX18f48aNi9WrV8fnPve5ePTRR6OwsDCtkgEAANiMlStXRlVVVVRXV0dJSUlUVFREaWlp2mXxCdTU1MQZXx8d69dl0y4lcVOmTEm7hMR16ZqJu++6UygPkKBUA/nhw4dHLpfb7P6CgoL44Q9/GD/84Q8TrAoAAIBPavbs2VFZWRlFRUUxYMCAWLBgQdx7770xadKkGDlyZNrlsZVqa2tj/bpsvLfHEdFQWJx2ObSiTmtrI/42N2prawXyAAlqs3PIAwAA0D6sXLkyKisro6KiIiZMmBCZTCay2WxMmzYtKisrY8iQIUbKtzMNhcXR0GOntMsAaLZ8m34rIn+n4Gpv028J5AEAANgmVVVVUVRU1BjGR0RkMpk4//zz46mnnoqqqqoYN25cylUCkC/yefqtiPybgqu9Tb8lkAcAAGCbVFdXx4ABAxrD+I0ymUwMHDgwqqurU6oMgHxk+q380R6n3xLIAwAAsE1KSkpiwYIFkc1mm4Ty2Ww2li5dGqNGjUqxOgDylem3aIs6pV0AAAAA7VtFRUWsWbMmpk2bFtnsB9MDbJxDvr6+PioqKlKuEACgbTBCHgAAgG1SWloakyZNisrKypg3b14MGDAgli5dGvX19TFp0iQLugIA/D8CeQAAALbZyJEjY8iQIVFVVRXV1dUxatSoqKioEMYDAHyIQB4AAIAWUVpaGuPGjUu7DACANssc8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAizqCkCHs3Llyqiqqorq6uooKSmJioqKKC0tTbssAAAAIM8ZIQ9AhzJ79uwYPXp0zJo1K1atWhWzZs2K0aNHx+zZs9MuDQAAAMhzRsgD0GGsXLkyKisro6KiIiZMmBCZTCay2WxMmzYtKisrY8iQIUbKAwAAAKkxQh6ADqOqqiqKiooaw/iIiEwmE+eff3706NEjqqqqUq4QAAAAyGdGyEMeW7t2baxYsSLtMlJRXl4ehYWFaZdBC6uuro4BAwY0hvEbZTKZGDhwYFRXV6dUGQAAAIBAHvLaihUrYty4cWmXkYrp06fHXnvtlXYZtLCSkpJYsGBBZLPZJqF8NpuNpUuXxqhRo1KsDgAAAMh3AnnIY+Xl5TF9+vTEr7t8+fKYMmVKXHzxxdG/f//Erx/xwXOn46moqIh77703pk2bFueff36TOeTr6+ujoqIi7RIBAACAPCaQhzxWWFiY6ijx/v37G6VOiyotLY1JkyZFZWVlzJs3LwYMGBBLly6N+vr6mDRpkgVdAQAAgFQJ5AHoUEaOHBlDhgyJqqqqqK6ujlGjRkVFRYUwHoC8ks9rBUVYLwgAaLsE8gB0OKWlpXm7PgIAROT3WkER1gsCANougTwAAEAHk9ZaQRHWCwIA2BKBPAAAQAeT9lpBEdYLAgDYFIE8AAB5q6amJmpra9MuIzHLly9v8t98UlxcHH379k27DAAA8pxAHgCAvFRTUxNnfH10rF+XTbuUxE2ZMiXtEhLXpWsm7r7rTqE8AACpEsgDAJCXamtrY/26bLy3xxHRUFicdjm0ok5rayP+Njdqa2sF8gAApEogDwBAXmsoLI6GHjulXQYAAJAHOqVdAAAAAAAA5AOBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAizqCgAAAEDiOq2tTbsEWpHfL2yaQB4AAACAxBQXF0eXrpmIv81NuxRaWZeumSguLk67DGhTBPIAAAAAJKZv375x9113Rm1t/oygXr58eUyZMiUuvvji6N+/f9rlJKa4uDj69u2bdhnQpgjkAQAAAEhU37598zKo7d+/f+y1115plwGkyKKuAAAAAACQAIE8AAAAAAAkwJQ1AAAAQBOd3luddgm0Mr9jgHQI5AEAAIAmur36VNolAECHJJAHAAAAmnhv98Ojodv2aZdBK+r03mofvACkQCAPAAAANNHQbfto6LFT2mUAQIdjUVcAAAAAAEiAQB4AAAAAABIgkAcAAAAAgAQI5AEAAAAAIAECeQAAAAAASIBAHgAAAAAAEiCQBwAAAACABAjkAQAAAAAgAQJ5AAAAAABIgEAeAAAAAAAS0OYD+SuuuCIKCgqa3AYNGpR2WQAAAAAA8Il0TruArfHpT386Hn/88cb7nTu3i7IBAAAAAKBRu0i2O3fuHCUlJWmXAQAAAAAAzdYuAvlXXnkldtlllygsLIxhw4bF1KlTo7y8fJPHZrPZyGazjffr6uqSKhMA8poeDADp0IMBoP1o83PIH3LIITFjxox49NFH45ZbbolXX301Pv/5z8c777yzyeOnTp0axcXFjbeysrKEKwaA/KQHA0A69GAAaD/afCA/cuTI+P/+v/8v9t133xgxYkRUVVXF6tWr41e/+tUmj588eXLU1tY23l577bWEKwaA/KQHA0A69GAAaD/axZQ1H7b99tvHXnvtFUuXLt3k/kwmE5lMJuGqAAA9GADSoQcDQPvR5kfI/7s1a9bEsmXLol+/fmmXAgAAAAAAW63Nj5D/3ve+F6NGjYr+/fvH66+/Hpdffnlst912cdppp6VdGgBt1MqVK6Oqqiqqq6ujpKQkKioqorS0NO2yAAAAgDzX5kfIr1y5Mk477bT41Kc+FaecckrsuOOOMX/+/Nh5553TLg2ANmj27NkxevTomDVrVqxatSpmzZoVo0ePjtmzZ6ddGgAAAJDn2vwI+XvvvTftEgBoJ1auXBmVlZVRUVEREyZMiEwmE9lsNqZNmxaVlZUxZMgQI+UBAACA1LT5QB4AtlZVVVUUFRXFSSedFP/1X//VOGXNySefHE899VRUVVXFuHHj0i4TAAAAyFMCeQA6jOrq6th+++3j7LPPjqKiohgwYEAsWLAg7r333igtLY3q6uq0SwQAAADymEAegA6je/fusWLFihg5cmRMnDixccqa66+/Ph599NHYb7/90i4RAAAAyGNtflFXANhaBQUFERGRy+VSrgQAAADgo4yQB6DDqK+vj/Ly8vjd734XzzzzTAwYMCCWLl3auL2+vj7tEgEAAIA8ZoQ8AB1GSUlJrF69On72s5/FF7/4xdhhhx1i1KhR8bOf/SxWrVoVJSUlaZcIAAAA5DEj5KENqKmpidra2rTLSMzy5cub/DefFBcXR9++fdMuo8OqqKiIe++9N+6///44//zzG+eQnzZtWtTX10dFRUXaJQIAAAB5TCAPKaupqYkzvj461q/Lpl1K4qZMmZJ2CYnr0jUTd991p1C+lZSWlsakSZOisrIy5s2b12TKmkmTJkVpaWnaJQKQZ/Jt4EVE/g6+MPACANgaAnlIWW1tbaxfl4339jgiGgqL0y6HVtRpbW3E3+ZGbW2tP9Za0ciRI2PIkCFRVVUV1dXVMWrUqKioqBDGA5C4fB54EZF/gy8MvAAAtoZAHtqIhsLiaOixU9plQIdQWloa48aNS7sMAPKcgRf5w8ALAGBrCeQBAABakYEXAABs1CntAgAAAAAAIB8I5AEAAAAAIAECeQAAAAAASIBAHgAAAAAAEmBRVwA6nJUrV0ZVVVVUV1dHSUlJVFRURGlpadplAQAAAHnOCHkAOpTZs2fH6NGjY9asWbFq1aqYNWtWjB49OmbPnp12aQAAAECeM0IegA5j5cqVUVlZGRUVFTFhwoTIZDKRzWZj2rRpUVlZGUOGDDFSHgAAAEiNEfIAdBhVVVVRVFTUGMZHRGQymTj//POjR48eUVVVlXKFAAAAQD4TyAPQYVRXV8eAAQMaw/iNMplMDBw4MKqrq1OqDAAAAEAgD0AHUlJSEsuWLYtsNttkezabjaVLl0ZJSUlKlQEAAAAI5AHoQCoqKmLNmjUxbdq0xlB+4xzy9fX1UVFRkXKFAAAAQD6zqCsAHUZpaWlMmjQpKisrY968eTFgwIBYunRp1NfXx6RJkyzoCgAAAKRKIA9AhzJy5MgYMmRIVFVVRXV1dYwaNSoqKiqE8QAAAEDqTFkDAAAAAAAJEMgD0KHMnj07Ro8eHbNmzYpVq1bFrFmzYvTo0TF79uy0SwMAAADynClrAOgwVq5cGZWVlXHEEUfEzjvvHP/85z/j+OOPjzfffDMqKytjyJAhpq4BAAAAUiOQB6DDqKqqiq5du8aTTz4ZPXv2jAEDBsSCBQvinXfeiUwmE1VVVTFu3Li0ywQAAADylEAegA7jb3/7W6xduza++MUvxoQJEyKTyUQ2m41p06bFI488En/729/SLhEAAADIY+aQB6DDqKuri4KCghg3blxkMpmIiMhkMvGtb32rcT8AAABAWgTyAHQYvXr1ilwuF7fddltks9mIiMhms3Hbbbc17gcAAABIiylrAOgw9thjj1i0aFHMnj075s2bFwMGDIilS5dGfX19FBYWxh577JF2iQAAAEAeM0IegA6joqIi1q1bF4cffniMHDkydthhh6ioqIjDDz881q1bFxUVFWmXCAAAAOQxI+QB6DBKS0tj0qRJUVlZGUVFRTFgwID44x//GPX19TFp0qQoLS1Nu0QAAAAgjwnkAehQRo4cGUOGDImqqqqorq6OUaNGRUVFhTAeAAAASJ1Avo1au3ZtrFixIu0yUlNeXh6FhYVplwG0U6WlpTFu3Li0ywAAACBFnd5bnXYJtLL2+DsWyLdRK1asyOswafr06bHXXnulXQYAkAfa45t4Phm/YwDIT91efSrtEuAjBPJtVHl5eUyfPj2Vay9fvjymTJkSF198cfTv3z+VGsrLy1O5bpr8odjx+R0DtE3+UAMA6Jje2/3waOi2fdpl0Io6vbe63b2fF8i3UYWFhamPEO/fv3/qNeST9vZ/HgDQUfhDreNrj3+oAdDy0pweePny5U3+m4Z8nB64odv20dBjp7TLgCYE8tBGCAM6PmEAQNvkDzUAyA9tYXrgKVOmpHZt0wND2yCQhzZCGAAAALQVndbWpl0CrSwff8dpTg/cFuTj9MDQFgnkP0ZNTU3U1uZXk2oLX6NKQ3FxcfTt2zftMgAA6GCsI9PxdaTfcXFxcXTpmon429y0SyEBXbpmori4OO0yEtMWpgcGEMhvQU1NTZzx9dGxfl027VJSkebXqNLQpWsm7r7rTqE8QJ5Lc27RtOXjvKKQBFPW0Z707ds37r7rzrwamLZ8+fKYMmVKXHzxxdG/f/+0y0mUgWkAyRPIb0FtbW2sX5eN9/Y4IhoK8+cT43zUaW1txN/mRm1trTcjdFhpfOMnm81GdXV1otdsS0pKSiKTySR+XX9YbZu2MLdoWswrCq3DWkEdX0dbK6hv3755+V6if//++iAArU4gvxUaCovN7Q20a/n+jZ984xs/2ybNuUXTHqFnXlFoHdYKAgBgI4E8QB5I7Rs/De9Hp+ya5K7XxjRkiiI6JdtqfeNn27WFuUWN0EtWPi5ql2/8jgEAaCsE8gB5JI1v/DT0TPRyAFvNwoX5Jd8WLgQAoG0SyAMAkJcsXGjhQgCgY/MtuY6vPf6OBfIAeaTTe6vTLoFW5ncMn4yFCwEAOh7fhMwv7e2bkAJ5aCPa4yd6fDJt4Xfc7dWn0i4BtlpNTU1ejVyO+GD08of/my+MXKajawvvAWhdfscAbUs+fhMyIn+/Ddne/p4QyEPKfGqbX9L+1Pa93Q+Phm7bp3Z9Wl+n91Z3iA9eampq4oyvj47167Jpl5KKKVOmpF1Corp0zcTdd93Zrt5Eb6u1a9fGihUrEr9uW/jQp7y8PAoLC1O7fpK8z8svab/PA6CpfP0mZIRvQ7Z1AnlIWT5+apuvn9hGtIFPbQsK0rs2yeggv+Pa2tpYvy4b7+1xRDQUCjc6sk5rayP+Njdqa2vz6g+mFStWxLhx41K7fpof+kyfPj1v/kDMx/d5Efn7Xi/193kAQLvQLgL5m266KSorK6O6ujr222+/+M///M84+OCDE7u++Xg7vrR/x2l9apvW6Ly2IJ9G50UYoZdvOtQIvVwu7QpobXn6Oy4vL4/p06enXUYqysvL0y4hUUbn5ceHLwCwUZpZS9rfhsy3rKW52nwg/8tf/jIuvPDCuPXWW+OQQw6JG264IUaMGBFLliyJPn36JFJDR/jqP2yK0Xn58wdiWiP0stlsVFdXJ3rNjd544424/fbb46yzzop+/fqlUkNJSUlkMpnEr9uRRujpwXRUhYWFedWHSF4+hwERAgEA0pN21hKRXt6Sb1lLc7X5QP4nP/lJfPOb34xvfOMbERFx6623xiOPPBK33357/OAHP0ikhrW7HhC5rkWJXIt0FKxbE4X/+J+0y0ic0Xn5JY0Rei+//HLqc2HffvvtqV3bm5HmKy4ujs5dusb769elXQoJ6Nyla8f5Vge0EfkcBkTowe2V9TV8iAQdgayFj1OQy7Xd7wmvW7cuunfvHvfdd1+ceOKJjdvPPPPMWL16dTz88MMfeUw2m41s9n8XgKurq4uysrKora2NXr16faLr5/uCcvkmHxeUg9aWz9MiReTfH1Yt2YMjPujDacy7nM/f7PCtDug49GA9eFt6cFpefvnl1D9ISosPkQDyR5sO5F9//fXYdddd45lnnolhw4Y1br/oooti7ty58dxzz33kMVdccUVceeWVH9ne3sKAjQsh5as0FoASBgBsm5buwWkRBggDANqbjtKD8/mDpHz7EAkgn3W4QL6jjAzI5zciEd6MALRHenD7p/8CtE8dpQcDQD5o03PI77TTTrHddttFTU1Nk+01NTVRUlKyycdkMplUvmrd0iz0BUB7owcDQDo6Sg8GgHzQKe0CtqRr165x4IEHxpw5cxq3NTQ0xJw5c5qMmAcAAAAAgLauTY+Qj4i48MIL48wzz4yhQ4fGwQcfHDfccEPU19fHN77xjbRLAwAAAACArdbmA/lTTz013nrrrbjsssuiuro6PvOZz8Sjjz5q8U0AAAAAANqVNr2oa0uoq6uL4uJii9kAQML0YABIhx4MAG1Xm55DHgAAAAAAOgqBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAnonHYBrS2Xy0VERF1dXcqVAED70LNnzygoKNjm8+jBAPDJ6MEAkLyW6r9bq8MH8u+8805ERJSVlaVcCQC0D7W1tdGrV69tPo8eDACfjB4MAMlrqf67tQpyGz8676AaGhri9ddfT/yTjvasrq4uysrK4rXXXkv0xUj+8BojCV5nzddSPVMP/uS8bmltXmMkweus+fTgdHjNkgSvM5LgddY8Rsi3sE6dOkVpaWnaZbRLvXr18j9eWpXXGEnwOkuPHtx8Xre0Nq8xkuB1lh49uHm8ZkmC1xlJ8Dpr2yzqCgAAAAAACRDIAwAAAABAAgTyfEQmk4nLL788MplM2qXQQXmNkQSvM9ojr1tam9cYSfA6o73xmiUJXmckweusfejwi7oCAAAAAEBbYIQ8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPIAAAAAAJAAgTwAAAAAACRAIA8AAAAAAAkQyAMAAAAAQAIE8gAAAAAAkACBPAAAAAAAJEAgDwAAAAAACRDIAwAAAABAAgTyAAAAAACQAIE8AAAAAAAkQCAPAAAAAAAJEMgDAAAAAEACBPK0e3//+9+joKAgFi1atE3nGT58eEycOLFFaupoxowZEyeeeGLaZXxiV1xxRXzmM5/ZpnO01OtrzJgxccUVV2zTOf5dSzw/gG2hB7c+PVgPBvh3+m/r03/1X2hNAnn4fx544IH40Y9+1CLnaqkG1pE8+eSTUVBQEKtXr067lE+krKws3njjjdhnn33SLqXNeOONN+JrX/ta7LXXXtGpUydv4oFtpge3Lj2443jggQfi2GOPjZ133jl69eoVw4YNi8ceeyztsoB2Sv9tXfpvx/GHP/whDjvssNhxxx2jW7duMWjQoLj++uvTLot2TCAP/0/v3r2jZ8+eaZdBG7PddttFSUlJdO7cOe1SPrF169a1ynmz2WzsvPPOcckll8R+++3XKtcA8osezKbowR/11FNPxbHHHhtVVVWxcOHCOPLII2PUqFHx/PPPt8r1gI5N/2VT9N+P6tGjR5x33nnx1FNPxeLFi+OSSy6JSy65JKZPn94q16PjE8jTLjQ0NMR1110XAwcOjEwmE+Xl5TFlypQmx/ztb3+LI488Mrp37x777bdfPPvss4373n777TjttNNi1113je7du8eQIUPiF7/4RZPH//vX9Xbbbbe4+uqr46yzzoqePXtGeXn5Vv+f7e677x4REfvvv38UFBTE8OHD46mnnoouXbpEdXV1k2MnTpwYn//85yMiYsaMGbH99tvHQw89FHvuuWcUFhbGiBEj4rXXXmvymIcffjgOOOCAKCwsjD322COuvPLKeP/997eqtm1x5ZVXNo7IOuecc5o0u4aGhpg6dWrsvvvu0a1bt9hvv/3ivvvui4gPRksceeSRERGxww47REFBQYwZMyYiIh599NH43Oc+F9tvv33suOOO8cUvfjGWLVu21TWtXLkyTjvttOjdu3f06NEjhg4dGs8991yTY+66667Ybbfdori4OL761a/GO++807jv467/7yM9No5ymDNnTgwdOjS6d+8en/3sZ2PJkiWf6GfZnOdyzTXXRN++faNnz54xduzYWLt2bZPHb/xa5ZQpU2KXXXaJT33qU9tc06bstttuceONN8bo0aOjuLi4Va4BtB16sB68OXrw/0qqB99www1x0UUXxUEHHRR77rlnXH311bHnnnvGb3/721a5HpAe/Vf/3Rz9938l1X/333//OO200+LTn/507LbbbnHGGWfEiBEjYt68ea1yPTo+gTztwuTJk+Oaa66JSy+9NP7yl7/EzJkzo2/fvk2Oufjii+N73/teLFq0KPbaa6847bTTGhv02rVr48ADD4xHHnkkXnzxxRg3blx8/etfj//7f//vFq/74x//OIYOHRrPP/98nHvuufHtb397q5rOxvM+/vjj8cYbb8QDDzwQhx9+eOyxxx5x1113NR63fv36uOeee+Kss85q3Pbuu+/GlClT4s4774ynn346Vq9eHV/96lcb98+bNy9Gjx4d3/nOd+Ivf/lL3HbbbTFjxoyPvDn7sHvuuSeKioq2ePu4RjJnzpxYvHhxPPnkk/GLX/wiHnjggbjyyisb90+dOjXuvPPOuPXWW+Oll16KCy64IM4444yYO3dulJWVxf333x8REUuWLIk33ngjbrzxxoiIqK+vjwsvvDAWLFgQc+bMiU6dOsWXv/zlaGho+Nif85o1a+KII46If/zjH/Gb3/wmXnjhhbjooouaPHbZsmXx0EMPxaxZs2LWrFkxd+7cuOaaaxr3N/f6F198cfz4xz+OBQsWROfOnZv8Dpvj457Lr371q7jiiivi6quvjgULFkS/fv3i5ptv/sh55syZE0uWLInf//73MWvWrE1ea968eR/7erjnnnu26fkAHYcerAdvih7cNnpwQ0NDvPPOO9G7d+/mPXmgzdJ/9d9N0X/bRv99/vnn45lnnokjjjiieU8ectDG1dXV5TKZTO6nP/3pJve/+uqruYjI/exnP2vc9tJLL+UiIrd48eLNnvcLX/hC7rvf/W7j/SOOOCL3ne98p/F+//79c2eccUbj/YaGhlyfPn1yt9xyy8fWvLGm559/vsn2a6+9Njd48ODG+/fff3+uqKgot2bNmlwul8vdcccduYjIzZ8/v/GYxYsX5yIi99xzz+VyuVzu6KOPzl199dVNznvXXXfl+vXrt9l66urqcq+88soWb+++++5mH3/mmWfmevfunauvr2/cdsstt+SKiopyGzZsyK1duzbXvXv33DPPPNPkcWPHjs2ddtppuVwul3viiSdyEZFbtWrVZq+Ty+Vyb731Vi4icn/+85+3eFwul8vddtttuZ49e+befvvtTe6//PLLc927d8/V1dU1bps0aVLukEMO2err//vvcuPzePzxxxsf88gjj+QiIvfee+9t9rxnnnlm7vLLL2/2cxk2bFju3HPPbbLtkEMOye23335NrtG3b99cNpvd7HVyuVzu3Xff/djXw4d/Zlvy7/+7AToWPVgP3hw9OP0enMt98LreYYcdcjU1NVv9GKDt03/1383Rf9Ptv7vuumuua9euuU6dOuV++MMffuzxsDntb0Io8s7ixYsjm83G0UcfvcXj9t1338Z/9+vXLyIi3nzzzRg0aFBs2LAhrr766vjVr34V//jHP2LdunWRzWaje/fuW33OgoKCKCkpiTfffLPZz2XMmDFxySWXxPz58+PQQw+NGTNmxCmnnBI9evRoPKZz585x0EEHNd4fNGhQbL/99rF48eI4+OCD44UXXoinn366yWiADRs2xNq1a+Pdd9/d5HPq2bPnNs8NuN9++zU597Bhw2LNmjXx2muvxZo1a+Ldd9+NY489tslj1q1bF/vvv/8Wz/vKK6/EZZddFs8991z885//bPw0fMWKFR+7iMyiRYti//333+KosN12263Jc+/Xr1+T32Fzr7+511t5efkWa27uc1m8eHGcc845TbYNGzYsnnjiiSbbhgwZEl27dt3itbp16xYDBw5sVp1AftGD9eDN0YPT78EzZ86MK6+8Mh5++OHo06dPi5wTaBv0X/13c/TfdPvvvHnzYs2aNTF//vz4wQ9+EAMHDozTTjttm89L/hHI0+Z169Ztq47r0qVL478LCgoiIhobS2VlZdx4441xww03xJAhQ6JHjx4xceLEj13w48Pn3Hjerfka2eb06dMnRo0aFXfccUfsvvvuMXv27HjyySc/0TnWrFkTV155ZZx00kkf2VdYWLjJx9xzzz3xrW99a4vnnT17duM8fp/UmjVrIiLikUceiV133bXJvkwms8XHjho1Kvr37x8//elPY5dddomGhobYZ599tmoxlq15bXzc77C519/S6605tvZ1/nE+/MZ2c+bNmxcjR47c4jG33XZbnH766S1SE9B+6cFN6cH/Sw/+qCR78L333htnn312/PrXv45jjjnmE9UJtH36b1P67//Sfz8qyf67ca2EIUOGRE1NTVxxxRUCeZpFIE+bt+eee0a3bt1izpw5cfbZZzfrHE8//XSccMIJccYZZ0TEB03j5Zdfjr333rslS2208dPZDRs2fGTf2WefHaeddlqUlpbGgAED4rDDDmuy//33348FCxbEwQcfHBEfzDe3evXqGDx4cEREHHDAAbFkyZJP9Onul770pTjkkEO2eMy/v4n4dy+88EK89957jU1z/vz5UVRUFGVlZdG7d+/IZDKxYsWKzc6htqmfydtvvx1LliyJn/70p41vhP7whz9s9fPad99942c/+1n861//atbcqdt6/Zb0cc9l8ODB8dxzz8Xo0aMbt82fP79Z1xo6dGjjAj2b8+/zUwL5SQ/WgzdHD06vB//iF7+Is846K+699974whe+0Kw6gLZN/9V/N0f/bTt/Azc0NEQ2m21WPSCQp80rLCyM73//+3HRRRdF165d47DDDou33norXnrppRg7duxWnWPPPfeM++67L5555pnYYYcd4ic/+UnU1NS02puRPn36RLdu3eLRRx+N0tLSKCwsjOLi4oiIGDFiRPTq1Suuuuqq+OEPf/iRx3bp0iUmTJgQ06ZNi86dO8d5550Xhx56aOObk8suuyy++MUvRnl5eXzlK1+JTp06xQsvvBAvvvhiXHXVVZuspyW+rrdu3boYO3ZsXHLJJfH3v/89Lr/88jjvvPOiU6dO0bNnz/je974XF1xwQTQ0NMTnPve5qK2tjaeffjp69eoVZ555ZvTv3z8KCgpi1qxZUVFREd26dYsddtghdtxxx5g+fXr069cvVqxYET/4wQ+2uqbTTjstrr766jjxxBNj6tSp0a9fv3j++edjl112iWHDhn3s47f1+i3p457Ld77znRgzZkwMHTo0DjvssLjnnnvipZdeij322OMTX6slvq638c3MmjVr4q233opFixZF165dW+1/U0A69GA9eHP04HR68MyZM+PMM8+MG2+8MQ455JCorq5uPO/G1znQ/um/+u/m6L/p9N+bbropysvLY9CgQRER8dRTT8V//Md/xPnnn9/sc5LfOqVdAGyNSy+9NL773e/GZZddFoMHD45TTz31E81jd8kll8QBBxwQI0aMiOHDh0dJSUmceOKJrVZv586dY9q0aXHbbbfFLrvsEieccELjvk6dOsWYMWNiw4YNTT7p3ah79+7x/e9/P772ta/FYYcdFkVFRfHLX/6ycf+IESNi1qxZ8bvf/S4OOuigOPTQQ+P666+P/v37t9rziYg4+uijY88994zDDz88Tj311PjSl74UV1xxReP+H/3oR3HppZfG1KlTY/DgwXH88cfHI4880viVrl133TWuvPLK+MEPfhB9+/ZtfCNz7733xsKFC2OfffaJCy64ICorK7e6pq5du8bvfve76NOnT1RUVMSQIUPimmuuie22226rHr+t129JH/dcTj311Lj00kvjoosuigMPPDCWL18e3/72t1OpNSJi//33j/333z8WLlwYM2fOjP333z8qKipSqwdoPXqwHrwpenA6PXj69Onx/vvvx/jx46Nfv36Nt+985zup1AO0Hv1X/90U/Ted/tvQ0BCTJ0+Oz3zmMzF06NC46aab4tprr93kB0ywNQpyuVwu7SIg34wdOzbeeuut+M1vftNk+4wZM2LixImxevXqdAqj1YwZMyZ22223Jm/gAEieHpx/9GCA9Om/+Uf/hc0zZQ0kqLa2Nv785z/HzJkzP/JGBABoPXowACRP/wX4KFPWQDNcffXVUVRUtMnbllbuPuGEE+K4446Lc845J4499tgEK26fmvtzBqDj0oOToQcD8GH6bzL0X8gPpqyBZvjXv/4V//rXvza5r1u3bh+7WjtbpyP9nB966KHYfvvtY/jw4WmXAtCudaTe0JZ1pJ+zHgyw7Tp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      "text/plain": [
       "<Figure size 1511.11x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(cdr_d_scores.sort_values('comparison'),\n",
    "            row='chain_type', col='cdr',\n",
    "            x='comparison',\n",
    "            y='d_score',\n",
    "            kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe3f2d07fd0>"
      ]
     },
     "execution_count": 17,
     "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(cdr_d_scores,\n",
    "            row='chain_type', col='cdr',\n",
    "            hue='comparison',\n",
    "            x='d_score',\n",
    "            kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=123.32856486268929, pvalue=3.571547724890704e-18)\n"
     ]
    }
   ],
   "source": [
    "treatment_options = ['comparison', 'chain_type', 'cdr']\n",
    "treatments = [(group, df['d_score'].to_numpy()) for group, df in cdr_d_scores.groupby(treatment_options)]\n",
    "treatments\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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>-2.450947</td>\n",
       "      <td>1.42e-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>-1.811616</td>\n",
       "      <td>7.00e-02</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>-3.887710</td>\n",
       "      <td>1.01e-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>-3.404588</td>\n",
       "      <td>6.63e-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>-1.910822</td>\n",
       "      <td>5.60e-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>0.437287</td>\n",
       "      <td>6.62e-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>-1.870166</td>\n",
       "      <td>6.15e-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>-2.061494</td>\n",
       "      <td>3.93e-02</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>-0.935083</td>\n",
       "      <td>3.50e-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>-0.093704</td>\n",
       "      <td>9.25e-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>-1.992133</td>\n",
       "      <td>4.64e-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>-0.124939</td>\n",
       "      <td>9.01e-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>2.009592</td>\n",
       "      <td>4.45e-02</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>1.186616</td>\n",
       "      <td>2.35e-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>3.685817</td>\n",
       "      <td>2.28e-04</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>-0.407477</td>\n",
       "      <td>6.84e-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>1.296519</td>\n",
       "      <td>1.95e-01</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>3.556165</td>\n",
       "      <td>3.76e-04</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   -2.450947  1.42e-02        False  \n",
       "1   -1.811616  7.00e-02        False  \n",
       "2   -3.887710  1.01e-04         True  \n",
       "3   -3.404588  6.63e-04         True  \n",
       "4   -1.910822  5.60e-02        False  \n",
       "5    0.437287  6.62e-01        False  \n",
       "6   -1.870166  6.15e-02        False  \n",
       "7   -2.061494  3.93e-02        False  \n",
       "8   -0.935083  3.50e-01        False  \n",
       "9   -0.093704  9.25e-01        False  \n",
       "10  -1.992133  4.64e-02        False  \n",
       "11  -0.124939  9.01e-01        False  \n",
       "12   2.009592  4.45e-02        False  \n",
       "13   1.186616  2.35e-01        False  \n",
       "14   3.685817  2.28e-04         True  \n",
       "15  -0.407477  6.84e-01        False  \n",
       "16   1.296519  1.95e-01        False  \n",
       "17   3.556165  3.76e-04         True  "
      ]
     },
     "execution_count": 19,
     "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",
    "d_score_statistics_tcr = 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",
    "d_score_statistics_tcr['p_val'] = d_score_statistics_tcr['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "d_score_statistics_tcr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Per-residue D-score of loop residues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df['resi'] = tcr_d_score_df['residue_seq_id'].apply(str) + tcr_d_score_df['residue_insert_code'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe3f331ce80>"
      ]
     },
     "execution_count": 21,
     "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(tcr_d_score_df.query(\"comparison == 'apo-holo' and not anchor\").sort_values(['resi', 'chain_type', 'cdr']),\n",
    "            row='chain_type', col='cdr',\n",
    "            x='resi', y='d_score',\n",
    "            sharex=False,\n",
    "            kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comapring the D-scores of Loop residues versus anchor residues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df['classification'] = tcr_d_score_df['anchor'].map(lambda anchor: 'anchor' if anchor else 'loop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_loop_vs_anchor = (tcr_d_score_df.query(\"comparison == 'apo-holo'\")\n",
    "                                     .groupby(['cdr_sequences_collated',\n",
    "                                               'chain_type', 'cdr',\n",
    "                                               'classification'],\n",
    "                                              dropna=False)['d_score']\n",
    "                                     .apply('sum')\n",
    "                                     .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe3f2e12e30>"
      ]
     },
     "execution_count": 24,
     "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(apo_holo_loop_vs_anchor,\n",
    "            row='chain_type', col='cdr',\n",
    "            x='classification',\n",
    "            y='d_score',\n",
    "            kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe3f0b9f910>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q+QEAQOMQfsDW3L5gw1k9/JCk+I5yyuM7h/QDAAAAABA+xVblBwPPAQAIlRYRfqxcuVIZGRmKiYnRmDFjtHXr1jrPf+6559SvXz/FxMRo8ODBevXVV/1uf/DBB9WvXz/Fx8erXbt2Gj9+vN55552mfAloocxh5hExSf43JHSWyxd+0PYKAAAAABBOVTM/6hp47m7WNQEA0NqFPfx49tlnNWfOHC1cuFA7duzQkCFDlJWVpSNHjgQ8f/PmzZoyZYpmzpypnTt3Kjs7W9nZ2frwww+tc/r27asVK1bogw8+0Ntvv62MjAxdddVVOnr0aHO9LLQQlR6HJMkZWyP8iG1fVflB+AEAAAAACCMz/AhU+WEGIkVlFc26JgAAWruwhx9Lly7VbbfdphkzZmjAgAFatWqV4uLi9MQTTwQ8f/ny5ZowYYLmzp2r/v37a9GiRRo+fLhWrFhhnfM///M/Gj9+vHr16qWBAwdq6dKlKiws1Pvvvx/wMcvKylRYWOh3gT14fNd+A88lKbadVflB2ysACD32VgAAQo/91b7MllZ1zfwopvIDAIAGCWv4UV5eru3bt2v8+PHWMafTqfHjxys3NzfgfXJzc/3Ol6SsrKxazy8vL9fq1auVnJysIUOGBDxn8eLFSk5Oti7p6emNfEVoadyGt/LDFZvsf0Nce9peAUATYm8FACD02F/tq67KD2Z+AADQOGENP44dOya3263U1FS/46mpqcrLywt4n7y8vKDOf/nll5WQkKCYmBj9+te/1oYNG9SxY8eAjzlv3jwVFBRYl0OHDp3Dq0JLYRiGPL5vcddZba9SGHgOAE2IvRUAgNBjf7Wv4GZ+EH4AANAQZ3+kwCYuv/xy7dq1S8eOHdMf//hH3XTTTXrnnXfUuXPns86Njo5WdHR0GFaJplR9locrrkblR2w7uRzHJYOZHwDQFNhbAQAIPfZX+yop9wYb8VR+AAAQMmGt/OjYsaNcLpfy8/P9jufn5ystLS3gfdLS0oI6Pz4+Xr1799ZFF12kP//5z4qIiNCf//zn0L4AtGhuj8f62hmT4n9jtZkfHio/AAAAAABh4vEYVuVHbICZH2blR3G5m7bNAAA0QFjDj6ioKI0YMUI5OTnWMY/Ho5ycHGVmZga8T2Zmpt/5krRhw4Zaz6/+uGVlZee+aLQanrIz1teuuBT/G2PbySHvD43VMhIAAAAAAJrVmYqqQeZ1VX5IUnE51R8AAAQr7G2v5syZo2nTpmnkyJEaPXq0li1bpuLiYs2YMUOSNHXqVHXr1k2LFy+WJM2ePVvjxo3TkiVLNHHiRK1du1bbtm3T6tWrJUnFxcV65JFHdN1116lLly46duyYVq5cqa+++kqTJk0K2+tE83OfOWV97YpJ8L+xWuUHMz8AAAAAAOFiVn04HFJM5NmfUY2JdMrpkDyGVFzmVmJMZHMvEQCAVins4cfkyZN19OhRLViwQHl5eRo6dKjWr19vDTU/ePCgnM6qzX/s2LFas2aN5s+fr/vvv199+vTRunXrNGjQIEmSy+XSxx9/rL/85S86duyYOnTooFGjRumtt97SwIEDw/IaER7uklPW1y5XjR8gq7e9omwYAAAAABAm1ed9OByOs253OBxKiI5QYWklQ88BAGiAsIcfkjRr1izNmjUr4G0bN24869ikSZNqreKIiYnRCy+8EMrloZVynym0vnbV/AEytp2cZuVHZXlzLgsAAAAAAEtxWe3zPkxm+MHQcwAAghfWmR9AU3KXF1tfO501wo/oJLnMmR9lJc25LAAAAAAALFWVH7WHH9bQc8IPAACCRvgB2/KUnpYkRch99o0Oh5wR3h8e3WXFZ98OAAAAAEAzMGd+xAUYdm4yw4/ThB8AAASN8AO2Vemr6KhZ9GFyuXzhRzmVHwAAAACA8DArP+LqaXslUfkBAEBDEH7Atjyl3ooOlyPwQHNXRKT3vPIzzbYmAAAAAACqM2d+xEXXXvlB+AEAQMMRfsC2zIoOVy2VH05XpO88wg8AAAAAQHg0ZOZHUVmAts4AACAgwg/YljnwvLbww6z8IPwAAAAAAIRLMDM/EqK9wQiVHwAABI/wA7blLqun8iMiSpJkVJQ215IAAAAAAPBTbIUfwVR+EH4AABAswg/YllnR4axl4rkzMsrvPAAAAAAAmluJL9CIiyb8AAAglAg/YFtmqOGqJfxw+So/3BVlzbYmAAAAAACqK6nwVn7E19H2KjGGgecAADQU4Qdsy1PubWcVUVv4ERntPa+S8AMAAAAAEB5W5Uddba+iqPwAAKChCD9gW5X1tb2K8IYf7oryZlsTAAAAAADVFQcx8Nxse0XlBwAAwSP8gG15Ksy2V4E/PeOKjPGeV0n4AQAAAAAIj5Jyb6ARX8fMjwQr/HA3y5oAALADwg/YltvX9srpDPxt7ozyVX4QfgAAAAAAwqQkqMoPbzBC2ysAAIJH+AHbMgeZR7gCf5ublR/uygrJ42m2dQEAAAAAYCopMwee11/5QfgBAEDwCD9gT5Xlcru9P0A6XbW0vYqKlSR55JTKCpttaQAAAAAAmIp9ba9i6wo/YqpmfhiG0SzrAgCgtSP8gD2VF8nt+/aubeaHw+X94dEjh1R6qrlWBgAAAACAxWx7ZQ41D8S8rdJjqKySzgUAAASD8AP2VHbaW9EhyVVb2yuHQ5K8IcmZU821MgAAAAAALObA87g6Kj/iq80DKab1FQAAQSH8gD2Vna5W+eEIeIqZibjlovIDAAAAANDs3B5DpRXeSo74Ogaeu5wOKxxh7gcAAMEh/IA9lRd5Qw1VVXjUZA5Cp/IDAAAAABAOZtWHVPfMD6mq9RXhBwAAwSH8gD1Vq/xw1lL5EeE7XmlQ+QEAAAAAaH5nfPM+XE6HoiPq/hVNQrQ59Nzd5OsCAMAOCD9gT9XbXtVW+WGGH3JJZ04229IAAAAAAJCkYl/4ERflkqOWf7uaEqzKj4omXxcAAHZA+AF7qjbwPMJVy8wPX9srb/hxqrlWBgAAAACApKrh5XUNOzfFR5szP6j8AAAgGIQfsKfyIlWaba9q+fRMpFX54aTtFQAAAACg2ZX4Kj/qGnZuSoiOlCQVlTLzAwCAYBB+wJ7KTstj+Npe1TLzw2WFHxFUfgAAAAAAmp058Dwuuv7KjwTfOcUMPAcAICiEH7Cn6gPPa6v88LW9clP5AQAAAAAIgxJr5kcQlR8x5swPwg8AAIJB+AF7Ki+ywo+Ieio/Kpj5AQAAAAAIg4bN/CD8AACgIQg/YE9lp+WW94fH2tpeRfoGobsNF5UfAAAAAIBm16CZH75zaHsFAEBwCD9gT2VVlR/OWis/vLdT+QEAAAAACIeqtldBzPzwtb06TfgBAEBQCD9gT2Wn5fF9e7sCZx+KMCs/5JJKCySPp7lWBwAAAACANfDcbGlVF/McKj8AAAgO4Qfsqbxq4LlZ4VFTRPWZHzKkssLmWh0AAAAAACou81Z+xAZR+ZFI+AEAQIMQfsCeyk6r0go/Ap8S4QtFKhXpPcDcDwAAAABAM7IqPxow8Px0KeEHAADBIPyAPZUVVbW9qmXmh9X2yukLP5j7AQAAAABoRlUzP4IYeO6b+VFcTvgBAEAwCD9gT2VVba+cjlrCD6vtle+HTCo/AAAAAADNqGrmRxADz32VH0VUfgAAEBTCD9iPu0Jyl8ljeL+9I2qr/PC1vXI7fOHHmZPNsjwAAAAAAKTqMz8aMvDc3aRrAgDALgg/YD9lpyVJlfJ+csZZT9srq/KDtlcAAAAAgGZUUuENMoKZ+WFWfpS7PSqrJAABAKA+hB+wH1/4Yc7ycNXT9srtC0loewUAAAAAaE4lZd4WVsHM/KgekFD9AQBA/Qg/YD/lRZIkjytaUl0Dz73f/pW+9lhUfgAAAAAAmlPVwPP6Kz8iXE7FRnrPKy5j7gcAAPUh/ID9mJUfrhhJdYQfvuOV5n8GVH4AAAAAAJpRcQMGnnvP81aInGboOQAA9SL8gP2UeSs/3M76Kj984YfHdzuVHwAAAACAZlRV+VF/2ytJSozxDT0vJ/wAAKA+LSL8WLlypTIyMhQTE6MxY8Zo69atdZ7/3HPPqV+/foqJidHgwYP16quvWrdVVFTo3nvv1eDBgxUfH6+uXbtq6tSp+vrrr5v6ZaClKCuUJLl9ba+ctc78MNte+Q5Q+QEAAAAAaCYVbo/KKz2SpPggww+zQqSItlcAANQr7OHHs88+qzlz5mjhwoXasWOHhgwZoqysLB05ciTg+Zs3b9aUKVM0c+ZM7dy5U9nZ2crOztaHH34oSSopKdGOHTv0wAMPaMeOHXrhhRe0d+9eXXfddc35shBO5WblR5Sk+tteGXLIbTio/AAAAAAANBuz6kOSYoOY+SFVhSRFtL0CAKBewX20oAktXbpUt912m2bMmCFJWrVqlV555RU98cQTuu+++846f/ny5ZowYYLmzp0rSVq0aJE2bNigFStWaNWqVUpOTtaGDRv87rNixQqNHj1aBw8eVI8ePc56zLKyMpWVlVl/LiwsDOVLRHPzzfzwBNn2SpIq5ZKLyg8ACBn2VgAAQo/91V5KfK2rIl0ORUUE99lUq+0VlR8AANQrrJUf5eXl2r59u8aPH28dczqdGj9+vHJzcwPeJzc31+98ScrKyqr1fEkqKCiQw+FQSkpKwNsXL16s5ORk65Kent7wF4OWwzfzo9IZKamuyo+qb/9Kuaj8AIAQYm8FACD02F/tpaHzPqSqgee0vQIAoH5hDT+OHTsmt9ut1NRUv+OpqanKy8sLeJ+8vLwGnV9aWqp7771XU6ZMUVJSUsBz5s2bp4KCAuty6NChRrwatBjlvsoPhy/8qG3mR43KD5UWSB5P068PANoA9lYAAEKP/dVeSsq84Ud8kC2vJCmB8AMAgKCFve1VU6qoqNBNN90kwzD0+9//vtbzoqOjFR0d3YwrQ5Pytb2qdAQ380PyhR8yvMPSY1OaeoUAYHvsrQAAhB77q70U+9peBTvvQ6oKP2h7BQBA/cJa+dGxY0e5XC7l5+f7Hc/Pz1daWlrA+6SlpQV1vhl8HDhwQBs2bKi16gM2VGYOPPdWfkS6AocfDofDCkbcrnjvQeZ+AAAAAACagTnzw2xlFQzaXgEAELywhh9RUVEaMWKEcnJyrGMej0c5OTnKzMwMeJ/MzEy/8yVpw4YNfuebwcenn36q//znP+rQoUPTvAC0TGblh6+wyeWs/dvcDD8qYtp5D5w52bRrAwAAAABA1Wd+NKbtlbtJ1gQAgJ2Eve3VnDlzNG3aNI0cOVKjR4/WsmXLVFxcrBkzZkiSpk6dqm7dumnx4sWSpNmzZ2vcuHFasmSJJk6cqLVr12rbtm1avXq1JG/w8Z3vfEc7duzQyy+/LLfbbc0Dad++vaKiosLzQtF8yn0Dzx3eb++IWtpeSVKk06FySZXR7aQSMfQcAAAAANAsqmZ+BP+rGSv8KK1okjUBAGAnYQ8/Jk+erKNHj2rBggXKy8vT0KFDtX79emuo+cGDB+Ws9sn9sWPHas2aNZo/f77uv/9+9enTR+vWrdOgQYMkSV999ZVeeuklSdLQoUP9nuv111/XZZdd1iyvC2FUVijJrPww/Aab12RWflRGJ3sP0PYKAAAAANAMGjXzI4a2VwAABCvs4YckzZo1S7NmzQp428aNG886NmnSJE2aNCng+RkZGTIMI5TLQ2tjzvxwREiqqHXguSRFurzBWmVUivcAlR8AAAAAgGZgtr1qSOVHUox3tuXpUsIPAADqE9aZH0CT8M38qDC8394RQcz8qIxK8h6g8gMAAAAA0AzMgedx0cFXfiTFeoMSwg8AAOpH+AH78c38cMv7A2Rdba+syo9IX/hB5QcAAAAAoBkUlzV84LlZ+VF4hpkfAADUh/AD9uKukCpLJUmVVuVHEDM/qPwAAAAAADQjq/KjIW2vYn1tr8oq5fbQ8hsAgLoQfsBefC2vJKnS8AYbdc38MKtCKqMSvQeo/AAAAAAANIPi8oZXfiTGVAUlRbS+AgCgToQfsBdfyytFxMjt+xCM2doqkEjfPBB3hC/8oPIDAAAAANAMSsq84UV8dPCVH5Eup2IjvWFJYSmtrwAAqEvwOyzQGpiVH1EJqvSVANdV+WHeVhEZ7z1A5QcAAI12pLBUSzd8oo++LtSF3ZN15/i+6pQYHe5lAQDQIpkzPxIaEH5I3qHnZyrcKjhTofSmWBgAADZB+AF7KfNVfkQnqrLcI6numR+RvrZX7ogE7wEqPwAAaJTDBWf07ZWblVfonb31wVcFevPTo3r+9rHqnBQT5tUBANDyFFszP4JveyV5h57nF5Yx9BwAgHrQ9gr2YlZ+RFdVfkTU0fbKqvyIiPMeoPIDAIAG83gM/e/fdiqvsFS9OsXrFzdeqB7t43ToxBnd9dx7MgwGsgIAUFOxr+1Vwys/vEPPaXsFAEDdCD9gL+Vm26tEuc3wo86B5+bMD1/4UVogeTxNukQAAOzmpfe+1rv7TyouyqW/zBitm0al68kZoxQd4dRbnx7Tvz7MC/cSAQBocaoGnjcw/PANPS88w8BzAADqQvgBe7EqPxJV4a5/5ocZjFS4fOGHDKmsoClXCACArbg9hn7z308lST++vLfS23v31PM7JeiH486XJP16wydUfwAAUAOVHwAANC3CD9iLNfMjQW5fBYc51yOQSF/lR4VcUkSs9yCtrwAACNqbnxzV50eLlRQToWljM/xu+/43eiohOkKfHilS7ufHw7NAAABaII/HUIlZ+RHd8Jkfkpj5AQBAPQg/YC/l1Qaee8zKj9q/zaMivLeVV3qk2BTvQYaeAwAQtH/s+FKSdOOI7md9cjUpJlLXDe0qSVq79VCzrw0AgJaqpMJtfd3wyg9f26tS2l4BAFAXwg/YS1mh9zoqQZXu+md+VIUfbikmxXuQyg8AAIJScKZCG3bnS5Ju1H+l56ZL/3lQOlUVdEwZ1UOStP7DPJ0sLg/DKgEAaHlKfC2vnA4pOqJhv5qh8gMAgOAQfsBerLZXSVUDz+toexXta3tV7vZIcR28B0toywEAQDBe/eCwyis9usB1WAO3zZc++qf09q+l34+VPsuRJA3unqyBXZNU7vbopfe+DvOKAQBoGYp84Ud8dIQcjtr/zRoIMz8AAAgO4QfsxRp4nqBK38yPugae+7W9iif8AACgIV7esV+SdIPjdTk69pGuWCh1G+GtxHz2e9Lh9yRJ2UO7SZL+/VFeuJYKAECLYs77iI9qWMsrqXrlB22vAACoC+EH7MU388MTlShf4Ycigp35QeUHAABBO32mXO/sL5AkZXU6KX3/P9I35kgz1kvnXyFVlEgv/ECqLNNVA1MlSe98cUKnSmh9BQBAVeVHw4adS9VnflD5AQBAXQg/YC++yo/KiATrUF1tryJ9ba/K3B4prqP3YPGxplsfAAA28fab/1GlnOrlOKyMKUul2BTvDRFR0o1/kuI7SUc/lrb8Tud1iFe/tES5PYZy9hwJ67oBAGgJiqu1vWooZn4AABAcwg/Yiy/8cEclWoeCGXheUWlI8b7wo4TwAwCAOhmGcrbukiRd3t0hde7nf3tce+mqh71fv/Vr6cxJXTUwTRKtrwAAkKTic2l7Zc38oO0VAAB1IfyAvfjCj4qIeOtQnTM/rIHn7qq2V8W0vQIAoC6efa9rY3GGJOmbl10R+KTBN0mdB0hlBdL2v+iqAd7WV5s+O6YKt6eZVgoAQMtUfC5tr2K8gUlRWaUq2VMBAKgV4QfsxTfzwx1Z1fYqkpkfAACE1J43/q5jSla8q1KjLugR+CSnU8qc5f1662oN6ByrDvFRKi53a+fBU822VgAAWqJzaXuV6Gt7JVXNDgEAAGcj/IC9WDM/vJUfDofkrKPyI7p6+EHbKwAA6nd8n3K/OCVJGtUj2fogQUCDbvTO/ij8Ss69/6exvb177VufHm2GhQIA0HIVl3nbXsU1ou1VVIRTsZHeipEC5n4AAFArwg/Yh7tSqiyVJFVGxEmqe96HVK3yo/rA85ITkofSYQAAAtr+lLZ4BkiSMvul131uZIw0cqb363f+oG9Y4QcfNAAAtG0l5d6KjYRGtL2SpPbxUZKkkyWEHwAA1IbwA/ZRftr60qz8iKij5ZVUbeZHpcc7nFWSDLdUeqpJlggAQKvm8cj9wQt6x+MdcJ55fof67zNyhuRwSofe0SWdSiRJ7395SgX8sgYA0IaZ7aoaU/khSSlx3tZXJ0vKQ7YmAADshvAD9uFreaWIGLnl/fRMfZUfkb7wo6zSI0VES9FJ3huY+wEAwNkO5mp3QaROK16J0S4N6JJU/30S06Rel0mSuu7/p87vFC+PIW35gr0WANB2lZR7214lNGLmh1St8qOY8AMAgNoQfsA+yrzDzhWVoEpf2yqXK7i2VxVuX5src+h5Me04AAA4y4f/UK6v5dXonh0U4QryR8kLb/Zev/+sLurl3Wu3fnGiKVYIAECrYFV+NLLtVbs4b/hxgvADAIBaEX7APszKj+gEVXoMSUG0vao+8FyqCj8Yeg4AgD93hfTROm3z9JUkje7ZPvj79psoRcZJJz7X6KRTkgg/AABtW9XMj3Os/KDtFQAAtSL8gH2UFnivY5JV6TbDjwYMPJekeN/Qcyo/AADwd2CTjJIT2ml4w4/h57UL/r7RCVK/ayVJo07nSJI++rrA+tQrAABtTVGZt+1VY2d+VFV+MEMLAIDaEH7APsoKvdcxyVblh6ue8CPaVbPywxd+UPkBAIC/vev1pdFRR41kRTgdGtwtuWH3H3CdJKnrgX8qvV2sPIa0/cDJJlgoAAAtX7HvAwDxjWx71T7eN/CctlcAANSK8AP2UXrKex2TLLdv5kdEkDM/rPCDyg8AAM5mGNLeV7XD6CNJGtg1STGRDfxlzfnflCJipJP7NbqLd//dytBzAEAbVWKGH42t/KDtFQAA9SL8gH1Ua3tV4Wt7FVnPINbImpUfiWne69N5TbJEAABapSN7pFMHtNPoJ0ka1qMBLa9MUfHeAETSGOcnkpj7AQBou4qsyo9GzvyII/wAAKA+hB+wDyv8SFGFb4ZHfeHHWTM/ElK910X5TbJEAABapU/+JUnaETlMkjSsR0rjHqffREnS6FMvS5LeO1Sg0gr3OS8PAIDWxDAMlZR797/Gtr1KYeYHAAD1IvyAfZjhR3SSFX5EBdn2qqyS8AMAgFp98m+VGpHaXdpBkjS8MZUfktR3guRw6ryjb6hzQoTK3R7tOnQqdOsEAKAVKKv0WHMqGzvwvH21tleGYYRsbQAA2AnhB+yjtGrgudnGqr7KD7NfeVlFzbZXhB8AAEjyfrjgy236wOilSsOhTonR6t4utnGPFd9RSr9IDoc0Otm7b9P6CgDQ1pwurbS+Tmhk26uUOO/Ac7fHUGG1xwMAAFUIP2Af1WZ+lAc58yPOF36Uuz2qdHuqKj8qiqWyoiZbKgAArcYXb0mGWztiLpIkDe+RIoej7srKOpmtryq3SyL8AAC0PadLva2qEqMj5HI2bk+NiXQpPsr779kTxcz9AAAgEMIP2Ef1gedm5UdE3d/isVFV/VVLKtxSdIIUGe89QOsrAACkz1+XJO2IHC7pHFpemfpdI0kaXeCdI7L9wEmrXSUAAG2BWfmRGNO4qg9Tx8RoSdKxorJzXhMAAHZE+AH7sMKP4Gd+REc4ZX7Q5oxv4JwSfdUfp/OaYpUAALQu+16XYUg7SjpJkoada/jRvpfUsa/6GgeUHGXoTIVbH35VEIKFAgDQOlSFH5Hn9DidfeHHkULCDwAAAiH8gH1Ur/xwBzfzw+FwWAPmSszwI8E394PKDwBAW3fqoHRin75SZx09I0U4Hbqwe/K5P27fLDkdhkbFeffad/fT+goA0HYUmm2vzrHyo3NijCTpyOnSc14TAAB2RPgB+yirNvDcN/Mjqp62V1JV66uSct+QOLPyg/ADANDW7fO1vGqXJUka0DVJMZGuuu4RnD7exxtdliuJuR8AgLbldIjCj06+yo98Kj8AAAiI8AP2UFEqVfo+7dKAyg9JivOFH1bbqwTaXgEAIKlq3kfUKEkhmPdh6nGRFJ2s0e4dkqR395+Ux2OE5rEBAGjhzLZXSbHn2PYqydf2isoPAAACCnv4sXLlSmVkZCgmJkZjxozR1q1b6zz/ueeeU79+/RQTE6PBgwfr1Vdf9bv9hRde0FVXXaUOHTrI4XBo165dTbh6tBhm1YccUlRi1cDzIMKP2Eiz8qNG+FF0JNSrBACg9fB4pM/fkCTtLPXujcN6pITmsV2RUu9vaqBjv+JcbhWcqdAnR06H5rEBAGjhCkM08Nxse3X0NJUfAAAEEtbw49lnn9WcOXO0cOFC7dixQ0OGDFFWVpaOHAn8S+fNmzdrypQpmjlzpnbu3Kns7GxlZ2frww8/tM4pLi7WJZdcoscff7y5XgZaAnPeR3SS5HQGPfBcqqr8sMKPRN/Mj9OHQ75MAABajbz3pDMnVBrZXh8d8+6rIav8kKS+ExTpcGt45EFJtL4CALQdVW2vGHgOAEBTCmv4sXTpUt12222aMWOGBgwYoFWrVikuLk5PPPFEwPOXL1+uCRMmaO7cuerfv78WLVqk4cOHa8WKFdY5t9xyixYsWKDx48cHvY6ysjIVFhb6XdDKVBt2Lsma+RFc2yvvp23OVPhmfiR19V4Xfh3aNQJAG8LeagP7/itJ+jD1W6r0GOqYEK3u7WJD9/i9x0tyaHTlNkmEHwAQDPZXezgdqsoP2l4BAFCnsIUf5eXl2r59u19I4XQ6NX78eOXm5ga8T25u7lmhRlZWVq3nB2vx4sVKTk62Lunp6ef0eAiDGuGHNfOjQQPPfZUfyb6//4IvJYP+4wDQGOytNmAOO4+5SJI0vEeKHI76KyqDFt9R6j5Ko517JHnDD4N9FwDqxP5qD4VnQlX54W17dbKkQuW+1s8AAKBK2MKPY8eOye12KzU11e94amqq8vICD5rOy8tr0PnBmjdvngoKCqzLoUOHzunxEAa1hR8NmPlhDTxP6ua9riiWzpwM7ToBoI1gb23lykukQ+9IknaUeffFYaFseWXqe5WGOvYp0uHWkdNlOniiJPTPAQA2wv5qD9bA83Os/GgXF6lIX6vno0W0vgIAoKawDzxvCaKjo5WUlOR3QStjhR/ev7tzmvkRGSPFd/Z+XcA/JgCgMdhbW7kDmyV3uYzE7tqR5/106vBQDTuvru8ExTgqNMT5uSTpHVpfAUCd2F/t4XSZd29NOsfKD4fDodQkb/VHXsGZc14XAAB2E7bwo2PHjnK5XMrPz/c7np+fr7S0tID3SUtLa9D5aENqVH6UVTag8qNm+CFJyd2916cIPwAAbdDn3pZXX3a/WkdOlynC6dCF3VNC/zypg6Skbhql3ZKY+wEAaBtCNfNDkjWP69AJwg8AAGoKW/gRFRWlESNGKCcnxzrm8XiUk5OjzMzMgPfJzMz0O1+SNmzYUOv5aEPO+H5ZEtteklTRgIHn5qdtTpdWVB00w4+CL0O3RgAAWgtz3kfcxZKkgV2TrA8LhJTDIfW5UqOdH0uS3t1P+AEAsL+q8OPcKj8kqXu7OEnSIVpHAgBwlnP/mME5mDNnjqZNm6aRI0dq9OjRWrZsmYqLizVjxgxJ0tSpU9WtWzctXrxYkjR79myNGzdOS5Ys0cSJE7V27Vpt27ZNq1evth7zxIkTOnjwoL7++mtJ0t69eyV5q0aoELGxkuPe6zhvP/KKyuAHnqfEeX/gPHWmWviR0sN7TdsrAEBbczpfOvKRJId2lKdLym+aeR+mvhM0YtuzcsqjA8dLlFdQqrTkmKZ7PgAAwsgwDOuDd6Go/Eg3w4+ThB8AANQU1pkfkydP1q9+9SstWLBAQ4cO1a5du7R+/XprqPnBgwd1+PBh6/yxY8dqzZo1Wr16tYYMGaJ//OMfWrdunQYNGmSd89JLL2nYsGGaOHGiJOnmm2/WsGHDtGrVquZ9cWheJb7B5FblR/AzP8zwo6AkUOUH4QcAoI35fKP3usuF2v61t4XGiPOaMPzoeamSIjzq7zggSdpK9QcAwMZKKzxWp4KQhB/taXsFAEBtwlr5IUmzZs3SrFmzAt62cePGs45NmjRJkyZNqvXxpk+frunTp4dodWg1zLZXcR0kSeXu4Gd+pMRGSZJOnSmvOugLP746VqBtu75S1sA0xUQ2QbsPAABamn3/lSSVZIzXnjdOS2ri8CMqXup5qUbv+VgfuXtq6xfHdd2Qrk33fAAAhJH5784Ip0MJ0aEIP6j8AACgNmGt/ABCxmp75V/5EUz4kWy2vfKr/EhXsRGtb385WbPX7tLcf7wf2vUCANASGYY17Py9+Ivl9hhKS4pR15TYpn3evlka49wjSdq873jTPhcAAGF0stj7786UuEg5HPV3KqiP2fbqcEGpKn3/DgYAAF6EH7CHksADz6OCmfkRG6DtVUoPveD+ho54kiVJL7//tU4Wlwe6OwAA9nFkt1SUL0XEakdZN0lNXPVh6nOVMp275ZRHnx8t1lenaN0BALCnUyXef1emxEWF5PE6J0YryuWU22PocEFpSB4TAAC7IPxA62cY0hnfzI+4mjM/6v8W7xAfLUk6XVap0gq39TgbHaP8nuKtz46FcNEAALRA+7xVH8q4WDsOeVteDW+O8KPdeUrufJ6GOj6TJL396dGmf04AAMLgpO9Dd+1DFH44nQ519839+OJYcUgeEwAAuyD8QOtXWiAZvtDCV/lRXukNPyKCGHieFBuhWN88jzzfJ2XKKz3KrbxAkjSik/exPvqqIKTLBgCgxfHN+zB6fVM7Dno/WDC8R0rzPHffLF3i/ECS9NanfOAAAGBPJ63Kj8iQPWbfzomSpE/yT4fsMQEAsAPCD7R+5ryPyHgpMkZSVfgRHVH/kHKHw6EuKd77fV3gbbOx4+BJlRhR6qgCZXfKkyR9nMcPkgAAG6solQ5sliR90f4SnSypUFSEUwO7JjfP8/fN0qUub/jx9qfH5PYYzfO8AAA0I7OdcrsQVX5IUt/UBEnSp/lFIXtMAADsgPADrV+NlleSrPZVMZHBfYt3SfaGH4dPeSs/3vZ94vQS5wfq7/G24OBTNAAAWzv0jlR5RkpI0/Yi7546pHtyUPOzQqL7aA2JPaYElejUmQp99DUVlwAA+zHbXqXEh7DyI81X+XGEf7MCAFAd4QdaP2vYeVVP8tIGVH5IUo/2cZKqeqS+6es1fonrA/U5s0uSdLigVCXllaFYMQAALY+v5ZXOv7xay6tmmPdhckUoss83lencLYnWVwAAezIHnoe28sMbfnyaXyTDoHISAAAT4QdavzO+8KNa5UdZAys/BnRJkiTtPlyo40Vl+sA33+NS5wdKPrVbSTERkqSvTp4J1aoBAGhZPvcNO+91ud753Lu3jsxoX8cdmsCA63Wp831J0pufMPQcAGA/J63wI3SVHxkd4hXpcqiorFJf++ZYAgAAwg/YQdER73V8J+uQWfkRExlc5ceArt7wY+fBk8r5+IgMQ+qXlqDOjgKprFDdk70/mH5J+AEAsKPiY9Lh9yRJ+Z3G6vNjxXI6pNE9mzn86HOlxsXskyRt239CBb7WIAAA2IXV9iqElR9REU6d38k79+Ojr2gbCQCAifADrV9Rvvc6IVWSVOH2WENSo4PsU35h9xQlx0bqZEmF7vmH9xOnVw/qKrU7T5LUPdb76ZwvT5aEcuUAALQMn/zbe512oXLzvXvnwK7JSo4N3adSgxIZqx4DRusCx0G5Dem/e/Ob9/kBAGhiZuVH+/jQhR+SNDQ9RZK04+CpkD4uAACtGeEHWr9iX1uMhM6SpDJf1YcUfOVHpMup74zobv05NtKlyaPSpbTBkqTuTm/7Dyo/AAC29Mm/vNcXXKPcfcclSZnndwjPWgbeoKuc2yVJGz7KC88aAABoIieLQ9/2Sqqa07XjwMmQPi4AAK0Z4QdavxqVH6W+eR9S8JUfkvTTK/tqwsA0dW8Xq19OulBpyTFSlyGSpO4VByQRfgAAbKiiVPrMN+z8gquV+7k3/LioVzO3vDKd/01dGfuxJGnjx/l++zoAAK1ZpdujwtJKSaFteyVJw89LkSS9/9UpVbg9dZ8MAEAbQfiB1s+c+eGr/DB/SRIV4ZTD4Qj6YRKiI7TqlhF6+95v6toLu3oPpnnDj24luyXR9goAYEP735YqiqXErvoqtq8OniiRy+nQqOYedm6KiNLgQUPURcdVUim99emx8KwDAIAQO3WmapZVSohbS/bqmKCkmAiVVni0++vCkD42AACtFeEHWr8alR9m26uYBlR91MpX+dG16ENJ0tcFpef+mAAAtCR7X/VeXzBBWz73tnkc1C1ZiTHNPO+jGsegG3SN6x1J0rodh8K2DuD/t3fn8VFVed7HP1XZ932FAAEiiAjIakDFFhQVbbG11dERtJ3x0UbFpbW1p0GfHh23cca23bvbrVtcaLen1RYR2UR2mk0gLAYICUkIIVslqSRV9/njFFlIhSVUUiT5vl+v87q37lanTvLK76Z+95wjIuJLJVVOwMz3ERjg269j7HYbYzPNkJXf7dKDAyIiIqDkh3R1rnqoNsNzENGy58eJzvdxTFEpEJVOOmZekYOVTpwNGn5DRES6CcuCHV+Z9TOahrzK7u+n+T6OyJzI1VE5AHyzrZDK2vrjnCAiInL6O1hpkh9JkSEdcv2Jg5IAWJJzsEOuLyIi0tUo+SFd25HJzm0BEG6G56itNz0/QoJ89Ovd51ziqSTEbq5bVO70zXVFRET8rXATVORDUDhWv/NZ7nlS1G+TnR8REMhZYy5kgC0fp8vGV1s08bmIiHR9xRWe5EdUByU/skzyY92+w1TowQERERElP6SLOzLfR0QS2E1PD+eRnh+BPuj5AdB3PDYbpAVWAlBQrknPRUSkm9juGfJqwEXklNZzoLyW0CA74zL9NN9HM7aR/8rVAd8B8LdVu/xcGxERkVN30DPsVXIHJT/6JITTPzECl9tS7w8RERGU/JCurnG+j6TGTTWe5EdYsK+SHxMASHcVAFBQpuSHiIh0A5YFP3xi1gdfwaLt5kuS8QMSfTN05KmK68c1mS4CcLEqr5odRZX+rpGIiMgpaRz2qoOSHwBThqYC8MWmAx32HiIiIl2Fkh/StZV7JkGN7t24yVFnkh8RwYG+eY+kwRCRTJplepkc0KTnIiLSHRT9ACU5EBACgy9nUY6Jcz8ZlHScEztP2vgbuNi+DoB3vlPvDxER6do6I/kx9ew0ABblFFPlbOiw9xEREekKlPyQrq3Mk/yIzWjc5PDc4EWE+OipVbsdBl1GL5uZBPaEe37kLoOP/h0+vh3yVvumLiIiIr6y5SOzzLqYciucdXsPA3DhoGQ/Vuoogy5neuxmAD5ev5/yGo1fLiIiXVdxpXmQriOTH2elR5OZGIGzwc3CbUUd9j4iIiJdgZIf0rWV7zfLGG/JDx/1/AAYPJW0k0l+rHgZ3r4CNn8Imz6ANy6FrZ/5rj4iIiKnwrKakh9nX8vinGJcbouByZFkxIf7t27N2QPInngZg2z7qHbZeWPZbn/XSEREpN0KPaMIpMWEddh72Gw2rhhmen98tqGgw95HRESkK1DyQ7q28tY9P6o9w16F+2rYK4DMiaSHmBvVA8Ulxz52299h/iNmfdgNMPgKsFzwyZ1NyRoRERF/ylsNZXshKAKypvDlZjMu+KVnpfq5Yq3ZzrmRu8MXAPDGsl2UV6v3h4iIdD1ut0WBJ/mRHhvaoe817ZxeACzOKaa4QsM2i4hIz6Xkh3RtR4a98tLzI9JXw14BBIWSPmQ8APnlzraPK9lpkhwA5/4Srn4Vfv42ZJwL9Q5YMMd3dRIREWmv9e+Y5ZCrcFjBLM4xk51fdvbpl/wgOILLJ57HINs+KuttvLJoh79rJCIictIOOeqoa3Bjt0FKdMcmPwYkRTKqbxxuCz7+Z36HvpeIiMjpTMkP6boa6qDSPKnaIvlRZ5IfPu35AaSdex0Ale5gKnPXtT6grho+nA51ldB3Alz8O7DZICAQLn8GsJkhRg7m+LReIiIiJ6W2An742KyPmsG324txNrjplxDOkLRo/9atDfZz7+BXkV8D8KfvctlRVOnnGomIiJycI8Mnp0SHEhTQ8V/FXDe6NwAfrt6DVboHirdD/nrY+z3s+gZyvoId82HH17Bzgdm2+1vYuwIObDQP9pXnQ81hcGnidBER6Zp8++2wSGeqyAcsCAiBiKTGzQ6nGfYq0pdzfgCRvQYTHbCNClcwB/7xLFH/568msQFm7PQvfwXFWyEiGa59EwKCmk5OGw6Dp8L2z2Hly3Dl731aNxERkRO25W9QXw2JgyBjHJ8vMQn9y85Ow2az+blybQgO5+KLL2fyp2v5xj2a3/xtPe/fcT6BnfDlkYiIiC/ke5If6bE+mu+jzgGH95phLI8sK/LBUQKOEqY6KnmMx/nxEKx//lpG2Xee2vuFxkJ4AkQkmmV4vOd1EkSlQUxviE43683/FxYREfEjJT+k6zq8xyxjM8De9OXHkWGvwn057JVHenw0FQdrKTiQzxlf/gqmPmd2fPMobHgXbHa49s8QldL65OyZJvmx8X24aLa5aRQREelMlgVr3zDrI6dzyFHHwm3FAFw1It2PFTsBo27h/676KSvyz2JtHvz31zt4+LLB/q6ViIjICck/3M7kh7PKjB5Q/AMUbTUP3BVvA0fxMU+LBC63r+Ij90Q+tCYzKuwQBIVDUCgEhYE9CLDMvcGRpeWG+hrzkERdtRm62e3p9VFbZkrp7uNU2AaRKSYREtMLYvtCfCbE9YO4TIjto+SIiIh0GiU/pOsq8Yz5nTioxeaqxjk/fP/rnZ4QzfaDtRywEmDdm7DnO7PjkOcpmsuegcwLvJ/cJxvSz4GCf8I//wLn3efz+omIiBxT7hIo3AyBYTDiRj5Zl0+D22J47xgGp56eQ141Cgii19WP8/Rrv+Gu+nt4dcluspIjuWZUb3/XTERE5LhyDzkA6JcQ3vZBrnoo2gL715ohqvLXmuGnsLwfHxpjkgqxfSGurxkOOiLR9MYIT+S6khA++st2PrddyKP3Pd6+oaEb6sBZAdWHTHGUNK1Xl0JVkRmOunw/VBSAux6qCk0pWN/6eja76SUSl+lJingSI0fWQ0/z+xEREelSlPyQruvgdrNMOqPF5rLqegBiw4N9/pZpMWZiuoLBt8K+tU1Jj+AomPrfMPyGtk+22WDMv8FnM2HtmzB+VoseKyIiIh1u+QtmOfJmrLA4Ply7GYCfj844xkmnkd6juGLCSDYt+5zXXVfwq3kbcbktrhvTReovIiI91o8HqwDITIxo2uiqh/x1sHuReUCh4J/QUNv65MgUSB7iKWeaZcIACIs95nuOTbbol7CPPYeq+XzTAa5rT7wPDIbAxBMbucDthuqSpkRI+X4zHFdpLhzONaM3NNRC2T5Tcpe0vkZ4IsT395TMpvW4TDPU1uk6RKeIiJyWlPyQruugp+dHUsshL8prPMmPMN93pT3SRbkguB/ctxl+XGJuvjIvgLC441/grJ/B/N+YG8Dd30LWZJ/XUURExKvCLbB7oXniMnsmpF8+TQAAJzlJREFUq3NL2VFURUignSuHn+ZDXjU3+TEe3ncZjr2hvOuazEMfbWLj/jIeufzMDun1KSIi4gu5JabnR//AUlj1Bfy4GHKXQV1lywNDY6HXKOg9GnqNhl4j2z1kss1m47oxGTzzVQ7vrtzbvuTHybDbITLZlF4jW+93u01PkcO5noTInmbruZ7eJCWm7F/d+vyQGE9C5KikSHx/iEpVYkRERFrRf4jSdTX2/Gg57FVZdR0AseEdkfzw9PwoqzHJjrOmndwFgsNh+I2w6hVY+2clP0REpPMs+i+zHDIN4vrx2mdrALhmVG9iOuCBgQ4TGIL9urf5z9cuJKGighdcP+PdVfv4emsRMy8cwLWjM5QEERGR00fVQRw5iymqMMNdZX50KdgcTfvD4qH/ROj/E+g7wfTo8OGX+NeNzuD5BTvZuL+cjXllDM+I9dm1T5rdDtFppvQd33p/bXlTIqT0R0/ZY5aVBeAshwMbTDlaYJiZTySmF0T38kzA3svzurd5z+BIJUhERHoY/WcoXVN1qXkaBCCxadirugY3jjoXALFhvh/2Kj3G9Pw4UF7T/ouM/oVJfuz4CsryzITtIiIiHWnv95DzBdgC4MJH2FlUybfbi7HZ4N/P7+/v2p282AzsN3/E/e/8lNGOHGZbd7C3Mo7H/r6Vp77azvlZSUwanMw5feIYkBRBYICGmRQRkU5SX2Pi7o+LYPdiKNrMDvcA4D9JpIyYwAboMxEG/MQkPFKHdehwyImRIVx+diqfbijgryv3tiv5Udfg5t1Ve1my4yARwYFcO6o3Pxmc7PvKhsZA+ghTjlZfY3qKlP5oEiSlPzYlScryoKEGSnJMaUtgqBlWK8JTmq+HRJnkSHAkBEd4XkeY14EhYA8Ee4Bn6Sk2u0mmuN1mYvi2iqveU5xm2eA8ar2uqTQcWfeyv8HZ7DrNjgUzibw9CAICPcsgU8fAUPNZQqMhJNq0cUi0eR0aAxHJGk5MRLo1JT+kazrypEdcP3ND4lFWYwK/zQZRoR0w4fmRYa/Ka7EsC1t7bhCSzoB+58OeZbD+HbjoP3xcSxERkWbcbvh6tlkfOR2SzuCF9/4JwCVDUlqOPd6VpI+Af/2EC969lq+rZ/FB8JW8FXANP1bCgq1FLNhaBEBYUACDUqPITIygX0IE/RLDzXpiBNGhXajHi4iInJ7cbijcaObt+HER7FtlvpxuZkvkBCiFs3rHw+17zIgAnejm7L58uqGA/7exgN9OHULMSYySUF5dz/Q3V7Mxr6xx2xebD/CLCZnMvuLM9v1P3B5BYZ75Ts5svc9V3zSPSEU+lOdDxX7P0vO6rtLMN1Kx3xSfsdHmhPRdhT3IzCsTlQKRqU3L2AzznUtcptmvOUtFpAtS8kO6pv3rzLLX6BabDzvMfB8xYUHY7b6/CUuJDsVmM0++lFTVkRQV0r4Ljf5FU/Jj4kPmqQwREZGOsO4NyF8LQRFw4cP8c99h/r6xAJsNZk064/jnn856j4LbFxPywU1ML/yEm12f8EPfaSyI/xdWHI7mh4IKHHUuNuSVsaHZlzZHJEYG0z8pkoHJkQzwLAcmR5IeE9p5X+aIiEjXc3ivp2fHIshdCjWlLfdH9zK9Ogb8BDIn8sP8A1Cax9CszE5PfACM7BPHmWnRbDtQwXtr9nHHxAEndJ6zwdWY+IgND2LmhQMpKK/hzeV7eGN5LqFBdh66dPDxL9TRAoLMcGEJx/hcziozeoTDM6+I4yA4Spq21VWaY+ocUFflWfdsc9cf482Pkfiwe3ph2APNxPEBzUpgiKl3gGcZGHLU/iPrx9vvGfHCVW/q6WrwLD2v62vBWQnOCqitMMsj67VlUHPYHHe8pFBgKMT2NfOtxPWD+AHmwc6kwSYxovsmETlNKfkhXdN+M045vVsmPworagFIjQ7tkLcNDrTTOy6MvNIadhVXtT/5MfgK0720qhC2f3Hyc4eIiIiciMN74Os5Zn3SHNwRKTz+1xUAXDuyN0PSo/1XN1+J6wu3fQPLf49t2XMMLfqUoUWfcl90b9znXsmPsePZEZhFriOIPSUO9hxykFtSTUmVk5KqOkqqSlmd2/JLq/DggMZEyBkpUZyREklWchS9YsM65OEKERE5zVWXwp7vzCTlPy4yQy01FxwFmec3JTwSBrb4MnjjfjNf5VnpMZ1Y6SY2m41fTOjHg3/bxJ+W5XLL+H6EBgUc97z/+mJbY+Ljg9uzGZQaBcDg1Ch+/dFmXl68mxEZsVxyVmpHf4RTFxJpSly/kz/XssA6emgrlymW2zPEVBtDYp3uGpxQVWwmoq8sNN9RVBZB5QHTk+bwHijfb3rNtDWsWGiMSYIkepIhSYPN3KwxvbtGG4hIt6bkh3Q9rnrYu9ys9zm3xa5Cz1wcaTEdk/wAODM1mrzSGrYdqCB7QEL7LhIYbIYeWfbfZuJzJT9ERMTXGpzw0b9DvcNMoDr2dt78fg/r9h4mLCiABy4Z5O8a+k5QKFz4axhxI6x8xfSsrNiPffUrDOQVBoKZUDauHyT0g8x0KoOS2ONOZJczjl2OMHZVBrDrsIu9pbVU17nYtL+cTfvLW7xNhCcpkuVJiAxIiiQlOpSU6FASIoKVGBER6S6clbB3BeQuMT07CjfT4gl/WwD0HuOZt+NC6DWqzd78hx11bC+sAGB037iOr3sbpp3Ti+e/2Ul+WQ0frMljxvh+xzz+H5sP8PaKvQD873UjGhMfANeP6UNOYRVvLM/lgXkb+TItmoz4zu/R0mlsNvMztwcA7XwA8nQVGGKGtzrWXKSueijPM4mQw3vMnCuHdsHBHDPvSm055K0ypbngSJMISR4MyUM8w5YNUU8REelUSn5I15O32nRDDU+E1OEtdh0o9/T88ExM3hEGp0Xz9daixhvYdhs1A5Y9Z26mi7d5H7tURESkPSwL/j4L9q+GkBi46kW2FVXx9FfmydPfXnEmqR34oIDfxGbApf8Fk+bArgWw+1tz31D0gxmSpKYUCtYDEAWc7SnN1YeEsDd8KDuDBrHD1pcdDansrI0h1xGMo87Fxv3lbDwqKQIQYLeRGBlMXHgw8RFmGRse1Lg8eltceDDRYYEaXktE5HRQXWpGF9i30vTwyF8HlqvlMUmDIfMC07uj33lmwugTsCr3EJYFWcmRJHfQCAUnIijAzh0T+zP7sx94adEurhnVm8gQ718J7T3k4KG/bQLgjokDvE5u/sjlg9mQd5j1+8q4+71/Mu+ObIICNCdEtxQQBPH9TTlafa0nEbIdSnaY5cEcOLTbfG+Tv9aU5kJjmyVDzmxKioTHd8rHEZGeRckP6Xq2fmaWAye1mnAr/3Bn9PwwT7xsO1B5aheK7QNnXgHb/g7fPAY3fnDqlRMREbEsmP8fsPE985TidW9RFJjOba8tp67BzYWDkrhxbB9/17JjBYXCmVeaAmb87uZPK1YVmi+6HCVQ7Rn7u7oUnBUEWU4GOtYxkHVc1uyS9UEB7LVS2GHLZEfoUHbaMsl1JVJcH8qhWjsut0VRhZOiCqeXCnkXFRLImenRDE2P4Zw+sWQPSCAx0vNEqdttxuRucJrPExSuOcJERHzB7TZD9+StMgnyvNVwaGfr4+L6mWRH5kTod76ZBLodFmwtBmDCwMRTqLRvXDcmgz99l8veQ9X8YeFOHrm89QN4tfUu7vzreiqdDYzuG8cDl3ifHywowM4L/3IOl/9+GRvyyvjv+TleryfdXFAopA41pTlXvRkerniredjzSCndbeYa2fe9Kc1FpjQlQpKO9BYZDCFRiIi0l5If0rU0OOGHj8360Gtb7d5RXAXAgKTIDqvCsIxYALYeqKCytp6o0FP4ImLSo5DzD9jxFexaaBI6IiIi7VVfA188ABveNa+nPkd+QjYz/rSKgvJa+idF8Pz1I3peb4PgCEg5y5RjaXCaCVArDkBlgVlW5EPZXoJKdjHw0C4Gugq43Lm85WkhdkqIoTh0AKVRZ1AWnMbhgHgO22Ioc4VRWh9AWV0Ah502ypwWh502ql12Kp0NrM71zDniueTgwANk27dxAWs517aVMFtd0xvZgyA6DWIyTEnMgrThkHo2RHWB8dZFRDqbq948hV64GQo3wYFNZt3ZugcfCVmQMRb6ZJukR1zfU3772noX838oBGDqsLRTvt6pCgkMYM4VQ7jt7bW8vuxHzs9K4ryspqSMy23x4N82sfVABQkRwbx448hj9uboHRfOM9cO446/rue1pT9y7oAEfjKodS8R6YECgsy8H0mD4Kyrm7bX1zb1EGlMjGw184tUFZny4+KW14rp4xk6y5MYScyCuEz1FBGRE6Lkh3QtG+aaLyWi0s34qs243RY7i0xvjObjkfpar9gw+iaEs/dQNSt/LOXiIe17AggwQXvMv8GqV+Gzu+CO7yCinfOIiIhIz1bwT/j0l+YfSJsdfvoi30dP4b6Xl1NU4SQlOoS3bx1LbHiwv2t6+goMMZNzxvT2vt/tMmNel+w0/7iX7ISSnQQe2klqVRGpzrXgXOv93OaCoDYwiFwrlR+sfmxxZ7LKPZhtVj+2N6SxnTTe5CKCqWOcfTsT7ZuYaN/IQCsfW9k+8wXB0SKSIX2EGXe+1yhIH6l7ChHpOY4MvVOyo6kc9CxdXnrkBYWbv5UZYyFjnJm/owO+SJ23No8qZwO9YsMY1cd/8300N+nMFK4fncEHa/O486/reG36KMYPSKSytp6HP97MF5sOEGi38cK/nHNCQ2ReOjSN6dl9eWfFXu7/YAPvN5sYXaSVoFBIG2ZKc84qk6g8khA56OkpUnkAyveZsvPrlueExpgkSHxm0zK+v1mPSms1UoiI9Ew2y7Ks4x/WsV566SWeffZZCgsLGT58OH/4wx8YO3Zsm8fPmzeP2bNns2fPHrKysnj66ae5/PLLG/dblsWjjz7KH//4R8rKypgwYQKvvPIKWVlZJ1SfiooKYmJiKC8vJzr6xMbxlE5QWQSvjDdDU0x5ErJ/2WL3pv1l/PTF5USGBLJhzsUEduB4o7M/3cJfVu7lqhHp/P6Gc07tYnUOeG2i6WqdPhL+9SM9wSAi3Y5iawexLMhfDytfhi1/M9siktg76RVe2JXMR+v3AzAwOZK3fzGWXrEdNydWj1dbDiW7zJdvVYVQVWyeXqyrBledKVgQFAHB4eaLt9AYM+51WCyExnLIimZlaTjfFcCSvbUUVNS1eIv06CAu6GVnYmIF2aF7iS37wTzFfGgnWO7WdYrr15QM6TUKUoeZ9xaRbqNHxFfLMsPkVBaZBHTZXijL86zvM+tVhW2fHxJtesilnm3+DqYNg6QzIaBjnwUtddQx5fmlHKx08rurzmJ6dr8Ofb+TUVvvYvobq03PQ2BwahT5ZTVU1jYQYLfx+xtGcMWw9JO63vWvr2RjXhkJEcH8+ZYxjPCMmCBySqpLm/US2W4SIkfutY7FHmQSINHpLcuRbeGJEJFo7sV6Wo9okR7G78mPDz74gOnTp/Pqq68ybtw4nn/+eebNm0dOTg7Jya27S37//fdccMEFPPnkk1xxxRXMnTuXp59+mvXr1zN0qBlj8Omnn+bJJ5/k7bffJjMzk9mzZ7N582a2bt1KaOjxn1zoETeQXU3JLvjgJhP0kofA7UsgsOWTq098sZU/LstlylkpvHbz6A6tzub95Vz54ncEBdj48p7zyUo5xSdbirfDm5eZiVhjMsxwWEN+ap5AFRHpBhRbfcTtMl/6FG2F3CVmQu9Du6i1gthu9WFV8vUsCshm5d6mealuHNeH/7j8TCLamNRUTk+WZbH7YBWLcw6yZMdBVuWWUtfQMsGRER/G2b1iOCslnP6BJWQ4d5FRvpaYolXex6+3BTSNn31k4tK4TPMlQGSy7jtEuqAuE1/dbmioNcVZaeY0qq04alkONYfNfEyOg+Ao9qyXgLv++O8RGgOJgyDxDEg6w7McBLH9Ov0J8IKyGu58dz0b88oYmBzJ53efR2hQQKfW4Xhq613M+WwLH67d37gtMzGCZ68dxuh+J/8wXnl1PTf9eSVb8isICrBx07i+3HZeJhnxSrpLB6ir9sznlmvmc2u+LNsH7oYTu449CMITTCLkyDI0xiRNQ6LMMrTZekiUeR0Ubu6bAsPMUgkUkdOW35Mf48aNY8yYMbz44osAuN1uMjIyuPvuu3n44YdbHX/99dfjcDj4/PPPG7ede+65jBgxgldffRXLskhPT+eBBx7gV7/6FQDl5eWkpKTw1ltvccMNNxy3Tl3mBrKnqHPA88NMj4/IFLj1H5AwoMUhJVVOzn96ETX1Lt64ZTQXDT6FoahO0K1vrmZRzkESI4P55YUDuXVCv1MbQ73oB3j/JhOsAYKjoNdIGDgZJtzjm0qLiPiJYqsPvHGp6eXRbPiOf6u7n61WJgVWy+GNbDaYeEYS904+Q09edhM1dS5W5R5iyY6DLN1xkN0HHW0eGxEcQGxYILGBdcRSRUxDCaG1xQQ3VBJCPcHUE0I9E+xbyA7Y1nRiSAxEJpl/7oMjzD/2weFNvVXsQeZJaXsghMXB+Ls74ZOLyLH4PL4u/E+ToHA3HFXcR712maXlanrtqm9KcDTUmqGoGmrM0tvQUycrJAZiPXMexfYx67F9ml6HJ5wWX0Dml9Uw5X+XUuVsIDY8iPdvP5fBqafvvU9BWQ3bDlQQHxHMsN6xBNjb34YVtfU8/NEmvtzc9FR+/8QIBiZHkh4bxr+e25eByR03P6cIAK4G0zOkoqBZyTdDaFUUmKXjENRVHv9aJyowtGUyJDDUDPEVGGrun+wB5v6p+dJ2ZP1IsTet2wI8x9gAmxnS1uZZHnmdPgIGT/XdZxDppvz6CGBdXR3r1q3jkUceadxmt9uZPHkyK1as8HrOihUruP/++1tsmzJlCp9++ikAubm5FBYWMnny5Mb9MTExjBs3jhUrVnhNfjidTpzOppux8nIz+VlFRUW7P5v42LhfwZaPYdrLEJQER/1s3PUu7p3YixW7DzEqLbRTfna/vbgfeUWH2FFUxsJNuVw77BTH1Q7LgBu/hLV/hnVvQeVB2L4YiISzbzn1CouItENUVFS7EruKrR2g0gHVtWAPNg8B9B7F3u1nsv+wDagmJiyQc/rEMaZfHBcPSSXdM8SV2rz7GJkWysi0DO6bmEF5dT3bD1Tww4FythdWkldaQ0FZNYcc9VQ6obIS8gCwAUme0pI7MZGz7OVmKBmrAZxlUFF2YpWJSoehM3z10UR6nNM2vi55GerbTq76REBIs6eqoyA0yjz4FRrdlIRt/iR2uGcZdIxRHFyYP3yngSg7jM8I40B5LY9fPZT08NM7FkfaYUwvc8/gqDr1NnzyioFcNSSePy79kbV7S9mVX82u/IMATMyMIDnUyzCNIr5mi4aYaIgZ3PYx9bVQfahZKYWaQ1BbaeYgqavw9FTzvD6yrbbSJHZp9iy5swaoAco69nM1N+wGSD+/896vC2hvbJVuzvKj/Px8C7C+//77FtsffPBBa+zYsV7PCQoKsubOndti20svvWQlJydblmVZy5cvtwCroKCgxTE///nPreuuu87rNR999FEL81dLRUVFRUVFpVkpLy9vV4xXbFVRUVFRUWm7KL6qqKioqKj4trQ3tkr3psGfgUceeaRFbxK3201paSkJCQldKmNYUVFBRkYGeXl5GlKkGbWLd2oX79Qu3qld2tbd2yYqqn1zGim2dm9qF+/ULm1T23indvGuJ7RLT46vPeHn2x5ql7apbbxTu3indvGuJ7RLe2OrdG9+TX4kJiYSEBBAUVFRi+1FRUWkpqZ6PSc1NfWYxx9ZFhUVkZaW1uKYESNGeL1mSEgIISEtJ3iMjY09mY9yWomOju62f8hOhdrFO7WLd2oX79QubVPbtKTY2jOoXbxTu7RNbeOd2sU7tUtr3Sm+6ufrndqlbWob79Qu3qldvFO7SE9j9+ebBwcHM2rUKBYuXNi4ze12s3DhQrKzs72ek52d3eJ4gAULFjQen5mZSWpqaotjKioqWLVqVZvXFBERERERERERERGR7sPvw17df//9zJgxg9GjRzN27Fief/55HA4Ht956KwDTp0+nV69ePPnkkwDMmjWLiRMn8txzzzF16lTef/991q5dy+uvvw6AzWbj3nvv5fHHHycrK4vMzExmz55Neno606ZN89fHFBERERERERERERGRTuL35Mf111/PwYMHmTNnDoWFhYwYMYKvvvqKlJQUAPbt24fd3tRBZfz48cydO5ff/va3/OY3vyErK4tPP/2UoUOHNh7z0EMP4XA4uP322ykrK+O8887jq6++IjQ0tNM/X2cKCQnh0UcfbdUNuqdTu3indvFO7eKd2qVtapvuTT9f79Qu3qld2qa28U7t4p3apXvTz9c7tUvb1DbeqV28U7t4p3aRnspmWZbl70qIiIiIiIiIiIiIiIj4il/n/BAREREREREREREREfE1JT9ERERERERERERERKRbUfJDRERERERERERERES6FSU/RERERERERERERESkW1Hyo5t44oknGD9+POHh4cTGxno9Zt++fUydOpXw8HCSk5N58MEHaWho6NyKdrKXXnqJfv36ERoayrhx41i9erW/q9Tpli5dypVXXkl6ejo2m41PP/20xX7LspgzZw5paWmEhYUxefJkdu7c6Z/KdqInn3ySMWPGEBUVRXJyMtOmTSMnJ6fFMbW1tcycOZOEhAQiIyO55pprKCoq8lONO8crr7zCsGHDiI6OJjo6muzsbP7xj3807u+JbeLNU089hc1m4957723cprbpfhRb29bT46tiq3eKrd4ptp4YxdaeQbG1bYqtiq3eKLZ6p9h6YhRbRZT86Dbq6ur4+c9/zp133ul1v8vlYurUqdTV1fH999/z9ttv89ZbbzFnzpxOrmnn+eCDD7j//vt59NFHWb9+PcOHD2fKlCkUFxf7u2qdyuFwMHz4cF566SWv+5955hleeOEFXn31VVatWkVERARTpkyhtra2k2vauZYsWcLMmTNZuXIlCxYsoL6+nksuuQSHw9F4zH333cff//535s2bx5IlSygoKOBnP/uZH2vd8Xr37s1TTz3FunXrWLt2LRdddBFXXXUVP/zwA9Az2+Roa9as4bXXXmPYsGEttqttuh/FVu8UXxVb26LY6p1i6/EptvYciq3eKbYqtrZFsdU7xdbjU2wV8bCkW3nzzTetmJiYVtu//PJLy263W4WFhY3bXnnlFSs6OtpyOp2dWMPOM3bsWGvmzJmNr10ul5Wenm49+eSTfqyVfwHWJ5980vja7XZbqamp1rPPPtu4rayszAoJCbHee+89P9TQf4qLiy3AWrJkiWVZph2CgoKsefPmNR6zbds2C7BWrFjhr2r6RVxcnPWnP/1JbWJZVmVlpZWVlWUtWLDAmjhxojVr1izLsvT70t0ptrak+NqSYmvbFFvbptjaRLG1Z1JsbUmxtSXF1rYptrZNsbWJYqtIE/X86CFWrFjB2WefTUpKSuO2KVOmUFFR0ZgZ707q6upYt24dkydPbtxmt9uZPHkyK1as8GPNTi+5ubkUFha2aKeYmBjGjRvX49qpvLwcgPj4eADWrVtHfX19i7YZPHgwffr06TFt43K5eP/993E4HGRnZ6tNgJkzZzJ16tQWbQD6fempelpsBcXXE6HY2kSxtTXF1tYUW6U5xVZDsbUlxdYmiq2tKba2ptgq0iTQ3xWQzlFYWNjiBhJofF1YWOiPKnWokpISXC6X18+8fft2P9Xq9HPkZ++tnbrj70Vb3G439957LxMmTGDo0KGAaZvg4OBWYxH3hLbZvHkz2dnZ1NbWEhkZySeffMKQIUPYsGFDj20TgPfff5/169ezZs2aVvt68u9LT9bTYisovp4IxVZDsbUlxVbvFFvlaIqtTRRbmyi2GoqtLSm2eqfYKtKSen6cxh5++GFsNtsxi26GRE7dzJkz2bJlC++//76/q3JaGDRoEBs2bGDVqlXceeedzJgxg61bt/q7Wn6Vl5fHrFmzePfddwkNDfV3deQUKLaKdA7F1pYUW1tTbO0+FFtFOodia0uKra0ptoq0pp4fp7EHHniAW2655ZjH9O/f/4SulZqayurVq1tsKyoqatzX3SQmJhIQEND4GY8oKirqlp+3vY60RVFREWlpaY3bi4qKGDFihJ9q1bnuuusuPv/8c5YuXUrv3r0bt6emplJXV0dZWVmLpyJ6wu9QcHAwAwcOBGDUqFGsWbOG3//+91x//fU9tk3WrVtHcXExI0eObNzmcrlYunQpL774IvPnz++xbdPVKLaeGsXX41NsVWz1RrG1NcXW7kOx9dQoth6fYqtiqzeKra0ptoq0pp4fp7GkpCQGDx58zBIcHHxC18rOzmbz5s0UFxc3bluwYAHR0dEMGTKkoz6C3wQHBzNq1CgWLlzYuM3tdrNw4UKys7P9WLPTS2ZmJqmpqS3aqaKiglWrVnX7drIsi7vuuotPPvmEb7/9lszMzBb7R40aRVBQUIu2ycnJYd++fd2+bY7mdrtxOp09uk0mTZrE5s2b2bBhQ2MZPXo0N910U+N6T22brkax9dQovh6fYqti64lQbFVs7U4UW0+NYuvxKbYqtp4IxVbFVhFv1POjm9i3bx+lpaXs27cPl8vFhg0bABg4cCCRkZFccsklDBkyhJtvvplnnnmGwsJCfvvb3zJz5kxCQkL8W/kOcv/99zNjxgxGjx7N2LFjef7553E4HNx6663+rlqnqqqqYteuXY2vc3Nz2bBhA/Hx8fTp04d7772Xxx9/nKysLDIzM5k9ezbp6elMmzbNf5XuBDNnzmTu3Ll89tlnREVFNY5vGRMTQ1hYGDExMdx2223cf//9xMfHEx0dzd133012djbnnnuun2vfcR555BEuu+wy+vTpQ2VlJXPnzmXx4sXMnz+/x7YJQFRUVOO4ukdERESQkJDQuL2ntk13ptjqneKrYmtbFFu9U2z1TrG1Z1Js9U6xVbG1LYqt3im2eqfYKuKFJd3CjBkzLKBVWbRoUeMxe/bssS677DIrLCzMSkxMtB544AGrvr7ef5XuBH/4wx+sPn36WMHBwdbYsWOtlStX+rtKnW7RokVefzdmzJhhWZZlud1ua/bs2VZKSooVEhJiTZo0ycrJyfFvpTuBtzYBrDfffLPxmJqaGuuXv/ylFRcXZ4WHh1tXX321deDAAf9VuhP84he/sPr27WsFBwdbSUlJ1qRJk6yvv/66cX9PbJO2TJw40Zo1a1bja7VN96PY2raeHl8VW71TbPVOsfXEKbZ2f4qtbVNsVWz1RrHVO8XWE6fYKj2dzbIsy/cpFREREREREREREREREf/QnB8iIiIiIiIiIiIiItKtKPkhIiIiIiIiIiIiIiLdipIfIiIiIiIiIiIiIiLSrSj5ISIiIiIiIiIiIiIi3YqSHyIiIiIiIiIiIiIi0q0o+SEiIiIiIiIiIiIiIt2Kkh8iIiIiIiIiIiIiItKtKPkhIiIiIiIiIiIiIiLdipIfIuIzF154Iffee6+/qyEiItJtKLaKiIj4lmKriEjPoeSHiIiIiIiIiIiIiIh0K0p+iEi3VldX5+8qiIiIdCuKrSIiIr6l2Coi0jGU/BCRdnE4HEyfPp3IyEjS0tJ47rnnTvjcl19+maysLEJDQ0lJSeHaa69t3Od2u3nmmWcYOHAgISEh9OnThyeeeKJx/+bNm7nooosICwsjISGB22+/naqqqsb9t9xyC9OmTeOJJ54gPT2dQYMGAZCXl8d1111HbGws8fHxXHXVVezZs+fUG0JERMRHFFtFRER8S7FVRKRnU/JDRNrlwQcfZMmSJXz22Wd8/fXXLF68mPXr1x/3vLVr13LPPffwu9/9jpycHL766isuuOCCxv2PPPIITz31FLNnz2br1q3MnTuXlJQUwNy4Tpkyhbi4ONasWcO8efP45ptvuOuuu1q8x8KFC8nJyWHBggV8/vnn1NfXM2XKFKKioli2bBnLly8nMjKSSy+9VE/YiIjIaUOxVURExLcUW0VEejhLROQkVVZWWsHBwdaHH37YuO3QoUNWWFiYNWvWrGOe+9FHH1nR0dFWRUVFq30VFRVWSEiI9cc//tHrua+//roVFxdnVVVVNW774osvLLvdbhUWFlqWZVkzZsywUlJSLKfT2XjMX/7yF2vQoEGW2+1u3OZ0Oq2wsDBr/vz5J/SZRUREOpJiq4iIiG8ptoqIiHp+iMhJ2717N3V1dYwbN65xW3x8fGNX3WO5+OKL6du3L/379+fmm2/m3Xffpbq6GoBt27bhdDqZNGmS13O3bdvG8OHDiYiIaNw2YcIE3G43OTk5jdvOPvtsgoODG19v3LiRXbt2ERUVRWRkJJGRkcTHx1NbW8vu3btP+vOLiIj4mmKriIiIbym2iohIoL8rICI9S1RUFOvXr2fx4sV8/fXXzJkzh8cee4w1a9YQFhbmk/dofpMJUFVVxahRo3j33XdbHZuUlOST9xQREfEXxVYRERHfUmwVEeke1PNDRE7agAEDCAoKYtWqVY3bDh8+zI4dO07o/MDAQCZPnswzzzzDpk2b2LNnD99++y1ZWVmEhYWxcOFCr+edeeaZbNy4EYfD0bht+fLl2O32Yz69M3LkSHbu3ElycjIDBw5sUWJiYk7wU4uIiHQcxVYRERHfUmwVERElP0TkpEVGRnLbbbfx4IMP8u2337JlyxZuueUW7Pbj/0n5/PPPeeGFF9iwYQN79+7lnXfewe12M2jQIEJDQ/n1r3/NQw89xDvvvMPu3btZuXIlf/7znwG46aabCA0NZcaMGWzZsoVFixZx9913c/PNNzdOLufNTTfdRGJiIldddRXLli0jNzeXxYsXc88997B//36ftYuIiEh7KbaKiIj4lmKriIho2CsRaZdnn32WqqoqrrzySqKionjggQcoLy8/7nmxsbF8/PHHPPbYY9TW1pKVlcV7773HWWedBcDs2bMJDAxkzpw5FBQUkJaWxh133AFAeHg48+fPZ9asWYwZM4bw8HCuueYa/ud//ueY7xkeHs7SpUv59a9/zc9+9jMqKyvp1asXkyZNIjo6+tQbQ0RExAcUW0VERHxLsVVEpGezWZZl+bsSIiIiIiIiIiIiIiIivqJhr0REREREREREREREpFtR8kNEfGrZsmVERka2WUREROTkKLaKiIj4lmKriEjPoGGvRMSnampqyM/Pb3P/wIEDO7E2IiIiXZ9iq4iIiG8ptoqI9AxKfoiIiIiIiIiIiIiISLeiYa9ERERERERERERERKRbUfJDRERERERERERERES6FSU/RERERERERERERESkW1HyQ0REREREREREREREuhUlP0REREREREREREREpFtR8kNERERERERERERERLoVJT9ERERERERERERERKRb+f9MIO07WeRhlwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1601.11x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(apo_holo_loop_vs_anchor,\n",
    "            row='chain_type', col='cdr',\n",
    "            hue='classification',\n",
    "            x='d_score',\n",
    "            kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=100.36714473266193, pvalue=1.5105310954050626e-16)\n"
     ]
    }
   ],
   "source": [
    "treatment_options = ['classification', 'chain_type', 'cdr']\n",
    "treatments = [(group, df['d_score'].to_numpy()) for group, df in apo_holo_loop_vs_anchor.groupby(treatment_options)]\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008333333333333333\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>classification_x</th>\n",
       "      <th>chain_type_x</th>\n",
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       "      <th>classification_y</th>\n",
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       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anchor</td>\n",
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       "      <td>1.11e-01</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>anchor</td>\n",
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       "      <td>4.96e-08</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anchor</td>\n",
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       "      <td>beta_chain</td>\n",
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       "      <td>0.891960</td>\n",
       "      <td>8.14e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anchor</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>loop</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>1.596139</td>\n",
       "      <td>9.45e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>anchor</td>\n",
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       "      <td>2.36e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  classification_x chain_type_x  cdr_x classification_y chain_type_y  cdr_y  \\\n",
       "0           anchor  alpha_chain      1             loop  alpha_chain      1   \n",
       "1           anchor  alpha_chain      2             loop  alpha_chain      2   \n",
       "2           anchor  alpha_chain      3             loop  alpha_chain      3   \n",
       "3           anchor   beta_chain      1             loop   beta_chain      1   \n",
       "4           anchor   beta_chain      2             loop   beta_chain      2   \n",
       "5           anchor   beta_chain      3             loop   beta_chain      3   \n",
       "\n",
       "   statistic     p_val  significant  \n",
       "0  -1.220053  1.11e-01        False  \n",
       "1  -1.547078  6.09e-02        False  \n",
       "2  -5.328286  4.96e-08         True  \n",
       "3   0.891960  8.14e-01        False  \n",
       "4   1.596139  9.45e-01        False  \n",
       "5  -4.577162  2.36e-06         True  "
      ]
     },
     "execution_count": 27,
     "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 ((classification_x, chain_type_x, cdr_x), sample_x), ((classification_y, chain_type_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y, alternative='less')\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "d_score_statistics_loop_anchor = pd.DataFrame({\n",
    "    'classification_x': [name for ((name, _, _), _), _ in combos],\n",
    "    'chain_type_x': [name for ((_, name, _), _), _ in combos],\n",
    "    'cdr_x': [name for ((_, _, name), _), _ in combos],\n",
    "    'classification_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",
    "d_score_statistics_loop_anchor['p_val'] = d_score_statistics_loop_anchor['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "d_score_statistics_loop_anchor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008333333333333333\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>classification_x</th>\n",
       "      <th>chain_type_x</th>\n",
       "      <th>cdr_x</th>\n",
       "      <th>classification_y</th>\n",
       "      <th>chain_type_y</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>-1.547078</td>\n",
       "      <td>9.39e-01</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>anchor</td>\n",
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       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>anchor</td>\n",
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       "      <td>beta_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>0.891960</td>\n",
       "      <td>1.86e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anchor</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>loop</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>2</td>\n",
       "      <td>1.596139</td>\n",
       "      <td>5.52e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>anchor</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>loop</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>-4.577162</td>\n",
       "      <td>1.00e+00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  classification_x chain_type_x  cdr_x classification_y chain_type_y  cdr_y  \\\n",
       "0           anchor  alpha_chain      1             loop  alpha_chain      1   \n",
       "1           anchor  alpha_chain      2             loop  alpha_chain      2   \n",
       "2           anchor  alpha_chain      3             loop  alpha_chain      3   \n",
       "3           anchor   beta_chain      1             loop   beta_chain      1   \n",
       "4           anchor   beta_chain      2             loop   beta_chain      2   \n",
       "5           anchor   beta_chain      3             loop   beta_chain      3   \n",
       "\n",
       "   statistic     p_val  significant  \n",
       "0  -1.220053  8.89e-01        False  \n",
       "1  -1.547078  9.39e-01        False  \n",
       "2  -5.328286  1.00e+00        False  \n",
       "3   0.891960  1.86e-01        False  \n",
       "4   1.596139  5.52e-02        False  \n",
       "5  -4.577162  1.00e+00        False  "
      ]
     },
     "execution_count": 28,
     "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 ((classification_x, chain_type_x, cdr_x), sample_x), ((classification_y, chain_type_y, cdr_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y, alternative='greater')\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "d_score_statistics_loop_anchor = pd.DataFrame({\n",
    "    'classification_x': [name for ((name, _, _), _), _ in combos],\n",
    "    'chain_type_x': [name for ((_, name, _), _), _ in combos],\n",
    "    'cdr_x': [name for ((_, _, name), _), _ in combos],\n",
    "    'classification_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",
    "d_score_statistics_loop_anchor['p_val'] = d_score_statistics_loop_anchor['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "d_score_statistics_loop_anchor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Germline (CDR1 and CDR2) loops compared with CDR3 loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_d_score_df['loop_type'] = tcr_d_score_df['cdr'].map(lambda cdr: 'Germline' if cdr in (1, 2) else 'CDR3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_loop_type = (tcr_d_score_df.query(\"comparison == 'apo-holo'\")\n",
    "                                .groupby(['cdr_sequences_collated',  'loop_type', 'classification'],\n",
    "                                         dropna=False)['d_score']\n",
    "                                .apply('sum')\n",
    "                                .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='loop_type', ylabel='d_score'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(apo_holo_loop_type, x='loop_type', y='d_score', hue='classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe3f2e5f280>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 1101.11x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(apo_holo_loop_type, x='d_score', col='loop_type', hue='classification', kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=46.51058594112732, pvalue=4.416965854082892e-10)\n"
     ]
    }
   ],
   "source": [
    "treatment_options = ['classification', 'loop_type']\n",
    "treatments = [(group, df['d_score'].to_numpy()) for group, df in apo_holo_loop_type.groupby(treatment_options)]\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008333333333333333\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>classification_x</th>\n",
       "      <th>loop_type_x</th>\n",
       "      <th>classification_y</th>\n",
       "      <th>loop_type_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>anchor</td>\n",
       "      <td>CDR3</td>\n",
       "      <td>anchor</td>\n",
       "      <td>Germline</td>\n",
       "      <td>-3.591312</td>\n",
       "      <td>3.29e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anchor</td>\n",
       "      <td>CDR3</td>\n",
       "      <td>loop</td>\n",
       "      <td>CDR3</td>\n",
       "      <td>-5.375231</td>\n",
       "      <td>7.65e-08</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anchor</td>\n",
       "      <td>CDR3</td>\n",
       "      <td>loop</td>\n",
       "      <td>Germline</td>\n",
       "      <td>-4.037292</td>\n",
       "      <td>5.41e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anchor</td>\n",
       "      <td>Germline</td>\n",
       "      <td>loop</td>\n",
       "      <td>CDR3</td>\n",
       "      <td>-4.600635</td>\n",
       "      <td>4.21e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anchor</td>\n",
       "      <td>Germline</td>\n",
       "      <td>loop</td>\n",
       "      <td>Germline</td>\n",
       "      <td>-1.713502</td>\n",
       "      <td>8.66e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>loop</td>\n",
       "      <td>CDR3</td>\n",
       "      <td>loop</td>\n",
       "      <td>Germline</td>\n",
       "      <td>3.708675</td>\n",
       "      <td>2.08e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  classification_x loop_type_x classification_y loop_type_y  statistic  \\\n",
       "0           anchor        CDR3           anchor    Germline  -3.591312   \n",
       "1           anchor        CDR3             loop        CDR3  -5.375231   \n",
       "2           anchor        CDR3             loop    Germline  -4.037292   \n",
       "3           anchor    Germline             loop        CDR3  -4.600635   \n",
       "4           anchor    Germline             loop    Germline  -1.713502   \n",
       "5             loop        CDR3             loop    Germline   3.708675   \n",
       "\n",
       "      p_val  significant  \n",
       "0  3.29e-04         True  \n",
       "1  7.65e-08         True  \n",
       "2  5.41e-05         True  \n",
       "3  4.21e-06         True  \n",
       "4  8.66e-02        False  \n",
       "5  2.08e-04         True  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = list(itertools.combinations(treatments, 2))\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "print(significance_level)\n",
    "statistics = []\n",
    "p_vals = []\n",
    "\n",
    "for ((classification_x, loop_type_x), sample_x), ((classification_y, loop_type_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y, alternative='two-sided')\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "d_score_statistics_loop_type = pd.DataFrame({\n",
    "    'classification_x': [name for ((name, _), _), _ in combos],\n",
    "    'loop_type_x': [name for ((_, name), _), _ in combos],\n",
    "    'classification_y': [name for _, ((name, _), _) in combos],\n",
    "    'loop_type_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",
    "d_score_statistics_loop_type['p_val'] = d_score_statistics_loop_type['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "d_score_statistics_loop_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of peptide D-scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pMHC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df = pd.read_csv(os.path.join(DATA_DIR, 'pmhc_per_res_apo_holo_d_score.csv'))\n",
    "\n",
    "pmhc_d_score_df = pmhc_d_score_df.merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_x_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_y_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    apo_holo_summary[['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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_score_df_holo = pd.read_csv(os.path.join(DATA_DIR, 'pmhc_per_res_holo_holo_d_score.csv'))\n",
    "\n",
    "d_score_df_holo['mhc_slug'] = None\n",
    "d_score_df_holo['peptide_sequence'] = None\n",
    "\n",
    "mhc_pattern = r'^hla|h2'\n",
    "mhc_alinged_complex_ids = d_score_df_holo['complex_id'].str.contains(mhc_pattern, regex=True)\n",
    "\n",
    "mhc_slug_peptides = d_score_df_holo[mhc_alinged_complex_ids]['complex_id'].str.rsplit('_', n=1)\n",
    "mhc_slugs = mhc_slug_peptides.map(lambda composite: composite[0])\n",
    "peptides = mhc_slug_peptides.map(lambda composite: composite[1])\n",
    "\n",
    "d_score_df_holo.loc[mhc_alinged_complex_ids, 'mhc_slug'] = mhc_slugs\n",
    "d_score_df_holo.loc[mhc_alinged_complex_ids, 'peptide_sequence'] = peptides\n",
    "\n",
    "d_score_df_holo = d_score_df_holo.merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_x_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    apo_holo_summary[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_y_name',\n",
    "    right_on='file_name',\n",
    ")\n",
    "\n",
    "d_score_df_holo_pmhc = d_score_df_holo[mhc_alinged_complex_ids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df = pd.concat([pmhc_d_score_df, d_score_df_holo_pmhc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df['comparison'] = pmhc_d_score_df['state_x'] + '-' + pmhc_d_score_df['state_y']\n",
    "pmhc_d_score_df['comparison'] = pmhc_d_score_df['comparison'].map(\n",
    "    lambda entry: 'apo-holo' if entry == 'holo-apo' else entry\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df['structure_comparison'] = pmhc_d_score_df.apply(\n",
    "    lambda row: '-'.join(sorted([row.structure_x_name, row.structure_y_name])),\n",
    "    axis='columns',\n",
    ")\n",
    "pmhc_d_score_df = pmhc_d_score_df.drop_duplicates(['structure_comparison', 'chain_type', 'tcr_contact',\n",
    "                                                   'residue_name', 'residue_seq_id', 'residue_insert_code'])\n",
    "pmhc_d_score_df = pmhc_d_score_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df['peptide_positon'] = None\n",
    "\n",
    "peptide_positions = (pmhc_d_score_df.query(\"chain_type == 'antigen_chain'\")\n",
    "                                    .groupby(['complex_id', 'structure_x_name', 'structure_y_name'])\n",
    "                                    .cumcount() + 1)\n",
    "\n",
    "pmhc_d_score_df.loc[pmhc_d_score_df['chain_type'] == 'antigen_chain', 'peptide_position'] = peptide_positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df['peptide_length'] = pmhc_d_score_df['peptide_sequence'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df = pmhc_d_score_df[~pmhc_d_score_df['d_score'].isnull()].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmhc_d_score_df = pmhc_d_score_df.groupby(['mhc_slug',\n",
    "                                           'peptide_sequence',\n",
    "                                           'comparison',\n",
    "                                           'chain_type',\n",
    "                                           'tcr_contact',\n",
    "                                           'residue_name',\n",
    "                                           'residue_seq_id',\n",
    "                                           'residue_insert_code',\n",
    "                                           'peptide_position',\n",
    "                                           'peptide_length'], dropna=False)['d_score'].apply('mean').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mhc_slug</th>\n",
       "      <th>peptide_sequence</th>\n",
       "      <th>comparison</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>tcr_contact</th>\n",
       "      <th>residue_name</th>\n",
       "      <th>residue_seq_id</th>\n",
       "      <th>residue_insert_code</th>\n",
       "      <th>peptide_position</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>d_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>h2_db</td>\n",
       "      <td>ASNENMETM</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>False</td>\n",
       "      <td>ASN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.065791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>h2_db</td>\n",
       "      <td>ASNENMETM</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>False</td>\n",
       "      <td>ASN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.174347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>h2_db</td>\n",
       "      <td>ASNENMETM</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>False</td>\n",
       "      <td>GLU</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.215112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>h2_db</td>\n",
       "      <td>ASNENMETM</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>False</td>\n",
       "      <td>GLU</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.015743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>h2_db</td>\n",
       "      <td>ASNENMETM</td>\n",
       "      <td>apo-apo</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>False</td>\n",
       "      <td>MET</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.019902</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42111</th>\n",
       "      <td>hla_e_01_03</td>\n",
       "      <td>RLPAKAPLL</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>THR</td>\n",
       "      <td>1073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0.001763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42112</th>\n",
       "      <td>hla_e_01_03</td>\n",
       "      <td>RLPAKAPLL</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>TRP</td>\n",
       "      <td>1077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0.004126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42113</th>\n",
       "      <td>hla_e_01_03</td>\n",
       "      <td>RLPAKAPLL</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>TYR</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0.015783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42114</th>\n",
       "      <td>hla_e_01_03</td>\n",
       "      <td>RLPAKAPLL</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>TYR</td>\n",
       "      <td>1070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0.004527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42115</th>\n",
       "      <td>hla_e_01_03</td>\n",
       "      <td>RLPAKAPLL</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>VAL</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0.011465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42116 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mhc_slug peptide_sequence comparison     chain_type  tcr_contact  \\\n",
       "0            h2_db        ASNENMETM    apo-apo  antigen_chain        False   \n",
       "1            h2_db        ASNENMETM    apo-apo  antigen_chain        False   \n",
       "2            h2_db        ASNENMETM    apo-apo  antigen_chain        False   \n",
       "3            h2_db        ASNENMETM    apo-apo  antigen_chain        False   \n",
       "4            h2_db        ASNENMETM    apo-apo  antigen_chain        False   \n",
       "...            ...              ...        ...            ...          ...   \n",
       "42111  hla_e_01_03        RLPAKAPLL   apo-holo     mhc_chain1         True   \n",
       "42112  hla_e_01_03        RLPAKAPLL   apo-holo     mhc_chain1         True   \n",
       "42113  hla_e_01_03        RLPAKAPLL   apo-holo     mhc_chain1         True   \n",
       "42114  hla_e_01_03        RLPAKAPLL   apo-holo     mhc_chain1         True   \n",
       "42115  hla_e_01_03        RLPAKAPLL   apo-holo     mhc_chain1         True   \n",
       "\n",
       "      residue_name  residue_seq_id residue_insert_code  peptide_position  \\\n",
       "0              ASN               3                 NaN               3.0   \n",
       "1              ASN               5                 NaN               5.0   \n",
       "2              GLU               4                 NaN               4.0   \n",
       "3              GLU               7                 NaN               7.0   \n",
       "4              MET               6                 NaN               6.0   \n",
       "...            ...             ...                 ...               ...   \n",
       "42111          THR            1073                 NaN               NaN   \n",
       "42112          TRP            1077                 NaN               NaN   \n",
       "42113          TYR              59                 NaN               NaN   \n",
       "42114          TYR            1070                 NaN               NaN   \n",
       "42115          VAL              76                 NaN               NaN   \n",
       "\n",
       "       peptide_length   d_score  \n",
       "0                   9  0.065791  \n",
       "1                   9  0.174347  \n",
       "2                   9  0.215112  \n",
       "3                   9  0.015743  \n",
       "4                   9  0.019902  \n",
       "...               ...       ...  \n",
       "42111               9  0.001763  \n",
       "42112               9  0.004126  \n",
       "42113               9  0.015783  \n",
       "42114               9  0.004527  \n",
       "42115               9  0.011465  \n",
       "\n",
       "[42116 rows x 11 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmhc_d_score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing *apo*-*apo*, *apo*-*holo*, and *holo*-*holo* peptide D-scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "peptide_d_scores = (pmhc_d_score_df.query(\"chain_type == 'antigen_chain'\")\n",
    "                                   .groupby(['mhc_slug', 'peptide_sequence', 'comparison'], dropna=False)['d_score']\n",
    "                                   .apply('sum')\n",
    "                                   .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='comparison', ylabel='d_score'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(peptide_d_scores, x='comparison', y='d_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='d_score', ylabel='Density'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(peptide_d_scores, hue='comparison', x='d_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "comparison\n",
       "apo-apo      2.788440\n",
       "apo-holo     5.380116\n",
       "holo-holo    6.488355\n",
       "Name: d_score, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peptide_d_scores.groupby('comparison')['d_score'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=26.286830514084954, pvalue=1.958336328723338e-06)\n"
     ]
    }
   ],
   "source": [
    "treatments = [(group, df['d_score'].to_numpy()) for group, df in peptide_d_scores.groupby('comparison')]\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.016666666666666666\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>comparison_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>apo-holo</td>\n",
       "      <td>-5.022685</td>\n",
       "      <td>5.10e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>-2.529214</td>\n",
       "      <td>1.14e-02</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>1.781758</td>\n",
       "      <td>7.48e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  comparison_x comparison_y  statistic     p_val  significant\n",
       "0      apo-apo     apo-holo  -5.022685  5.10e-07         True\n",
       "1      apo-apo    holo-holo  -2.529214  1.14e-02         True\n",
       "2     apo-holo    holo-holo   1.781758  7.48e-02        False"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = list(itertools.combinations(treatments, 2))\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "print(significance_level)\n",
    "statistics = []\n",
    "p_vals = []\n",
    "\n",
    "for (comparison_x, sample_x), (comparison_y, sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y, alternative='two-sided')\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "d_score_statistics_peptide = pd.DataFrame({\n",
    "    'comparison_x': [name for (name, _), _ in combos],\n",
    "    'comparison_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",
    "d_score_statistics_peptide['p_val'] = d_score_statistics_peptide['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "d_score_statistics_peptide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Per-residue (per length) D-scores of peptides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe3f3400820>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 511.111x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(pmhc_d_score_df.query(\"chain_type == 'antigen_chain'\").sort_values('peptide_position'),\n",
    "            row='peptide_length',\n",
    "            x='peptide_position', y='d_score',\n",
    "            sharex=False,\n",
    "            kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Overall, the analysis of D-scores shows very similar results to the RMSD calculations done in other notebooks. The flexibility of TCRs seems apparent in the CDR3 loops, and the peptides have flexibility in the canonical TCR contact regions (p3 to pn-1)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}