notebooks/16_david_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Create figures for different structure stability scores.\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00044642857142857147"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.025 / (14 * 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"max_row\", 1000)\n",
    "pd.set_option(\"max_column\", 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/kimlab1/strokach/workspace/proteinsolver/notebooks/16_david_analysis')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOTEBOOK_NAME = \"16_david_analysis\"\n",
    "NOTEBOOK_PATH = Path(NOTEBOOK_NAME).resolve()\n",
    "NOTEBOOK_PATH.mkdir(exist_ok=True)\n",
    "NOTEBOOK_PATH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_scores_total(file):\n",
    "    columns = [\n",
    "        \"protein_name\",\n",
    "        \"pdb_name\",\n",
    "        \"molpdf\",\n",
    "        *[f\"ga341_score_{i}\" for i in range(8)],\n",
    "        \"dope_score\",\n",
    "        \"dope_score_norm\",\n",
    "        \"nope_hr_score\",\n",
    "    ]\n",
    "    results = []\n",
    "    with file.open(\"rt\") as fin:\n",
    "        for line in fin:\n",
    "            protein_name, pdb_name, molpdf, ga341_score, dope_score, dope_score_norm, nope_hr_score = line.strip().split(\n",
    "                \":\"\n",
    "            )\n",
    "            results.append(\n",
    "                (\n",
    "                    protein_name,\n",
    "                    pdb_name,\n",
    "                    float(molpdf),\n",
    "                    *eval(ga341_score),\n",
    "                    float(dope_score),\n",
    "                    float(dope_score_norm),\n",
    "                    float(nope_hr_score),\n",
    "                )\n",
    "            )\n",
    "    return pd.DataFrame(results, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_rosetta(file):\n",
    "    header = \"SCORE: total_score dslf_fa13    fa_atr    fa_dun   fa_elec fa_intra_rep fa_intra_sol_xover4              fa_rep              fa_sol hbond_bb_sc hbond_lr_bb    hbond_sc hbond_sr_bb lk_ball_wtd       omega     p_aa_pp pro_close rama_prepro         ref yhh_planarity description \\n\"\n",
    "    buf = io.StringIO()\n",
    "    buf.write(header)\n",
    "    with file.open(\"rt\") as fin:\n",
    "        for line in fin:\n",
    "            if line.startswith(\"SEQUENCE:\") or line.startswith(header):\n",
    "                continue\n",
    "            buf.write(line)\n",
    "    buf.seek(0)\n",
    "    df = pd.read_csv(buf, sep=\" +\", engine=\"python\",)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workflow\n",
    "\n",
    "Note from David:\n",
    "\n",
    "> The way to read the file (i.e., scoresTotal) is:\n",
    "> \n",
    "> protein_name + ':' + pdb_name + ':' + molpdf + ':' + 'GA341 score' + ':' + 'DOPE score' + ':' + 'Normalized DOPE score' + ':' + 'DOPE-HR score + '\\n'\n",
    "> \n",
    "> The measure that we used in the plots was molpdf, then you should read the third column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "modeller-and-rosetta-score-distributions-1n5u.pdf\n",
      "modeller-and-rosetta-score-distributions-1n5u.png\n",
      "modeller-and-rosetta-score-distributions-1n5u.svg\n",
      "modeller-and-rosetta-score-distributions-4beu.pdf\n",
      "modeller-and-rosetta-score-distributions-4beu.png\n",
      "modeller-and-rosetta-score-distributions-4beu.svg\n",
      "modeller-and-rosetta-score-distributions-4unu.pdf\n",
      "modeller-and-rosetta-score-distributions-4unu.png\n",
      "modeller-and-rosetta-score-distributions-4unu.svg\n",
      "modeller-and-rosetta-score-distributions-4z8j.pdf\n",
      "modeller-and-rosetta-score-distributions-4z8j.png\n",
      "modeller-and-rosetta-score-distributions-4z8j.svg\n",
      "modeller-and-rosetta-score-distributions-5vid.pdf\n",
      "modeller-and-rosetta-score-distributions-5vid.png\n",
      "modeller-and-rosetta-score-distributions-5vid.svg\n",
      "modeller-and-rosetta-score-distributions-5vli.pdf\n",
      "modeller-and-rosetta-score-distributions-5vli.png\n",
      "modeller-and-rosetta-score-distributions-5vli.svg\n",
      "modeller-and-rosetta-score-distributions-5vmr.pdf\n",
      "modeller-and-rosetta-score-distributions-5vmr.png\n",
      "modeller-and-rosetta-score-distributions-5vmr.svg\n",
      "modeller-and-rosetta-score-distributions.pdf\n",
      "modeller-and-rosetta-score-distributions.png\n",
      "modeller-and-rosetta-score-distributions.svg\n",
      "molpdb-scores-1n5u.svg\n",
      "molpdb-scores-4beu.svg\n",
      "molpdb-scores-4unu.svg\n",
      "molpdb-scores-4z8j.svg\n",
      "quark-aligned-1n5uA03.png\n",
      "quark-aligned-4beuA02.png\n",
      "Scores\n",
      "screenshot.png\n"
     ]
    }
   ],
   "source": [
    "!ls {NOTEBOOK_PATH}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1n5u\t4beu\t4unu\t      4unuWT  4z8jFiltered  5vid    5vli    5vmr\n",
      "1n5uWT\t4beuWT\t4unuFiltered  4z8j    4z8jWT\t    5vidWT  5vliWT  5vmrWT\n"
     ]
    }
   ],
   "source": [
    "!ls {NOTEBOOK_PATH}/Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load model scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SCORES = {}\n",
    "\n",
    "PDB_IDS = [\"1n5u\", \"4beu\", \"4unu\", \"4z8j\", \"5vid\", \"5vli\", \"5vmr\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1n5u\n",
      "4beu\n",
      "4unu\n",
      "4z8j\n",
      "5vid\n",
      "5vli\n",
      "5vmr\n"
     ]
    }
   ],
   "source": [
    "for pdb_id in PDB_IDS:\n",
    "    print(pdb_id)\n",
    "\n",
    "    SCORES[f\"{pdb_id}_modeller_wt\"] = read_scores_total(\n",
    "        NOTEBOOK_PATH.joinpath(\"Scores\", f\"{pdb_id}WT\", \"scoresTotal\")\n",
    "    )\n",
    "    assert len(SCORES[f\"{pdb_id}_modeller_wt\"]) == 1\n",
    "\n",
    "    SCORES[f\"{pdb_id}_modeller\"] = read_scores_total(\n",
    "        NOTEBOOK_PATH.joinpath(\"Scores\", f\"{pdb_id}\", \"scoresTotal\")\n",
    "    )\n",
    "\n",
    "    SCORES[f\"{pdb_id}_rosetta_wt\"] = load_rosetta(\n",
    "        NOTEBOOK_PATH.joinpath(\"Scores\", f\"{pdb_id}WT\", \"scoresTotal_rosetta.sc\"),\n",
    "    )\n",
    "    assert len(SCORES[f\"{pdb_id}_rosetta_wt\"]) == 1\n",
    "\n",
    "    SCORES[f\"{pdb_id}_rosetta\"] = load_rosetta(\n",
    "        NOTEBOOK_PATH.joinpath(\"Scores\", f\"{pdb_id}\", \"scoresTotal_rosetta.sc\"),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>SCORE:</th>\n",
       "      <th>total_score</th>\n",
       "      <th>dslf_fa13</th>\n",
       "      <th>fa_atr</th>\n",
       "      <th>fa_dun</th>\n",
       "      <th>fa_elec</th>\n",
       "      <th>fa_intra_rep</th>\n",
       "      <th>fa_intra_sol_xover4</th>\n",
       "      <th>fa_rep</th>\n",
       "      <th>fa_sol</th>\n",
       "      <th>hbond_bb_sc</th>\n",
       "      <th>hbond_lr_bb</th>\n",
       "      <th>hbond_sc</th>\n",
       "      <th>hbond_sr_bb</th>\n",
       "      <th>lk_ball_wtd</th>\n",
       "      <th>omega</th>\n",
       "      <th>p_aa_pp</th>\n",
       "      <th>pro_close</th>\n",
       "      <th>rama_prepro</th>\n",
       "      <th>ref</th>\n",
       "      <th>yhh_planarity</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SCORE:</td>\n",
       "      <td>-90.798</td>\n",
       "      <td>-1.393</td>\n",
       "      <td>-201.482</td>\n",
       "      <td>47.2</td>\n",
       "      <td>-64.385</td>\n",
       "      <td>0.422</td>\n",
       "      <td>7.817</td>\n",
       "      <td>23.292</td>\n",
       "      <td>144.088</td>\n",
       "      <td>-6.16</td>\n",
       "      <td>-0.902</td>\n",
       "      <td>-5.112</td>\n",
       "      <td>-23.635</td>\n",
       "      <td>-4.487</td>\n",
       "      <td>1.405</td>\n",
       "      <td>-8.081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.308</td>\n",
       "      <td>8.923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5vmr_0001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SCORE:  total_score  dslf_fa13   fa_atr  fa_dun  fa_elec  fa_intra_rep  \\\n",
       "0  SCORE:      -90.798     -1.393 -201.482    47.2  -64.385         0.422   \n",
       "\n",
       "   fa_intra_sol_xover4  fa_rep   fa_sol  hbond_bb_sc  hbond_lr_bb  hbond_sc  \\\n",
       "0                7.817  23.292  144.088        -6.16       -0.902    -5.112   \n",
       "\n",
       "   hbond_sr_bb  lk_ball_wtd  omega  p_aa_pp  pro_close  rama_prepro    ref  \\\n",
       "0      -23.635       -4.487  1.405   -8.081        0.0       -8.308  8.923   \n",
       "\n",
       "   yhh_planarity description  \n",
       "0            0.0   5vmr_0001  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SCORES[f\"{pdb_id}_rosetta_wt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5vmr'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdb_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>protein_name</th>\n",
       "      <th>pdb_name</th>\n",
       "      <th>molpdf</th>\n",
       "      <th>ga341_score_0</th>\n",
       "      <th>ga341_score_1</th>\n",
       "      <th>ga341_score_2</th>\n",
       "      <th>ga341_score_3</th>\n",
       "      <th>ga341_score_4</th>\n",
       "      <th>ga341_score_5</th>\n",
       "      <th>ga341_score_6</th>\n",
       "      <th>ga341_score_7</th>\n",
       "      <th>dope_score</th>\n",
       "      <th>dope_score_norm</th>\n",
       "      <th>nope_hr_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1n5u_11115</td>\n",
       "      <td>1n5u_11115.B99990001.pdb</td>\n",
       "      <td>331.188141</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333770</td>\n",
       "      <td>-199.935623</td>\n",
       "      <td>-11.192048</td>\n",
       "      <td>-14.278540</td>\n",
       "      <td>-6.215987</td>\n",
       "      <td>-6.553811</td>\n",
       "      <td>-9.149229</td>\n",
       "      <td>-8536.583984</td>\n",
       "      <td>-2.325544</td>\n",
       "      <td>-7893.746094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1n5u_10104</td>\n",
       "      <td>1n5u_10104.B99990001.pdb</td>\n",
       "      <td>291.176971</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.335036</td>\n",
       "      <td>-216.271133</td>\n",
       "      <td>-11.589787</td>\n",
       "      <td>-14.527915</td>\n",
       "      <td>-7.145832</td>\n",
       "      <td>-6.461433</td>\n",
       "      <td>-8.887251</td>\n",
       "      <td>-8651.523438</td>\n",
       "      <td>-2.291999</td>\n",
       "      <td>-8350.359375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1n5u_11709</td>\n",
       "      <td>1n5u_11709.B99990001.pdb</td>\n",
       "      <td>321.634644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.343180</td>\n",
       "      <td>-220.915268</td>\n",
       "      <td>-11.113292</td>\n",
       "      <td>-14.798443</td>\n",
       "      <td>-7.288752</td>\n",
       "      <td>-6.346551</td>\n",
       "      <td>-9.109745</td>\n",
       "      <td>-8701.623047</td>\n",
       "      <td>-2.355440</td>\n",
       "      <td>-8406.374023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1n5u_11569</td>\n",
       "      <td>1n5u_11569.B99990001.pdb</td>\n",
       "      <td>358.094788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.331383</td>\n",
       "      <td>-193.088791</td>\n",
       "      <td>-11.931268</td>\n",
       "      <td>-14.321241</td>\n",
       "      <td>-6.849178</td>\n",
       "      <td>-6.600010</td>\n",
       "      <td>-8.736956</td>\n",
       "      <td>-8500.715820</td>\n",
       "      <td>-2.295815</td>\n",
       "      <td>-7982.669434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1n5u_10496</td>\n",
       "      <td>1n5u_10496.B99990001.pdb</td>\n",
       "      <td>320.189484</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.344019</td>\n",
       "      <td>-200.976791</td>\n",
       "      <td>-11.313426</td>\n",
       "      <td>-14.269778</td>\n",
       "      <td>-6.716456</td>\n",
       "      <td>-6.372287</td>\n",
       "      <td>-8.620751</td>\n",
       "      <td>-8604.061523</td>\n",
       "      <td>-2.389728</td>\n",
       "      <td>-8127.271973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  protein_name                  pdb_name      molpdf  ga341_score_0  \\\n",
       "0   1n5u_11115  1n5u_11115.B99990001.pdb  331.188141            1.0   \n",
       "1   1n5u_10104  1n5u_10104.B99990001.pdb  291.176971            1.0   \n",
       "2   1n5u_11709  1n5u_11709.B99990001.pdb  321.634644            1.0   \n",
       "3   1n5u_11569  1n5u_11569.B99990001.pdb  358.094788            1.0   \n",
       "4   1n5u_10496  1n5u_10496.B99990001.pdb  320.189484            1.0   \n",
       "\n",
       "   ga341_score_1  ga341_score_2  ga341_score_3  ga341_score_4  ga341_score_5  \\\n",
       "0       0.333770    -199.935623     -11.192048     -14.278540      -6.215987   \n",
       "1       0.335036    -216.271133     -11.589787     -14.527915      -7.145832   \n",
       "2       0.343180    -220.915268     -11.113292     -14.798443      -7.288752   \n",
       "3       0.331383    -193.088791     -11.931268     -14.321241      -6.849178   \n",
       "4       0.344019    -200.976791     -11.313426     -14.269778      -6.716456   \n",
       "\n",
       "   ga341_score_6  ga341_score_7   dope_score  dope_score_norm  nope_hr_score  \n",
       "0      -6.553811      -9.149229 -8536.583984        -2.325544   -7893.746094  \n",
       "1      -6.461433      -8.887251 -8651.523438        -2.291999   -8350.359375  \n",
       "2      -6.346551      -9.109745 -8701.623047        -2.355440   -8406.374023  \n",
       "3      -6.600010      -8.736956 -8500.715820        -2.295815   -7982.669434  \n",
       "4      -6.372287      -8.620751 -8604.061523        -2.389728   -8127.271973  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SCORES[\"1n5u_modeller\"].head()"
   ]
  },
  {
   "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>total_score</th>\n",
       "      <th>dslf_fa13</th>\n",
       "      <th>fa_atr</th>\n",
       "      <th>fa_dun</th>\n",
       "      <th>fa_elec</th>\n",
       "      <th>fa_intra_rep</th>\n",
       "      <th>fa_intra_sol_xover4</th>\n",
       "      <th>fa_rep</th>\n",
       "      <th>fa_sol</th>\n",
       "      <th>hbond_bb_sc</th>\n",
       "      <th>hbond_lr_bb</th>\n",
       "      <th>hbond_sc</th>\n",
       "      <th>hbond_sr_bb</th>\n",
       "      <th>lk_ball_wtd</th>\n",
       "      <th>omega</th>\n",
       "      <th>p_aa_pp</th>\n",
       "      <th>pro_close</th>\n",
       "      <th>rama_prepro</th>\n",
       "      <th>ref</th>\n",
       "      <th>yhh_planarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "      <td>16487.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-227.796927</td>\n",
       "      <td>-3.253338</td>\n",
       "      <td>-515.209958</td>\n",
       "      <td>115.376251</td>\n",
       "      <td>-167.747010</td>\n",
       "      <td>0.920853</td>\n",
       "      <td>18.741575</td>\n",
       "      <td>58.585529</td>\n",
       "      <td>380.262940</td>\n",
       "      <td>-7.587940</td>\n",
       "      <td>-1.683784</td>\n",
       "      <td>-12.933979</td>\n",
       "      <td>-58.805364</td>\n",
       "      <td>-16.070841</td>\n",
       "      <td>3.702669</td>\n",
       "      <td>-12.222696</td>\n",
       "      <td>0.524877</td>\n",
       "      <td>-13.537225</td>\n",
       "      <td>3.070913</td>\n",
       "      <td>0.069587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.927876</td>\n",
       "      <td>0.871608</td>\n",
       "      <td>8.385177</td>\n",
       "      <td>4.087143</td>\n",
       "      <td>6.924111</td>\n",
       "      <td>0.044577</td>\n",
       "      <td>1.104581</td>\n",
       "      <td>2.184559</td>\n",
       "      <td>7.287774</td>\n",
       "      <td>1.868239</td>\n",
       "      <td>0.836989</td>\n",
       "      <td>2.651065</td>\n",
       "      <td>1.318788</td>\n",
       "      <td>1.524308</td>\n",
       "      <td>1.072899</td>\n",
       "      <td>1.134971</td>\n",
       "      <td>0.238970</td>\n",
       "      <td>1.947257</td>\n",
       "      <td>3.149636</td>\n",
       "      <td>0.134742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-251.468000</td>\n",
       "      <td>-4.410000</td>\n",
       "      <td>-547.958000</td>\n",
       "      <td>100.699000</td>\n",
       "      <td>-197.329000</td>\n",
       "      <td>0.804000</td>\n",
       "      <td>15.070000</td>\n",
       "      <td>50.799000</td>\n",
       "      <td>353.131000</td>\n",
       "      <td>-18.687000</td>\n",
       "      <td>-4.753000</td>\n",
       "      <td>-25.329000</td>\n",
       "      <td>-64.671000</td>\n",
       "      <td>-22.138000</td>\n",
       "      <td>0.116000</td>\n",
       "      <td>-16.190000</td>\n",
       "      <td>0.032000</td>\n",
       "      <td>-19.415000</td>\n",
       "      <td>-9.323000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-232.574000</td>\n",
       "      <td>-3.918000</td>\n",
       "      <td>-520.985500</td>\n",
       "      <td>112.576500</td>\n",
       "      <td>-172.355000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>17.966500</td>\n",
       "      <td>57.092500</td>\n",
       "      <td>375.303000</td>\n",
       "      <td>-8.761500</td>\n",
       "      <td>-2.269000</td>\n",
       "      <td>-14.658500</td>\n",
       "      <td>-59.683000</td>\n",
       "      <td>-17.075000</td>\n",
       "      <td>2.957000</td>\n",
       "      <td>-12.991000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>-14.838000</td>\n",
       "      <td>0.982000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-227.989000</td>\n",
       "      <td>-3.627000</td>\n",
       "      <td>-515.094000</td>\n",
       "      <td>115.272000</td>\n",
       "      <td>-167.583000</td>\n",
       "      <td>0.914000</td>\n",
       "      <td>18.701000</td>\n",
       "      <td>58.526000</td>\n",
       "      <td>380.120000</td>\n",
       "      <td>-7.410000</td>\n",
       "      <td>-1.777000</td>\n",
       "      <td>-12.807000</td>\n",
       "      <td>-58.799000</td>\n",
       "      <td>-16.085000</td>\n",
       "      <td>3.647000</td>\n",
       "      <td>-12.242000</td>\n",
       "      <td>0.488000</td>\n",
       "      <td>-13.703000</td>\n",
       "      <td>3.102000</td>\n",
       "      <td>0.003000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-223.228000</td>\n",
       "      <td>-2.721500</td>\n",
       "      <td>-509.394500</td>\n",
       "      <td>118.050500</td>\n",
       "      <td>-162.977000</td>\n",
       "      <td>0.945000</td>\n",
       "      <td>19.453500</td>\n",
       "      <td>59.994000</td>\n",
       "      <td>385.120000</td>\n",
       "      <td>-6.212500</td>\n",
       "      <td>-0.961000</td>\n",
       "      <td>-11.080500</td>\n",
       "      <td>-57.931000</td>\n",
       "      <td>-15.059000</td>\n",
       "      <td>4.383000</td>\n",
       "      <td>-11.486000</td>\n",
       "      <td>0.627000</td>\n",
       "      <td>-12.479000</td>\n",
       "      <td>5.163000</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-175.451000</td>\n",
       "      <td>3.033000</td>\n",
       "      <td>-485.357000</td>\n",
       "      <td>132.835000</td>\n",
       "      <td>-144.925000</td>\n",
       "      <td>1.242000</td>\n",
       "      <td>23.342000</td>\n",
       "      <td>74.875000</td>\n",
       "      <td>410.456000</td>\n",
       "      <td>-2.352000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.595000</td>\n",
       "      <td>-53.795000</td>\n",
       "      <td>-10.490000</td>\n",
       "      <td>11.260000</td>\n",
       "      <td>-6.371000</td>\n",
       "      <td>2.513000</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>14.215000</td>\n",
       "      <td>0.913000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_score     dslf_fa13        fa_atr        fa_dun       fa_elec  \\\n",
       "count  16487.000000  16487.000000  16487.000000  16487.000000  16487.000000   \n",
       "mean    -227.796927     -3.253338   -515.209958    115.376251   -167.747010   \n",
       "std        6.927876      0.871608      8.385177      4.087143      6.924111   \n",
       "min     -251.468000     -4.410000   -547.958000    100.699000   -197.329000   \n",
       "25%     -232.574000     -3.918000   -520.985500    112.576500   -172.355000   \n",
       "50%     -227.989000     -3.627000   -515.094000    115.272000   -167.583000   \n",
       "75%     -223.228000     -2.721500   -509.394500    118.050500   -162.977000   \n",
       "max     -175.451000      3.033000   -485.357000    132.835000   -144.925000   \n",
       "\n",
       "       fa_intra_rep  fa_intra_sol_xover4        fa_rep        fa_sol  \\\n",
       "count  16487.000000         16487.000000  16487.000000  16487.000000   \n",
       "mean       0.920853            18.741575     58.585529    380.262940   \n",
       "std        0.044577             1.104581      2.184559      7.287774   \n",
       "min        0.804000            15.070000     50.799000    353.131000   \n",
       "25%        0.890000            17.966500     57.092500    375.303000   \n",
       "50%        0.914000            18.701000     58.526000    380.120000   \n",
       "75%        0.945000            19.453500     59.994000    385.120000   \n",
       "max        1.242000            23.342000     74.875000    410.456000   \n",
       "\n",
       "        hbond_bb_sc   hbond_lr_bb      hbond_sc   hbond_sr_bb   lk_ball_wtd  \\\n",
       "count  16487.000000  16487.000000  16487.000000  16487.000000  16487.000000   \n",
       "mean      -7.587940     -1.683784    -12.933979    -58.805364    -16.070841   \n",
       "std        1.868239      0.836989      2.651065      1.318788      1.524308   \n",
       "min      -18.687000     -4.753000    -25.329000    -64.671000    -22.138000   \n",
       "25%       -8.761500     -2.269000    -14.658500    -59.683000    -17.075000   \n",
       "50%       -7.410000     -1.777000    -12.807000    -58.799000    -16.085000   \n",
       "75%       -6.212500     -0.961000    -11.080500    -57.931000    -15.059000   \n",
       "max       -2.352000      0.000000     -3.595000    -53.795000    -10.490000   \n",
       "\n",
       "              omega       p_aa_pp     pro_close   rama_prepro           ref  \\\n",
       "count  16487.000000  16487.000000  16487.000000  16487.000000  16487.000000   \n",
       "mean       3.702669    -12.222696      0.524877    -13.537225      3.070913   \n",
       "std        1.072899      1.134971      0.238970      1.947257      3.149636   \n",
       "min        0.116000    -16.190000      0.032000    -19.415000     -9.323000   \n",
       "25%        2.957000    -12.991000      0.385000    -14.838000      0.982000   \n",
       "50%        3.647000    -12.242000      0.488000    -13.703000      3.102000   \n",
       "75%        4.383000    -11.486000      0.627000    -12.479000      5.163000   \n",
       "max       11.260000     -6.371000      2.513000      1.074000     14.215000   \n",
       "\n",
       "       yhh_planarity  \n",
       "count   16487.000000  \n",
       "mean        0.069587  \n",
       "std         0.134742  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.003000  \n",
       "75%         0.070000  \n",
       "max         0.913000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SCORES[\"1n5u_rosetta\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_scores(scores_modeller, scores_rosetta):\n",
    "    modeller_columns = {\n",
    "        \"molpdf\": [\"Modeller\", \"(molpdf)\"],\n",
    "        \"dope_score_norm\": [\"Modeller\", \"(dope_norm)\"],\n",
    "        \"nope_hr_score\": [\"Modeller\", \"(dope_hr)\"],\n",
    "        \"ga341_score_1\": [\"Modeller\", \"(ga341)\"],\n",
    "    }\n",
    "    rosetta_columns = {\n",
    "        \"total_score\": [\"Rosetta\", \"(score)\"],\n",
    "    }\n",
    "\n",
    "    data = {\"column\": [], \"name\": [], \"value\": []}\n",
    "\n",
    "    for column, column_name in modeller_columns.items():\n",
    "        column_name = \"\\n\".join([s for s in column_name])\n",
    "        data[\"column\"].append(np.repeat([column], len(scores_modeller)))\n",
    "        data[\"name\"].append(np.repeat([column_name], len(scores_modeller)))\n",
    "        data[\"value\"].append(scores_modeller[column])\n",
    "\n",
    "    for column, column_name in rosetta_columns.items():\n",
    "        column_name = \"\\n\".join([s for s in column_name])\n",
    "        data[\"column\"].append(np.repeat([column], len(scores_rosetta)))\n",
    "        data[\"name\"].append(np.repeat([column_name], len(scores_rosetta)))\n",
    "        data[\"value\"].append(scores_rosetta[column])\n",
    "\n",
    "    df = pd.DataFrame({key: np.hstack(value) for key, value in data.items()})\n",
    "    return df\n",
    "\n",
    "\n",
    "def normalize(df, df_wt):\n",
    "    dfs = []\n",
    "    for name, gp in df.groupby(\"name\"):\n",
    "        #         mean = gp[\"value\"].mean()\n",
    "        mean = df_wt[df_wt[\"name\"] == name][\"value\"][0]\n",
    "        gp = gp.copy()\n",
    "        gp[\"value\"] = (gp[\"value\"] - mean) / gp[\"value\"].std()\n",
    "        dfs.append(gp)\n",
    "    df = pd.concat(dfs, ignore_index=True, sort=False)\n",
    "    return df\n",
    "\n",
    "\n",
    "df = process_scores(SCORES[\"1n5u_modeller\"], SCORES[\"1n5u_rosetta\"])\n",
    "df_wt = process_scores(SCORES[\"1n5u_modeller\"], SCORES[\"1n5u_rosetta\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1n5u\n"
     ]
    },
    {
     "data": {
      "image/png": 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MwkSmks3GjRv1MksTjh07Rm19AzZFDXEZlqZGy8fSzQCGQHVKYWEhR44cxpc/Wr/WUwCTGV/uSNatX6/pU2zNmjXs2L4D6zhLuyu1dIS31gtuwA3eCp+6HQbmwWbMeQqvvPpK3M7T27RpE8lKMqmmtFb7cyx5HDx4sMc4+kNh+/btANgU9T72T3aQpMiYtxQNgeqEVatWIRQLZA7R2xSVnBNASs1yKrndbl586UV1vt0wbRzjHSGEIGmClYb6Bt58882YXjsUfD4fmzdtJlvJ+5ZPLsecDyTW8lrffPMNfaw+kkyqz0kRcGJaAxu+WR9TP5QhUB3g9XpZs+ZLfBmDQIntj7VDbH0QqbmsWbNGk9N/8sknVJRXYIlwvl13CczTW/LeEhoaGmJ+/c7Yu3cvjU2N5JjzvvVeHyUDq5KUMN28hoYGvv56LeMzalrtPyWrnvKKypi2ogyB6oCioiIaGxsgc5DeprTClzGIXbt3t0zijBZSSt5+523MWQrmvto7xjsiabQFl9PFRx99pJsN7bFhwwZA7c61RQhBtimXDd9s0H1aUjRYvnw5breHKbmt69jErDqSzZIlS5bEzBZDoDpgw4YNqt9Jb+d4WzIGgJRs2rQpqqfdv38/pYdKMQ83axpW0BVKpoI5V2HVJ6t0s6E9Vn+xmkxzNkmm9uPg8q39qK2rjftQia6w2+28vegtRvdpZGBK6zxXVpNkal4Fa9eupbi4OCb2GALVARs3boTUPDBHZ4Js1EjJwWSxRb078dVXX4FAs6DMcFAGKRw8cJDDhw/rbQqgBmcW7y+mr6Xjh1W+pQBFKKxaFV/CGi6LFy+mvqGRi/u3P33nvPxKks2S+a++GhN7DIFqB7vdzp49e5BpUU5GFw2EwJtWwJYox0OtW78Oc46CKYyRO62w+Fcq/uabb3S2RGXp0qWYhIn+1o67+2ZhocDSn1UrV8XtKGRX1NTU8Pait5iQWcfQVHu7x6SYfcwoOMY3GzawZcsWzW0yBKodSkpKVF9Caq7eprRPSi6VFRVRcySXl5ezb+8+lH76+Z6CMaWZMGcoaqtOZ2pra/lo2Uf0tQzAauq8NT0kaQQOp4OlS5fGyLro8vLLL+Nxu7hsQOeTn6fmV5GT5OG5Z5/RPBOFIVDt0NK1SM7o/MBQ8Lqw2Wx873vfw2azgTcKK5okq9MrotUFWr16NRAf3bsApoEmtm/frnsqk0WLFuFyuxhuO7HLYzPMmeRa8ln01qIe14oqLCzkk08+4bz8CvJtnddRi0kye+BhDsYgE4UhUO3QkgRfiUKKEa+bmTNncvfdd3PhhReCNwrL+Cjqiip2e/vN8HCQUrLso2WYcxWU9PipDtYhFqSUfPzxx7rZcPToURa/s5gB1sGkKekhfWakbTT1DfUsXLhQY+uih8vl4qk5/yYnycOFfUNLwzwuo4GxGfW8Nn9+S1oWLYifGhlHmALJ6GQUhowVC8uWLeOZZ55Rh86V8JZrahe/XYoSeZds9+7dHDl8BHOMAzO7wpRuwpyn8PHKj3VJZSKl5N///jdIGJkcevbUDHMW/a2DWLRoUUs2gHhn/vz5HD5SxjWDS0lSQvuuhYDvDy7D5HPxj78/qdkcSkOg2iEvzx/r4oxCMnzFisPh4N1331VbZtFolbnU7kNubuQ+ss8++wyhCCwD40ugAMxDzJQdKWPfvn0xv/by5cvZtGkTI5PGYDMlh/XZk5LHYsbMk397UveFL7ti48aNLF68mHPyqhjdJ7z6nmV1c9Wgw+zYWaRZ9L8hUO0wfPhw9UVTnOb4aazAlpxMQUEYmT07oHB7IaYcE8Kq/+hdW8z91RZiYF5YrCgtLWXu3LnkWPIYnDQs7M9bTUmMtp3C3n17ee211zSwMDocOXKEJx5/jP4pLr43sHvdtEnZtZyeXct//7uAdevWRdlCQ6DaJScnh379+yPqj+htyreRElNjGePHjYu4iyel5MCBAyiZkVcD6ZbYbDZmzZqFzWZDuiPvlpmSTSg2hQMHDkR8rlBxu9089uhj4IFxKad2O2i1r7U/A61DePPNN+Nyjl5jYyN/fPghcDdx5/D9WE3du19CwHVDDjMwxcFfn3g86t1aQ6A64DtTpkBDGXjibNXY5iqko4EpU6ZEfCqv14vX441K60m6YcaMGdx1113MmDEDGaWejbCKqAwGhMqrr77KvuJ9nJw8MeyuXVtOShlHqpLGX5/4a1zNLfR4PDz6yCMcLSvjjmEl5CZFdrOsJsldw0tIknb++NAfojoNyxCoDpg6darqjK4u0duU1lTtQzGbOeuss/S2pBXCovptnnvuOZYvX46IwlgAADH0j2/dupVFixYx0DqUAmu/iM9nFmbGJZ9GbW0tc+bMiZu85fPmzWPrtm1cO7iUE9KjsypyptXD3SP2U1tTzeOPPRa1+ChDoDpg+PDhDBk6FFPVXr1NOY7Pi6lmP2dNnkx6emjD3p1hNpvV7pgz8h+OsAgcDgdLlizB4XBEbZEFn8tHnz59onKuzmhsbORvf/0bKUoqJ6WMjdp5M8yZjEg6iS+++CIupsF8/vnnvPvuu0zNq2JybnRXChqU4uC6wYfYvmNH1Bb5MASqA4QQXDRzJrKpMn6c5TUHkG4HM2fOjNopc3Jz8DXH5wx86Zb4nD5ycnK0vY6U/Ouf/6KyqopxyadiFp2PaO5s3kqDp44GTx1fN3zBzubOpx0Nt40k25LDnDlzdFkZJcChQ4eY8+9/MTzNzhWDtFlC6oycOqbmVbFkyZKopAUyBKoTzj//fKxJSXAstqNI7SIlpvId9O3bj4kTJ0Z8umnTpjFv3jz69e2Hr8HHykdWsX9NCQAep4eVj6ziwNqDALiaXax8ZBWH1qs/LmeDk5WPrOLwJjWS3V5rp+Zg66dxzcFajhaqI0ON5Y2sfGQV5UVqEGB9WT0rH1lFxR5V+GtLa1n5yCqqitWo8ZoDNer2LjVDZbRTy7RlyZIlrF6zmpG2k8gMyjfeEfXeOjz+fzWeKuq9dZ0eL4RgfMrpSA/85c9/OR4IHEOcTiePPfoIZp+DW4cdQNFw0PZ7A48yNNXOv/75j4iDOA2B6oTU1FRmXXYZono/2GO/cGYr6kqRTZVce+01xwNJo8DQoUPx1vuIE/dIK2SD2rLr1y9yf1BH7Ny5k+eee558S1+GJWm3ao/NlMy45FMpOVDCf/7zn5j7o1544QUOHirl5iEHybSG5h9adKgvpc02Sptt/GvXMBYdCm3yvNkk+cGwgwiPgycej8wfJbr4ojT9Fi+77DIArefzhPqsaLestbW13HrbbTgsWciRF4Sfm7zoQ3XBzsBF0grgpIvCO4fPi2nnEgoyU3n+uWcxm9vtgnSrnKtXr+axxx4jZXoy5tzuhy00rWrGW3G8q6jkmUj9bkq3zwdg/8aB6bDCG6+/0bbMoZS1y7pbV1fHT378Exz1DianTsViCi2I9uuGL6jxHJ8jmGXO4cz0c0L67B77TvY5dvHzn/9cnfrUORHV3QBbtmzhd7/7Hd8tqOSKMOKd/rVrGHsbU1u2T0hr4t5Roa+Nt6mmDy8VD+bmm2/m6quv7uzQDstptKC6IDMzkx/ccgvUH4GaEn2MOFqItNfx05/8uCNx6jbjxo1DCIHnqLaz0sNFSomvXDJu7Liolzlw/r///e/U1NQwPvmMkMUpUk6wnUSOJY///Oc/MYnvcjqdzPn3v8i3ubmkgxxPWjExq54JmXX8778Luj2x3RCoELj00ksZMeIETKXrYh8XZa/FdHQr55xzDqedFuay6yGQkZHBmJPH4D3ki5thcABftQ9vg5ezzz5bk/N/9NFHrFu3jhNtY8jwL74ZC1R/1GkIn4m//vVvmqcrWbhwIcfKK7h2cGm3gzEj4erBZZjx8PTTc7tVvwyBCgFFUbjvvl8gPC44FGYStZRspMmi/k8rgJSunbAtSIk4+CUpycn8+Mc/Du+6YTD9u9Px1ntbddH0xrXPjcVi0USgKioqePbZZ8mx5DEkaUTUz98VSSYbY2ynsG/fXt566y3NrrN3714WvfUWk7JrGJmuT/qXPhYPl/YrY8uWrd1a89AQqBAZPnw4V111JaJqL9SHMUQ7+ExVlFKyVd/T4DND/2zlHmgo5+677yIzU7un/LnnnktaWhquXVHIVRUFfA4fnoNepk+fTlpaWtcfCJO5c+fidro5OXmCbvnX+1r709fSnwULFmgSeuB0Ovn7k38jzexm9iDt0qGEwnfyqhmZ3sTzzz0b9qieIVBhcP3115OXl6929aKRiqUzPE5MRzZy8sljOf/88zW9lM1m4/LLL8dzxIu3Wv+lx1073eCDK664Iurn3rBhA2vXrmWEbRQpSmrXH9CQ0SnjwSd4/rnno37u+fPnc6j0MDcMOUSqWd97ahJw49BS8DrDTs0SFwIV7ykpAiQlJXH33Xchm2ugYpe2FzuyCTxOfvKTH8fkKT9r1ixS01JxbtO3FeVr8uHe52H69OkMGDAgqueWUvLC8y+Qak5lqA5du7YkmWwMTzqRdevXsTWKOeb37t3Lu+++260UKlqRbXVz1UA1Ncvy5ctD/pxuAhXsMHO54qNrEQpnn30248ePx1S2BTxObS5ir0VU7OKiiy5i2LDw0310h9TUVK679jo8R714yvQb0XNsdaGYFK6//vqon3vDhg2UHChhuHUUJhEf+deHJA0nSUmKaj6ld955B5siu8wtHmsmZdcyJNXBO28vCrkVpZtABbeaepJACSG46667kB4nHNFmVQtRuh6bzcZNN92kyfk74pJLLqGgbwGuLW6kL/YjPp4qL56DHmZfMZv8/Pyon3/p0qXYFBv9rAOjfu7uogiFQZZhfPPNNxw7FrmgeL1evvryS87IriZZiZ9BD1BDCM/Jq6Ts6DH27w8tnko3gQpOoRHLdBrRYMSIEcy88EJERVH0I8xrS6HuMDfecIOmjvH2sFgs3HH7HXjqvLj3hdeKUjIVsAAWNUhTyQyvhSKlxLXZRUZmBldddVVYnw2FpqYmvln/DQXmAZhEZNXeI92tFsLwRJhbJrCc1RdffBHReUAdoXR7PAxKiTwcxu41tcrxZfdGLheDUtTfeqhxUYZAdZNbbrmFlORkxMG1RG2eiM+DqXQd/fsPYNasWdE5Z5hMnjyZk8eejGuHO6ykc7ZTk1AyTSiZagS57dTwFjz1lHrxVHq55eZbSEmJLAK9PbZv347H6yHfEvlah27pabUQhltG1iVOUVJJN/eJSmK7ujp1XmC6JXK/rt2rMGPGDO68804uuOAC7N7Iu8UZFvW7qq+vD+l43QSqsfG4866nLdEDaoT5bbfdCg1HoSpKObOPbEU66vnZz36KxRKthErhIYTgjtvvwOfw4dwZm6639EpcW90MGjSI6dOna3KNPXv2AOqiBpFiEeZWC2FYush+EAoZpix2FUU+8BJwnVhE5A/NZMXbkuNrxYoVJCuRjwaahdrtDNWto5tABStoqGoaCwKz/EG92dOmTWvJK93c3My0adN44403ANVhvm1bIeXfvAtuO67meta9+jDluzcA4GysZd2rD+N2qGJsr6tk3asPU1W8TT1fzTHWvfow1Qd2QHM1TfvWsnv3bpqb1SRihYWFTJs2jfXr1wOwefNmpk2b1vKkDeyPNiNHjmTatGm4d3vwNWnvx3DtceNt9HLHHXdEZaWa9qiuriZJsXWZSiUUzMLSaiEMcxSy89lMyTQ2NUYcWR74/nwy8pHfZMWHw+Hgvffew+FwRMWn5fXbFep91k2gamuP+26qq6v1MiMiTCYTAwcOQPi8ULqh+yeSEnFwLWmpKfTv3z96BkbALbfcglkx49ikbSvKZ/fh3uHhtNNP02QqTwCPx6NbUGYoBPxikQpURoa62Gy1S58WeFfU+O0KNQmhbmsNlZcfXyCwoqJCLzO+xaefftry2mKxtNpOSUlptZ2RkcHatWt5+eWXWbhwIda8E5l08x9a3k9Ky1S3iz4EIDkjt9X7KVkF6nblXqgs57777uOCCy5oeX/s2LGtrjdhwoRW22eccUbkBe6AvLw8rrv2Ol599VXcRzxY+mtTVRybXJikibvvuluT8wfIzc3F4bHjk964CTEIxu5tJjUlVWnztA0AACAASURBVF19OgL69etHVmYGO+vrmJKnbR6t7rCzXs0EO27cuJCO160FdfjwYTIk5Ht9HDkSh6unhMF1111Hn4wMxJFN4X/Y58N0dAsnnHCCZv6X7nLFFVcwcNBAXBvcSFf0ww7chz14Dnm45pprNG85jho1CoAqT5xkRw1CSkm1r5JRJ42K+FxCCM777nS21mZwuDm8gQqtcXhNrCrP5+QxY46vPdkFuglUSXExeU4n+S4XxXvjKO93N7DZbFx7zTXqHL3G0JaObqF6H9LRwE033RTVRHTRwGKx8Iuf/wKf3YdjS3SDUqVL4trgZsiQIV3lCoqYadOmsXXrVpKTkzlk38+clU/yzf6vAXB5XMxZ+SQbD6iTwO0uO3NWPsmWQ+rDptHZyJyVT1J4WI30rrfXcbim9dy5wzWl7Dq6E4DKxgrmrHySveW7AThWf5Q5K59kf4U6kFJWe5g5K5/kYFUJAKU1h/jnx49TVn2Ec845Jyp+xauvvprU1BQWHhqIDuFsHfLBkXwa3CZuve22kD+jyy+iubmZA4cO0d/tpr/bTWV1dY/1QwW46KKLSElNDS89sD+N7+AhQzTtrkXCqFGjuOKKK3AXe6KaM8qx0Yl0Su677z5N8j21xWw2M2vWLI66j+CV+s83bEFK7NJOWloa06ZNi8op09LSuPOuuyluTObjY5GvPh0NdtWn8kl5LhdffHFLazYUdPFB7d+/HyklA9xubH6J37t3L5MmTdLDnKhgs9m49JJLWPjmm0hHPdhCcALWH0E213DVlbfGtQP3xhtvZO3Xazm24RjKhQrCHJmtnqMe3Ac8XHvttYwYof2cuIDfrqGhgfffe5+zzjyL09LUumY1W/nZ9F+2HJtsTW61nZaU1mq7T3IGA7IGtsqoOSBrIKPSRwOQm5bX6viCPn1bbffLHNBqW0kzceoZE/nRj36EzWaL2oPqvPPOY926dXzw5RpOSm9kcKp+6zs2eRReOzCYAf37ceutt4b1WV1aUIHVR4uSktiWrDoFQw19j2cuv/xydfg0xFaUOFZIVlZ21J6cWmG1WvnZT3+Gt9GLc0dko3rSK3FudNO3X1++//3vR8nC0EhPT+fuH95NjaeKA84oxa5FgNPnoMixjRNHnsgll1wS1XMLIfjJT35CZlYWr5QMweXT5wEoJbx+oD8NXgv3/+rXYQ8C6CJQ+/btIwWoNpupMJvJkpLi4mI9TIkq2dnZnD99urqWnruLBREbK6C+jNmzr9AtKDMcxo0bx9SpU9XYqAiWqXLtdeNt8PKjH/4IqzU2aXaDOf/885k0aRK7HTuo8+g3yiWlZGvzBqRJct8v79Mk/is9PZ37fnk/5Q4L7x0uiPr5Q2FDTQabazO48cabOOGEE8L+vC4CtauoiH6O407X/g4nu4uK4irlbHf5/ve/r+aKOraj0+PE0W2kpKZG/cmpJTfffDMmYcJZ2L1WlHRL3Ds9nHLKKZx66qlRti40hBDcf//9ZOdks7l5HQ5f+NOs+igZmP3/ssw59FEywvq8lJKd9m1UuSv46c9+ypAhQ8K2IVTGjx/PZZddxqfluexpiG3+qzq3mTcPDWTUqBO7ndsr5gJVU1PDwUOHGBoU6j7E5aKyupqyMm0WE4wl/fv3Z8qUKZgqd4O3gx+yow5qD3L5rFkkJyfH1sAIKCgo4NJLLsVd4sFbH34ryrXLjc/p49Zb9fW5paen8/DDDyMtkg1NX+HyhSe4o1PGk27OIN2cwZnp56iJ58Jgv3MvB53FzJ49u1Xcm1bcfPPN9C3IZ8HBQTjCmPA7MMWOzeTFZvJyQloTA1NCF3O1azcAt7Dwi190v4UYc4EKrDY60nm8Uox0qq2paMzmjgeuvPJKpMcFVR10W8uLUBSlZdmtnsTVV19NUlISzm3hhR34HD7cuz2cffbZ3WrqR5vhw4fz+9//HrtsZkPTV7jDFKnuUuLYy277ds4991xuv/32qJ67o2laNpuNu3/4I97/ZD3/95kFKaHe4ePaeWUs26nOg61u9nLtvDI+3qW6JioaPby5chNpoomBKQ6u7ruHN1duYnWxKlIHa9xcO6+MtSWq831fpbq94ZC6/d+iZJ77cBtnnz2FAQMGdHuaVkwFSkrJRx9+SH+Ph4KgkP5Mr49hLhfLly0LKx1ovDJq1CiGDh2m+qLa4vNiqinm7LPPJisr8omrsSYzM5MrZ1+pZh+oCv1eObe7was+zeOFiRMn8n+//z+aZAPfdKMlFS4ljr0U2QuZMmUKv/rVr2Ia9zZmzBjy8/Mpbkrlk3Jtl5Lf05DKymN59ElP59xzz43oXDFduHP79u088MADXFpbz2l2O/Oy1R/oD6pr2G5L4q2sTH7/+99HOyYoKosfhsuiRYt44YUXkOOuhP2r1Z0nXQR1pYg9H/OHP/yByZMnR/OSMSun3W7n9jtup9naRPJ5tpbuWtMq9enbdsFOb4OP5mV2LpxxIT/96U8jvTxEaeHOAOvWreNPf/oTKaRyeurZWE1dR2B/3aC29kNdsLPYsZvd9h1MmTKFBx54INTYr6jeU6/Xy+OPP8ZXX63l+iGHOSu360GCf+1SM7qGumBnSVMyT+0ZTn6/gTz+xF9JT08P5WPxsXDnypUrSQLGOb7dlz3J4SRN0q2laeKRluWSag+2fqP2EElJNt2cxNEgOTmZG2+4EU+FF09Z160oZ6ELi8XCDTfcEAPrwmfSpEk8/PDDOISd9U1f4vRFN2p+r72I3fYdTJs2jQcffDAmgantoSgKv/rVr5k4cQL/OzCAz8vDWAItBPY0pPKfPcPJzMnnz395JFRx6pSYCZTX62Xd2rWMtDuwtqP3CjCquZmNGzb0qBTAHdGvXz/69e+PqG89z9DUUMYpp5yiyxB7NJkxYwZ5+Xm4t7s7HX311qlpfC+fdXlcd2lPPfVUHv7jwziFnW+a1uCKkkjts+9ir6OI6dOnc//992uWTiZULBYLv/3t7zhj0iTePNSfZWWhzYnrim216czdO4y8fgN57PEnyM6OjvjFTKC2bt1KXUMDJzk6jmgd7XDidLk0y3MUa0479VRE8Nw8ZyPSUc/EiRP0MypKmM1mdZGFai/e8o5bUa4iN9YkqyZLSEWbCRMm8PAf1ZbUhqa1EafyPeAoZo9jJ+eddx6/+MUvdBenAElJSfzmN7/hvPPOY+mRgohFqrA2nReLhzB02HAee/wJcnOjN70mZgK1dOlSbMAoR8dPpmEuF32k5P333kuImKhx48YhvW7w+it6o5oUf/z48Ial45XzzjuPPhl9cO1q/4fss6sLcF4448KQ8//ozYQJE/jtb39Lg6+OTU3r8XVz/cOjrsPstG9l8uTJ/PKXv4wbcQqgKAr33ntvi0itruhe63Z/YzIv7h/CsOHD+dOf/xL1+xwTgdq4cSNff/01ZzU0djr5zwRMqW+gcPt2Vq9eHQvTNOXkk09WX/gCAlWOLTmZoUOH6mZTNLFYLFx80cV4yrztZt507/cgfZJLL71UB+u6z5lnnsm9995LlbucInth2J+v99SyrXkjo0aN4sEHH4w7cQoQEKnTTj2VRaUDONgU3jSUJo/CyyVDycnN5Y9/+rMmq0BrLlAOh4O5Tz1Fjs/H2Y1d5x4/vdlOP4+X5599tlXe8p5ITk4O2dk5LS0oU3MVo048Me7SqkTCjBkzQIC7pHWmAyklnhIvY04eE/UFOGPBjBkzmD17NgedxZS5Ql+a3CPdbLGvJ9M/Ih3vvkZFUbjvl78kIzOL1w4MxhtGx+Xt0r7Ueyz8vwd+ExWHeHto/kuZN28exyoquKy6NqTUCSbgsppa6urqeOaZZ3p8V2/EiOEIn//Ha6+Jyez9WJKfn8/Yk8fiOdDaD+Wt9uFt8HL+dG2XbdeSW2+9lZNGncQO+xbs3i7mVvrZ0byVZm8zD/7mwag5irWmT58+3P3DH1Fmt/JZiDFS+xpTWFeVxRVXzGbkyJGa2aapQK1atYqlS5cyubGJIWEsb97P4+GchkY+++wz3n//fQ0t1J7BgweDzwPSi/R5GTRokN4mRZ1p06bhbfASvPqSp9SDoijHwy16IGazmQcefADFolBo39zlw7LcVcYR1yGuv/764937HsLkyZM5/bTT+LCsL3XuzpsSXglvHhpAbk4211xzjaZ2aSZQX331FXP+/W+GuVyc3/DtrtqyPmkctZg5ajEzLzuLZX1a91+nNjYxyuHkheef55NPPtHKTM3p16+f+sLfzevbN/J12eKNM888E6BVWmDvER/jxo3TxC8RSwoKCrj9jtupcpd32tXzSA87HVsZMniI5j9aLRBCcOddd+ERZt4t7TzzwZqKbA43J3HHnXdFnEO9KzQRqI8++ohHH32UAqeTq6trac9FeNRswWky4TSZOJBk5ai5dcoRAVxRW8cQp4u///3vvPPOOz2yu9eyhLdHje0qKNAn7YWWZGVlMXjI4BaB8tl9eOu9TJw4UWfLosPFF1/MyBNGstu5A08Hi3QWO3Zj99q59+f39oj0Oe3Rv39/rrhiNuurs9jf2P4k9iaPwtKyfowfNy4mreOoCpTD4eCf//wnTz31FCMcTm6urCI5AlFJkpLrq6oZ7XDw0ksv8cTjj/e4RT5bBMrrQggR1RiReGLc2HHg/+16q9QRvZ7WzekIk8nEj378IxxeOwed354A7vI5OeDcx7Rp0xg9erQOFkaPq6++mqzMDJYc6dfugtkrjuZi95q48667YpKRImoCtXfvXn5x772sWrmScxsaua66pt2I8XAxA1fX1DG9voEv16zh5/fcQ1FRUeQnjhGBFpOQXjKzsnvs07UrRowYARKkF7y1XoQQCRNOATB69GgmTpzIAVfxt3KaH3Duw4eP66+/XifrokdycjLfv+Za9jaksLtN/qgGt8LnFXlMnTo1Zvc2YoHyer28/vrr3P/LX1J3+DA3VNVwXmNTVJtmAvhOUzO3VFbjOHaMX//617zyyistyzzHMzabDZM/DqZvQb7O1mhHS9I1j8RX5yO/IF9z/0Ssueqqq3B6HZS7j+ct80kfpe6DnHHGGQkzAHLhhReSmdGHVeWtW/urK7Jx+4hpquaIdKS6uprfPPAACxYsYExTEz86VsEIDefRDXa7+eGxCiY0NfPWW2/xq/vv59ixY5pdL1qkpaqO4pbuXgISiHWSXqARBg8arK9BGjBhwgTycvM47Do+AbzKU47T62DmzJk6WhZdLBYLF118CTvq0nH7c5n7JKypyuPUUyfGVIi7LVDbt2/n3p/9jD1FRcyuqWN2bX1E/qZQSZKSWXX1XFNdS2lxMT+/5x42btyo+XUjYdSoEwF6TFxMd0hLS1Mjpr0Sb4OPgQMH6m1S1DGZTEydNpUqT2XLgM1R1xGSbcmcfvrpOlsXXc4/X41fa/CoIQe7GtKoc5mYMePCmNrRLYH65JNP+O1vfoNSW8sdFZWM62QCcEc4hcBmszFr1ixsNhvOMB1uJzmd3FVeSWpDAw8/9BAffvhh2DbEikBa354yH627WK1WpEtduaUlvCLBOOuss5DSh1u6QEKVt5xJZ05KON9ifn4+Y8aMptEvUFtq+mBLssZ8/caQBCo4lei7777LVVddRXPxfm4vr6SPy8Xdu4pYXq2uE9bo9XD3riJW1agLcdZ63Ny9q4jPa2sBqHSr27VIZsyYwZ133skFF1xArT/nVqnTwd27itjQUA9AicPO3buK2NLYAMBeezN37ypie1Mj2V4v3zlwkI2rV/Poo4+ycOFC1q9fH3fLOAVGO3p6TFBXJCUlgX9KXiKGU4CaLTUlJQW3dOPFi8Pr4LTTTtPbLE04/fQzcPlMeCUUNfThlAkTYz51J6wWVGFhIS88/zwpPsnZjY0RdeksPh/Lly/nueeeY8WKFVh83Zs1bpGQ7/Yw0ulk/vz57NjR+WoqetKTFkjoDsGtiEQVKEVRGDduHB7paUnHkijZKdoSCBNp8pipcpoZO3ZszG0IKbVfYGXWBx94gAwp+d8JI7H5xclmUnh21Ektx6Yp5lbbmWZLq+1ci7o9T5g44HDw3nvvAVAgVK0cmGRrdfxQW3Kr7ROSU1ptj0pRt91eyXNeLxvWr2+xN95IpEnC7REsUDk52ua91pOTTjqJr7/+Grd0kZmRmbCDH8OGqel+A36owHYsCesXk5mVhRQCJQ4juk2o4QiZceyIjuflzaNBcCrbRG4tBn6obulm+IjhCXtfk5OTURQFu1cNk9HDrxiWQE2ePJl6IXi2II9DceQULDObeT4/lwpF4ayzztLbnA7piVN1wiFR4oC6Ijh9TE9MJRMOAZ+TYjLp0ioOK3v71KlTyczM5F//+Acvm0yMtds5q7GZfp725ydpTblZ4avUVLamJJORkcH/3XMPkyZN0sUWA7juuuta1j1MZIKnKyXq1KUAFosFu91OVlamLon3wl5e4pRTTuGpuXN5/fXX+fCDD9iWnMxQl5vJjY2MdLpCbpL19bg5alEv39ftoa8ntKhwCRRbrXyVlsq+JCtWi4WLL7yQG264Ie5HyRK1KxAgJSWl64MSgOAI+USObQNVgOvr68nNi87iCuHSrfVvUlJSuO2227jmmmv46KOPWLJ4Ma9bLWRIycTGJiY22+nTxajczPrGlgwGP6juen2uRpOJzck2NqalUWMSZPbpw02XX87MmTN7THxRonfxEtnv1BE9pe51l6lTp1JcXIzNps+9jWiBrtTUVGbPns2sWbP4+uuvWbZsGZ9u3sxn6WmMdDg5o6mZES5XyKsPtkUCJVYL61NS2JVswweMPflkbps5kylTpvS44LhEb0HFe3pbLdAq1W28EBBgvepuVFYQNJvNTJkyhSlTpnD06FGWL1/Oio8+YkF9PTk+H2c0NDLB7iApxBaES8DW5GTWp6VSriikp6Zy+YwZzJgxo0dPoUj0FlRvFKhE79ampqoZDXq0QAXTt29fbr755haH6XtLlrBszx4+yejD1PoGJjU1t5vADtQg5I0pyXyS0YdmYPjQoVx3+eWcc845apSyQVyT6C3E9ki0jA1t0buXotkazBaLhWnTpjFt2jR2797NgtdeY/mmTWxOTeGSmjoGt0mVcsRiZmlmBkfMZsaefDI33nQTY8aMSahKn0hlMVAxHpzaEpNF4k888UQeevhh1q5dy/PPPsurisL3gxzjJVYL/83JJi0jg/vvvJNzzz3X+DEb9Aj0bmEkOjERKFBbD2eddRbjxo3jd7/9LQspJtftxiThfznZ9B0wgEcee4zMzMxYmWRgEDHxuihnohDzyWFpaWn86c9/JjcvjxqzmWqzQkqfPvzl0UcTXpwS3UneG0n0+ZV6o8u3m56ezrXXX49LCJwmE1defTVZWd1bG97AQE+MFpS26Cb/U6ZMaXl97rnn6mVGTDH8aomH0YLSFt2+3eCo40SfLhDA6OIlHsZDR1sM+TcwMIhbDIEyMDCIW3QVqKysrF4xPeKEE04AEjvLZDB6pIaNNYm2iktHBEbWR40apcv1RRd+EU2dJocPH8bj8Rxf9FEbQnUSaFZWl8vF5s2bOeOMM7T0WeheToCdO3fSt29frUdlQymrpuWsra2ltLRUazGOi3u6YcMGxo4dq2XUfIfl1FWgYkRc3OQY0FvKCXEgUDGit9zTDstp+KAMDAziFkOgDAwM4hZDoAwMDOIWQ6AMDAziFkOgDAwM4pZOR/GEEMsArdfVyQUqNTx/pZRyZlcHxaCsRjmjR5dlNepuVNGtnF2FGWiOEOIbKWXCR70Z5Uw8ektZ9Syn0cUzMDCIWwyBMjAwiFviQaCe09uAGGGUM/HoLWXVrZy6+6AMDAwMOiIeWlAGBgYG7RKWQAkhpBBiftC2WQhRIYR4P8zzlAghOh0WDT5GCNEYzvkjpbeU03/NXlHW3lLONnZ4hRCbhRCFQoj3hBBRW5VECDFBCHFx0PY0IcTZ0Tp/gHBbUE3AWCFEIF/vBcDh6JoUGUIl0pZhbykn9J6y9pZyBmOXUk6QUo4FqoGfRPHcE4CLg7anAboLFMCHwCX+19cB/wu8IYTIFkIsFkJsFUKsFUKM9+/PEUIsF0JsEkI8S1B6BSHEjUKIdX6lf1YI0ekyGUKIXwkh1vuv8bB/31AhxE4hxFxgIzCoG+XqreXsTWXtLeVsj6+AAf5rTvCXcasQ4h0hRJZ//z1CiB3+/a/796UKIV7y271JCHG5EMIK/BG4xl/2/wf8EPiFf/scIcRlQoiv/Z/5WAhR0C2rpZQh/wcagfHAW4AN2IyqnO/7358D/MH/+rvAZv/rfwO/97++BDV/TS4wGngPsPjfmwvc7H9dAuQGruv/OwN1REGgiuv7wLnAUMAHTA6nPL29nL2prL2lnG3L7P+rAG8CM/3bW4Gp/td/BP7pf30ESPK/zvT/fQS4MbAP2A2kAj8Angq61kPA/UHbWRwfhLsDeLI7ZQh7ZWEp5VYhxFDUJ9AHbd7+DnCl/7hV/qdPhv9GzPbvXyqECKx7Ph04DVgv1EyTyUB5J5ef4f+/yb+dBowEDgIHpJRrwy1PR/SWcvpt7RVl7S3lDCJZCLEZVQQ3ACv8ZcqUUn7mP+YVVPECVbgWCCEWA4uD7J4lhLjfv20DBodw7YHAG0KIfoAV2N+dAnR36fMlwN9Qn0DBibbby4wn2/wNRgCvSCkfDPG6AnhUSvlsq51qpWsK8Rzh0FvKCb2nrL2lnOD3QflF6X1UH9QrnRx/CaogzwL+TwhxMqrdV0opdwUfKIQ4s4trzwH+LqVcIoSYhtrCCpvuOuReAv4opdzWZv/nwA2gevVRJwHWt9l/EWrzD2AlcJUQIt//XrYQorME5R8Btwkh0vzHDwh8ViN6Szmh95S1t5SzBSllHXAPcD/QDNQIIc7xv30T8JlQnfODpJSfAL9G7c6l+e3+mfA3E4UQE/2fawDSgy7TdjuD44MQt0RifNh92jb7pnG8H58NvIvaVFwLjPfvzwGWozoB/wEc4Hgf/RpUf8BW1Gbo5I768f7X9wLb/P+/AkagNmELo913T/Ry9qay9pZydlZmVJ/ZTagjcGv9di9GFV0LsNpvWyHwgP8zycCzQfuDv6/1/vJfA5zoP99m4BzgcqAY+AL4K/Bpd8pgRJIbGBjELUYkuYGBQdxiCJSBgUHcYgiUgYFB3BK2QAkhkoUQn3UVNRvCeR4Kiq0I9TPBc5zu8UfgLhBCXBqIzDUw6IpI6rAQ4k/+SOvNQo0w79/m/cFCiMbgui2E+IsQ4pBoMy9PCPFTIcSt3S9J4tOdFtRtwNtSSm+0jQmTHwMXSylvAJaiBpOldPWhjiqnEGKeEOIqjWyNOV2Jtt7fQzgPKP9UiWiupR5JHf6rlHK8lHICamzR79u8/w/UKTXBvAdMaudcL6EO/2tOtBoWYV4z4oZDdwTqBuBdoc5e/kwIsVAIsVsI8ZgQ4gahzk3aJoQY4TdyiBBipf+ps1II8a0oVCHEp0KIfwohvhTqzOtJ/v3tzoMSQjwDDAeWCCF+IdWhyE+BS0OwP14ENmyESqj3rCvRjvvvIai881EfSNEiUIdNQoi5QojtQoj3hRAfBMRZCPF7oc4/KxRCPBeIA5JqbFSAVIKCOIUQ30MdWt8efDEp5VopZVlbI6SUzUBJoL5rjCb3WwjRWbB3yA2Hjgg33YoVGC6lLPHvOgU1tmMcanzFiVLKScALwM/8xzwFvCqlHA8sQJ3b1B6pUsqzUSviS/59fwBWSyknokYADwaQUv4Qdd7QeVLKf/iP/QY1/qIrApVTCCGeEurkyKVAS9CcEGK6XxS3CXWiZJJ/f4kQ4nG/CK8TQpzg358nhFjkr9DrhRBTOrq4v+Xwkl+Ui4UQ9wS9d5//B1EohPi5f1/byaTnCCGKhBAv+I9bIIQ4XwixRgixJ1DZQxBtXb8HP2Pafg/tlHcQ6r2/rotzhUSbOjwbNQ5pHOp8sbOCDn1KSnmGVDMBJBP0PQp/lw31O/y9f18q8P+AcFsModbbSAnc735CiM/F8TQs5wAIIWYKITYKIbYIIVb693U0gfohv2gvB17t6L6H2XBonzADv/oDRUFBbiuC3vscmOJ//V1gsf91JccnVFpQI3QhaHKhvxDfDTrXQdRI1s2olSmwv5rjAXAlgdf+7QuARV3YbwWO+l/PBlagTqTsD9QCV6HONTqEKrYArwI/D7rmb/2vb+Z40Np/ge/4Xw8GdnZiw0PAl0AS6qTTKv/3chpqMFwqagTvdmAibSaT+rc9qD8qE2qA4EuorcvLA9+7/9gbgDk97HtoVd6g4/cAOd0NWuygDv8TuDXovbeBq/yvrwS+9t+Tw/gDF9uc60HgYf/rvwHfb1u32xzfXrDonXRzIm0YZQ6+378MuncKavR3nv9eD/Pvz/b/7WgC9UP+epfc1X3vqA6G+j/cuXh21IobwBn02he07aPjeX4dRYa23d/ZPKj2sPnt64xc1B8gqHOO/ifVJu8RIcQq//5RwH4p5W7/9iuoc5j+6d/+X9DfQOvtfNTWQOA6fYQQ6VLKhg7sWCqldAJOIUQ5UIA6WfUdKWUTgBDibdQn6xK+PZl0v/RP1RBCbAdWSimlEGIb6g88QDnqD7InfQ+0U97gslR1cK5QCa7D7c2/QwhhQ81OcLqU8pAQ4iFa1/sA/0XtxvwBOBN16ssTqA9XnxDCIaV8qgt7Qqm3kRJ8v9cDLwkhLKgPs81CndrzuZRyP4CUstp/bEcTqAGWSCkDdnd23zuqgyERVhdPSlkDKP4bGCpfAtf6X9+AGk7fHtcACCG+A9RJdf5QR/Og2uNE1FD8zmgrsB1NAu0M2c5rE3CWVJODTZBSDujkRwmthd2LKuadXbftZNJQHwwdVf54/h6g/cmzUfkht6nDq4ErheqLKkDtFQSuBVAp1LlzLYMGQoiRQaebBRT5z3uOlHKolHIoqog/EoI4QWj1NlJa7reU8R7+AgAAAm5JREFU8nPUh9JhYL4Q4mbUex1qHQgcF3yPOrvvEd237jjJl6Mqa6jcA9wqhNiK6qe6t4PjaoQQXwLPALf79z0MnCuE2Iia9uFgJ9c5D/Vp1iFtKufnwLVCCEWoKSHO8x9WBAwN+FX8Nn8WdJprgv5+5X+9HPhp4AAhxITO7OiAz4HvCSFS/P6MK1DnMUVCu5U/zr+HbyHUR3Nf1K5lNAjU4UVAKep39Cxql65OSlkLPI/avVuM2uoI8Jjfd7MVtU52VJ+D7X9CCFEKpAghSv0tsgBTgI8jL1LHBN9voU5oLpdSPg+8CJyKev+mCiGG+e3N9n+0ownUbensvkcmwN3oz04E5ke5j/wpanO6u58vQO3mhHLsi6hNUoHqwN+BWgkXc9z/MB01b882VP9OIIlXCWpz/mvUSnuCf38u8AbqZMkdwDOdXP8hWif2KgSG+l/f598u5Li/ZyhBk0nb2Z4XZHfb994HxvWk76FtGfzvnU4X/sXu1mEgzf83B9gH9I1m3Y71bymEen+L/7vehPoADPidLvLv24Lft0zHE6jb3rsO73tndTAku7tZ2NsAJYpf3qdEJlBnABO0rhS0cczH83+6EO2e9D0A/wKmR/mct6E6iT9FHYzZAfwgxvfoAvwPpxhcK2ZiGHTNkBsOHf3vldkMhBC3oSYbCysmRAhRgiqklZoYFkWEEGcAbinl5k6O6RHfgxDiTql2SQwioLv3O4LrdVkHuzxHbxSoWCDUKQxt/RNrpJTRXFkj7jG+B4NIMATKwMAgbjGyGRgYGMQthkAZGBjELYZAGRgYxC2GQBkYGMQthkAZGBjELf8fIegWVLM+e/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4beu\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4unu\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4z8j\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5vid\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5vli\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5vmr\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.get_cmap(\"Set1\")\n",
    "\n",
    "columns = df[\"column\"].unique()\n",
    "\n",
    "for pdb_id in PDB_IDS:\n",
    "    print(pdb_id)\n",
    "\n",
    "    fig, axs = plt.subplots(ncols=len(columns), figsize=(4, 3))\n",
    "\n",
    "    df = process_scores(SCORES[f\"{pdb_id}_modeller\"], SCORES[f\"{pdb_id}_rosetta\"])\n",
    "    df_wt = process_scores(SCORES[f\"{pdb_id}_modeller\"], SCORES[f\"{pdb_id}_rosetta\"])\n",
    "\n",
    "    for i, (column, ax) in enumerate(zip(columns, axs)):\n",
    "        sns.violinplot(x=\"name\", y=\"value\", data=df[df[\"column\"] == column], color=cmap(i), ax=ax)\n",
    "        ax.hlines(\n",
    "            df_wt[df_wt[\"column\"] == column][\"value\"].values[0], *ax.get_xlim(), linestyle=\":\"\n",
    "        )\n",
    "#         ax.set_xticklabels(ax.get_xticklabels(), rotation=\"vertical\")\n",
    "        ax.set_xlabel(None)\n",
    "        ref = df\n",
    "\n",
    "    for ax in axs[1:]:\n",
    "        ax.set_ylabel(None)\n",
    "\n",
    "    for i, ax in enumerate(axs):\n",
    "        ax.set_ylabel(None)\n",
    "#         ax.set_ylabel(\"Score\" if i == 0 else \"\")\n",
    "        ax.set_yticks([])\n",
    "        #     ax.spines[\"top\"].set_visible(False)\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        ax.spines[\"left\"].set_visible(False)\n",
    "        ax.grid(axis=\"y\", linestyle=\":\")\n",
    "\n",
    "    fig.subplots_adjust(top=0.98, bottom=0.15, left=0.01, right=0.99, hspace=0.1)\n",
    "\n",
    "    fig.savefig(NOTEBOOK_PATH.joinpath(f\"modeller-and-rosetta-score-distributions-{pdb_id}.pdf\"))\n",
    "    fig.savefig(NOTEBOOK_PATH.joinpath(f\"modeller-and-rosetta-score-distributions-{pdb_id}.svg\"))\n",
    "    fig.savefig(NOTEBOOK_PATH.joinpath(f\"modeller-and-rosetta-score-distributions-{pdb_id}.png\"), dpi=300)\n",
    "    \n",
    "    plt.show()\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"216pt\" version=\"1.1\" viewBox=\"0 0 288 216\" width=\"288pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 216  L 288 216  L 288 0  L 0 0  z \" style=\"fill:#ffffff;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 2.88 183.6  L 51.542069 183.6  L 51.542069 4.32  L 2.88 4.32  z \" style=\"fill:#ffffff;\"/>\n",
       "   </g>\n",
       "   <g id=\"PolyCollection_1\">\n",
       "    <defs>\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import SVG, Image\n",
    "\n",
    "display(SVG(\"16_david_analysis/modeller-and-rosetta-score-distributions-5vmr.svg\"))\n",
    "# display(Image(\"07_protein_analysis_bestsel/reference-vs-design-secondary-structure-from-cd.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1n5u\n"
     ]
    },
    {
     "data": {
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ULwZiIsHi/HykQOQhlZWVsWHDzzhadQGDG78XQuBo053du3eTl5fnUelMaGgoISEhLkMJIwGIAIKqDHow7SCG7g0v4I2JBk7tKiA3N5d27dq5PfbGjRt58MEHURSFOXPm8Mwzz5CRkcHx48ezgClSSs+XoT5yzliFaJpnVbP947nnngNw/phKYKRmB2kBNFbIvIRGTupghIaDhOZ5SCtXrsRqtUCiBw7zhJ6Qv5cffviBBx90v2xx2LBhvPLKK5jNZlatWgXwDbDE0zl7w/bt27Hb7IS2D2nwOcb2Rthl4ZdffuGWWy6ol2+QP/7xj8yfP5/y8nLGjRvHokWLuOaaaxBC3ATMBq72/R14RmVlJSbFhBGTph2n166tk/Y3QrOBWxiN+axmoVrxhi6/FjRdhVitVhYuXATRSbXFym4REols1ZmlS5d5JAn82muvkZCQwIABA/jggw8AlqLq6PuddevWYQg31Otcd6JEKhjbGFi9ZrVHelBWq5UBAwZw5ZVXkpCQwDXXXAPUbo3CfZ27N1RUVGBUTBgwaKoxf/bsWZ544gkGDx6MEGK7EOKNi6Vhiyc0eBZJKdc7L6hJoadrLpuCGR5u6axZs4bTp4uQbQd4/uLkAZjNlSxZ4v7CSFEUpk2bxrPPPsvzzz8P8KkMgEpcUVER27Ztw9BFUdVBG8HY1UjmiUyOHDni9viu0TZnsqQLDS/l/EhlZSVGTJiEyWfZZlfuv/9+oqOjnZ2KpgClwL81O4AXBEMJtclooBBiOHAEVWnhXeCwEOJaP8+rOaFZHpLNZuPLeV8hIuMhpr3nA0TEQWwHvv12gdu/3D/88APdu3dnxowZPPbYY6BGcv0uTLd06VKklIR0uzC/6nxMnYwoJsUjIzxz5szardbNN99ce78QojsBFhZ0UlFRgQEDRmHSdGWVkZHBiy++6KxfPVbjRw64hr4rwdCYdyd14Q1gtJRymJTyWmAM8H/+nVZgCVQe0sqVKyksOIWjXUqDGetNIdulUFFRzqJF7tXqPvnkk6xdu5Z169axfv16UH0efv38qqqq+GHpDxjbG1Cimj7FRIjA2MXA+p/WU1TkXg+SCRMm1CtKKKXMkFL+w+NJa4C50owRI0ZhxFylXRPX8PBwfv7559rbQoirgaB2iW2uxsokpUx33pBSHgaa/rlsIQQqD8lisTBv3leqZpU3qyonkfHQqhMLFnxHWVlZk09PTEykR48erncdAwq8n0DTrFmzhoryCkJ6ur8bC+lpwmF38MMPP3h0rMLCQp566iluuOEGRo4ciRBijRBiTdOv1B6z2YwiDCgYsFRrl7rw3nvv8eijj9KlSxeEECeAf6H2SAgazdVYbRdCfCKEGF5z+Qi1qcJFwTvvvMPGjRuJiYkB1DwkVHVUTVm5cqXqq/JhVeVEtkvBbK5k4cKFTT63X79+3HDDDcydO5dPP/0U1EjgNiHELfXJVfuKw+Fg0aJFGNsYMCS4X++nRCkYOxj4YekPHoX977zzTvr06cPx48edPrkTBKnY3mKxYMCAQShYbVbNGkikpKSwZ88e9u7dCzBQSpkqpQyoQOT5BKNawJ2z6ffAAWAG8AfgIEG26loSiDwkq9XK11//B6ITIVoDeeGINsjWnVm06Hu1cWgjVFVVkZSUxPr1651SM4VAG1TZ6vG+T6YuO3bsIDc3F1NPo8cqESE9Q6isqGTNGvcXRqdPn+aBBx7AZDI5ZY7vB4Z6OG1NsFgsKEJBwYCUUrOuzH/96185c+YMMTExSClLhRCthRB/12RwL9FSVcJd3NGzqpZSvimlvEVKOVFK+X9SysDP1E8EIg/pxx9/rIkAXurzqqqW5IGYzZV8//33jT7t3//+d52LlPI+l8v92kzmHAsXLcQQYcDY0V31oXMY4tU0hoWLFrodbTKZVI9EcnIyP/zwA0KIVNStfMCxWCyU2s9QZC2ova0Fy5Yto1Wrc7LyUsoS4AZNBvcA162f2Rx4l5k70cDxQohdQohiIUSpEKJMCHHRCNT7Ow/JarXy1VdfIyIT1KJlrYiIg1Yd+W7hwkYjT9nZ2UycOJHExESSkpIQQiwQQvjly5yRkcHePXsx9jA0ma5QH0IITL2M5OXmsW2bezu5Z555hrNnz/LGG28wa9YsgI+BP3p8cA2oqqrC6rBSLdVtrFZfaLvdXmclI4QIB0I1GdwDXFfxTa3o/YE728B/AtOAOClljJQyWkoZ4+d5BQx/5yGtXr2awsICHO00XFXVIJNTqKyoYPHihuuu77vvPiZMmEBubi45OTmgrhr9kqPz3XffoZgUQrp7H38xdjBiiDTwzbffuPX88ePHExsbS//+/Vm7di1SykFSSr8Xop+PxWLBZrchav4BmmWx33XXXYwaNYpPPvkEIcT9wCrgU00G94DS0nNrlDNnzgT68G4VMmcB+wORSBgMfvjhBx5++GG6d+/udIgeFUJMl1Iu83Vsq9XKl/PmISLjkb5EABsiUl1dffvtAiZMmFBvd+PCwkLuu++cpLyUcq4QQnNJ3NzcXH766SdMvYz1Fi27i1AEpp4GDu06xP79+5vsvDxjxow6t2fPnv02qjDddill43tkDXGuNIQQiJo1gDvRWnd4+umnGThwoDO9ph8wU0q5QpPBPcDVQLkarkDhzsrqaWCpEOIvTlmYi0Eaxok/85BWrVpFUWGhT3lVTSHbpVBZWdFg3lV8fDxffPEFdrsdu92OEOIu1EoETfn2229BUVMQfMXUzYQhTOGrr79q8rlVVVXs3r2bSy65xNmWayBqAOEBIcQ/fZ6Mm5w9qwp3ChSUms/aeZ+vVFRUMHr0aOc290MgVAgR8PQhV2NVUlIS6MO7ZaxeBipRC3qjXS4XBf7KQzqXV5XgW15VU0TENZp3NWfOHObPn0/btm2dag2TAU0d66dOneLH1T9i7GpECfdMnrg+hFFg7Glkz+49TQrzHT16lDVr1vD444/z+OOPA/wW6ANMBEb7PBk3cX6RFc6trLTaKl177bVUVVU5t/E/onZfmqvJ4B5wzkDJoBgrd7aBbaSUAfvQA40zD2nKlCnOUHttHhKAlNKrztMrVqxQI4CXXOe3VZUT2S4F88HFLFy48IIOL506dTrfp3UzGjN//nyklIT20e7HPqSHCWu6jXnz5vHSSy81+LycnBwqKiqIja2t640E2kkp7UKIgEWtnV9eIRQUjY2VlJKIiAg++eQTgNlSyn8IIXZpMrgHFBcXAxCiyNrrgcQdY/WjEGK0lHKl32cTBFzzkGpwzUOSgMfGqrq6Wl1VRSd5FgE8uQUqa06CQ8tUVYZOQ5p+XUQbZOsufPfdQm666SZiY2N5/PHH681zqvHpIKWcccGDXlBQUMCPP/6IsasBJcL3VZUTYVIjgzt37iQ9PZ1evXrV+7ynn36alJQUhg8f7vQ57gJeEUJEoq5CAoLzy6uggIBQQ5hmX2gpJZs3b+bLL78EcKb4e54b4iOFhYUYhcQoHBSeOhXow7u1DXwUWC6EMF+MqQv+yENaunQpZ86UNNq1pl4qixEOq3opP3XOcLlDuxSqLdWq7wgYPHgwgwYNoqqqip07d7r6dFLQsOP0t99+i0M6CO2jvdBBSA8TSqjC119/3eBzHnjgATZt2sTNN9/sLGi+Rkr5sZSyQkr5J80n1QBFRUUYFVNtJDBUhFFYWKjJ2G+99RavvvoqEydOREp5oKZreMAbDZ/Kz8ekODApklMFpzTL0HcXd5RCLxr/VH1kZ2fz+OOPs3HjRoQQFBQULAD+IKXM9ma86upq/jN/PkS3VS+BIrwVsnVXFi9ZwuTJk5k2bRoAc+fOZe3atbXJkzNmzBgFaLJKLioqYsXKFRi7GFAitVtVOREmgekSI9u2bSMjI4Pu3c/p1Z84cYIuXboAakLoTTfdBICUMhdAqMvK9t5+jp6Sm5tLhHKusDpcRJCTk6vJ2Ndeey3XXntO6ERKeQy1oiRgSCnJyc7CJByYFAdnzVWcOXOG1q1bB2wODZ5hQogujb1QqAQlU1hLtM5D+vHHHzl75gwy+VLN5ug2yQOxVFfXkVrJzc093/EeBWiSnbpw4ULsdrtfVlVOQi4xoYRcuLr605/+xKRJk/jss884cOAABQUFnDx5EiHESCHETGAjqqM9IGSdzCJcnEsdiTREcSo/36eSm9/97nfs27ev3seEEJFCiPuFEHd6fQAPOHv2LOWVZkIUByGKuqLKzg7I70Atja2sXhdCKKgNInag+nLCgB6o4f1RwPOorbpaLFrmIUkpWbhoESIyDhnIVZWT8FYQ24ElS/7LlClTCAkJ4X/+539ITU1lxIhaNdydwAu+HurMmTMsXbYUU2ejWzIw3iJCBMYeBjZv3lxnNfXNN99w8OBBvvzyS+bMmUNeXp5TMuZB1CqEl6WU2gmhN0J5eTn5p/K5JKwPRTY1kBxtiMXusJOZmXl+tNltHnnkEWbOnMm+ffvo378/CQkJvPfee3OAS4AY1N6dX2r1PhojMzMTgBDDOWOVmZnJgAFeiEh6SWNKobcCzwK9UIX3NqAargeBdGCklHJVICbpT7TMQzpw4AA52dk4Enr7PQLYEDKxD6WlZ9myZQugrhy3bNnCxIkTmThxIsCVUkqfs58XLlyI1WolxI+rKichPUNQTIpTKbOWvn378vLLL7Nu3TrS09PZtWsXUsqpUsovAmWogFqF0xjjufq9WIN6/fDhw16Pm5KSwvz589m2bRuPPvoov/nNbwAWAw9KKS+VUr4VqDrdEydOABCqODAKSaRJ1t4XKBr9SZRSHpRS/k1KOVxK2atGmiLgJ4M/0TIPac2aNQiDEVp30XKKnhGTjAiJYMGCBbV3tW3blptuuombbroJKWU++LaNLy0tZcl/l2DsaMQQ4/6qqmpnNfYzDuxnHFSsqaRqp3vfMyVUYOxuYMOGn8jKan79dZ25YK0M5/w34UoEoYZQrxq4nk9UVBTDhw/njjvuQEq5yFVfLlAcP36cmBAHhprf4HZhlRw/diygc/Df+r2F4MxDKiwspKCgACnlzVLKTE/HsdvtbPj5ZxyxHcEQRG1CoeBo1YX/zJ/PzTff7BefzpIlS7BUWwjt69n7tJ+xgxWwgr3Qod52k5BeJjCIOka4uZCWlkaUMQaTUkdqiBilNQcPHAzizLTjcPohOoSdK5jvGGHm+PHjmsnguEPAczWaC1rnIR0+fJjysjLomqrRDH2gVUdSLr2UyZMnk5aWpqlPp6qqisVLFmNsb8QQe2GXZX+hhCkYuxhZu3Ytd911F/Hx8Ugpyc7OpmPHjgGbx/lIKUlLSyNWubBTUStDG47kHqSsrIzo6JYbVC8tLSUrO4fx7So5VBoFQLfICtacsnHkyBH69u0bkHk0urKq2SoE70zwI1rnIe3cWdMcN9aPpTXuEpWEMIZQUlKiuU/np59+UiWLewV+9Rjay4TdbmflSjXzQghRp1lEMMjJyaG8vJxWxnqMlVHdFmqxFQwm27dvB6Bn9DlZmB7RlSgCt6V8tKApn5XERY/8YmLatGlMmzaNI0eOsHbtWtfaslGoBssj9uzZg4iMA2PAZYYuRFFwRCaye7f2yrcrVq7AEGvAEB94D4ISpWBsa2D5iuW14nxDhw4N6BfmfNLS0gDqNVaxxtYIBAcP+rYVdNWZd2rMB1JnfsNPP9Em1E6XyHP6XJFGOz2jy9nw07qAteVy54z7RQhxud9nEiS0yEOyWq0cOnQIR2SSpnPziagkcnNzNNUdKikpIf1QOsZOBo8li7XC2NlI8eliMjIyALVb8dChQ+nevTsDBw5ECLFPCLE3UPPZs2cPIYZQopQLt3lGYSTW2Irdu3b7dAxXnXngRQKoM19QUMCOnTsZ1Lr4ggD3FW1KOFVQxJ49gZGDd8dYjUA1WBlCiL2BPhn8jTMP6d577+Xee+8FNQ/pFU/GOHr0qOpojNa8z4T3RCUipTy/9bhP7Nih9gkxJgfOV3U+xraqm9W5NVm2bBnHjh1jzZo1zmTY8ah1nX7H4XCwc8dO2ijxDRrvNsYEDh857JOypqvOfE3j4YDpzC9duhSQXJNwYelXSutSokwO/vvf/wZiKm4Zq7GoDRVHcq7JQEBOhkCgRR5SbS5NZBP9+XwAACAASURBVIL2E/SWyDiEYuCPf9RO4Xffvn0YwhSUVsELIithAmNrg7PTC507dyYrK4s1a9bQuXNnUOWMAjLB9PR0Ss6UkGBqeEWdaGqLw+GozXvzBledeSHEuEDpzJvNZpYvW8qlsWdpE3Jh1M+kSK6OK2Lbtq3k5mpTWtQY7jSMyAQ6oiaBZhLAk8GfuCa0+ZqHdOTIEZSQCAi5UKkzaChGRERr4uLiNPPp7Nu/DxEngrYFdKLEKxxKP4TVauXFF1/kf//3f11byJuALwIxj/Xr16MIhcSQhjsWxRpaE26IcHYW8gpXnXngKVSdec3VXs9n1apVVFSaGZnUcOPZaxOLMQjcagvnK+40jHge+DPwl5q7AnYy+BMta8tOZmVhD41t+okBxhEay9GMDE18Ovn5+RScKsCYGLwtoBNDogGrRfUTLly4kMWLF9dKOtcUMvs9T8BisbD6x9UkGNtiakS0UwhBsqkDO3bscLvb9Pm0bt26VmdeSjlCSjkI8KuglMViYcG339A9qpKuUQ03vogx2RjSppgff1zl9ftzF3dWSBOBCUAFBO5k8DfffPMNM2fOJD09vbaUoaZy3+Nyory8PAjV4E9itxAWFsbNN99MWFgY2H1s5RQazWWpqaSnp/vs03GmZhjaBj81z5hoAKH60EJCQlTd85rVXo2Old/ZsGED5RXldAzt0uRzO4R0RkrJsmXeyfrXRKnPZ7ZXg7nJqlWrKC45w9jkpnWrrmtbiLTb+eYb95p8eIs7Z55FSimFEBICdzIEAmdt2XlM9XSc6qoqiNCgRs5u5fobrmf69OlIKVm0bLVv4xlCCAsL4/jx42RnZzsLtitRI54esWnTJgzRBpTo4G4Boaa4OcHApk2buPXWW5k+fTpnzpzho48+AlVw7yN/Hl9KyTfzvyHaGEOcsWk/ZYQhkgRTEosXL+HWW29Vf4jcYPPmzWzatInCwkLefPNNAJ588sknUIuY/bbEraqq4uuv5tEjupKe0Q23eXMSF2plaFwxK1YsZ+LEibRt658ifndWVvOFEB8ArYQQDxGAk6GlYbc7tClcNphYvnw577//PitWrPC9bEcIjh49yptvvumTT6e8vJy9e/diaK8E3V/lxNjBSG5uLrfddhuTJ09m0qRJpKenAzwnpfTrqmPLli1knsykc0h3t/8e3cJ6Ul5eVhNdcw+LxUJ5eTk2m42ysjJnik00UIpaw+oXFi1axJmzpdzYLs/t03pscgGKdPDZZ5/5a1puie/NEkJch/oH6oV6MrR4tQUtiYqO4qxNg+J3QwhV5SXnuixH+egHs1Vz6tQp0tLSuPxyNVVOSpkrhPBoz7pjxw4cDgfG9s0g4bUGY3sD7FQzqCdNmsR1110HwKxZs/x6btrtdj75+BMijdG0C3G/uKO1MY44UyJfzfuK0aNHExXV9OJ22LBhDBs2jPDwcJ5++mkAnn/++RcBhBC3Ake8excNU1hYyDfz55PS6izdGvFVnU9siI1RSQUs37CBcePG0a9fP62n5l5UT0q5Skr5JynlUxeToZJSalLFnxCfAJbAd6htEks5JpMJo9Hok09nz549KKEKhjbNJwi84P8tZMWKFUydOpWYmJjai79lt5ctW0Z2TjaXhPZBEZ79PXqF9aW8opyvvmq6xZgrDcg6/6W+O31BSsmHH3yAw27l5g75Hr/+t0mFtAq18947//JLgXNjSqFlNZrr518uGg12rWrLunfvhsFcDM2sD6xiLiY19TKffTrHjx9HxAqvWsL7i1s/nMT46eOYfOtkSktLay/+7Bh+9uxZ5v57LnGmBJJMDacrNESMsRUdQjqzaNGiWjG7xli2bBmPP/44OTk5zJgxgxkzZiCEeFsIMRewef4OGmfdunX8smUL45PziAv13NiEGiS3dcgmMyvbY4PsDo2J70XXtIs//3JRtY/XorasV69eOKxVUKVNU0tNsFUjzWe47757ffbp5OblauJYl1ZJWFgYEyZMICwsDGn13rhXl1djM9ooLCgkNzeX4uJiiouLEUK0EUJcWKinAXPnzqWyspI+4QO89t31DO+LESPvvvtukw0X2rVrx+DBgwkLC2PQoEEMGjQIVNXexcAYrybQAAUFBbz/3rt0jTIzIsn7Hrj9W5UxJK6Eb7/9VvMC7gZ9Vk194FLKwDcO8wNr167l/fffp0uXLkRGRrJPFb2WUsqB7o6RmlojC1Oaq0oLNwdKc0FKUlNT6du3r08+HYvFgjBqYaxg9OjR/O53vwNgyeolTbyiYVY8twppk8hqyZVXXomi1P7u7kBtodbN1/m6cvjwYVasWEHn0G5EGbz/rQ5RQukR1oe9e/ewYcOGOo0gzufSSy/l0ksvZerUqdhsNk6ePIkWKq/n43A4+Of/vYndYubuPln4uoCe1DGPo+XRzHr9H7w9+19OaSKfaczB7vzQ65u65idDsDg/96VLly7jPR0jOTmZ5HbtyDubjUwKjLZPU/z44XMIJEOH1i0hK1NDStKj1bFGu1tholbeZeXKlQj3Ivj1MuHN8ViOWKjaaeGLL76gVavaH4muPk/0PKSUvPvuu6qhCe/t83gdQ7qQbcnko48+4oorrmgylWH58uU89dRTWCwWdUsuRArwkpRygs+TAX744Qf27T/A1M45JIT6mNsHhBsc3N3lJG+lG5k7dy6PPPKIBrNsfBvYVUrZreb/8y8XhaEC7WrLrr7qKijLB5vvH7bPSAejx47j1VdfrePP8danExbu25bNiTAJVbxv8WKqqqoQJt9+wqVVNSSLFi1i5syZ6jGE6CSEuMLnybqwdu1a0tPTuSS0D8ZGstXdRQhB77D+FBUVuaV8+sILL7B169Zagyyl3A108XkiqJUJn879N31iyhkap51CR/eoSoYnFrFs2bIGO/R4ijvlNkIIcZcQ4tma25qfDMFEq9qyq666CqQDzjYDjfDSfKorSunXr1+tL8cXn05MTAzS0ryCBwCyWlXp3LZtG/PmzXPeXYba4EQTKioq+Pijj4k1tqZ9SCethqWNKZ62pvb85z//IT+/8cib0WgkNlb7ci4pJe/8619gt3B75xzNe5yMb3+K+DAbs99+i+pq31N73FlBvAtcybnMbk1PhmCjVW1Zr169iIuLRxQf13qKnlNynF9++YVHHnmk1jF7noN2uyfDJcQnIN1PuQkYDrOktLSUd955p3YrJaUsATRrufP5559TcqaEvuEDm3Sqp1Xupcx2ljLbWbaUbSCtsvESzN4R/ZF2yXvvvteos71///7MmzfP2X3pEiHEbGCTN+/HlXXr1rF7zx5ubJdXr6qCr4Qokts6ZpGXf4r//Oc/Po/njrEaIqV8FKgC7U+GYKNVbZmiKIwcOQJKc8DSdImC37BbUUpO8NJLL3H8+PELLp5s44cPH87cuXNJTEzEUe5g9StrOL7xBAC2ahurX1lD5i8nAbBUWlj9yhqytqltJKvLqln9yhpyduUAYD5jpuRk3W1Gyckz5O9XVxXlBeWsfmUNBYfUvnuleaWsfmUNhUfU4tgz2WdY/coaTh9TI1UlmSX8vPBnFEVxfokBEEIkAJpIV6anp7N48WI6hnQh1th05+FS+1lsNf9KbKcptTceHQ5Twuke2put27ayYcOGBp83e/ZsDhw4QGhoKMBXqAnaPqkulJeX88nHH9El0sxv6tGq0oreMRVcEVfCd98t8Dmn0R1jZRVCGKhxs2p5MjQHpkyZollt2Q033KBeKQx4p6RznD6KtFsZN24coC71v/jiC598Ou3atcNutjerNDIpJdjV1JOJEydSUFDA3/72N4Cf8VA8sT5sNhv/9+b/EaqE0TNC+2xsJ51DuxFrbM2777x7vmJtLREREbz88svOFJshwP/62grv888/p7SsjNs65bgd/VuQ1ZbsyjCyK8N4K70rC7LcqwG8uX0+ocLOe26kazSGO4XMbwMLgUQhxMuoNUnPeH3EZsZTTz3FqlWriImJcc1D8ipLv23btgwdOpQt23biSOoPxgAvQB12lFMHuKRnL3r16gWoXX0VRWHNmjU8++yzoG7jFwBNSlU7NZi2bduGoigMf2hYrUSMMdTIqL+OrH1uSERInduh0aF1boe3Cqd1J4m98NzvXOtOrYjsr4a1oxKj6jw/Jjmmzu1WHVrVvR3fiqFDhvLwww/TrVs3Vq9e7fwi3CylTGvqvTXFd999R+bJTFIjhzQqAeMrilDoF57C5tJ1fPzxx/WKJU6dOpX3338fg8EAcACIFUK8KaV83Ztj5uTksHz5Mq6JP02HCPdtXnZlOFUO9fM/Wu7+BiTaZGdcch7f7Bds3769tvTLU9wR3/sSeBp4FchDPRn8qwURYK677jpef/11Zs2aha/lRHdOnYq0VUNBEPrFnT6KrC7nrrvurN0WbdmyxWefTs+ePQGwn/a46Y/fMGeZsVgsJCUlkZiYyB133MHUqVMBTvmaFJqfn88XX3xBkimZpEaE9bQixhhLl9DurFy5kv3791/w+MGDB4mJiWHRokWgtlHrBNzt7fE+++wzTMLB2OQCr+fsKVcnFJMQZuXTuf/2usFEY+U2bVwiRwWoe+V5aHAyNAeio6Pr1JRpVVvWo0cPNUmx4CBYA9i02m7DkL+XXr16MXjw4Nq7TSaTzz6d2NhYOnbqiD2/+RirH/+1hk2bNjF58mQSEhLo2bOns5VaIWoQwWs++fgTpF3SJ8LtvGCf6R7em3BDBO+/9/4FX2ar1YrVanUaq++llFa8zH47duwYmzZtYkRiIdGmwH2eBgHjkvPJPJnFzz//7NUYja2snFGjHagnwGHUKm+fT4bmQFlZ2QU5SFrVlt17771gt0Keh4KcEW2Qikm9RCVBhAe/CQVpOKoreOCBB+pErWbMmKGJT2fokKHYCu04qoPvuJIOyajrRvHCCy8wZswYlixZQlFREadPnwZVXPA7b8fev38/P2/8mS4hPQhTwjWbc1MYhZFLwvqQcSyD1avr6phNnz6dLl26UFFRAfCTEKIzqpPdYz7//DPCjZIRif5V9ayP1NZnSQ638OUXn2OzeV7a2GRSKLACuFFKGS+ljMPHk6G5cH7+kZa1ZZ06deK3v/0tSlG6Z5HBTkNUAxXRBnqPVW+7g82Ccmo/gwYNZsCAAXUeuvPOO/nHP/7BX/7yF5KTk8HLbfw111wDEmwnNa+f9Rhbvh1HtYNrrrmGbdu2nQtsAFLKZcAwb8aVUjLnkzmEGcLpGtZDq+m6TbKpA7HG1nz22WdYLOeSi2fMmEFOTg5Lly5FqmSidp3yiF27drF9+w5GJ50iwhj4GJki4MZ2eeTm5Xuk61X7ejeec7mUsnZkX06G5sSgQYNquzL7modUH3feeacaZckLQE+1goNIWzX33jut9i5XA6yFT6dbt2506twJ24ngGyvrcStR0VEMGjSI+Ph4/v73v3PixAkyMzMRQvwN8KoSd9u2baQdSqN7aC8MIvDyzUIIeob1paioqE4Z2NmzZ3niiScYPHgwQojtQog3AI9SbOx2Ox9/9CHxYTaGJXpfqOwr/WPL6B1Tzrwvv6C01LPFoTvGqkgI8YwQoosQorMvJ4OW+BICBVX25NixYz7lITVGUlISY8aMQTmd4d+8K7sFpSCNoVdeSY8e51YDrsZYC5+OEIKx14/FVmzHFkRHu6PSgS3HzujrRmMymfjqq68oLCxk4sSJTrmfROAOb8b++uuvCTdEaJqp7ilxpgTamOL4Zv43tZpQ999/P9HR0cyfPx9gCuoW8N+ejLt8+XJOZmVzc7scTErwtvJCwC0d8qg0mz2WkXHHWN0BJKCmLyzCh5NBS7zZ89aHFnlIDTFlyhS1Cjz/wgiPZhQcQtqqmXpH3Y/EaYy19OmMGjWKsPAwLIe1z3Z2F8sRKwJRm0fWpk0b3nrrLdavX8+GDRuQUv7BG0WQtLQ00tLS6BLSw2NRPa3pGtqT08WnaxNFMzIyePHFF+nWrRtSymNSyhfxQEjAarUy78svuCS6goGt6s/lCiTJ4dVcE3+apUt/oKDA/YikO6kLxVLKP6Bu/X7j7cmgNVopET7yyCNs3rzZL7VlSUlJjBw5AuX0Ef9EBh02lMI0Lhs0yLlqugAtfToRERHcMPYGbFk2HOWB93lIi8SWYefqq68mKUltLLpv3z5SU1MZMGAA/fr1QwixQwjR39OxV65ciVEx0j7UfalifxFvTCTSGMXy5csBCA8PrxNBE0JcDbhdALV161ZKy8q5LqlQ8/o/bxmVVITDIVmzZo3br3GnkHmAEGIXsA844O3JoAWuIV2tjJUWeUiNceuttyLtNijwOU/xQoqOIi1mbpsypcGnaOnTAbjpppswGAxUpwd+dWXJsOKwOpg8+VyvhOnTp/Pmm2+SmZnpVN98EvjQk3GtVivr1q0j0ZisiaqCrwghaGfsyL59+ygsLOS9997j0UcfpUuXLgghMoF/AdPdHW/Tpk3EhDjoFdN8pLfjQq30iK5kkwdpDO6sdz8AnpBSdpZSdsaLk0ErXCMkrtd9QYs8pMbo1KmTmndVeEhNZ9AKhwOl4AC9eve+IALoipY+HYC4uDhGjRyF7bgNR5VnfyZDK4OqaWECQ4Ki3nYTaZfYjthISU2p45urqKhgxIhzgTEp5To8dD6npaVRVVVFkqmdJy+7AJu01un7aJPef97OLs87duwgJSWFPXv2sHfvXoABqNUHDX/o55GXm0O7sAqfRfW0pkN4JXn5eW77n90xVpFSyrXOG96cDFrhKjNRVaXNtkqrPKTGuP3222uy2jVcXRVnIKvKuOP22xtVA9DKp+PKpEmTkA7pse8q7LJQDK0UDK0UIkdGEHaZ+91yrMdt2M0OptxadxXZrVs3Zs6cyYkTJzhx4gRCiGcAj6Qvdu/ejUAQZ4r35GUXzlHauP56te/jmDFjsErv/apRSjRGaeKf//wnjz32GKtWrSI6OhrgHuAoqqPdLYqKCmll8t3Ha7YrdWSpzXbffHutQqxUVVuorKx06/nuxGeP1WhZfV5z+y48PBm0wmw+t03XyljdeeedDBo0SPPaMld69uzJ5ZdfzvZde3Ek9Pa9ZtDhQMnfS7fuPbjiisZjAfv27eOee+6huFi1TydPntwBTJNSeu31b9++PUOHDmXrzq3IPtJnEb2mkA6J9bCN7j26X7CKnDNnDs8//zy33HKL8/NLAO7zZPyMjAyijNE+bwFNwsjy5cuRUrJixQpMwvvPWQjB7t27CY8OY+LEiXz00Uf84x//ALgV9Rzd7e5YFouV0CjfI7hmu4HR14/moYceQkrJxhVNCwc2RqjiqJmfpVaiqTHcMVb3Ay+iRpAE8BMengxa4WqB3bXGDeH88gK1eUgAjz/++CkhRButgwh3330327bNgFMHoH2qb4MVHUZWlXHPPU81qbHk9Ok4t0pCCOc2/ipfpjB50mR+2fwLlmNWQnv5t2DblmvHXmZn8iOTL3i/rVu35u2333a96w+ejn8y8ySRnrVSrBejMFFWVVrb9zHc6NsGpNJcyYDL+vPggw/y4IMPEh8fDzBeSulRSM9ut3sufVsP4QY7K1euRErJqlWriDP4ZgCd21J3I/vuRANLpJQzpJSXSSlTa7YRJT7N0kvKy8vrve4NWuQhOfWeQHXSDh8+nC++UEVGKysrGT58eK3oWGJiIocOHaJgz2qwmrFUlrL1sxcpOKweqrr8DFs/exFrlfq+zGeL2PrZi5w+pkrCVpacYutnL1J8fC9K/l7atk3m6aefZtMmVYNt//79DB8+vLZTz+7duxk+fDhFRUU++3Tqo3fv3vTt1xfbYTvS7r+8HSkl1jQrSW2TVDXWGiZMmFDvRQixWAix2JNjlJeXE6I0P4k2gzAgpcRsNmMwGOjatSueGioAo8GAQ/q++g03OKiqqmLJkiVUVVURbvDNtWuvmZPR6F4CbmPdbRr9wLUSq/cE14xXT7Nfz+f4cXUn+/DDDzNhwoTa8L4QYjzwW58Gb4C2bduqfrf8/RDXy7tBzmQh7ZVMmnQfs2bNavLpHTp0YObMmdx9t1qk37VrV499Og1x25TbeP7557GesBHS3T9RNHu+HVuxnVsfu9UpkQLA5s2b6dixI3fccQdDhgypddIuWbLkDY+PYbcj6u2LElyKzhayatUq2rVrV2u0aorsBR40/YiKjuZsdeAz8pui1Ko233W3+01j7+BKIAtVbWEL9Xe5CSiuW7eSEm0Wd9u2beP999+vvS2lXCaEmOnOa516T6BGFV1vR0RE1LkdGxvL5s2bmTVrFmvWrSekbX+uuOf52sdDo1qptw+pZRbhsfF1Ho9oncQVd/4NZf8CUgZexvjx4xk//lwjnv79+9c5XkpKCuvWraOkpMRnn05DXHbZZVzS8xKOpR3D1MWIMGh7ikgpsRywEp8Qz6hRo+o8lp+fz6pVq/jqq6+YN28e48aN44477kBKud7T47Ru3ZrqIt81wrXm9zc/TqYlg8VLFru2GvO4yL5nr97s3ZKHlDSbPCuAE+URdOnUyamA2iSNbQPbAn8F+gNvAdcBRVLK9d6cEFpQVFSEEYgCCgsLNRlT6zykprj99tvVvCtv1ESLM5BWM1Onup954PTp7Ny5k127dqHlNl4Iwd133Y29wo4lQ/u8K1uOWtpzx+13YDLVXbkZDAauv/56Pv30U3755Rd69OjB8OHDEUI87ulx2rdvT7lsfk3Gy+xnSUpKcjVUXjFw4EDOVBvIKNemf58WnLEYOVIexaWp7vtvG1NdsEspl0sppwFDUcOl67w5GbQiLy+P1g4HbSxWcnNzNRlT6zykpujQoQODBg1CKToCnoiQSYlSmE7Xrl3p169pmV0tfTqNkZqayoCBA7Cl2TTtgCMdEss+K+3at+O3v61/V15dXc13333HXXfdxTvvvMOMGTPAi1KilNQUym1lmO2+BW20xCHtlNhPc9mgy3wea9iwYcRER7H6VIIGM9OGdQVxOFzKptyhUZMthAgVQtyC2prqUVSJ46DJw5w8cYI4i4U4m5WszEyfi5nBP3lITTFu3DikpUJtLuEulaeRlcWMHz/erdblmzdvJjs7m9/85jc89dRTPPnkkzz55JMAb9RcNEEIwQP3P4C9ykH1Ie16JlqP2bCX2nng/gfq+KqcTJs2jauuuoqdO3fy/PPPs23bNp599lmklG7/UZ0BkqFDh+JwOJi9+g22H98CgMVmYfbqN9iZqQpwmC1mZq9+gz1ZuwAory5n9uo32J+japaVms+SU5JdZ/yckmzS89UsmKLyQmavfoOjBYcBOFWaz+zVb3C8MAOAvDM5zF79BidPnwBgT+EuNm7eSOvWTTeqaIqwsDBuunki+89Gc6QsKCmSdThdbeKnwgSuvfZa2rZ1T8cdGlcK/RS13c9lwItSysullDM9ORm0pKqqirz8fJKsNtpabZRVVNTxYXmLVrVlnjB48GCioqPh9FH3X3T6KEajqdF2467k5+fzyiuvsH//fv7whz+watUq4uPj8cc23rkFsx624aj0PflfWlVfVb/+/RrMI/v88885fPgwb731FldddZVPSq/t2rUjNTWValmNoxn0QpFSkm/JITQ0lN69fe8ADWqZVEJ8HAuy2+HH4K1bLMpuizCYmDZtWtNPdqExB/vdQAXQE5jh8mvuUSRCK7Kzs5FAos1GRM32KSsri7i4OJ/G9VceUmOYTCZGjRzJ4iX/xWGrAmMTfdQddpSS41x11ZVERUW5dQynT+f666+nurqar776ypnK8LiUcrYGb6MO99xzDxt+3kD1AQvhl/vQFx6wHLbiqHJw/333N7iKbETH2+2EKdeAxJQpU9izZw+J4YkAhBhDeHzUk7WPh4eE17kdFRpV53ZMeCztW3egxHbO3dm+dQd6RfcBID4qoc7zk2La1rmd3Kp97e0Caz4iUvLxxx9z2WW+bwMBQkNDeeDBh3jttdfYWNiGaxODo0WQXhrJ7jOxTJ16KwkJnm1LG/NZKTUSv9FSyhiXi8+yv97g9FEdCg1lb03RcU6O74s8LWrLvGH06NFIhx1Ou5FFcCYLaa1m9OjRHh1DK5+OOyQmJqp6VyfsPikySIuarT5kyJDaDj2BIDU1lYEDB3LcchirQ7vtrKc4pIMjVQdpm9SWMWPGaDr2VVddxaUDB/DfvGTKbe7XZWqFXcKC7PYkJcZzyy23ePz64Ar3eEBWVhYCOGswUGw0Eoq62vIVLWrLvD1u9+49UE4fpqmGfKLoMHFx8aSkpLg9vhY+HU+ZPHkyilB80ruyHLPisDhqKwoChRCC3/3ud1gcFg6bg9CZqIbM6gzKbKU8+NCDbidLuosQgod+N51qh8Ly3ERNx3aHTUVtyDOH8MCDv3M7XcGVFmWsWktZm+wVb7WReeKEz+POmTOHwsJCbrnlFiZOnAga5iE1xQ03jEVWlkBFI+L91WVQmsvYsdfX62huCC19Ou4SHx/PsGHDsB23Ib1oLCEdEtsROwMGDqijrBAounfvzsSJE8mynOC01fPUmBhDLMaaf62NccQYYj16fYW9nIzqdIYMGVInW19LOnfuzJgx17OhKI5TVYHL2jfbFZbmtWVA/34MHTrUqzFajLFKT0sjuepc4l6yxcLRI0ew232rT/JnHlJTDBs2DFNICJw+0vCTio6CEFx33XUeje1wOCgrK7ugi4+/t/ETJ07EYZNYjnu+urJl27BX2rlloudbBK246667aJfcjv3mnVgcniWK9okYSLQxlmhjLEOif+NRKy+HtLO3cjth4WE8+uijbkV8vWXq1KmEhobyfbb7kTiADhFmwhQ7YYqdHlEVdIhwW/+PVfkJlFsV7n/gQa/fW4swVoWFhRQVF9PeRXCvg9WKubqakydPejVmoPKQGiMyMpJrf/MblJIT4KinmFNKlJJjpKSkkJgY+GW7N3Tt2pX+/ftjO2pHOtxfXUmp7sXozQAAEoBJREFU+qqS2iY5G3cEhfDwcP7y179gxcreyh2apMe4Q5p5P2dtZ3jyqSc9djw3REO1q61atWLCTTfzwbL9fLhNPe9KqxzcPjeP5Wlqv4DiSju3z83jx3Q196yw3MY3q3cRJSroEFHFrW2P8M3qXfx8TDVYJ0us3D43j19OqGooGUXq7R1ZVRRVm1h8NJS0tLTayhNn7eru3W6LR7QMY7Vli5r7conLyqp7tQUB/PLLL16NGag8pKYYMWIE0maB0rwLH6w8jawqY6RLAKAlMHHiROwVdmzZ7mso2Ysc2E7bufmmm33O2PaVHj168PtHfk+RtYB08wG/H+9k9XGyqo8zefJkr7dInjJu3DhCQkLYfLoNVof/VnFSwvyT7TGYQmqlqL1F+OmXQ9NB//I//8Opvft4pKCQuW3UJLl7i0v4d1wbZLeuvPveex6Pabfba2vL9u7dW1tb1q9fP3c+Oc3en81mY8ptt1MZngxdr6mtDaT3WMjegaHgIF9//ZVTeE0L/P7+HA4H0x+eTmFVAeG/Dauz7K9Yo/5SR46sW/pR+bOZkDOhfPbpZ7US016i2ft79913WbJkCf0jUugQ2sWtg28pU5s8DIn+jVvPL7IWsKPiFwYPHsTzzz/vjl9Ss/e3a9cunnvuOYYnFjGpY747L+Gt9K4A/KGXezGojYWt+fpkex566CEmTHBL+6DB99fsV1anT5/mwIED9KtHv6qf2UxWdrZTe9sjtKwt8wWj0cigy1IxlOddEBVUyvLo3ae3loYqICiKwqRbJmErtmMvbDqNwVGmtte6cfyNvhoqTZk+fTqpqakcMO/1yuHeFOX2UvZUbqdTx478+c9/9iiAogWpqamMHz+edQXxbC/2LBjgDicqwvk2uz2pKSkeldU0RLM3Vr/88gsS6G++UBm0b1U1Amo1nTwlkHlIjZGamoqjugKqXYJ0tmpkRRGXeVDo2ZwYMWIE0THRWNxoLFF92IrBaKijItEcMBgM/O1vf6Njhw7srtxKuV27NlYWRzU7K7cQER3OSzNfcksp0x/cf//99O3Tm3mZHTlWHq7ZuKerTXx0rCtt4uJ56k9/0sQQN3tjtWfPHlpJSVw9Ub8oh4O2NrtTSN8jgpGH1BC1hcnlLj3UKgrrPtbCCA0NVdt25dpwVDS8upJWiT3TzrBrh2lSB6c1kZGRvDTzJcIjw9lVuQWLBgmjDulgd+U2rMLCiy++GNTgiclk4q9/e4b4xCQ+PNaNfLPn+U/nU24z8O7RbtiNkTz3/AvExGgTfG7Wxspms7F3zx661LOqctKlqopDaWkeyxwHIw+pITp27Eh4eEStgQKgvFBtJ96zZyCnoiljx45FCNGofIw104bD6tBkm+AvkpKSePa5Z6mSZvZpECE8ZN5HsbWIJ554IqBZ+g0RGxvLSzP/jikilg+OdaXCh+x2m0PwybHOlNjCePa55+ncubNm82zWxurAgQNUVFbSs6rhfJee1dXY7HZ27drl0djBykOqD0VR6NnzEpRKFxmtytN06NDRbRXF5kh8fDyXXXYZ9pOOBr/gtkwbHTp2aPZGuX///jz88MMUWk9xrOqw1+PkWbI5WX2cW265heHDh2s3QR9p27Ytf3vmWc7aQvnkWCevi52/zUrmaFkEM/7w/zTfFTRrY7V82TJCgO6Who1VJ4uVKAlLly4N3MT8QM+ePdVs9hoM5mJ69qy/y3JLYuTIkdgr6ne0O8od2Irs/HbUb/2aBKkV48aNY/jw4RytOsQZm+eFwGZ7JQfMe+jTpw/33ReUniuN0rt3bx597HGOlEWy7pTnAgH7z0axsagNkyZN8oshbrbGKjMzk40bN3JFeTkhjVh5BbiqrIy9e/dy4ID/c2L8Rbdu3UA61ORQ6cBhqaR79+7BnpbPXHHFFZhMJqxZF+ZcOe+75pprAj0trxBC8NhjjxEXH8c+807sHvQFlFKy37wLo8nA008/rXndn1aMHDmSK664gqX5yRRXu6+rX20XzM/qSMcO7bnzzjv9Mrdma6w+++wzQoAry5v2RQ2uqCRKwqdz5wYs41hrunZV81dw2Gqz2bXc7weL8PBwBg8ejCP3wq2gPddOt27dPBJgCzaRkZE88cQTVNjKPdoO5lqyOG0t5KHfPdSs368QgunTpyMVI8vy3Hf8byiMo6TawCOPPnaBBLVWNEtjtX//frZu3crVpWVEuGF8TMCw0lLSDh3yOqM92CQnJ6tbIYcNHGrks0OHDkGelTZcccUV2CvtOM6c2wo6qiS2InvAMra1JDU1lREjRnC8+igV9qZbwlkdFg5XH6B3r95cf/31AZihbyQmJjJ27A1sLW5NoRvFzv+/vXsNiuq84zj+Pbss7C6wAiJesBk1kCBCFLEgJiVV46WJoxW1sYmpmibRmWQmjU00mnSSyXRap5npjNMZ28YXRmvj4NhMZ5waR0sniSbGGJvaVoWqQbwgAnLZC7vsnktfrMAqARF29+xZns8ruSjPweXHc57zPP9/pyJR1ZhFUdE0CgoiV7cy5sLK5/Pxu23bSNM0ZnoG/oRveoeXLEXhD9t/P+SegnpITEwkLT0jGFSqjNls7mpqaXgzZswAQG7o2X6iNAZnj3qeAxyK5557jsTExAGVk/nGdx6/6ufFl17U/SjRQC1fvhxzQgKHG+5+TvFo00jcARNPPRWZ278uMfed27lzJ/UNDSxuaeXOyeQhRwoNlgQaLAm8n5HOIUdP1UwTsKSljba2VrZv327I28GsrFHBdStNJWPkSMO8sO8mPT2d8ePHozT2hJV8Q8Fmt+lSCiYcMjIyWL5iOTcC9f0utvtUL5f93zB79mxDXWt6enr37Kq5n7Urv3prVjVtWthKMPclpn4aPvzwQw4ePEiZ28NEf++9OQ0JFjpNJjpNJuqSEmlIuP2bOE6W+b7TxdGjR9m9e3e0hh02ozIzkTQFSVPiZlbVpaCgALWl5xeI1qKRPzk/6kdMwqmiooKUlFQu+vpuq1brO48mBcs+G01FRQUms5m/9zO7+rwpA3fAxMooFEuMmbD66KOP2LlzJ1O8Pua5Bn8b94ing2JPB/v376eystJQM6yMjIxgey5NJXOIteVjTW5uLqpfRVMADeR2Jeb3Vt2NzWajomIpTYEbuOT2Xh/3q51c9dcxZ87sIVcc0MPIkSOZN28+J1oyaPf3fnop35pVFUzJJz8/P+Lj0T2sNE3jgw8+YPv27eR2dvLDtvYhtX6WgMedLgq9Xvbs2cOOHTv6ay4QU4LNLzRQlWBwxZHup52y1h1YEyZM0HNIYbFo0SIsFguX/Zd6feya/zKKprBs2bLoDyxMli5diqLBseber8fTbQ7a/GaWLV8RlbHoGlaBQIBt27axd+9epnV4ebKlrd92OwNlApa2OSl1ezhw4ABbt27F5+v7yE6sCO3UM9SuPbGm68mmpgBycLabnZ2t44jCIzU1lfLycq4HrqJoPWtymqZxLXCZyXmTDR3KY8eOpbh4Bp/fzOy1q/3TppGMGZ0Vtg48d6NbWLW2tvLG5s1UVVVR7nKzuN1JOFcvJGCBy818p4svjh9n46uv0tjYeNe/p6fQ2VS8hZXdbifVkQrKrZkVwR+EeDB37lxkNUBzoOf15VZduGUXc+bO0XFk4bFgwQKcfhPVzp4HWs2dFr5x25m/YGHUHgTpElb19fW8umEDF2pqWN7axmy3Z0i3fn2RgDJPBz9uaaW+ro6fv/IKtbURb1wzaKFhFYsVCIZqzOgxwaBSNUakjRhUh5NYVFhYSLI9mRuB+u73NfqvI0lSxBo/RFNxcTEpyXa+aknrft+pW39+9NFHozaOqIdVbW0tm157DXdzM2uabzKln0PK4ZLb6eenTc2o7e1s3rSJc+fORfxrDsaIET0F0NLS0vr5TGMaPXo0qMFbQSMuOPclISGB6cXTaVGau2t03pSbmDTp/rhYe7RYLJTOLOOsc0R3CdL/tI/ggdzcqJa3iWpY1dbWsuX111Hb21nb2My4wMDPVgF0ShJWq5XFixdjtVrpvIfDr6NkhbVNzSR5PPzizTc5e1a/3nB9Ce22PNDOy0aSlZUFCqDC6Kz4CSsI7mr3KV4UFNA02pQWiooG3ucx1pWUlNAhS/gUE4oGdR4rJaWlUR1DRMMqtLvGxYsXmVlaSl1NDWuabpIS8LOupprDLcGyKG5FZl1NNf9oDW6wa5MDrKup5tO2NgCaAwGuKjLz58/n+eefZ968eVxVZE44g4+Mr3b6WFdTzSlXsBTVJZ+XdTXVnHYHqzte8Haw6exZZtbWkeLz8bOXX6a0tPSeumtEWujhViOXhulL994xhbB1cIkVXcdMZE1G1mRUTaWwsFDnUYVP1/X5FDNeJbi6/NBDA281Fg5ROfrt8/n41S9/iaYoPOJyk56QyGCezamyzOHDh9E0jSNHjqDK9zYzA7BrKj9puslvkyxcunQJj8cziJFEXizVIg+X0I2u8fYAITs7m2R7MgFvAE0K/jBHekd3NDkcDrLHjaXtxhVUDcxmU9SrgkQ0rD7++GMg2PW4sbmZ7Q/mMeHWznSrycwfH+z5z0wxJ9z2dlqC5ba3My0WchOTqPP5OHDgAAC5iUmUOoLrPOOTrLd9/gSr7ba3c2z2nrdVlfUBBXNJCSdPnuThhx8O74WHgZF3dvcl9KFBvK3JmUwmcnJy+O+/z6ChMSpzVNjK+caKCRMn8WVDPaiQPXYsiYnR6+gMUVqzOnTwIPleX3dQxYIxskyRp4OqqioCgdgZVzwLDah4CyuAiZMmoiCjagoTJ03Uezhhl52dTUCV8Ktmsr9zX9S/flTCKiMjgwj2URw0VZJwpKRErP6OcLvQlmLxNusAGDduHAAKSlxseL1T1218QJN0WXOMSljl5edTY7VyLNke3u6ng6QBJ+02TtttPDh5st7DGTZCHxro1XoqkkK3Y8TT1owuep+wiMoC+/r16/F3dlJ17BjXLRYedzpJVvWJLa8kcdiRyr/sNmYUF7NhwwZdxjEche50jsewCt1TFQ/7q+6k96blqISV1WrltY0buT8nh927dnHBZqXM5abM00HSPVRFGCMHaLAEhzwmIDNGHvhakwycSLZzzJFKJ7BixQqefvrpuFzINoJ4fNoZ72tyel9f1KrWS5LEsmXLKC0t5U+7d/PJ8eN8lZrC99qdTO/w9iq0920WOt3dNazWtLTe5bODFOC0zcanI1JplySKp09n9Zo1PVUABF3E4y+JeN/Uq/cJi6i32Bg/fjybt2yhurqaXe+/z6EzZzg6wsFMp4vvdnjvaabVnwDwT7uNzx2pOCWJ3JwcNq5dG/WNbMLwEXrWMR7DKvRBlB4PSHTrB5SXl8evt27lzJkzVFZWUvX113zmSKXU5WampwPrIEPLL8FJu53jjlQ8wJT8fH705JMUFRUZojedYFyhry+bzabjSCJPjzVH3ZuXTZkyhXfeeYfz58+zr7KST06c4MvUFGY5XZR0eEkcYGjJwFd2G585HLglmDZtGitXrgx7V1hBGIh4Dys9KmboHlZdcnNzeePNN7lw4QJ/3rOHqlOn+MKRysLWNgruUpnhf0mJ/C09DackUVhQwKpnnolKmVVB6Es8rsmF0uMuJWbCqktOTg5vvf021dXV7HjvPf5y/jwXO7z8wOnqNcuSgSOOVL5MtjPhvvvY+MILTJ06VZ+BC4IQUTEXVl3y8vL4zbvvsnfvXvbt28c1axJrGpu7P+6XJHZljqQ+wczixYtZvXp11M8qCYIQPTEbVhCcSq9atYrCwkLefust9mekdRc3+2uag+uWBLZs3kxZWZm+AxUEIeJ0724zEFOnTuXFl16iNjGRdrMZl9nEOauVZ599VgSVIAwThggrgMcee4wZxcU4zWacZjMP5OayZMkSvYclCEKUGCasAB5/4glUCWRJ4olFi8S+KUEYRgwVVqFlYsVTP0EYXgwVVqGHX+PxVLsgCH0zVFiFEreAgjC8GDasBEEYXkRYCYJgCDG9KfTblJSU4HQ69R5GxJSVldHS0qL3MCJm1qxZNDY26j2MiCkvL+fatWt6DyNiysvLuXLlii5fW9LCVD/qDhGrWawoCqqqRrLJw0AWwyJ6fYqiRPLokO7XJ/7/hmTYXp/hwioKdH0xRIG4PnF9sazP6xNrVoIgGIIIK0EQDEGElSAIhiDCShAEQxBhJQiCIYiwEgTBECKydUGSpENAZtj/4eho1jRtYX+fIK4vponri9Pri9Q+K0EQhLASt4GCIBiCCCtBEAxBhJUgCIYgwkoQBEMQYSUIgiGIsBIEwRBEWAmCYAgirARBMAQRVoIgGML/Ab482D5Iyjo4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4beu\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4unu\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4z8j\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5vid\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5vli\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5vmr\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 288x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cmap = plt.get_cmap(\"Set1\")\n",
    "\n",
    "# columns = df[\"column\"].unique()\n",
    "\n",
    "# for pdb_id in PDB_IDS:\n",
    "#     print(pdb_id)\n",
    "\n",
    "#     fig, axs = plt.subplots(ncols=len(columns), figsize=(4, 3))\n",
    "\n",
    "#     df = process_scores(SCORES[f\"{pdb_id}_modeller\"], SCORES[f\"{pdb_id}_rosetta\"])\n",
    "#     df_wt = process_scores(SCORES[f\"{pdb_id}_modeller\"], SCORES[f\"{pdb_id}_rosetta\"])\n",
    "\n",
    "#     for i, (column, ax) in enumerate(zip(columns, axs)):\n",
    "#         sns.violinplot(x=\"name\", y=\"value\", data=df[df[\"column\"] == column], color=cmap(i), ax=ax)\n",
    "#         ax.hlines(\n",
    "#             df_wt[df_wt[\"column\"] == column][\"value\"].values[0], *ax.get_xlim(), linestyle=\":\"\n",
    "#         )\n",
    "# #         ax.set_xticklabels(ax.get_xticklabels(), rotation=\"vertical\")\n",
    "#         ax.set_xlabel(None)\n",
    "#         ax.set_xticks([])\n",
    "#         ax.set_ylabel(df[df[\"column\"] == column][\"name\"].values[0].replace(\"\\n\", \" \"))\n",
    "#         ref = df\n",
    "\n",
    "# #     for ax in axs[1:]:\n",
    "# #         ax.set_ylabel(None)\n",
    "\n",
    "#     for i, ax in enumerate(axs):\n",
    "# #         ax.set_ylabel(\"Score\" if i == 0 else \"\")\n",
    "#         ax.set_yticks([])\n",
    "#         #     ax.spines[\"top\"].set_visible(False)\n",
    "#         ax.spines[\"right\"].set_visible(False)\n",
    "#         ax.spines[\"left\"].set_visible(False)\n",
    "#         ax.grid(axis=\"y\", linestyle=\":\")\n",
    "\n",
    "#     fig.subplots_adjust(top=0.98, bottom=0.02, left=0.05, right=0.99, hspace=0.5)\n",
    "\n",
    "#     fig.savefig(NOTEBOOK_PATH.joinpath(f\"modeller-and-rosetta-score-distributions-{pdb_id}.pdf\"))\n",
    "#     fig.savefig(NOTEBOOK_PATH.joinpath(f\"modeller-and-rosetta-score-distributions-{pdb_id}.svg\"))\n",
    "#     fig.savefig(NOTEBOOK_PATH.joinpath(f\"modeller-and-rosetta-score-distributions-{pdb_id}.png\"), dpi=300)\n",
    "    \n",
    "#     plt.show()\n",
    "# #     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"216pt\" version=\"1.1\" viewBox=\"0 0 288 216\" width=\"288pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 216  L 288 216  L 288 0  L 0 0  z \" style=\"fill:#ffffff;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 14.4 211.68  L 61.075862 211.68  L 61.075862 4.32  L 14.4 4.32  z \" style=\"fill:#ffffff;\"/>\n",
       "   </g>\n",
       "   <g id=\"PolyCollection_1\">\n",
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       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import SVG, Image\n",
    "\n",
    "display(SVG(\"16_david_analysis/modeller-and-rosetta-score-distributions-5vmr.svg\"))\n",
    "# display(Image(\"07_protein_analysis_bestsel/reference-vs-design-secondary-structure-from-cd.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"Modeller\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.0, 6.0)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg, ax = plt.subplots()\n",
    "\n",
    "ax.boxplot([df[df[\"name\"] == name][\"value\"] for name in df[\"name\"].unique()])\n",
    "\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=\"vertical\")\n",
    "ax.set_ylim(-4, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e8cb0fcd0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"name\", y=\"value\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Modeller' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-3f7f4d3777b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mModeller\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmolpdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeller\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdope_normalized\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeller\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdope_hr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeller\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mga341\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mRosetta\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Modeller' is not defined"
     ]
    }
   ],
   "source": [
    "Modeller (molpdf), Modeller (dope_normalized), Modeller (dope_hr), Modeller (ga341),  Rosetta (score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!head {NOTEBOOK_PATH}/Scores/1n5u/scoresTotal_rosetta.sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(NOTEBOOK_PATH.joinpath(\"Scores\", \"1n5u\", \"scoresTotal_rosetta.sc\"), skiprows=1, sep=\" +\", engine=\"python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "molpdf, dope_normalized, dope_hr and ga341, rosetta energy units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = plt.cm.get_cmap(\"Set1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for pdb_id, *xlim in [\n",
    "    #\n",
    "    (\"1n5u\", 250, 490),\n",
    "    (\"4beu\", 850, 1350),\n",
    "    (\"4unu\", 450, 950),\n",
    "    (\"4z8j\", 250, 850),\n",
    "    #     (\"5vid\", 250, 1450),\n",
    "    #     (\"5vli\", 250, 1450),\n",
    "    #     (\"5vmr\", 250, 1450),\n",
    "]:\n",
    "    with plt.rc_context(rc={\"font.size\": 18}):\n",
    "        fg, ax = plt.subplots(figsize=(8 * 0.9, 6 * 0.9))\n",
    "        ax.hist(np.clip(SCORES[f\"{pdb_id}\"][\"molpdf\"].values, *xlim), bins=80, range=xlim, color=cmap(8))\n",
    "        ylim = ax.get_ylim()\n",
    "        ax.vlines(SCORES[f\"{pdb_id}_wt\"].iloc[0][\"molpdf\"], *ylim, colors=\"k\", linestyle=\"--\", color=cmap(0))\n",
    "        ax.set_ylim(ylim)\n",
    "        ax.set_xlim(xlim)\n",
    "        ax.set_xlabel(\"MolPDF score\")\n",
    "        ax.set_ylabel(\"Number of sequences\")\n",
    "    fg.tight_layout()\n",
    "    fg.savefig(NOTEBOOK_PATH.joinpath(f\"molpdb-scores-{pdb_id}.svg\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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