analysis/0_fragsys_validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STRUCTURE VALIDATION METRICS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we explore different structure validation metrics provided by the PDBe to assess the quality of the structures used in this analysis. The input file needed to run this analysis is not on the repository due to file size storage limits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTING NECESSARY PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import statistics\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SOME FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(data, feat_name, bns, col, show = True, out = None, dpi = 100, figsize = (5, 5), yticks = None, et = None, logx = False, logy = 10):\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "    if type(data) != list:\n",
    "        data = data.tolist()\n",
    "    st_median = round(statistics.median(data), 2)\n",
    "    st_mean = round(statistics.mean(data), 2)\n",
    "    st_max = round(max(data),2)\n",
    "    st_min = round(min(data), 2)\n",
    "    print(\"\\tMIN = {}\\tMEAN = {}\\tMEDIAN = {}\\tMAX = {}\".format(st_min, st_mean, st_median, st_max))\n",
    "    \n",
    "    fig = plt.figure(figsize = figsize, dpi = dpi)\n",
    "    sns.histplot(data, fill = True, color = col, stat = \"count\", element = \"step\", bins = bns, log_scale = (logx, logy))\n",
    "    plt.ylabel(\"log(count)\")\n",
    "    plt.xlabel(feat_name)\n",
    "    plt.axvline(x = st_median, linewidth = 1, color = \"k\", linestyle = \"--\")\n",
    "    if et != None:\n",
    "        plt.axvline(x = et, linewidth = 2, color = \"r\", linestyle = \"--\")\n",
    "    \n",
    "    if yticks != None:\n",
    "        plt.yticks(yticks)\n",
    "    #plt.legend(edgecolor = \"k\")\n",
    "    if out != None:\n",
    "        plt.savefig(out)\n",
    "    if show == False:\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### READING INPUT DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_dir = \"/cluster/gjb_lab/2394007/pandda_analysis/phase4/\"\n",
    "val_d = pd.read_pickle(os.path.join(main_dir, \"validation_data.pkl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1121927, 34)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>pdb_id</th>\n",
       "      <th>validation_NatomsEDS</th>\n",
       "      <th>validation_altcode</th>\n",
       "      <th>validation_avgoccu</th>\n",
       "      <th>validation_chain</th>\n",
       "      <th>validation_cis_peptide</th>\n",
       "      <th>validation_ent</th>\n",
       "      <th>validation_flippable-sidechain</th>\n",
       "      <th>validation_icode</th>\n",
       "      <th>validation_ligand_chirality_outlier</th>\n",
       "      <th>...</th>\n",
       "      <th>validation_rama</th>\n",
       "      <th>validation_resname</th>\n",
       "      <th>validation_resnum</th>\n",
       "      <th>validation_resnum_full</th>\n",
       "      <th>validation_rota</th>\n",
       "      <th>validation_rscc</th>\n",
       "      <th>validation_rsr</th>\n",
       "      <th>validation_rsrz</th>\n",
       "      <th>validation_said</th>\n",
       "      <th>validation_seq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5fzl</td>\n",
       "      <td>6.0</td>\n",
       "      <td>.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>??</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SER</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>p</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.369</td>\n",
       "      <td>3.393</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5fzl</td>\n",
       "      <td>8.0</td>\n",
       "      <td>.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>??</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Allowed</td>\n",
       "      <td>MET</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>mmt</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.905</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5fzl</td>\n",
       "      <td>11.0</td>\n",
       "      <td>.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>??</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Favored</td>\n",
       "      <td>PHE</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>t80</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.153</td>\n",
       "      <td>-0.087</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  pdb_id  validation_NatomsEDS validation_altcode  validation_avgoccu  \\\n",
       "0   5fzl                   6.0                  .                 1.0   \n",
       "1   5fzl                   8.0                  .                 1.0   \n",
       "2   5fzl                  11.0                  .                 1.0   \n",
       "\n",
       "  validation_chain validation_cis_peptide  validation_ent  \\\n",
       "0                A                    NaN               1   \n",
       "1                A                    NaN               1   \n",
       "2                A                    NaN               1   \n",
       "\n",
       "   validation_flippable-sidechain validation_icode  \\\n",
       "0                             NaN               ??   \n",
       "1                             NaN               ??   \n",
       "2                             NaN               ??   \n",
       "\n",
       "  validation_ligand_chirality_outlier  ... validation_rama validation_resname  \\\n",
       "0                                 NaN  ...             NaN                SER   \n",
       "1                                 NaN  ...         Allowed                MET   \n",
       "2                                 NaN  ...         Favored                PHE   \n",
       "\n",
       "   validation_resnum  validation_resnum_full  validation_rota validation_rscc  \\\n",
       "0                 -1                      -1                p           0.937   \n",
       "1                  0                       0              mmt           0.839   \n",
       "2                 26                      26              t80           0.982   \n",
       "\n",
       "   validation_rsr  validation_rsrz  validation_said  validation_seq  \n",
       "0           0.369            3.393                A               1  \n",
       "1           0.209            0.905                A               2  \n",
       "2           0.153           -0.087                A               3  \n",
       "\n",
       "[3 rows x 34 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_d.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = [\n",
    "    \"pdb_id\", \"validation_chain\",\n",
    "    #'validation_ligand_chirality_outlier', 'validation_ligand_clashes_outlier', 'validation_ligand_geometry_outlier',\n",
    "    #'validation_ligand_num_clashes', 'validation_ligand_num_symm_clashes',\n",
    "    'validation_resname', 'validation_resnum', #'validation_resnum_full',\n",
    "    'validation_rsr', 'validation_rscc', 'validation_rsrz',\n",
    "    #'validation_said', 'validation_seq'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VALIDATION ANALYSIS FOR ALL RESIDUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111797\n",
      "\tMIN = 0.01\tMEAN = 0.14\tMEDIAN = 0.11\tMAX = 4.27\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_rsr = val_d.query('validation_rsr == validation_rsr')\n",
    "print(len(d_rsr))\n",
    "bns_rsr = np.linspace(0, 4.5, 50)\n",
    "plot_hist(d_rsr.validation_rsr, \"RSR\", bns_rsr, \"firebrick\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111797\t933518\t83.96%\n",
      "\tMIN = -0.76\tMEAN = 0.91\tMEDIAN = 0.94\tMAX = 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_rscc = val_d.query('validation_rscc == validation_rscc')\n",
    "rscc_len = len(d_rscc)\n",
    "rscc_t_len = len(d_rscc.query('validation_rscc > 0.85'))\n",
    "print(\"{}\\t{}\\t{}%\".format(rscc_len, rscc_t_len, round(100*(rscc_t_len/rscc_len),2)))\n",
    "bns_rscc = np.linspace(-0.75, 1.0, 50)\n",
    "plot_hist(d_rscc.validation_rscc, \"RSCC\", bns_rscc, \"gold\", et = 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "643193\t590313\t91.78%\n",
      "\tMIN = -1.74\tMEAN = 0.38\tMEDIAN = -0.06\tMAX = 38.63\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_rsrz = val_d.query('validation_rsrz == validation_rsrz')\n",
    "rsrz_len = len(d_rsrz)\n",
    "rsrz_t_len = len(d_rsrz.query('validation_rsrz < 2'))\n",
    "print(\"{}\\t{}\\t{}%\".format(rsrz_len, rsrz_t_len, round(100*(rsrz_t_len/rsrz_len),2)))\n",
    "bns_rsrz = np.linspace(-2, 40, 75)\n",
    "plot_hist(d_rsrz.validation_rsrz, \"RSRZ\", bns_rsrz, \"cornflowerblue\", et = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APPLY FILTERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = 'validation_rsr == validation_rsr & validation_rscc == validation_rscc & validation_rsrz == validation_rsrz & validation_rscc > 0.85 & validation_rsrz < 2'\n",
    "val_d_filt = val_d.copy().query(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tMIN = 0.85\tMEAN = 0.95\tMEDIAN = 0.95\tMAX = 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bns_rscc = np.linspace(-0.75, 1.0, 50)\n",
    "plot_hist(val_d_filt.validation_rscc, \"RSCC\", 50, \"gold\", et = 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tMIN = -1.74\tMEAN = -0.05\tMEDIAN = -0.16\tMAX = 2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bns_rscc = np.linspace(-0.75, 1.0, 50)\n",
    "plot_hist(val_d_filt.validation_rsrz, \"RSRZ\", 50, \"cornflowerblue\", et = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOOKING AT QUALITY OF LIGANDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_resnames = [\n",
    "    \"ALA\", \"CYS\", \"ASP\", \"GLU\", \"PHE\", \"GLY\", \"HIS\", \"ILE\", \"LYS\", \"LEU\",\n",
    "    \"MET\", \"ASN\", \"PRO\", \"GLN\", \"ARG\", \"SER\", \"THR\", \"VAL\", \"TRP\", \"TYR\"\n",
    "]\n",
    "\n",
    "simple_ions = [\n",
    "    \"ZN\", \"MN\", \"CL\", \"MG\", \"CD\", \"NI\", \"NA\", \"IOD\", \"CA\", \"BR\", \"XE\", \"TB\", #FE, K, CO, NO3, AZI, R\n",
    "]\n",
    "\n",
    "acidic_ions = [\n",
    "    \"PO4\", \"ACT\", \"SO4\", \"MLI\", \"CIT\", \"ACY\", \"VO4\", \"WO4\"\n",
    "]\n",
    "\n",
    "non_relevant_ligs_manual = [\n",
    "    \"DMS\", \"EDO\", \"HOH\", \"TRS\", \"GOL\", \"OGA\", \"FMN\", \"PG4\", \"PGR\",\n",
    "    \"MPD\", \"TPP\", \"MES\", \"PLP\", \"HYP\", \"CSO\", \"UNX\", \"EPE\", \"PEG\",\n",
    "    \"PGE\", \"DOD\", \"SUI\", \"BEN\", \"BOG\"\n",
    "]\n",
    "\n",
    "non_relevant = non_relevant_ligs_manual + simple_ions + acidic_ions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_d_lig = val_d.query('validation_resname not in @pdb_resnames & validation_resname not in @non_relevant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2189, 34)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_d_lig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2133\t632\t29.63%\n",
      "\tMIN = -0.18\tMEAN = 0.71\tMEDIAN = 0.76\tMAX = 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_rscc_lig = val_d_lig.query('validation_rscc == validation_rscc')\n",
    "rscc_len = len(d_rscc_lig)\n",
    "rscc_t_len = len(d_rscc_lig.query('validation_rscc > 0.85'))\n",
    "print(\"{}\\t{}\\t{}%\".format(rscc_len, rscc_t_len, round(100*(rscc_t_len/rscc_len),2)))\n",
    "bns_rscc = np.linspace(-0.75, 1.0, 50)\n",
    "plot_hist(d_rscc_lig.validation_rscc, \"RSCC\", bns_rscc, \"gold\", et = 0.85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SOME EXAMPLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = val_d.query('pdb_id == \"5rhg\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>pdb_id</th>\n",
       "      <th>validation_chain</th>\n",
       "      <th>validation_resname</th>\n",
       "      <th>validation_resnum</th>\n",
       "      <th>validation_rsr</th>\n",
       "      <th>validation_rscc</th>\n",
       "      <th>validation_rsrz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>NY7</td>\n",
       "      <td>704</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.665</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>NY7</td>\n",
       "      <td>705</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.823</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pdb_id validation_chain validation_resname  validation_resnum  \\\n",
       "442   5rhg                A                NY7                704   \n",
       "443   5rhg                A                NY7                705   \n",
       "\n",
       "     validation_rsr  validation_rscc  validation_rsrz  \n",
       "442           0.217            0.665              NaN  \n",
       "443           0.166            0.823              NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.query('validation_resname == \"NY7\"')[cs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "BS0 = [264, 265, 267, 268, 271, 293, 493, 536, 537, 540]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>pdb_id</th>\n",
       "      <th>validation_chain</th>\n",
       "      <th>validation_resname</th>\n",
       "      <th>validation_resnum</th>\n",
       "      <th>validation_rsr</th>\n",
       "      <th>validation_rscc</th>\n",
       "      <th>validation_rsrz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>ALA</td>\n",
       "      <td>264</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.966</td>\n",
       "      <td>-0.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>THR</td>\n",
       "      <td>265</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.971</td>\n",
       "      <td>-0.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>THR</td>\n",
       "      <td>267</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.977</td>\n",
       "      <td>-0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>SER</td>\n",
       "      <td>268</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.978</td>\n",
       "      <td>-0.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>LEU</td>\n",
       "      <td>271</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>SER</td>\n",
       "      <td>293</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.980</td>\n",
       "      <td>-0.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>LEU</td>\n",
       "      <td>493</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.959</td>\n",
       "      <td>-0.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>MET</td>\n",
       "      <td>536</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.981</td>\n",
       "      <td>-0.531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>LYS</td>\n",
       "      <td>537</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.865</td>\n",
       "      <td>1.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>5rhg</td>\n",
       "      <td>A</td>\n",
       "      <td>ASP</td>\n",
       "      <td>540</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.952</td>\n",
       "      <td>-0.466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pdb_id validation_chain validation_resname  validation_resnum  \\\n",
       "82    5rhg                A                ALA                264   \n",
       "83    5rhg                A                THR                265   \n",
       "85    5rhg                A                THR                267   \n",
       "86    5rhg                A                SER                268   \n",
       "89    5rhg                A                LEU                271   \n",
       "111   5rhg                A                SER                293   \n",
       "313   5rhg                A                LEU                493   \n",
       "356   5rhg                A                MET                536   \n",
       "357   5rhg                A                LYS                537   \n",
       "360   5rhg                A                ASP                540   \n",
       "\n",
       "     validation_rsr  validation_rscc  validation_rsrz  \n",
       "82            0.066            0.966           -0.400  \n",
       "83            0.058            0.971           -0.612  \n",
       "85            0.053            0.977           -0.731  \n",
       "86            0.043            0.978           -0.904  \n",
       "89            0.088            0.917           -0.027  \n",
       "111           0.053            0.980           -0.703  \n",
       "313           0.066            0.959           -0.690  \n",
       "356           0.065            0.981           -0.531  \n",
       "357           0.213            0.865            1.554  \n",
       "360           0.071            0.952           -0.466  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.query('validation_resnum in @BS0')[cs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GETTING RESOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(\"https://www.ebi.ac.uk/pdbe/api/pdb/entry/experiment/5rhg\", convert_axes = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_experimental_data(pdb_ids, exp_data_dir, out):\n",
    "    \"\"\"\n",
    "    Retrieves experimental data from PDBe rest API\n",
    "    and saves subset of columns to pandas dataframe, and pickle.\n",
    "    \"\"\"\n",
    "    exp_data_dfs = []\n",
    "    for pdb_id in pdb_ids:\n",
    "        pdb_expd_out = os.path.join(exp_data_dir, \"{}_exp_data.pkl\".format(pdb_id))\n",
    "        if os.path.isfile(pdb_expd_out):\n",
    "            exp_data_df = pd.read_pickle(pdb_expd_out)\n",
    "            exp_data_dfs.append(exp_data_df)\n",
    "        else:\n",
    "            try:\n",
    "                exp_data = pd.read_json(\"https://www.ebi.ac.uk/pdbe/api/pdb/entry/experiment/{}\".format(pdb_id), convert_axes = False)\n",
    "                exp_data_dict = exp_data.loc[0, pdb_id]\n",
    "                exp_data_df = pd.DataFrame({k: v for k, v in exp_data_dict.items() if k in exp_data_cols}, index = [0])\n",
    "                exp_data_df[\"pdb_id\"] = pdb_id\n",
    "                exp_data_df.to_pickle(pdb_expd_out)\n",
    "                exp_data_dfs.append(exp_data_df)\n",
    "                #print(\"Experimental data retrieved for {}\".format(pdb_id))\n",
    "                time.sleep(sleep_time)\n",
    "            except HTTPError as e:\n",
    "                print(\"Experimental data not retrieved for {}\".format(pdb_id))\n",
    "                continue\n",
    "    master_exp_data_df = pd.concat(exp_data_dfs)\n",
    "    master_exp_data_df.reset_index(drop = True, inplace = True)\n",
    "    master_exp_data_df.to_pickle(out)#, index = False)\n",
    "    return master_exp_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_ids = val_d.pdb_id.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_data_cols = [\n",
    "    \"resolution\", \"resolution_low\", \"resolution_high\",\n",
    "    \"r_factor\", \"r_free\", \"r_work\", \"experimental_method\", # columns of interest from experimental data table\n",
    "    \"structure_determination_method\"\n",
    "]\n",
    "from urllib.error import HTTPError\n",
    "import time\n",
    "sleep_time = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experimental data not retrieved for 5s9h\n",
      "Experimental data not retrieved for 5s9f\n",
      "Experimental data not retrieved for 5s9i\n",
      "346.26417326927185\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "master_exp_data_df = get_experimental_data(pdb_ids, \"./../results\", \"./../results/strucs_ress.pkl\")\n",
    "tf = time.time()\n",
    "print(tf-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>resolution_low</th>\n",
       "      <th>resolution_high</th>\n",
       "      <th>r_factor</th>\n",
       "      <th>r_free</th>\n",
       "      <th>experimental_method</th>\n",
       "      <th>r_work</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structure_determination_method</th>\n",
       "      <th>pdb_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.035</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.1937</td>\n",
       "      <td>0.2398</td>\n",
       "      <td>X-ray diffraction</td>\n",
       "      <td>0.1912</td>\n",
       "      <td>2.55</td>\n",
       "      <td>MOLECULAR REPLACEMENT</td>\n",
       "      <td>5fzl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.940</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.1848</td>\n",
       "      <td>0.2243</td>\n",
       "      <td>X-ray diffraction</td>\n",
       "      <td>0.1828</td>\n",
       "      <td>2.49</td>\n",
       "      <td>MOLECULAR REPLACEMENT</td>\n",
       "      <td>5fzm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.566</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.1806</td>\n",
       "      <td>0.2186</td>\n",
       "      <td>X-ray diffraction</td>\n",
       "      <td>0.1786</td>\n",
       "      <td>2.33</td>\n",
       "      <td>MOLECULAR REPLACEMENT</td>\n",
       "      <td>5fz6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resolution_low  resolution_high  r_factor  r_free experimental_method  \\\n",
       "0          71.035             2.55    0.1937  0.2398   X-ray diffraction   \n",
       "1          70.940             2.49    0.1848  0.2243   X-ray diffraction   \n",
       "2          96.566             2.33    0.1806  0.2186   X-ray diffraction   \n",
       "\n",
       "   r_work  resolution structure_determination_method pdb_id  \n",
       "0  0.1912        2.55          MOLECULAR REPLACEMENT   5fzl  \n",
       "1  0.1828        2.49          MOLECULAR REPLACEMENT   5fzm  \n",
       "2  0.1786        2.33          MOLECULAR REPLACEMENT   5fz6  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_exp_data_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tMIN = 14.5\tMEAN = 56.05\tMEDIAN = 54.53\tMAX = 130.45\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resolution_low_len = len(master_exp_data_df)\n",
    "#resolution_low_t_len = len(d_rscc.query('validation_rscc > 0.85'))\n",
    "#print(\"{}\\t{}\\t{}%\".format(rscc_len, rscc_t_len, round(100*(rscc_t_len/rscc_len),2)))\n",
    "bns_rscc = np.linspace(15, 130, 25)\n",
    "plot_hist(master_exp_data_df.resolution_low, \"resolution_low\", bns_rscc, \"lavender\", logy = False)#, et = 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1496\t17\t1.14%\n",
      "\tMIN = 0.9\tMEAN = 1.55\tMEDIAN = 1.57\tMAX = 3.28\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_len = len(master_exp_data_df)\n",
    "d_t_len = len(master_exp_data_df.query('resolution_high > 2.5'))\n",
    "print(\"{}\\t{}\\t{}%\".format(d_len, d_t_len, round(100*(d_t_len/d_len),2)))\n",
    "bns_d = np.linspace(1, 3.5, 50)\n",
    "plot_hist(master_exp_data_df.resolution_high, \"resolution_high\", bns_d, \"orchid\", logy = False)#, et = 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1496\t134\t8.96%\n",
      "\tMIN = 0.9\tMEAN = 1.55\tMEDIAN = 1.57\tMAX = 3.28\n"
     ]
    },
    {
     "data": {
      "image/png": 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8gtDQUMPZt9Rx+Pn54fXXX4efn5/YUYiIzBK1PI8fP47x48cb3i9duhQAMGfOHOh0OuzZswcAMGTIEKPPHTp0COPGjQMAbNiwAU5OTpg5cyYqKysxYcIEpKSkwNHRsV3+BrIdPz8/6HQ6sWMQETVL1PIcN24cBEEwOd/cvAbOzs7YuHEjNm7caMtoJIKSkhJ89913GDlyJI9DE5GkSfqYJ9mXS5cu4aGHHjLalU9EJEUsTyIiIguxPImIiCzE8iQiIrIQy5MkQ6lUok+fPlAqlWJHISIyS9SzbYl+a9CgQTxZiIg6BI48iYiILMTyJMk4c+YMfHx8cObMGbGjEBGZxfIkyaitrUVBQQFqa2vFjkJEZBbLk4iIyEIsTyIiIguxPImIiCzE8iTJCA4ORkZGBoKDg8WOQkRkFq/zJMno0qULRo4cKXYMIqJmceRJknH9+nUsXboU169fFzsKEZFZLE+SjPz8fGzYsAH5+fliRyEiMovlSUREZCGWJxERkYVYnkRERBZieZJkeHt7Y+HChfD29hY7ChGRWbxUhSSjZ8+eePfdd8WOQUTULI48STIqKipw8uRJVFRUiB2FiMgslidJxs8//4zw8HD8/PPPYkchIjKL5UlERGQhlicREZGFWJ5EREQWYnmSZDg4OEClUsHBgZslEUkbL1UhyRgyZAhKSkrEjkFE1CyWJ3UIhdmFqC6tbnKeQqWAV5BXOyciInvG8iTJ+Omnn/DEE0/gs88+w8CBAw3TC7MLkRycbPaz0RejWaBE1G5YniQZd+/exU8//YS7d+8aTW8YcXoP8IbcVW40r6aiBgVZBSZHpUREbYHlSR2G3FUOpUopdgwiIp5tS0REZCmWJxERkYVELc8jR45g2rRp0Gq1kMlk2L17t9F8QRCg0+mg1Wrh4uKCcePG4fz580bLVFVVYfHixfD29oabmxuioqJw/fr1dvwryFZ69+6NL774Ar179xY7ChGRWaKWZ3l5OQYPHozk5KbPpFy7di3Wr1+P5ORkHDt2DL6+vnjwwQdRWlpqWCYmJgapqanYsWMHjh49irKyMkydOhV1dXXt9WeQjXTt2hVRUVHo2rWr2FGIiMwS9YShyZMnY/LkyU3OEwQBSUlJWLlyJWbMmAEA2Lp1KzQaDbZv34758+ejuLgYW7Zswccff4yJEycCALZt2wZ/f38cPHgQkyZNare/hVpPr9fjww8/xHPPPQdfX1+x4xARmdTqkWdVVZUtcjSSk5MDvV6PyMhIwzSlUomIiAhkZGQAAE6cOIGamhqjZbRaLUJCQgzLmMpcUlJi9CLx3bx5E6+99hpu3rwpdhQiIrMsLs/9+/fj2WefRZ8+fSCXy+Hq6gqVSoWIiAi89dZbNvsXn16vBwBoNBqj6RqNxjBPr9dDoVDAw8PD5DJNSUhIgFqtNrz8/f1tkpmIiOxDi3fb7t69G6+++iqKi4sxZcoULFu2DN27d4eLiwvu3LmDc+fO4eDBg/j73/+OZ599Fn//+9/h4+PT6oAymczovSAIjab9XnPLxMXFYenSpYb3JSUlLNAO7nbW7San89Z9RNQWWlye8fHxWLduHR5++OEmn3oxc+ZMAMCNGzfwzjvv4KOPPkJsbKzVwRqOeen1evj5+Rmm5+fnG0ajvr6+qK6uRlFRkdHoMz8/H6NGjTL53UqlEkolL7bvDBwc722Lqc+kmlyGt+4jIltrcXn++OOPLVque/fuWLt2rdWBGgQGBsLX1xdpaWkICwsDAFRXVyM9PR2JiYkAgPDwcMjlcqSlpRnKOy8vD+fOnbNJBmpfXbt2xeOPP27R2bZyVzm6D++O+rr6RvN46z4iaitWnW37xhtv4JVXXoGrq6vR9MrKSvzjH//A3/72txZ9T1lZGS5dumR4n5OTg1OnTsHT0xM9e/ZETEwM4uPjERQUhKCgIMTHx8PV1RWzZs0CAKjVasybNw+xsbHw8vKCp6cnXnnlFYSGhhrOvqWOo3fv3vjss88s/tzv73f7e9ylS0S2ZlV5rl69GgsWLGhUnhUVFVi9enWLy/P48eMYP3684X3Dccg5c+YgJSUFy5cvR2VlJRYuXIiioiKMGDECBw4cgEqlMnxmw4YNcHJywsyZM1FZWYkJEyYgJSUFjo6O1vxpJKLq6mrk5+ejW7duUCgUrf4+7tIlorZiVXmaOiHn9OnT8PT0bPH3jBs3DoIgmJwvk8mg0+mg0+lMLuPs7IyNGzdi48aNLf5dkqajXx3FhEcnYN+2fbhvwH2G6aZGjs3hLl0iaisWlaeHhwdkMhlkMhmCg4ONCrSurg5lZWVYsGCBzUNS51eYXYhPH/0UALDrmV34Ht83WqZhJGmJ5nbpEhFZw6LyTEpKgiAImDt3LlavXg21Wm2Yp1AoEBAQgJEjR9o8JHV+vx0B+gzwgZ+rn9F8B0cHFiERSYZF5TlnzhwA986EHTVqFORy/suMbI/P7SQiqbPqmGdERATq6+tx8eJF5Ofno77e+JjS2LFjbRKOiIhIiqwqz++//x6zZs3C1atXG53wI5PJ+EQTsoovfJEclgzXLq7NL0xEJCKrynPBggUYOnQo9u7dCz8/v2Zvl0fUEg5wgNxBDgcZn9FORNJmVXlmZ2fj888/R9++fW2dh+xYAQqw/cJ2zB04F76ufCQZEUmXVf+JP2LECKM7AxHZQjWqcbHsIu7W3RU7ChGRWVaNPBcvXozY2Fjo9XqEhoY2Ouv2vvvuM/FJIiKijs+q8nzssccAAHPnzjVMk8lkhjsP8YQhIiLqzKwqz5ycHFvnICIi6jCsKs9evXrZOgcR1FBjdq/Z8FLyRu1EJG1WledHH31kdv6f//xnq8JQx1eYXWjyZuvNPQLMDW4Y4z0GSgXvLkRE0mZVeb700ktG72tqalBRUQGFQgFXV1eWp50qzC5EcnCy2WXMPQKsHOU4WnAUw5XDoVKomlyGiEgKrCrPoqKiRtOys7Px4osvYtmyZa0ORR1Tw4jTe4B3o5u4t+QRYMUoxsdXP0Yf7z4sTyKSNKvKsylBQUFYs2YNnnnmGfz888+2+lrqgHhjdyLq7Gx6HzRHR0fcvHnTll9JREQkOVaNPPfs2WP0XhAE5OXlITk5GaNHj7ZJMCIiIqmyqjwfeeQRo/cymQw+Pj544IEH8Pbbb9siF9khBRQI7hIMZ0dnsaMQEZllVXn+/vmdRLbgDW/E9ouF0pXHS4lI2lp9zFMQhEbP9CSyRj3qUVNfg3qB/3FGRNJmdXl+9NFHCA0NhYuLC1xcXHDffffh448/tmU2sjN66BGdGY1rZdfEjkJEZJZVu23Xr1+PVatWITo6GqNHj4YgCPj222+xYMECFBQU4OWXX7Z1TiIiIsmwqjw3btyITZs2Gd1JaPr06Rg0aBB0Oh3Lk4iIOjWrdtvm5eVh1KhRjaaPGjUKeXl5rQ5FREQkZVaVZ9++ffGf//yn0fRPP/0UQUFBrQ5FREQkZVbttl29ejWefPJJHDlyBKNHj4ZMJsPRo0fx3//+t8lSJWqJbuiGNaFr4O3mLXYUIiKzrBp5PvbYY/jhhx/g7e2N3bt3Y9euXfD29saPP/6IRx991NYZyU44wQkeCg84OdjslstERG3C6n9LhYeHY9u2bbbMQnbuDu5gz+U9eKrfU+jm0k3sOEREJlk18vzqq6+wf//+RtP379+Pr7/+utWhyD7dxV2c/PUkKmorxI5CRGSWVeW5YsUK1NXVNZouCAJWrFjR6lDUed3Ouo28k3mNXrezbosdjYioxazabZudnY2BAwc2mt6/f39cunSp1aGo83FwvPffaanPpIqchIio9awqT7VajV9++QUBAQFG0y9dugQ3Nzdb5KJORu4qR/fh3VFfZ/q+tbV3a4Hz7RiKiMhKVu22jYqKQkxMDC5fvmyYdunSJcTGxiIqKspm4ahzkbvKoVQpTb683b3xeODj6KroKnZUIiKzrCrPf/zjH3Bzc0P//v0RGBiIwMBADBgwAF5eXli3bp3NwtXW1uKvf/0rAgMD4eLigt69e+ONN94weiSaIAjQ6XTQarVwcXHBuHHjcP48hy8dUVdlV0ztNRVdlV3FjkJEZJbVu20zMjKQlpaG06dPG56qMnbsWJuGS0xMxPvvv4+tW7di0KBBOH78OJ577jmo1Wq89NJLAIC1a9di/fr1SElJQXBwMN588008+OCDuHDhAlQqlU3zUNsqrynHxeKLCFYHw03O3f9EJF1WX+cpk8kQGRmJyMhIW+Yx8t1332H69Ol4+OGHAQABAQH45JNPcPz4cQD3Rp1JSUlYuXIlZsyYAQDYunUrNBoNtm/fjvnz57dZNrK923dv451z70AXrmN5EpGktXi37Y4dO1r8pbm5ufj222+tCvRbY8aMwX//+19cvHgRAHD69GkcPXoUU6ZMAQDk5ORAr9cbFbhSqURERAQyMjJMfm9VVRVKSkqMXkRERC3V4vLctGkT+vfvj8TERGRlZTWaX1xcjK+++gqzZs1CeHg47ty50+pwr776Kp5++mn0798fcrkcYWFhiImJwdNPPw0A0Ov1AACNRmP0OY1GY5jXlISEBKjVasPL39+/1VmJiMh+tLg809PTsW7dOnzzzTcICQmBu7s7goKCEBoaih49esDLywvz5s1DQEAAzp07h2nTprU63Keffopt27Zh+/btOHnyJLZu3Yp169Zh69atRsvJZDKj94IgNJr2W3FxcSguLja8cnNzW52ViIjsh0XHPKdOnYqpU6eisLAQR48exZUrV1BZWQlvb2+EhYUhLCwMDg5WncDbpGXLlmHFihV46qmnAAChoaG4evUqEhISMGfOHPj6+gK4NwL18/MzfC4/P7/RaPS3lEollEqlzXKSbcgd5NC6aiF3kIsdhYjILKtOGPLy8sL06dNtnaWRioqKRmXs6OhouFQlMDAQvr6+SEtLQ1hYGACguroa6enpSExMbPN8ZFvd3bojfni82DGIiJol6Wc/TZs2DW+99RZ69uyJQYMGITMzE+vXr8fcuXMB3NtdGxMTg/j4eAQFBSEoKAjx8fFwdXXFrFmzRE5PRESdlVXl6eHh0eQxRZlMBmdnZ/Tt2xfPPvssnnvuuVaF27hxI1atWoWFCxciPz8fWq0W8+fPx9/+9jfDMsuXL0dlZSUWLlyIoqIijBgxAgcOHOA1nh3Q1dKrSDiVgLghceil6iV2HCIik6wqz7/97W946623MHnyZAwfPhyCIODYsWPYt28fFi1ahJycHLz44ouora3FX/7yF6vDqVQqJCUlISkpyeQyMpkMOp0OOp3O6t8haRAg4G7dXQgQxI5CRGSWVeV59OhRvPnmm1iwYIHR9H/96184cOAAdu7cifvuuw///Oc/W1WeREREUmTVqbH79+/HxIkTG02fMGGC4SHZU6ZMwS+//NK6dERERBJkVXl6enriyy+/bDT9yy+/hKenJwCgvLycxx2JiKhTsmq37apVq/Diiy/i0KFDGD58OGQyGX788Ud89dVXeP/99wEAaWlpiIiIsGlY6tz8XP2gC9fBz9Wv+YWJiERkVXn+5S9/wcCBA5GcnIxdu3ZBEAT0798f6enpGDVqFAAgNjbWpkGp81M6KhGgChA7BhFRs6y+znP06NEYPXq0LbOQnSu8W4i91/bi4Z4Pw8vZS+w4REQmWV2edXV12L17N7KysiCTyTBw4EBERUXB0dHRlvnIjpTWlOKbm99grN9YlicRSZpV5Xnp0iVMmTIFN27cQL9+/SAIAi5evAh/f3/s3bsXffr0sXVOIiIiybDqbNslS5agT58+yM3NxcmTJ5GZmYlr164hMDAQS5YssXVGIiIiSbFq5Jmeno7vv//ecFkKcO9m8WvWrOFxUCIi6vSsKk+lUonS0tJG08vKyqBQKFodiuyTu9wdk3pMgrvcvd1+83bWbZPzFCoFvIJ47JWIGrOqPKdOnYoXXngBW7ZswfDhwwEAP/zwAxYsWICoqCibBiT74ensiaf7Pt0uv+XgeO+IReozqWaXi74YzQIlokasKs9//vOfmDNnDkaOHAm5/N6Di2tqajB9+nSzN3EnMudu7V1cL7+OHm494Ozk3Ka/JXeVo/vw7qivq29yfk1FDQqyClBdWt2mOYioY7KqPLt27YovvvgCly5dQlZWFgRBwMCBA9G3b19b5yM7oq/U483MN6EL17XLzRLkrvI2/w0i6pxaXJ5Lly41O//w4cOGf16/fr3VgYiIiKSuxeWZmZnZouWaekg2ERFRZ9Li8jx06FBb5iAiIuowrLpJAlFbcJQ5QiVXwVHGWzwSkbRZfW9bIlvz7+KPjaM3ih2DiKhZHHkSERFZiOVJknGj/AaWf78cN8pviB2FiMgslidJRk19DfLv5qOmvkbsKEREZrE8iYiILMTyJCIishDLk4iIyEIsT5IMjYsGsffFQuOiETsKEZFZvM6TJMPFyQWhnqFixyAiahZHniQZv1b9itScVPxa9avYUYiIzGJ5kmT8Wv0rvrj6BX6t/lXsKEREZrE8iYiILMTyJCIishDLk4iIyEI825YsVphdiOrS6kbTb2fdbtX3ujm5YWS3kXBzcmvV9xARtTXJl+eNGzfw6quv4uuvv0ZlZSWCg4OxZcsWhIeHAwAEQcDq1auxefNmFBUVYcSIEXj33XcxaNAgkZN3ToXZhUgOTja7jIOjdTs0fFx8MH/gfKs+S0TUniRdnkVFRRg9ejTGjx+Pr7/+Gt26dcPly5fRtWtXwzJr167F+vXrkZKSguDgYLz55pt48MEHceHCBahUKvHCd1INI07vAd6Qu8obzXdwdGhyeou+u64aRVVF8FB6QOGoaFVOIqK2JOnyTExMhL+/Pz788EPDtICAAMM/C4KApKQkrFy5EjNmzAAAbN26FRqNBtu3b8f8+RzFtBW5qxxKldKm33mz4iZ0J3TQhesQoAqw6XcTEdmSpE8Y2rNnD4YOHYonnngC3bp1Q1hYGP79738b5ufk5ECv1yMyMtIwTalUIiIiAhkZGWJEJiIiOyDp8vzll1+wadMmBAUFYf/+/ViwYAGWLFmCjz76CACg1+sBABqN8b1QNRqNYV5TqqqqUFJSYvQiIiJqKUnvtq2vr8fQoUMRHx8PAAgLC8P58+exadMm/PnPfzYsJ5PJjD4nCEKjab+VkJCA1atXt01oIiLq9CRdnn5+fhg4cKDRtAEDBmDnzp0AAF9fXwD3RqB+fn6GZfLz8xuNRn8rLi4OS5cuNbwvKSmBv7+/LaPblKlLQwBAoVLAK8irnRMREdk3SZfn6NGjceHCBaNpFy9eRK9evQAAgYGB8PX1RVpaGsLCwgAA1dXVSE9PR2JiosnvVSqVUCpte7JLW2nJpSHRF6M7RYEGqAKQMi5F7BhERM2SdHm+/PLLGDVqFOLj4zFz5kz8+OOP2Lx5MzZv3gzg3u7amJgYxMfHIygoCEFBQYiPj4erqytmzZolcvqmmbqRgKkRpLlLQ2oqalCQVWByVEpERG1D0uU5bNgwpKamIi4uDm+88QYCAwORlJSEP/3pT4Zlli9fjsrKSixcuNBwk4QDBw5I7hrPhhsHpD6TanIZcyPItrg0RGryKvLwwc8f4Pn+z8PP1a/5DxARiUTS5QkAU6dOxdSpU03Ol8lk0Ol00Ol07RfKCnJXOboP7476uvpG8ziCvKeqrgqXSy6jqq5K7ChERGZJvjw7E2vvvENERNIi6es8iYiIpIjlSUREZCGWJ0mGt7M3Xuj/ArydvcWOQkRkFo95kmR0kXfBKN9RYscgImoWR54kGSXVJTh44yBKqnmvYSKSNpYnScadqjvYlr0Nd6ruiB2FiMgslicREZGFWJ5EREQWYnkSERFZiOVJkuHs6IwQjxA4OzqLHYWIyCxeqkKS4evqi1cGvyJ2DCKiZnHkSZJRL9SjsrYS9ULjm+cTEUkJR54kGdfKrkF3QgdduA4BqgCx4wCw/PmrRGQfWJ5ETWjt81eJqHNjeRI1gc9fJSJzWJ5EJvD5q0RkCk8YIiIishBHniQZPdx64J+j/glXJ1exoxARmcXy7AQ6yxmhTg5OcFe4ix2DiKhZLM8OrLOdEZpfmY/tl7ZjVt9Z6ObSTew4REQmsTw7sM52RmhFbQVOFZ7CIwGPiB2FiMgslmcHxzNCiYjaH8+2JSIishDLk4iIyEIsT5IMD6UHnurzFDyUHmJHISIyi8c8JaSpS05MXYbSGakVajzk/5DYMYiImsXylICWXHLSsExnVl5TjvNF5zHIYxDc5G5ixyEiMonlKQHmLjkB7hWnPZxVe/vubbz303vQhes6dHkWZheavESoo924goiaxvKUCHsoR3tQmF2I5OBks8t0pBtXEFHTWJ5ENtQw4vQe4N3oP4g64o0riKhpLE+iNiB3lUOpUoodg4jaSOc/C4U6DIWDAr269ILCQSF2FCIiszjy7OTMXeoitZNXtG5arB66WuwYRETN6lDlmZCQgNdeew0vvfQSkpKSAACCIGD16tXYvHkzioqKMGLECLz77rsYNGiQuGFF1pLLXwCevEJEZI0Os9v22LFj2Lx5M+677z6j6WvXrsX69euRnJyMY8eOwdfXFw8++CBKS0tFSioNDZe/+IX7NfnyHuANAJI6eeVq6VU8n/48rpZeFTsKEZFZHaI8y8rK8Kc//Qn//ve/4eHx/9+6TRAEJCUlYeXKlZgxYwZCQkKwdetWVFRUYPv27SImloaGk1aaeknx0hgBAmqFWggQxI5CRGRWhyjPRYsW4eGHH8bEiRONpufk5ECv1yMyMtIwTalUIiIiAhkZGSa/r6qqCiUlJUYvIiKilpL8Mc8dO3bg5MmTOHbsWKN5er0eAKDRaIymazQaXL1qetdfQkICVq/miSlERGQdSY88c3Nz8dJLL2Hbtm1wdnY2uZxMJjN6LwhCo2m/FRcXh+LiYsMrNzfXZpmJiKjzk/TI88SJE8jPz0d4eLhhWl1dHY4cOYLk5GRcuHABwL0RqJ+fn2GZ/Pz8RqPR31IqlVAqeQG71GhdtXhr2FvwcfYRO0qL2PtTcIjsmaTLc8KECTh79qzRtOeeew79+/fHq6++it69e8PX1xdpaWkICwsDAFRXVyM9PR2JiYliRKZWUDgq0N2tu9gxmsWn4BCRpMtTpVIhJCTEaJqbmxu8vLwM02NiYhAfH4+goCAEBQUhPj4erq6umDVrlhiRqRUK7hZgz5U9iAqIgrezt9hxTOJTcIhI0uXZEsuXL0dlZSUWLlxouEnCgQMHoFKpxI5GFiqrKcMR/RE80P0BSZcnwKfgENm7Dleehw8fNnovk8mg0+mg0+lEyUNERPaHB2aIiIgs1OFGnmRbps4OldpN44mIpITlaadacsZoe980Xq1Q4+GeD0OtULfbbxIRWYPlaafMnTFaU1GDgqyCdr9pvIfSA0/0fqJdf5OIyBosTzsmtTNGK2srcaX0CgJUAXBxchE7DhGRSTxhiCTjVuUtJJ5OxK3KW2JHISIyiyNPMom3nyMiahrLkxrh7eeIiMxjeVIjvP0cEZF5LE9qkhjl6ChzhIfCA44yx3b/bSIiS7A8STL8u/hjw6gNYscgImoWD1wRERFZiOVJkpFblouXM15Gblmu2FGIiMxieZJk1Al1KKouQp1QJ3YUIiKzWJ5EREQWYnkSERFZiOVJRERkIZYnSYbGRYNXB78KjYtG7ChERGbxOk+SDBcnFwzwGCB2DCKiZnHkSZJRVFWEz375DEVVRWJHISIyi+VJklFcXYy91/aiuLpY7ChERGZxty1ROzP1WDeFSgGvIK92TkNE1mB5ErWTljzqLfpiNAuUqANgeRK1E3OPequpqEFBVgGqS6tFSEZElmJ5kmR0kXfBWN+x6CLvInaUNsPnoBJ1DixPkgxvZ2/M7T9X7BhERM3i2bYkGdV11bhRfgPVddx1SUTSxvIkybhZcRMrj63EzYqbYkchIjKLu22JJMTUZSzN4WUuRO2L5UkkAS25jKU5vMyFqP2wPIkkwNxlLM3hZS5E7Y/lSZIhgwxOMifIIBM7iih4GQtRx8HyJMnopeqFDyI+EDsGEVGzeLYtERGRhSRdngkJCRg2bBhUKhW6deuGRx55BBcuXDBaRhAE6HQ6aLVauLi4YNy4cTh//rxIiak1bpbfxOvHX8fNcl6qQkTSJunyTE9Px6JFi/D9998jLS0NtbW1iIyMRHl5uWGZtWvXYv369UhOTsaxY8fg6+uLBx98EKWlpSImJ2tU11fjatlVVNfzxBcikjZJH/Pct2+f0fsPP/wQ3bp1w4kTJzB27FgIgoCkpCSsXLkSM2bMAABs3boVGo0G27dvx/z588WITUREnZykR56/V1x87yHJnp6eAICcnBzo9XpERkYallEqlYiIiEBGRobJ76mqqkJJSYnRi4iIqKU6THkKgoClS5dizJgxCAkJAQDo9XoAgEajMVpWo9EY5jUlISEBarXa8PL392+74ERE1Ol0mPKMjo7GmTNn8MknnzSaJ5MZXxcoCEKjab8VFxeH4uJiwys3N9fmeclyPs4+WDhwIXycfcSOQkRklqSPeTZYvHgx9uzZgyNHjqBHjx6G6b6+vgDujUD9/PwM0/Pz8xuNRn9LqVRCqVS2XWCyipvcDcO7DRc7BhFRsyQ98hQEAdHR0di1axe++eYbBAYGGs0PDAyEr68v0tLSDNOqq6uRnp6OUaNGtXdcaqXi6mLsy92H4upisaMQEZkl6ZHnokWLsH37dnzxxRdQqVSG45hqtRouLi6QyWSIiYlBfHw8goKCEBQUhPj4eLi6umLWrFkipydLFVUVYcflHejftT/UCrXYcYiITJJ0eW7atAkAMG7cOKPpH374IZ599lkAwPLly1FZWYmFCxeiqKgII0aMwIEDB6BSqdo5LRER2QtJl6cgCM0uI5PJoNPpoNPp2j4QERERJH7Mk4iISIokPfIk++Lq5IohXkPg6uQqdhS7UZhdaPI5oAqVgg/XJjKB5UmS0c2lG2JCY8SOYTcKswuRHJxsdpnoi9EsUKImsDxJMmrra1FRWwFXJ1c4OXDTtBVTo8vbWbcBAN4DvBs9iLumogYFWQUmR6VE9o7/hiLJuF5+HboTOujCdQhQBYgdp1NoyehSqVI2Kk8iMo/lSdSJNYwcmxpdAoCDowOLk8gKLE8iOyB3lUOp4i0piWyFl6oQERFZiOVJRERkIe62Jcno2aUnNo3ZBKUjdy8SkbSxPEkyHGQOcHFyETsGEVGzuNuWJENfoce60+ugr9CLHYWIyCyWJ0nG3bq7OFd0Dnfr7oodhYjILJYnERGRhXjMk4hMariF3+/xpvFk71ieRNSIg+O9nVKpz6SaXIY3jSd7xvIkyfBUeuKZoGfgqfQUO4rdk7vK0X14d9TX1Teax5vGE7E8SULcFe6Y2H2i2DHo/8N73hKZxhOGSDLKasqQoc9AWU2Z2FGIiMziyJMko+BuATb/vBm6cB26yLuIHafDaerkHlMn/BBR67A8iTq4lpzc07AMEdkGy5OogzN3cg/AZ3YStQWWJ1EnwHIkal8sT5IMpaMSfdz78Kkqdqwwu9DkJTC8MQNJCcuTJMPP1Q+r/rBK7BgkksLsQiQHJ5tdhjdmIKlgeRKRJDSMOL0HeDfaDc0bM5DUsDxJMq6UXoHuhA66cB0CVAFix6FmmLsMpjW7WOWucihV3HVP0sbyJCKLtOTSGIC7WKlzY3kSkUWauzSmYRfrjR9vNLmblSf+UGfA8iQii5m7NIZPZCF7wPIkIptqyRNZmhqVtuRWgny+KEkFy5MkQ+uqReLwRHgoPcSOQq1kamRq7a0EOZolqWF5kmQoHBXQuGrEjkFtyNpbCbb2+aLmbr5gLamNdnmDifbVacrzvffewz/+8Q/k5eVh0KBBSEpKwh//+EexY5EFblfexq6cXZgROAM+Lj5ix6E2Yu2tBK39XEtuvmAtqYx2eYOJ9tcpyvPTTz9FTEwM3nvvPYwePRr/+te/MHnyZPz000/o2bOn2PGohcpry/Fd/neY5D8JPmB5km2Yu/mCtVp70wZbj4QbjgW39w0m7Hm02ynKc/369Zg3bx6ef/55AEBSUhL279+PTZs2ISEhQeR0RCQFUrn5QluOhJUqZbs9JMDeR7sdvjyrq6tx4sQJrFixwmh6ZGQkMjIyREpFRNS0thgJA+3/6Dl7v51ihy/PgoIC1NXVQaMxPtFEo9FAr9c3+ZmqqipUVVUZ3hcXFwMASkpKWpWltKwUd3EXJaUlkNfxEVGWKqsou/e/pWX4te5XccNQh1FdWo27uIvv/p/v4P2td6P5BRcKcBd3UfxrMRR1inb5TXMa8lRUV0ChtE0eAEAdgCa6qjVZzTH3d1RXt81vNkfdS43AcYFWf76hAwRBaHbZDl+eDWQymdF7QRAaTWuQkJCA1atXN5ru7+9vmzAXbfM19mrdxXViR6CO6N1m5l8W4TfNaYs85rQmqznm/o62+s02VlpaCrVabXaZDl+e3t7ecHR0bDTKzM/PbzQabRAXF4elS5ca3tfX1+POnTvw8vIyWbgtUVJSAn9/f+Tm5sLd3d3q7+lsuF6axvViGtdN07heTLPFuhEEAaWlpdBqtc0u2+HLU6FQIDw8HGlpaXj00UcN09PS0jB9+vQmP6NUKqFUGp840LVrV5tlcnd354bdBK6XpnG9mMZ10zSuF9Nau26aG3E26PDlCQBLly7F7NmzMXToUIwcORKbN2/GtWvXsGDBArGjERFRJ9QpyvPJJ59EYWEh3njjDeTl5SEkJARfffUVevXqJXY0IiLqhDpFeQLAwoULsXDhQlEzKJVKvP766412Cds7rpemcb2YxnXTNK4X09p73ciElpyTS0RERAaNH19AREREZrE8iYiILMTyJCIishDLk4iIyEIszxY6cuQIpk2bBq1WC5lMht27dzf7mfT0dISHh8PZ2Rm9e/fG+++/3/ZB25ml6+Xw4cOQyWSNXj///HP7BG4nCQkJGDZsGFQqFbp164ZHHnkEFy5caPZz9rDNWLNu7GG72bRpE+677z7DRf4jR47E119/bfYz9rC9AJavm/bYXlieLVReXo7BgwcjOblljxLKycnBlClT8Mc//hGZmZl47bXXsGTJEuzcubONk7YvS9dLgwsXLiAvL8/wCgoKaqOE4khPT8eiRYvw/fffIy0tDbW1tYiMjER5ebnJz9jLNmPNumnQmbebHj16YM2aNTh+/DiOHz+OBx54ANOnT8f58+ebXN5ethfA8nXToE23F4EsBkBITU01u8zy5cuF/v37G02bP3++cP/997dhMnG1ZL0cOnRIACAUFRW1SyapyM/PFwAI6enpJpexx21GEFq2bux1u/Hw8BA++OCDJufZ6/bSwNy6aY/thSPPNvLdd98hMjLSaNqkSZNw/Phx1NTUiJRKOsLCwuDn54cJEybg0KFDYsdpcw2PvfP09DS5jL1uMy1ZNw3sZbupq6vDjh07UF5ejpEjRza5jL1uLy1ZNw3acnthebYRvV7f5DNGa2trUVBQIFIq8fn5+WHz5s3YuXMndu3ahX79+mHChAk4cuSI2NHajCAIWLp0KcaMGYOQkBCTy9njNtPSdWMv283Zs2fRpUsXKJVKLFiwAKmpqRg4cGCTy9rb9mLJummP7aXT3J5Pipp6xmhT0+1Jv3790K9fP8P7kSNHIjc3F+vWrcPYsWNFTNZ2oqOjcebMGRw9erTZZe1tm2npurGX7aZfv344deoUfv31V+zcuRNz5sxBenq6yZKwp+3FknXTHtsLR55txNfXt8lnjDo5OcHLy0ukVNJ0//33Izs7W+wYbWLx4sXYs2cPDh06hB49ephd1t62GUvWTVM643ajUCjQt29fDB06FAkJCRg8eDDeeeedJpe1t+3FknXTFFtvLyzPNjJy5EikpaUZTTtw4ACGDh0KuVwuUippyszMhJ+fn9gxbEoQBERHR2PXrl345ptvEBgY2Oxn7GWbsWbdNKUzbje/JwgCqqqqmpxnL9uLKebWTVNsvr202alInUxpaamQmZkpZGZmCgCE9evXC5mZmcLVq1cFQRCEFStWCLNnzzYs/8svvwiurq7Cyy+/LPz000/Cli1bBLlcLnz++edi/QltwtL1smHDBiE1NVW4ePGicO7cOWHFihUCAGHnzp1i/Qlt4sUXXxTUarVw+PBhIS8vz/CqqKgwLGOv24w168Yetpu4uDjhyJEjQk5OjnDmzBnhtddeExwcHIQDBw4IgmC/24sgWL5u2mN7YXm2UMOpz79/zZkzRxAEQZgzZ44QERFh9JnDhw8LYWFhgkKhEAICAoRNmza1f/A2Zul6SUxMFPr06SM4OzsLHh4ewpgxY4S9e/eKE74NNbVOAAgffvihYRl73WasWTf2sN3MnTtX6NWrl6BQKAQfHx9hwoQJhnIQBPvdXgTB8nXTHtsLH0lGRERkIR7zJCIishDLk4iIyEIsTyIiIguxPImIiCzE8iQiIrIQy5OIiMhCLE8iIiILsTyJ7NCVK1cgk8lw6tSpVn+XTCbD7t27W/09RB0Jy5OIWkSn02HIkCGNpufl5WHy5MntH4hIRHwkGZEEVVdXQ6FQiB2jRXx9fcWOQNTuOPIkkoBx48YhOjoaS5cuhbe3Nx588EH89NNPmDJlCrp06QKNRoPZs2cbPeT4888/R2hoKFxcXODl5YWJEyeivLwcAFBfX4833ngDPXr0gFKpxJAhQ7Bv3z6Tv5+SkoKuXbsaTdu9e7fhuZApKSlYvXo1Tp8+DZlMBplMhpSUFACNd9uePXsWDzzwgCHXCy+8gLKyMsP8Z599Fo888gjWrVsHPz8/eHl5YdGiRaipqWnlWiRqPyxPIonYunUrnJyc8O2332LNmjWIiIjAkCFDcPz4cezbtw+3bt3CzJkzAdzbVfr0009j7ty5yMrKwuHDhzFjxgzDw5DfeecdvP3221i3bh3OnDmDSZMmISoqyurnGT755JOIjY3FoEGDkJeXh7y8PDz55JONlquoqMBDDz0EDw8PHDt2DJ999hkOHjyI6Ohoo+UOHTqEy5cv49ChQ9i6dStSUlIMZUzUIdj0NvNEZJWIiAhhyJAhhverVq0SIiMjjZbJzc0VAAgXLlwQTpw4IQAQrly50uT3abVa4a233jKaNmzYMGHhwoWCIAhCTk6OAEDIzMwUBEEQPvzwQ0GtVhstn5qaKvz2XxGvv/66MHjw4Ea/BUBITU0VBEEQNm/eLHh4eAhlZWWG+Xv37hUcHBwEvV4vCMK9J2D06tVLqK2tNSzzxBNPCE8++WSTfwuRFHHkSSQRQ4cONfzziRMncOjQIXTp0sXw6t+/PwDg8uXLGDx4MCZMmIDQ0FA88cQT+Pe//42ioiIAQElJCW7evInRo0cbff/o0aORlZXVpn9DVlYWBg8eDDc3N6Pfra+vx4ULFwzTBg0aBEdHR8N7Pz8/5Ofnt2k2IlviCUNEEvHbwqmvr8e0adOQmJjYaDk/Pz84OjoiLS0NGRkZOHDgADZu3IiVK1fihx9+gJeXFwAYjlc2EASh0bQGDg4Ohl2+Daw5BmnuN347XS6XN5pXX19v8e8RiYUjTyIJ+sMf/oDz588jICAAffv2NXo1lKxMJsPo0aOxevVqZGZmQqFQIDU1Fe7u7tBqtTh69KjRd2ZkZGDAgAFN/p6Pjw9KS0sNJxwBaHQNqEKhQF1dndncAwcOxKlTp4y+59tvv4WDgwOCg4MtWQVEksbyJJKgRYsW4c6dO3j66afx448/4pdffsGBAwcwd+5c1NXV4YcffkB8fDyOHz+Oa9euYdeuXbh9+7ahHJctW4bExER8+umnuHDhAlasWIFTp07hpZdeavL3RowYAVdXV7z22mu4dOkStm/f3ugEnoCAAOTk5ODUqVMoKChAVVVVo+/505/+BGdnZ8yZMwfnzp3DoUOHsHjxYsyePRsajcbm64lILCxPIgnSarX49ttvUVdXh0mTJiEkJAQvvfQS1Go1HBwc4O7ujiNHjmDKlCkIDg7GX//6V7z99tuGmxUsWbIEsbGxiI2NRWhoKPbt24c9e/YgKCioyd/z9PTEtm3b8NVXXyE0NBSffPIJdDqd0TKPPfYYHnroIYwfPx4+Pj745JNPGn2Pq6sr9u/fjzt37mDYsGF4/PHHMWHCBCQnJ9t8HRGJSSb8/kAHERERmcWRJxERkYVYnkRERBZieRIREVmI5UlERGQhlicREZGFWJ5EREQWYnkSERFZiOVJRERkIZYnERGRhVieREREFmJ5EhERWYjlSUREZKH/F2bUe0MGLK73AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_len = len(master_exp_data_df)\n",
    "d_t_len = len(master_exp_data_df.query('resolution > 2'))\n",
    "print(\"{}\\t{}\\t{}%\".format(d_len, d_t_len, round(100*(d_t_len/d_len),2)))\n",
    "bns_d = np.linspace(1, 3.5, 50)\n",
    "plot_hist(master_exp_data_df.resolution, \"resolution\", bns_d, \"purple\", logy = False)#, et = 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G87WTyK/fmyVL1mTJKdmfNZx1hV1QLly4oKVLl+r3v/+9Pvaxj+nIkSO666671NLSIknKy8sLmZ+Xl6crV65Ikjo6OpSenq7Zs2ePmNPR0THmPvfs2aPdu3ePGG9qalJmZma4EeKqubk53kuImUTMWrN49PEnFw3FdiFxEqucR48ejcl+xpOIr9+xJEvWZMkp2Zu1r69v0nPDLih33HGHzp8/r9/97nd65ZVXtH79ep08eTJ4v8PhCJlvjBkxdrOJ5uzYsUNVVVXB7e7ubuXn58vr9SonJyfcCHERCATU3Nys8vJyOZ3OeC8nqhI5a5HveMi2K8XoyUVD+sbZFPmHxn+dz2SxztnmG/1qaiwk8uv3ZsmSNVlySvZnHX4HZDLCLijp6en61Kc+JUlatGiRzpw5o3/8x3/U3/3d30n66CrJ/Pnzg/M7OzuDV1XcbrcGBgbU1dUVchWls7NTy5YtG3OfLpdLLpdrxLjT6bTyAIxnJq55qhIxq39w9H+c/UOOMe9LJLHKacPrJhFfv2NJlqzJklOyN2s4a5r270Exxsjv96ugoEButzvkstLAwIBOnjwZLB8lJSVyOp0hc9rb29XW1jZuQQEAAMklrCsof//3f69Vq1YpPz9fPT09amxs1Ouvv65jx47J4XCosrJS1dXVKiwsVGFhoaqrq5WZmamKigpJUm5urjZs2KAtW7Zo7ty5mjNnjrZu3ari4mKVlZVFJSAAAJh5wiooH3zwgR599FG1t7crNzdX99xzj44dO6by8nJJ0rZt29Tf369Nmzapq6tLS5YsUVNTk7Kzs4PPsW/fPqWlpWndunXq7+/XihUrdOjQIaWmpkY2GQAAmLHCKih1dXXj3u9wOOTz+eTz+cack5GRodraWtXW1oazawAAkET4Lh4AAGAdCgoAALDOlH+TLDBdH9/+o3gvAQBgKa6gAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdcIqKHv27NHnPvc5ZWdn69Zbb9UXvvAFvf322yFzjDHy+XzyeDyaNWuWli9frosXL4bM8fv92rx5s+bNm6esrCytWbNGV69enX4aAACQEMIqKCdPntTjjz+u06dPq7m5WR9++KG8Xq9u3LgRnFNTU6O9e/fqwIEDOnPmjNxut8rLy9XT0xOcU1lZqSNHjqixsVGnTp1Sb2+vVq9ercHBwcglAwAAM1ZaOJOPHTsWsn3w4EHdeuutam1t1Z/+6Z/KGKP9+/dr586dWrt2rSSpvr5eeXl5amho0MaNG3X9+nXV1dXpxRdfVFlZmSTp8OHDys/P14kTJ7Ry5coIRQMAADNVWAXlZtevX5ckzZkzR5J0+fJldXR0yOv1Bue4XC6VlpaqpaVFGzduVGtrqwKBQMgcj8ejoqIitbS0jFpQ/H6//H5/cLu7u1uSFAgEFAgEphMhZobXOVPWOx2TzepKNbFYTlS5UkzIn4kq1jnjeZ5wriaeZMkp2Z81nHVNuaAYY1RVVaXPf/7zKioqkiR1dHRIkvLy8kLm5uXl6cqVK8E56enpmj179og5w4+/2Z49e7R79+4R401NTcrMzJxqhLhobm6O9xJiZqKsNYtjtJAYeHLRULyXEBOxynn06NGY7Gc8nKuJJ1lySvZm7evrm/TcKReUr3/96/qv//ovnTp1asR9DocjZNsYM2LsZuPN2bFjh6qqqoLb3d3dys/Pl9frVU5OzhRWH3uBQEDNzc0qLy+X0+mM93KiarJZi3zHY7iq6HClGD25aEjfOJsi/9D4r/GZLNY523zxe6uXczXxJEtOyf6sw++ATMaUCsrmzZv12muv6Y033tCCBQuC4263W9JHV0nmz58fHO/s7AxeVXG73RoYGFBXV1fIVZTOzk4tW7Zs1P25XC65XK4R406n08oDMJ6ZuOapmiirfzBx/kH3DzkSKs9YYpXThnOEczXxJEtOyd6s4awprE/xGGP09a9/Xa+++qr+4z/+QwUFBSH3FxQUyO12h1xaGhgY0MmTJ4Plo6SkRE6nM2ROe3u72traxiwoAAAguYR1BeXxxx9XQ0ODfvjDHyo7Ozv4MyO5ubmaNWuWHA6HKisrVV1drcLCQhUWFqq6ulqZmZmqqKgIzt2wYYO2bNmiuXPnas6cOdq6dauKi4uDn+oBAADJLayC8vzzz0uSli9fHjJ+8OBBfeUrX5Ekbdu2Tf39/dq0aZO6urq0ZMkSNTU1KTs7Ozh/3759SktL07p169Tf368VK1bo0KFDSk1NnV4aAACQEMIqKMZM/BFDh8Mhn88nn8835pyMjAzV1taqtrY2nN0DAIAkwXfxAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGCdsL4sEABi4ePbfxS3fbtSjWoWS0W+4/IPOib9uPeefjCKqwKSD1dQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOuEXVDeeOMNPfTQQ/J4PHI4HPq3f/u3kPuNMfL5fPJ4PJo1a5aWL1+uixcvhszx+/3avHmz5s2bp6ysLK1Zs0ZXr16dVhAAAJA4wi4oN27c0Kc//WkdOHBg1Ptramq0d+9eHThwQGfOnJHb7VZ5ebl6enqCcyorK3XkyBE1Njbq1KlT6u3t1erVqzU4ODj1JAAAIGGkhfuAVatWadWqVaPeZ4zR/v37tXPnTq1du1aSVF9fr7y8PDU0NGjjxo26fv266urq9OKLL6qsrEySdPjwYeXn5+vEiRNauXLlNOIAAIBEEHZBGc/ly5fV0dEhr9cbHHO5XCotLVVLS4s2btyo1tZWBQKBkDkej0dFRUVqaWkZtaD4/X75/f7gdnd3tyQpEAgoEAhEMkLUDK9zpqx3Oiab1ZVqYrGcqHKlmJA/E1Wy5JSmnnUmntvJ8vdSsuSU7M8azroiWlA6OjokSXl5eSHjeXl5unLlSnBOenq6Zs+ePWLO8ONvtmfPHu3evXvEeFNTkzIzMyOx9Jhpbm6O9xJiZqKsNYtjtJAYeHLRULyXEBPJklMKP+vRo0ejtJLoS5a/l5Ilp2Rv1r6+vknPjWhBGeZwOEK2jTEjxm423pwdO3aoqqoquN3d3a38/Hx5vV7l5ORMf8ExEAgE1NzcrPLycjmdzngvJ6omm7XIdzyGq4oOV4rRk4uG9I2zKfIPjf8an8mSJac09axtvpn39nSy/L2ULDkl+7MOvwMyGREtKG63W9JHV0nmz58fHO/s7AxeVXG73RoYGFBXV1fIVZTOzk4tW7Zs1Od1uVxyuVwjxp1Op5UHYDwzcc1TNVFW/2Di/EPnH3IkVJ6xJEtOKfysM/m8Tpa/l5Ilp2Rv1nDWFNHfg1JQUCC32x1yaWlgYEAnT54Mlo+SkhI5nc6QOe3t7WpraxuzoAAAgOQS9hWU3t5e/c///E9w+/Llyzp//rzmzJmj2267TZWVlaqurlZhYaEKCwtVXV2tzMxMVVRUSJJyc3O1YcMGbdmyRXPnztWcOXO0detWFRcXBz/VAwAAklvYBeXs2bP6sz/7s+D28M+GrF+/XocOHdK2bdvU39+vTZs2qaurS0uWLFFTU5Oys7ODj9m3b5/S0tK0bt069ff3a8WKFTp06JBSU1MjEAkAAMx0YReU5cuXy5ixP37ncDjk8/nk8/nGnJORkaHa2lrV1taGu3sAAJAE+C4eAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWicqvukfsfXz7j+K9hCBXqlHN4o9+lX2y/NZRAEBkcQUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALBOWrwXYKOPb/9RxJ/TlWpUs1gq8h2Xf9AR8ecHACCRcAUFAABYh4ICAACsw1s8ABAB0XhrONqG33oGbMQVFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYJ67fxfPcc8/pO9/5jtrb23X33Xdr//79euCBB+K5JABIOkW+4/IPOuK9jEl77+kH470ExEDcrqD84Ac/UGVlpXbu3Klz587pgQce0KpVq/TLX/4yXksCAACWiFtB2bt3rzZs2KCvfvWr+pM/+RPt379f+fn5ev755+O1JAAAYIm4vMUzMDCg1tZWbd++PWTc6/WqpaVlxHy/3y+/3x/cvn79uiTpt7/9rQKBQMTXl/bhjcg/55BRX9+Q0gIpGhyaOZdSp4KsiSdZckpknQmuXbsW1vxAIKC+vj5du3ZNTqczSqsa35I9/x6T/bhSjP7fvUP6zM5X5Z/mMf3ZjhURWtX/6enpkSQZYyaebOLgV7/6lZFkfvrTn4aMf+tb3zK33377iPm7du0ykrhx48aNGzduCXB7//33J+wKcf0hWYcjtN0ZY0aMSdKOHTtUVVUV3B4aGtJvf/tbzZ07d9T5Nuru7lZ+fr7ef/995eTkxHs5UUXWxJMsOSWyJqJkySnZn9UYo56eHnk8ngnnxqWgzJs3T6mpqero6AgZ7+zsVF5e3oj5LpdLLpcrZOyP/uiPornEqMnJybHyRRMNZE08yZJTImsiSpackt1Zc3NzJzUvLj8km56erpKSEjU3N4eMNzc3a9myZfFYEgAAsEjc3uKpqqrSo48+qkWLFmnp0qX6/ve/r1/+8pf62te+Fq8lAQAAS8StoDz88MO6du2avvnNb6q9vV1FRUU6evSoFi5cGK8lRZXL5dKuXbtGvFWViMiaeJIlp0TWRJQsOaXEyuowZjKf9QEAAIgdvosHAABYh4ICAACsQ0EBAADWoaAAAADrUFAm6bnnnlNBQYEyMjJUUlKin/zkJ2PObW9vV0VFhe644w6lpKSosrJyxJwXXnhBDzzwgGbPnq3Zs2errKxMb775Zsgcn88nh8MRcnO73ZGONkKksx46dGhEDofDod///vdT3m+kRDrr8uXLR8364IP/9/Xw8Tiu4eR89dVXVV5erltuuUU5OTlaunSpjh8/PmLeK6+8orvuuksul0t33XWXjhw5Mq39Rkqks9p6rkY6Z6Kcp5PJaut5KoWX9dSpU7r//vs1d+5czZo1S3feeaf27ds3Yp6t5+qEIvLlOgmusbHROJ1O88ILL5if//zn5oknnjBZWVnmypUro86/fPmy+du//VtTX19vPvOZz5gnnnhixJyKigrz7LPPmnPnzplLly6Zxx57zOTm5pqrV68G5+zatcvcfffdpr29PXjr7OyMVkxjTHSyHjx40OTk5ITkaG9vn9Z+IyEaWa9duxaSsa2tzaSmppqDBw8G58T6uIab84knnjDf/va3zZtvvmneeecds2PHDuN0Os1bb70VnNPS0mJSU1NNdXW1uXTpkqmurjZpaWnm9OnTU96vrVltPFejkTNRztPJZLXxPJ1K1rfeess0NDSYtrY2c/nyZfPiiy+azMxM873vfS84x9ZzdTIoKJOwePFi87WvfS1k7M477zTbt2+f8LGlpaWj/kN2sw8//NBkZ2eb+vr64NiuXbvMpz/96XCXOy3RyHrw4EGTm5sbtf1OVSyO6759+0x2drbp7e0NjsX6uEbif9u77rrL7N69O7i9bt068xd/8Rchc1auXGkeeeSRiO43XNHIejMbztVo5EzE83TYRMfUhvPUmMhk/eu//mvz5S9/Obht67k6GbzFM4GBgQG1trbK6/WGjHu9XrW0tERsP319fQoEApozZ07I+LvvviuPx6OCggI98sgj+sUvfhGxfd4smll7e3u1cOFCLViwQKtXr9a5c+dist+xxGqfdXV1euSRR5SVlRUyHqvjGomcQ0ND6unpCXlt/ud//ueI51y5cmXwOWfqMR0t683ifa5GM2cinqeTOabxPk+lyGQ9d+6cWlpaVFpaGhyz8VydLArKBH7zm99ocHBwxJcY5uXljfiyw+nYvn27/viP/1hlZWXBsSVLluif//mfdfz4cb3wwgvq6OjQsmXLdO3atYjt9w9FK+udd96pQ4cO6bXXXtNLL72kjIwM3X///Xr33Xejut/xxGKfb775ptra2vTVr341ZDyWxzUSOZ955hnduHFD69atC451dHSM+5wz9ZiOlvVm8T5Xo5UzUc/TiY6pDeepNL2sCxYskMvl0qJFi/T444+HZLHxXJ2suP2q+5nG4XCEbBtjRoxNVU1NjV566SW9/vrrysjICI6vWrUq+N/FxcVaunSpPvnJT6q+vl5VVVUR2fdoIp31vvvu03333Rfcvv/++/XZz35WtbW1+u53vxu1/U5GNPdZV1enoqIiLV68OGQ8Hsd1qjlfeukl+Xw+/fCHP9Stt94a9nPOpGM6XtZhNp2rkc6ZiOfpZI6pTeepNLWsP/nJT9Tb26vTp09r+/bt+tSnPqUvfvGLYT1nPI7rRCgoE5g3b55SU1NHNMnOzs4RjXMq/uEf/kHV1dU6ceKE7rnnnnHnZmVlqbi4OPj/aCIt2lmHpaSk6HOf+1wwR6z2+4eivc++vj41Njbqm9/85oRzo3lcp5PzBz/4gTZs2KCXX3455GqBJLnd7nGfc6Yd0/GyDrPlXI12zmEz/TydTFZbzlNpelkLCgokfVSkPvjgA/l8vmBBsfFcnSze4plAenq6SkpK1NzcHDLe3NysZcuWTeu5v/Od7+jJJ5/UsWPHtGjRognn+/1+Xbp0SfPnz5/WfscSzax/yBij8+fPB3PEar9/KNr7/Jd/+Rf5/X59+ctfnnBuNI/rVHO+9NJL+spXvqKGhoaQj14OW7p06YjnbGpqCj7nTDqmE2WV7DpXo5nzD83k83SyWW05T6XI/e9rjJHf7w9u23iuTlrMfyx3Bhr+CFZdXZ35+c9/biorK01WVpZ57733jDHGbN++3Tz66KMhjzl37pw5d+6cKSkpMRUVFebcuXPm4sWLwfu//e1vm/T0dPOv//qvIR9j6+npCc7ZsmWLef31180vfvELc/r0abN69WqTnZ0d3O9Myerz+cyxY8fM//7v/5pz586Zxx57zKSlpZmf/exnk97vTMk67POf/7x5+OGHR91vrI9ruDkbGhpMWlqaefbZZ0Nem7/73e+Cc37605+a1NRU8/TTT5tLly6Zp59+esyPLtp8TCeT1cZzNRo5E+U8nUzWYTadp8aEn/XAgQPmtddeM++884555513zD/90z+ZnJwcs3PnzuAcW8/VyaCgTNKzzz5rFi5caNLT081nP/tZc/LkyeB969evN6WlpSHzJY24LVy4MHj/woULR52za9eu4JyHH37YzJ8/3zidTuPxeMzatWtH/ccw0iKdtbKy0tx2220mPT3d3HLLLcbr9ZqWlpaw9hstkc5qjDFvv/22kWSamppG3Wc8jms4OUtLS0fNuX79+pDnfPnll80dd9xhnE6nufPOO80rr7wS1n6jJdJZbT1XI50zUc7Tyb5+bTxPjQkv63e/+11z9913m8zMTJOTk2Puvfde89xzz5nBwcGQ57T1XJ2IwxhjonBhBgAAYMr4GRQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArPP/ASO+/lW5U9iAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "master_exp_data_df.resolution_low.hist()\n",
    "plt.show()\n",
    "master_exp_data_df.resolution_high.hist()\n",
    "plt.show()\n",
    "master_exp_data_df.r_factor.hist()\n",
    "plt.show()\n",
    "master_exp_data_df.r_free.hist()\n",
    "plt.show()\n",
    "master_exp_data_df.r_work.hist()\n",
    "plt.show()\n",
    "master_exp_data_df.resolution.hist()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:myenv2]",
   "language": "python",
   "name": "conda-env-myenv2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}