example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of finding protein interface\n",
    "\n",
    "Goal: Find insights regarding the interface between proteins complexes:\n",
    "\n",
    "- SARS-CoV-2 spike protein (prot_A, monomer)\n",
    "- Angiotensin-converting enzyme 2 - ACE2 (prot_B, monomer)\n",
    "- Antibody P5A3C8 (prot_H and prot_L, dimer)\n",
    "\n",
    "\n",
    "Structural information available in Protein Data Bank:\n",
    "\n",
    "- [6M0J: Crystal structure of SARS-CoV-2 spike receptor-binding domain bound with ACE2](https://www.rcsb.org/structure/6M0J)\n",
    "\n",
    "- [7Z0X: THSC20.HVTR26 Fab bound to SARS-CoV-2 Receptor Binding Domain](https://www.rcsb.org/structure/7Z0X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mon/opt/anaconda3/envs/instadeep_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "#Customized Classes and methods by M. Penaloza-Amion\n",
    "from my_tools import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Interface Analysis of Monomer-Monomer system (PDB:6M0J)\n",
    "\n",
    "<img src=\"./images/prot_AB_chain1.png\" width= \"80%\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating working directory and changing directory\n",
    "\n",
    "os.system('mkdir 6M0J_Analysis')\n",
    "os.chdir('6M0J_Analysis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#First a instance of MyPDBTool is created adding the name of PDB file that is going to be analysed\n",
    "PDB_6M0J = MyPDBTool('../6m0j')\n",
    "#then we load the file.\n",
    "PDB_6M0J.load_pdb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** PDB file ../6m0j.pdb ***\n",
      "Total of chains: 6\n",
      "Chains available: [<Segment A>, <Segment E>, <Segment A>, <Segment E>, <Segment A>, <Segment E>]\n",
      "Total atoms : 6571\n",
      "total residues : 878\n",
      "*******************************\n",
      "<Segment A>\n",
      "Chain id : 0\n",
      "Total atoms : 4877\n",
      "Total residues : 597\n",
      "*******************************\n",
      "<Segment E>\n",
      "Chain id : 1\n",
      "Total atoms : 1542\n",
      "Total residues : 194\n",
      "*******************************\n",
      "<Segment A>\n",
      "Chain id : 2\n",
      "Total atoms : 58\n",
      "Total residues : 6\n",
      "*******************************\n",
      "<Segment E>\n",
      "Chain id : 3\n",
      "Total atoms : 14\n",
      "Total residues : 1\n",
      "*******************************\n",
      "<Segment A>\n",
      "Chain id : 4\n",
      "Total atoms : 71\n",
      "Total residues : 71\n",
      "*******************************\n",
      "<Segment E>\n",
      "Chain id : 5\n",
      "Total atoms : 9\n",
      "Total residues : 9\n",
      "*******************************\n"
     ]
    }
   ],
   "source": [
    "#Using method describe to now basic information of the PDB file structure\n",
    "PDB_6M0J.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# According to number of residues and the chain id information from describe method, SARS-CoV-2 spike protein is chain_id : 1 and ACE2 is chain_id: 0.\n",
    "prot_A = PDB_6M0J.select_chain(1)\n",
    "prot_B = PDB_6M0J.select_chain(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A new object is created to analyse the interface between both proteins\n",
    "\n",
    "contacts_6M0J = ProteinAProteinB(prot_A, prot_B, PDB_6M0J.dimensions())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** PDB files with modified occupancies were generated ***\n",
      "*** Contact matrix csv file was generated ***\n"
     ]
    }
   ],
   "source": [
    "# with method search_protein_A_protein_B_interface residues in protein A and B are scanned with a cutoff.\n",
    "\n",
    "cutoff = 8\n",
    "second_prot_name = 'B'\n",
    "contacts_6M0J_df = contacts_6M0J.search_protein_A_protein_B_interface(cutoff, second_prot_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Prot_A res_id</th>\n",
       "      <th>Prot_B res_id</th>\n",
       "      <th>Distance (Angstrom)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TYR-505</td>\n",
       "      <td>LYS-353</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GLY-502</td>\n",
       "      <td>GLY-354</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASN-501</td>\n",
       "      <td>LYS-353</td>\n",
       "      <td>5.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASN-487</td>\n",
       "      <td>GLN-24</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>THR-500</td>\n",
       "      <td>ASP-355</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Prot_A res_id Prot_B res_id Distance (Angstrom)\n",
       "0       TYR-505       LYS-353                4.24\n",
       "1       GLY-502       GLY-354                4.48\n",
       "2       ASN-501       LYS-353                5.04\n",
       "3       ASN-487        GLN-24                5.74\n",
       "4       THR-500       ASP-355                5.89"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display in pandas DF the closest 5 residue contacts\n",
    "contacts_6M0J_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we can plot the result of the residue interface search\n",
    "\n",
    "contacts_6M0J.interface_contacts_plot(second_prot_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Aminoacid restype % prot_A ****\n",
      "non-polar count : 70.6 %\n",
      "polar charged count : 29.5 %\n",
      "positive count : 0 %\n",
      "negative count : 0 %\n",
      "********* \n",
      "\n",
      "***** Aminoacid restype % prot_B ****\n",
      "non-polar count : 40.0 %\n",
      "polar charged count : 20.0 %\n",
      "positive count : 15.0 %\n",
      "negative count : 25.0 %\n",
      "********* \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666x333 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The frequency of aminoacids involve in the interface interaction is visualized as bar plot\n",
    "contacts_6M0J.aminoacid_content(second_prot_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image generated with VMD and PDB with modified occupancy values\n",
    "\n",
    "<img src=\"./images/prot_AB_chain3.png\" width= \"80%\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mon/Desktop/Desktop/InstaDeep_technical_assignment_M_Penaloza-Amion\n"
     ]
    }
   ],
   "source": [
    "# going back to main directory\n",
    "os.chdir('../')\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Interface Analysis of Monomer-Dimer system (PDB:7Z0X)\n",
    "\n",
    "<img src=\"./images/prot_AHL_chain1.png\" width= \"80%\">\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mon/Desktop/Desktop/InstaDeep_technical_assignment_M_Penaloza-Amion/7Z0X_Analysis\n"
     ]
    }
   ],
   "source": [
    "#creating working directory and changing directory\n",
    "os.system('mkdir 7Z0X_Analysis')\n",
    "os.chdir('7Z0X_Analysis')\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** PDB file ../7z0x.pdb ***\n",
      "Total of chains: 7\n",
      "Chains available: [<Segment H>, <Segment L>, <Segment R>, <Segment A>, <Segment H>, <Segment L>, <Segment R>]\n",
      "Total atoms : 10126\n",
      "total residues : 1245\n",
      "*******************************\n",
      "<Segment H>\n",
      "Chain id : 0\n",
      "Total atoms : 3278\n",
      "Total residues : 224\n",
      "*******************************\n",
      "<Segment L>\n",
      "Chain id : 1\n",
      "Total atoms : 3140\n",
      "Total residues : 215\n",
      "*******************************\n",
      "<Segment R>\n",
      "Chain id : 2\n",
      "Total atoms : 3008\n",
      "Total residues : 195\n",
      "*******************************\n",
      "<Segment A>\n",
      "Chain id : 3\n",
      "Total atoms : 61\n",
      "Total residues : 3\n",
      "*******************************\n",
      "<Segment H>\n",
      "Chain id : 4\n",
      "Total atoms : 200\n",
      "Total residues : 169\n",
      "*******************************\n",
      "<Segment L>\n",
      "Chain id : 5\n",
      "Total atoms : 155\n",
      "Total residues : 155\n",
      "*******************************\n",
      "<Segment R>\n",
      "Chain id : 6\n",
      "Total atoms : 284\n",
      "Total residues : 284\n",
      "*******************************\n"
     ]
    }
   ],
   "source": [
    "#First a instance of MyPDBTool is created adding the name of PDB file that is going to be analysed\n",
    "PDB_7Z0X = MyPDBTool('../7z0x')\n",
    "#then we load the file.\n",
    "PDB_7Z0X.load_pdb()\n",
    "#and display information\n",
    "PDB_7Z0X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# According to number of residues and the chain id information from describe method, \n",
    "#SARS-CoV-2 spike protein is chain_id : 2 and \n",
    "#Antibody is split in two \n",
    "#chain Light (L) is chain :1 and \n",
    "#Heavy(H) is chain : 0.\n",
    "#To avoid confusions original segment naming for antibody will remain.\n",
    "\n",
    "prot_A2 = PDB_7Z0X.select_chain(2)\n",
    "prot_L = PDB_7Z0X.select_chain(1)\n",
    "prot_H = PDB_7Z0X.select_chain(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a individual instance for each protein segment will be calculated\n",
    "\n",
    "#for prot_A2 // prot_L\n",
    "\n",
    "contacts_prot_A_prot_L = ProteinAProteinB(prot_A2, prot_L, PDB_7Z0X.dimensions())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** PDB files with modified occupancies were generated ***\n",
      "*** Contact matrix csv file was generated ***\n"
     ]
    }
   ],
   "source": [
    "#for prot_A2 // prot_L interface search algorithm\n",
    "cutoff = 8\n",
    "contacts_prot_A_prot_L_df = contacts_prot_A_prot_L.search_protein_A_protein_B_interface(cutoff, 'L')"
   ]
  },
  {
   "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>Prot_A res_id</th>\n",
       "      <th>Prot_L res_id</th>\n",
       "      <th>Distance (Angstrom)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PHE-486</td>\n",
       "      <td>SER-95</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GLY-485</td>\n",
       "      <td>SER-95</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PHE-486</td>\n",
       "      <td>TYR-91</td>\n",
       "      <td>6.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASN-481</td>\n",
       "      <td>SER-94</td>\n",
       "      <td>7.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>THR-478</td>\n",
       "      <td>TYR-91</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Prot_A res_id Prot_L res_id Distance (Angstrom)\n",
       "0       PHE-486        SER-95                 5.7\n",
       "1       GLY-485        SER-95                5.98\n",
       "2       PHE-486        TYR-91                6.66\n",
       "3       ASN-481        SER-94                7.21\n",
       "4       THR-478        TYR-91                7.22"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display in pandas DF the closest 5 residue contacts\n",
    "contacts_prot_A_prot_L_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for prot_A2 // prot_H\n",
    "contacts_prot_A_prot_H = ProteinAProteinB(prot_A2, prot_H, PDB_7Z0X.dimensions())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** PDB files with modified occupancies were generated ***\n",
      "*** Contact matrix csv file was generated ***\n"
     ]
    }
   ],
   "source": [
    "\n",
    "contacts_prot_A_prot_H_df = contacts_prot_A_prot_H.search_protein_A_protein_B_interface(cutoff, 'H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Prot_A res_id</th>\n",
       "      <th>Prot_H res_id</th>\n",
       "      <th>Distance (Angstrom)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLY-485</td>\n",
       "      <td>TYR-52</td>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GLU-484</td>\n",
       "      <td>SER-53</td>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GLU-484</td>\n",
       "      <td>SER-56</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TYR-489</td>\n",
       "      <td>SER-53</td>\n",
       "      <td>5.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GLY-485</td>\n",
       "      <td>SER-53</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Prot_A res_id Prot_H res_id Distance (Angstrom)\n",
       "0       GLY-485        TYR-52                4.44\n",
       "1       GLU-484        SER-53                4.66\n",
       "2       GLU-484        SER-56                 5.3\n",
       "3       TYR-489        SER-53                5.47\n",
       "4       GLY-485        SER-53                5.55"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display in pandas DF the closest 5 residue contacts\n",
    "contacts_prot_A_prot_H_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image generated with VMD and PDB with modified occupancy values\n",
    "\n",
    "<img src=\"./images/prot_AHL_chain9.png\" width= \"80%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bcHfGCsB5jRo1MpLMunXrnDquZ8+eRpIZPny4TfmqVauMJFOqVCm7PU/FihUzJ0+etJafO3fO5M6d2+TMmdPcunXLWn53j509x44dM3FxcTZlCQkJ1rh+/vlnm321atUyksyMGTOSnCsqKsr678S/5DvbA5AYr7e3t/n111+t5TExMaZAgQImICDAhISEmCNHjlj3nThxwvj4+JhKlSrZnOvMmTPmypUrSa4xZ84cI8mMHj3apjyxbQsWLGiio6Ot5VeuXDEVK1Y0ksymTZscug+l0MPoqveuS5cuRpKZOXOmTfnp06dNWFiYCQ4ONjdu3LCWJ/YwVqlSxaaHLNH69euT7RVLr8/J+fPnTVBQkClcuLA5fvy4Tb358+cbSebll1+2e//30v/3Oj333HMmISHBWr5hwwZjsVhMyZIl7X6X7F07Pj7elCpVykgyq1atstk3fPhwI8n06tXLpjyxB84V2rVrZySZXbt2GWOMOXTokJFkmjVr5vS5RowYYSwWi81n2hhjrl69avz8/EyfPn0cOk9iL3STJk1sepUPHDhg/P39Te7cuU1MTIy1PLGHMTAw0Pz3v/9Ncr4mTZoYSWb69Ok25dOnTzeSTNOmTW3KEz+/94MeRgBZBQkj8AAqU6aMkWQOHjzo8DG3bt0y/v7+Jl++fObatWtJ9rds2dJIshlemPhL7r1Jwt377v7lLLWkIzk7d+40ksyoUaOsZb/++quRZBo0aJDq8febMHbv3j3JvsTk5K233kqyr0mTJsbT09Pcvn071WskJCSYoKAg06hRI5vyxPZ79913kxzz9ddf200SkpNSwuiK9+7cuXPG09MzyS/ViSZPnmwkmW+//dZalvgLt73hg8aknDAm534/JxMmTDCSzOeff253f7Vq1Uz+/PkdikWS8fT0TDKc0Rhj2rZtm+x36aOPPkpSf9OmTUaS3WGcV69eNfny5TP+/v42Cb6rEsbTp08bLy8vU758eZvy2rVrG4vFYo4dO+bU+SpWrGhq1aqVpHzRokVGkvn+++8dOk9iwmhveG+/fv2SvI+JCeOgQYOS1D9x4oSRZMqVK2eT3Btz5/tZtmxZI8nmvSRhBID/YVkNIJs4ePCgbty4oVq1atmdUTNxONuePXuS7KtWrVqSssRppS9duuRwDLGxsZowYYJq1aqloKAgeXh4yGKxqHr16pL+NxGKJP3666+SZDOTWXqpWrVqkrLQ0FBJ9oehhYaGKj4+XmfOnLEpX7x4sVq2bKng4GB5eXnJYrHIw8NDMTExNvd2t0cffTTZMnvvhbNc8d5t375d8fHxunnzpnVSgbu3bdu2SbrzGbtXrVq1nI45vT4niXFu27bN7n3cvHlT58+f1/nz5x2KMzw8XGFhYUnKU3r/7LXH7t27JdkfUhoYGKgaNWroxo0b+uOPPxyKyxlz5sxRXFxckiUwnn/+eRljNGvWLIfPFRkZqb179yY7O2qOHDnUpEkTh8/n7e2tOnXqJClPa/s2bNgwyVIZFotFDRo0kCT99ttvDscGwHV++ukn+fr66vjx43b3V6tWTRaLRePHj8/gyFI2f/586+MBmc0ff/whHx8fu5P5pAXPMAIPoJCQEB08eFDR0dEqXbq0Q8fExMRIUrLPPCbOVnn58uUk++w9e5f4bFB8fLxD15ekp556St9++60efvhhde7cWQUKFJC3t7cuXbqkjz76SLdu3bLWTUxmHnroIYfPn1ZBQUFJyhLvL6V9t2/ftpZ9+OGHGjJkiIKDg9WiRQsVLlxY/v7+kqRJkybZ3NvdChQoYLfMw8PD7nvhLFe8dxcuXJAkbd68WZs3b0623rVr15KUpfaMrT3p9TlJvI/EhZuTc+3aNeXPnz/V89l776T/3bO9989ee9zPd/N+zZo1Sx4eHkmeB3z66ac1aNAgzZo1S//+97/l4ZH635eXLVsmSUkSxvj4eK1YsUKtWrVKsgh2SvLly2f3ug9S+wJImTFGAwcOVO/evRUeHp5k/549e6x/9ImIiNCQIUMyOsRkzZ8/X/v27cuUawg//PDD6tq1qwYNGqSNGzfe9/lIGIEHUL169bRhwwb98MMPDv/FPjHxubdXLFFiub0EyRW2b9+ub7/9Vi1bttSKFSvk6elp3bdt2zZ99NFHNvVz584tSYqOjk6XeFwpLi5O77zzjgoVKqQ9e/bYrA1ljNG4ceOSPfbs2bNJkv6zZ88qISHBbrLnDomficQlF5zh7OLn6fk5SbyPvXv3qkKFCk7FZc/Zs2ftlid+l+y9f/baw13fzc2bN1t7he31lErSiRMntG7dOod6cJctW6YSJUqofPnyNuU///yz/v77b7s9jyn5+++/lZCQkCRpfFDaF0DqVq1apV27dmn+/Pl29ycua9G2bVutWLFCW7Zs0SOPPJKRIbpEfHy84uLinPqj2f16+eWXVaNGDZe0GUNSgQdQ9+7d5enpqRkzZujcuXMp1k3sjSlTpoz8/Py0fft2Xb9+PUm9xL9A3c9MgIm/3NvruTpy5IikOz/0704CpDvDUe6VOLRszZo193XdjHD+/HldvnxZderUSbKQ8I4dO3Tjxo1kj7V374llGTkrY0ptWLNmTVksFm3dujXd40jPz0nt2rUlyWX3cfz4cUVFRSUpd/b9SxwSfe/aXpJ0/fp17dixQ/7+/jZ/WPD09Lzvz3tERISkO2uC9erVK8mWOOttYr2UXLhwQT///HOyw1G9vLzUpk0bp+K7ffu2dRjx3Zxt38R6mzZtSrImmjHG7vnc/TMFyC6mTp2qmjVr2h0tdfPmTc2fP1/Vq1fXxIkTJf1vzdi7jRo1ShaLRfv379czzzyjXLlyqWDBgurZs2eSkQOXLl1Sr169lDdvXuXIkUNt27bV0aNHZbFYbNZwPHfunF588UWFhYXJ19dXwcHBqlevntatWyfpziMEK1as0PHjx2WxWKybJB07dkwWi0Xjxo3T6NGjVaxYMfn6+mr9+vWS7szWXbduXQUEBChnzpxq3rx5kv+XEu/pv//9rzp27KhcuXIpb968Gjx4sOLi4nTo0CG1atVKOXPmVNGiRe3+Ybp69eoqW7aspk2b5sQ7Yh8JI/AAKlmypIYOHarz58+rdevWioyMTFLn5s2bmjBhgvUHoI+Pj5555hmdP39eY8eOtam7bt06rVy5UiVLllS9evXSHFfevHklSX/99VeSfYlDTX7++Web8v379yeJR7qTpNSqVUubNm3Sp59+mmT/3T1KefLkkcVisXvdjFCgQAH5+/tr165dNsn4xYsX9corr6R47OTJk22eybt69arefvttSXeeI8soKb13ISEh6tSpk7Zs2aIPPvjA7kLEv/zyi90/RDgrPT8nPXr0UM6cOfXGG29o//79Sepev37dboKSnPj4eL3xxhs27bFx40Z9//33KlmypMN/0a1Xr55KlCihlStXWn8ZSTR27FidP39ezzzzjHx8fKzlefPm1fnz53Xz5k2H473b1atX9dVXXykwMFBfffWVPvvssyTb119/rQIFCmjp0qX6+++/UzzfihUrFBcXZzdhXL58uR599FHrZ8wZb775ps3Q74MHD2rmzJnKlSuXwz2WRYoUUePGja3LaNxt5syZ2r9/v5o0aWLTy5rS9wGAa8TGxmrdunVq3Lix3f2LFy/WxYsX1bNnT5UqVUr169fXwoULdfXqVbv1O3TooIcffliLFi3Sv/71L82fP1+DBg2y7k9ISNDjjz+u+fPna9iwYVqyZIlq166tVq1aJTnXc889p6VLl+rf//631qxZo88++0zNmjWz/iz8z3/+o3r16ikkJERbt261bnebPHmyfvzxR40fP14rV65UmTJlNH/+fLVr105BQUH68ssvFRERoYsXL6pRo0ZJ/t+TpE6dOqly5cpatGiRevfurYkTJ2rQoEF68skn1bZtWy1ZskRNmjTRsGHDtHjx4iTHN2rUSCtXrrT7/7YzGJIKPKBGjx6tmzdvauLEiSpdurSaNGmiChUqyNvbW5GRkVq3bp3+/vtvjR492nrM+++/r40bN2r06NHasmWLateubV2HMSAgwPo8U1rVrVtX/v7+mjRpkmJiYqy9bf/6179Uq1Yt1apVS1999ZVOnTqlOnXq6MSJE1q+fLnatm2rb775Jsn55s2bp0aNGunFF1/U559/rrp16+rmzZvav3+/du/ebf3BnSNHDtWsWVObNm1Sjx49VKpUKXl4eKhLly4qUqRImu/HUR4eHurbt68+/PBDVa5cWY8//rhiYmK0cuVKhYeHq1ChQskeW7NmTVWuXFmdO3eWr6+vFi9erGPHjql3797WyTgyQkrvnXTnP8dDhw5p6NCh1vciV65cioqK0s6dO/Xnn3/q1KlTdidUckZ6fk6Cg4P15ZdfqmPHjqpcubJatWqlMmXK6ObNmzp+/Lg2btyoRx55RKtWrXIo1kqVKmnDhg2qU6eOmjRpopMnT2rBggXy9vbWp59+6vB3ycPDQ7Nnz1bLli3Vpk0bdezYUeHh4frll1/0448/qkSJEnrvvfdsjmnSpIl27Nihxx9/XI8++qh8fHxUv3591a9f36FrLliwQNeuXVOPHj2UI0cOu3W8vLz07LPPasKECZo3b54GDBiQ7PmWLVumfPnyJfmD0759+3TkyJFU/3BiT2hoqC5duqQqVaqobdu2unz5sr788kvdvHlTn376qXLmzOnwuaZOnar69eurd+/e+vbbb1WuXDn9/vvvWr58uYKDgzV16lSb+k2aNNE333yjjh07qk2bNvLz81PFihXVtm1bp+9j79696t69u9191apVU//+/Z0+J5AV7NmzRzdu3LA7OZt0Z3SDn5+funTpIknq1auXevTooa+++ko9e/ZMUr9Xr1567bXXJEnNmjXT4cOHNXPmTEVERMhisWjVqlX6+eefNXXqVL300kuSpObNm8vHx0fDhw+3OdfmzZv1wgsvqHfv3tayu/9IVa5cOeXOnVu+vr52J+eSJD8/P61evVre3t6S7iSs9erVU8WKFbVy5Urr/xFt2rRRiRIlNGzYsCTzBLz44osaPHiw9Z7WrFmjKVOmaPHixfrHP/4h6U5S+N133+mLL75Q+/btbY6vVq2apk6dqkOHDqlMmTJ243SI+yZoBeAK27dvNz179jQlS5Y0/v7+xtfX1xQtWtQ888wzZs2aNUnqnzt3zvTv39+Eh4cbb29vkz9/fvPUU0+ZvXv3JqmbuBRAZGRkkn2J09ivX7/epnzFihWmZs2axt/f37pWXaKzZ8+anj17mkKFChk/Pz9TsWJF88knn5ijR48muyzG6dOnzYABA0zx4sWNj4+PyZs3r6ldu7aZMGGCTb1Dhw6ZNm3amNy5cxuLxWI3tnultJREcveXXLvExsaad99915QqVcr4+vqaIkWKmMGDB5srV67YXQIh8RyHDx82Y8aMsd5fiRIlzPvvv59kHcKUKIVlNVz13hljzPXr1824ceNM9erVTWBgoPH39zfFihUzTz75pJk7d67NMiOpLUuQ0rIa6fk5McaYgwcPml69epnw8HDj4+Nj8uTJYypWrGj69+9vsx5nShLb/Pjx46Zjx44mT548xt/f3zRo0CDJOpHGpPx+JPrvf/9rnnrqKZM/f37j7e1twsPDTf/+/c25c+eS1L1y5Yrp3bu3CQ0NNR4eHk4vUVKnTp0kS3/Ys3fvXiPJVKxYMdk6N2/eNDly5LD7vowePdpIMkePHnU4NmP+t2zI33//bV544QVToEAB4+vra2rUqGF3qZaUvq+Jjh07Znr06GFCQ0ONl5eXCQ0NNT169LC7dMjt27fN0KFDTZEiRYyXl1ealu1JXFYjpa1du3ZOnRPISpYsWWIkmR9//DHJvqNHjxqLxWK6dOliLbt69arJmTOnqVevnk3dxO//vUuNTZs2zUgyp0+fNsYYM3ToUCMpydrAx44dS/IztEmTJiZ37tzmnXfeMVu3brVZDzZR27Zt7S5vlPjdv3eZn99//91IMuPGjUtyzD//+U/j4eFhXfYs8Z4OHTpkU++ZZ54xFovFZt1jY4ypW7euqV69epLzLlu2zCgN63bfi4QRANzAkQQCmZe9JD27WrFihZFkFi9enGRfzZo1TaVKlZw+p6vWmQSQec2fP99IMlu2bEmyb8SIEdafKxcvXrRuXbt2NZLMgQMHrHUTk6t7/7iW+EfhxP9ne/XqZby8vJJc68aNG0kSxnPnzpkBAwZY14TNkSOHee6558ypU6esdVJLGO9NDH/66adk1wJ+5513jCTz119/pXhP3bp1M4GBgUmOb9iwYZL1dI0xZvXq1UnWSU4LnmEEAABptmzZMvn5+SWZSfXUqVPasWOHnnjiCTdFBiAzS1y+KHHJo0QJCQmaPXu2JKl9+/bKkyePdfviiy8k2Z/8JjX58uVTXFxckuudPn3abmyTJk3SsWPHdPz4cY0dO1aLFy9Odni5PffO2pwvXz5Jd3423uvkyZPy8PBQnjx5HD6/IxLv1ZGlolJCwggAANJs+vTpunHjhgIDA23KQ0NDlZCQoHfeecdNkQHIzMqWLSvpf7NjJ1q9erX++usv9evXT+vXr0+ylS9fXnPnzlVcXJxT12vYsKEkaeHChTblCxYsSPG4IkWK6OWXX1bz5s21a9cua7mvr2+Ks6Dfq3Tp0nrooYc0f/58m0lorl27pkWLFllnTnWlo0ePysPDw+E1u5PDpDcAAGQBly5d0qRJkxyqe/f08XCco+02cOBA6xqhAOwrXLiwihcvrm3bttlM/hQRESEvLy+9/vrrdieN69Onj/r3768VK1Y4tb5rq1atVK9ePb366quKiYlR9erVtXXrVs2dO1eSrJPQXL58WY0bN1aXLl1UpkwZ5cyZU9u3b9eqVatsJpWpWLGiFi9erKlTp6p69ery8PBQjRo1kr2+h4eHxo0bp65du+qxxx5Tnz59dOvWLX3wwQe6dOlSksnNXGHbtm2qUqXKffdckjACgBvMnj3bOuQGDx5zn1OUp4dLly7prbfecqhuZk8Yjx075u4Q7HK0fbt3707CCDiga9eumjJlim7duiVfX1+dP39e3377rR577LFkZxh/7rnnNGzYMEVERDiVMHp4eOjbb7/Vq6++qvfee0+xsbGqV6+e5s2bpzp16li/s35+fqpdu7Y+//xzHTt2TLdv31aRIkU0bNgwDR061Hq+AQMGaP/+/Xr99dd1+fJlmTtzw6QYQ5cuXRQYGKixY8eqc+fO8vT0VJ06dbR+/XqHl2Jy1NWrV/XDDz+4ZJSHxWTG//UAAAAAZGknT55UsWLFNHfuXHXu3NktMcyfP19du3bV5s2bXZ60uVNERIQGDBigqKio++5hJGEEAAAA4BbDhg3TypUrtWfPnvtaC9oRX375paKjo1WxYkV5eHho27Zt+uCDD1S1alVt3LgxXa+dkeLi4lSuXDl169ZNb7zxxn2fjyGpAAAAANxixIgRCggIUHR0tMLCwtL1Wjlz5tSCBQs0evRoXbt2TaGhoerevbtGjx6drtfNaFFRUXr22Wf16quvuuR89DACAAAAAOxiWQ0AAAAAgF0kjAAAAAAAu0gYAQAAAAB2kTACAAAAAOwiYQQAAAAA2EXCCAAAAACwi4QRAAAAAGAXCSMAAAAAwC4SRgAAAACAXSSMAAAAAAC7SBgBAAAAAHaRMAIAAAAA7CJhBAAAAADYRcIIAAAAALCLhBEAAAAAYBcJIwAAAADALhJGAAAAAIBdJIwAAAAAALtIGAEAAAAAdpEwAgAAAADsImEEAAAAANjl5e4A0ltCQoJOnjypnDlzymKxuDscAAAAIFswxujKlSsqVKiQPDwevH6qmzdvKjY2NtV6Pj4+8vPzy4CI3CPLJ4wnT55UWFiYu8MAAAAAsqWoqCgVLlzY3WE45ebNmypWrJhOnz6dat2QkBBFRkZm2aQxyyeMOXPmlHTngxoUFOTmaAAAAIDsISYmRmFhYdbfxx8ksbGxOn36tKKOR6aYQ8TExCgsvJhiY2NJGB9UicNQg4KCSBgBAACADPYgPxYWlCNQQTkCk6+QEJ9xwbhJlk8YAQAAACBN4m/d2VLan8WRMAIAAACAPQnxKfciZoMexgdvuiIAAAAAyAgJ8VJCXAqbcwlj0aJFZbFYkmz9+vWzW3/Dhg126x88eNAVd+cQehgBAAAAwB4Tf2dLab8Ttm/frvj4/x2zb98+NW/eXB07dkzxuEOHDtnMxxIcHOzUde8HCSMAAAAyXKfSpR2q99WhQ+kcSdYQGRnpUL1ixYqlcyRZTNwtKc4n5f1OuDfRe++991SiRAk1bNgwxeMKFCig3LlzO3UtV2FIKgAAAADYk9jDmNKWRrGxsZo3b5569uyZ6kyyVatWVWhoqJo2bar169en+ZppQQ8jAAAAANhj/v8ZxpT26856jHfz9fWVr69viqdeunSpLl26pO7duydbJzQ0VDNmzFD16tV169Ytff7552ratKk2bNigBg0aOHwb94OEEQ+GM3sdq1ewYvrGAdxrYXvH6nVenL5xAAAA13NwltSwsDCb4pEjR2rUqFEpnjoiIkKtW7dWoUKFkq1TunRplb5r+HbdunUVFRWl8ePHkzACAAAAgFvF35LivVPeLykqKspmUprUehePHz+udevWafFi5/+gXKdOHc2bN8/p49KKhBEAAAAA7HGwhzEoKMgmYUzNrFmzVKBAAbVt29bpkHbv3q3Q0FCnj0srEkYAAAAAsCchlWcYnVyHUZISEhI0a9YsdevWTV5etunY8OHDFR0drblz50qSJk2apKJFi6p8+fLWSXIWLVqkRYsWOX3dtCJhBAAAAB5wrVq1cqjeIZYpcY6L12GUpHXr1unEiRPq2bNnkn2nTp3SiRMnrK9jY2M1ZMgQRUdHy9/fX+XLl9eKFSvUpk0bp6+bViSMAAAAAGBP3C0pLoWUycl1GCWpRYsWMsbY3Td79myb10OHDtXQoUOdvoYrkTACAAAAgD3p0MP4oCFhBAAAQIb7iqGRLsVQ03SSEJfKM4wp7MsiSBgBAAAAwB6TkPLENiYh42JxExLG9PJ1J8fqdfwqfeMAAABAlrezfOnUK0mqvp+eSKfE35LiPVPen8WRMOLBULCiuyMA7Ovs/IK7AADgAeHgOoxZGQkjAAAAANjDM4wkjAAAAABgV/xtKT425f1ZHAkjAAAAANhDDyMJIwAAAADYxTqMJIwAAAAAYBc9jCSMAAAAAGBXwu2Ul9VI4BlGAAAAAMie6GEkYUw3Hb9ydwQAAADIJqrvP+TuELIm1mEkYQQAAAAAu+hhJGEEAAAAALvib0vxHinvz+JIGAEAAADAnoQ4KSGlSW/oYQQAAACA7Il1GEkYAQAAAMAuehhJGAEAAADAroTbUrwl5f1ZHAkjAAAAANiTECclpDDpDT2MAAAAAJBNsQ4jCSMAAAAA2EUPIwkjAAAAANgVHyul1IkYH5thobgLCSMAAAAA2JMQn3IvIkNSAQAAACCbMvGSSWFIKuswAgAAAEA2lRAnJaS0rAbPMAIAAABA9hQfK8WbFPazDiMAAAAAZE8mPuUeRoakAgAAAEA2lZBKwsikNwAAAACQTZl4ydDDCAAAAAC4V9wtKS6FpDCOSW8AAAAAIHtKiJcSUtmfxZEwAgAAAIA9Jl5KYZJUhqQCAAAAQHZFD6M83HnxuLg4jRgxQsWKFZO/v7+KFy+ut99+WwkJ/3tXjDEaNWqUChUqJH9/fzVq1Ej79+93Y9QAAAAAsoX4W6lvWZxbE8b3339f06ZN05QpU3TgwAGNGzdOH3zwgT7++GNrnXHjxmnChAmaMmWKtm/frpCQEDVv3lxXrlxxY+QAAAAAsryE+NS3LM6tCePWrVvVrl07tW3bVkWLFtVTTz2lFi1aaMeOHZLu9C5OmjRJb7zxhtq3b68KFSpozpw5un79uubPn+/O0AEAAABkdUZ3hqQmt6X0fGMW4daEsX79+vrhhx/0xx9/SJJ+++03/fzzz2rTpo0kKTIyUqdPn1aLFi2sx/j6+qphw4basmWL3XPeunVLMTExNhsAAAAAOM04sGVxbp30ZtiwYbp8+bLKlCkjT09PxcfH691339UzzzwjSTp9+rQkqWDBgjbHFSxYUMePH7d7zrFjx+qtt95K38ABAAAAZH1x/7+ltD+Lc2sP48KFCzVv3jzNnz9fu3bt0pw5czR+/HjNmTPHpp7FYrF5bYxJUpZo+PDhunz5snWLiopKt/gBAAAAZGEpDUdN3LI4t/Ywvvbaa/rXv/6lp59+WpJUsWJFHT9+XGPHjlW3bt0UEhIi6U5PY2hoqPW4s2fPJul1TOTr6ytfX9/0Dx4AAABA1pbasNNsMCTVrT2M169fl4eHbQienp7WZTWKFSumkJAQrV271ro/NjZWGzdu1COPPJKhsQIAAADIZuhhdG8P4+OPP653331XRYoUUfny5bV7925NmDBBPXv2lHRnKOrAgQM1ZswYlSpVSqVKldKYMWMUEBCgLl26uDN0AAAAAFld/P9vKe3P4tzaw/jxxx/rqaeeUt++fVW2bFkNGTJEffr00TvvvGOtM3ToUA0cOFB9+/ZVjRo1FB0drTVr1ihnzpxujBwAAABAlufiHsaiRYvKYrEk2fr165fsMRs3blT16tXl5+en4sWLa9q0aWm8mbSxGGNSHXk7efJkh0/Yv3//+wrI1WJiYpQrVy5dvnxZQUFB7g4HAAAAyBYe5N/DrbH/q4SCfD2Tr3crXrneO+LwPZ47d07x8f/rlty3b5+aN2+u9evXq1GjRknqR0ZGqkKFCurdu7f69OmjzZs3q2/fvvryyy/VoUOHNN2bsxwakjpx4kSb1+fOndP169eVO3duSdKlS5cUEBCgAgUKZLqEEQAAAADSxMWT3gQHB9u8fu+991SiRAk1bNjQbv1p06apSJEimjRpkiSpbNmy2rFjh8aPH59hCaNDQ1IjIyOt27vvvqsqVarowIEDunDhgi5cuKADBw6oWrVqNkNJAQAAAOCBFufAlkaxsbGaN2+eevbsmeySgVu3blWLFi1sylq2bKkdO3bo9u3bab+4E5x+hvHNN9/Uxx9/rNKlS1vLSpcurYkTJ2rEiBEuDQ4AAAAA3MbBZxhjYmJstlu3bqV66qVLl+rSpUvq3r17snVOnz6dZDnBggULKi4uTufPn0/LHTnN6YTx1KlTdrPZ+Ph4nTlzxiVBAQAAAIDbGQc2SWFhYcqVK5d1Gzt2bKqnjoiIUOvWrVWoUKEU693b+5g4BU1yvZKu5vSyGk2bNlXv3r0VERGh6tWry2KxaMeOHerTp4+aNWuWHjECAAAAQMZLbSbU/98XFRVlM+mNr69viqc9fvy41q1bp8WLF6dYLyQkRKdPn7YpO3v2rLy8vJQvX74Uj3UVp3sYZ86cqYceeki1atWSn5+ffH19Vbt2bYWGhuqzzz5LjxgBAAAAIOPFO7BJCgoKstlSSxhnzZqlAgUKqG3btinWq1u3rtauXWtTtmbNGtWoUUPe3t5puiVnOd3DGBwcrO+//15//PGHDh48KGOMypYtq4cffjg94gMAAAAA93Cwh9GpUyYkaNasWerWrZu8vGzTseHDhys6Olpz586VJL300kuaMmWKBg8erN69e2vr1q2KiIjQl19+6fyF08jphDHRww8/TJIIAAAAIOtKh4Rx3bp1OnHihHr27Jlk36lTp3TixAnr62LFiun777/XoEGD9Mknn6hQoUKaPHlyhi2pITmYMA4ePFjvvPOOAgMDNXjw4BTrTpgwwSWBAQAAAIA7GUkmhbUWnVyGUZLUokUL68Q195o9e3aSsoYNG2rXrl1puJJrOJQw7t692zoz6u7du5Otl1Ez9QAAAABAeouPk+JTmPUl/j7WYXxQOJQwrl+/3u6/U/LXX3+pUKFC8vBwel4dAAAAAHC7BHNnS2l/Vpdu2Vy5cuV07Nix9Do9AAAAAKSvBAe2LC7Nk96kJrlxuQAAAADwIEhIuLOltD+rS7eEEQAAAAAeZPHxd7aU9md1JIwAAAAAYIdJuLOltD+rI2EEAAAAADtIGNMxYWSJDQAAAAAPMmNSWYcxG0zbwqQ3AAAAAGBHfHzKay1mh2cY73tZjZiYGC1dulQHDhywKf/9998VHh5+v6cHAAAAALdI7GFMacvqnE4YO3XqpClTpkiSbty4oRo1aqhTp06qVKmSFi1aZK0XFhYmT09P10UKAAAAABnIJPxvaQ17W3Z4htHphHHTpk169NFHJUlLliyRMUaXLl3S5MmTNXr0aJcHCAAAAADukDjpTUpbVud0wnj58mXlzZtXkrRq1Sp16NBBAQEBatu2rf7880+XBwgAAAAA7pAQn/qW1TmdMIaFhWnr1q26du2aVq1apRYtWkiSLl68KD8/P5cHCAAAAADuQA9jGmZJHThwoLp27aocOXKoSJEiatSokaQ7Q1UrVqzo6vgAAAAAwC0Sn1VMaX9W53TC2LdvX9WqVUtRUVFq3ry5PDzudFIWL16cZxgBAAAAZBmsw5jGdRhr1KihSpUqKTIyUiVKlJCXl5fatm3r6tgAAAAAwG3i46V4S8r7szqnn2G8fv26evXqpYCAAJUvX14nTpyQJPXv31/vvfeeywMEAAAAAHdgHcY0JIzDhw/Xb7/9pg0bNthMctOsWTMtXLjQpcEBAAAAgLsw6U0ahqQuXbpUCxcuVJ06dWSx/K9/tly5cjpy5IhLgwMAAAAAd2HSmzQkjOfOnVOBAgWSlF+7ds0mgQQAAACAB1lCvJSQQorDOox21KxZUytWrLC+TkwSP/30U9WtW9d1kQEAAACAO6U2HJUexqTGjh2rVq1a6ffff1dcXJw++ugj7d+/X1u3btXGjRvTI0YAAAAAyHAMSU1DD+MjjzyiLVu26Pr16ypRooTWrFmjggULauvWrapevXp6xAgAAAAAGY5ZUp3sYbx9+7ZefPFFvfnmm5ozZ056xQQAAAAAbpcQn/KoU55hvIe3t7eWLFmSXrEAAAAAQKaRYFLfsjqnh6T+4x//0NKlS9MhFAAAAADIPFiHMQ2T3pQsWVLvvPOOtmzZourVqyswMNBmf//+/V0WHAAAAAC4i0mQTArLapAw2vHZZ58pd+7c2rlzp3bu3Gmzz2KxkDACAAAAyBLi46WUHlOMzwbPMDqdMEZGRqZHHAAAAACQqSQkSAkp9DBmh2U1nE4YAQAAACBbSJCUQsKY4hSqWQQJIwAAAADYkdpai6zDCAAAAADZVEIqzzBmh3UYSRgBAAAAwA7z/1tK+7M6p9dhTM7Fixc1d+5cV50OAAAAANwqwYEtq3NZwnjixAn16NHDVacDAAAAALcyDmxZncsSxrSKjo7Ws88+q3z58ikgIEBVqlSxWd/RGKNRo0apUKFC8vf3V6NGjbR//343RgwAAAAgO4h3YMvq3JowXrx4UfXq1ZO3t7dWrlyp33//XR9++KFy585trTNu3DhNmDBBU6ZM0fbt2xUSEqLmzZvrypUr7gscAAAAQJZHD6ObJ715//33FRYWplmzZlnLihYtav23MUaTJk3SG2+8ofbt20uS5syZo4IFC2r+/Pnq06dPRocMAAAAIJtg0hsnEsbJkyenuD86Otrpiy9fvlwtW7ZUx44dtXHjRj300EPq27evevfuLUmKjIzU6dOn1aJFC+sxvr6+atiwobZs2ULCCAAAACDdkDA6kTBOnDgx1TpFihRx6uJHjx7V1KlTNXjwYL3++uv69ddf1b9/f/n6+ur555/X6dOnJUkFCxa0Oa5gwYI6fvy43XPeunVLt27dsr6OiYlxKiYAAAAAkFJ/TjE7PMPocMIYGRnp8osnJCSoRo0aGjNmjCSpatWq2r9/v6ZOnarnn3/eWs9isdgcZ4xJUpZo7Nixeuutt1weKwAAAIDshR7GdJz0pmLFioqKikqxTmhoqMqVK2dTVrZsWZ04cUKSFBISIknWnsZEZ8+eTdLrmGj48OG6fPmydUstBgAAAACwh0lv0jFhPHbsmG7fvp1inXr16unQoUM2ZX/88YfCw8MlScWKFVNISIjWrl1r3R8bG6uNGzfqkUcesXtOX19fBQUF2WwAAAAA4KwEB7aszq3LagwaNEjbtm3TmDFjdPjwYc2fP18zZsxQv379JN0Zijpw4ECNGTNGS5Ys0b59+9S9e3cFBASoS5cu7gwdAAAAQBZnlHKymJYextTWob/Xhg0bZLFYkmwHDx5Mw9Wd59ZlNWrWrKklS5Zo+PDhevvtt1WsWDFNmjRJXbt2tdYZOnSobty4ob59++rixYuqXbu21qxZo5w5c7oxcgAAAABZnaufYUxch75x48ZauXKlChQooCNHjtisQ5+cQ4cO2YyeDA4OdvLqaePWhFGSHnvsMT322GPJ7rdYLBo1apRGjRqVcUEBAAAAyPZSG3bq7JDU1NahT0mBAgUcSixdza1DUgEAAAAgs3L0GcaYmBib7e5l/u62fPly1ahRQx07dlSBAgVUtWpVffrppw7FUrVqVYWGhqpp06Zav379fd6Z40gYAQAAAMAOR2dJDQsLU65cuazb2LFj7Z4vcR36UqVKafXq1XrppZfUv39/zZ07N9kYQkNDNWPGDC1atEiLFy9W6dKl1bRpU23atMmFd5o8p4ekzp07V507d5avr69NeWxsrBYsWGBdP3H69OnJLn0BAAAAAJmdo0NSo6KibJ4vvDdXstZ3cB36u5UuXVqlS5e2vq5bt66ioqI0fvx4NWjQwJnbSROnexh79Oihy5cvJym/cuWKevToYX3dpUsXBQYG3l90AAAAAOAmjvYw3rusX3IJY2rr0DuqTp06+vPPP506Jq2c7mE0xshisSQp/+uvv5QrVy6XBAUAAAAA7ubqWVJTW4feUbt371ZoaKiTV08bhxPGqlWrWtf8aNq0qby8/ndofHy8IiMj1apVq3QJEgAAAAAyWvz/byntd8agQYP0yCOPaMyYMerUqZN+/fVXzZgxQzNmzLDWGT58uKKjo63PNU6aNElFixZV+fLlFRsbq3nz5mnRokVatGiR0/eTFg4njE8++aQkac+ePWrZsqVy5Mhh3efj46OiRYuqQ4cOLg8QAAAAANzB1T2MjqxDf+rUKZshqrGxsRoyZIiio6Pl7++v8uXLa8WKFWrTpo2TV08bizHGqfucM2eOOnfuLD8/v/SKyaViYmKUK1cuXb582eZBVAAAAADp50H+PTwx9k9LlFCAp2ey9a7Hx6v3kSMP5D06yulnGLt16yZJ2rlzpw4cOCCLxaJy5cqpatWqLg8OAAAAAOA+TieMZ8+e1dNPP60NGzYod+7cMsbo8uXLaty4sRYsWKDg4OD0iBMAAAAAMlSCUn5OMaUlN7IKp5fVeOWVVxQTE6P9+/frwoULunjxovbt26eYmBj1798/PWIEAAAAgAyX4MCW1Tndw7hq1SqtW7dOZcuWtZaVK1dOn3zyiVq0aOHS4AAAAAAA7uN0wpiQkCBvb+8k5d7e3kpIyA45NgAAAIDsILVexOyQ/Tg9JLVJkyYaMGCATp48aS2Ljo7WoEGD1LRpU5cGBwAAAADuEu/AltU5nTBOmTJFV65cUdGiRVWiRAmVLFlSxYoV05UrV/Txxx+nR4wAAAAAkOGMA1tW5/SQ1LCwMO3atUtr167VwYMHZYxRuXLl1KxZs/SIDwAAAADcIrWkkITxHnFxcfLz89OePXvUvHlzNW/ePL3iAgAAAAC34hlGJxNGLy8vhYeHKz4+O4zWBQAAAJCdkTCm4RnGESNGaPjw4bpw4UJ6xAMAAAAAmQLPMKbhGcbJkyfr8OHDKlSokMLDwxUYGGizf9euXS4LDgAAAADchWcY05AwtmvXThaLJT1iAQAAAIBMI0EpL52RHYakOp0wjho1Kh3CAAAAAIDMhR5GJ55hvH79uvr166eHHnpIBQoUUJcuXXT+/Pn0jA0AAAAA3IZnGJ1IGEeOHKnZs2erbdu2evrpp7V27Vr985//TM/YAAAAAMBtEhzYsjqHh6QuXrxYERERevrppyVJzz77rOrVq6f4+Hh5enqmW4AAAAAA4A7xSvkZxuyw2KDDPYxRUVF69NFHra9r1aolLy8vnTx5Ml0CAwAAAAB3YkiqEz2M8fHx8vHxsT3Yy0txcXEuDwoAAAAA3I1Jb5xIGI0x6t69u3x9fa1lN2/e1EsvvWSzFuPixYtdGyEAAAAAuAEJoxMJY7du3ZKUPfvssy4NBgAAAAAyi9QmtmHSm7vMmjUrPeMAAAAAgEyFhNGJhBEAAAAAshOGpJIwAgAAAIBdRin3IpIwAgAAAEA2xZBUEkYAAAAAsIshqSSMAAAAAGAXPYwkjAAAAABgFz2MJIwAAAAAYBc9jCSMAAAAAGAXCSMJIwAAAADYRcIoebg7AAAAAABA5kQPIwAAAADYEf//W0r7szoSRgAAAACwg1lSSRgBAAAAwC6jlJ9TzA4JI88wAgAAAADsoocRAAAAAOzgGUYSRgAAAACwi2U1MtGQ1LFjx8pisWjgwIHWMmOMRo0apUKFCsnf31+NGjXS/v373RckAAAAAGQjmSJh3L59u2bMmKFKlSrZlI8bN04TJkzQlClTtH37doWEhKh58+a6cuWKmyIFAAAAkF0kOLBldW5PGK9evaquXbvq008/VZ48eazlxhhNmjRJb7zxhtq3b68KFSpozpw5un79uubPn+/GiAEAAABkB/EObFmd2xPGfv36qW3btmrWrJlNeWRkpE6fPq0WLVpYy3x9fdWwYUNt2bIl2fPdunVLMTExNhsAAAAAOMs4sGV1bp30ZsGCBdq1a5e2b9+eZN/p06clSQULFrQpL1iwoI4fP57sOceOHau33nrLtYECAAAAyHZSSwqzQ8Loth7GqKgoDRgwQPPmzZOfn1+y9SwWi81rY0ySsrsNHz5cly9ftm5RUVEuixkAAABA9pEezzBGR0fr2WefVb58+RQQEKAqVapo586dKR6zceNGVa9eXX5+fipevLimTZuWhiunjdt6GHfu3KmzZ8+qevXq1rL4+Hht2rRJU6ZM0aFDhyTd6WkMDQ211jl79mySXse7+fr6ytfXN/0CBwAAAJAtuHpZjYsXL6pevXpq3LixVq5cqQIFCujIkSPKnTt3ssdERkaqTZs26t27t+bNm6fNmzerb9++Cg4OVocOHZyMwHluSxibNm2qvXv32pT16NFDZcqU0bBhw1S8eHGFhIRo7dq1qlq1qiQpNjZWGzdu1Pvvv++OkAEAAABkI64ekvr+++8rLCxMs2bNspYVLVo0xWOmTZumIkWKaNKkSZKksmXLaseOHRo/fnzWThhz5sypChUq2JQFBgYqX7581vKBAwdqzJgxKlWqlEqVKqUxY8YoICBAXbp0cUfIAAAAALIRo5R7ERMTxnsn2kxu1OPy5cvVsmVLdezYURs3btRDDz2kvn37qnfv3sleY+vWrTYTgUpSy5YtFRERodu3b8vb29vBu0kbt8+SmpKhQ4dq4MCB6tu3r2rUqKHo6GitWbNGOXPmdHdoAAAAALI4R2dJDQsLU65cuazb2LFj7Z7v6NGjmjp1qkqVKqXVq1frpZdeUv/+/TV37txkYzh9+rTdiUDj4uJ0/vz5+73FVLl1ltR7bdiwwea1xWLRqFGjNGrUKLfEAwAAACD7cvQZxqioKAUFBVnLk5tTJSEhQTVq1NCYMWMkSVWrVtX+/fs1depUPf/888lex95EoPbK00Om7mEEAAAAAHdxdJbUoKAgmy25hDE0NFTlypWzKStbtqxOnDiRbAwhISHWJQcTnT17Vl5eXsqXL1+a7ssZJIwAAAAAYIejQ1IdVa9ePetqEIn++OMPhYeHJ3tM3bp1tXbtWpuyNWvWqEaNGun+/KJEwggAAAAAdrk6YRw0aJC2bdumMWPG6PDhw5o/f75mzJihfv36WesMHz7cZnjqSy+9pOPHj2vw4ME6cOCAZs6cqYiICA0ZMuQ+784xJIwAAAAAYEe8A5szatasqSVLlujLL79UhQoV9M4772jSpEnq2rWrtc6pU6dshqgWK1ZM33//vTZs2KAqVaronXfe0eTJkzNkSQ1JspjEJyazqJiYGOXKlUuXL1+2eRAVAAAAQPp5kH8PT4y9TYkS8vb0TLbe7fh4fX/kyAN5j47KVLOkAgAAAEBmkdqw0yzd8/b/SBgBAAAAwA5Hl9XIykgYAQAAAMAOEkYSRgAAAACwiyGpJIwAAAAAYJdRyr2IJIwAAAAAkE3Rw0jCCAAAAAB28QwjCSMAAAAA2EXCSMIIAAAAAHYxJJWEEQAAAADsImEkYQQAAAAAu+IlWVLZn9WRMKaXpd0dq/fk7PSMAgAAAEAa0cNIwggAAAAAdpEwkjACAAAAgF3MkkrCCAAAAAB2kTCSMAIAAACAXUYpJ4UMSQUAAACAbIpnGEkYAQAAAMAuEkYSRgAAAACwK7V1FrPDOowe7g4AAAAAAJA50cMIAAAAAHYkSLKksj+rI2FML0/OdncEAAAAAO4DzzCSMAIAAACAXTzDSMIIAAAAAHbRw0jCCAAAAAB2kTBmg4TRmDtvY0xMjJsjAQAAALKPxN+/E38ffxDFJSSkOLFNQkLWn/YmyyeMV65ckSSFhYW5ORIAAAAg+7ly5Ypy5crl7jCc4uPjo5CQEEVGRqZaNyQkRD4+PhkQlXtYzIOc8jsgISFBJ0+eVM6cOWWxpDQpbvqKiYlRWFiYoqKiFBQU5LY4sgra07VoT9ehLV2L9nQt2tO1aE/Xoj1dKzO0pzFGV65cUaFCheTh8eAt/37z5k3FxsamWs/Hx0d+fn4ZEJF7ZPkeRg8PDxUuXNjdYVgFBQXxQ9CFaE/Xoj1dh7Z0LdrTtWhP16I9XYv2dC13t+eD1rN4Nz8/vyydCDrqwUv1AQAAAAAZgoQRAAAAAGAXCWMG8fX11ciRI+Xr6+vuULIE2tO1aE/XoS1di/Z0LdrTtWhP16I9XYv2hKtk+UlvAAAAAABpQw8jAAAAAMAuEkYAAAAAgF0kjAAAAAAAu0gYXWTs2LGyWCwaOHCgtcxisdjdPvjgA2ud06dP67nnnlNISIgCAwNVrVo1ffPNN264g8wlre155MgR/eMf/1BwcLCCgoLUqVMnnTlzxg13kLnYa8+rV6/q5ZdfVuHCheXv76+yZctq6tSpNsfdunVLr7zyivLnz6/AwEA98cQT+uuvvzI4+swnre05Y8YMNWrUSEFBQbJYLLp06VLGBp5JpaU9L1y4oFdeeUWlS5dWQECAihQpov79++vy5ctuuIPMJa2fzz59+qhEiRLy9/dXcHCw2rVrp4MHD2Zw9JlLWtsykTFGrVu3lsVi0dKlSzMm6Ewsre3ZqFGjJP/3P/300xkcfeZzP5/PrVu3qkmTJgoMDFTu3LnVqFEj3bhxIwOjx4OEhNEFtm/frhkzZqhSpUo25adOnbLZZs6cKYvFog4dOljrPPfcczp06JCWL1+uvXv3qn379urcubN2796d0beRaaS1Pa9du6YWLVrIYrHoxx9/1ObNmxUbG6vHH39cCQkJ7riVTCG59hw0aJBWrVqlefPm6cCBAxo0aJBeeeUVLVu2zFpn4MCBWrJkiRYsWKCff/5ZV69e1WOPPab4+PiMvo1M437a8/r162rVqpVef/31jA4700pre548eVInT57U+PHjtXfvXs2ePVurVq1Sr1693HEbmcb9fD6rV6+uWbNm6cCBA1q9erWMMWrRokW2/b7fT1smmjRpkiwWS0aFnKndb3v27t3b5neA6dOnZ2T4mc79tOfWrVvVqlUrtWjRQr/++qu2b9+ul19+WR4epAVIhsF9uXLliilVqpRZu3atadiwoRkwYECyddu1a2eaNGliUxYYGGjmzp1rU5Y3b17z2WefpUe4md79tOfq1auNh4eHuXz5srXswoULRpJZu3ZteoadaaXUnuXLlzdvv/22Tf1q1aqZESNGGGOMuXTpkvH29jYLFiyw7o+OjjYeHh5m1apVGRJ/ZnM/7Xm39evXG0nm4sWL6Rxx5uaq9kz01VdfGR8fH3P79u30CjlTc3V7/vbbb0aSOXz4cHqFnGm5oi337NljChcubE6dOmUkmSVLlmRA5JnT/bZnar8PZDf32561a9dO8bsP3Is/Jdynfv36qW3btmrWrFmK9c6cOaMVK1Yk+et3/fr1tXDhQl24cEEJCQlasGCBbt26pUaNGqVj1JnX/bTnrVu3ZLFYbNYb8vPzk4eHh37++ed0izkzS6k969evr+XLlys6OlrGGK1fv15//PGHWrZsKUnauXOnbt++rRYtWliPKVSokCpUqKAtW7Zk2D1kJvfTnkjK1e15+fJlBQUFycvLKz3DzrRc2Z7Xrl3TrFmzVKxYMYWFhaV36JnO/bbl9evX9cwzz2jKlCkKCQnJyNAzJVd8Nr/44gvlz59f5cuX15AhQ3TlypWMCj/TuZ/2PHv2rH755RcVKFBAjzzyiAoWLKiGDRtm29+T4Jjs+b+qiyxYsEC7du3S9u3bU607Z84c5cyZU+3bt7cpX7hwoTp37qx8+fLJy8tLAQEBWrJkiUqUKJFeYWda99uederUUWBgoIYNG6YxY8bIGKNhw4YpISFBp06dSs/QM6XU2nPy5Mnq3bu3ChcuLC8vL3l4eOizzz5T/fr1Jd15vtbHx0d58uSxOa5gwYI6ffp0usef2dxve8KWq9vz77//1jvvvKM+ffqkZ9iZlqva8z//+Y+GDh2qa9euqUyZMlq7dq18fHwy4hYyDVe05aBBg/TII4+oXbt2GRV2puWK9uzatauKFSumkJAQ7du3T8OHD9dvv/2mtWvXZtRtZBr3255Hjx6VJI0aNUrjx49XlSpVNHfuXDVt2lT79u1TqVKlMuxe8OAgYUyjqKgoDRgwQGvWrJGfn1+q9WfOnKmuXbsmqTtixAhdvHhR69atU/78+bV06VJ17NhRP/30kypWrJhe4Wc6rmjP4OBgff311/rnP/+pyZMny8PDQ88884yqVasmT0/P9Aw/03GkPSdPnqxt27Zp+fLlCg8P16ZNm9S3b1+Fhoam2MNrjMl2z+SkZ3tmR65uz5iYGLVt21blypXTyJEjM+IWMhVXtmfXrl3VvHlznTp1SuPHj1enTp20efNmh34uZwWuaMvly5frxx9/zNZzESRy1Wezd+/e1voVKlRQqVKlVKNGDe3atUvVqlXLkHvJDFzRnolzOvTp00c9evSQJFWtWlU//PCDZs6cqbFjx2bY/eAB4rbBsA+4JUuWGEnG09PTukkyFovFeHp6mri4OGvdTZs2GUlmz549Nuc4fPiwkWT27dtnU960aVPTp0+fDLmPzMIV7Xm3c+fOWZ8PK1iwoBk3blx630Kmklp7Xr161Xh7e5vvvvvO5rhevXqZli1bGmOM+eGHH4wkc+HCBZs6lSpVMv/+978z7F4yA1e0592y+zOMrmzPmJgYU7duXdO0aVNz48aNjLyNTMPVn89Et27dMgEBAWb+/PnpfQuZhivacsCAAdb6d5/Dw8PDNGzY0A135T7p9dlMSEhI8ox9duCK9jx69KiRZD7//HObOp06dTJdunTJsHvBg4UexjRq2rSp9u7da1PWo0cPlSlTRsOGDbPp0YqIiFD16tVVuXJlm/rXr1+XpCSzUnl6ema7WT1d0Z53y58/vyTpxx9/1NmzZ/XEE0+kT+CZVGrtGR8fr9u3b6f42atevbq8vb21du1aderUSdKdmWr37duncePGZcyNZBKuaE/8j6vaMyYmRi1btpSvr6+WL1+ebXrB7pWen09jjG7duuXymDMrV7Tlv/71L73wwgs2+ytWrKiJEyfq8ccfT98byGTS67O5f/9+3b59W6GhoekSd2blivYsWrSoChUqpEOHDtnU+eOPP9S6dev0vQE8sEgY0yhnzpyqUKGCTVlgYKDy5ctnUx4TE6Ovv/5aH374YZJzlClTRiVLllSfPn00fvx45cuXT0uXLtXatWv13Xffpfs9ZCauaE9JmjVrlsqWLavg4GBt3bpVAwYM0KBBg1S6dOl0jT+zcaQ9GzZsqNdee03+/v4KDw/Xxo0bNXfuXE2YMEGSlCtXLvXq1Uuvvvqq8uXLp7x582rIkCGqWLFithti6Yr2lO48F3r69GkdPnxYkrR3717lzJlTRYoUUd68eTPuhtzMFe155coVtWjRQtevX9e8efMUExOjmJgYSXeGp2enYeiuaM+jR49q4cKFatGihYKDgxUdHa33339f/v7+atOmTYbfk7u4oi1DQkLsTnRTpEgRFStWLP1vIhNxRXseOXJEX3zxhdq0aaP8+fPr999/16uvvqqqVauqXr16GX5P7uSK9rRYLHrttdc0cuRIVa5cWVWqVNGcOXN08OBB1gFH8tzdxZmV2Jv2efr06cbf399cunTJ7jF//PGHad++vSlQoIAJCAgwlSpVSrLMRnaVlvYcNmyYKViwoPH29jalSpUyH374oUlISMiAaDO/e9vz1KlTpnv37qZQoULGz8/PlC5dOkl73bhxw7z88ssmb968xt/f3zz22GPmxIkTbog+80lLe44cOdJISrLNmjUr428gk3G2PROH9drbIiMj3XMTmYiz7RkdHW1at25tChQoYLy9vU3hwoVNly5dzMGDB910B5lHWr7r91I2X1bjbs6254kTJ0yDBg1M3rx5jY+PjylRooTp37+/+fvvv910B5lLWj+fY8eONYULFzYBAQGmbt265qeffsrgyPEgsRhjjHtSVQAAAABAZsY6jAAAAAAAu0gYAQAAAAB2kTACAAAAAOwiYQQAAAAA2EXCCAAAAACwi4QRAAAAAGAXCSMAAAAAwC4SRgAAAACAXSSMAAA4wGKxaOnSpcnuP3bsmCwWi/bs2ZNhMQEAkN5IGAEgG+revbssFossFou8vb1VvHhxDRkyRNeuXbuv844aNUpVqlRJ07FjxoyRp6en3nvvvfuKIb2cOnVKrVu3dncYAABkKBJGAMimWrVqpVOnTuno0aMaPXq0/vOf/2jIkCF2696+fTvd45k1a5aGDh2qmTNnpun49I4xJCREvr6+6XoNAAAyGxJGAMimfH19FRISorCwMHXp0kVdu3a1DrlM7CmcOXOmihcvLl9fXxljdOLECbVr1045cuRQUFCQOnXqpDNnzkiSZs+erbfeeku//fabtfdy9uzZDsWyceNG3bhxQ2+//bauXbumTZs2pVg/cfjnV199pUaNGsnPz0/z5s2TdCfxLFu2rPz8/FSmTBn95z//sR4XGxurl19+WaGhofLz81PRokU1duxYh2K8d0jqr7/+qqpVq8rPz081atTQ7t27HToPAAAPEi93BwAAyBz8/f1teukOHz6sr776SosWLZKnp6ck6cknn1RgYKA2btyouLg49e3bV507d9aGDRvUuXNn7du3T6tWrdK6deskSbly5XLo2hEREXrmmWfk7e2tZ555RhEREWrQoEGqxw0bNkwffvihZs2aJV9fX3366acaOXKkpkyZoqpVq2r37t3q3bu3AgMD1a1bN02ePFnLly/XV199pSJFiigqKkpRUVFOt9W1a9f02GOPqUmTJpo3b54iIyM1YMAAp88DAEBmR8IIANCvv/6q+fPnq2nTptay2NhYff755woODpYkrV27Vv/9738VGRmpsLAwSdLnn3+u8uXLa/v27apZs6Zy5MghLy8vhYSEOHztmJgYLVq0SFu2bJEkPfvss6pXr54+/vhjBQUFpXjswIED1b59e+vrd955Rx9++KG1rFixYvr99981ffp0devWTSdOnFCpUqVUv359WSwWhYeHOxzn3b744gvFx8dr5syZCggIUPny5fXXX3/pn//8Z5rOBwBAZsWQVADIpr777jvlyJFDfn5+qlu3rho0aKCPP/7Yuj88PNyaLErSgQMHFBYWZk0WJalcuXLKnTu3Dhw4kOY45s+fr+LFi6ty5cqSpCpVqqh48eJasGBBqsfWqFHD+u9z584pKipKvXr1Uo4cOazb6NGjdeTIEUl3JvvZs2ePSpcurf79+2vNmjVpivnAgQOqXLmyAgICrGV169ZN07kAAMjM6GEEgGyqcePGmjp1qry9vVWoUCF5e3vb7A8MDLR5bYyRxWJJcp7kyh01c+ZM7d+/X15e//svKSEhQREREXrxxRdTPPbuGBMSEiRJn376qWrXrm1TL3FIbbVq1RQZGamVK1dq3bp16tSpk5o1a6ZvvvnGqZiNMU7VBwDgQUXCCADZVGBgoEqWLOlw/XLlyunEiROKioqy9jL+/vvvunz5ssqWLStJ8vHxUXx8vMPn3Lt3r3bs2KENGzYob9681vJLly6pQYMG2rdvnypUqODQuQoWLKiHHnpIR48eVdeuXZOtFxQUpM6dO6tz58566qmn1KpVK124cMHm+qkpV66cPv/8c924cUP+/v6SpG3btjl8PAAADwoSRgCAQ5o1a6ZKlSqpa9eumjRpknXSm4YNG1qHhhYtWlSRkZHas2ePChcurJw5c6a4FEVERIRq1apld4KbunXrKiIiQhMnTnQ4xlGjRql///4KCgpS69atdevWLe3YsUMXL17U4MGDNXHiRIWGhqpKlSry8PDQ119/rZCQEOXOnduptujSpYveeOMN9erVSyNGjNCxY8c0fvx4p84BAMCDgGcYAQAOSVxWIk+ePGrQoIGaNWum4sWLa+HChdY6HTp0UKtWrdS4cWMFBwfryy+/TPZ8sbGxmjdvnjp06GB3f4cOHTRv3jzFxsY6HOMLL7ygzz77TLNnz1bFihXVsGFDzZ49W8WKFZMk5ciRQ++//75q1KihmjVr6tixY/r+++/l4eHcf4c5cuTQt99+q99//11Vq1bVG2+8offff9+pcwAA8CCwGB7EAAAAAADYQQ8jAAAAAMAuEkYAQLr54osvbJa4uHsrX768u8OzelDiBAAgozEkFQCQbq5cuaIzZ87Y3eft7a3w8PAMjsi+ByVOAAAyGgkjAAAAAMAuhqQCAAAAAOwiYQQAAAAA2EXCCAAAAACwi4QRAAAAAGAXCSMAAAAAwC4SRgAAAACAXSSMAAAAAAC7SBgBAAAAAHb9H2IHHbdnvIXkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x333 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting contact maps for prot_A and prot_L\n",
    "contacts_prot_A_prot_L.interface_contacts_plot('L')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rOjpaJ06c0OTJk63nzuznAgDueO7OXAHcntatWxtJZuPGjdk67rHHHjOSzIQJE2zK161bZySZKlWqZNoTVaFCBXP69Glr+Z9//mmKFStmAgMDTWJiorX81h68zBw/ftwkJyfblKWmplrj2rZtm82+Ro0aGUnm/fffz3Cu2NhY6//Tv9nP7rf26fF6e3ub7777zloeHx9vSpcubYoUKWLCwsLMb7/9Zt138uRJ4+PjY2rXrm1zrj/++MNcuXIlw3MsWrTISDJTp061KU9v29DQUHPq1Clr+ZUrV0ytWrWMJLN161anrkMOehxd9dr179/fSDILFiywKT979qyJiIgwpUqVMgkJCdby9B7HunXr2vSYpdu0aZPdXrLcep+cP3/eBAUFmbJly5oTJ07Y1Fu6dKmRZJ566qlMr/+v9H+9UI8++qhJTU21lm/evNlYLBZTuXLlTH+WMnvulJQUU6VKFSPJrFu3zmbfhAkTjCQzZMgQm/L0HjlX6NGjh5Fk9u3bZ4wx5vDhw0aSad++fbbPNWnSJGOxWGze08YYc/XqVePn52eGDx/u1HnSe6Xbtm1r08t86NAh4+/vb4oVK2bi4+Ot5ek9jgEBAebHH3/McL62bdsaSea9996zKX/vvfeMJNOuXTub8vT37+1I/71UqVIlM3ny5Ey3IUOG0OMIIF8jcQQKuKpVqxpJ5pdffnH6mMTEROPv729CQkLMtWvXMuzv1KmTkWQz7DD9j92/Jgu37rv1j7Sskg979u7daySZKVOmWMu+++47I8m0bNkyy+NvN3EcNGhQhn3pScqLL76YYV/btm2Np6enuXnzZpbPkZqaaoKCgkzr1q1tytPb75VXXslwzH/+859MkwV7HCWOrnjt/vzzT+Pp6Znhj+t0b731lpFkPv/8c2tZ+h/emQ0rNMZx4mjP7b5PZs2aZSSZxYsXZ7r/3nvvNSVLlnQqFknG09MzwzBHY4zp1q2b3Z+lN998M0P9rVu3GkmZDu+8evWqCQkJMf7+/jaJvqsSx7NnzxovLy9To0YNm/LGjRsbi8Vijh8/nq3z1apVyzRq1ChD+fLly40k89///tep86QnjpkN+33yySczvI7pieOYMWMy1D958qSRZKpXr26T5BuT9vNZrVo1I8nmtXRl4ujMRuIIIL9iOQ6gEPrll1+UkJCgRo0aZToDZ/owt/3792fYd++992YoS5+S+vLly07HkJSUpFmzZqlRo0YKCgqSh4eHLBaL6tevL+l/E6ZI0nfffSdJNrOf5ZZ69eplKCtTpoykzIenlSlTRikpKfrjjz9sylesWKFOnTqpVKlS8vLyksVikYeHh+Lj422u7Vb33Xef3bLMXovscsVrt3v3bqWkpOjGjRvWCQhu3Xbu3Ckp7T32V40aNcp2zLn1PkmPc+fOnZlex40bN3T+/HmdP3/eqTgjIyMVERGRodzR65dZe3z//feSMh9qGhAQoAYNGighIUFHjhxxKq7sWLRokZKTkzMsnfG3v/1NxhgtXLjQ6XPFxMTop59+sjubatGiRdW2bVunz+ft7a0mTZpkKM9p+7Zq1SrDEhsWi0UtW7aUJP3www9Ox5YdnTp1kkn70j7DtmPHjlx5TqAg+Oabb+Tr66sTJ05kuv/ee++VxWLR66+/nseRObZ06VLr7QL5zZEjR+Tj45PphD85xT2OQAEXFhamX375RadOndI999zj1DHx8fGSZPeeyPTZLePi4jLsy+zevPR7h1JSUpx6fkl68MEH9fnnn+vuu+9W3759Vbp0aXl7e+vy5ct68803lZiYaK2bntTcddddTp8/p4KCgjKUpV+fo303b960lr3xxhsaO3asSpUqpY4dO6ps2bLy9/eXJM2ZM8fm2m5VunTpTMs8PDwyfS2yyxWv3cWLFyVJ3377rb799lu79a5du5ahLKt7cDOTW++T9OtIXwTanmvXrqlkyZJZni+z10763zVn9vpl1h6387N5uxYuXCgPD48M9ws+/PDDGjNmjBYuXKgXXnhBHh5Zf+e8atUqScqQOKakpGjNmjXq3LlzhgW1HQkJCcn0eQtS+wLInDFGo0eP1rBhwxQZGZlh//79+61f+kRFRWns2LF5HaJdS5cu1YEDB/Ll2sN33323BgwYoDFjxmjLli0uOSeJI1DANW/eXJs3b9ZXX33l9Df46QnQX3vJ0qWXZ5YoucLu3bv1+eefq1OnTlqzZo08PT2t+3bu3Kk333zTpn6xYsUkSadOncqVeFwpOTlZL7/8ssLDw7V//36b9aWMMZo5c6bdY8+dO5ch+T937pxSU1MzTfrcIf09kb5UQ3ZkdxH13HyfpF/HTz/9pJo1a2YrrsycO3cu0/L0n6XMXr/M2sNdP5vffvuttZc4s55TSTp58qQ2btzoVI/uqlWrVKlSJdWoUcOmfNu2bbpw4UKmPZGOXLhwQampqRmSx4LSvgDsW7dunfbt26elS5dmuj99KYxu3bppzZo12r59u5o1a5aXIbpESkqKkpOTs/Wl2e166qmn1KBBA5e1GUNVgQJu0KBB8vT01Pvvv68///zTYd303pmqVavKz89Pu3fv1vXr1zPUS/9m6nZmDkz/Iz+znqzffvtNUtqHwK3JgJQ2XOWv0oecrV+//raeNy+cP39ecXFxatKkSYZFiffs2aOEhAS7x2Z27elleTmLo6M2bNiwoSwWS54Mq8vN90njxo0lyWXXceLECcXGxmYoz+7rlz5U+q/rg0nS9evXtWfPHvn7+9t8weDp6Xnb7/eoqChJaeuKDRkyJMOWPktuej1HLl68qG3bttkdpurl5aWuXbtmK76bN29ahxffKrvtm15v69atGdZVM8Zkej53/04B7nTz5s1Tw4YNMx01dePGDS1dulT169fX7NmzJf1vrdlbTZkyRRaLRQcPHlS/fv0UHBys0NBQPfbYYxlGEFy+fFlDhgxRiRIlVLRoUXXr1k3Hjh2TxWKxWf/xzz//1OOPP66IiAj5+vqqVKlSat68uTZu3Cgp7ZaCNWvW6MSJE7JYLNZNko4fPy6LxaKZM2dq6tSpqlChgnx9fbVp0yZJabN6N23aVEWKFFFgYKA6dOiQ4fMo/Zp+/PFHPfTQQwoODlaJEiX09NNPKzk5WYcPH1bnzp0VGBio8uXLZ/rFdP369VWtWjW9++672XhF7CNxBAq4ypUra9y4cTp//ry6dOmimJiYDHVu3LihWbNmWX8h+vj4qF+/fjp//rymT59uU3fjxo1au3atKleurObNm+c4rhIlSkiSfv/99wz70oeibNu2zab84MGDGeKR0pKVRo0aaevWrZo/f36G/bf2MBUvXlwWiyXT580LpUuXlr+/v/bt22eTlF+6dEn/+Mc/HB771ltv2dyzd/XqVb300kuS0u4zyyuOXruwsDD16dNH27dv12uvvZbposa7du3K9AuJ7MrN98ngwYMVGBioiRMn6uDBgxnqXr9+PdNExZ6UlBRNnDjRpj22bNmi//73v6pcubLT3/Q2b95clSpV0tq1a61/nKSbPn26zp8/r379+snHx8daXqJECZ0/f143btxwOt5bXb16VZ988okCAgL0ySef6IMPPsiw/ec//1Hp0qX12Wef6cKFCw7Pt2bNGiUnJ2eaOK5evVr33Xef9T2WHf/85z9thoT/8ssvWrBggYKDg53uwSxXrpzatGljXX7jVgsWLNDBgwfVtm1bm15XRz8PAG5PUlKSNm7cqDZt2mS6f8WKFbp06ZIee+wxValSRS1atNDHH3+sq1evZlq/d+/euvvuu7V8+XI999xzWrp0qcaMGWPdn5qaqgceeEBLly7V+PHjtXLlSjVu3FidO3fOcK5HH31Un332mV544QWtX79eH3zwgdq3b2/9Hfivf/1LzZs3V1hYmHbs2GHdbvXWW2/p66+/1uuvv661a9eqatWqWrp0qXr06KGgoCB99NFHioqK0qVLl9S6desMn3eS1KdPH9WpU0fLly/XsGHDNHv2bI0ZM0Y9e/ZUt27dtHLlSrVt21bjx4/XihUrMhzfunVrrV27NtPP6+xiqCpwB5g6dapu3Lih2bNn65577lHbtm1Vs2ZNeXt7KyYmRhs3btSFCxc0depU6zEzZszQli1bNHXqVG3fvl2NGze2ruNYpEgR6/1OOdW0aVP5+/trzpw5io+Pt/a+Pffcc2rUqJEaNWqkTz75RGfOnFGTJk108uRJrV69Wt26ddOnn36a4XxLlixR69at9fjjj2vx4sVq2rSpbty4oYMHD+r777+3/iIvWrSoGjZsqK1bt2rw4MGqUqWKPDw81L9/f5UrVy7H1+MsDw8PjRgxQm+88Ybq1KmjBx54QPHx8Vq7dq0iIyMVHh5u99iGDRuqTp066tu3r3x9fbVixQodP35cw4YNs07akRccvXZS2ofl4cOHNW7cOOtrERwcrNjYWO3du1e//vqrzpw5k+nES9mRm++TUqVK6aOPPtJDDz2kOnXqqHPnzqpatapu3LihEydOaMuWLWrWrJnWrVvnVKy1a9fW5s2b1aRJE7Vt21anT5/WsmXL5O3trfnz5zv9s+Th4aHo6Gh16tRJXbt21UMPPaTIyEjt2rVLX3/9tSpVqqRXX33V5pi2bdtqz549euCBB3TffffJx8dHLVq0UIsWLZx6zmXLlunatWsaPHiwihYtmmkdLy8vPfLII5o1a5aWLFmiUaNG2T3fqlWrFBISkuGLpwMHDui3337L8guUzJQpU0aXL19W3bp11a1bN8XFxemjjz7SjRs3NH/+fAUGBjp9rnnz5qlFixYaNmyYPv/8c1WvXl0///yzVq9erVKlSmnevHk29du2batPP/1UDz30kLp27So/Pz/VqlVL3bp1y/Z1ALC1f/9+JSQkZDp5m5Q2ysHPz0/9+/eXJA0ZMkSDBw/WJ598osceeyxD/SFDhujZZ5+VJLVv315Hjx7VggULFBUVJYvFonXr1mnbtm2aN2+ennjiCUlShw4d5OPjowkTJtic69tvv9XQoUM1bNgwa9mtX1JVr15dxYoVk6+vb6aTd0mSn5+fvvzyS3l7e0tKS1ybN2+uWrVqae3atdbPhq5du6pSpUoaP358hvkDHn/8cT399NPWa1q/fr3mzp2rFStW6P/9v/8nKS05/OKLL/Tvf/9bvXr1sjn+3nvv1bx583T48GFVrVo10zid5o6pXAHkjt27d5vHHnvMVK5c2fj7+xtfX19Tvnx5069fP7N+/foM9f/8808zcuRIExkZaby9vU3JkiXNgw8+aH766acMddOXEIiJicmwL336+02bNtmUr1mzxjRs2ND4+/tbp5pPd+7cOfPYY4+Z8PBw4+fnZ2rVqmXeeecdc+zYMbtT0p89e9aMGjXKVKxY0fj4+JgSJUqYxo0bm1mzZtnUO3z4sOnataspVqyYsVgsmcb2V46WoLB3ffbaJSkpybzyyiumSpUqxtfX15QrV848/fTT5sqVK5kunZB+jqNHj5pp06ZZr69SpUpmxowZGdYxdEQOluNw1WtnjDHXr183M2fONPXr1zcBAQHG39/fVKhQwfTs2dN8+OGHNsuTZLWcgaPlOHLzfWKMMb/88osZMmSIiYyMND4+PqZ48eKmVq1aZuTIkTbreTqS3uYnTpwwDz30kClevLjx9/c3LVu2zLDOpDGOX490P/74o3nwwQdNyZIljbe3t4mMjDQjR440f/75Z4a6V65cMcOGDTNlypQxHh4e2V7apEmTJhmWDMnMTz/9ZCSZWrVq2a1z48YNU7Ro0Uxfl6lTpxpJ5tixY07HZsz/lhu5cOGCGTp0qCldurTx9fU1DRo0yHSJF0c/r+mOHz9uBg8ebMqUKWO8vLxMmTJlzODBgzNdcuTmzZtm3Lhxply5csbLyytHS2akL8fRqVMnu3V27NjBchwodFauXGkkma+//jrDvmPHjhmLxWL69+9vLbt69aoJDAw0zZs3t6mb/nP/16XJ3n33XSPJnD171hhjzLhx44ykDGsKHz9+PMPvzrZt25pixYqZl19+2ezYscNmHdl03bp1y3Q5pPSf+b8uC/Tzzz8bSWbmzJkZjvn73/9uPDw8rMukpV/T4cOHber169fPWCwWm/WSjTGmadOmpn79+hnOu2rVKqMcrPedGRJHAHAzZxIJ5F+ZJeuF1Zo1a4wks2LFigz7GjZsaGrXrp3tc7pqnUoA+c/SpUuNJLN9+/YM+yZNmmT9fXLp0iXrNmDAACPJHDp0yFo3Pcn665dr6V8Kp3++DhkyxHh5eWV4roSEhAyJ459//mlGjRplXUu2aNGi5tFHHzVnzpyx1skqcfxrgvjNN9/YXUP45ZdfNpLM77//7vCaBg4caAICAjIc36pVqwzr8BpjzJdffplhfeWc4h5HAADgEqtWrZKfn1+GmVfPnDmjPXv2qHv37m6KDEB+lL7cUfoSSelSU1MVHR0tSerVq5eKFy9u3f79739LynySnKyEhIQoOTk5w/OdPXs209jmzJmj48eP68SJE5o+fbpWrFihQYMGOf18f53dOSQkRFLa78S/On36tDw8PFS8eHGnz++M9Gt1ZmmprJA4AgAAl3jvvfeUkJCggIAAm/IyZcooNTVVL7/8spsiA5AfVatWTdL/ZtFO9+WXX+r333/Xk08+qU2bNmXYatSooQ8//FDJycnZer5WrVpJkj7++GOb8mXLljk8rly5cnrqqafUoUMH7du3z1ru6+vrcLb0v7rnnnt01113aenSpTaT1Vy7dk3Lly+3zrTqSseOHZOHh4fTa307wuQ4AADcYS5fvqw5c+Y4VffW6efhPGfbbfTo0dY1RgHYKlu2rCpWrKidO3dq5MiR1vKoqCh5eXnp+eefz3RSueHDh2vkyJFas2ZNttaF7dy5s5o3b65nnnlG8fHxql+/vnbs2KEPP/xQkqyT1cTFxalNmzbq37+/qlatqsDAQO3evVvr1q2zmXymVq1aWrFihebNm6f69evLw8NDDRo0sPv8Hh4emjlzpgYMGKD7779fw4cPV2Jiol577TVdvnw5w+RnrrBz507VrVvXJT2ZJI4A4GbR0dHWITkoeIwLpjh3tcuXL+vFF190qm5+TxyPHz/u7hAy5Wz7Dho0iMQRcGDAgAGaO3euEhMT5evrq/Pnz+vzzz/X/fffb3cm8kcffVTjx49XVFRUthJHDw8Pff7553rmmWf06quvKikpSc2bN9eSJUvUpEkT68+qn5+fGjdurMWLF+v48eO6efOmypUrp/Hjx2vcuHHW840aNUoHDx7U888/r7i4OJm0+WMcxtC/f38FBARo+vTp6tu3rzw9PdWkSRNt2rTJ6aWbnHX16lV99dVXLhvtYTH58RMPAAAAwB3v9OnTqlChgj788EP17dvXLTEsXbpUAwYM0Lfffuvy5M2doqKiNGrUKMXGxrqkx5HEEQAAAIDbjB8/XmvXrtX+/ftvaw1pZ3z00Uc6deqUatWqJQ8PD+3cuVOvvfaa6tWrpy1btuTqc+el5ORkVa9eXQMHDtTEiRNdck6GqgIAAABwm0mTJqlIkSI6deqUIiIicvW5AgMDtWzZMk2dOlXXrl1TmTJlNGjQIE2dOjVXnzevxcbG6pFHHtEzzzzjsnPS4wgAAAAAcIjlOAAAAAAADpE4AgAAAAAcInEEAAAAADhE4ggAAAAAcIjEEQAAAADgEIkjAAAAAMAhEkcAAAAAgEMkjgAAAAAAh0gcAQAAAAAOkTgCAAAAABwicQQAAAAAOETiCAAAAABwiMQRAAAAAOAQiSMAAAAAwCESRwAAAACAQySOAAAAAACHSBwBAAAAAA6ROAIAAAAAHCJxBAAAAAA4ROIIAAAAAHCIxBEAAAAA4JCXuwPID1JTU3X69GkFBgbKYrG4OxwAAADgjmGM0ZUrVxQeHi4Pj4LVb3Xjxg0lJSVlWc/Hx0d+fn55EJH7kDhKOn36tCIiItwdBgAAAHDHio2NVdmyZd0dhtNu3LihChUq6OzZs1nWDQsLU0xMzB2dPJI4SgoMDJSU9mYOCgpyczQAAADAnSM+Pl4RERHWv7kLiqSkJJ09e1axJ2Ic5gjx8fGKiKygpKQkEsc7Xfrw1KCgIBJHAAAAIBcU1FvCgooGKKhogP0KqSl5F4wbkTgCAAAAgD0piWmbo/2FAIkjAAAAANiTmuK4V5Eex9y3detWvfbaa9q7d6/OnDmjlStXqmfPntb9xhi9+OKLev/993Xp0iU1btxY77zzjmrUqGGtk5iYqLFjx+qjjz5SQkKC2rVrp3/961/5/8bbpfc7V6//F7kbRw7NLVvMqXpP/X45V+PIia333ONUvZaHD+dyJDm0pLNz9R5Zl7txAABy1ycPZl2nz6e5H0cOfOXkZ227/PpZC9wqNUVKTXa8vxBw63y4165dU506dTR37txM98+cOVOzZs3S3LlztXv3boWFhalDhw66cuWKtc7o0aO1cuVKLVu2TNu2bdPVq1d1//33KyWlcLyAAAAAAHKRScl6KwTc2uPYpUsXdenSJdN9xhjNmTNHEydOVK9evSRJixYtUmhoqJYuXarhw4crLi5OUVFRWrx4sdq3by9JWrJkiSIiIrRx40Z16tQpz64l2+Ji3R0BAABA7rh2zt0RFF5//ORcvdBauRvHnSQ5UUr2cby/EMi3K3DGxMTo7Nmz6tixo7XM19dXrVq10vbt2yVJe/fu1c2bN23qhIeHq2bNmtY6mUlMTFR8fLzNBgAAAAAZ0OMoKR8njukLbYaGhtqUh4aGWvedPXtWPj4+Kl68uN06mZk+fbqCg4OtW0REhIujBwAAAHBHMP93j6O9rZAkjvl+VtW/rvdijMlyDZis6kyYMEFPP/209XH6oqR5KfXmTafq5dvM3tENwgAAoFBz9u8coEBgVlVJ+TgvCQsLk6QMPYfnzp2z9kKGhYUpKSlJly5dslsnM76+vgoKCrLZAAAAACCD9HUcHW2FQL5NHCtUqKCwsDBt2LDBWpaUlKQtW7aoWbNmkqT69evL29vbps6ZM2d04MABax0AAAAAyLH0HkdHWyHg1qGqV69e1dGjR62PY2JitH//fpUoUULlypXT6NGjNW3aNFWpUkVVqlTRtGnTVKRIEfXv31+SFBwcrCFDhuiZZ55RSEiISpQoobFjx6pWrVrWWVYBAAAKJGPcHUHOFeTYgb9iHUdJbk4c9+zZozZt2lgfp993OHDgQEVHR2vcuHFKSEjQiBEjdOnSJTVu3Fjr169XYGCg9ZjZs2fLy8tLffr0UUJCgtq1a6fo6Gh5enrm+fVkR+KlJKfq+edyHDn11L+cWJQ4n2pZ0BcbfmSduyMAAOSFq/Yn+svvGky8z90hAK6T1cypTI6T+1q3bi3j4Bspi8WiKVOmaMqUKXbr+Pn56e2339bbb7+dCxECAAAAKNSSE6VkB2lTIVnHMd/PqgoAAAAAbkOPoyQSR7f5fqlzQ2mbTc7lQAAAAFzso+e+dqreiFG5HEhhFFrL3RHcedLXa3S0vxAgcQQAAMiPEi66OwIAkmRSHU+AY1LzLhY3InEEAAAAAHtSEqUUB6MFC8k6jiSObtKsoM/s2TPa3REAAIB8qmWtCu4OAXCdrNZqZDkOAAAAACjkuMdREokjAAAAANiXclNKcbAGe8rNvIvFjUgcAQAA8qN7h7g7ghyr+V0BvyUHuBU9jpJIHAEAAADAPtZxlETiCAAAAAD20eMoicQRAAAAAOxLvel4OY5U7nEEAAAAgMKNHkdJJI4AAAD5U7Ox7o4AgMQ6jv+HxBEAAAAA7KHHURKJIwAAAADYl3JTSvFwvL8QIHEEAAAAAHtSk6VUR5Pj0OMIAAAAAIUb6zhKInEEAAAAAPvocZRE4ggAAAAA9qXelFIsjvcXAiSOAAAAAGBParKU6mByHHocAQAAAKCQYx1HSSSOAAAAAGAfPY6SSBwBAAAAwL6UJMlRp2JKUp6F4k4kjgAAAABgT2qK415FhqoCAAAAQCFnUiTjYKgq6zgCAAAAQCGXmiylOlqOg3scAQAAAKBwS0mSUoyD/azjCAAAAACFm0lx3OPIUFUAAAAAKORSs0gcmRwHAAAAAAo5kyIZehxJHAEAAADAnuREKdlBcpjM5DgAAAAAULilpkipWewvBEgcAQAAAMAekyI5mFSVoaoAAAAAUNjR4yiJxBEAAAAA7EtJlFI8HewvHImjh7sDAAAAAIB8KzUl6y0bypcvL4vFkmF78sknc+kCXIMeRwAAAACwx8jxUFVH9z9mYvfu3Uq5pZfywIED6tChgx566KEchZdXSBwBAAAAwB6jLCbHyd7pSpUqZfP41VdfVaVKldSqVatsh5aXGKoKAAAAAPYkO7HlUFJSkpYsWaLHHntMFovl9mPNRfQ4AgAAAIA9qcpiVtW0f+Lj422KfX195evr6/DUn332mS5fvqxBgwbdVoh5gR5HAAAAALDHOLFJioiIUHBwsHWbPn16lqeOiopSly5dFB4enkvBuw49jgAAAABgj5M9jrGxsQoKCrIWZ9XbeOLECW3cuFErVqy4/RjzAIkjAAAAANiT8n+bo/2SgoKCbBLHrCxcuFClS5dWt27dbiu8vOJU4vjX8bqOZKexAAAAACBfc7LHMVunTE3VwoULNXDgQHl5FYy+PKfucSxWrJiKFy/u1OZqycnJmjRpkipUqCB/f39VrFhRL730klJT//cKGWM0ZcoUhYeHy9/fX61bt9bBgwddHgsAAACAQiZ9HUd7WzaX45CkjRs36uTJk3rsscdcF2cucyq93bRpk/X/x48f13PPPadBgwapadOmkqQdO3Zo0aJFTt0Aml0zZszQu+++q0WLFqlGjRras2ePBg8erODgYI0aNUqSNHPmTM2aNUvR0dG6++67NXXqVHXo0EGHDx9WYGCgy2MCAAAAUEi4eB1HSerYsaOMycGBbmQx2Yy4Xbt2Gjp0qPr162dTvnTpUr3//vvavHmzK+PT/fffr9DQUEVFRVnLevfurSJFimjx4sUyxig8PFyjR4/W+PHjJUmJiYkKDQ3VjBkzNHz48CyfIz4+XsHBwYqLi2OoLQAAAOBCBfVvbWvcz1RSkK+n/XqJKQp+47cCd33Zle3lOHbs2KEGDRpkKG/QoIG+++47lwR1qxYtWuirr77SkSNHJEk//PCDtm3bpq5du0qSYmJidPbsWXXs2NF6jK+vr1q1aqXt27e7PB4AAAAAhYijYapZ3f94B8n2nZgRERF699139cYbb9iUv/fee4qIiHBZYOnGjx+vuLg4Va1aVZ6enkpJSdErr7xi7fE8e/asJCk0NNTmuNDQUJ04cSLTcyYmJioxMdH6ODuT/wAAAAAoRHJhqGpBlO3Ecfbs2erdu7e+/PJLNWnSRJK0c+dO/fbbb1q+fLnLA/z444+1ZMkSLV26VDVq1ND+/fs1evRohYeHa+DAgdZ6FovF5jhjTIaydNOnT9eLL77o8lgBAAAA3GFyYVbVgijbQ1W7du2qI0eOqHv37rp48aIuXLigHj166MiRI9bho6707LPP6rnnntPDDz+sWrVq6dFHH9WYMWOsE/GEhYVJ+l/PY7pz585l6IVMN2HCBMXFxVm32NhYl8cNAAAA4A6Q4sRWCORo0ZCIiAhNmzbN1bFk6vr16/LwsM1vPT09rctxVKhQQWFhYdqwYYPq1asnSUpKStKWLVs0Y8aMTM/p6+srX1/f3A0cAAAAQMFHj6MkJxPHH3/8UTVr1pSHh4d+/PFHh3Vr167tksDSPfDAA3rllVdUrlw51ahRQ99//71mzZplXfPEYrFo9OjRmjZtmqpUqaIqVapo2rRpKlKkiPr37+/SWAAAAAAUMiSOkpxMHOvWrauzZ8+qdOnSqlu3riwWS6brjlgsFqWkuLav9u2339Y///lPjRgxQufOnVN4eLiGDx+uF154wVpn3LhxSkhI0IgRI3Tp0iU1btxY69evZw1HAAAAALfFSHK0gGEhmRvHuXUcT5w4oXLlyslisdidqTRdZGSky4LLKwV1bRkAAAAgvyuof2unx31hSCUF+ThYxzEpRSFRd/46jk71ON6aDDqbGHbr1k0ffPCBypQpk7PIAAAAAMDNUk3a5mh/YZCjyXGcsXXrViUkJOTW6QEAAAAg93GPo6RcTBwBAAAAoKBLTU3bHO0vDEgcAQAAAMCOlJS0zdH+woDEEQAAAADsMKlpm6P9hQGJIwAAAADYQeKYhsQRAAAAAOwwJot1HJlV9fY8//zzKlGiRG6dHgAAAAByXUqKlJLseH9h4JHdAxYtWqQ1a9ZYH48bN07FihVTs2bNdOLECWv5hAkTVKxYMZcECQAAAADukN7j6GgrDLKdOE6bNk3+/v6SpB07dmju3LmaOXOmSpYsqTFjxrg8QAAAAABwF5P6vyU5Mtu4x9GO2NhYVa5cWZL02Wef6cEHH9Tjjz+u5s2bq3Xr1q6ODwAAAADchslx0mS7x7Fo0aK6cOGCJGn9+vVq3769JMnPz08JCQmujQ4AAAAA3Cg1JeutMMh2j2OHDh00dOhQ1atXT0eOHFG3bt0kSQcPHlT58uVdHR8AAAAAuA09jmmy3eP4zjvvqGnTpvrzzz+1fPlyhYSESJL27t2rfv36uTxAAAAAAHAXR/c3pm+FQbZ7HIsVK6a5c+dmKH/xxRddEhAAAAAA5Bes45gm2z2OkvTNN9/okUceUbNmzXTq1ClJ0uLFi7Vt2zaXBgcAAAAA7pS+jqPdrZDc45jtxHH58uXq1KmT/P39tW/fPiUmJkqSrly5omnTprk8QAAAAABwF9ZxTJPtxHHq1Kl69913NX/+fHl7e1vLmzVrpn379rk0OAAAAABwp/TJcRxthUG273E8fPiwWrZsmaE8KChIly9fdkVMAAAAAJAvZDUBTmGZHCfbPY5lypTR0aNHM5Rv27ZNFStWdElQAAAAAJAfsI5jmmwnjsOHD9eoUaO0a9cuWSwWnT59Wv/+9781duxYjRgxIjdiBAAAAAD3yGqYaiHpccz2UNVx48YpLi5Obdq00Y0bN9SyZUv5+vpq7Nixeuqpp3IjRgAAAABwC4aqpslW4piSkqJt27bpmWee0cSJE/Xzzz8rNTVV1atXV9GiRXMrRgAAAABwC9ZxTJOtxNHT01OdOnXSoUOHVKJECTVo0CC34gIAAAAAt0tNcTwalXsc7ahVq5aOHTuWG7EAAAAAQL6SarLeCoNsJ46vvPKKxo4dqy+++EJnzpxRfHy8zQYAAAAAdwrWcUyT7clxOnfuLEnq3r27LBaLtdwYI4vFopSUQtJXCwAAAOCOZ1IlY3G8vzDIduK4adOm3IgDAAAAAPKdlBTJUddYYek3y3bi2KpVq9yIAwAAAADyndRUKdVBjyPLcQAAAABAYZcqyUHi6HDK1TsIiSMAAAAA2ME6jmlIHAEAAADAjtQs7nEsLOs4kjgCAAAAgB3m/zZH+wsDEkcAAAAAsCNVjm9jLCS3ODqfONarV89m3UZ79u3bd1sBAQAAAEB+QY9jGqcTx549e1r/b4zR9OnT9cQTT6hEiRK5ERcAAAAAuF2KsljHMa8CcTOLMTmbBygwMFA//PCDKlas6OqY8lx8fLyCg4MVFxenoKAgd4cDAAAA3DEK6t/a6XGvqVRJAZ6edutdS0lRt99+K3DXl13c4wgAAAAAdjBUNY2HuwMAAAAAgPzKOLFl16lTp/TII48oJCRERYoUUd26dbV3716XxZwb6HEEAAAAADtcfY/jpUuX1Lx5c7Vp00Zr165V6dKl9dtvv6lYsWI5DzIPOJ04vvXWWzaPk5OTFR0drZIlS9qUjxw50jWRAQAAAICbuXqo6owZMxQREaGFCxday8qXL5+DyPKW05PjVKhQIeuTWSw6duzYbQeV1wrqDbsAAABAfldQ/9ZOj3uFE5Pj9MrG5DjVq1dXp06d9Pvvv2vLli266667NGLECA0bNsyV4buc0z2OMTExuRkHAAAAAOQ7qf+3OdovpSWat/L19ZWvr2+G+seOHdO8efP09NNP6/nnn9d3332nkSNHytfXV3/7299cFrer5drkOLVq1VJsbGxunR4AAAAAcp3R/5LHzLb04ZsREREKDg62btOnT8/0fKmpqbr33ns1bdo01atXT8OHD9ewYcM0b9683L+Y25Brk+McP35cN2/ezK3TAwAAAECuc/Yex9jYWJuhqpn1NkpSmTJlVL16dZuyatWqafny5bcXaC4rEMtxZDVdrTFGU6ZMUXh4uPz9/dW6dWsdPHjQjREDAAAAuBM46m28dRhrUFCQzWYvcWzevLkOHz5sU3bkyBFFRkbm0hW4Rr5PHNOnq/X29tbatWv1888/64033rCZrnbmzJmaNWuW5s6dq927dyssLEwdOnTQlStX3Bc4AAAAgALP2cTRWWPGjNHOnTs1bdo0HT16VEuXLtX777+vJ5980qVxu1q+X8cxq+lqjTGaM2eOJk6cqF69ekmSFi1apNDQUC1dulTDhw/P65ABAAAA3CFcvRxHw4YNtXLlSk2YMEEvvfSSKlSooDlz5mjAgAG3EWXuy/c9jqtXr1aDBg300EMPqXTp0qpXr57mz59v3R8TE6OzZ8+qY8eO1jJfX1+1atVK27dvz/SciYmJio+Pt9kAAAAA4K9c3eMoSffff79++ukn3bhxQ4cOHcr3S3FIBSBxTJ+utkqVKvryyy/1xBNPaOTIkfrwww8lSWfPnpUkhYaG2hwXGhpq3fdX06dPt5nxKCIiIncvAgAAAECBZJzYCoNsD1X98MMP1bdv3ww3eyYlJWnZsmXWtUfee++9DMlcTqSmpqpBgwaaNm2aJKlevXo6ePCg5s2bZ7POicVisTnOGJOhLN2ECRP09NNPWx/Hx8eTPAIAcKd5t55z9Z74PnfjAFCguXqoakGV7R7HwYMHKy4uLkP5lStXNHjwYOvj/v37KyAg4Paik/3pak+ePClJCgsLk6QMvYvnzp2zm7j6+vpmmPUIAAAAAP4qxYmtMMh24mivJ+/3339XcHCwS4K6VVbT1VaoUEFhYWHasGGDdX9SUpK2bNmiZs2auTweAAAAAIUHQ1XTOD1UtV69erJYLLJYLGrXrp28vP53aEpKimJiYtS5c2eXBzhmzBg1a9ZM06ZNU58+ffTdd9/p/fff1/vvvy8pbYjq6NGjNW3aNFWpUkVVqlTRtGnTVKRIEfXv39/l8QAAAAAoPLKaACcnk+MURE4njj179pQk7d+/X506dVLRokWt+3x8fFS+fHn17t3b5QE6M13tuHHjlJCQoBEjRujSpUtq3Lix1q9fr8DAQJfHAwAAAACFjcUYk63e1UWLFqlv377y8/PLrZjyXHx8vIKDgxUXF8f9jgAA3CmYHAfIFwrq39rpcb9XqZL8PT3t1ktISdHw334rcNeXXdmeVXXgwIGSpL179+rQoUOyWCyqXr266tVz8pczAAAoMMbdc0+WdWb+ZS6C/OJKTIxT9RifBMARhqqmyXbieO7cOT388MPavHmzihUrJmOM4uLi1KZNGy1btkylSpXKjTgBAAAAAG6S7VlV//GPfyg+Pl4HDx7UxYsXdenSJR04cEDx8fEaOXJkbsQIAAAAAG6R6sRWGGS7x3HdunXauHGjqlWrZi2rXr263nnnHXXs2NGlwQEAAACAO2W1VmNhWccx24ljamqqvL29M5R7e3srNbWw5NsAACC/W/dZqFP1HpqRy4EAKNCyWquRdRztaNu2rUaNGqWPPvpI4eHhkqRTp05pzJgxateuncsDBAAA7nPZyQlmAOBOReKYJtv3OM6dO1dXrlxR+fLlValSJVWuXFkVKlTQlStX9Pbbb+dGjAAAAADgFtzjmCbbPY4RERHat2+fNmzYoF9++UXGGFWvXl3t27fPjfgAAAAAwG1YjiNNthLH5ORk+fn5af/+/erQoYM6dOiQW3EBAIB8YP7Nm1nXsVhkTP4brHWRYbbIqVVDsq7TIyr348iBV5xYe1WSJubT9VfzI4aqpslW4ujl5aXIyEilpBSWuYMAAEBBdbVCBXeHAOAOQOKYJtv3OE6aNEkTJkzQxYsXcyMeAAAAAMg3UvW/JTky2xiqasdbb72lo0ePKjw8XJGRkQoICLDZv2/fPpcFBwAAkFNjjxxxqt4zuRxHTv3UIOshh7X2MNwQyG30OKbJduLYo0cPWSyW3IgFAAAAAPIVEsc02U4cp0yZkgthAAAAAED+w6yqaZy+x/H69et68sknddddd6l06dLq37+/zp8/n5uxAQAAAIBbObq/MX0rDJxOHCdPnqzo6Gh169ZNDz/8sDZs2KC///3vuRkbAAAAALiVcWIrDJweqrpixQpFRUXp4YcfliQ98sgjat68uVJSUuTp6ZlrAQIAAACAu3CPYxqnexxjY2N13333WR83atRIXl5eOn36dK4EBgAAAADuRo9jGqd7HFNSUuTj42N7sJeXkpOTXR4UAADIH8y3r2VdqdnY3A8EyEs9otwdQY5NPMwSLa7G5DhpnE4cjTEaNGiQfH19rWU3btzQE088YbOW44oVK1wbIQAAAAC4CYljGqcTx4EDB2Yoe+SRR1waDAAAyGfoTXSbWnvoOQLyA+5xTON04rhw4cLcjAMAAMCljCksf84ByE1GjnsVC8tvGqcTRwAAAAAobBiqmobEEQAAAADsYKhqGhJHAAAAALCDHsc0JI4AAAAAYAc9jmlIHAEAAADADnoc05A4AgAAAIAdJI5pSBwBAAAAwA4SxzQe7g4AAAAAAJC/0eMIAAAAAHak/N/maH9hQOIIAAAAAHYwq2oaEkcAAAAAsMPI8X2MhSVx5B5HAAAAAIBD9DgCAAAAgB3c45iGxBEAAAAA7GA5jjQMVQUAAAAAOESPIwAAAADYQY9jGhJHAAAAALCDexzTkDgCAAAAgB2s45iGxBEAAAAA7CBxTMPkOAAAAABgR6oTW3ZMmTJFFovFZgsLC3NpzLmBHkcAAAAAsCM3JsepUaOGNm7caH3s6emZg7PkLRJHAAAAALAjN4aqenl5FYhexlsVqKGq06dPl8Vi0ejRo61lxhhNmTJF4eHh8vf3V+vWrXXw4EH3BQkAAADgjmHkeJhqeuIYHx9vsyUmJto956+//qrw8HBVqFBBDz/8sI4dO5a7F+ECBSZx3L17t95//33Vrl3bpnzmzJmaNWuW5s6dq927dyssLEwdOnTQlStX3BQpAAAAgDuFcWKTpIiICAUHB1u36dOnZ3q+xo0b68MPP9SXX36p+fPn6+zZs2rWrJkuXLiQ+xdzGwpE4nj16lUNGDBA8+fPV/Hixa3lxhjNmTNHEydOVK9evVSzZk0tWrRI169f19KlS90YMQAAAIA7gbOT48TGxiouLs66TZgwIdPzdenSRb1791atWrXUvn17rVmzRpK0aNGiXL6S21MgEscnn3xS3bp1U/v27W3KY2JidPbsWXXs2NFa5uvrq1atWmn79u15HSYAAACAO4yziWNQUJDN5uvr69T5AwICVKtWLf3666+uD96F8v3kOMuWLdO+ffu0e/fuDPvOnj0rSQoNDbUpDw0N1YkTJ+yeMzEx0WbMcXx8vIuiBQAAAHAnye11HBMTE3Xo0CHdd999t3mm3JWvexxjY2M1atQoLVmyRH5+fnbrWSwWm8fGmAxlt5o+fbrN+OOIiAiXxQwAAADgzuHsPY7OGjt2rLZs2aKYmBjt2rVLDz74oOLj4zVw4ECXxu1q+Tpx3Lt3r86dO6f69evLy8tLXl5e2rJli9566y15eXlZexrTex7TnTt3LkMv5K0mTJhgM/44NjY2V68DAAAAQMGU4sSWHb///rv69eune+65R7169ZKPj4927typyMhIl8btavl6qGq7du30008/2ZQNHjxYVatW1fjx41WxYkWFhYVpw4YNqlevniQpKSlJW7Zs0YwZM+ye19fX1+kxxwAAAAAKL1cPVV22bNltROM++TpxDAwMVM2aNW3KAgICFBISYi0fPXq0pk2bpipVqqhKlSqaNm2aihQpov79+7sjZAAAAAB3kNy+x7GgyNeJozPGjRunhIQEjRgxQpcuXVLjxo21fv16BQYGujs0AAAAAAXcrTOn2ttfGFiMMYUlSbYrPj5ewcHBiouLU1BQkLvDAQAAAO4YBfVv7fS4O1SqJG9PT7v1bqakaMNvvxW468uuAt/jCAAAAAC5haGqaUgcAQAAAMAOI8fDUUkcAQAAAKCQo8cxDYkjAAAAANjB5DhpSBwBAAAAwA4SxzQkjgAAAABgB0NV05A4AgAAAIAdJI5pSBwBAAAAwI4USZYs9hcGJI4AAODONL+xc/WG7crdOHLofJ97sqxT8pPDeRBJDkQ1d67ekG9zNw7ABehxTEPiCAAAAAB2kDimIXEEAAAAADuYVTUNiSMAAAAA2EHimIbEEQAAAADsMHKcHDJUFQAAAAAKOe5xTEPiCAAAAAB2kDimIXEEAAAAADuyWqexsKzj6OHuAAAAAAAA+Rs9jgAAAABgR6okSxb7CwMSRwAAcGcatsvdEdyWkp8cdncIOTfkW3dHALgM9zimIXEEAAAAADu4xzENiSMAAAAA2EGPYxoSRwAAAACwg8QxDYmjJGPSXu74+Hg3RwIAAADcWdL/xk7/m7ugSU5NdTgBTmpq4Zgeh8RR0pUrVyRJERERbo4EAAAAuDNduXJFwcHB7g7DaT4+PgoLC1NMTEyWdcPCwuTj45MHUbmPxRTU1N+FUlNTdfr0aQUGBspicTTZLuyJj49XRESEYmNjFRQU5O5wChXa3n1oe/eh7d2Htncf2t49aPfbZ4zRlStXFB4eLg+PgrWM/I0bN5SUlJRlPR8fH/n5+eVBRO5Dj6MkDw8PlS1b1t1h3BGCgoL4peomtL370PbuQ9u7D23vPrS9e9Dut6cg9TTeys/P745PCJ1VsFJ+AAAAAECeI3EEAAAAADhE4giX8PX11eTJk+Xr6+vuUAod2t59aHv3oe3dh7Z3H9rePWh3IA2T4wAAAAAAHKLHEQAAAADgEIkjAAAAAMAhEkcAAAAAgEMkjnDK9OnTZbFYNHr0aGuZxWLJdHvttddsjt2xY4fatm2rgIAAFStWTK1bt1ZCQkIeX0HBldO2P3v2rB599FGFhYUpICBA9957rz799FM3XEHBlVnbX716VU899ZTKli0rf39/VatWTfPmzbM5LjExUf/4xz9UsmRJBQQEqHv37vr999/zOPqCLSdtf/HiRf3jH//QPffcoyJFiqhcuXIaOXKk4uLi3HAFBVdO3/fpjDHq0qWLLBaLPvvss7wJ+g5xO23PZ+3tyWnb81mLwsTL3QEg/9u9e7fef/991a5d26b8zJkzNo/Xrl2rIUOGqHfv3tayHTt2qHPnzpowYYLefvtt+fj46IcffpCHB99ZOON22v7RRx9VXFycVq9erZIlS2rp0qXq27ev9uzZo3r16uVJ/AWZvbYfM2aMNm3apCVLlqh8+fJav369RowYofDwcPXo0UOSNHr0aH3++edatmyZQkJC9Mwzz+j+++/X3r175enp6Y7LKVBy2vanT5/W6dOn9frrr6t69eo6ceKEnnjiCZ0+fZo/5Jx0O+/7dHPmzJHFYsnLsO8It9P2fNbenttpez5rUagYwIErV66YKlWqmA0bNphWrVqZUaNG2a3bo0cP07ZtW5uyxo0bm0mTJuVylHem2237gIAA8+GHH9qUlShRwnzwwQe5Ee4dxVHb16hRw7z00ks29e+9917r+/zy5cvG29vbLFu2zLr/1KlTxsPDw6xbty5P4i/IbqftM/PJJ58YHx8fc/PmzdwK+Y7hirbfv3+/KVu2rDlz5oyRZFauXJkHkRd8t9v2fNbm3O22PZ+1KEz4KgoOPfnkk+rWrZvat2/vsN4ff/yhNWvWaMiQIdayc+fOadeuXSpdurSaNWum0NBQtWrVStu2bcvtsO8It9P2ktSiRQt9/PHHunjxolJTU7Vs2TIlJiaqdevWuRj1ncFR27do0UKrV6/WqVOnZIzRpk2bdOTIEXXq1EmStHfvXt28eVMdO3a0HhMeHq6aNWtq+/bteXYNBdXttH1m4uLiFBQUJC8vBthk5Xbb/vr16+rXr5/mzp2rsLCwvAy9wLudtuez9vbc7vuez1oUJnySwq5ly5Zp37592r17d5Z1Fy1apMDAQPXq1ctaduzYMUnSlClT9Prrr6tu3br68MMP1a5dOx04cEBVqlTJtdgLuttte0n6+OOP1bdvX4WEhMjLy0tFihTRypUrValSpdwK+46QVdu/9dZbGjZsmMqWLSsvLy95eHjogw8+UIsWLSSl3e/i4+Oj4sWL2xwXGhqqs2fP5nr8Bdnttv1fXbhwQS+//LKGDx+em2HfEVzR9mPGjFGzZs0yDF2FY7fb9nzW5pwr3vd81qIwIXFEpmJjYzVq1CitX79efn5+WdZfsGCBBgwYYFM3NTVVkjR8+HANHjxYklSvXj199dVXWrBggaZPn547wRdwrmh7SZo0aZIuXbqkjRs3qmTJkvrss8/00EMP6ZtvvlGtWrVyK/wCzZm2f+utt7Rz506tXr1akZGR2rp1q0aMGKEyZco47B02xnDflwOubvv4+Hh169ZN1atX1+TJk/PiEgosV7T96tWr9fXXX+v777/P4+gLNle0PZ+1OeOq3zl81qJQcec4WeRfK1euNJKMp6endZNkLBaL8fT0NMnJyda6W7duNZLM/v37bc5x7NgxI8ksXrzYprxPnz6mf//+eXIdBZEr2v7o0aNGkjlw4IBNebt27czw4cPz5DoKoqza/urVq8bb29t88cUXNscNGTLEdOrUyRhjzFdffWUkmYsXL9rUqV27tnnhhRfy7FoKGle0fbr4+HjTtGlT065dO5OQkJCXl1EguaLtR40aZa1/6zk8PDxMq1at3HBVBYMr2p7P2pxxRdvzWYvChh5HZKpdu3b66aefbMoGDx6sqlWravz48TYzQ0ZFRal+/fqqU6eOTf3y5csrPDxchw8ftik/cuSIunTpknvBF3CuaPvr169LUoYZ9Tw9Pa3fTiOjrNo+JSVFN2/edNiu9evXl7e3tzZs2KA+ffpISpsF98CBA5o5c2beXEgB5Iq2l9J6Gjt16iRfX1+tXr3aqV77ws4Vbf/cc89p6NChNvtr1aql2bNn64EHHsjdCyjAXNH2fNbmjCvans9aFDYkjshUYGCgatasaVMWEBCgkJAQm/L4+Hj95z//0RtvvJHhHBaLRc8++6wmT56sOnXqqG7dulq0aJF++eUXpsZ3wBVtX7VqVVWuXFnDhw/X66+/rpCQEH322WfasGGDvvjii1y/hoLKmbZv1aqVnn32Wfn7+ysyMlJbtmzRhx9+qFmzZkmSgoODNWTIED3zzDMKCQlRiRIlNHbsWNWqVSvLiY4KM1e0/ZUrV9SxY0ddv35dS5YsUXx8vOLj4yVJpUqVYikUO1zR9mFhYZlOiFOuXDlVqFAh9y+igHJF2/NZmzOuaHs+a1HYkDjitixbtkzGGPXr1y/T/aNHj9aNGzc0ZswYXbx4UXXq1NGGDRu4adwFHLW9t7e3/vvf/+q5557TAw88oKtXr6py5cpatGiRunbt6oZo7xzLli3ThAkTNGDAAF28eFGRkZF65ZVX9MQTT1jrzJ49W15eXurTp48SEhLUrl07RUdHk7jcpqzafu/evdq1a5ckqXLlyjbHxsTEqHz58nkd8h3Dmfc9coczbc9nbe7Iqu35rEVhYzHGGHcHAQAAAADIv1jHEQAAAADgEIkjAAAAAMAhEkcAAAAAgEMkjgAAAAAAh0gcAQAAAAAOkTgCAAAAABwicQQAAAAAOETiCAAAAABwiMQRAIAcslgs+uyzz+zuP378uCwWi/bv359nMQEAkBtIHAEAkqRBgwbJYrHIYrHI29tbFStW1NixY3Xt2rXbOu+UKVNUt27dHB07bdo0eXp66tVXX72tGHLLmTNn1KVLF3eHAQBAriNxBABYde7cWWfOnNGxY8c0depU/etf/9LYsWMzrXvz5s1cj2fhwoUaN26cFixYkKPjczvGsLAw+fr65upzAACQH5A4AgCsfH19FRYWpoiICPXv318DBgywDsVM7zlcsGCBKlasKF9fXxljdPLkSfXo0UNFixZVUFCQ+vTpoz/++EOSFB0drRdffFE//PCDtTczOjraqVi2bNmihIQEvfTSS7p27Zq2bt3qsH76sNBPPvlErVu3lp+fn5YsWSIpLQGtVq2a/Pz8VLVqVf3rX/+yHpeUlKSnnnpKZcqUkZ+fn8qXL6/p06c7FeNfh6p+9913qlevnvz8/NSgQQN9//33Tp0HAID8zsvdAQAA8i9/f3+bXrujR4/qk08+0fLly+Xp6SlJ6tmzpwICArRlyxYlJydrxIgR6tu3rzZv3qy+ffvqwIEDWrdunTZu3ChJCg4Oduq5o6Ki1K9fP3l7e6tfv36KiopSy5Ytszxu/PjxeuONN7Rw4UL5+vpq/vz5mjx5subOnat69erp+++/17BhwxQQEKCBAwfqrbfe0urVq/XJJ5+oXLlyio2NVWxsbLbb6tq1a7r//vvVtm1bLVmyRDExMRo1alS2zwMAQH5E4ggAyNR3332npUuXql27dtaypKQkLV68WKVKlZIkbdiwQT/++KNiYmIUEREhSVq8eLFq1Kih3bt3q2HDhipatKi8vLwUFhbm9HPHx8dr+fLl2r59uyTpkUceUfPmzfX2228rKCjI4bGjR49Wr169rI9ffvllvfHGG9ayChUq6Oeff9Z7772ngQMH6uTJk6pSpYpatGghi8WiyMhIp+O81b///W+lpKRowYIFKlKkiGrUqKHff/9df//733N0PgAA8hOGqgIArL744gsVLVpUfn5+atq0qVq2bKm3337buj8yMtKaNErSoUOHFBERYU0aJal69eoqVqyYDh06lOM4li5dqooVK6pOnTqSpLp166pixYpatmxZlsc2aNDA+v8///xTsbGxGjJkiIoWLWrdpk6dqt9++01S2qRA+/fv1z333KORI0dq/fr1OYr50KFDqlOnjooUKWIta9q0aY7OBQBAfkOPIwDAqk2bNpo3b568vb0VHh4ub29vm/0BAQE2j40xslgsGc5jr9xZCxYs0MGDB+Xl9b+PqdTUVEVFRenxxx93eOytMaampkqS5s+fr8aNG9vUSx9qe++99yomJkZr167Vxo0b1adPH7Vv316ffvpptmI2xmSrPgAABQmJIwDAKiAgQJUrV3a6fvXq1XXy5EnFxsZaex1//vlnxcXFqVq1apIkHx8fpaSkOH3On376SXv27NHmzZtVokQJa/nly5fVsmVLHThwQDVr1nTqXKGhobrrrrt07NgxDRgwwG69oKAg9e3bV3379tWDDz6ozp076+LFizbPn5Xq1atr8eLFSkhIkL+/vyRp586dTh8PAEB+RuIIAMix9u3bq3bt2howYIDmzJljnRynVatW1iGj5cuXV0xMjPbv36+yZcsqMDDQ4RIWUVFRatSoUaYT4TRt2lRRUVGaPXu20zFOmTJFI0eOVFBQkLp06aLExETt2bNHly5d0tNPP63Zs2erTJkyqlu3rjw8PPSf//xHYWFhKlasWLbaon///po4caKGDBmiSZMm6fjx43r99dezdQ4AAPIr7nEEAORY+nIUxYsXV8uWLdW+fXtVrFhRH3/8sbVO79691blzZ7Vp00alSpXSRx99ZPd8SUlJWrJkiXr37p3p/t69e2vJkiVKSkpyOsahQ4fqgw8+UHR0tGrVqqVWrVopOjpaFSpUkCQVLVpUM2bMUIMGDdSwYUMdP35c//3vf+Xhkb2PyKJFi+rzzz/Xzz//rHr16mnixImaMWNGts4BAEB+ZTHclAEAAAAAcIAeRwAAAACAQySOAIA89e9//9tmaYxbtxo1arg7PKuCEicAAHmBoaoAgDx15coV/fHHH5nu8/b2VmRkZB5HlLmCEicAAHmBxBEAAAAA4BBDVQEAAAAADpE4AgAAAAAcInEEAAAAADhE4ggAAAAAcIjEEQAAAADgEIkjAAAAAMAhEkcAAAAAgEMkjgAAAAAAh/4/q/h1HzQgmhoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x333 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting contact maps for prot_A and prot_H\n",
    "contacts_prot_A_prot_H.interface_contacts_plot('H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Aminoacid restype % prot_A ****\n",
      "non-polar count : 60.0 %\n",
      "polar charged count : 30.0 %\n",
      "positive count : 0 %\n",
      "negative count : 10.0 %\n",
      "********* \n",
      "\n",
      "***** Aminoacid restype % prot_L ****\n",
      "non-polar count : 78.5 %\n",
      "polar charged count : 14.3 %\n",
      "positive count : 0 %\n",
      "negative count : 7.1 %\n",
      "********* \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666x333 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Aminoacid content in interface prot_A and prot_L\n",
    "contacts_prot_A_prot_H.aminoacid_content('L')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Aminoacid restype % prot_A ****\n",
      "non-polar count : 60.0 %\n",
      "polar charged count : 30.0 %\n",
      "positive count : 0 %\n",
      "negative count : 10.0 %\n",
      "********* \n",
      "\n",
      "***** Aminoacid restype % prot_H ****\n",
      "non-polar count : 78.5 %\n",
      "polar charged count : 14.3 %\n",
      "positive count : 0 %\n",
      "negative count : 7.1 %\n",
      "********* \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666x333 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Aminoacid content in interface prot_A and prot_H\n",
    "contacts_prot_A_prot_H.aminoacid_content('H')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "instadeep_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}