search_spacesize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\robbi\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\robbi\\appdata\\roaming\\python\\python310\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\robbi\\appdata\\roaming\\python\\python310\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install unimod_mapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from utils import tryptic_digest_pyopenms\n",
    "import itertools\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Proteomics imports\n",
    "from unimod_mapper import UnimodMapper\n",
    "from pyteomics.mass.mass import std_aa_mass\n",
    "from collections import defaultdict\n",
    "\n",
    "from itertools import combinations\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_possibilities(possibilities,max_mods=3):\n",
    "    N = len(possibilities)  # Total number of positions\n",
    "    total = 0\n",
    "\n",
    "    # Iterate over the number of positions that can vary (0 to 3)\n",
    "    for k in range(0, max_mods+1):\n",
    "        # Generate all combinations of positions that can vary\n",
    "        for varying_positions in combinations(range(N), k):\n",
    "            # Calculate the product of possibilities for the chosen positions\n",
    "            product = 1\n",
    "            for pos in varying_positions:\n",
    "                product *= possibilities[pos]\n",
    "            # Add this product to the total sum\n",
    "            total += product\n",
    "\n",
    "    return total\n",
    "\n",
    "\n",
    "def modification_list_to_dict(all_amino_acids, modifications_search_space):\n",
    "    modification_dict = defaultdict(list)\n",
    "    for modification, amino_acids in modifications_search_space:\n",
    "        for aa in amino_acids:\n",
    "            modification_dict[aa].append(modification)\n",
    "\n",
    "    for aa in all_amino_acids:\n",
    "        modification_dict[aa].insert(0, \"\")\n",
    "\n",
    "    modification_dict = dict(modification_dict)\n",
    "    return modification_dict\n",
    "\n",
    "\n",
    "def apply_modifications(\n",
    "    peptides, modifications, charges=[2, 3, 4, 5], max_var_mods=3, # max_len=30, min_len=7\n",
    "):\n",
    "    unique_peptides = set([p[-1] for p in peptides])\n",
    "    tot_num = 0\n",
    "    tot_num_fragments = 0\n",
    "\n",
    "    for idx, peptide_seq in enumerate(unique_peptides):\n",
    "        #if len(peptide_seq) > max_len or len(peptide_seq) < min_len:\n",
    "        #    continue\n",
    "        all_pos = [len(modifications[aa]) - 1 for aa in peptide_seq]\n",
    "        poss = calculate_possibilities(all_pos, max_mods=max_var_mods) * len(charges)\n",
    "        tot_num += poss\n",
    "        tot_num_fragments += poss * len(peptide_seq) * 2\n",
    "\n",
    "    return tot_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from itertools import product\n",
    "\n",
    "# Your modification data\n",
    "modifications_search_space = [\n",
    "    (\"[Oxidation]\", [\"M\"]),  # , \"P\"\n",
    "    (\"[OxidationP]\", [\"P\"]),  # , \"P\"\n",
    "    (\"[Carbamidomethyl]\", [\"C\"]),\n",
    "    (\"[Alkyl]\", [\"C\"]),\n",
    "    (\"[DeamidationN]\", [\"N\"]),\n",
    "    (\"[DeamidationQ]\", [\"Q\"]),\n",
    "    (\"[PhosphorylationS]\", [\"S\"]),\n",
    "    (\"[PhosphorylationT]\", [\"T\"]),\n",
    "    (\"[PhosphorylationY]\", [\"Y\"]),\n",
    "    (\"[Nitrosyl]\", [\"C\"]),\n",
    "    (\"[OxidationC]\", [\"C\"]),\n",
    "    (\"[Methyl]\", [\"K\"]),\n",
    "    (\"[MethylR]\", [\"R\"]),\n",
    "    (\"[Dimethyl]\", [\"K\"]),\n",
    "    (\"[DimethylR]\", [\"R\"]),\n",
    "    (\"[Trimethyl]\", [\"K\"]),\n",
    "    (\"[CitrullinationR]\", [\"R\"]),\n",
    "    (\"[Propionyl]\", [\"K\"]),\n",
    "    (\"[Butyryl]\", [\"K\"]),\n",
    "    (\"[Malonyl]\", [\"K\"]),\n",
    "    (\"[Succinyl]\", [\"K\"]),\n",
    "    (\"[Glutarylation]\", [\"K\"]),\n",
    "    (\"[Crotonyl]\", [\"K\"]),\n",
    "    (\"[Hydroxyisobutyryl]\", [\"K\"]),\n",
    "    (\"[Biotin]\", [\"K\"]),\n",
    "    (\"[GG]\", [\"K\"]),\n",
    "    (\"[NitroY]\", [\"Y\"]),\n",
    "]\n",
    "\n",
    "\n",
    "charge_min_range = range(1, 3)\n",
    "charge_max_range = [5] #range(2, 6)\n",
    "maximum_length_range = [35] #range(30, 50, 2)\n",
    "max_var_mods_range = [3] #range(1, 4)\n",
    "missed_cleavages = [2] #range(0,4)\n",
    "num_var_mods = range(0, len(modifications_search_space))\n",
    "\n",
    "combinations_df = product(\n",
    "    charge_min_range,\n",
    "    charge_max_range,\n",
    "    maximum_length_range,\n",
    "    max_var_mods_range,\n",
    "    missed_cleavages,\n",
    "    num_var_mods,\n",
    ")\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    combinations_df,\n",
    "    columns=[\n",
    "        \"charge_min\",\n",
    "        \"charge_max\",\n",
    "        \"maximum_length\",\n",
    "        \"max_var_mods\",\n",
    "        \"missed_cleavages\",\n",
    "        \"num_var_mods\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fasta_analyze = \"fasta/human_22032024.fasta\"\n",
    "\n",
    "peptides = tryptic_digest_pyopenms(\n",
    "    fasta_analyze,\n",
    "    min_len=6,\n",
    "    max_len=35,\n",
    "    missed_cleavages=int(2),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 charge_min           1\n",
      "charge_max           5\n",
      "maximum_length      35\n",
      "max_var_mods         3\n",
      "missed_cleavages     2\n",
      "num_var_mods         0\n",
      "Name: 0, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:02, ?it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 13\u001b[0m\n\u001b[0;32m     11\u001b[0m i \u001b[38;5;241m=\u001b[39m r[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_var_mods\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Read peptides from fasta using a tryptic digest function\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m peptides \u001b[38;5;241m=\u001b[39m \u001b[43mtryptic_digest_pyopenms\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfasta_analyze\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmin_len\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     16\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_len\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximum_length\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     17\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmissed_cleavages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmissed_cleavages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     18\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m modification_dict \u001b[38;5;241m=\u001b[39m modification_list_to_dict(\n\u001b[0;32m     21\u001b[0m     all_amino_acids, modifications_search_space[\u001b[38;5;241m0\u001b[39m:i\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     22\u001b[0m )\n\u001b[0;32m     24\u001b[0m \u001b[38;5;66;03m# Apply modifications to peptides\u001b[39;00m\n",
      "File \u001b[1;32m~\\OneDrive\\Documents\\GitHub\\ASMS_DIA_presentation\\utils.py:51\u001b[0m, in \u001b[0;36mtryptic_digest_pyopenms\u001b[1;34m(file_path, min_len, max_len, missed_cleavages, decoy_method, decoy_prefix, seq_types)\u001b[0m\n\u001b[0;32m     48\u001b[0m digestor\u001b[38;5;241m.\u001b[39mdigest(pms\u001b[38;5;241m.\u001b[39mAASequence\u001b[38;5;241m.\u001b[39mfromString(protein_sequence), result)\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m peptide \u001b[38;5;129;01min\u001b[39;00m result:\n\u001b[1;32m---> 51\u001b[0m     peptide_sequence \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpeptide\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoString\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m     len_pep_seq \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(peptide_sequence)\n\u001b[0;32m     53\u001b[0m     start \u001b[38;5;241m=\u001b[39m protein_sequence\u001b[38;5;241m.\u001b[39mfind(peptide_sequence)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fasta_analyze = \"fasta/human_22032024.fasta\"\n",
    "\n",
    "# Define all standard amino acids\n",
    "all_amino_acids = set(\n",
    "    \"ABCDEFGHIKLMNPQRSTUVWXY\"\n",
    ")  # 20 standard amino acids + additional ones that can be in the fasta, they are ignored if they are not in there\n",
    "\n",
    "search_space_list = []\n",
    "for idx,r in tqdm(df.iterrows()):\n",
    "    print(idx,r)\n",
    "    i = r[\"num_var_mods\"]\n",
    "    # Read peptides from fasta using a tryptic digest function\n",
    "    peptides = tryptic_digest_pyopenms(\n",
    "        fasta_analyze,\n",
    "        min_len=6,\n",
    "        max_len=r[\"maximum_length\"],\n",
    "        missed_cleavages=int(r[\"missed_cleavages\"]),\n",
    "    )\n",
    "\n",
    "    modification_dict = modification_list_to_dict(\n",
    "        all_amino_acids, modifications_search_space[0:i+1]\n",
    "    )\n",
    "\n",
    "    # Apply modifications to peptides\n",
    "    all_peptidoforms = apply_modifications(\n",
    "        peptides,\n",
    "        modification_dict,\n",
    "        charges=list(range(r[\"charge_min\"], r[\"charge_max\"])),\n",
    "        max_var_mods=r[\"max_var_mods\"],\n",
    "    )\n",
    "    print(f\"Total number of unique peptidoforms: {all_peptidoforms}\")\n",
    "    search_space_list.append(all_peptidoforms)\n",
    "    \n",
    "df[\"search_space\"] = search_space_list\n",
    "df.to_csv(\"data/human_searchpace.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/human_searchpace.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df_search_space = df[df[\"charge_min\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sub_df_search_space[\"num_var_mods\"], sub_df_search_space[\"search_space\"],s=20,alpha=0.5)\n",
    "plt.plot(sub_df_search_space[\"num_var_mods\"], sub_df_search_space[\"search_space\"])\n",
    "plt.show()\n",
    "# plt.scatter(\n",
    "#    sub_df_search_space[\"num_var_mods\"],\n",
    "#    sub_df_search_space[\"search_space\"]\n",
    "#    * (sum([len(p[-1]) for p in peptides]) / len(peptides)),\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7nUdbLrVr165qv0P79u3R6XT07NmTiooKMjIyGDx4cK3XKC8vZ+/evabnT0hIIDc3l86dOwPq+/1qI4+dO3fmv//9L6WlpaZRoJ07d1Y757vvvqvy57d9+3ZcXV0JDg6u8bpt27Y11VRVzogzGAzs2bOH55577ooxXStfX18mTJjAhAkTGDx4MLNmzZIESAhLqjBA+mFI3KzW5xRlqqM/l7N3VROdVhcSnla9wdm7+nmWHL1x8rJa4lPJ6glQYWEhJ06cMG0nJSURGxuLl5cXoaGhTJ8+nQkTJtCnTx/69u3L0qVLTbNLmrJR3YMAiEnOISW3BGc7G26O8DftbwjdunXjwQcf5L333quyf9iwYWRmZrJo0SLuvvtu1q1bx9q1a3Fzc7PI8y5fvpz27dvTuXNn3nnnHXJycpg0aRKgjsB9+OGH3H///bzwwgt4eXlx4sQJvvrqKz766COzVv6eNWsWc+fOpW3btvTo0YNPP/2U2NhYPv/8c7NjPn36NNOnT+eJJ55g3759LFu2jMWLFwPQoUMHHnzwQR5++GEWL15Mz549yczMZNOmTXTv3p3bb78dUEdJpk6dynvvvYeNjQ3PPPMM/fv3NyVE4eHhpvd/cHAwrq6u2NvbV4njgQce4B//+AeTJ09mzpw5JCcnV0sann76aZYuXcrUqVN55plnSEhIYO7cuUyfPr3KLctLOTs789RTT5lm8IWGhrJo0SKKi4t59NFHzX69ruaVV16hd+/edOnShbKyMtasWWNKBIUQNSjOvvqISXG2eivrzC44sxvOxYCh+LKTNOrtK7cg9TsauPVN9bbW1TSi0RtLsHoCtHfvXoYPH27anj59OgATJkxg5cqVjB8/nszMTF555RXS0tJMU5krawosrSFqgAAcbHXc3TuEEZ38G7QP0OXmzZvH119/XWVf586d+de//sWbb77JP//5T+666y5mzpzJf/7zH4s854IFC1iwYAGxsbG0a9eOn376yTQDqHLU5sUXX+SWW26hrKyMsLAwbr311lo/vGvz7LPPkpeXx4wZM8jIyCAiIoKffvqJ9u3r8Bf9Mg8//DAlJSX07dsXnU7HtGnTePzxx03HP/30U15//XVmzJjBuXPn8PHxoX///owaNcp0jpOTEy+++CIPPPAA586dY/DgwXz88cem43fddRfff/89w4cPJzc3l08//bTa7UkXFxd+/vlnnnzySXr27ElERAQLFy7krrvuMp3TqlUrfv31V2bNmkVkZCReXl48+uijvPTSS1f8HRcsWIDRaOShhx6ioKCAPn368Ntvv9VLvyg7OztTAufo6MjgwYP56quvLP48QjR5tdXuRNwBeecuJDsXvs4fq/54B3cI6gklueASAP4R6uwsgMIMtX7HqYbRnitpBKM3lqBRLi/2EACmGqC8vLxqIx+lpaUkJSXRunXrKsWoonkaNmwYPXr0YOnSpdd8jZUrV/Lcc8+Rm5trsbhaMvk7KFqM2C8u1t0YiiA7GfLOqMmQoYbbWd7tIaQfhPRVv/t0AK324nWc/dS+O6X5F+t3ejzQ4L9WfbrS5/elrD4CJIQQQojLFGfD8fXw1zK1dqc4C5TLJpTYOKh1O5XJTvANNdfuQKOafdVYSAIkhBBC1Ker1e8oCuSeVpOT03+p3zOPVj/P3hXcgsHFD7Q6iJ4Pfp3qFkMzq9+xBEmALtNQNUCi6bh0SYtr9cgjj1Sr5xFCNHNXqt/JSYZTF5Kd0zvUdbMu590eNJoLtTtd1HoeuFi743L19TCraSb1O5YgCdBlGqIPkBBCiBagsveOozdotJB+BBLWwi/T1eToUlobCOwBYQMgdACE9FdvZ1XW7pSXqWtoXVq7I4nMdZEESAghhLAkQykk/g67/g2F6WoNj7G86jm2TmrtTuhAdVQouI86QnQ5qd2pN5IACSGEEDWpS+8dUBOes3sg+U91cdAzu6GirOo5tk7gHqzeztLZwK0LwLdjzder8jip3akvkgAJIYQQl6qtdqfLODUhMZSoSc6p7WrSc3Zv9YTH2Vd9nGsQ+EWovXY0mov1O86+5sUktTsWJwnQZaQIWgghWrjK2h1nP3XUpjgbYlZC3E9Qmqt2WK7QV32MSwCE3wjhgyB8MHi3gwNfqtdRjGqCJPU7jYp5rXVbgClTphAXF8eePXusHUqzk5ycjEajsei6Ytfr6NGj9O/fHwcHB3r06FHjOYqi8Pjjj+Pl5dXo4reW8PDw62oMKUSjVZwNydvBWKFORT/0LcT+n9pp+dhadcZWhV4d2el2L4x+F6bugxlH4e6Poc8kdVkJjUYdMep0uzrik3dW/S71O42GjAC1II888girVq1i/vz5zJ4927T/hx9+YNy4cdVWgG8J5s6di7OzMwkJCbWu3L5u3TpWrlzJli1baNOmjWnZjuaorl2v9+zZU+Oq8EI0ScYKSI2Fk1vh2G9qPY9y2V0AOxe1G3PvR6DrOPBsrSY5VyL1O42aJEAtjIODAwsXLuSJJ56olzWerEGv12Nnd23rqCUmJnL77bebVkCv7ZzAwEAGDhxYLzE0Rb6+ZtYvCNHQrlTArCiQmQBJ2yBpKyT/AaV5Vc+xcQSv1uARpn6VlwFG6DPR/CRG6ncaJbkFZm3F2ZCVqH5vAFFRUQQEBDB//vxaz3n11Ver3Q5aunQp4eHhpu1HHnmEsWPH8uabb+Lv74+Hhwfz5s2jvLzctKJ4cHAwn376abXrHz16lIEDB+Lg4EDXrl3ZunVrleOHDx9m5MiRuLi44O/vz0MPPcT58+dNx4cNG8YzzzzDc889h4+PD9HR0TX+HkajkXnz5hEcHIy9vb1pId1KGo2GmJgY5s2bh0aj4dVXX612jUceeYSpU6dy+vRpNBqN6TWoLYYlS5bQrVs3nJ2dCQkJ4emnn6awsLDKNT/88ENCQkJwcnJi3LhxLFmyBA8Pj2qv/yeffEJoaCguLi48/fTTVFRUsGjRIgICAvDz8+ONN96oct3c3Fwee+wxfH19cXNz46abbuLAgQPVrvvf//6X8PBw3N3due+++ygoKDD9rlu3buXdd99Fo9Gg0WhITk6u8bW9/BbY6dOnueOOO3BxccHNzY17772X9PT0Oj83wLfffku3bt1wdHTE29ubqKgoiopqWOtIiCsxlKi9cza+CpvfVL/HfqEuFLr//+C7ybC4E/yrH6ydBUfXqMmPvTt0vB1uXQg3vQxtb1J78fh2VG95FWeqhdCSyDQbkgBZS21/SS9vjmVhOp2ON998k2XLlnH27Nnrutbvv/9OSkoK27ZtY8mSJcydO5dRo0bh6enJrl27ePLJJ3niiSeqPc+sWbOYMWMG+/fvZ8CAAYwePZqsrCxA/RC/6aab6NmzJ3v37mXdunWkp6dz7733VrnGqlWrsLOzY/v27axYsaLG+N59910WL17M22+/zcGDB4mOjmbMmDEcP34cgNTUVLp06cKMGTNITU1l5syZNV6jMolKTU2tUhtWUwxarZb33nuPI0eOsGrVKn7//XdeeOEF02O2b9/Ok08+ybRp04iNjeXmm2+ulsiAOuq0du1a1q1bx5dffsnHH3/M7bffztmzZ9m6dSsLFy7kpZdeYteuXabH3HPPPWRkZLB27VpiYmLo1asXI0aMIDs7u8p1f/jhB9asWcOaNWvYunUrCxYsMP2uAwYMYPLkyaSmppKamkpISEiNr+2ljEYjd9xxB9nZ2WzdupUNGzZw8uRJxo8fX+13qu25U1NTuf/++5k0aRLx8fFs2bKFO++8s0XelhXXqbKAucIAhmJ1JtcvM+D9G+DHKXDof1CYpq6j1WYYjHgFHvsdXjgJ938B/Z+EAVOg8yip3WnuFFHF+++/r3Tu3Fnp0KGDAih5eXnVzikpKVHi4uKUkpKSa3+i/Z8rypcPKMpPzynK+lfU718+oO6vJxMmTFDuuOMORVEUpX///sqkSZMURVGU1atXK5e+FebOnatERkZWeew777yjhIWFVblWWFiYUlFRYdrXsWNHZfDgwabt8vJyxdnZWfnyyy8VRVGUpKQkBVAWLFhgOsdgMCjBwcHKwoULFUVRlH/+85/KLbfcUuW5z5w5owBKQkKCoiiKMnToUKVnz55X/X2DgoKUN954o8q+G264QXn66adN25GRkcrcuXOveJ3Lf3dzYvjmm28Ub29v0/b48eOV22+/vco5Dz74oOLu7m7anjt3ruLk5KTk5+eb9kVHRyvh4eHVXu/58+criqIof/zxh+Lm5qaUlpZWuXbbtm2Vf//737Ved9asWUq/fv2q/F7Tpk276u8VFhamvPPOO4qiKMr69esVnU6nnD592nT8yJEjCqDs3r27Ts8dExOjAEpycvJVn1tRLPR3UDQvhjJFif9FUf41SFEWtlaUuW6Xfbkryr+HKcrGeYpycqui6Ovw3inKUpTzJ9TvosnIy8ur9fP7UlIDdJkGWQqjOFv9X4mz38W1XFwc1O+nd6oFc/U8zLpw4UJuuummGkc96qpLly5otRcHEf39/enatatpW6fT4e3tTUZGRpXHDRgwwPSzjY0Nffr0IT4+HoADBw6wefPmGguSExMT6dChAwC9e/e+Ymz5+fmkpKQwaNCgKvsHDRpU5bbQ9agpho0bNzJ//nyOHj1Kfn4+5eXllJaWUlxcjJOTEwkJCYwbV/V/kX379mXNmjVV9oWHh+Pq6mra9vf3R6fTVXu9K1/bAwcOUFhYiLd31ZWgS0pKSExMrPW6gYGB1f58zBUfH09ISEiV0aKIiAg8PDyIj4/nhhtuuOpzR0ZGMmLECLp160Z0dDS33HILd999d7OpUxP1QFHU8oHE3yFxEyT9AYbLbpk6+6r1O25BgAai5oJ327o/h9TuNGuSAFlDSY7aXMs9uOp+Bzd1uLUkp97/0g0ZMoTo6GjmzJlTbZFOrVZb7daDwWCodg1bW9sq2xqNpsZ9RqOxznEVFhYyevRoFi5cWO1YYGCg6efGMAPp8hiSk5MZNWoUTz31FG+88QZeXl78+eefPProo+j1epycnOp8bXNf28LCQgIDA2tcuPXS+qLr/fO5Hld6bp1Ox4YNG/jrr79Yv349y5Yt4x//+Ae7du2idevWDRKfaERqK2AuyVELl09sgsTNkHe66uOcvNVaHvdgCOgO9hf+I1XZfNBREmpxkSRA1uDoqXYILc2/OPID6radc4P9JV2wYAE9evSgY8eq7dh9fX1JS0tDURQ0F6Z5WrL3zc6dOxkyZAgA5eXlxMTE8MwzzwDQq1cvvvvuO8LDw7Gxufa3p5ubG0FBQWzfvp2hQ4ea9m/fvp2+ffte3y9Qi5iYGIxGI4sXLzaN1Pzvf/+rck7Hjh2r9ZiyRM+pXr16kZaWho2NTZVidXPZ2dmZ3QS0c+fOnDlzhjNnzphGgeLi4sjNzSUiIqLO19FoNAwaNIhBgwbxyiuvEBYWxurVq5k+fbpZ8Ygm7PIOzLaO4OKvNhJM/kNtQKhckrDr7NSFQ9vepH75d4WDX6k1QIZidckJaT4oaiEJkDU4eamzCY7+om47uFnlL2m3bt148MEHee+996rsHzZsGJmZmSxatIi7776bdevWsXbtWtzc3CzyvMuXL6d9+/Z07tyZd955h5ycHCZNmgSotyA//PBD7r//fl544QW8vLw4ceIEX331FR999BE6na7OzzNr1izmzp1L27Zt6dGjB59++imxsbF8/vnnFvk9LteuXTsMBgPLli1j9OjRNRZoT506lSFDhrBkyRJGjx7N77//ztq1a02J5rWKiopiwIABjB07lkWLFtGhQwdSUlL45ZdfGDduHH369KnTdcLDw9m1axfJycm4uLjg5eVV5bZbbc9d+V5aunQp5eXlPP300wwdOrTOz7tr1y42bdrELbfcgp+fH7t27SIzM5POnTvX6fGimTiyWm08aChRFxHNPQ3Gy0affTtdTHjCBlZfQFQWDxV1JLPArKWRdAidN29etVsgnTt35l//+hfLly8nMjKS3bt3X1et0OUWLFjAggULiIyM5M8//+Snn34yNResHLWpqKjglltuoVu3bjz33HN4eHhc9YP4cs8++yzTp09nxowZdOvWjXXr1vHTTz/Rvn17i/0ul4qMjGTJkiUsXLiQrl278vnnn1drNzBo0CBWrFjBkiVLiIyMZN26dTz//PM4ODjUctW60Wg0/PrrrwwZMoSJEyfSoUMH7rvvPk6dOoW/v3+drzNz5kx0Oh0RERH4+vpy+vTpqz5Go9Hw448/4unpyZAhQ4iKiqJNmzZ8/fXXdX5eNzc3tm3bxm233UaHDh146aWXWLx4MSNHjqzzNUQTVWFQ19P69QVYN0et5zn9F2QnqsmPzh48w9Xp6c8fgSm74Nb50P7mmldPr2w+GPUqDP+7+r3HA+p+IS6hUS4v9hAApiLovLy8aiMfpaWlJCUl0bp16+v+4KrzasOi2Zo8eTJHjx7ljz/+sHYoTYZF/w6KhleQBic2wvH1ai1PWf4lBzXg1kptQujZBhzdIT9FTWbMKWAWLdaVPr8vJbfALtPgi6HKLIMW5+233+bmm2/G2dmZtWvXsmrVKv71r39ZOywhrl9t/6EzVqj1O8fXq1+pl83EdPKB1kOg6Dy4h4DHJf2nCjMatDZStBySAF2mQabBixZt9+7dLFq0iIKCAtq0acN7773HY489Zu2whLh2lxcv2zlDQFewdYaTm9VZWyWXdbsP6gXtb1G/gnqCVqs2gz36CxTaW602UrQckgAJ0cAunxkmRJN3ZDXErwGtjZqwZCXCvlVVz3Fwh7Yj1ISn3YiLPdAuJQXMogFJAiSEEOLalOvh2DrY/i7knVFHfy7l6AmR90PnMRB8gzot/Upk9XTRgCQBug5SPy6EdcjfPSsqyVULmI/+on6/tIBZa6PO2PJupxYyl+bBDY+ZX7wstZGiAZidAO3btw9bW1u6desGwI8//sinn35KREQEr776KnZ2dhYPsrGp7EWj1+txdJSplUI0tOLiYqB6d2lxHa40IzXnFCSshYRf4dR2MJZfPObsAw6e6pITgZGgu/BnIsXLopEzOwF64oknmD17Nt26dePkyZPcd999jBs3jm+++Ybi4mKWLl1aD2E2LjY2Njg5OZGZmYmtra3Z/WmEENdGURSKi4vJyMjAw8PDrMaYohY1FTCH9AWvtmpPnoS1kH646mN8O0HH29SvVr0vdl8uyZHiZdFkmN0HyN3dnX379tG2bVsWLlzI77//zm+//cb27du57777OHPmTH3F2qCu1kdAr9eTlJTUYOsoCSEu8vDwICAg4Lo7aAsuzrxy9IayXMhIgNwkKC+7eI5GC6EDoeNI9evyW1o1JVGh/dXiZWlAKBpYvfUBUhTF9KG/ceNGRo0aBUBISAjnz5+/xnCbHjs7O9q3b49er7d2KEK0KLa2tjLyYym5Z+HA1+qSE/lnoeKSf8+0Nmq35Yix6sytK43kSPGyaILMToD69OnD66+/TlRUFFu3buWDDz4AICkpyayW+42VOY0QtVqtdKEVQjQtpflw7DeI/xGOb4Dy0ovH7FzAp71az4MGRrxsXgGzFC+LJsTsW2AHDx7kwQcf5PTp00yfPp25c+cC6iKPWVlZfPHFF/USaEOr6xCaEEI0esXZai1P/E+Q+HvVkR47F3XmVlBPcA0CjUYtYFYq1HW0JKERTUxdP78tthZYaWkpOp2u2czKkARICNFk1DSDqzADjq6BuJ8g+Y+qM7d8Oqi9eSLGQNphdXaXs1/1AuYeD1jn9xHiOtT7WmB6vZ6MjIxqRcChoaHXekkhhBDmuLz4GNSC5bwzcGYXKJf8++zf9ULScwf4dbq436eDOuoj3ZdFC2N2AnTs2DEeffRR/vrrryr7FUVBo9E03CKiQgjR0h1ZDQe/AX2BmvQUpFU9HtRLHeXpPKb2Wh4pYBYtlNkJ0MSJE7GxsWHNmjUEBgbKNFQhhGho2SfhwFew5yMozqp6zNkPPEJh1DsQ2L3u15QCZtHCmJ0AxcbGEhMTQ6dOna5+shBCCMvISlRHfOJ+hLSDlxzQgEcI+HRSb2fpbC7eyhJC1MrsBCgiIqJF9fsRQoh6c6XlJwAyj0HcD2rSc2k3Zo0OwgaAsQI8wsEz7OIxWYJCiDoxOwFauHAhL7zwAm+++SbdunWrNutLZkwJIcRV1NY5OWIs5CSrCU/cj5AZf/ExWhtoPRS6jIWOt4Oz98UuzoUZsgSFEGYyexp85bpXl9f+NLciaJkGL4SoN5WJi7Mf2LuqSU9GHJQVQOElhcxaW2g7XJ251fG26kmNLEEhRDX1Ng1+8+bN1xWYEEK0aMXZasJi4wTnj0HGEfU2WCWdHbQdcSHpGQmOHrVfS2ZwCXHNzE6Ahg4dWh9xCCFE82cogdjP1a7MRRkX92t0ajdmJ2+4/W0IjDTvujKDSwizXVMjxNzcXD7++GPi49X70126dGHSpEm4u7tbNDghhGjyFAXOxcD+/4PD30FZ/sVjHmEQ0A2820Npnrr8hHuI9WIVogUxuwZo7969REdH4+joSN++fQHYs2cPJSUlrF+/nl69etVLoA3l0sVQjx07JjVAQohrU5AOB7+C/Z/D+YSL+z1C1aRHX6KO+sjyE0JYVL2tBTZ48GDatWvHhx9+iI2NOoBUXl7OY489xsmTJ9m2bdv1Rd5ISBG0EOKKaprCXq6H47+pSc/x9eqIDoCNo1rT0/NBCLsRKsqkeFmIelJvCZCjoyP79++v1ggxLi6OPn36UFxcfG0RNzKSAAkhalTTzCv3EHUE5/B3VTszB/dVk54u48ChhhKBq/UBEkKYrd5mgbm5uXH69OlqCdCZM2dwdXU1P1IhhGhKjqxWp7Dbu6vJzsnNVZMeF3+IvA96/A18O1z5WlK8LITVmJ0AjR8/nkcffZS3336bgQMHArB9+3ZmzZrF/fffb/EAhRCi0SjKgiM/QNZxyDl18RaXRquOAo14RW1mqLum+SVCiAZk9t/St99+G41Gw8MPP0x5eTkAtra2PPXUUyxYsMDiAQohhNUVpKnNC/d+oq66XsnZFwIi1ZXWi7MgqKckP0I0EWbXAFUqLi4mMTERgLZt2+Lk5GTRwKxNaoCEaOEqyiFxE8SsgmPrLo72aG3Bq41atOwSABqNuhSFUgFRr8otLSGsrN5qgCo5OTnRrVu3a324EEI0TjnJas+e/Z9DQcrF/SH9odfDUKGHExvV5oUVZbL+lhBNVJ0SoDvvvJOVK1fi5ubGnXfeecVzv//+e4sEJoQQ9eby2VflZWph875VcHLLxfMcvdS+PD0fAr8LEz8MJWBjr84CyzurzgLrdLs600sI0WTUKQFyd3c3LX4q3Z6FEE3W5VPYKwygL4SU/VCSffG8NsOh9wR1AVIb+6rXkPW3hGgWrrkGqLmTGiAhmqHYLyDuJygrhOwTUJB68ZhrEPT8m9q3xzPcaiEKIa5PvdcACSFEk5KdDLs/hIx4KC+5sFMDHiHqWlzjVoCLnzUjFEI0IEmAhBDNW9452PkvdQq74UKnentXCOqlrsmltVFrecoKJAESogWRBEgI0Tylx8Ffy+DQ/8Co9izDwQMCukNIP9Dq1H2FGWohs6On1UIVQjQ8SYCEEM2HosCpv2D7u+qipJXCB8OgaWqyk/Cr2rTw8lXYpZBZiBbF7ATos88+Y/z48djbV50Zodfr+eqrr3j44YctFpwQQtSJsUKdxr79XTi398JODUSMgYHTILi3ustQojYulCnsQrR4Zs8C0+l0pKam4udX9V55VlYWfn5+VFRUWDTAuhg3bhxbtmxhxIgRfPvtt1fdXxcyC0yIRujy/j2GUjjwpXqrK1vtTI/OXp3JNeAZdYmKulxHCNFs1NssMEVRTD2BLnX27Fmr9QiaNm0akyZNYtWqVXXaL4RoYi7v36O1UQuaT++Aokz1HAcP6DsZ+j5+9WJmWYVdiBavzglQz5490Wg0aDQaRowYgY3NxYdWVFSQlJTErbfeWi9BXs2wYcPYsmVLnfcLIZqYI6vVW1y2zupIT9qhi4XN7iEwYIrardnexbpxCiGajDonQGPHjgUgNjaW6OhoXFwu/kNjZ2dHeHg4d911l9kBbNu2jbfeeouYmBhSU1NZvXq16bkqLV++nLfeeou0tDQiIyNZtmwZffv2Nfu5hBBNUHE2HN8I549D1omLi5I6eoJ/V7jrY3D1t26MQogmp84J0Ny5cwEIDw9n/PjxODg4WCSAoqIiIiMjmTRpUo3rjH399ddMnz6dFStW0K9fP5YuXUp0dDQJCQnV6pCuR1lZGWVlZabt/Px8i11bCHGNshJh46sQ/zNwoVzRPQRCB4BrIOSfU5eyQBIgIYR5zK4BmjBhAqDO+srIyMBoNFY5Hhoaatb1Ro4cyciRI2s9vmTJEiZPnszEiRMBWLFiBb/88guffPIJs2fPNjP62s2fP5/XXnvNYtcTQlyHzATY9jYc/haUC//GuAapa3R5hKjb0r9HCHEdzE6Ajh8/zqRJk/jrr7+q7K8sjrbkLDC9Xk9MTAxz5swx7dNqtURFRbFjxw6LPQ/AnDlzmD59umk7Pz+fkJAQiz6HEOIq0g7Dtrcg7kdMIz7toyGwu7qEhY09lJdK/x4hxHUzOwF65JFHsLGxYc2aNQQGBtY4I8xSzp8/T0VFBf7+VYe3/f39OXr0qGk7KiqKAwcOUFRURHBwMN988w0DBgyodX9N7O3tq/U2EkI0kJT9sPUtSPjl4r5Oo2DILAjqUXUWmPTvEUJYgNkJUGxsLDExMXTq1Kk+4rkmGzduNGu/EMIKauq9c2Y3bF0EJzZcOEkDXcaqiY9/l4uPtXWEHg9Ah1ulf48QwiLMToAiIiI4f/58fcRSjY+PDzqdjvT09Cr709PTCQgIqJfnXL58OcuXL7dKQ0chmqXLe/jYOavJy9m9kPyHeo5GB93ugcEzwLdD7deS/j1CCAvRmvuAhQsX8sILL7BlyxaysrLIz8+v8mVJdnZ29O7dm02bNpn2GY1GNm3aVOutrOs1ZcoU4uLi2LNnT71cX4gWp7KHD1q1oPnYb/DnO2ryo7VR+/dM3Qt3/vvKyY8QQliQ2SNAUVFRAIwYMaLK/mstgi4sLOTEiROm7aSkJGJjY/Hy8iI0NJTp06czYcIE+vTpQ9++fVm6dClFRUWmWWFCiEasOFsd+dHoIHGDWr8DoNGCd3u46yO1wFkIIRqY2QnQ5s2bLRrA3r17GT58uGm7cibWhAkTWLlyJePHjyczM5NXXnmFtLQ0evTowbp166oVRluK3AITwoJyTkHyn5B9ElDUEZ/ASAjsCaW56u0wIYSwArMXQ20pZDFUIa6D0Qix/wcb5kJJtrrPtzO0HQ72bmoPH6UCol6Vmh4hhEXV9fPb7BoggD/++IO//e1vDBw4kHPnzgHw3//+lz///PPaohVCNB/n9sHHUfDTVDX5cQ2EkP5q92adnZr8FGVAaH9JfoQQVmN2AvTdd98RHR2No6Mj+/btMy0fkZeXx5tvvmnxAIUQTURRFvw8DT68Cc7FgJ0rRL8JU3ZD7wnqiE/eWfW79PARQliZ2bfAevbsyfPPP8/DDz+Mq6srBw4coE2bNuzfv5+RI0eSlpZWX7E2KLkFJkQdGSsgZiX8/k+1Rw9A9/Fw8zxwvaRdRU19gIQQwsLq+vltdhF0QkICQ4YMqbbf3d2d3Nxccy/X6EgRtBBmOLMbfp0JqQfUbf+ucNtbEDaw+rnSw0cI0YiYfQssICCgyrT1Sn/++Sdt2rSxSFDWJH2AhKhBcba6MnvxhYLmwkz4YQp8fLOa/Ni7w8i34PGtNSc/QgjRyJg9AjR58mSmTZvGJ598gkajISUlhR07djBz5kxefvnl+ohRCGEtl3dxtnWE8jI4tg7KLjQ+7fE3dTaXi69VQxVCCHOYnQDNnj0bo9HIiBEjKC4uZsiQIdjb2zNz5kymTp1aHzEKIaylsouzsx+ggaNrLtb5BEbCbYsh5AarhiiEENfimvsA6fV6Tpw4QWFhIREREbi4uFg6Nqu4tAbo2LFjUgQtWq7ibNjwinq7K/cUZMar+3V2ENQLxv+fjPoIIRqduhZBSyPEWsgsMNFiGUog8XeI/RKOr4MKw8VjgT0gpB8UZ8Hwv4N3W6uFKYQQNam3WWClpaUsW7aMzZs3k5GRgdForHJ837595kcrhLCu0jw4th7if4ITG8FQfPGYjQP4dFBHfVwD1EaGds7qdHYhhGiizE6AHn30UdavX8/dd99N37590Wg09RGXEMISrtR7pzBDre85ugZObgXjJSM97iHQaZRa9JyZAC7+4OB2sYtzp9tlSrsQokkzOwFas2YNv/76K4MGDaqPeIQQlnD57C07Z3XpiVa91RGe+J/VY1xyB9ynI3QeBZ1Hq7e6NJqq18k7q15HujgLIZoBsxOgVq1a4erqWh+xCCEspXL2lpOvOoqTdkjdVzl1vVJQLzXp6TQafDtUv46tI/R4ADrcKl2chRDNitkJ0OLFi3nxxRdZsWIFYWFh9RGTVUknaNHkFZ1XR3hyz8Cp7RenrQOgUYuYu96pjuS4B9ftmtLFWQjRzJg9CywzM5N7772Xbdu24eTkhK2tbZXj2dnZFg3QWmQWmGhSKgyQ/KdazxP3IxRlXjym0YFnOHi1UQuab35NZm8JIZqtepsFdv/993Pu3DnefPNN/P39pQhaiPpytcVD9cWQuAni16idmUtzLx7T2qqjOwHdLyQ+9moBs1Ihs7eEEIJrSID++usvduzYQWRkZH3EI4SorYC5yzgoL4Vjv6m3uE5sgvKSi49z8oFOt6n1PAWpcHw9OHkDiszeEkKIy5idAHXq1ImSkpKrnyiEuDaXLj/hHgz5abBjOWx/F7JOgLH84rnuoRdnboX0A61O3W8oAZ2tzN4SQohamJ0ALViwgBkzZvDGG2/QrVu3ajVAUi8jxHUozlaTFhsHyElSe/AUpFQ9xy9C7dHTeZR6i6um29Aye0sIIa7I7CJorVarPvCyf3QVRUGj0TSb2VNSBC0aXFYi7P0E9v9f1XoeUDswO/nCLf+EtsOtEp4QQjQF9VYEvXnz5usKrLGTafCiQWUeU2dtxf0A6YcvOaABj1C1OaFPe/WWllKhrsAuhBDiusliqLWQESBxXWqbwaUokBF/Ien58eIK66BOV28zFFwD1aJl9xB1+YnS/IsFzD0eaPjfRQghmpB6GwE6ePBgjfs1Gg0ODg6EhoZib29v7mWFaB5qmsEV0g98O6pT1eN+hPPHLp6vtYU2w6DLWOh4m5osyfITQghR766pBuhKvX9sbW0ZP348//73v3FwcLjuAK1FRoDENYn94uISFBWlkH5Enbl16erqOjtoOwIi7oCOt9bel+dqfYCEEEJUU28jQKtXr+bFF19k1qxZ9O3bF4Ddu3ezePFi5s6dS3l5ObNnz+all17i7bffvvbfQIimpnIGV4UB4n+ougSFRgftoqDbPdAhWr21dTWy/IQQQtQbsxOgN954g3fffZfo6GjTvm7duhEcHMzLL7/M7t27cXZ2ZsaMGZIAiZalMB1O/QVZx9VtrQ14tVU7MevsIGquLEEhhBBATpGe3BIDHo62eDrbWSUGsxOgQ4cO1bgIalhYGIcOHQKgR48epKamXn90QjQVmQnw7aSLyU9QT2gzXE18ZAkKIYQAoNRQwZqDKexNzqFYX46TnQ19wj0Z1T0IB1tdg8aiNfcBnTp1YsGCBej1etM+g8HAggUL6NSpEwDnzp3D39/fclEK0Vgpitq7599DISNOLVhu1QcCe4JivLgERWh/uZ0lhGgWcor0JJ0vIqdIf/WTL/Nj7Dl+jD1HRn4ZuSUGyisUNsSls+ZgytUfbGFmjwAtX76cMWPGEBwcTPfu3QF1VKiiooI1a9YAcPLkSZ5++mnLRipEY1OUBT9NhYRf1O02w2HUUjj9l8zgEkI0O3UZvVEUhawiPaezizlj+irhdHYxyVlFpOWVcunMq3t6B+PtbE9Mcg4jOvk36O2wa+oDVFBQwOeff86xY+p03o4dO/LAAw/g6upq8QAb2qWNEI8dOyazwETNEjfD6iehME2dyh71KvR/Gi50SpcZXEKI5ubbmDNsiEvH3cEWNJCWV0pGQRm+rvbY22hNiU6J4cqNhHVaDW4ONrg72tKvtTeezrak5Jbw/M0dae3jfN1x1nUWmDRCrIVMgxc1KtfD7/Pgr2Xqtk8HuOsj6dAshGiWjEaFk+eL2H7iPP/dkUzWheLlK2UOGg0EujkQ7OVEqJcTIZ5OhHg54ulkxw/7z+Fkp8PP7WKbnMyCMhRF4YVbO1lkBKjepsFXiouL4/Tp01VqgQDGjBlzrZcUonE7f1wtdE670Ay090SIfhPsnKwblxBC1NHVZl/lFOmJPZvL/tO57D+dw4EzueSXllc7z95Gi7ujLS72Nmg0MCYyiB6hnoR6ORHk4YC9Tc0FzVlFZWyIS0ej0eDmaEN+STlZRWXcHNGwt7/gGhKgkydPMm7cOA4dOoRGo6FyAKmyOaKsoSWaHUWBfatg3Ry1oaGjJ4x5X12NXQghmoCa6nd6hnrQxteF+NR89p/OJfZMLknni6o91sFWS+cAN0oMFfi42NPB3wVXB1vg4ujNwwPC65TAjOoeBEBMcg4puSU429lwc4S/aX9DMjsBmjZtGq1bt2bTpk20bt2a3bt3k5WVJX1/RPNUnK0WOh9VC/xpPRTG/RvcAq0blxBCmGHNwRR+PpBChRHySw2k5pbyv71nMNZwK6uNjzM9Qj3oGeJBz1BPOga4YqvTmmqASg1G7Gwqrmn0xsFWx929QxjRyb/p9QHasWMHv//+Oz4+Pmi1WrRaLTfeeCPz58/n2WefZf/+/fURpxAN49Li5bRDaqFzQYpa6DziZRgw9WKhsxBCNFKGCiNHUwuIOZXNjpNZ/HH8PMX66ndo7HRa+oR7ckO4Fz1DPegR4oGHU80JiSVHbzyd7ayW+FQyOwGqqKgwzfby8fEhJSWFjh07EhYWRkJCgsUDFKJBXLoAaWkBZB1T1/FCAe92cNfHENTD2lEKIVqoq9Xu5Bbr2Xc6h5hT6teBM3nVZmNpAB8XewLcHQh0d8DL2ZaisnKm39KpTrOvGtPojSWYnQB17dqVAwcO0Lp1a/r168eiRYuws7PjP//5D23atKmPGIWof0dWq4uY2jjAqT/V5oUAYTfCg/9T+/kIIUQDq6l2p1eYB12D3Dl8Ll9NeE7ncCKjsNpj3Rxs6BXmSUSgG/Gp+fi42BPk4Wg6nllQhou9LR6OtmbF1BhGbyzB7ATopZdeoqhILZKaN28eo0aNYvDgwXh7e/P1119bPEAh6l3lIqYOnmpTw5JsNREKHQgeIVBeJgmQEMIq1hxMYd3hNBQFCkoNnM0p4bt9ZzFUVC/eaePrTO9QT3qHqV9tfV3QatUJSpX1O7YFWqvPvmoszE6ALl0EtV27dhw9epTs7Gw8PT1NM8GEaFJKcqCsENIPq8mPvSv0eAhs7NRuziU50sxQCNFgcov1xJzK4Y9j5/n5YAo5xfpqxco6rYYeIR70a+1F7zBPeoZ64nWFRKYxzb5qLMxKgAwGA46OjsTGxtK1a1fTfi8v+XAQTZijp1rofD4B0EDnO8DBTV3Hy85ZFjEVQlyXq9XvpOSWsCc5W/1KyiEhvaDaOc52OoI8HAnycMTbxQ59eQUz6li7A82vfscSzEqAbG1tCQ0NlV4/onnJT4Gze9WfW/UGZ5+Li5h2ul1Gf4QQ16S2+p2IQHcOnM1lT1I2e5JzOJdbUu2xbXydiQz2ICW3BH9XB8J9nEx3WTILyrDXac2u3YHmU79jCWbfAvvHP/7B3//+d/773//KyI9o+soK4ZtHwGgA/y7gFyGLmAohLGLNwRR+O5KGBg35pQbOZOfWWL+j02roEuTGDeFe3BDuRZ9wT3xc7IGLtTvnC/VSu2NhZidA77//PidOnCAoKIiwsDCcnasOv+3bt89iwVnDpYuhimZOUeCX6ZB1HFyD4OGf1UVsZBFTIcQ1MlQYOXg2jy0JGXyz9yznC8soN1ZPeHqHetK/rTd9L/Tfcbav+eNYanfqj9mLob722mtXPD537tzrCqixkMVQW4B9/4WfngGNDh5ZA2EDrR2REKIRuVrtDoC+3MjBs7nsSspm58ksYk7lVGs4aG+jpdWF+h1fVzsMFUaz6nfqGotQWXQx1Pfee4/HH38cBwcHJk6cSHBwMFrphiuasvQ4+HWW+vNN/5DkRwhhUlPtTp9wT0Z1D0KjgYNn89iZmMWupGz2nsqm1GCs8nhPJ1t6hnqSW6zH382B9n4uVep3HGyUFtt7pzGp0wiQjY0NKSkp+Pn5odPpSE1Nxc/PryHisxoZAWrG9EXwn+HqrK+2I+DBb2V5CyGESWXdjbezPc72OpIyi0jKKkKj0XAmu5iy8qoJj5ezHf1ae9G/jTf923jT3k/tv3PpdS6v37m7d4iVfrvmz6IjQEFBQXz33XfcdtttKIrC2bNnKS0trfHc0NDQa4tYiIbyy0w1+XENVBc2leRHCHFBZkEpvx1OI6OgjCMp+aTmlVJxWQ2Pt7PdhWTHi34XEp6a+uBJ/U7jVqcRoP/85z9MnTqV8vLyWs9RFAWNRtNsiodlBKiZ2v85/Pg0aLQwYQ2ED7J2REIIK1IUhWPphWw/cZ6/Es+zIzGLostqeJzsdAS6O+Bkp2PGLR0Z2sHXrMa/Ur/TsCw6AvT4449z//33c+rUKbp3787GjRvx9va2WLBCNIiMo/DrTPXn4X+X5EeIZuxKSceZ7OILCU8WfyVmcb6wrMpxW50GfzcH2vm6EOLlhKeTLecL9SiKQmSwh9mrHkj9TuNU52nwmZmZdO3alU8//ZRBgwZhb29fn3EJYVn6IvhmAhiKoc1wuHGGtSMSQtSDmgqYOwa44Opgy97kHLYnnudMdtXGgw62Wm4I92JQOx8GtfUhPi2PTfEZphqg84V66b3TDNU5AerevTvh4eGMGTOG2NhY+vXrV59xCWFZv74AmUfBJQDu/FDqfoRoptYcTGHtoVTKKyC7uIzT2SV8tcdQ5RybC+toDWznw8C23vQM9cDeRmc63t7fBa1GI7U7zVydE6Dz58+zYcMGfvzxR+644w40Gg2jRo1izJgx3HzzzTg4ONRnnEJcu9gvIfb/1Lqfuz4CF19rRySEsCCjUeFISj6/HUnjf3vPcL6wrNrioR5OtozuHsRNnfy4obUXLrU0HgRZN6ulMLsRIqhFYzt27OCnn37ip59+4vTp00RFRTFmzBhGjx6Nr2/T/4CRIuhmIjMB/jNMvfU1/B8w9AVrRySEuIq6FA2n5ZXyx/FM/jh+nj9PnCe7SF/luJuDDaHeToR6OuHrZk9OkZ7nb+5oVvNB0TTV9fP7mhKgyx0/fpyffvqJH3/8kV27drFkyRKmTJlyvZe1KkmAmgF9MXw0AjLioPVQeGg1aHVXf5wQwiqu1IBQUWB3cjZ/HMtk2/FMjqUXVnmsi70NfcI8KSgzEOTuSBtfF9OxzIIyFEXhhVs7yUhOC9CgCdClsrKyyM7Opn379pa8bIOTBKgZ+PEZ2P9fcPGHJ/8El+bdvFOIpu7SxoGuDjpOZ5eQmFmIosCp7GL0lzQg1Gige7AHQ9r7MKSDLz1CPLDVaaX5oLDsNPhLrVq1Ch8fH26//XYAXnjhBf7zn/8QERHBl19+SVhYmEyRF9Z34Gs1+UGjFj1L8iNEo5ZTpOevE1kUlpZzOquYU9nF1dbUCnJ3YEgHXwa392VQO288nKqP5kjzQVFXZo8AdezYkQ8++ICbbrqJHTt2EBUVxTvvvMOaNWuwsbHh+++/r69YG5SMADVhmccu1P0UwbA5MGy2tSMSQtRAURTiUwvYnJDBusNpHDqXV+W4jVZDkIcjbg42zIruyBAzGhBK88GWq95GgM6cOUO7du0A+OGHH7jrrrt4/PHHGTRoEMOGDbvmgIW4bsXZUJAK305Uk5/WQ2DILGtHJUSLUZeko7CsnD+Pn2dLQgabEzJIz6/ahNDd0ZZ2fi6EeTkR6OFATpEBRVHobmYDQmk+KK7G7ATIxcWFrKwsQkNDWb9+PdOnTwfAwcGBkpKSqzy6fowbN44tW7YwYsQIvv32W9P+NWvWMGPGDIxGIy+++CKPPfaYVeIT9cxQAkdWw+mdkLQNcpLA3g3GvC9Fz0I0gCsVL9vbaEnMLGTz0Uw2J2SwJzkbQ8XFGw+OtjoGtfNheCdfivXl7E3OMdXv5BQZpAGhqDdmJ0A333wzjz32GD179uTYsWPcdtttABw5coTw8HBLx1cn06ZNY9KkSaxatcq0r7y8nOnTp7N582bc3d3p3bs348aNk/qk5ujIajj6C5QVqMkPgF8EnNoOnmHWjU2IFmDNwRRT4XGQhyPZhXq+2HWa7/ed43R2MWdzqv7nuLWPM8M6+jK8ox99W3vhYKv+R6XUUIGnk53U74gGYXYCtHz5cl566SXOnDnDd999Z0ooYmJiuP/++y0eYF0MGzaMLVu2VNm3e/duunTpQqtWrQAYOXIk69evt1qMop4UZ6sjP+VlasIDEDoQfDup+zvcCk5e1o1RiGYsp0jP3uQcHG11pOaVsD2xiLM5JVVWULez0dK/jTfDO/oyrKNfrb14pAGhaEhmJ0AeHh68//771fa/9tpr1xTAtm3beOutt4iJiSE1NZXVq1czduzYKucsX76ct956i7S0NCIjI1m2bBl9+/a94nVTUlJMyQ9Aq1atOHfu3DXFKBqxkhxIPwLnYgAFvNpC+I1QoYe8s+pxSYCEsDhFUYhLzeebvWdZeziVvJLyKsdd7G3wdrHjiSFtGNuzFU52df+4kfod0RDMToAASktLOXjwIBkZGRiNl/Zl0DB69GizrlVUVERkZCSTJk3izjvvrHb866+/Zvr06axYsYJ+/fqxdOlSoqOjSUhIwM9Ppja3aIoCB7+Gc3vVbf+u0GGkuuRFaT7YOYOjp3VjFKIZ0Zcb2ZWUxca4dDbGZ3Aut+qtrUB3B9r4ONPax9k0AjSya6BZyY8QDcXsd+W6det46KGHyMrKqnZMo9FQUVFRw6NqN3LkSEaOHFnr8SVLljB58mQmTpwIwIoVK/jll1/45JNPmD279unNQUFBVUZ8zp07d8VRo7KyMsrKLs5GyM/PN+fXEA3NaITf/g67PlC3vdpCqxvAaIDiLCjKgE63y+iPEHVU2wyuvBIDWxIy2BCXztaETArKLo70ONhqGdLeFw8nWzIKyghydzQ1H8wu1kvxsmjUzE6Apk6dyr333ssrr7yCv79/fcRkotfriYmJYc6cOaZ9Wq2WqKgoduzYccXH9u3bl8OHD3Pu3Dnc3d1Zu3YtL7/8cq3nz58//5pv44kGVq6HH56Cwxdm/EW9pjY6PL1Tve1l56wmP13GWTdOIZqAmmZwtfFVa3S2JGSyOymb8kvqeXxc7Inq7MfNEf4MaueDg63OdA0pXhZNidkJUHp6OtOnT6/35AfUFegrKiqqPZe/vz9Hjx41bUdFRXHgwAGKiooIDg7mm2++YcCAASxevJjhw4djNBp54YUXrjgDbM6cOaYp/aCOAIWESNv0RqesAL7+G5zcAlobGPsBdL9XPdbhVrXmx9FTRn6EqKM1B1NYfyQNrUZLRkEpJzOLyC0xVDmng78LUZ39uTnCn8hgD7Taqv14pHhZNEVmJ0B33303W7ZsoW3btvURzzXZuHFjjfvHjBnDmDFj6nQNe3t77O3tLRmWsLTCTPj8bkiNBVtnGP9faDfi4nEnL0l8hKgjo1Fh67FMPtx2krM5JRRdsuyEBvB1teeh/mGM6RFEmHfdVlCX4mXRlJidAL3//vvcc889/PHHH3Tr1g1bW9sqx5999lmLBefj44NOpyM9Pb3K/vT0dAICAiz2PJdavnw5y5cvN7uWSdSz7CT4vzsh+yQ4ecOD30Cr3taOSogmpbzCyO7kbNYdTuO3I2lVujDbaDWEezvT1teZQA8Hsov0jIqse/IjRFNjdgL05Zdfsn79ehwcHNiyZUuV1uQajcaiCZCdnR29e/dm06ZNpqnxRqORTZs28cwzz1jseS41ZcoUpkyZYlpLRDQCqQfVkZ/CdPAIhb+tBp921o5KiEbjSktQGCqM/JWYxbrDqaw/kk5Wkd50zMXeBm9nO0K9nOgW7I6tTgtAZkEZznY2eDhW/Q+uEM2J2QnQP/7xD1577TVmz56NVqu97gAKCws5ceKEaTspKYnY2Fi8vLwIDQ1l+vTpTJgwgT59+tC3b1+WLl1KUVGRaVaYaOaS/oCvHoCyfHWa+4PfglugtaMSolGobQmKmzv7szs5h7WHU9kYl05+6cWZWx5Ottzc2Z+R3QIY1M6Hnw+oXZxziw2mGVyy/IRoCcxeDd7Ly4s9e/ZYrAZoy5YtDB8+vNr+CRMmsHLlSkC97VbZCLFHjx6899579OvXzyLPXxtZDb4ROPIDfD9ZbWoYNgju+wIcPawdlRCNxrcxZ0xLUDjaaUlIK+RERgE5xQbKyi/2aPNxsSe6iz8juwbSr42XaaQHqDKDq0hfjrOdDb0vrONVuUSFEE1JXT+/zU6Ann/+eXx9ffn73/9+3UE2RpfWAB07dkwSIGvZ/SH8OgtQoPNouPMjsHWwdlRCNBo5RXpe/yWO1LxS0vNLOZVVXGW6ur+bPSO7BjKyawB9wr3Qaa+8knpdVnIXoimotwTo2Wef5bPPPiMyMpLu3btXK4JesmTJtUXcyMgIUAMrzlansDt4wK4VsG2Rur/PJLjtbVnVXYgL8ksNbIpP55u9Z9l5MotLch7cHGxo7eOMm4MNr97Rlba+LtYLVAgrqevnt9k1QIcOHaJnz54AHD58uMqxSwuihagTQ4m6mvvpnWqPn/TDcP6YemzYHBj6Isj7SrRwecUGNsSn8+uhVP48fh59xcXbW672NnQKdKWdnwu+LvacL9SjKApeTjKKI8SVmJUAVVRU8Nprr9GtWzc8PWWNJWEBR1bD0V/A0QtSD0B2oro/8n4YVvtSJ0I0B1e67ZRTpGd9XBq/Hkpj+4nzVW5vtfNz4bZugdjq4NDZPHxcHHBztOF8oV4KmIWoI7MSIJ1Oxy233EJ8fHyzTYCkD1ADKs5WR37s3eDk7+oyFhodtB4KOjv1uDQ2FM1QbbO3Brb1YUtCJmsPp/JXYpZpQVGAjv6u3NYtkNu6BdDe37XKdWQJCiHMZ/YtsK5du3Ly5Elat25dH/FYnfQBakAlOZB7Gs7uBX0B6Oyh613qul55Z9XjkgCJZmjNwRTT7C0PJ1viUwpYH5dOTpGeS4syOwe6cXu3AG7tGkg7v+r1PLIEhRDXzuwE6PXXX2fmzJn885//pHfv3jg7V+0SKgXDok4URb39dXILoKjrd0WMU5Ofwgx1QVPH5jnKKFq2nCI9u05mU1haTnxqAWeyiy9LelwZHRnEyK6BtPaRJSiEqC9mJ0C33XYboK6zdWnRs6IoaDQauXUkrq40D36cAvE/q9uugdB2BDi4qclPUYa6mruM/ohmRFEUDp3L4+M/k/j1UCqGiqpT1lv7OONsp+OlUV3qnPgIIa6d2QnQ5s2b6yMO0VKkxMI3EyAnGbS2EPWqOtJzZpd628vOWU1+uoyzcqBCWEZWYRmr95/jm71nSUgvMO13stPRNcidzoGueDjZkVlQhqIosvyEEA3E7ARo6NCh9RFHoyFF0PVEUSDmU1g7GyrKwD0U7l15cUHTjiPVmh9HTxn5EU2eocLIloRMvtl7ht+PZphmcNnZaLm1SwAB7vYkny/Cx8UBRzsdmQVlMntLiAZmdiNEgNzcXD7++GPi4+MB6NKlC5MmTWpWRcPSCNGCygphzXNw6Bt1u8NIGPeB1PiIJq2mKezH0wv4JuYs3+87x/nCiyutRwa7c3efEMZ0D8LdyVaWnxCiHtVbJ+i9e/cSHR2No6Mjffv2BWDPnj2UlJSwfv16evXqdX2RNxKSAFlIRjz872G1uaFGB1FzYeCz0txQNFmXT2G30WrRaOB4RiEHz+aZzvNxsWNcz1bc0yeEDhemrV9Olp8QwvLqLQEaPHgw7dq148MPP8TGRr2DVl5ezmOPPcbJkyfZtm3b9UXeSEgCZAGxX8Iv08FQrBY63/0phA2wdlRCXJdvY86w/kga5RVwOqeYpMwiKi78M2qj1TC8kx/39A5meCe/KouOCiEaRr0thbF3794qyQ+AjY0NL7zwAn369Lm2aEXzYihRFzLd/191u81wuPNDcPG1blxCXKeEtHxW/XWKpPNFFJaVm/a7O9rSxseZt++JpG0N/XqEEI2P2QmQm5sbp0+fplOnTlX2nzlzBlfXmod5mxIpgr5OWYnqLa/0w4BGXc9ryExZzFQ0WeUVRjYnZPL1ntP8fjTDtPionU5LxwBXIoLc8HC0ITWvFO1VVlwXQjQeZidA48eP59FHH+Xtt99m4MCBAGzfvp1Zs2Zx//33WzzAhiadoM1UuYq7oyckbYUfp6pdnZ194a6PoM0wa0coxDU5lVXE//ae4Zu9Z8kouFjQ7OtiT1s/Z3qFeppucWUWlOFsZyNT2IVoQsxOgN5++200Gg0PP/ww5eXqELCtrS1PPfUUCxYssHiAopG6dBX30gLIjIPMo+qx0IFw9yfgFmjdGIWoRW3Fx6WGCtbHpfP1ntNsP5Fl2u/tbMddvYO5t08IsWdy2BCXTm6xATdHG/JLymUKuxBNUJ2KoA8ePEjXrl3Rai8W9BUXF5OYqK7c3bZtW5ycnOovSiuQIuiriP1CXcXdxhGSt0Fhurq/fTTc9wXozM6thah3tS1C2sHfldX7z7F6/zlyiw2AOlFxcHtf7r8hhBGd/bGz0Va5hkxhF6JxsmgRdM+ePUlNTcXPz482bdqwZ88evL296datm8UCFk1IcTac2gEluXB2PVTowcYBwgeDawCU5UszQ9EoXboIqa+rPYfP5bP2cBp5JQbTOUHuDtzTJ4R7+gQT7Fn9P3ayAKkQzUOdEiAPDw+SkpLw8/MjOTkZo9FY33GJxizrBJzYAAWp6rZbMHQeDTb2soq7aLRyivTsTc7BRqvl4NlcEtILTOtxaTQwvKMfDw0IY0h7X3R1KGaWBUiFaNrqlADdddddDB06lMDAQDQaDX369EGnq3mo9+TJkxYNUDQycT/Cz89BSTZotBA+BEL6qj/LKu6iEfvjxHm2JGSQln+xoNnD0ZaOAa64O9ow57YIWYRUiBakTgnQf/7zH+68805OnDjBs88+y+TJk5vFlPeayDT4WpTkwtoX4ODX6rZbMHi1Ub8q9FCaL6u4i0bHaFTYGJ/Ov7edJOZUjml/W19neoR40MrDkfOFelmEVIgWyOxO0BMnTuS9995rtglQJSmCvkTi7/DjM5B/Th3pufF5GDRNLYI+vRP0RerIT2h/dRV3W0drRyxauLLyClbvO8d//jjJycwiQO3b0zPUA1udhlAv52ozuO7uHWLlqIUQllBvS2Fc6uzZswAEBwdf6yUaLUmAUBObDXNhz4fqtldbGLdCveVV6dI+QDLyI6wsr8TA57tO8en2ZDIv9O5xdbDhof5hPDIwHDdHW5nBJUQzV29LYRiNRl5//XUWL15MYWEhAK6ursyYMYN//OMfVabKiybszB5Y/QRkq60OuGEy3PyaOtJzKScvSXxEg6mtf09qXgmf/JnEl7vPmJaoCHR34NEbW3Nf31Bc7C/+UyczuIQQcA0J0D/+8Q8+/vhjFixYwKBBgwD4888/efXVVyktLeWNN96weJCiAZXrYetC+HMJKEZwDYKxy6HtTdaOTLRgtfXv6ejvysq/TvFj7DnKL6xR0cHfhSeGtGV0ZJCpd8/lZAaXEMLsW2BBQUGsWLGCMWPGVNn/448/8vTTT3Pu3DmLBmgtLfIWWPoRddQn7ZC63X08jFwos7qE1X0bc8bUv8fVQUdiRhEHzuaSWag3ndOvtRdPDm3LsI6+aDSyJpcQLVW93QLLzs6uthAqQKdOncjOzjb3csKaKut37N3gwBfw++vqjC5HLxi9FCLusHaEQpj693g52ZFfauD3oxmk5Zeajkd19uOZm9rTI8TDekEKIZocsxOgyMhI3n//fd57770q+99//30iIyMtFpioR5eu41WQDin71CnsAB1Gwuh3wdXfujEKcUF2sZ4TGQUknS8mq0gd8dFpNXT0dyHA3YF/3C79e4QQ5jM7AVq0aBG33347GzduZMCAAQDs2LGDM2fO8Ouvv1o8QFEPjqyG+DXq6M/ZPWA0gNYGIu+HMcvUtrhCWFl5hZE1B1N5d9Nxks5fnMrePdidHiEeFOsrpH+PEOKamZ0ADR06lGPHjrF8+XKOHlVX/77zzjt5+umnCQoKsniADa3ZN0Iszoajv8LZ3VCUqe5zD4GQ/mqPH1nGQliZocLI6v3n+NfmEyRnFQPgYKullYcjPUI88HW1lxXYhRDX7br6ADVnzbIIujRf7eZ84Et1W2enLmDaqg9UlKnreA3/O3i3tW6cokUqK6/g25izfLAlkbM5JQB4Otny6I2tGX9DCFuPZUr/HiHEVVm0CPrgwYN1fuLu3bvX+VzRQBQFjnwP6/4OhWnqPs/W0PE2sL/Q0bs0X9bxElZRaqjgq92n+fe2k6TmqcXNPi52TB7chr/1D8P5Qg8f6d8jhLCkOiVAPXr0QKPRoChKlemllYNHl+5rtreOmqrzJ+DXmXBys7rt1UZdrys7SS2G1tnKOl6iQVzexLBYX87nO0/znz9Omro2+7vZ88SQttzfNxRHu+ojO9K/RwhhKXVKgJKSkkw/79+/n5kzZzJr1qwqRdCLFy9m0aJF9ROlMJ+hBP5YAtuXqlPbdfYweIa6hhfKxVlgeWfVkZ9Ot6vreAlhYZc3MbTVaSnWV7DzZBY5xQYAWnk48uSwttzTO1huaQkhGoTZNUB9+/bl1Vdf5bbbbquy/9dff+Xll18mJibGogFaS5OuATq+QR31yUlWt9uOgNveql7bI+t4iQZQ2cTQ1d6W5KwiDp7NQ19hBCDUy4kpw9syrmdwrV2bhRDCHPXWCPHQoUO0bt262v7WrVsTFxdn7uWEJeWdhXVzIP4ndds1CEYugM5jap7aLut4iXqWU6Rnd1I22UV6tiZkUlquJj5uDjZEBLqx7IGe+Lo6WDlKIURLZHYC1LlzZ+bPn89HH32EnZ16L16v1zN//nw6d+5s8QBFLS4dvbF3hZ0fwJYFYCgCjQ76PwXDZl8schbCCv5KPM9vR9LJK1FvdXk52dGvjRchXo6k5ZVSWFaBr7xFhRBWYHYCtGLFCkaPHk1wcLBpxtfBgwfRaDT8/PPPFg9QXObSLs76ItAXQtpByE9Rj4f0g9uXQEBX68YpWrSswjIWrjvK//aeBcBWp2FgWx+6t3JHq9WQWVCGs52NNDEUQliN2QlQ3759OXnyJJ9//rmpEeL48eN54IEHcHaWdvT17shqOPoL2LlCRpz6BWoh88hFEPkAaKWWQlhHeYWRz3edZvH6BPJLywHoE+aJi4OOIHdHDEYj+UXSxFAIYX1mJ0AAzs7OPP7445aORVxNcbY68qO1gbjVUH5hQUifDhDUU+3rI8mPsJI9ydm88uMR4lPzAegS5Ma8O7rQJcidNQdTiEnOISW3BGc7G26O8GdU96bfOV4I0XTVKQH66aefGDlyJLa2tvz0009XPHfMmDEWCUzUoCRHve11LkZNfpx9of2t4OytFkDLMhbCCjLyS1mw9ijf7z8HqAXOs6I78kC/MHRatfhemhgKIRqbOiVAY8eOJS0tDT8/P8aOHVvreRqNRhoh1idHT7VpYd4Zdd2urneBgwcUZkgXZ9HgDBVGVv2VzNKNxyksK0ejgftuCGHmLR3xdrGvdr40MRRCNCZ1SoCMRmONPzdHjXoxVEdPyDqm/uzTEWwc1ORHujiLenZ5F+cdiVnM/ekwx9ILAYgMdue1O7rSI8TDuoEKIUQdmd0I8bPPPmP8+PHY21f9H55er+err77i4YcftmiA1tIoGyEe/QW+ekBdxDRiLChGdeQntL/axdnW0doRimbm8i7OAKezi4k9kweoi5W+eGsn7u0TglZbQ68pIYRoYHX9/DY7AdLpdKSmpuLn51dlf1ZWFn5+fo1z5OQaNLoEyFgBHwyCzHi4cToMnCpdnEW9q+zi7OFoR3JWETGncig3KmiAv/UPY8YtHfBwkttaQojGo946QV++IGqls2fP4u7ubu7lRF0d+lZNfhzcYdCzkviIepddWMbGuHRSckvZeTLb1MzQx8WO3qGeTL9Zkh8hRNNV5wSoZ8+eaDQaNBoNI0aMwMbm4kMrKipISkri1ltvrZcgW7xyPWx+Q/150DQpdhb1xmhUOHA2l3VH0vjlYCpnc0pMxxxtddzY3oc2Pk6k5pWSW2KQomYhRJNV5wSocvZXbGws0dHRuLi4mI7Z2dkRHh7OXXfdZfEABbD/M8g9Bc5+0O9Ja0cjmhlDhZHdSdn8diSN346kkZ5fZjqm1UCAuwMRgW6083PB3kYnXZyFEM1CnROguXPnAhAeHs748eNxcJAFDBuEvhi2vqX+PGSWWvQsRB1dPnurUqmhgj+On2fd4TQ2HU0nt9hgOuZsp2N4Jz9u7RpAbrGeP46fx9tZnfSQWVAmXZyFEM2C2TVAEyZMAGDv3r3Ex8cDEBERQe/evS0bmVDt/g8UpoFHKPR+xNrRiCbi8tlbTnY2dGnlhoONjs0JGWxJyKRYf3HCgpezHTd39ie6qz8D2/rgYKszXcfBViddnIUQzY7ZCdC5c+e477772L59Ox4eHgDk5uYycOBAvvrqK4KDgy0dY8tVmgd/vqP+PGwO2Mj/uEXdrDmYwoa4dJzsbMgtMbAnOYev957h0jmfQe4ORHcNILpLAH3CPLHRVV9GxcFWJ12chRDNktkJ0KOPPorBYCA+Pp6OHTsCkJCQwMSJE3nsscdYt26dxYNssf5aBqW5atPD7uOtHY1oIg6fzeP/dp7mXE4JmYVlVY65OdhwT+8QxvZsRddWbjXO6KyJdHEWQjQ3ZidAW7du5a+//jIlPwAdO3Zk2bJlDB482KLBtWiFmbDjX+rPN70EWp114xGNlqIoJKQX8NvhdH47kkbchcVIK/m52tPOz4UQL0dK9BX8bUAYrX2klkwI0bKZnQCFhIRgMBiq7a+oqCAoSOoCLOaPxWAoUld57zza2tEIK6itgBnU6er7z+Sy/sLMreSsYtMxrQZ8XOwJ8XQiMsQdVwd1tpY6e0sjs7eEEIJrSIDeeustpk6dyvLly+nTpw+gFkRPmzaNt99+2+IBtki5Z2Dvx+rPI16BOt6mEM1DTQXMfcI9ie4SQOyZXNYdTmNDXDoZBRdvb9nZaBnS3odbugQQ1dmf34+msyEunVKDETubCvJLymX2lhBCXMLspTA8PT0pLi6mvLzc1Ayx8mdn56rD6tnZ2ZaLtIFZdSmMH6fA/v+D8MEw4WdJgFqYyuUnvJ3tcbTTciytkBOZheQWGygxXJy55Wpvw/BOfkR3CWBYR1+c7S/+f6YyiYpJzqFIX46znQ29wz0Z1T3INMNLCCGao3pbCmPp0qXXE5e4mvPHIfYL9ecRcyX5aWFyivTsSMyioKScExmFnMoqptx48f8oXs52RHcJILqLPwPaemNvU3MyI7O3hBDiyq65D5CoJ7+/rq7y3vE2CLnB2tGIBpJXYmBjXDrf7TvLzpNZXJLz4OZgQ7iPM672Nrx2R1fa+bnUfqHLyOwtIYSomdkJEEBiYiKffvopiYmJvPvuu/j5+bF27VpCQ0Pp0qWLpWO8Zm+//TaffvopGo2G2bNn87e//c3aIV1ZSizE/QBo1JlfolnLLdaz/kg6vx5OZfuJ8xgqLmY9bg42dApQl5/wcbHjfKEeRVHwlmRGCCEs4pqmwY8cOZJBgwaxbds23njjDfz8/Dhw4AAff/wx3377bX3EabZDhw7xxRdfEBMTg6IoDB8+nFGjRpmaNzZKv/9T/d7tHvBvPImkMF9tM7iyCstYH5fOr4dS2ZGYVeX2Vkd/V0Z2C8BGq+Hg2Vx8XBxwc7ThfKFeCpiFEMLCzE6AZs+ezeuvv8706dNxdXU17b/pppt4//33LRrc9YiPj2fAgAGmNcsiIyNZt24d9913n5Ujq0XydjixEbQ2MHyOtaMR16imGVwdAlzQoGFDXHq121udA924vVsAt3YNNN3aurSAWZafEEKI+mF2AlQ5snI5Pz8/zp8/b5GgALZt28Zbb71FTEwMqamprF692rQifaXly5fz1ltvkZaWRmRkJMuWLaNv374AdO3alddee43c3FwURWHLli106NDBYvFZlKLApnnqz70eBq821o1HXLPKJSgcbXVkFenZeTKbr/acqXJOt1bujOwWwMiugTU2JJQCZiGEqH9mJ0AeHh6kpqbSunXrKvv3799Pq1atLBZYUVERkZGRTJo0iTvvvLPa8a+//prp06ezYsUK+vXrx9KlS4mOjiYhIQE/Pz8iIiJ49tlnuemmm3B3d6d///7odI10+u/xDXBmJ9g4qCu+iyYpp0jP7qRsUnJLOZKSV2Wkx9vZjocHhHFnr2BCvJzqdD0pYBZCiPpTffXDq7jvvvt48cUXSUtLQ6PRYDQa2b59OzNnzuThhx+2WGAjR47k9ddfZ9y4cTUeX7JkCZMnT2bixIlERESwYsUKnJyc+OSTT0znPPHEE+zbt4/Nmzdja2tL+/bta32+srIy8vPzq3w1CKPx4uhP38fBTW5zNFVH0wrYGJ/OoXNq8hPg5sDg9j482C+UQe28GdOjVZ2THyGEEPXL7ATozTffpFOnToSEhFBYWEhERARDhgxh4MCBvPRSw8xc0uv1xMTEEBUVZdqn1WqJiopix44dpn0ZGRmAuljr7t27iY6OrvWa8+fPx93d3fQVEhJSf7/ApeJWQ/ohsHeDG59vmOcUFvfTgRQmf7aX7CIDNloN0RH+jL8hhF6hnigKONvZyBIUQgjRiJh9C8zOzo4PP/yQl19+mcOHD1NYWEjPnj2vOLpiaefPn6eiogJ/f/8q+/39/Tl69Khp+4477iAvLw9nZ2c+/fRTU+fqmsyZM4fp06ebtvPz8+s/Caooh9/fUH8eOBWcvOr3+YTFFZaV88qPh/l+3zkAwrycCPV2xNvFnrJyWYJCCCEaq2vqAwQQGhpqShA0jbRb8aWjQVdjb2+Pvb19PUZTg9jPITsRnLyh/1MN+9ziuu0/ncNzX8dyKqsYrQaeuak9jw9uzbojaTKDSwghGrlrSoA+/vhj3nnnHY4fPw5A+/btee6553jssccsGlxtfHx80Ol0pKenV9mfnp5OQEDAdV17+fLlLF++nIqKiquffD0MpbB1ofrz4Blg73rl80WjUWFU+GDLCd7ZeJwKo0IrD0eW3teDG8LVETyZwSWEEI2f2TVAr7zyCtOmTWP06NF88803fPPNN4wePZrnn3+eV155pT5irMbOzo7evXuzadMm0z6j0cimTZsYMGDAdV17ypQpxMXFsWfPnusN88r2fgz558CtFfR5tH6fS1jMudwS7v9wJ2+vP0aFUWF0ZBC/ThtsSn4qeTrb0drHWZIfIYRopMweAfrggw/48MMPuf/++037xowZQ/fu3Zk6dSrz5s2zSGCFhYWcOHHCtJ2UlERsbCxeXl6EhoYyffp0JkyYQJ8+fejbty9Lly6lqKiIiRMnWuT5601xNuSdhW1vq9tDXwRbB+vGJOpkzcEU/v79IfJLy3G20zHvjq7c2atVo70FLIQQonZmJ0AGg4E+ffpU29+7d2/Ky8stEhTA3r17GT58uGm7skB5woQJrFy5kvHjx5OZmckrr7xCWloaPXr0YN26ddUKoxsNQwkcWQ2nd8LZPVCSDc5+0LV6jyPRuBSWlfPqT0f4NuYsAD1CPHj3vh6EeVdvYiiEEKJp0CiKolz9tIumTp2Kra0tS5YsqbJ/5syZlJSUsHz5cosG2NAurQE6duwYeXl5uLm5Xf+FY7+Ao7+o090PfwsVegjqBX0nQ48Hrv/6wiIuX8Mr9kwu077az6msYjQamDKsHdOi2mOrM/vusRBCiAaQn5+Pu7v7VT+/rykB+uyzzwgJCaF///4A7Nq1i9OnT/Pwww9ja3ux18nlSVJTUtcXsE6Ks2Hjq6DRQfphOLsbXPygw+2AEaJelSnwVnb5Gl4OtjoKSsvZEJdOuVEhyN2Bd8b3oF8bb2uHKoQQ4grq+vlt9i2ww4cP06tXLwASExMBdVaWj48Phw8fNp0ndRGXKMkBfZHa5TnrQl1T+FBwdFfrgUpyJAGysso1vLyd7XFzsGV9XDoZBWUA3N49kDfHdsPdSRoZCiFEc2F2ArR58+b6iKN5c/QEO2coK4Q+k+D8MXXB06JMdb+jp7UjbNFyivTsTc7B29meYn05aw+nUVZuxEaroXeYJ/8c00WSHyGEaGakkOEyy5cvJyIightuuMFyF3XygtD+UJSh3g6rTH6KMtT9MvpjVbklBor15YDCrxeSH383e+7uHYy/mz15pZYr7hdCCNE4SAJ0mXrrA9RlHHS6HZQK9baXUqFud6l5sVfRcDwcbbG30bHuSDr6ciOB7g7c0zsErUYja3gJIUQzdc1LYQgz2Tqqs7063KrW/Dh6yshPI+HpbEdafinZRXrsdFpu6uRHdpFe1vASQohmTBKghubkJYlPI/PbkTT+OH4egP5tvCgoNcgaXkII0czVKQHq1asXmzZtwtPTk3nz5jFz5kycnJzqOzYh6t2Z7GJmfXMAgMmDW/P0sHayhpcQQrQAdaoBio+Pp6ioCIDXXnuNwsLCeg3KmuqlCFo0SvpyI898uZ/80nJ6hHjwwq2dZA0vIYRoIerUCHHAgAG4uLhw44038tprrzFz5kxcXFxqPLehFkStbxZthCgapdfXxPHRn0m4Odjw67TBBHvKqKYQQjR1Fm2EuHLlSubOncuaNWvQaDSsXbsWG5vqD9VoNM0mARLN24a4dD76MwmAt++JlORHCCFaGLOXwtBqtaSlpeHn51dfMTUKMgLUfJ3NKeb29/4kr8TAoze25uVREdYOSQghhIXU21IYRqPxugITwpoMFUamfrmfvBIDkSEevHhrJ2uHJIQQwgquaRp8YmIiS5cuJT4+HoCIiAimTZtG27ZtLRqcNVy6Grxoft76LYH9p3Nxc7Dh/ft7YmcjvUCFEKIlMvtf/99++42IiAh2795N9+7d6d69O7t27aJLly5s2LChPmJsUPXWCVpY3ab4dP6z7SQAb90TSYiX1P0IIURLZXYNUM+ePYmOjmbBggVV9s+ePZv169ezb98+iwZoLVID1Lyk5JZw23t/kFtsYOKgcOaO7mLtkIQQQtSDun5+mz0CFB8fz6OPPlpt/6RJk4iLizP3ckLUu8q6n9xiA92D3ZkzsrO1QxJCCGFlZidAvr6+xMbGVtsfGxvb7GeGiabp7fUJxJzKwdXBhvfv7yV1P0IIIcwvgp48eTKPP/44J0+eZODAgQBs376dhQsXMn36dIsHKMT12Hw0g39vvVD3c3d3Qr2l7kcIIcQ1JEAvv/wyrq6uLF68mDlz5gAQFBTEq6++yrPPPmvxAIW4Vql5JUz/XywAjwwM59augdYNSAghRKNhdhH0pQoKCgBwdXW1WEDWduk0+GPHjkkRdBNVXmHk/g93sic5h26t3Pn2qQHY2+isHZYQQoh6Vtci6OtKgJozmQXWtC1ad5R/bUnE1d6GNc/eSJi3s7VDEkII0QDqbRaYEI3dloQM/rUlEYAFd3WX5EcIIUQ119QJWojGKKdIz/GMQp77OhaAh/qHcXt3qfsRQghRnSRAoskrNVSw5mAKu5Oy2RifQW6xgVYeDsy8pYO1QxNCCNFImXULzGAwMGLECI4fP15f8QhhtjUHU9gQl86Rc/lkF+mx0Wpo7ePMhvh0a4cmhBCikTIrAbK1teXgwYP1FYsQZssp0rMnKZtzOSUcSc0H4OYIf0K9nIlJziGnSG/lCIUQQjRGZhdB/+1vf+Pjjz+uj1iEMFtWkZ6dJ7M5nKImP/3beNHB3xU3RxuK9OXklhisHKEQQojGyOwaoPLycj755BM2btxI7969cXauOsNmyZIlFgtOiCspNVTw5i/xnMouRgMM6+hL92APAPJLynG2s8HD0daqMQohhGiczE6ADh8+TK9evQA4duxYlWMajcYyUVnRpY0QReOVV2Jg8qq97E7ORqfV0K2VG4HujpSVV5BfUk5WURk3R/jj6Wxn7VCFEEI0QtIIsRbSCLHxSs8vZcInuzmaVoCrvQ3LH+xFRkEpMck5FOnVkZ/e4Z6M6h6Eg610fxZCiJakrp/f1zwN/sSJEyQmJjJkyBAcHR1RFKVZjACJxi0xs5CHP97NudwSfF3tWTWxLxFB6ht8RCd/cksMeDjaysiPEEKIKzK7CDorK4sRI0bQoUMHbrvtNlJTUwF49NFHmTFjhsUDFKJS7Jlc7v7gL87lltDax5nvnxpoSn4APJ3taO3jLMmPEEKIqzI7AXr++eextbXl9OnTODk5mfaPHz+edevWWTQ4ISptScjg/v/sJKfYQPdgd759cgAhXk5Xf6AQQghRA7Nvga1fv57ffvuN4ODgKvvbt2/PqVOnLBaYEJVW7z/LrG8OUm5UGNzehxV/642zvTQxF0IIce3M/hQpKiqqMvJTKTs7G3t7e4sEJUSlD7ed5I1f4wEY2yOIRXdHYmcja/gKIYS4PmZ/kgwePJjPPvvMtK3RaDAajSxatIjhw4dbNDjRchmNCm/+Gm9Kfh69sTVL7u0hyY8QQgiLMHsEaNGiRYwYMYK9e/ei1+t54YUXOHLkCNnZ2Wzfvr0+YhQtjKHCyAvfHmT1/nMAzBnZiceHtJFZhkIIISzG7ASoa9euHDt2jPfffx9XV1cKCwu58847mTJlCoGBgfURo2gBcor05JYYsNdpmbP6EFuPZaLTalh0V3fu6h189QsIIYQQZpBGiLWQRogNo9RQwZqDKexNziGnWM+e5Byyi/Q42Gr54MHeDO/kZ+0QhRBCNCH12ggxJyeHjz/+mPh4tT4jIiKCiRMn4uXldW3RihZrzcEUNsSl42CjY9fJbHJLDNjqNEwe3FqSHyGEEPXG7IrSbdu2ER4eznvvvUdOTg45OTm89957tG7dmm3bttVHjKKZyinSszc5B0WBjUfTyS0x4GJvwy0RAZwv0JNTpLd2iEIIIZops0eApkyZwvjx4/nggw/Q6dR1lioqKnj66aeZMmUKhw4dsniQDUkWQ204OcV6Dp/LIy41H6MC3s523NEjCDsbLSm5JeSWGKSrsxBCiHphdg2Qo6MjsbGxdOzYscr+hIQEevToQUlJiUUDtBapAapfBaUGpn99gA3x6QC09XXm5gh/7G10ZBaUoSgKL9zaSRIgIYQQZqnr57fZt8B69eplqv25VHx8PJGRkeZeTrRAcSn5jF72Jxvi09FqoFOAK33D1fqxzIIysorK6B3uKcmPEEKIelOnW2AHDx40/fzss88ybdo0Tpw4Qf/+/QHYuXMny5cvZ8GCBfUTpWgWFEXhf3vP8MqPRygrNxLk7sCS8T04m1NMTHIOKbklONvZcHOEP6O6B1k7XCGEEM1YnW6BabVaNBoNVztVo9E0m9oZuQVmWcX6cl764TDf71ObGw7v6MuSe3uYRnkq+wB5ONrKyI8QQohrZtFp8ElJSRYLTLQ8JzIKePrzfRxLL0SrgRm3dOSpoW3Rai92dvZ0tpPERwghRIOpUwIUFhZW33GIZurH2HPM+f4QxfoKfF3tWXZ/T/q38bZ2WEIIIVq4a2qEmJKSwp9//klGRgZGo7HKsWeffdYigYmmrdRQwbw1cXyx6zQAA9t68+59PfF1tbdyZEIIIcQ1JEArV67kiSeewM7ODm9v7yoLVGo0GkmABKeyinj6830cSclHo4Gpw9sxLaoDOq0sZiqEEKJxMLsPUEhICE8++SRz5sxBqzV7Fn2TIUXQdXN58fK6w2nM+vYABaXleDrZ8s74HgzrKEtaCCGEaBj1thZYcXEx9913X7NOfsTVXbqIabG+HHsbHen5pWw7fh6A3mGeLLu/J0EejlaOVAghhKjO7Czm0Ucf5ZtvvqmPWEQTUrmIqVajwdXBlt+PZpiSn8mDW/PV4/0l+RFCCNFomX0LrKKiglGjRlFSUkK3bt2wtbWtcnzJkiUWDdBa5BZY7XKK9CxcdxStRkNBmYENR9IpLTdiq9PQv7U3793fU6a0CyGEsIp6uwU2f/58fvvtN9NaYJcXQYvmL7fEQF6JnqTzxRxNKwDAz9WeqM5+FJaVyyKmQgghGj2zE6DFixfzySef8Mgjj9RDOKIpOJFRwLZj5ynSq12/e4Z4MLCtNznFBpztbPBwtL3KFYQQQgjrMjsBsre3Z9CgQfURi2jkSg0VvP1bAh9vT0JRwMFWy6C2PrT3dyGn2EBWURk3R/jL6I8QQohGz+wi6GnTprFs2bL6iEU0YofO5jF62Z989Kea/NzdO5iXbu+Mn6s9KbklKIoii5gKIYRoMsweAdq9eze///47a9asoUuXLtWKoL///nuLBXe93nnnHT766CMURSEqKop3331X6pTMZKgw8q/NiSz7/TjlRgUfF3sW3NmNqAh/QBYxFUII0TSZnQB5eHhw55131kcsFpWZmcn777/PkSNHsLW1ZciQIezcuZMBAwZYO7Qm40RGITP+F8uBs3kAjOwawBvjuuF1SaIji5gKIYRoisxOgD799NP6iKNelJeXU1paCoDBYMDPTzoS14XRqLBqRzIL1h6lrNyIq4MN/7yjK3f0CJIRNCGEEM1Co23nvG3bNkaPHk1QkPqh+8MPP1Q7Z/ny5YSHh+Pg4EC/fv3YvXu36Zivry8zZ84kNDSUoKAgoqKiaNu2bQP+Bk3TudwSHvxoF6/9HEdZuZHB7X1Y//wQxvZsJcmPEEKIZsPsEaDWrVtf8YPw5MmT1xVQpaKiIiIjI5k0aVKNt9y+/vprpk+fzooVK+jXrx9Lly4lOjqahIQE/Pz8yMnJYc2aNSQnJ+Po6MjIkSPZtm0bQ4YMqfH5ysrKKCsrM23n5+db5PdozC6t3/FwsuW7fed47acjFJSV42ir4++3deJv/cMk8RFCCNHsmJ0APffcc1W2DQYD+/fvZ926dcyaNctScTFy5EhGjhxZ6/ElS5YwefJkJk6cCMCKFSv45Zdf+OSTT5g9ezYbN26kXbt2eHl5AXD77bezc+fOWhOg+fPn89prr1ks/sbs8nW8tBoNxzMKOZKiJn29Qj1YfG8PWvs4WzlSIYQQon6YnQBNmzatxv3Lly9n79691x1QXej1emJiYpgzZ45pn1arJSoqih07dgDqqvV//fUXpaWl2NrasmXLFh5//PFarzlnzhymT59u2s7PzyckJKT+fgkrqlzHy9vZnrJyI1sSMikrN6LTaJgR3YEnhrRFp5VRHyGEEM2XxWqARo4cyXfffWepy13R+fPnqaiowN/fv8p+f39/0tLSAOjfvz+33XYbPXv2pHv37rRt25YxY8bUek17e3vc3NyqfDVHOUV69ibn4Gxnw77TOfx2JJ2yciMejrbcHOHH/TeESvIjhBCi2TN7BKg23377rel2U2Pxxhtv8MYbb5j1mOXLl7N8+XIqKirqKSrryi7WE5eaT0JaAWXlRjRArzBPeoV6kJ5fKut4CSGEaBHMToB69uxZpShWURTS0tLIzMzkX//6l0WDq42Pjw86nY709PQq+9PT0wkICLiua0+ZMoUpU6aYVpNtTg6fy2PO94c4dE7t6+PjYsdNnfwIdHcks6BM1vESQgjRYpidAI0dO7bKtlarxdfXl2HDhtGpUydLxXVFdnZ29O7dm02bNpniMRqNbNq0iWeeeaZBYmhKCkoNLF5/jM92JGNUwN5GS2sfZ3qEeODhZEtmQZms4yWEEKJFMTsBmjt3bn3EUU1hYSEnTpwwbSclJREbG4uXlxehoaFMnz6dCRMm0KdPH/r27cvSpUspKioyzQoT6ujczwdTeX1NHBkF6hT/Ud0DmRXdkT3J2cQk55CSW4KznY2s4yWEEKJFsVgNkKXt3buX4cOHm7YrZ2hNmDCBlStXMn78eDIzM3nllVdIS0ujR48erFu3rlphtLmaSw3QycxCXvnxCH+eOA9Aax9n5t3RhcHtfQEI83ZmRCd/WcdLCCFEi6RRFEWpy4larfaqDfE0Gg3l5eUWCczaKmuA8vLymtSMsFJDBcs3n+DfW0+irzBiZ6PlmeHteHxIGxxsddYOTwghhKhXdf38rvMI0OrVq2s9tmPHDt577z2MRqN5UQqL2nw0g1d+OsyZ7BIAhnX05bUxXQjzloaGQgghxKXqnADdcccd1fYlJCQwe/Zsfv75Zx588EHmzZtn0eBE7S5dxqLEUMFrPx/htyPqrLgANwfmjo7g1q4BsoyFEEIIUYNrqgFKSUlh7ty5rFq1iujoaGJjY+nataulY7OKxl4DdOkyFoVlBs5klxCfWoC+wohOq2HSoHCmRXXAxb7RlncJIYQQVlfnGiCAvLw83nzzTZYtW0aPHj1YuHAhgwcPrs/4rKax1gB9G3OGDXHpVBgVYk7lkFNsACDc24kVD/WmU0DjiVUIIYRoaBavAVq0aBELFy4kICCAL7/8ssZbYqJ+VS5jUao3svV4JgAOtloigz1o7e2Ev6uDlSMUQgghmoY6J0CzZ8/G0dGRdu3asWrVKlatWlXjed9//73FghNV5ZYYKCwzcOBcLgDt/VwY3skPrQZScktkGQshhBCijuqcAD388MNSUGtlHo62ZBToyS024GCjZURnP+xtdLKMhRBCCGGmOidAK1eurMcwGo/GXATtZK/jWHoBAJ0D1fuasoyFEEIIYT6ttQNobKZMmUJcXBx79uyxdijVfLnrNLnFBtwcbGjn50xKbgmKosgyFkIIIYSZZK50E1FUVs77m9W10V4c2YnbugbKMhZCCCHENZIEqIlY+Vcy5wv1hHk7cW+fEGx1Wkl8hBBCiGskt8CagNxiPSu2JgIw/eYO2Orkj00IIYS4HvJJ2gT8e9tJCkrL6RTgymip9RFCCCGumyRAl1m+fDkRERHccMMN1g4FgIz8Uj7dngTAzFs6otVKKwIhhBDiekkCdJnGNgvs/c0nKDUY6RXqwYjOftYORwghhGgWJAFqxM5kF/Pl7tMAzIruJI0ohRBCCAuRBKgRe2fjMQwVCoPb+zCgrbe1wxFCCCGaDUmAGqlj6QWs3n8OgFnRHa0cjRBCCNG8SALUSC1en4CiwK1dAuge7GHtcIQQQohmRRKgRujAmVx+O5KOVgMzbulg7XCEEEKIZkcSoMs0hmnwb/2WAMC4nsG093e1WhxCCCFEc6VRFEWxdhCNUX5+Pu7u7uTl5eHm5tZgz/vXifM88NEubHUafp8xjBAvpwZ7biGEEKKpq+vnt4wANSKKorDowujPA31DJfkRQggh6okkQI3IxvgMYs/k4mirY8pN7awdjhBCCNFsSQLUSFQYFd6+MPozcVA4fq4OVo5ICCGEaL4kAWokfj6QQkJ6AW4ONjwxpK21wxFCCCGaNUmAGgF9uZElG44B8MTQtrg72Vo5IiGEEKJ5kwSoEfjf3jOczi7Gx8WeiYPCrR2OEEII0exJAmRlJfoK3tt0HICpN7XDyc7GyhEJIYQQzZ8kQJdp6EaIn+1IJqOgjGBPR+7vG9ogzymEEEK0dJIAXWbKlCnExcWxZ8+een+u/FIDH2xNBOC5qA7Y2cgfhxBCCNEQ5BPXij7adpLcYgPt/FwY17OVtcMRQgghWgxJgKzkfGEZH/2ZBMDMWzqg02qsHJEQQgjRckgCZCX/2pxIsb6C7sHuRHcJsHY4QgghRIsiU44aWE6RnoT0Av67MxmAWdEd0Whk9EcIIYRoSJIANZBSQwVrDqawNzmHvxLPY6hQaOvrTJ8wT2uHJoQQQrQ4cgusgaw5mMKGuHQKS8s5k10CgI+LHb8cSrVyZEIIIUTLIwlQA8gp0rM3OQdvZ3sS0gtQgNY+zrT1dSUmOYecIr21QxRCCCFaFEmAGkBuiYFifTkuDjqMigLAgDbeuDnaUKQvJ7fEYOUIhRBCiJZFaoAagIejLU52NhSWVjCqexB5JQbcHW3JLCjD2c4GD0dZ/FQIIYRoSDIC1AA8ne3oE+5JVlEZmQVlONhqySwoI6uojN7hnng621k7RCGEEKJFkQToMvW1Ftio7kHcHOGPoiik5JagKAo3R/gzqnuQRZ9HCCGEEFenUZQLRSmiivz8fNzd3cnLy8PNzc1i180p0pNbYsDD0VZGfoQQQggLq+vnt9QANTBPZztJfIQQQggrk1tgQgghhGhxJAESQgghRIsjCZAQQgghWhxJgIQQQgjR4kgCJIQQQogWRxIgIYQQQrQ4kgAJIYQQosWRBEgIIYQQLY4kQEIIIYRocSQBEkIIIUSLI0th1KJyibT8/HwrRyKEEEKIuqr83L7aUqeSANWioKAAgJCQECtHIoQQQghzFRQU4O7uXutxWQ2+FkajkZSUFFxdXdFoNBa7bn5+PiEhIZw5c8aiq8wLeW3rk7y29UNe1/ojr239aeyvraIoFBQUEBQUhFZbe6WPjADVQqvVEhwcXG/Xd3Nza5RvnOZAXtv6I69t/ZDXtf7Ia1t/GvNre6WRn0pSBC2EEEKIFkcSICGEEEK0OJIANTB7e3vmzp2Lvb29tUNpduS1rT/y2tYPeV3rj7y29ae5vLZSBC2EEEKIFkdGgIQQQgjR4kgCJIQQQogWRxIgIYQQQrQ4kgAJIYQQosWRBKiBLV++nPDwcBwcHOjXrx+7d++2dkhN3quvvopGo6ny1alTJ2uH1eRs27aN0aNHExQUhEaj4YcffqhyXFEUXnnlFQIDA3F0dCQqKorjx49bJ9gm5mqv7SOPPFLtPXzrrbdaJ9gmZP78+dxwww24urri5+fH2LFjSUhIqHJOaWkpU6ZMwdvbGxcXF+666y7S09OtFHHTUZfXdtiwYdXet08++aSVIjafJEAN6Ouvv2b69OnMnTuXffv2ERkZSXR0NBkZGdYOrcnr0qULqamppq8///zT2iE1OUVFRURGRrJ8+fIajy9atIj33nuPFStWsGvXLpydnYmOjqa0tLSBI216rvbaAtx6661V3sNffvllA0bYNG3dupUpU6awc+dONmzYgMFg4JZbbqGoqMh0zvPPP8/PP//MN998w9atW0lJSeHOO++0YtRNQ11eW4DJkydXed8uWrTIShFfA0U0mL59+ypTpkwxbVdUVChBQUHK/PnzrRhV0zd37lwlMjLS2mE0K4CyevVq07bRaFQCAgKUt956y7QvNzdXsbe3V7788ksrRNh0Xf7aKoqiTJgwQbnjjjusEk9zkpGRoQDK1q1bFUVR36O2trbKN998YzonPj5eAZQdO3ZYK8wm6fLXVlEUZejQocq0adOsF9R1khGgBqLX64mJiSEqKsq0T6vVEhUVxY4dO6wYWfNw/PhxgoKCaNOmDQ8++CCnT5+2dkjNSlJSEmlpaVXev+7u7vTr10/evxayZcsW/Pz86NixI0899RRZWVnWDqnJycvLA8DLywuAmJgYDAZDlfdtp06dCA0NlfetmS5/bSt9/vnn+Pj40LVrV+bMmUNxcbE1wrsmshhqAzl//jwVFRX4+/tX2e/v78/Ro0etFFXz0K9fP1auXEnHjh1JTU3ltddeY/DgwRw+fBhXV1drh9cspKWlAdT4/q08Jq7drbfeyp133knr1q1JTEzk73//OyNHjmTHjh3odDprh9ckGI1GnnvuOQYNGkTXrl0B9X1rZ2eHh4dHlXPlfWueml5bgAceeICwsDCCgoI4ePAgL774IgkJCXz//fdWjLbuJAESTd7IkSNNP3fv3p1+/foRFhbG//73Px599FErRiZE3dx3332mn7t160b37t1p27YtW7ZsYcSIEVaMrOmYMmUKhw8flvq/elDba/v444+bfu7WrRuBgYGMGDGCxMRE2rZt29Bhmk1ugTUQHx8fdDpdtdkH6enpBAQEWCmq5snDw4MOHTpw4sQJa4fSbFS+R+X92zDatGmDj4+PvIfr6JlnnmHNmjVs3ryZ4OBg0/6AgAD0ej25ublVzpf3bd3V9trWpF+/fgBN5n0rCVADsbOzo3fv3mzatMm0z2g0smnTJgYMGGDFyJqfwsJCEhMTCQwMtHYozUbr1q0JCAio8v7Nz89n165d8v6tB2fPniUrK0vew1ehKArPPPMMq1ev5vfff6d169ZVjvfu3RtbW9sq79uEhAROnz4t79uruNprW5PY2FiAJvO+lVtgDWj69OlMmDCBPn360LdvX5YuXUpRURETJ060dmhN2syZMxk9ejRhYWGkpKQwd+5cdDod999/v7VDa1IKCwur/M8tKSmJ2NhYvLy8CA0N5bnnnuP111+nffv2tG7dmpdffpmgoCDGjh1rvaCbiCu9tl5eXrz22mvcddddBAQEkJiYyAsvvEC7du2Ijo62YtSN35QpU/jiiy/48ccfcXV1NdX1uLu74+joiLu7O48++ijTp0/Hy8sLNzc3pk6dyoABA+jfv7+Vo2/crvbaJiYm8sUXX3Dbbbfh7e3NwYMHef755xkyZAjdu3e3cvR1ZO1paC3NsmXLlNDQUMXOzk7p27evsnPnTmuH1OSNHz9eCQwMVOzs7JRWrVop48ePV06cOGHtsJqczZs3K0C1rwkTJiiKok6Ff/nllxV/f3/F3t5eGTFihJKQkGDdoJuIK722xcXFyi233KL4+voqtra2SlhYmDJ58mQlLS3N2mE3ejW9poDy6aefms4pKSlRnn76acXT01NxcnJSxo0bp6Smplov6Cbiaq/t6dOnlSFDhiheXl6Kvb290q5dO2XWrFlKXl6edQM3g0ZRFKUhEy4hhBBCCGuTGiAhhBBCtDiSAAkhhBCixZEESAghhBAtjiRAQgghhGhxJAESQgghRIsjCZAQQgghWhxJgIQQQgjR4kgCJIQQQogWRxIgIZqQ5ORkNBqNac2dxuDo0aP0798fBwcHevTo0aDPfS2vxyOPPHLV5TuGDRvGc889d12xNSSNRsMPP/wA1PyabN++nW7dumFra8vYsWPZsmULGo2m2iKhllaX11oIa5EESAgzPPLII2g0GhYsWFBl/w8//IBGo7FSVNY1d+5cnJ2dSUhIqLLoZEMICQkhNTWVrl27NujzNmY1vSbTp0+nR48eJCUlsXLlSgYOHEhqairu7u4Wec7aEtF3332XlStXWuQ5hLA0SYCEMJODgwMLFy4kJyfH2qFYjF6vv+bHJiYmcuONNxIWFoa3t7cFo7oyvV6PTqcjICAAGxtZ17lSTa9JYmIiN910E8HBwXh4eGBnZ0dAQEC9J+3u7u54eHjU63MIca0kARLCTFFRUQQEBDB//vxaz3n11Ver3Q5aunQp4eHhpu3K2wNvvvkm/v7+eHh4MG/ePMr/v717D6qq6hs4/uUicTmSiIqdMLS4eEQkvAaakjiijkcQQxNGvIch44VLSWp4GQsUNXUIsSl0GgaqESYbEhS8pAzKgQA1kZAxs6RUtByMFGG9f7zDft0eOIo8zzzzPqzPDDNnXfZav702Z/aatdeGhw+Jj4+nd+/eODs7k5GRYdT+pUuX8PPzw9ramqFDh3Ly5ElV+YULF5g6dSoajQYnJyfmzZvHrVu3lHJ/f3+io6NZtWoVffr06fC/jre2trJp0yacnZ157rnnePXVV8nPz1fKzczMKC8vZ9OmTZiZmbFhwwajNvbt24dWq6W1tVWVHxQUxKJFi4D/vUEHBQXh5OSERqNh1KhRFBYWquoPHDiQzZs3ExERgb29PW+//bbRykNLSwuLFy9m0KBB2NjY4OHhwa5du9o9t40bN9K3b1/s7e1ZtmyZyUng/fv3iYuL48UXX8TOzo4xY8Zw4sSJDuu3jU16ejrTp0/H1tYWnU5HSUkJly9fxt/fHzs7O/z8/Kirq1Mdl5aWxiuvvIKVlRUeHh588cUXqvLa2lrGjx+PtbU1Q4YM4ejRo6ryR8ek7XNDQwOLFi3CzMyM/fv3t/sIrLi4GH9/f2xtbXFwcCAwMFCZ5Ofn5zNu3Dh69eqFo6Mj06dPV8U9aNAgAHx8fDAzM8Pf3x8wfgR2//59VqxYQb9+/bC2tmbcuHEYDAalvC2uoqIiRo4cia2tLX5+ftTU1Ch1qqqqeOONN+jZsyf29vaMGDGCsrIyk9dCktojJ0CS1EkWFhZ8+OGH7Nmzh19//bVLbR07dozr16/z/fffs2PHDhITE5k+fToODg6cPXuWZcuWERkZadRPfHw8sbGxVFRU4Ovri16vp6GhAYA///yTiRMn4uPjQ1lZGfn5+fzxxx/Mnj1b1caBAwewsrKiuLiYvXv3thvfrl272L59OykpKZw7d47AwEBmzJhBbW0tAPX19Xh6ehIbG0t9fT1xcXFGbYSGhtLQ0MDx48eVvNu3b5Ofn094eDgAjY2NTJs2jaKiIioqKpgyZQp6vZ5ffvlF1VZKSgre3t5UVFSwfv16o75aW1txdnbm66+/5uLFi3zwwQe8//77fPXVV6p6RUVFVFdXc+LECbKyssjJyWHjxo3tjgFAdHQ0JSUlZGdnc+7cOUJDQ5kyZYoyDh1pm7BVVlYyePBgwsLCiIyMJCEhgbKyMoQQREdHK/Vzc3NZuXIlsbGxXLhwgcjISBYuXKiMXWtrKyEhIVhZWXH27Fn27t3Le++912H/bY/D7O3t+fjjj6mvr2fOnDlG9SorKwkICGDIkCGUlJRw+vRp9Ho9LS0tANy7d4+YmBjKysooKirC3NycmTNnKpPa0tJSAAoLC6mvrycnJ6fdeN59910OHjzIgQMH+OGHH3B1dSUwMJDbt2+r6q1du5bt27dTVlaGpaWlMlEGCA8Px9nZGYPBQHl5OWvWrKFHjx4mr4Mktes//N/oJen/lfnz54ugoCAhhBCvvfaaWLRokRBCiNzcXPHo1ykxMVF4e3urjt25c6dwcXFRteXi4iJaWlqUPA8PD/H6668r6YcPHwo7OzuRlZUlhBDiypUrAhBJSUlKnebmZuHs7CySk5OFEEJs3rxZTJ48WdX3tWvXBCBqamqEEEJMmDBB+Pj4PPF8tVqt2LJliypv1KhRIioqSkl7e3uLxMREk+0EBQUpYyWEEOnp6UKr1arO/XGenp5iz549StrFxUUEBwer6rSNR0VFRYftLF++XMyaNUtJz58/X/Tu3Vvcu3dPyUtLSxMajUaJZ8KECWLlypVCCCGuXr0qLCwsxG+//aZqNyAgQCQkJHTYLyDWrVunpEtKSgQgPvvsMyUvKytLWFtbK2k/Pz+xdOlSVTuhoaFi2rRpQgghCgoKhKWlpSqWw4cPC0Dk5uZ2OCbPP/+8yMjIUNLHjx8XgLhz544QQoi5c+eKsWPHdnguj7t586YAxPnz5zvsUwj196WxsVH06NFDZGZmKuUPHjwQWq1WbN26VRVXYWGhUicvL08AoqmpSQghRM+ePcX+/fufOlZJ6ohcAZKkZ5ScnMyBAweorq5+5jY8PT0xN/+/r6GTkxNeXl5K2sLCAkdHR27cuKE6ztfXV/lsaWnJyJEjlTiqqqo4fvw4Go1G+Rk8eDCA6rHFiBEjTMZ29+5drl+/ztixY1X5Y8eO7fQ5h4eHc/DgQe7fvw9AZmYmb731lnLujY2NxMXFodPp6NWrFxqNhurqaqMVoJEjRz6xr9TUVEaMGEHfvn3RaDTs27fPqB1vb29sbW2VtK+vL42NjVy7ds2ovfPnz9PS0oK7u7tqTE+ePGn0+Opxw4YNUz47OTkBqK6vk5MT//zzD3fv3gWgurra5HhXV1czYMAAtFqtKvaualsB6khtbS1z587l5Zdfxt7eXnmU+/i4mlJXV0dzc7Pq/Hr06MHo0aONfp8eHbcXXngBQPkOxMTEsGTJEiZNmkRSUtITr4EkdUTuHJSkZzR+/HgCAwNJSEhgwYIFqjJzc3OEEKq85uZmozYeX7o3MzNrN+/x/TOmNDY2otfrSU5ONipru5kA2NnZPXWbXaXX6xFCkJeXx6hRozh16hQ7d+5UyuPi4jh69CgpKSm4urpiY2PDm2++abQv50kxZ2dnExcXx/bt2/H19aVnz55s27aNs2fPPnPsjY2NWFhYUF5ejoWFhapMo9GYPPbRa9m24bi9vM5c338HGxsbk+V6vR4XFxc+/fRTZT/X0KFDu7R53hRTY7RhwwbCwsLIy8vj8OHDJCYmkp2dzcyZM/8tsUj/veQKkCR1QVJSEt9++y0lJSWq/L59+/L777+rJkH/yr/dc+bMGeXzw4cPKS8vR6fTATB8+HB+/PFHBg4ciKurq+qnM5Mee3t7tFotxcXFqvzi4mKGDBnSqXitra0JCQkhMzOTrKwsPDw8GD58uKrNBQsWMHPmTLy8vOjfvz8///xzp/poa8fPz4+oqCh8fHxwdXVtd4WgqqqKpqYmJX3mzBk0Gg0DBgwwquvj40NLSws3btwwGs/+/ft3OkZTdDqdyfHW6XRcu3aN+vp6VexdNWzYsA7/hEFDQwM1NTWsW7eOgIAAdDqd0RuQVlZWAMqeofa0bex+9Pyam5sxGAyd/n1yd3dn9erVHDlyhJCQkHZfFJCkJ5ETIEnqAi8vL8LDw9m9e7cq39/fn5s3b7J161bq6upITU3l8OHD/7J+U1NTyc3N5dKlSyxfvpw7d+4oG0WXL1/O7du3mTt3LgaDgbq6OgoKCli4cKHJG1R74uPjSU5O5ssvv6SmpoY1a9ZQWVnJypUrOx1zeHg4eXl5fP7558rm5zZubm7k5ORQWVlJVVUVYWFhz7Qq4ubmRllZGQUFBfz000+sX79e9ZZRmwcPHrB48WIuXrzId999R2JiItHR0arHkW3c3d0JDw8nIiKCnJwcrly5QmlpKR999BF5eXmdjtGU+Ph49u/fT1paGrW1tezYsYOcnBxlc/mkSZNwd3dn/vz5VFVVcerUKdauXdvlfhMSEjAYDERFRXHu3DkuXbpEWloat27dwsHBAUdHR/bt28fly5c5duwYMTExquP79euHjY2NsuH+r7/+MurDzs6Od955h/j4ePLz87l48SJLly7l77//ZvHixU8VZ1NTE9HR0Zw4cYKrV69SXFyMwWBQJv+S1BlyAiRJXbRp0yajm7VOp+OTTz4hNTUVb29vSktL231D6lklJSWRlJSEt7c3p0+f5tChQ/Tp0wdAWbVpaWlh8uTJeHl5sWrVKnr16tXuDd6UFStWEBMTQ2xsLF5eXuTn53Po0CHc3Nw6HfPEiRPp3bs3NTU1hIWFqcp27NiBg4MDfn5+6PV6AgMDVStETysyMpKQkBDmzJnDmDFjaGhoICoqyqheQEAAbm5ujB8/njlz5jBjxox2X+Fvk5GRQUREBLGxsXh4eBAcHIzBYOCll17qdIymBAcHs2vXLlJSUvD09CQ9PZ2MjAzltXJzc3Nyc3Npampi9OjRLFmyhC1btnS5X3d3d44cOUJVVRWjR4/G19eXb775BktLS8zNzcnOzqa8vJyhQ4eyevVqtm3bpjre0tKS3bt3k56ejlarJSgoqN1+kpKSmDVrFvPmzWP48OFcvnyZgoICHBwcnipOCwsLGhoaiIiIwN3dndmzZzN16lSTb/BJUkfMxOMbFSRJkiRJkv7LyRUgSZIkSZK6HTkBkiRJkiSp25ETIEmSJEmSuh05AZIkSZIkqduREyBJkiRJkrodOQGSJEmSJKnbkRMgSZIkSZK6HTkBkiRJkiSp25ETIEmSJEmSuh05AZIkSZIkqduREyBJkiRJkrqd/wE2aRLm4428tAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    sub_df_search_space[\"num_var_mods\"],\n",
    "    sub_df_search_space[\"search_space\"],\n",
    "    s=20,\n",
    "    alpha=0.5,\n",
    "    label=\"Number of peptidoforms\",\n",
    ")\n",
    "plt.plot(sub_df_search_space[\"num_var_mods\"], sub_df_search_space[\"search_space\"])\n",
    "\n",
    "factor_fragments = ((sum([len(p[-1]) for p in peptides]) / len(peptides))) * 2\n",
    "\n",
    "plt.scatter(\n",
    "    sub_df_search_space[\"num_var_mods\"],\n",
    "    sub_df_search_space[\"search_space\"] * factor_fragments,\n",
    "    s=20,\n",
    "    alpha=0.5,\n",
    "    label=\"Number of fragment ions\",\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    sub_df_search_space[\"num_var_mods\"],\n",
    "    sub_df_search_space[\"search_space\"] * factor_fragments,\n",
    ")\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_yscale(\"log\")\n",
    "\n",
    "plt.xlabel(\"Number of variable modifications\")\n",
    "plt.ylabel(\"Number of peptidoforms/fragment ions\")\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(\"img/peptidoforms_num_fragments.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>charge_min</th>\n",
       "      <th>charge_max</th>\n",
       "      <th>maximum_length</th>\n",
       "      <th>max_var_mods</th>\n",
       "      <th>missed_cleavages</th>\n",
       "      <th>num_var_mods</th>\n",
       "      <th>search_space</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>89604440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>130990944</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>199372532</td>\n",
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       "      <th>5</th>\n",
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       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>329032448</td>\n",
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       "      <th>6</th>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>689519328</td>\n",
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       "      <th>7</th>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1024408924</td>\n",
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       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1228708580</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1464423316</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1746393076</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2428642040</td>\n",
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       "      <th>12</th>\n",
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       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
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       "      <td>13</td>\n",
       "      <td>4106565840</td>\n",
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       "      <th>14</th>\n",
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       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>5100029324</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "      <td>5</td>\n",
       "      <td>35</td>\n",
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       "      <td>15</td>\n",
       "      <td>6292390688</td>\n",
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       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>7550486764</td>\n",
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       "      <th>17</th>\n",
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       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>9037088984</td>\n",
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       "      <th>18</th>\n",
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       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>10705364604</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>12564668872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>14624357036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>16893784344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>19382306044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>22099277384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>25054053612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>28255989976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>30375313144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    charge_min  charge_max  maximum_length  max_var_mods  missed_cleavages  \\\n",
       "0            1           5              35             3                 2   \n",
       "1            1           5              35             3                 2   \n",
       "2            1           5              35             3                 2   \n",
       "3            1           5              35             3                 2   \n",
       "4            1           5              35             3                 2   \n",
       "5            1           5              35             3                 2   \n",
       "6            1           5              35             3                 2   \n",
       "7            1           5              35             3                 2   \n",
       "8            1           5              35             3                 2   \n",
       "9            1           5              35             3                 2   \n",
       "10           1           5              35             3                 2   \n",
       "11           1           5              35             3                 2   \n",
       "12           1           5              35             3                 2   \n",
       "13           1           5              35             3                 2   \n",
       "14           1           5              35             3                 2   \n",
       "15           1           5              35             3                 2   \n",
       "16           1           5              35             3                 2   \n",
       "17           1           5              35             3                 2   \n",
       "18           1           5              35             3                 2   \n",
       "19           1           5              35             3                 2   \n",
       "20           1           5              35             3                 2   \n",
       "21           1           5              35             3                 2   \n",
       "22           1           5              35             3                 2   \n",
       "23           1           5              35             3                 2   \n",
       "24           1           5              35             3                 2   \n",
       "25           1           5              35             3                 2   \n",
       "26           1           5              35             3                 2   \n",
       "\n",
       "    num_var_mods  search_space  \n",
       "0              0      22287368  \n",
       "1              1      63904568  \n",
       "2              2      89604440  \n",
       "3              3     130990944  \n",
       "4              4     199372532  \n",
       "5              5     329032448  \n",
       "6              6     689519328  \n",
       "7              7    1024408924  \n",
       "8              8    1228708580  \n",
       "9              9    1464423316  \n",
       "10            10    1746393076  \n",
       "11            11    2428642040  \n",
       "12            12    3182322228  \n",
       "13            13    4106565840  \n",
       "14            14    5100029324  \n",
       "15            15    6292390688  \n",
       "16            16    7550486764  \n",
       "17            17    9037088984  \n",
       "18            18   10705364604  \n",
       "19            19   12564668872  \n",
       "20            20   14624357036  \n",
       "21            21   16893784344  \n",
       "22            22   19382306044  \n",
       "23            23   22099277384  \n",
       "24            24   25054053612  \n",
       "25            25   28255989976  \n",
       "26            26   30375313144  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df_search_space"
   ]
  }
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