figure5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a09fc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import gzip\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import matplotlib.colors as colors\n",
    "from scipy.stats import gaussian_kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e399df7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Open main objects generated below\n",
    "#OP_df=pd.read_pickle(\"./OP16_df.pkl\") #This pickle is not in repository, but it can be generated by code below\n",
    "OP_df_refprot_ms0=pd.read_pickle(\"./OP16_df_refprot_ms0.pkl\")\n",
    "OP_df_altprot_ms0=pd.read_pickle(\"./OP16_df_altprot_ms0.pkl\")\n",
    "MART_df=pd.read_pickle(\"./MART95_df.pkl\")\n",
    "MART_df_psdg=pd.read_pickle(\"./MART95_df_psdg.pkl\")\n",
    "OP_df_altprot_ms0_psdg=pd.read_pickle(\"./OP16_df_altprot_ms0_psdg.pkl\")\n",
    "OP_df_refprot_ms0_len300=pd.read_pickle(\"./OP16_df_refprot_ms0_len300.pkl\")\n",
    "OP_df_altprot_ms0_psdg_lngstorf=pd.read_pickle(\"./OP_df_altprot_ms0_psdg_lngstorf\")\n",
    "OP_df_altprot_ms0_psdg_lngstorf_len300=pd.read_pickle(\"./OP_df_altprot_ms0_psdg_lngstorf_len300\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c182c2",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "033017e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Path to unfiltered OpenProt .tsv file version 1.6\n",
    "#https://archive.openprot.org/p/download\n",
    "OP_file=\"human-openprot-r1_6-refprots+altprots+isoforms-+uniprot2019_03_01.tsv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7f77dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unzip OP_file\n",
    "with zipfile.ZipFile(f'{OP_file}.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall(\"./\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09803f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lera\\AppData\\Local\\Temp\\ipykernel_6584\\4042716307.py:1: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  OP_df=pd.read_csv(OP_file,sep=\"\\t\",skiprows=[0])\n"
     ]
    }
   ],
   "source": [
    "#Load OP_file\n",
    "OP_df=pd.read_csv(OP_file,sep=\"\\t\",skiprows=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4f61bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract only reference (canonical) proteins. _ms0 indicates no filtering on MS score\n",
    "OP_df_refprot_ms0=OP_df.loc[(OP_df[\"protein type\"]==\"RefProt\"),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ccd3a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract only alternative proteins\n",
    "OP_df_altprot_ms0=OP_df.loc[(OP_df[\"protein type\"]==\"AltProt\"),] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "acd9f003",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BioMart query of Ensemble 95 (compatible with OpenProt v1.6)\n",
    "#https://jan2019.archive.ensembl.org/biomart/martview/dace2a612a1691831d9f86d3c6c0e59c\n",
    "MART_file=\"./mart95_export.txt\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a64835b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unzip MART_file\n",
    "with gzip.open(f'{MART_file}.gz', 'rb') as f_in:\n",
    "    with open(MART_file, 'wb') as f_out:\n",
    "        shutil.copyfileobj(f_in, f_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "afa8769f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load MART_file\n",
    "MART_df=pd.read_csv(MART_file,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7071e07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract only pseudogenes\n",
    "psdg_ind_mart=[i for x,i in zip(list(MART_df[\"Gene type\"]),list(MART_df.index)) if \"pseudogene\" in x]\n",
    "MART_df_psdg=MART_df.iloc[psdg_ind_mart,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8d4060e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "MART_psdg_trxlist=list(MART_df_psdg[\"Transcript stable ID\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "816191b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lera\\AppData\\Local\\Temp\\ipykernel_6584\\1683176474.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  OP_df_altprot_ms0[\"id\"]=pd.DataFrame(OP_df_altprot_ms0['transcript accession'].apply(lambda x: x.split(\".\")[0]))\n"
     ]
    }
   ],
   "source": [
    "#Extract stable transcript id\n",
    "OP_df_altprot_ms0[\"id\"]=pd.DataFrame(OP_df_altprot_ms0['transcript accession'].apply(lambda x: x.split(\".\")[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bc2f4804",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Leave only pseudogenic alternative proteins\n",
    "OP_df_altprot_ms0_psdg=OP_df_altprot_ms0[OP_df_altprot_ms0['id'].isin(MART_psdg_trxlist)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7f511b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select pseudogenic proteins with the longest ORF by transcript coordinates\n",
    "OP_df_altprot_ms0_psdg_lngstorf=pd.DataFrame(columns=OP_df_altprot_ms0_psdg.columns)\n",
    "for psdg in set(list(OP_df_altprot_ms0_psdg['gene symbol'])):\n",
    "    df=OP_df_altprot_ms0_psdg.loc[OP_df_altprot_ms0_psdg['gene symbol']==psdg,]\n",
    "    orflen=list(df['stop transcript coordinates']-df['start transcript coordinates'])\n",
    "    maxorfidx=orflen.index(np.max(orflen))\n",
    "    df=df.iloc[maxorfidx,:].to_frame().transpose()\n",
    "    OP_df_altprot_ms0_psdg_lngstorf=pd.concat([OP_df_altprot_ms0_psdg_lngstorf,df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c60300f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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eqzPPPJMlS5Zwzz33sHXrVurWrUunTp2YM2cOp5xySpFcoywp8YWKE62wCxXHzqlZFPNbSvE7fPgwK1asoG3btlSuXDnRxZEyJGvO1sWLF9OpU6dEF6fCKcj/3fK0ULGIiEippcAoIiISosAoIpKLrJfw1Y1a8SgwioiIhCgwioiIhCgwioiIhCgwioiIhCgwioiIhCgwioiIhCgwioiIhCgwipRhU6dOxcyytypVqpCamsoNN9xQahebzcjIwMyYOnVqoosSl7JaboDx48cfc7nT0tKyF6Au7zSJuFRoZkvzz1TM3As3gXfYlClT+MlPfsK+fft4//33GT16NPPnz+ezzz6jVq1aRVBKOf7441m4cCEnnHBCoosSt/Hjx9OwYcNjCmyvv/46xx13XNEVqhRTYBQpB0499dTsGVp69OjB4cOHefjhh5kxYwbXXnttgktXPiQlJXHWWWcluhgA7N27l5o1a5boNX/605+W6PUSSV2pIuVQ1g/w1atXA7Bq1SoGDBhA06ZNSUpKokmTJvTs2ZNPPvkk6nPTpk3j7LPPplatWiQnJ9OnT58cC9V2794910V6r7/+etLS0qLSNmzYwNVXX03t2rWpU6cO/fv3Z9OmTbmW+c033+Tss8+mZs2a1K5dm969e7Nw4cIc+d544w06dOhAUlISrVu3Zty4cTz44IPZCzFncXfGjx/P6aefTo0aNahXrx79+vVj1apVOepz6qmnsnjxYs4991xq1qxJ69ateeyxxzhy5Eh2vry6Ur/88kuuueYamjRpQlJSEi1atGDw4MEcOHAg13qGzzVmzBgeeeQRWrRoQfXq1enUqROzZ8+OyptVt6VLl9KvXz/q1auX3Wrdv38/I0eOpFWrVlSrVo1mzZrxy1/+kh07dmR/Pi0tjf/+97/Mnz8/u8s9/D3t3LmTu+66K+ocv/71r9mzZ09UOWK7UufNm4eZ8dJLL3HffffRtGlTjjvuOHr16sVXX30V9dlly5Zx0UUX0bhxY5KSkmjatCkXXnghhVnpqCSoxShSDn3zzTdAsB4gQN++fTl8+DBjxoyhRYsWbNu2jQULFkT9AH300Ue5//77ueGGG7j//vs5ePAgY8eO5dxzz2XRokW0a9curjLs27ePXr16sWHDBkaPHk3btm2ZNWsW/fv3z5H3xRdf5Nprr+XnP/85L730EgcOHGDMmDF0796d2bNn07VrVwDeeecdrrjiCs477zymTZtGZmYmv//979m8eXOOc950001MnTqV2267jccff5zvvvuOUaNG0aVLFz799FOaNGmSnXfTpk1ce+213HnnnaSnp/P6668zcuRImjZtyuDBg/Os46effkrXrl1p2LAho0aNok2bNmzcuJE333yTgwcPkpSUdNR79Kc//YmWLVvyhz/8gSNHjjBmzBj+53/+h/nz53P22WdH5b3iiisYMGAAN998M3v27MHdueyyy5g9ezYjR47k3HPPZfny5aSnp7Nw4UIWLlxIUlISr7/+Ov369aNOnTqMHz8eILtce/fupVu3bqxbt457772XDh068N///pcHHniAzz77jH/96185fuGIde+993LOOefw7LPPsnPnTu655x4uvvhivvjiCypXrsyePXvo3bs3rVq14umnn6ZJkyZs2rSJuXPnsmvXrqOeO2HcvUJtzZo188JgbvQmZUNmZqZ//vnnnpmZmetx+L+Eb8diypQpDvhHH33khw4d8l27dvnMmTO9UaNGXrt2bd+0aZNv27bNAf/DH/6Q53nWrFnjVapU8V/96ldR6bt27fKUlBS/+uqrs9O6devm3bp1y3GOIUOGeMuWLbP3//znPzvgb7zxRlS+G2+80QGfMmWKu7sfPnzYmzZt6u3bt/fDhw9HXbtx48bepUuX7LTOnTt78+bN/cCBA1H5GjRo4MGPs8DChQsd8CeeeCLq2mvXrvUaNWr43XffHVUfwD/++OOovO3atfM+ffpk73/77bdR5XZ3P//8871u3bq+ZcuWHPfjaLLO1bRpU9+3b192+s6dO71+/freq1ev7LT09HQH/IEHHog6xzvvvOOAjxkzJip92rRpDvikSZOy00455ZRcv7PRo0d7pUqVfPHixVHpf//73x3wt99+OzutZcuWPmTIkOz9uXPnOuB9+/aN+uwrr7zigC9cuNDd3ZcsWeKAz5gxI5+7Ei2//7vu7sA6L4Y4oa5UkXLgrLPOomrVqtSuXZuLLrqIlJQU/vGPf9CkSRPq16/PCSecwNixY3nyySdZtmxZVBchwLvvvktmZiaDBw8mMzMze6tevTrdunVj3rx5cZdp7ty51K5dm0suuSQqfeDAgVH7X331FRs2bOC6666jUqUffyQlJydz5ZVX8tFHH7F371727NnDkiVLuOyyy6hWrVpUvosvvjjqnDNnzsTMGDRoUFR9UlJSOO2003LUJyUlhTPOOCMqrUOHDtld0bnZu3cv8+fP5+qrr85umcfriiuuoHr16tn7tWvX5uKLL+b999/n8OHDUXmvvPLKqP05c+YA5BhQc9VVV1GrVq0cXbK5mTlzJqeeeiqnn3561H3q06cPZlag7z32++3QoQPwYzf+iSeeSL169bjnnnuYMGECn3/+eb7nTDQFRpFy4LnnnmPx4sUsW7aMDRs2sHz5cs455xwAzIzZs2fTp08fxowZQ8eOHWnUqBG33XZbdldWVldk586dqVq1atQ2bdo0tm3bFneZtm/fHtVdmSUlJSVHPghGfcZq2rQpR44c4fvvv+f777/H3XM9Z2za5s2bs/PG1uejjz7KUZ8GDRrkOGdSUhL79u3Ls37ff/89hw8fJjW18AvIx96LrLSDBw+ye/fuqPTY+7N9+3aqVKmSIyibGSkpKdn39Wg2b97M8uXLc9yj2rVr4+4F+t5j711WN23WvatTpw7z58/n9NNP59577+WUU06hadOmpKenc+jQoXzPnwh6xihSDpx88slHXTewZcuWTJ48GYAVK1bwyiuv8OCDD3Lw4EEmTJhAw4YNAfj73/9Oy5Ytj3qt6tWr88MPP+RIzy3YLFq0KEe+2ME3WT9YN27cmCPvhg0bqFSpEvXq1cPdMbNcnyfGnrNhw4aYGR988EGuz/nye/ZXEPXr16dy5crHNIAkt4FImzZtolq1aiQnJ0elxz7ra9CgAZmZmWzdujUqOLo7mzZtonPnzvlev2HDhtSoUYO//OUveR4vCu3bt+fll1/G3Vm+fDlTp05l1KhR1KhRgxEjRhTJNYqSWowiFUzbtm25//77ad++PUuXBu9x9unThypVqrBy5Uo6deqU65YlLS2NFStWRI263L59OwsWLIi6To8ePdi1axdvvvlmVPqLL74YtX/SSSfRrFkzXnzxRYLHRoE9e/Ywffr07JGqtWrVolOnTsyYMYODBw9m59u9ezczZ86MOudFF12Eu7N+/fpc69K+fftC3r0f1ahRg27duvHqq68WqkUN8Nprr7F///7s/V27dvHWW29x7rnnUrly5aN+tmfPngC88MILUenTp09nz5492cch79bvRRddxMqVK2nQoEGu9yl2lPGxMjNOO+00nnrqKerWrZv976+0UYtRpJxbvnw5t956K1dddRVt2rShWrVqzJkzh+XLl2f/tp6WlsaoUaO47777WLVqFRdccAH16tVj8+bNLFq0iFq1avHQQw8BcN111zFx4kQGDRrEjTfeyPbt2xkzZkyOl78HDx7MU089xeDBg3nkkUdo06YNb7/9Nu+++25UvkqVKjFmzBiuvfZaLrroIm666SYOHDjA2LFj2bFjB4899lh23lGjRnHhhRfSp08fbr/9dg4fPszYsWNJTk7mu+++y853zjnn8L//+7/ccMMNLFmyhPPOO49atWqxceNG/v3vf9O+fXtuueWWY763Tz75JF27duXMM89kxIgRnHjiiWzevJk333yTiRMnUrt27aN+vnLlyvTu3Zs77riDI0eO8Pjjj7Nz587se300vXv3pk+fPtxzzz3s3LmTc845J3tU6k9/+lOuu+667LxZLbZp06bRunVrqlevTvv27fn1r3/N9OnTOe+88/jNb35Dhw4dOHLkCGvWrOG9997jzjvv5MwzzzymezRz5kzGjx/PZZddRuvWrXF3XnvtNXbs2EHv3r2P6dzFRYGxkGxezjTvXtKlEMlfSkoKJ5xwAuPHj2ft2rWYGa1bt+aJJ57gV7/6VXa+kSNH0q5dO8aNG5f9ykRKSgqdO3fm5ptvzs53zjnn8Ne//pXHHnuMSy+9lNatW5Oens7bb78dNVijZs2azJkzh9tvv50RI0ZgZvz85z/n5ZdfpkuXLlFlHDhwILVq1WL06NH079+fypUrc9ZZZzF37tyovBdccAHTp0/ngQceoH///qSkpDB8+HA2bNjA888/H3XOiRMnctZZZzFx4kTGjx/PkSNHaNq0Keecc06OgTaFddppp7Fo0SLS09MZOXIku3btIiUlhfPPPz9qgFBebr31Vvbv389tt93Gli1bOOWUU5g1a1b28+GjMTNmzJjBgw8+yJQpU3jkkUdo2LAh1113HY8++mhUd/FDDz3Exo0bufHGG9m1axctW7YkIyODWrVq8cEHH/DYY48xadIkvv32W2rUqEGLFi3o1atXkbQY27RpQ926dRkzZgwbNmygWrVqnHTSSUydOpUhQ4Yc8/mLg4W7LiqC1NRUL8wzgdwCYSwFxtLn8OHDrFixgrZt2+bbNSVl06FDhzj99NNp1qwZ7733XqKLUyAZGRm0atWKsWPHctdddyW6OKVSQf7vmtl6dy/86Kc8qMUoImXK0KFD6d27N8cffzybNm1iwoQJfPHFF4wbNy7RRZNyQoFRRMqUXbt2cdddd7F161aqVq1Kx44defvtt+nVq1eiiyblhAKjiJQpr7zySqKLcMzS0tKoaI+xyhK9riEiIhKiwCgiIhKirtRcFGQEqoiIlE9qMUq5lt+SOSJSuiXi/7ACo5RrZkaVKlXYu3dvoosiInHYu3cvVapUSUhgVFeqlGtmRoMGDdiwYQMNGjSgdu3aVKmif/YipVVmZia7du1i+/btNGrUSIFRpDjUr1+f6tWrs2XLFrZv355jLUIRKT0qVapEUlISzZs3p2bNmgkpgwKjVAg1a9bMfncsaxOR0sXMsrdEUmCUCqU0/KcTkdJNg29ERERCFBhFRERCFBhFRERCFBhFRERCEhIYzayNmS0wsxVmtsjM2uWRb6iZfW1mK81skplVCR1rYWZvmdlXZvalmf0qt3OIiIjEI1EtxonAJHdvC4wBJsdmMLNWwMNAV+BEIAUYGjlmwOvAc+5+EnAy8GrJFF1ERMqzEg+MZtYY6Ai8EEmaDrQys7SYrP2A1919swcvnU0Arokc6wnsc/dXATywqdgLLyIi5V4iWozNgQ3unglBUAPWAC1i8rUAVof2M0J52gFbzexlM1tmZq+bWeviLbaIiFQEiepKjZ12JK83rj2PPFWBXsDD7v5T4B/Ay7mdwMzuMLN1Wdvu3bsLW2YREakAEhEY1wKpWQNpIs8LmxO0GsPWAGmh/ZahPKuBZe7+38j+C8DPzKxy7MXc/Ul3T83akpOTi64mIiJS7pR4YHT3LcAyYFAk6Uogw90zYrJOBy43syaR4HkzP7YK/wE0M7Nmkf0LgP+4++FiLbyIiJR7iZor9SZgqpndC+wEhgCY2bPAm+7+pruvMrN04EOCAD6HyOhVd99jZsOBWZGguQMYWPLVEBGR8sYq2ioDqampvm7duqPmsXmFO7d3L9znREQkfma23t1Ti/q8mvlGREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkJFFzpZYqhZ0CTkREyh+1GEVEREIUGEVEREIUGEVEREIUGEVEREIUGEVEREIUGEVEREIUGEVEREIUGEVEREL0gn8Rip0owLsnohQiInIs1GIUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJUWAUEREJSUhgNLM2ZrbAzFaY2SIza5dHvqFm9rWZrTSzSWZWJZKeZmaZZvZJaDuhZGshIiLlUaJajBOBSe7eFhgDTI7NYGatgIeBrsCJQAowNJRlh7ufHtpWlkC5RUSknCvxwGhmjYGOwAuRpOlAKzNLi8naD3jd3Te7uwMTgGtKrKAiIlIhJaLF2BzY4O6ZAJGgtwZoEZOvBbA6tJ8Rk+c4M1tsZkvN7AEzq1yMZRYRkQoiUV2pHrNvBcgXzrMRSHX3zkAv4FzgztxOYGZ3mNm6rG337t2FLbOIiFQAiQiMa4HU0EAaI2hFronJtwZIC+23zMrj7gfcfUvk798BfyEIjjm4+5Punpq1JScnF2VdRESknCnxwBgJaMuAQZGkK4EMd8+IyToduNzMmkSC583AyxA8pzSzqpG/JwFXRM4pIiJyTBLVlXoTcJOZrQBGEBltambPmtklAO6+CkgHPgRWAlv4cfRqV2CZmX0KLAU2AY+UaA1ERKRcsmDsS8WRmprq69ati0qzecVzLe9ePOcVEREws/XunlrU59XMNyIiIiEKjCIiIiFVEl2A8iy2i1ZdqyIipZ9ajCIiIiEKjCIiIiEKjCIiIiEKjCIiIiEKjCIiIiEKjCIiIiEKjCIiIiF6jzHB9K6jiEjpohajiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiNZjLGVi12cErdEoIlKS1GIUEREJUYuxBOXWGhQRkdJFLUYREZEQBUYREZEQBUYREZEQBUYREZEQBUYREZEQBUYREZEQBUYREZEQBUYREZEQBUYREZGQAgdGM3vLzPoUZ2FEREQSLZ4W48nA22a2wsxuN7PjiqtQIiIiiVLgwOjuJwIXA98ATwLrzWyCmbUvrsKJiIiUtLieMbr72+7eF2gLPANcDXxiZvPMrJ+ZVS6OQoqIiJSUQg2+cfeV7n4HcAIwDzgPmAZkmNmvzMyKrogiIiIlp1CB0cxSzex3wBdAd+AfwA3AIuAPwB+LqHwiIiIlKq7AaGbnm9lrwCrgNuBV4CfufqG7P+fuVwJ3ANcWfVFFRESKX4EXKjazLwieLX4L3A38xd135pL1Y6BO0RRPRESkZBU4MALrCQLiTHf3o+RbCrQ6plKJiIgkSIEDo7v3KmC+g8DqQpdIREQkgeKZ+eYsM7s6j2NXm9mZRVcsERGRxIhn8M2jQF4v87cDfnfsxREREUmseAJjB+CjPI59DJxW0BOZWRszWxCZXm6RmbXLI99QM/vazFaa2SQzqxJz3MxstpltK3AtREREjiKewFgLyMzj2BGgdhznmghMcve2wBhgcmwGM2sFPAx0BU4EUoChMdluBTLiuK6IiMhRxRMYvwV65HGsBwUccGNmjYGOwAuRpOlAKzNLi8naD3jd3TdHRsFOAK4JnacNMAB4rKAVEBERyU88gfFl4DdmdkM40cyuB34NvFTA8zQHNrh7JkAk6K0BWsTka0F0sM3IymNmlQjmav0lcCiOOoiIiBxVPIHxMeBDYLKZ7Yk8+9tD0A36ITA6jnPFvgeZ19yqnkeeu4D33f2T/C5kZneY2bqsbffu3XEUU0REKpp4lp06CPQGhgAzCLpWZ0T2fx45XhBrgdSsgTSRCcebE7Qaw9YAaaH9lqE85wHXm1kG8G+gnpllmFm9XMr9pLunZm3JyckFLKaIiFRE8cx8g7sfBp6PbIXi7lvMbBkwCJgKXAlkuHtGTNbpwL/NbBSwBbiZoDsXd78oK1Pk2eQSd08rbJlKO5sXve/dE1EKEZGKoVCraxSBm4CbzGwFMILIaFMze9bMLgFw91VAOkE37UqC4Jhj9KqIiEhRsqNPexrKaFYVuAcYSNCtWT0mi7t7XC3QREhNTfV169ZFpcW2yEo7tRhFRMDM1rt7alGfN55ANhr4DcHaizOAA0VdGBERkUSLJzBeDYxy94eKqzAiIiKJFs8zxnrA+8VVEBERkdIgnsD4PnB6MZVDRESkVIgnMN4GDDWzK8ysWnEVSEREJJHiecb4CVAVeBVwM9sbc9zdvU5RFUxERCQR4gmM08k5lZuIiEi5UuDA6O7XF2M5JA65vXepdxtFRIpGoma+ERERKZXiCoxm9hMze8nMNprZQTPrGElPN7O81moUEREpMwocGM3sdGAx0A2YB1QOHU4mmORbRESkTIt3PcblwInAdUSvj7gI6FyE5RIREUmIeEalngMMcve9ZlY55thmIKXoiiUiIpIY8bQYDchrMeJ6aFJxEREpB+IJjMuBy/M4dgHwf8deHBERkcSKpyt1HPCime0Bno+ktTCz84FfAP2KunAiIiIlLZ4X/KeZ2QnAgwTzpkIwG04mkO7ubxV98UREREpWPC1G3P1RM3sO6AM0AbYB77r76uIonIiISEmLKzACuPs6YHIxlEVERCThChwYzaxFfnncfc2xFUcKK3b+VM2dKiJSOPG0GDPIf3WN2PcbRUREypR4AuMvyBkYGwKXAKnA74qqUCIiIokSz6jUqXkcesLMXgWaF0mJREREEqiolp2aCgwronOJiIgkTFEFxipA3SI6l4iISMLE/bpGmJlVBToADwGfFkmJREREEiie1zWOkPeo1O8JXvoXEREp0+JpMY4iZ2DcT/Aax9vuvquoCiUiIpIo8YxKfbAYyyEiIlIqHNMzRim9YmfCAc2GIyJSEPE8Y/xLHOd1dx9aiPKIiIgkVDwtxh4Er2TUIVhqajvQIHKOH4Adobz5TR0nIiJSKsXzHuPVwG7gWqCGux8P1AAGAXuA/u7eKrK1LvqiioiIFL94WoxPAL9395eyEtz9MPCimTUBngLOKeLyiYiIlKh4Wow/A/6Tx7HPgNOPuTQiIiIJFk9g3An0yuNYr8hxERGRMi2ertTngd+aWRXgRWATkELwzPHXwJNFXjoREZESFk9gvBdoDNwJ3BFKN+CFyHEREZEyLZ6ZbzKB681sNHA+UJ/glY157v5lMZVPRESkRMU98427fwV8VQxlERERSbi41mM0syQzu8nMXjKz98ysTST9UjPTu4siIlLmxTMlXENgLnAKwcCbJkDtyOHLCJadGl7E5RMRESlR8bQYxxBMCdcJaEEw6CbLXKBb0RVLREQkMeJ5xngRcI+7LzWzyjHH1gGpRVcsERGRxIinxXgcsDqPY1XRElYiIlIOxBMYvwXOzuPYGWikqoiIlAPxtPL+BtxjZv8BZkXS3Mw6A7cDjxR14aRoxS5erIWLRURyiicwPk6wesbrwPeRtHcJ1mR8BxhXtEUTEREpefHMfHPIzPoC/YELCV7X2AbMBF529yPFU0QREZGSU6DAaGY1gH8B6e7+MvBysZZKREQkQQo0+Mbd9wHtgcyiuKiZtTGzBWa2wswWmVm7PPINNbOvzWylmU2KrOyBmbUys/8zs0/M7DMze9XM6hVF2UREpGKLZ1TqQoLRp0VhIjDJ3dsSTBwwOTaDmbUCHga6AicSLHE1NHJ4A9DV3U939/bAeuD/FVHZRESkAosnMN4J3GRmg80subAXNLPGQEeCpaoApgOtzCwtJms/4HV33+zuDkwArgFw9wORViyRyQaSAT3jFBGRYxZvizEVmAL8YGa7zGxnaPuhgOdpDmyILGNFJOitIZhmLqwF0RMKZITzmFk1M/uEYADQicCoOOoiIiKSq6MOvjGzDsAKd98P/L0Ir+uxlypAvqg87n4QON3MqgF/BG4m6JaNPrHZHYQWVq5Tp05hyisiIhVEfqNSlxHMdrMIaAkML4JFidcCqWZWxd0zzcwIWpFrYvKtAdJC+y1zyYO7HzSzKcAz5BIY3f1J4Mms/dTU1NigLCIiki2/rtQDQLXI37sTzJd6TNx9C0HAHRRJuhLIcPeMmKzTgcvNrEkkeN5M5DURM2thZrUif68EXA0sP9ayiYiI5NdiXAXcaWYpkf3uZpbnKhru/loBr3sTMNXM7gV2AkMAzOxZ4E13f9PdV5lZOvAhQQCfw4+jV08FHgviJZWApcBtBby2iIhIniwY+5LHQbP+wHMEq2c4eT8LhGAcTexyVKVOamqqr1u3Liotdg7RikJzpYpIWWZm6929yJc8PGqL0d2nmdls4CTgA+CXwOdFXQgREZHSIt8p4dx9G7DNzP4KvOPu3xZ/sURERBIjnknEbyjOgoiIiJQG8bzgLyIiUu4pMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQoMIqIiIQUeBJxKX9yW4dSazSKSEWnFqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIFiqWKLGLF2vhYhGpaNRiFBERCVFgFBERCVFgFBERCVFgFBERCVFgFBERCVFgBOixNNElEBGRUkKBUUREJESBUUREJKTCB0YzdaOKiMiPKnxgFBERCdOUcHJUsVPEgaaJE5HyTS1GERGREAVGERGREAVGERGREAVGERGREAVGERGRkIQERjNrY2YLzGyFmS0ys3Z55BtqZl+b2Uozm2RmVSLp7c3sfTP70sw+ixxLKtlaiIhIeZSoFuNEYJK7twXGAJNjM5hZK+BhoCtwIpACDI0c3g/c6u4/AU4H6gB3Fn+xRUSkvCvxwGhmjYGOwAuRpOlAKzNLi8naD3jd3Te7uwMTgGsA3P1rd18e+fthYDHQugSKLyIi5VwiWozNgQ3ungkQCXprgBYx+VoAq0P7GbnkwcxqAcOAt4qjsCIiUrEkqivVY/atAPly5DGzqsA04D13fyO3E5jZHWa2LmvbvXt3oQosIiIVQyIC41ogNTSQxghakWti8q0B0kL7LcN5IkHxFWAjcHteF3P3J909NWtLTk7Ou2Ral1FEpMIr8cDo7luAZcCgSNKVQIa7Z8RknQ5cbmZNIsHzZuBlgEhQfRn4DvjfSHfssVFQFBEREteVehNwk5mtAEYQGW1qZs+a2SUA7r4KSAc+BFYCW/hx9Gp/4AqgE7DMzD4xs6dLtgoiIlIeWVE0tsqS1NRUX7duXfZ+jvUY53Ys4RKVPVpdQ0RKAzNb7+6pRX1ezXwjIiISovUYJW6xazSqBSki5YlajCIiIiEKjCIiIiEKjLH02oaISIWmwCgiIhKiwTdyzDQYR0TKE7UYRUREQhQYRUREQhQYRUREQhQYRUREQhQYRUREQhQYRUREQhQYRUREQhQYRUREQvSCvxS52Bf+QS/9i0jZoRajiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiAKjiIhIiCYRlxIRO7G4JhUXkdJKLUYREZEQBUYREZEQBUYREZEQBUYREZEQBcbc9FgabCIiUuFUuFGp6w/kHCEpIiKSpcIFRikdcvvlRK9wiEhpoK5UERGREAVGERGREAVGERGREAVGERGREAVGERGREI1KlVJDE42LSGmgFqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAqOIiEiIAmN+tPyUiEiFosB4NAqKIiIVTkICo5m1MbMFZrbCzBaZWbs88g01s6/NbKWZTTKzKpH0ZDN718y2mdm2ki29iIiUZ4lqMU4EJrl7W2AMMDk2g5m1Ah4GugInAinA0MjhQ5HP9SqR0kqpYfOiNxGRolbigdHMGgMdgRciSdOBVmaWFpO1H/C6u292dwcmANcAuPsBd58N7CiRQqtLNSFig6ACoYiUhES0GJsDG9w9EyAS9NYALWLytQBWh/YzcskjIiJSpBLVleox+1aAfHnlOSozu8PM1mVt7NtdmNOIiEgFkYjAuBZIDQ2kMYJW5JqYfGuAtNB+y1zy5Mvdn3T31KyNGsmFK7WIiFQIJR4Y3X0LsAwYFEm6Eshw94yYrNOBy82sSSR43gy8XGIFFRGRCilRXak3ATeZ2QpgBJHRpmb2rJldAuDuq4B04ENgJbCF0OhVM1sKLATqRbpJny/ZKoiISHmUkIWK3f0r4Oxc0ofF7D8DPJPHOToWT+lERKQi08w3IiIiIQqMIiIiIQnpShUpKrm99O/dS7oUIlKeqMUoIiISosAoIiISosAoIiISosAYD00mLiJS7ikwFpSCoohIhaDAKCIiEqLXNaTcKci6jXqlQ0TyohajiIhIiAKjiIhIiAKjiIhIiJ4xSoUU+xxSzxxFJItajPHSaxsiIuWaAqOIiEiIAqOIiEiInjGKoOWrRORHajGKiIiEqMUokgeNXBWpmNRiFBERCVGLsTB6LIW5HRNdCilhakGKVAwKjCKFpAE7IuWTulJFRERCFBhFRERC1JUqUoT0HFKk7FOLUUREJESBsbA0mbiISLmkrlSRYqSRqyJlj1qMIiIiIQqMIiIiIepKLQqaCUfioJGrIqWbWozHQgNwRETKHQVGERGREAXGY6VWo4hIuaJnjCIJVpBXOvTah0jJUWAUKYVyC4T55VGgFCkaCowi5YRalSJFQ88YRUREQtRiLCp6l1FKIXW3isRPLUYREZEQBcailN+rG3q1Q0ohmxe9iVR06kotDupWlVKqsIEvv8+pi1bKEwXG4qYgKWWMWo1S0akrtaipu1REpExTi1FEjllBWpm5dbcWZNSsRtZKSVNgFJESUZjZfEQSQYGxJBztOaOeQYrEpbCtU5GCUmAsTuHnjbk9e9TzSJFioeApx8LcPdFlKFHWKNV5dd2PCaUtOBW29aiWp0iRK8gqJ/l9Jjea17ZomNl6d08t6vMmpMVoZm2AvwINgR3A9e7+eS75hgIjCEbPzgaGu3tm5NhFwO8J6vApMMTdd5dIBUqKgp1IQhXmmWdRPSdVqzdxEtWVOhGY5O5TzawfMBk4O5zBzFoBDwM/BbYAbwBDgYlmlhz5TDd3/9LM/gTcB4wswToUDwVDkQopkUG4MK3c8hyUS7wr1cwaAyuAhu6eaWYGbATOcveMUL7fAmnu/svIfl/gbnfvbmZXEbQyL4wcawe87e5p+V6/tHel5mZuxx/LmRU0swJo7J8iIglSmK7nwp7HuxdfV2oiXvBvDmzI6hL1IDKvAVrE5GsBrA7tZ4Ty5HasmZmVzwkLYgfxZO3n9mdux/P6bEF/KcjtPIU5ll/+eB3rLzWJvPaxSvT1RXJRVPPuFuQ8xflqT6K6UmObqVaAfLF5CtTUNbM7gDtCSUfoYRsL8tkyrUfMn5AM7I5Ji/57Qc6X2348x/LLH6/8P/tjvQv3+WO5dvE6+vWPXu/yS/WuWFKK46SJCIxrgVQzqxLqSm1O0GoMWwOkhfZbhvKsAc4PHUsD1rv7kdiLufuTwJNZ+2a2rjia3qWd6l2xqN4VS0Wud3Gct8S7Ht19C7AMGBRJuhLICD9fjJgOXG5mTSLB82bg5cixd4DOZvaTyP7w0DEREZFCS9QzuZuAm8xsBcHrGEMBzOxZM7sEwN1XAenAh8BKgpGpkyPHdgHDgBlm9g3QDHi0pCshIiLlT0KeMbr7V8S8nhFJHxaz/wzwTB7neBN4sxCXfzL/LOWS6l2xqN4Vi+pdhCrczDciIiJHUz5fbxARESkkBUYREZGQChMYzayNmS0wsxVmtigyW065YGYZZvalmX0S2fpH0hub2Ttm9rWZ/cfMuoY+U9PMXjKzbyL35IrE1aBgzOz/i9TVzezUUHqh6mlmlczsj2a2MnJ8eEnXqSCOUu95ZrYq9L3/JnSsPNS7upnNiJT/k8h3nBY5Vm6/83zqXd6/8/fMbHmkbh+Y2emR9JL9vt29QmzAHIJp5AD6AQsTXaYirFsGcGou6X8BHoz8vTPBbEFVIvsPAFMjf28FbALqJbou+dTzPCA1tr6FrScwmGBy+spA/ch5f5LoesZR73nARXl8pjzUuzrQlx/HQtwKvFfev/N86l3ev/O6ob9fBixNxPed8BtRQje7McEqHlk30iI3Ly3RZSui+kX9wAyl7wYahfYXAd0jf/8v0Dl07BUivziU9i22voWtJzALuCp0bEzWf77SuOVS76P9kCw39Q6VsxPwTUX6znOpd4X5zoEhwJJEfN8VpSu1oPOzlmV/M7PPIu+CNjKzBkAld98aypPB0eebLXP34xjrWR7uwdjI9z7NzFqH0stjvW8D3qqA3/ltwFuh/XL9nZvZc2a2FvgdMCQR33dFCYxQ8PlZy6Lz3P00oCOwnWCtS8i/zkebi7YsOZZ6luV7cJ27nwx0AD4AZsYcLzf1NrN7gTYEy8tBBfnOc6l3uf/O3X2wuzcH7gfGZiXHZCvW77uiBMbs+VkBzPKcn7VMcvc1kT8PAX8AznX37QBm1iiUNXa+2bQ8jpUZx1jPMn0P3H1t5E939z8BrSO/XUM5qreZ3QVcAfyPu++tKN95bL2h4nznAO7+V0JT5Zfo953ofuQS7K+eR/Tgm48SXaYiqlctoh9Y3wG8H/n7VKIfWK/hx+esDxL9wHozUD/R9SlgnTOIftZWqHoC1wP/4scH86uBkxNdv4LUm2DWqiahY1cCq0P75aLekX/P/0fMwLDy/p3nVu/y/p0DxwFNQ/uXA+sIWnkl+n0n/GaU4E0/CVhIsEjyEuCURJepiOrVmmBS9uXAZ8AbRAYVAU2A94CvCR5Qdwt9rhYwDfgmck/6JbouBajr05H/KJkEg6eyBiQUqp6R/yxPE8zFuxK4NdF1LGi9I/VaEvnOPyUYeXdaOat3KkE32Ergk8j2cXn/zvOqd3n/zgl68RaF6vcv4PREfN+aEk5ERCSkojxjFBERKRAFRhERkRAFRhERkRAFRhERkRAFRhERkRAFRpEQM2tqZg9mzepfyHN0j6yE0b0IyzXPzOYV1fmOlZkNN7Prc0nPqnu/Yzz/A2b2uZkV288oM3vYzJYW5zWkbNI/CJFoTYF04PRjOMdS4OzIn+XVcIKXp4ucmTUF7gYecPcjxXGNiN8TvBA+pBivIWWQAqOUe2ZWoySv5+473f0jd99ZktctR24nWA3nteK8iLv/ALwAjIhMEykCKDBKGRDp2nQz+6mZvWZmO83sBzN7IWb+xKxFm2ea2RVmtszM9hO0ADGzU83sDTP73sz2RxZDHRL6bHdgcWR3SuSabmYPhvJ0MrM3zey7yDmWmdnVMWXI0ZVqZlPNbLeZnWhmb0f+vtbMnjCzpELel2pmdr8Fi1QfMLOtZjblKPfkgkjX4b7IZ36Ryzm7mtnCSN3WR7obh0Xqk5Z1PuAUoFvoHmXEnKqqmT1iZhsi39e/zOykgtQJGAq8GNtaNLN0M/s4cu93RuoyNDaomVl/Cxa83Rip6xdm9piZ1crlks8DbQnNySlSJdEFEInD6wRrrU0g+MH8MNDOzM70YAL1LB2BkwmWrfkW2BP5obwA2EKwjM92YBAw1cyauPsYgq7PG4Apkc/OipxvHYCZ9QDeIZie62bgB2AAMM3Marr71HzKXxV4E5gMPEGwAPH/i5xnVDw3IvJc7A3gXII15hYQTJD8EDDPzDq5+77QR06LXPMxgrkkhwGTzewbd38/cs4OwD8JptUaAuyN1HNQzOUvB/4eKXfWiugHYvI8CnwYuc5xwOMES0ad7O6Hj1K1M4EGwNxcjqUBE/lxEuizgD8CzYi+f22Atwkm1N8D/AS4BzgDOD/mnP9HsNbfhQSLmYtUnLlStZXdjWCSYAeejEkfGEm/NpSWQTCnaNuYvC8B+4HmMelvE/zwrBPZ7xQ55/W5lOMLguBZJSb9LWADwZpxAN0j5+geyjM1knZVzGdnAV8W4B7MA+aF9gdEzndFTL6s8t8Sc0/2AS1CadUJfjmYEEp7hSBINAylVSKYm9IJLewN/CdcnlB6Vt1nxaRfFUk/K5963h3J1ySffJUIfrH/f8A2Iqvd55LPIvnOi5y3Qy55/k05WVRAW9Fs6kqVsuRvMfuvEATB2G6w5e6+IibtfGC2R5btCZkK1CQYLJMnMzuRoOXxt8h+layNILgeTzBR/dE40QvOQjD5e8t8Ppebiwiew70VU5ZPCCYa7x6T/xOPLE8G4O77CVqG4Wt3A+a4+7ZQviME9zleb8bsL4/8mV9dmxLcp22xB8zs/EiX7A/AYeAQQUuxAdA4lK+1mb1oZptC+eZHDp+cyzW3ELQ6RQB1pUrZsim84+6ZZrad4Adj2MZcPtsgj/QNoeNH0yTy5+8jW24a5nOOvZGAFHaAoPUWryZAXeBgAcuyPZc8B4DwwKQGBN2ssXJLy0/s9bK6WvMbCFUDOOQx3a1mdgbB6grzgBsJurcPApcRLOJbI5IvmWAB3/0EC92uIOgSbk4wmCe36+8vQLmkAlFglLIkBViftRNpITUg5w/h3JaM2U7QqovVNPJnjhZKjKzjo8l7tORX+ZyjKG0jqNMFeRzfVYhzbufHXwDCUgpxrsLaBlQzs1ruvieUPoCg5XdR+JcLM7ss5vPnE3yn3d19fihf3aNcsz75f/9SgSgwSllyLcFgiSxXE/wbnleAz84GLjezpu6+IZQ+mKBF8VFkP9eWjbt/ZWZfE6x/d28hyl7UZhIEi8ru/nERnXM+0NfMGmZ1p0YG+VyVS97Y1mZR+TLy5wn82P0KwS87mQRdo0TKVgO4LubzWb8UxQ4Guuko12xN8MxUBFBglLLlCjPLJBg5mTUq9VMK9gzsIYLncnPNbBTwHUGgvRC424N32iBYzHQfcK2ZfUEwGGVDJJjeBPzDzN4leDa5nqC1cTLQ0d1zCyDF5eVI+d82s3EEC7weIljktgfwhru/Huc5HwEuBmab2SME9+FmgoVgAcKvT3wGDDCz/sAqYL+7f1bYyoTMi/x5FtGBcRbBqvYvmtkkgp6Cu8gZABcA3wMTzOwhgntyLcGo3BzMrAHBKNY/FkHZpZzQ4BspS64gGADzGsGgi7eAn7t7Xs/Zsrn7V0AXgu7Op4EZwKnADe4+NpRvL/ALgh+87xG81/i/kWNzCYb87yB4FeBfwJ+BXpG/l5jIM7hLCF6LuILgVZYZwAiCZ2ZxByl3/xToTRAQnwMmEYxIHR/J8kMoezpBC/MZgqAcO6ioUCKDoz4ALo1Jn0PwvbSPXOsRgldGHovJt53gl529BC/v/4Xgl5v+eVzyUoLgWZgBRlJOmXtuj2NESo/IC/bpQKPwiEkpGWb2HsGrGm1L6HpXAtOAlu6+Pr/8x3itD4A17n5tcV5HyhZ1pYpINjN7ElgGrCXoJr6WoBU5tASL8RpBS30kcGtxXcTMzgM6o7lSJYYCo4iEVSbopk4hGMjyOXCdu79QUgVwdzezG4FLzKySF99E4g2Awe6+qpjOL2WUulJFRERCNPhGREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQkRIFRREQk5P8HiXbKZMqsp3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 480x560 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Build a frequency histogram of protein length\n",
    "figure(figsize=(6, 7), dpi=80)\n",
    "plt.hist(list(OP_df_refprot_ms0['protein length (a.a.)']),bins=1000,color=\"deepskyblue\",weights=np.zeros_like(list(OP_df_refprot_ms0['protein length (a.a.)'])) + 1. / len(list(OP_df_refprot_ms0['protein length (a.a.)'])));\n",
    "plt.hist(list(OP_df_altprot_ms0_psdg_lngstorf['protein length (a.a.)']),bins=1000,color=\"mediumblue\",weights=np.zeros_like(list(OP_df_altprot_ms0_psdg_lngstorf['protein length (a.a.)'])) + 1. / len(list(OP_df_altprot_ms0_psdg_lngstorf['protein length (a.a.)'])));\n",
    "plt.xlim(0,3000);\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.ylabel(\"frequency\",size=15);\n",
    "plt.xlabel(\"protein length (aa)\",size=15);\n",
    "plt.legend([\"Canonical proteins\",\"Pseudogenic proteins\"],prop={'size': 15});\n",
    "plt.savefig(\"freq_protlen4.png\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f25a5c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select canonical proteins with protein lengths < 300 aa\n",
    "OP_df_refprot_ms0_len300=OP_df_refprot_ms0.loc[OP_df_refprot_ms0['protein length (a.a.)']<300,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "72d0319c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Percent of canonical proteins with protein length < 300 aa = 42.826223915587%'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Percent of canonical proteins with protein length < 300 aa = {(len(OP_df_refprot_ms0_len300)/len(OP_df_refprot_ms0)*100)}%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "01982c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=list(OP_df_refprot_ms0_len300['protein length (a.a.)'])\n",
    "y=list(OP_df_refprot_ms0_len300['MS score'])\n",
    "# Calculate the point density, might take a while\n",
    "xy = np.vstack([x,y])\n",
    "z = gaussian_kde(xy)(xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2cb95436",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set cmap\n",
    "def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n",
    "    new_cmap = colors.LinearSegmentedColormap.from_list(\n",
    "        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n",
    "        cmap(np.linspace(minval, maxval, n)))\n",
    "    return new_cmap\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "new_cmap = truncate_colormap(cmap, 0.32, 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fc6c45de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lKIqiKC1GY8TuDqAWOBR4GfizZ9044N8tOC5FURRFaTEaU0HFAnfGWHdai41IURRFUVqYJiWVK4qiKEpnQsVOURRFSXhU7BRFUZRmMXs/fFTc3qOoHxU7RVGUDsq0RZAyC/5U2N4jic3Tu2DmUjh6Odyztb1HExsVO0VRlA5IbQjmlkkI/D0dWOzml4YfzyuNvV170yyxM8b0MMY0qk2Q7/W3GGOsMWac87yXMeYdY8waY8xSY8zhnm0zjTHPGGPWGmNWG2PO9KwLGGPuN8asc9Zf05zzUhRFaW+SA3B8DmQYuLUDl9n//UA4riscng1PDG/v0cSmXqFyRGiStfZJ3/LzgD8hlVPKjTF3W2sbVRvTGDMFmIFUZnG5E5htrT3RGDMNeNEYM8xaW4sksVdZa4cbY4YAXxhjPrTW7gUuAsYAI4EcYL4x5gNr7crGjElRFKUj8e7Y9h5BwyQlwX/HtfcoGqYhy+4G4CrvAmPMeOAJIAt4FdiEVFb5VrwHNcakAQ8C1wDWs+pcZznW2jnADsC17s7zrNsAfIKULXPXPWStDVpr9wDPA+fHOx5FURQlsWlI7KYBL/mWfQ9IAk601p4JTAA+Aq5sxHFvA550RAsAY0x3IGCt3eXZbiPSbQHn/6YmrIvAGHOdMabA/Sst7cBOZkVROjQf7YNR8+G2Le09EqUhGhK7vtSteXkCsMRa+xmAtTYEPIqIXoMYY2YiIvqXKKutf/N61jdmXXgja++11ua7f1lZWQ0NWVEUJSr3FcLqSrijoL1HojREQ2KXDFS4T4wx3YAhwCzfdluA7DiPeSRwELDBGLMRyAf+A0x3jtHTs+0gwnN6m4HBTVinKIrSKlzXD0ZnwC357T0SpSEaErtNRFpsboufL33b5QB74zmgtfZOa20/a+1ga+1goAA4wVr7NvACcC2AE6DSB/jMeal33RBENF/zrLvaGJPkCPJ5wHPxjEdRFKWpHJEDyyfDjR04WlIRGkobeA24wRizANgO3AhUAW/5tptG5JxZU/kZ8IQxZg1QDVzsRGIC3A08ZoxZC4SAa51gFJCAmWmEXa53W2tXtMB4FEVRlATASDODGCvFSvoSGOouAn5lrb3Ds40BVgGvWGtvaMWxtgr5+fm2oEAd7oqiKJ0dY8xWa21Up3K9lp21do8xZhKSEtAN+MJa65+v6wn8DXijBcaqKIqiKC1OvZbdgYBadoqiKIlBfZad1sZUFEVREp6GyoV90Ih9WWvtsc0cj6IoiqK0OA1FYx4F7Efy6BRFURSlU9KQ2K1HIjH3AY8Bz1lry1p9VIqiKIrSgtQ7Z2etHQ4cjYje/cA2Y8yjxphD22JwiqIoitISNBigYq392Fp7CVIn8wZgPPCZMWaFMeanxpjerT1IRVEURWkOcUdjWmv3W2sfstYegpQQex/4LdELOiuKoihKh6HRqQfGmNHApcDZSEWVVS09KEVRFCVx+GwfmFny9/iO9hlDXGJnjMkyxlxpjPkCWAZ8E/gzMNBae2Mrjk9RFEXp5Px+a/jxHwrbZwwN5dkdAXwHOAux4l4Efm6t/bgNxqYoiqIkAP8YCv3mSwX/p0e0zxgaSj34CMmzewp4xnmMMWZKtI2ttfNbcnCKoihK56dHOlS3cwx/Q2IH0BW4wvmLhUE6hSe1xKAURVEUpSVpSOwub5NRKIqiKEor0lCLn3+21UAURVEUpbXQrgeKoihKwqNipyiKoiQ8KnaKoigJzvB5ktD9w/XtPZL2Q8VOURSlFXipCE5cDl+VtPdIYF2V/H+hqH3H0Z7Ek3qgKIqiNJKr1kFRLVSE4ONx7TuWH/WF53bDX4e27zjaE7XsFEVRWoHLekKXgPxvb/44BAqnwend23sk7Yex1rb3GNqV/Px8W1BQ0N7DUBRFUZqJMWartTY/2jq17BRFURQAzl0JgVlw9sr2HknLo2KnKIqiAPDKXqn7+Nre9h5Jy6NipyiKogDw8/6QFYCf9WvvkbQ8KnaKoigx2FgJp66Au7c2vG1bc+VaGVt5bXjZ7moIhZq+z9sGQskMuH1Q88fX0VCxUxRFicH92+DNvXDDJthb2/D2bcUzu+DRnfDGXvjJJll2ynLoORcGaaO1qKjYKYqixOCM7pCXDCfnQW4HamA2ITP8eGa2/P/CSV7fWt324+kMaFK5oihKDA7vCkXT2/64JUGoCUG3lOjrx3aBHVMlYX1Quix7eiRctxGu6t1mw+xUqNgpSifljT3wrdUwIwv+284VOtoCa2FfEHIT/KpVWA2j5kOVhdnjYUpW9O16pUY+PyEPluW1/vg6K+rGVJROylkroTQE7+2H3ZXtPZrW57K1kPcV3LipvUfSumytEovNAGua8LkuKYPec2DGYqhqRrBKopHg90iKkrgMToPVToHfRLd2AD7ZL/8/2te+42htpmXDY8OhOAhnN6G81zvFUFQDO2tgYxWMymjxIXZKDoCfiKIkJqsOhk+Kxc2VfAD8kp8fCU/vhqsPgDmpS3o1/bWX9YI5pTAyXf4UQWtjam1MRVGUhEBrYyqKorQyJUGoTMA5svIglAXbexTNR8VOUZROyRf7pPu2mQWby9t3LHNLoftXkD8Xdte071hako2V0GsO9JwDaypaZp8ry+FfO9v+xkDFTlGUTskpnsr8X2/nKv1LyyV6cm9tx07qrqqCH62HXVXxbb+2UlIgai2sbgGxC1o4ZAl8Zx3c1MZRtQfAtLaiKInId3vDbwvl8S/7t+9YLugB26qhdwpM7NK+Y6mPjHnS1eBP28Ee2vD2x+bAvYNFpL7RAjl8BuiaBKVByIuRMN9aaICKBqgoihIHtSGxSrZWw/tjpIpJZ8PMCj+OR+xag6vWSr3RZ0fA13Jbdt8aoKIoitJM3twL88tgRw3cuLl5+6oMSS5cW3NDX7GupmQ2uGmrUBmCR3ZCYQ18e13bHlvFTlEUJQ6OzhEXXBJwZTNy/cqDMHy+BH38p42bpP5+CIQOhXmT2va4LqlAD2fy7IwmJMw3B52zUxQlbkqDcN4qCVh4YRR0PYCuIF2TYd8hzd+PG8SSYmBJudS0PFAIBGDXdHEJJ7exqXUAfVUVRWku/90L/ymWx+/tgzPb+O48EeifBi+OglUVcE2f9h5N+9DWQgfqxlQUJQq3bYGjl0bmVt2zFc5cDUEk4vDonHYbXoOU18ING+GDDlpH86zucGM+ZHagHnmJjoqdoigR7KuFW7bAx/vhL9vDy5/ZHX58QQ9patpR+cYKuLsQjl0mwqcoKnaKokTQNQnO7Q59U+G8HuHlz42EbkkwIg1+1MHdb2nOlc0g80QHCusr4fQV8Jdt7T2Sjofm2WmenaIkHLUh+NM2cbXGan6aiHx3Hfxth4h82SGQcYC5STXPTlGUA4rkAPyk/4EldCABQ12T4LQ8SHeu7scthR5fwWdxzF/eugWSZ8Etzcwj7Iio2CmKoiQIX8+V9IhXRoMxEmD0/n4oqoXrNjb8+ud2SwDSc7sb3LTToWKnKEqTeX43HL8MFpa290iUaAxLk3qdBrgqjnnWh4dJd/RHhrf60NocnbPTOTtFaZBgEO4qlDmwGV3Dy5NmQQi5oG6f1m7Dq0PQQpJpv+PfVSDlxR4dDiMy2m8cBxo6Z6coBzghC825rz10Gdy4BWYuhV2eFjZdnQCIYWnNG19L8sA2SPkCLl/TPscvrYWfbYZPSuCsVe0zhrYgaOGVIljRzr0E40XFTlESnLmlkP0ljF0IFU3sOL3bI3Clnn1sPRg+HAufjmvWEJtEbS0cuQROXh65/JU90sbm9WbWnSyshivWwkdxJqZXhmD6Ijh0MaQ5VuX0ODojlNfA99bCthZqjtpW/HkbnLsaJi+K/E50VFTsFCXB+XgfVIdgRQUUNLGx6JwJ8LVsuD0fhnjccpnJcFRO83PZrIU9jewCcPIqsZ7eKoZr1oaXX9oTeibD5T3rvua4JZA6S6rBNMQRS+DvOyUxPR7+XAhzymBJJVzRC5ZPgkdHNPy67Dnw0E7otyC+43QU0gPyuaWYziEknWGMiqI0gyt6w3d6wz2DYXh60/bRLRU+GQ83DWyZMZUHoSoUfn7+aug+B25uRMh7T0/zz76p4cd/3wm7auHRnXVf834J1AC/jOM4bpHr5Djn/k7Ok44IBvhWDxgdZxudUMObdEi+2xveGwvLJ3eOsmcduOCPoigtQU4yPDSsfY69sRIWl8NJeWHRWFQGhyyGrCRYMVlEa1aJiMQn++Pf95MjYWianN9PPJ3Kv9cHlpbD1VHa8OQEYF8Ijoujrufs8fD3HSJi8TC2C1TPkMeNsXQfGCxzfFdGsUQ7MsbAkR24PqofjcbUaEzFg5kFPYBd7dTFua3YXwvLKuCQLAhEsVzeL4Y5pXBtX8iu5669oEpqaJ7eTcTLS62Fnl9BaQhu7A+3OlbhEzvhO+skwGH2eJiWDbP2w6M74Of9YWQ7NRZVOj/1RWOqZacoDmaW/N8N3LoGboljvqWzcsgSWF0B1/eD3w+OXFcahBOWi6VVEoQ7BsXez5FLYXM1nNMdnh5Zd32SAXxpAOf1gE1V0D0FpjoVTm4vgHeKxXV435DmnZuiREPFTlGi8J9iuKW9B9GK7K0Vi25XlKCQ9AAMSIONVTCmASurS5KkNUSz/pINLJwIy8rhuNzw8tQA3DQgcttVFTLf9VqR1LTsnQTbW6BRqtI6dPkCyh2noO0kXhAVO0VxmJkCXzgX/1kdKEG6NfhsnMyPnduj7rpkI5GEe2ql0Wh9fDIOFpTB4dnR1+enyV9DvDEaXt0DNzqBIzuCUFwJuU0MqFFal/JOOPulYqcoDokucF6GZ8ifl02VYqF1S5Fq+f3jiLDLTW6ZJq6DU2Gsz4o80ITOdaMDPDsMzosSYNNRyA1AcScLI9XUA0VJUG7dJDllpy5veNv/FsPQ+TBkPhQ1Mt+tJRgwD05fCeMy4Ff9YcOUpu9rfSVct0GCXjorP9zQtNetq4BQI0WoqkqE1syCifPie83eGeK+7CwuTFCxU5SE44FtMG0R/Hqr5JS9UdzwazZVSXJwaRD2NaIaxpt74PxVsLSsqaMV9jvHLKyB2wbB4GZYdd9bB/dtgzNWSTL9dRvgZxslOrQleXQHDJgL9xe2zP7GeHIFd8xo/OuPXwrDF0D/OAXL5TFPh4PFVY0/bmdB3ZiKkmD8oAlWwWW9JNBkQBoMbYTQXLwG9gZlfu/dsY0/rstro+HBbfCbFkhaPzIH3t0Hh2bDG3vh/u1S6ePYXGmBE4s91SL0Q+Is3PyD9VBp4bpN8IN+zR/3sqnNe/0Cp0bljkZa5hf2hms2yeM+CawIatkpSpxsrISZi+DZKJU5OiLdkiAV+L++DW+bbKQFzDeiJFDvq4bvrIGvSuquO6O7RFGe1b15Y/1GHrwxBia1QLPVG/Nhz3R4aZSkNuQlSVeGCfVEli4tg+5zYegC+MVG2OYrq1YehBqfe/AwJyhnahz1L9uCl0bBwZnw0NDGva5rMiydCI8Og63NFNyOjCaVa1K5Eieps8QtCB17rmJ1Bfx7N9zQD5JaoIxTjy+hyHEzRjtva6WaRkfFvcTVN8a/boNrHIs4OwAlIfhZP0nPGJoG126AbslS8SXXY/2EQk2vC3rNWnhqN6wZB72ypDbojZthfKYk81eHoLgWeqU2vC8vy8vFQq+vGEA0akKwpRqGpDX989xZBs/thR9ETeuGx3dKKsqv8sPl2FqS+pLKVexU7JQ4SZsF7g1/Rxa7liZnNux3rJpEPu/DFktFmP0hmUPskwLbaiDdQC0y57d4IoxrAUuutByyF4af20PhjgK4ZbN0Cl8+CSYughoLvx8IN8QQDz8PboPvb4CBqbD+4Lo9/bZXQ58Y4nnYEinbdnN+uNpNYygrg6xF8tgAId93ZUsVDJwnc2d9UqCgBi7oAU9FKUbQVLSfnaK0AJumwBHZ8EoL/jg7A8snw0m58PpB7T2S1uXzCbBpGrw9Rlyhl/eSVkHTs+EX/eFvwxoWur21Es16xVoJXvk0RkRocZRlR3aVIKHxmdITr8axQ/4bZ4shkEIAKQZ21kg5Ni/jF0DfuTB1YfTXrq4QQXhut4h+Y/nUE6RkkejO45aGl/VIhmHpYjUWOC6SZ3fTZqhlp5adoiQ8LxdJ4MYVvePvYgBSYaZ7cvT6oX42VErPwFobFqpJmfDKQTAoStDP5AWwuALePgi+3k2WBa0IjjFwzTrpRfjiKBgYZ9BQWVBchYdkh0uxuaR9AdVWLNWKmXVfu6BUrLsKC7lJsDdKBZuKCninFM6IUbQ6eZZYpkmE/9d6LLygFffsIYthSQV8Mw9eHh3fucWD1sZUFKVDUBaUcmDjM+HUbm1zzBXlcPYqEbnMAFzSK/7XetsI1cfmKjhmqTRwTTZwQXd4YQ8sKoer18E7nkjV0iAEg7Bgct39eN2Of2lCp4oQMobqKLl2fx8Gd22tW6rNZXKWlIqrr8Fvpttzb010l7YrbPcVwq1b4Jo+keuTjBQsWBzl3FsbdWMqitJm3LcNfr0FTlsZvS5nvCwvhx5fwcGLJGl+5mKZj4pG92ToEhCLqzFpFX5qbexcvbf2SnCHdbZ7ugjGZsjzaZ5Sah8VS9f43Lnw9+31H6+gCj5rhAsT4Neb4eeb4Zhlkf0CAS7qJSITrUScy8KJEti0aGLjjuvnR/3EMqyviDjAW3ug91dw6ermHS8eVOwURWkzxmeKiy8/tfHRgl7OXwVFtTC/TJLm55bC63ujb9srFTZPhS1T4fCuTTteQRX0niMCu7Gy7vqzusMxOTDBETiAtRVQOBVu9wR7/Lso/PjFPbGPt7Nagjm+tgxu2Bj/OEdmiNj2T5W5u8YyMF26YMRym+Y5inFYPXOX9xXC6AUiZA3x/Q2wsxb+tRtqW7n8mIqdoiiNoiYEp62AsQukNFdjOK0b7JwGa6aIy6ypHOKxlo7rCtOz4NR6mqzmJkO/Robwe1lWLj0Ay0LSjNZPzxRJqv+npy3UYV0jO6gvK4fHd8k8Vr9keGp47OMV1YZFc3mU48Xi6j6wbgosmSTzjIvK4IN94fSL5rLHKRP2WT2W33UbYWUFXLSm4f1d5Mz9DUyF5FZWI52zUxSlUayqECsqCQn88HYJj4d458Hq45HhcEY3GJIOo9ug2etxuXDzAAmwOMkR1dHzYGUVDEqBjU4R8UlZsG4SbKiCY33iO7cUqqyI2EujoVs94js6E+4aCF+WwmP1iGI0XFfthkqYuljSAJ4fCd9sZuJ/vAxKhY3VMDNGJwwvl/aEL0rgzDaYv9VoTI3GVJRGEbRw5TpYVwlPj2i4DVCi4u1SEE/+YWVI8ui6p8BP+7V+Iv6mShixQMT136PEqm4r9tRI94yGGD0fVjregZoZzbfuNBpTUToZhdUSOff13LAl4fJuMfx1O/y8f6Q7r61IMo23NhKR6V3gqzIYG2fQS3qgbld4P3trpEt8TjK8OVoa3TaVQemwYKLk/jV1rrKpxCN0/y6SLvcAaab159RU7BSlA/LLTfDPXXD/Nnh0KFzuCeH+9lrYWg1bq+CrZkbNKU3ny1Z4728vgDlOcva/dkleYEMMmiui8cPe8CdfuoK/R6CXkBUXp2thhmx8+YQtxZ0FUB6SMmybpjS97Fq8tHmAijEm3RjzijFmtTFmoTHmHWPMYGddL+f5GmPMUmPM4Z7XZRpjnjHGrHVee6ZnXcAYc78xZp2z/pq2Pi9FaUmOyhH3Uwj49npY5QlS+FYPibT7VozEXqXzcqRjgRngtSKYsFDm3urDtY7+vCP+4ywoha5fwkELoKQWDl0C6bPhnRgRra3BLQNgTAb8fhBktYHZ1V7RmA8Do6y1k4A3nOcAdwKzrbUjgMuBp4wx7ttwPVBlrR0OnAD8xRjjOnguAsYAI4HpwA3GmAQvbqQkMpf2gis9YtbFE6Z/92Congk/boG2MonKynJJ8r6rlafjG9sotT5WV8AtW+C0XHhlFLxeDEvKpfRYPAxsRLTppyUyh7i6EmYskSCRWgvvFTdh4E3klG6wbHJ81mtL0OZiZ62ttNa+ZcORMbMBtynFucCDznZzgB2Aa92d51m3AfgEON2z7iFrbdBauwd4Hji/tc9FUVqTh0dIAMiCCZB/gAaBNJU/boMP98PPNtdfEQRgUQlsrRQLak6UNkYuQSvCc/RScSEftxSSZsOMRU0f5+IyCVrZVAnP7BZxe61YakimOi7FhirNuB3DNzWiPc9lPeH4HBGAVRXwgz7S09AbWfvWHkiaBRlfwG7HepxXKonry8paLp2hregIc3Y/BF43xnQHAtbaXZ51GwE3JXMgsKkR6xK4M5NyoKCuyvgYMkeKC88aB9O6SjX91/bAsTlSnioWP9kA926LXHbPYLguitW8sgL+vlNSLp7dDZ87wjivEXlwfk5bKd0APtsPDw2DV/fAxExJPag4RKyvzFa4SndNhhcOggtXyzzd7wZFeg9AcgJDSIPa9/fDeT3g5BVSY/TWAriil6SAdBbaNancGHMjMAL4pbPIf6/gny61TVznPeZ1xpgC96+0tLQxQ1aUdqEsKL3N2gJr4ZQVkPclfNKIclUf7YPM2TL/46+435r8tRA21kgbnhlOlf0jc2DbNHiygQ4Vn0TpSrCkrO4ygJHpEhk7MkNy1u4fCgNS4d4GSmLF4s4CSVQPAZO7SC3JBWUiMnuqJGCjNYTOJSsJXh0NLx9UV+gA7h4Eg1NhRhac5ViX4zwBL7E6OnRU2s2yM8ZcD5wJHGetLQfKjTEYY3p6rLtBwGbn8WZgMOBd95Zv3Zwor4vAWnsvcK/7PD8/v5MZ48qBxrZqCSSoCMGX46Vgb2uyPwhv7pU7xlf2wBE58b3u3WKpx/hFiYS7V1vYXQMTmtH/zVq4fqOUBfvHcBgcJcx/vGf/mY2MJnzlIDh8iYjWkTmwrgr+GqUAc00IfrIRBqdJR/C0gLgamzrfVBmCXzhXqGFpMg/b7avw+nGLxeJaOrH+5POm8HIRXLBaanfOnRR7u0HpsMHnH/vPGLmpeXNv4wpqdwTaxbIzxlwHfAs43lpb7Fn1AnCts800oA/wWZR1Q4Ajgdc86642xiQZY7ohc3jPtfJpKAcw1sKLu6X3V2vPXRRUiWVngFWNLM/VFHKSxZV3Sh78sG/8r/thX3EfPjBELiwj5sOURfBSUYMvjUlBtbgZP9kP/9gZfZvDc+C54XBhdyiJ0rqmPvqnyQX9kwlw+yB4emT0Mmb/3Qd/2Q4Pb4e3Gxmx+PE+GDIPfroxvGxLheSWARzRVcL/p3tEe0etNI7tM1fmxxrDwhJpuDtyfjiA5i/bpFj27P3wnTXimpxXDusqGrfvJAPH5sK9Q2BSnDcxn+yT1j8ZX0j+aHvR5hVUjDH5wBZgPeBOB1dZaw8xxvQGngCGIE2hr7HWfuy8rgvwGHAwYvnfaK190VmXBPwZONHZ3x+ttQ/EMx6toKI0hU/2wXHL5fFbo6WcVGthrfQoKw7CD/pKGsJDO6QKR7x9ztqawmoYOFcujvcPgav6NPyaaIQsnLVKajy+elCkFdeW7KiGQxz37OzxjasaM3SelA8DCM6Qx8OdVjmn5MDrTvufC1ZLkEo0bujXcEK6y9gFsNwRscecHM2ML0TgRqZLBCaIW6/yEEhqRkHueLh8jbhmAR4aKvU7W4v6KqhouTAVO6UJrCyHCYtkonj+hLa9CGfOFpdmr2TYMb3tjuvFWkluf38fPDcqeh3EL0qkQ8C5PSL7tLU15UE4ehlsr4EPxor7sS25bI0UCMhLgj2HwH/2wokrZN24DFji9HarDcHNW+DgLhCwcO4amYcEp95knCF3t2yC27bK44Ip0D9drMond8GgNKnR2T0FdkxrybOEL0tgUxWc3T0yOb2gSprC5iRJEYTmFABvCBW7elCxU5pKYbVYHs1NC6gJwdeWSp7Vu2Prdpj2kz0bSkPSJmdLO8UclwUh60t5/L3eTWs02la8thtOd/qlReuMva0aZjhzZLPHQ+8WniOzViytZGRuMDMZLlglaQb/HQt9ohxvaxXkz5PH6UbmFk+op6vDpkp4ba8ITUlQzmdAKizyNUndXi0RpSflRp/7dZuu3jygcXmc26phgGPJPzAUrmyj3Dk/WhtTUVqB5rSM8bK1WqrbJyFBHg2J3YrJ8MQu+F4LuYP21UpZskOy4fjc+F7TJQl+PUDG+/1GzOu1JcW10jPv7q3hZdEqhHxVKjcuFphTKsnOzeWnG2Vu718jYEoWvFgkTWuTgaLp8PSo2K/9xw4RO5eBaWGhs1aiIPuniYX63G6YVQJv74T1Qak3ubsG9gZhbwX8pRBO6x6+IeuTCr+MKgXCQ9vFXf7X7Y0TuzQjQTtVTvmvjohadmrZKe2MtfC7ArnT/9MQaTbaltywEe4tlMf7D4HMVp7DaQpz9sMfCuFfQyAtDkv6mV1wwRrJWXtmGIxZIstv7g+3+lIFqkLwow1i2f1xcPOKL4OkE+Q4kZVD0yTY5+xV4Oa2L50IY2O4ve/ZCtdvilx2cQ/4l5NC8cRO+PY6uTH6cjxMWhy57UU9RNjudAQ+A6gCPhwbX1TtO3vhd1vhZ/3rFiBviK1VsKtW3KR/2y4RpkfFGcnbUtRn2WnzVkVpQx7YBl1mSxUKF2PgxgHwzKi2FzqQrtZB5I48rQNeEaqqYPpSeH4PZM6Lvs1LRVKJ5INiuHqdJJQHgKXl0CsdJmVC3xS40BMuf2+htOkZOBceHAIPDhWhK6oRi7UmjlJgrxZB3zlw7brwMm/B/xQjQSdBJJr2x31iCx1Iuoaf9/bBKcthfql0eTdIhF60TuT/GCFzfjjHq0Deh01VdbeNxol58PG4sNB9sU/eIzMLPvB0Hi+thf/uha6zYbpTQaZ/mkRoXr0O5pbBWStl+XXrwvtY2I5pzR3wq60oTWdfrYS89/hKyiC1FLdtgZRZkSLVFB7bIZXeH4sRRl9rYW1F25ZiWudE5+2qlcCXjob3Ou0dXq2V9+ntvRKxeUeBWFAP74AVFXB9P4ng3FgFi8phV4107b5sNaTOgt84n+XOWtjuSdg/dAmctBz+b0PDY/veegl8+YunCLO3OplFCh6fnAePDYN7h/r3EMkv8sW6vHsgTM6A3IDs/81iOHYZfFUigldjISsgpeTSHdE7K08svj9vCx8b5CYmXve0n/M83cYvcGp0Hr4Esr+SbvUlIenSsNQjYr0dtZ/oiO4fPe/NoT5LtC1RsVMSipUVsL4S9gXh8xas8PDsbomMezZGaHi83DcETsiBv8a46J26Qhpu/nhj847TGE7vJhfOE3Ohi+eKsKNaLKTKdhbArmkwKUMuVo8OkWWz9kPWbBg2X6q9gIjMDCcq9OQ8CdU/uRv0TJYLfy2wowr+uRtqgL0huTCfkRc5/1rtWE/xnPf5PeT/MI9rNT0QDoaYmAljMuGN0XBZnEEbp3eDX26BRRUiJq5oZQbgb56bpHnl0hm9YqbUxnxxNDy4HT7z1ffcVdv0XMd7PC7fO53ijPMcYfPehPTwmLMFB8OaSVJKLGhhSkZ43W2Og7E6JHVBQ214U9dBpxIVpWlMy4Ib+8vd+nk9Wm6/Dw+TSLUf9JE6hkPTmxagckRO/XMnqyrk7nxZM+otNpZjcyXopXtyZPfsQ5eI++vyGDUQ99SIi+6rEvh5vtRzjMZn++H53ZIjOCIj+jZentoJF6+VUPVt00Q8FviiCj/dL5bdhiq5iFng8GxpKts1KXLe8YyV4ce3eYJVAsB2J/y+wklPKKiW4tvbaySpviHuHSJ/XpIDsHWqlP6qL4IyFqXBcK8510rMMPJ9K6yR50dmwxnd6752UxUkGzBWxD2ERGWOieN9j8Y5PcH66rM+MkwiNi/uKRbnzOzICNZAAO7ZJrmgZ3SDeZPh37vg7DXw0wIot/BxqVjZJ+TAi6PapsWPip2SUASMVMJoaQ7vKn8PbJNghmzPhTgWqypgW5XkNMWbh/fGaCnRdUkjC0AX1UjYd24TftH/2gmXrpVgitVTwjlx9aXG1VoYu1BEAeT/f8ZG3/bsVVI8eHkFvBdjGy9/3yniVRwUwYiWw3d1H9hSDaMzYEoXOHSpFGZ+cDvcPjBy24eGypwfhN2gEzNgzrjwNqsrJSozCXF5/qCZEaa9UuEERwC2VsE9hTIf9vVciaAsCcJpeRLMcWJuZOPSYanwwEDYh1jWX5WKR+B3HqG+uT9MnA8rK0VcXxolwnrrABiSBtOzxaoKWThzFRy9HP41HC6OUeLrpSLJi/x5f3ndYzvhhNzo6QnP7Ya1VfDRfvhyQvT9LSmXm4nFZXDRarHEXR7ZGZ4b/s8+GDRfIlRbGxU7RWkEZUER1MpQ/S6YD/bB8cvk4poEfDY+7GKrjzGO26sxLC6DqYslYGH5JKlp2BiWlcuFYHOVWDi3FcjjN0bDmkq5QPuptRLabxBhOrWbhLzfvEXKXm2plvfo1wOkHNYLRXCMx6K1zgW1LATX9olMOr+yt1xIAS5dIxfUPN+VKjdZ8rlALs4zs0SkUo1EV3oDbaZ1FTcfwKWr5WbihnxI8bjexmfCj/uKZXRBDI/AZmcOeGAjraSfbYKnd0u5sedHwvlOzt/VyPfDm/t3/Xq4Z3v4tbfmw2eOoOSnwiVrJRH9+JVh4a4OiaV1Qh78cyf8cAMMT4eVUyQydFeNfDdiBamUBOWGxCB/m6okheEPhbA7igi9USz/P6xnmuCpEZIes61aLLwQ4Tmzb/eCT0ok5abSyvHbAhU7RWkEP+kvLsyxmfWH6O9yLC3XHVXeivNe6yvlGNUhuYA0VuxuGiBBDNOzpRDyHwpFoA/rWtfCKQnCN1eKwLw7VoJpzukhFfRv2iwh539FLmwBIzlmz42Ev4fEGnaZXQrfXSfb9E+FsxyXnLUisCAiuq5SuhBEc/1aK/lryUYsx/KQ5LLtqAkLoZ+X9ojAvlwEF3is54CBe4ZEfw3AC7ukognAP4dLEWRrI92+foJW8ve+lg1P7YZDsqSFj4v7lVjnEaFnfXNrtxSI1XzPYDipm4jPR54bKZCL+E+cnLh7CsX1uaoSFpfChCyJrlxUBt+OMWeYGZBuBkvKZT6uNCj7GBHje9TFQJkFfwaIteIyfn8fvDBKvlef7ZfqMeMz4dPxsl3qF84NUq68f42pv9ocVOwUpREkG7m4N8S5zsW7sAqGpMuddmtxWjfJz8sMRHf5WQvV1bHz07KT4GdO4EBFUNyCW6vhuCgC8/E++TPAzhq43HMBPaIr3GVgdLoEt9QgoejGRAodiBCkGJlXGum8N/dshRs2SWPRr+dKtY9jc+DQGBbx28Vw4Rq5oXCNA4uU3TpnFfyiv9wAHLpUxrtooljX7+2TDgfRmLlYLvrPjoxMLn/KE5j05E74eL9YUQ/WU+vx1BUyxhv7S/7iq0XwnXVyI/DTfmLBv7InUpjfHwtjFkZGnf5th3zv/uxsd1QOfDhOXOj+AgReZ8P0xVAwTW5aDusafYwgN2ULJkL3r+ADx1obkirfp99sEdHyMiIDFpbXrctabaWKiwVe3iNu22Qj1nZqQD6DJCO/jbeL4Xt94RtNmNNsKppUrknlByRlQafaQ0rD2zaHfbVS8b44CG+ObtsfN8Cbe+AUT4DGp2OlS0As3D50sWpZlgQlfyoI/Kq/zPP8NF8uaiC5aclGLtaW8HIvn+2Ho5bKujmeuqIzF4vF1z8VCqZKAMzBi6Xl0OzxdYNb5pTAzCVyEXUzB2ZkiXXiTCVycq4EUYAkXP9rhOwvJ8pt/qZKGDxfHk/KhAWTwutm7YPDlsnjD0bDWaulSsnxOWLhRqPrbImmnJgJCyfJuGYsFo/AqsnRy4S5vLEHTvV8bqfnSQ6d353r58JV8LTHOvxsXP1C5+Wk5SJCIAEtbjHpC3rAP0eEP8uFZVJh5Tu9xBvg5ZebxHr99ygYlQk3b5aUkBBQOBX6tnIeqSaVK4qH3TVSx6/XHJhd0vD2zWF/UIQuxYi7sa15w1ce6x5PkMPqCskfdPPsNlfJe9J7jnTP9mKtuM/21crF/dVREvRw45ZwUjFASkAsuSQTXehA3IxJRub9HtguFlBxrZSxGpch83wguXKbq+Q9/KJE/q5ZF45UnZYtlpDXCppXGpnndkN/SEXKWd0xQMYWTehAohaHpYm7y1sqq7ZW5t2+kQufjoOj8+CJEXBOLnytq1jD0XBdnO784cFZUnx569TYQrelSm4ovMWqM418jheuls/h3WJYUS7v34zFUqTAbbL71CjI9VjRHxVHP0403hwNe6bB5oMl4jLbscae2S3zwi6TusDfhtUVuuqQBNEsq4Br18uy7/aRiMzfDYwUul9sktSRR7bTZqgbU+nwbKyUi0NLVUvfXi15eMlGLhrxBI40lQFp0gJoXWXrFce1Vu6mMwJ1Q91/kQ/zSmBOuVy4nvUUbD5rFSwvh9f3wJyJckHb75hIS8pl7C73b4cfb5B8vB3TZV7HZWcNDVIdksjDyV3gzG7w+HDYWQU/chK7j10CXVPEmvhdgTRFnZktLsA9QSlwPGahBE8sKIMvnKCNvqlyx+4K3mU9Zb70o/1QHJJzLJsZW3i9BAKw9uC6y6culeAXEME7vKvk731rNbxQLJbLnul153AfHAJ/3CYu7EVlkmRdnyfhtT0yH9ojGTYcDLf3h89L5UbgqzJJxXhsB1y9XoR00USpqRpA+u2585qvjoJjlovI90lueG7RxRjIS4E85LNfPAmOXy6BMWOjBE29XyyRnqGQBCg9NiLsRq1yHvRLhRcPinzd0jKJai4LSY/CK1ux5Y8XteyUDs2fC2HIfJi6qOWqiozrIu6s3w6EixoI8X9rr5ShKq6tf7v6ODEPru3b/JqLsfhPMZy7WpKr3YRfl4Fp8OxBEiBwRjdI8txdT8gUkXA7iZ+YJ+L4y3wJO3d5eDtct0G2LQlBVRD6pMFvBkjQxewY4edeLlsrnR1OWSkX1W/1lM4NLksqZIwhJFgCwmkkDw6Fa9ZLZB+Ex7akTHLqZo2Hd8dAaCZ8UAKvFIsbMwmZR2rO92ZhmVjALjkeQStzxl9lYcaSuq+9qJfMVz2zW6IdG2JNhXgAdtfC77bAr7bCO/tEqN8fKzmEzxWJ5VrrpBX8eYi4Gb/vEYxlFbJNBXDVRml8Gy9flkgE7OwSsSDXVsr8ZEEVDJkjJb+WOFbkDzaI1V1q4Zki+Z68P1bmI/8zOvr+a0MwcZF89nlJ8IfB8Y+tuahlp3RolpXLRWt9ldw1xnGDGhcXxpHHVhIUF5tB5vfuHNxCB29h8pLDQRpTF8Oc8WK5PrgdftpfErCXl4u1tqYinPz9xAi4Y6C4V3t9JZbUq1EuUjdtDrsGDVKI2CDzODfmxxdpurNG7qx3VMu8YJKBa/vBTc50eS2S1L5hSqRFCXJuT+wSITynG/x6oHSJP3+1WLNbpobzC4/NESu6LCQXt3kTxLXaVP61U8LjQdygl3is82dHSMBJqRMFG41Ds2Ue8pCGOlmUS6ukAPBWMdxRGF43t0yCUn6zRSIdAbKM3Mj8oC/giWasDsrn5RIAFpeLG/QX/SMty+qQdGcIGLhrkLxP31krYvlVKUx3xmyBR7bBRseCn7BMUjm2+1zdv9ws1uBdg+s/V/frMiIdDo1zPrElUMtO6dDcOVguyB+MjWwI2RZkBMQFFUTmW2Lx50Kxqta1wpzchkopNDxyPuyNYV0ekg2TPW6mY5bBFeskIu7adVLSanqWJKqP9AR5BAwMTpck+V21EklXGOWifXVvEbeBqXD7AHGZvrpHLMpzVklfO7drQmE1TFssNwlVHhF8dqRE4S2rEAsPRKCCM8MVUFaXy3j8wTEBA38ZKtU2bnESxnfVynYVociyXn8bJo1OQSzIy9bAFWvrf48XlkLyLPnzzk2BuFMnd5EcvBt9UYnn9ITNUyXh++MYQSp/GAw7psqNhZ97CuGIpTBhgbhoRy2UoJf39kVud59TJKHUyfHMCsD6g6FrMpy4DPp8BavKYPICSPtS3L7/G2N3ybu7txDu3SZBP/dvk4IH7xRL7t/928SKA4nsNch7XRmCsRlwVW9xl3qpDImV71/m9yz4mblY5gIv6C5WYFuiYqd0aPKSJSy+NefVvNSEJNLsjT3iPlo6CXZNi51uUBaE/9so7s4/Fkbfpjl8XiJuLTffLBZXeSyO6VkicCmOu3BgGsyaIJZYkhHr7t+eUPpvO2LWJ0XmeFwKq+GF3WK9hQ51IgrLZE5pTIakGrjJ3+7F8q/bYEEpvLlXXIAuPVLkQh1AlruuxZLacCTls54cND9X94F3xobnjq7qDf8YLrlb/mCP+RPFipmQIXNef98J7+6tu0+XFxzXYBDJ2/MyJhM+Hy/uy79skwhLM0tuPpaUiWD/sK+4xqNhjFRTKQ/BbwtkfhTEDXn9RrG6Vzpu0m3V8hn4+dlm+SxuGwhPjpA0gZ6p8GGxVCDZUQuTF4sr2M/JueFSYTOy4LvrxSV9zFJJW+iVCj1TZFmX2fI7q54pc4zPF8nNybe6wUceEfu/XrK9Py6nFuieJBVTMr6IDIYCyW2cW+4Ujy5tmxJhXtSNqSgeHt8llk6tldJZw9KhRz23hJkB6JcC22pgRJrc2Y7PbLn5uTO6wUc9JcLODSG/ai08uUt6jrmWzmTPzcBFveCyXvCbfEjyzDFtqpQAlImLxZK6fK8EFfyon/z5OWyJzNVc3FO2e3qXBFEEEeHpkSJ1DV/YLdGLj++EO7aK2/n0bmIRebl7sLgov54rIvD8btjvsSQzGmG5J5nIpHCXj/bJzcpP+slYXdyyZtH4WX9nWwM39JOI08XlYvHdsMlxY9vIqM81lRJm/9tBcOZKWFMFXQOwd3pk6S8QC/e+bVLlpMbCzmmRc389U6QH3Q/7SAuiwWmwsAR+6YjFvpAkkS+bHFnv1evurbBi3a+ukDnNOWVS/u3C3nB+L7GAs5Lk/Qki7Xj6pcJN/eCajeAGRd6yWay7welAidycTOwCp+RK5RQD9EyDolpZ1y9Fbgo3OJ/jG8XhnMTrN0kRBhdvDmhLNMhtLCp2iuJhaJpckCwwaj5smQx96ykPtaUyXJz3Z5slIOLkPHg9xgR9Y9hYCZeskYvN3YPDEXWPOJXvbysIi93BXaQ1zK4aser6fCV3/MPTYM3BdfO2QIoe10eKky/3yX7ppXZsjghcv9RwIvhROeEGnS8WOa+xUmXEL/h9UkUcQIpHX7harL07BshF+pF6KpjEy+VrpaXPmgpJtD9phdwonFtPIYCuybBksjx+cid83wm8CBBpvXRPlou8y+vFIoqbnPdxf0iCUQ7r6ogF0hLq1gKpf1ljJbIxO8mpMIO8v6MzwzVD39ojtVTv9YXk9/dYr5srYdIicXkCpAOVyDHmT6p7fklGhA5kPm1khszVnbICtvisQbcSyz9GSKHpiZlQa+DmfLEia6ykjtw7BMpq4eFdUkllRpasu2WACHsIyIvy+dceIu7YnHbo26huTEXxcGxuuF1LEKlBWB99UsNBMxkB+UE1JCLx8s9d4sZ8YLvkm7n0dG5RveWcjBHr7I5BkqKxw7kou6WovvK4ob7VXUL0XxwV+9jzS52alc4+Dl8MB2VK8evnR0Y/x+v6Seuiz8eLgNRHr5Rw8vmJefD4yMhalSBNW5Ocpp+9Z0FglnQsr49zustncE4P+Sx3TxfX3EWrYcRcmZf7LEa7m93V0m1hX1BudrxClwS8OxqKpoaXpSA1QL1ctlb6Kc4vlTm0W50AnJUVMne3ZorTAigAKyeLJZkJbKgQ1+DJKyWys8hz8KOz4R3PzdM9hWGhAxE6kO+Jl62e9++5XbIsYKShbUlIXM3/HRne/tnh4hUor4U7C6TIdpWF/LlSSLvGcT1/tl8+u387ruEqJAhnS5VEW5ZOhaeHQ9Ehdd/jeeXwomNx/3iDfB5nrKi7XWuglp2i+PheH/jJJudxA7lxqUlQdojMqXVPkerxpzXDRXPcUinZdHUv+EE/cVdOyIx0We2cDsFgpIvSz7W9pdDyj/uJG61rkrTAGZoO/xhW19XmUloLQ+fBrqBcHNxrahVw7iqZr/ur04yzW5LkwV3bTy7gqYHI+ouF1RJlGDBwZFf5XxuCO7dKK6bNU8Vd3DdV5j7/s1cspXN6yNzcZE/9R7eN2/Sl4aLOXrZWSB+3uwbD7wdJ8rmZJblmIcLzggBHrYLSGfDO3sj2NOmBcGHr3slyw/Dd3jC1i8x1zVwqnQN+2kesuId3Rpbnumsg/GKznOfOGljosZqeGiE3CP2BdOd4IzLgLmee980FkW7Si7vDE44ol9vw57W5ou68okuakc/PnQt7dEd4n3cXwnmO2/fYXJnHnJgJvbLg8WGSYuDOSx+yBJZWSFHvd8eE92GQjgrPOTdJEzLgQ09Rhp210j2j3Eoh9Hll8AePtf6rTfAbxzX7apHkBgaRwKi2QMuFabkwJQo1jmvSb220NoFZcgHNCkDJjKbt4z97Jfn4r0OhWyr8bGP4ovqNrvDv0ZARQyif2x2uyg9ivbg5a7GK0/+yP/xmUOSyPTUwYJ4EZgSQ4Ipf5sP5qyRXDGDLwTJXBZA/B7Y67/mwNEnuvqtAXMN+/GI3Zy9Md6yDLgEYlQ7zy8Prx2TA/iooCIXP6aKeks7QO0UiO7MCsHWaWEMLyqQw9Z5aKZD9YhFcsDpSMC/tIcvLnMtntySxlkIW7hsseZXjF0qAx3ndxXq9Yq24E7dOhS7O+29m1T2/Pw2C83tC77ny/Ns9RXAe3xV2WXq5sqfcFFUgVWA2O9ZnRVAq4pSFJFXh4/HSsuf76yUtBeDdg+Drjvfi+33g/qEwdK7MwQWA2pnSsWFVBYzPgLN6yFiWl0tw0VHLIt+X50fI/ncG4bhs+O94cVF/vl9SVvwY5/wejRKt2hS0XJiiNJKUlPiE7u874MZNUB5LCRrJ93rLhffWgY17XVVIIkkBTlwBz++BgxbK8w89oexv7w9HUL6zF45bBu8Vh9ef3k3u3rMD8MwImUv6eo6E0KfFCCDpFeV9OnNlZP6dW1IrWvpIKBSe9wSx6opqZI7ohBzo6RHmKxyrubhWSlI9UAjneVILykKRQpcO/H1YWOiyArB9qrh9LTL/VG0lXP9v22BYBpzdQ9Z3T5H/Z3eHXw2IzPH8124RuoHJUDNDIl1rnVzHt4olcfrV0SLMp+TBt9eKSOwPwkHzYPQ82c9N/SVY5fw8sb5Py5PjvuKJli2slpsQqCt0AI/sAteb6m2Xs6xccgAtUGKlOS2ErdUb+kF3z9xZ1yS4vzD8WWQE5Hv9843w6HYJLAo46RwTF8EJK8JC99kYmY/7RjcROoAvyuDZ3XDQAvjRxigDR97TlhK6hlA3pqI0kdUVks+WYsQV19yGnwAPDpM/P/tqZc6kRxRhWV0BUxaJGEXrnn7rQAlGsMCY9HBU3Dmr5GI4rxT2OvMr6U4Olxe3DNWVvSVhOT9V5v3WVEJ2cmTUZchKWPvHjntraBqclAu5Aej2pVxofzdQ6irmp4nQ5X4lYwsgEYl/GCKWyif7RCDeHxsOgnH523ap2OFPPcwAhjhFjLsYKJ0pEYgup3cTa/fBoXIhd1vqBBALDOCS1ZKjeMdACSY5MkdyDR/aLlG3EHZfBgz8dJNYsu6yt4vFEn5ht3Rwf3NvuCCCBQpqgVqpSLJhmkSxPrtXrNJU4CJfXuDH+yXaMhX4v36SR/j9jZHb9E6RMma3eXIB/XmZrliP6yLl4Vw+HCtzwpf0grQvRPxBcku7zwmX/rpyHTx/UDgX0a0gk2YkyKbbXBFzl7KQ7DfFiAhnGDkPLyFg2qLI8bQWKnaK0khCVi5yfVPlwr+jpm6rlVgsLINbN0vE4JW94Zo4BHJrFYxaINbb7Al1E9wXl8kFqiwEe5x5ouwArJkkjx/aHp53WVUp+Vy5yeFyXcVxWqVdkuCPnjmYflHaFj23Gz71zOOsr4IHdoSf/65QrJ07t0BBJZzbM5ycHAK+KJXowRNzxeXWNUkE/oN9cHTXcETq17pKkETflMggkUt7w189NwvWynt9WU9xAz61W4TtXyOlusnzRRJl+btB8P5+uDAdnnCsqJ9sBIwEBBVOk04M398gPfxuyheReqFIog+9jEuXebOzusmc5W8GiiCkBmSuzP0s3Jgj15IqC8FL9cxfpQQkFeYJZx53sceCLayRSji9PTc7U7JEQMtC0C8pXE/Uj3sjEbISVflJifTgO7e7uB9d3FZLa33mZZWFPnPDnSZcMoxEA0/uIu7Vl2PkUc4tk2ourVVOz0XFTlF8VIakMgVGJui9P8L/Fkt1kIOzpAL+hoPlh1pfI1cvV66VHzfAtRvE0okWLFIdkhJPB3eROaXKkIjAuspIsVtXIRXkh6RKeL0bADCxC3xWKoES3gAAi0QbAgxNhfXVcFB6+Ly/v15SDfqmwLOjwpXqg1bGMzojsgqLn35OdGoysq2b6OzO/eUkwZkr4GVnTMvLxfpbXyUpAj918rJ6p4poZCfLnX8QuKa3iPVtA6TMVMkMeGonXObMBX09Jyx0e2qkIME7xXD1urC1AtJDDuD7fWFmV5i1Xyx09/3xErKyn4Iqyan8y9DwupNWhK0bL5MyocpIKbFPS6T6ytRsuG5juKEvSPg+iEvxK+c74e6uZxKkJUl5tbSAuIHLnM8nRLjzt/fwc8tg7AKZezxzueTrlYekPuftg8Md1iuCcO9WOLYrTMsRF+vScgluWV0F/VNgUynsyJG5zdJaibz88SYIGcnR8wteDfId3Fgt76FrxX17nVh1XyJ5n7cVhF2ueM6h21cSNNSaqNgpio/fFoTdcHduhZs9rqF3i+XCOatEXDa5yZDsETpr5UIwMC3c2sXLKd3CYtclEF3oXt0j8yL7giIAu6fLnFBxbbijt8u162GtYyJM7gIvjJRln5VI0WVvovbJuWJJutVo1k2VMPNM5yrwn2KxVoKIqL5QFO4ifW+h1D5MMVJRJpa4H5kjyfhLyuCbTvHj/FSp0L+pQqynP3osobucxwZxr/65UIT+l5vCicoBRCQe2C5j+6IEig8Ry+7iniJeO2ukOzZEBn0snuj01SPs8jzWsWSMEYvcW+nlEo8L8aIeUpG/yAm2STFSPPwn/eS1XqH790gJ7Ek38KQjphkBcWeurhQ3n+sR+HqORHtmGLnBmBOlMs4zI+E4J+jGa3knG2kGPDpDSrCdsyrcMBVkf9mzw1b72TkwJSdc8HxXDQyaK0J0E9IF41+7Io/tBgr9oRA+Hyfl31y+KoFf94OL10feGAwNwPLJkjaxsFz6Nr60B47pKnOv4zKlruivfLGAGUbmPstCEqmb3IrWnYqdckCwpEzmHL6RJ4mv9XFCDtzu/Ci91f9Bcsl21YgbLTfKr+e3BXDTFhGe+RNF/G7YJO6svwyVY/+iv+Rd3bQBntgOF/tanPxmS/gCtzcISV/I41UTJUKwp2fe7rwecjFJAy5YI0ES3iCQCiti8ZO+cFeUpG1X6HZWw682hOfOhqVKsMSGCvj1FnFDBa2cz42bZB6wJBiOpnxjj1gI3+opydxZgXAE5/X9pADxTzdJYWWvpZyE9JzbXCUiXxISofZaLKflwVV9pBr/rtrIeclAAF6uJ4F/fBeJ+iwNwvAFcm4HZUpwzte6imv2it5ybsvK4H7H5RoA/jFSqv+f50Sn1lixou/bGs5jdDmzB+zOhR1VMGqRvI/ndhfX8+0Dxf24sVJSAN5z5iKfKJIbgWjx8KmIC9C9kXHHdHSOlEr7pnPT8/JBEmD07G4RutsGwogF4de8WwIPjwq/599dFzlvdv668Fyin2t7S3CMq7VTM6UrghtN6+Xvo6TE2OJyp8pKClTNiPysS4IS3bqnVsR+V224WXBboGKnJBRlQenBtbsG/jsGBjkuuge2SzHbL0slCi1W6D1EJuzu8V3U+qbC457osRXlckE4Nlcu5L/dKj/2DY6bZ02l3CGDdMH+cR+Zl9lSJReR1/dL9N2OWglTv9lpsXP52rpzaaMXyUXSdfud4lRqubQnrKiEiQvDTVG9hIC7t8HGcng+SvHd/bVw7DJY6rmwXtpbqoCkzhIXVTIwpYtYpX/aDo/skG4A74yB43NlLmpXrdTG7JsiEZr7p0s+mlu7cmS6WFejnKCVCivzWkflyvqDF0okpXu3DzL3+NRIyPtS3F8jUuGr8SK6s0pE+Ib45g6zAXfacE2FuCGHZUhi9q2b4Q9bxWI/KRceGArHLpU8xldHhcVuYIqkGyTZSHEwBrY63wlXzKd3kZupQ5ZI25pFE6TtzaVr5HtQY6UCzr+L5OKenRR2JRfXyn6+ngsj0+DRnTA8Aw7Lkco3SbPCwt83WYJf3i6GasflN2GRnONJufK97JYCb42CbzhWdUlI3MB/HCxehWguaO/XJZWwm/GJ3WLJued6bB783lfv0uWEFZHuyf8WQd5Oybu833H9ZifBuily7hevgR375YZxu/MetHZqgKYeKAnF4nK5+9xQKUmrIBfG43Kkav+VveoXOpD5C5eFpZHVS7ysqZBq9cctl+oXd22VOZLcpHBF9yFpcISnJuC92yVgwqtjb+6RuY0/FUpdx292h8KpMl/nxb3orXfG88G+cMX/sZnSafofw+HPg6OP94V90nDTy/ZqOHmFCK2Xr3WNPKYl0qIstyLq310n79GpeXBYtlzsf7FZ5qvu3hoWuln7Jdjh78PgR33hvu1ScX95hURWVodgzgTpTXdsbvg4JSF4ryh8IV1TLW2MflMgOV5jFoibEeRz/20BLJ4iwpYdgJELYOh8mcM7vKuEw7v3L5UWfrBe3KWflMj7eVi2zHFtqpHSX08WyXyjS40TFel9b/YEJZilwkmhOGyplPfKTpJtjIGZS2S+MYS4Qc/rLo1gr3CiU42FP+0QkV9ULvmIQQsVh4Qv0oWeG6/zVkqS+soK56apWDooPLJdhK5fCkzKkM9tXRWcvkqqonQNiGty2xSZqz2ua3j/BnmfD3PmhMuD8IlTeWd8RljoomWg+AvqrK+V74hb1aUmBFetg++tF+vuP2Pgu71E6P5HjNSWlkLFTkkopmXB5b3EXXmmk5P1i03S3HR4Bjw8vOF93NBPRPHKXiI+g+ZJKLyfnZ5f+B+3SReA8ZkS1j7FuWCkBCSZ9/mR4n7K8f3isgOS19XPyely59MykqT8l0ECI7ZPFSEB+FYPCSI4IhsyZkt4eCgkXcov6CkVTby415AkZHxevrtOSpJZJMBg9US4sLsE0hy3VKydaZly4X67WKyuZ0fI3GAQEd5vrpQAjzQTzrEC+PVWeNmJbPzBBrkgf2+9WLspRlyI126QFjR/3S4uyZnZ4Zw8kDmf0Z6bBRDRuLcwXLvSdYUdv0xSI4bMF6vMjfIsDcLDO6D/3PA++qXAv0dJyL7LNRvgtYOgYkakteOPMvyl4wZ3Xb6X9gzfWIEc9+Tl8NxIeZ9m7ZeIXfezuHmLuALfLpb2OhuqwvOTLi/slZuD1KTIG45058N8uRiOXCKfqUuXgIhfABFd7w2MBW7ZAjduga8vlwT0FVPgnyNlHjCJ8L4yk+TGw+vC91aDyYqhGtOjdH7o4fgOPyuRnNQXdou7+uJV8Ned4e2CQG09xbpbAhU7JaEorpUyWa/vlTwtkNJHSUgViHgIBEQU7x0iF41kI+5KP4flwK88Vd1fLpLmld+KUo3/nB7w0DAomiZVJqoOkRD8x0fAMSvkODf1lwAPl/xUubj1T5Xw94KpUrnjk31iwW1wLLzioLSKiTgH3+MzuklZs9SARBa6TOgiF8IZWWIZjlsMTxVJVN77++G9/XKhc12qlRZOyoN3PWHkW50IvM9K6l5QLlwDK8ulWa5Bgnv+UAiPDw0nQFdbGOQph/bYCBnP93rLnNTkRfL5HZ0tnwWIS/T+ITB3grSpcffjipQ33yvViLXpug6PypYo2q6eGwoQV3LfOdIh4YSudT5CjPN3Um54/sci1rJ/PmhphVjMZ62OTJj3T1EFkZ5xb46OFK7cgMw3+qn07GBLjdRHTUNee2kvmRM+KU+e+7XDfV4WgtELpOvEp8XyvgWd98kguZZTsyT9Ixo5STDTSWvw4kaUugxKgV3T5fHkLuJ9GJgGc0vhxSgpFtX+N6eFUbFTEooLV4cvKFesl/+PDJOJ+7ca2YkgK0kqiPx2IPwiagEiKZb8Pyy8swdq/ZnOHpKSpOlnqnNl+7I4vO7WAvjA+3yLzBetqZQ5pqd2wT93S7TcleskQrNnsvyIj10uyeUuXvEIIjlOqyslxHvAPJiyUHqOPbUTru0lwQXHL697wbl/WzhU3jlFHt4Oj3gqfHQJyBi6BCIDS0DG/+JuOCYnXMDaUrd7uNsPryIoVvT6SnET19qwiM0qlefZAYlgvG6jHHfqIhg8L5z8HCDsqsxLqhsEURKSG6LkL6QkmZdq4ObNkvIwe5zcgDw+BEanhy251ZXSCNc9l09L4M5BUij6u57aoKt94fmuVebn8xKZbw0SFt6SkAS0gNz0xGJlpeTrBYE7CiSC9o29sS/srqDWWJk3+9bayHUGCaYBsUCjkWzgyZGRLXuiscVR1zeKIO8rWFIOl3aDP22Lvn16K5fm09qYWhuzUxEKSamijVUSnOGvrLGwBCYvkce/GygVLFqbM1bInE9prVwsU4DqKMWK/dy6SVx9Xty6kABvFsGZq6USyF5HRTIDMi94fT8JenjBqWUZQlyMKyZL0Mb2avjaEpl7AgkaGZImguEnzYSFIlZkXgAYnAI/zoei6vC4U4DVkyXcfG0F3LA5/PoTuop1WIs0hXXnZwySMzcgFX7uXFCPyBb3WXaSXLBdXh4FF6wSt1s00oyIYRIwKkNSJrICsDsoYyuYKsE0oZC4zXbWwGPDZc5vTaVYZIvHwxjnOzMqXSzfMisW3E/7S0k111CclClJ/rt8wUNTMyXHL9VIv0CQepl7guJyvn+IzGt+d71E875WHH6tNy3Ce14rJ0uQ0J5qeGwXvLgLviyP/IyGp8l4axCr7sN98KHHyu+VJJVR/rJd5tCmZEpLoiOyJWUhicj5tl/nw00DJNDp6Z1woSOGaUhwi5s3OSpdgreKauUmxh+d6pLr3Gx4u5p/LTuy8IBrTZYeEr2cXGPQ2phKwjCnTFxEpSFpNOknNQnWTBYXoSt0NSFx85UE624PYk18sk8m5N8rlkjJ+thUCUPmwozFkhv0WYn88N2LRrSph38XSSX+Qs+V5ekoIdyXORbD7wvglFUiUGd7cusyjMwzZSdJt+ztNfCHQU7SthWX4owlMHB+WOhAylzNKoXTciOPlxmQ6vcQ7rEW7aIQAtbXyNzbbVvlog7w2kgYnCFBNd/pLSkOLsXB8EXcey20wIM7pCr+syNljnR5uSSAf+qZ+wI4K4bQGcTCqLIiFifkilu32kpCukE6LeQmywW51DpdFg6Vi7/3Hn9Vddji2V4j0aUpRtoFvbEnMphoaXldoQPpwD1tiQTPuJzaTaImC6bBwHSZW/37rkihA8m783JuN2nEevIK+PcO6DtP0jbcmp/em5Gf9oPxWTLG27dIabHsgNwggcyh/mGbCB3Ib6eoVpqsBpHvrPfzvrNAOndsqpSUlh85bZ5qCbeLApk33VUr34sdtbGFpDgIh/rcsf5oYYPM7bVyfIqmHiidi4O7hEt0ebsggyQbH7FEAj0WT5R8KpCqE4/uhEldYN5Eucv/+nK5sL03VnqQ/bcYDsoQV2BWEuyYFrt80U82SqWIjdUSWLHbuZKnIUmxV/eO3L4qJO1xLPLnVvl4bZSEjhskf+/WgeLeq6mRyE6Qi8qc8fDKXmmWWRQEguLiDCFRoFUzIT1JhGtmtlTtj8YZefCCk5f16T4J2LmkpxTqhUgX5K394ZYYYeYhpPrGw8PDeXYPbBMhPLKrRBo+XyRidlxXCWJ5bDh8czkUey50L+yRElS/HywlsAjKe+U/lhviD3JRPTVPxP2zErnQ1gCBkLjflpRLhKdF5ht/sEECI0CiPL+eK4/znRy2jICkcrj7LwlCz1S5wZiaJX351lTIhf2LUrnoG0QMo80xufcXg1Ph0WHhJOlX9kgaRjQGZ8C5AXjemcd63jMfeva68OMU5PjuYX/cF67qG25H5R57XBeJPr7d57BKRjwHm6sjE+K9w8pIkuCeLp70iOlZ0g/RW9jbX+cyhXD5Mz//KYl8vtp393L7QGmrZVpZ7dSyUzoVyQHYMhWqZ9btG1cSlB9MyEb+MPcG5Yte7IjSc0XiXltWIcJV7NyZlofk9TUWzloplfvLguImcsPj394bLjeVjkRG9nTuSm8ZIO1u/rQNZi4KH3+XU5LLIEnBLr3T5Bwu6y3u2Ed3yjEy5oj7C+QO/chlMrm/Y3o4ItM9vVrE9TM0TaxAYyTwwiUHuTBPzpTIu4Pmi/DevQ0uXyfh6muiXKUe3gnf6Rm+2/ZPp1zTJyx0IJazAeaUiHD0TZF+Z0srnJzAFZFC5/KUE+W63blS7/MJQv+USMsqBMxxAhy214Yv/K/vlyr/EF6WgURhBpH35NEd0lh1YZm410As5O+uD+/fIm7PZ3bDMcvEsvy8VNIrXJKQHMI54+GnfeW8+yWHrSkQ16M3IOrK3nBkl3Dagjs+EBdjr1SJPI3FlAwpOeaeW8/kcLTxu2Ph9DwR2LsGwlPDJcncO95xGXKTuKJSAkyi8Zt+MtdbS7hRK4jL0ksSYaHrnixWfhWR35EAstx7qIcHy/8in2V8/zbJhWxt1LJTEoav58IrB0kgwCM74Kt10jTz0WFwcl5YaL7WNTxPcnY3qbP4drFEn72+V0pouXfLfyyUCiJBpKDy8bnhC8726TL/MjhNhHZyloThB4HZZfDMLnGvjVroCCmwuFSiNt/YK+7DL0vlAv63Hc5cXyjy4l5mYUG5XDzmloXvTt2+ZqlITtaLRbL/rybA+d0lybsWmJErHckzAxL8stYnbJ9HmcMDSfx+dAT8ZZgEj/xzRziCbv0kGOJLYbhnsMzBzSsLNxe9NR+u3CCPSz0i5tbJBLGKu38ZtlpCiDi7F9uHh8DJTgUT18LbHs1PTN25Rq8BsTcoc4EGeHpXeM6osCbSQvPuY5dzc1RUKykjFc45/KSX1K2cmi1imgTUGnhlBBzvjHU/MG4RfC1LPAA/6gvbgpHzY96P4uGdYqH/a4fs0/+5bKqWIt+5STKX+HQRfG0ZvDdGLNZXnOCrW7fA0AWRrzXAN7tJ9CXIjc+IULgknsttheHxud9XS7gwtov3+1lUK4K3tzbSfZ9m6nY4+NFGuLwH/GN3eFyZAZm/bQtU7JROQ62F/9sgCcIPDRUrr4vvLvWkPKnq/9By+TH9axf8dpC4pC5YLYJ20wApZQQyD/Jgobi/Kmvhexsl+XxSJlgTFjqQNjvX9xNhG5cpeXbjFoUvlm/uFRfbi47ld8EauKpX2DVngDsLw/ubXerU0DQSVVeD1KYclSrzSF76JsOG8vBdcSWw6WAp3fWUc/EIIG65v+0MHy8AZH8pibwX+Szh+thUA3mzxdK6a1Bku5gjl8pYq4JSVWN0hjQD/WFfmS9yecp3keydLJ/FdzyuuY2e8zwkS6JCj82Rmw+Ax3fDqTmwqELOZ1N13YhPlwcHwdBMce0uj9L47chsuQAfmiVlwD4vkX3trpX3xq1pGQ2vxfmFR0X/OAQO6yrVZd6KMgf7WakIxk831R2397n7Hbqkt7jTv70O9taEE8mDThBOrxTpcO5ywnKp/nJlL3hyd9156UGpcs73bw+7Jd/0zYv+bwy+52d3E1dzQxTVymfjdTfnJklUcg1h0Sx3jnFrvuTaTcuS7+qaaE36WgGNxtRozE7Dp/vhqKVyAR+aLj+Sx4aJG9BLTUhajuyplYod3+4tle8fduZuyg8JV1HJ/TJ8EXAxQMl0mZA/wZnbG54WTjN4o1iqbWQFZO7DvWi5xxo2L1zlBGSMD+8QN5s3Ku2+wTJXNS9KftImn/XinydKRoohf31ZOMKye0A2LAuGm3x6Iy29eMtCNYQbAdocsowk22+slhsI75CSkMol4zLgLzvh7FwYkyWh9JZwhf9r+0gX9tVV8tydA01y/k/IlKjQ3CQJHCr1RJiC5LKdkAPJs6NHnLoEiC2oIFb1T/MlneO+wfL+nrpC3ifXgs00Eh1a4ERu5qeG8xFdLukhTWBd7KFSbu0axxr+dKyUcasGZmSKFbq5JjKy1fseBpGbjZpQ+PN/bRSc2l1yFRfGmMv14o0M9YqXl77JEj3pT4Q/PVes1Vn7JWH/pBV1g7VmjZNOEwCHL5EbDm939eai0ZhKQjAxUyqA9E+TcmAWyTe7s0DmivY7v9K9tSJ0ASRFAaQ6fn6qVFfxlguLFqFpkYvy1MVSYzM3SYTvx5vk7/19sk1ZCJ4cEU42di8mGz1C1ydJXKqzSyOFLhkJhIkWTDIgSp84iwjd44MkodoCF62RFjAuRSGx/Lw3yt/IlRJRfhoSurEe68EfNBIvrmUJTq3IdfDwtuiJ1ZkBETqAF4slNy/ThEUnhHRlWF0Fh2dJsvmQNAmeCDrr3TJvxcFw8nUAKSR9Xnc4qiv0nlu/0IGsd6NNo1GJBH8sLZfyZF+UyLl6XbXGiPt5V1A+601T4PuenMwA8jmPdeY9eyZJcepXPZbUrzaHP6fZ5SJ0ECl07lf5fwE2ISmK4A7/jFVSt3PWOPh8jMwxHhslWd3Fq6FH+L6HacAx2XIMv9ABzC+ViNr9IbFkve/z8U5ZskOXwkPbJABrnuNG3VItpd5aG7Xs1LLrlLy9V+4cIXwHmmqcWoIBqRE4txTuGBTZ3fvtIjjJKZI7a5z8+Fzyk6XIbwBYMQnGOJGSfVMkgs3LwFSZpzq7h5TW+mAf3DcIllVKtRX3jj0nEHaBueO8qAc8PEzy0qYujhSeFOCQbIk0dDktNxyu3j1JcqB21kKPJMkna4gzu0laxTs+95Wbo+ZeAdKdubLBqTC6i0SoulZhbpK4cCdkSlUQbwBDBjI/lkbsiLyWJAAcnyOpJXNKpasEwMGZMM9X6cYAlTOkhujeWinG7ZIFxJiyjGBShkS7JhGeS+uZJG71K/vA//WVMTyzOyzOfuvwoHRxVXtJNvDgEHntvqDs/+EhcO1G2Xc9tQkAKZLwjTw4bYXMNUcjALw/JlxsG8S9eJ7TQeLTGCKT5oyvzCcP07PkBmuT86UdlBp+XDgZ+jnzhcPT4M9Dw79RL9FyOQ1QO7N18+xU7FTsOh2XrZa75rwkSebOCUCxc2UpmS5dol3GzRcBOjwLPp0QrqwP0vl6bEa4k7YB/jwExqfCIV1gbRD+sVNcUad1k0TtIekyR9PLqWX51X44ZCl1cBOKvRjgj4Pg/5yUiSvWSiCJn2/mScKve7FcMA4mLw3vo7G/2C4BaUL7yX4pm+XiDRQB+M8o+Mcumb9cFmMe5exu0qesKcZeMhIJ6I/Gc2nIfRiN2WMlqrQ8BLMmiBvw7sJI1+2X4yX30Pu+Te0iFUrqu1noYqB0Zvj5u3ulur+ffikyX/lz5701zmvLbP2fVbIR6zSeOSs3IMklAJTPCPdMrLWQOzssTinABT0kZWV/UF5/YS/pJD8gBZZHuSs5M0+a6lpE7DI8vyuXmV1EmN050YyAjAMk6riXU380CUnrOXZZ/J9p+TTIaGYVFRW7elCxk7yubTUwMLPhbduTPxVCcU24esexXeG9ceLSPGm53DmnGCma3M350XgbeW6fCq/tgaucUPMTc+Cj/ZH1BidmSDAEwNPD4AJPMAWIO+xPQyRZeWMlHLSw8efRNwUKp8n804lRLp4gwRw7aqUCBshFZWB65J14Bk6YOOHu4AEjiel/iyKifrICka63tiArIG2M7tkMO4hfvC/Mg9XVYlk+vityLikZ6RG4pRp+Pwjy54ZFPA1xNZ+zJnL7G/Phnq11LRcvBvjdIPjdFglA+Uk/SfSPtt2g1Mhgm3iYmCnzq/4IWT9ZiNAlEyl4P+0rPQqvXQcP7WjaDYifc7pJnuK6ysjfBUT/rE7NkxvBPili9R2+JJzKkp8qnRniJdjKlp3O2R3g7KuB1DkwaCFMXtjeo4nN7wokdPnXW8MhxKd0k1D9IenhnmY1Npxv9VJROJcJJIrQexf97j6JqkxCtusaCAsdOPk/vmjPd4sl+OWgebDK5zJLw/c8xg93m3MlHlDPXaxbfmlnUP421UiumDfnqYLwRd0CSyZJL7zHo3Ro8JNC9N5mrU1pSEqK7SU81+klynQlAK/ukzmv/+6rGzQRAm7fKpG3P9oYaa1WAad0l3k7l1qkXJhX6Caki4s4zYTHZIGfbxI39IclcGoUoXMDZfxu7njC3EekyZywy0Cn8kuvFNgzOby83Bmz3wBcUwH/3Al/rUfoTs6puywpxmOAlaWSa+cXuuv61hW6JOQm4Mp1cOpK6DtXhK6fc/Le9zIeWtvuUrE7wPnMU0dvWXns7dobb8DAK6PEXbmqAtJmS2fvh4fBmAy5yJy9WuYVLlgtgjAoVb7oW6okiXh0uuwvK0m6DtTMhIzkuhUuXh4e2cg1KxB+vrEGPvLNgVUB2c44k5AcKO9Fb5ijhtkGfrwexi6OfH20H+NgTxbym8WwtrJui5UBqSJyRbVSvDda9KWfGqT1jZdJbWjZVxOOtPRSSWTJMZfSkAS4+EUFpGpLVkD25U2mdimqkqLZXvzHvayHVOg/Lje2pRlteQj5jLv7PpN1k2LsxMM3u4fdhAb4aKxUlDmne2R9y1hC9mqxVP/xjiuAWIIGmVc8swdc4Ck3lwZM9dzk+G8cllTL++kVqR5JcEt+XQEPIsXL/eN00yXWVYmnpSECwHndIKmV1UjdmOrGpL/T1uTp4XBer9Y/3mf7pKP3Y0Ohj3Mrv7kyHLpdWC1lr4xxcup2SCTdqgqZf3poh0TCBYyUDTskC2ZPgD8WwHXOvMktfeG5YilN9PfhUt1+he/W+JMx8LVceXzVWnhkp8zhLXXuqvfXQs5X8viEHJiWLRaBlz7JcFO+WImv7ZU5KTeVoVuSuHj+NEQq9P9zZ8NBB/HQxUitQ/eX6waGeC2SePGGl0dzUwUQkfZWWclERKm1PKCZRhrFborhAmvsvOWLIyTgZuzC+rf7WR/4/faG99c1ELv0F0iqyeW9Yfi8yHqSA1LCnQAApnWB+WXy/vdOhu/1lbQMgG5AfSlu3ZNE4AMmemSkF//cbDy45cBSgb1TYIetm6zekhikjmhyMwVP5+zqQcWu7XHn0QJIIeDvrA2H5acZ+fE/Ngwu7gVXO0EcqQGpil5YLS1qAkjTTID/6wcTu0DGrEhXTzIiRLcMrHvhARGNQWlwTK6UbPpxX7htAFyyVvqE/bS/FO91SUGizFZ49mOQ+pLX94HfFUrawezSSCF4eGh4ntBPP6CwMW9eA9ycD7c18utsgDHpsYNSol0sk5CqHP+OckXuFoA9raSEqcj3I8OE8+iikR2QmyfXHZyM9Cf84YbGHSuajtQntgZ4ZAgckyeude+c8Yej4egYc7QHpUsgya6a+ISpMXmSDRErn85LUwKjGkMAqJkhkdTNQefslDZlV43UIdzWwK8xBHxvXWT+Wa2VL6Vbmml8F/khDndqS/ZPFUE6NQ+OyJGghP1BCRZJ83ybswNiRbkdw693IiAHpcBhzrIyK1FlD2yXsTy4XUTp33uk0slRSyPdOTVECh3IBeCfu2DCEinhNKtUUgO8XL1eJvABLuoO+Z65uuoAXNE9PHcywOO27GKcqLhGTHz8fmvdeZJYc4fec/BXHOmdHB5TNN3qmxqZ4+dlT6juXFBDxHuK1cj3IZbQuW9fSSiy7UwtMsc1PFXG5p9fjXWsaNR30bfAFRtg6Hw4xhel+9PNUV8CSO+7wjiFLtbYDHB+XpQVDrHS64LAeXn1zzO2lNCNy5AALS8GmJbZfKFrCLXs1LJrcY5aCp/vF2tr7sS66wfOFZECyDEwtStsqZQalT/uJ61aTs0LR2ZtrpK2Nmm+H4PbVdoN83Z5ZxQcnSeiWxKUShbGQFEN5CRLHtyERXXHNSEDLugZDiGPRlcD+xv4yfyin5zfs7vlIuu9cz48W+bKFsY5P+rN02sMY9PEJbSoIpwH1xjSEJFrrPsLYEhqw661WLSkxRILg8yNfV4iAUjuPGxLWy/17e/EHPlePr07XIOzsRybDe/Xk4w9KAk2xZGH2VYY4PPx0pkk2ilXHRJuatzkY6hlp8SLtbCyvOlVM0B6U4WITOb24g1H3mfhi/2wcgr8YwRM6AKndxN34APbRLAGptUVOgh30PYK3am5cFw3cXt+YwWMXiiRnADdUyS3aXwXKR3lt5gWV8h8YH1Wxs/617PS3U+5uFYxInT9Pe/DnJL4hQ6aJnQAy6rCkaWNFTqQOcBoQjc6HU7Jrf+1XqHzWwv1NN0Gmh96Hg8W2FEFRdPgyeFOKTbiuxg2KrqwnnXv7IM/bZei4/XRPyX2MS9qYH69IwmdiwlGF7o003yhawgVOyWCGzeLQByzrOn7eGqkVHfomQyfFNdd/4v+kV+8cls37PjE5fCjDXDpGmIy2hc92D9ZKtE/vlOOu6pCjrM0Skmuk/KkVqOfjVV1l3n5ZRxOgA/3SemkWisW3WaPari7z6R+t1EbXPPjxjuWFZVSG7Sh7Xslydyd/8LWkNXmD3mPl8ZmUaytlvnYk1eJKNXQ8LwVtPy8VUPlKrfWxD7m5etirIiTtr74W+C+LdHXbW2h2pj1oWKnRLC6QqyR9ZWwoQL2NcGnlBaAi9dIFfYjl9ddf8cgcXGCfAG/3zvSX2+tzHGFkMRUl7UVMGWRdOg+dTksKg1bTdkGttXCLVvginVy3Gor+3hmDwyaCws8taEKq6WyvMtYzyROUy9oqciFflyX2F3RXcqJnNcKIBF46YiV0ZEmFxo7FovkBsYTpBLrArRuEsyfEPt1Ob4XNsV6jXcsjaGelnStTrc4LCNvz7366n+2Fm/GUPfh8zXPTmljHhoGdw6Cb+RIqHHuXJnjaixBzxf3/m11858u6xWuVv/PXeEvurViVX5eAr8dCHcOkOVzS6Wj9oIySQx/o1hylIprYc1kuH1Q/RflzdXSqPSadfBKkfSUc7m8J9w8UEK505txAahGhGp5GfwhRpdvL14jMoSEmldS130Yz5AeHAjHZUdf1w6543ETSw+HL4TTVsZ+XVPduxDbom6JANL9DW/SahzcpeHvivd32VQr2ku/Rpb3KvO9ye54i4ORY2sNNEDlAAhQmV8K9xVKMdwb8qW3W0McvEhygEDamJzbQyLwolEdkvmxjZXw9hipzLGrWiysYekS5VhrYeFEmS8rDUqJo2eLxPry5rbVWkj/Qi48WQGJqutqJFftcid8/8iu8LFzVckLwN6QJPXuCTXNIvpOL6mB2caVs1qU1gz3VxIT97fTVhik5+A1m6Kvr5gG6a1YG1MtuwOAaYul2/ALe6SvWzy8dhAMTJGCyDdvkXqD/oohLmsqper/pip43cm76pkKLx0E5/QQActKkohKkB5y/9od7s12UU+pEwkSQPL8KLiyd/gucL+F73nyowKeH6jbZ62oHqHrlwQ/6xf7XP8ep9C18vx5szhQhC6zI01mdnLaUuggnOLy26hS1PqpB9qp/ADjm93Cj0uDToX2JCirkSog3+wuYfr902DTNOkzNWOJbH/mSlg+MVz1xGV0hjTWXFsJFzqJ3o/vkI7Up3WD7dMk2qqr823zT2fdvEUEcec0qYxSVAsPDJG+bdc7d4Fel8uHnrm3eKK2Q8DvCxuuqp/qrI+1T/+4m1KxBMRV2hIupAOR8g7wvrk1PQ+Q+4sWxe0w4iezDaIxVewOAOZNgFf2SM3BbMe6WlUBkxZKAvSySeE+X72SYcf08GvzPN+QvUGJ1Nw6DTI9X8yAgQd8kY1v7A033Ozpc00MTJU5tPwU+NUASbpOQpJwl1XImOaVhiuk1IdXgNxOAX62Oxs1dHFyY3GyA3BhN3goSp1FLxbpkrC2qu5cRH0k0bQSTtHwdpZW6pJpWl4gLR0rgCgaTWmX1J6UW+ku39xyYfWhYncAMClL/rysrhChqLWw3hMpsdt35RycKhFm7sR7EOjypTSsvG9I3WPdsFGqp9wyQIoun94NntwldSanZUn1hFvyYUwXaVvz3bXhpOslTqpAjYWHd8CzDVTvH5EKwzPg7X0i0r/Ihx9vbP6FqCQU22UL4bqBENklIV7qayvTWFTo6qcjWILtQWcSOpfWFDpQsUtISoLiKkup58tzch7cNUiqkh/VVfq0vb0X/uqz0O7bHha6bgHomybW13O74bu94SBfrtvDO6QQ8j93irVTFoSX99StJDIkVZLO5/hCkd0fqSVcMiwW47Ngn9NZfGettHfpmyJVVaK1I2lMju3KesyuigP0AqoorUlxJeTG6vHUAqjYJRgf7oPjl0G/VFg5OdLdCBAKyURwwMCPPEEbz46quy9rwQbDZY9+MwhmZMO5q6Sk11HLZD7u/WIRkq/nylzbQzukq/e6KrEao2nW5mqpc1kf1dRfcumlKEWIt0URqbYoQaUoSvPQCipKo5hXKkK2pbquS/KCVZA0G0bPj/7at/fCUUtEvAD+XQS/2ioRkv8dIy1IuidLF/AQkvj9UTEcv1wqnry8W9IFNlaKyKUauL4vLJ8Mbx0Er3oENTdJAmGg/tygFCNWYDwRyTlJ8Iu+dfuydWuFWzr94ShKy5LZzLSDhtA8uwTLsysLSuX7g5yixi7vFsPJy8WdaIDQobI8FIKRC8TSslbWZwbg9YPgWKf6SZqR5qibq+CurTKnlRUQq+6xHfDDjfWPqVuSRHxuq27aHFND/cMaIlbgiqIoHYeSqZDVUPHUBqgvz07dmAlGlyS4bWDd5VetE6FJAW70fBXWVIb7vLk3VlkBsdZcF+J/x8DRyyLnvFKMHOviXnBvIWz0+Qm97sc9QflrKs0ROlChU5TOQKiWhiuFNwP1xiQw314DpyyXyuoX9xAL7c5B8GtHDN/ZI611pmRKB/CezrchZMPh1Y8Ph5ldYYCnduSAVLG2rt8AhyyK7CjdPRn6BOS1qUhxZkVRlIZIa+VrhboxE8yN6fLANviBU3Xk6l7w0PDI9TdulM7aIG1bdteKtVZYI0WVj8mVEmGuK7QiCJ/uh9sL4LMYPbSSgaWT4KTlsL4JESHn5sLzxY1/naIonZ+aGc1PP6jPjalil4BiN79USoS53r8nnJ5dvy+EX/aXQJS5pbDM053aIFaZG9SSBHwwFnqlhNMLSoNS6iuWRzLDyH6amtukCdKKcuCic3ZKoymslgjKWgsvjYTTe4S7g1+0Jiwog5LFdXBhN5hdBt/tA1euk5w1AxznzNutnwI/3ACv7gkLXbSUgObmn6nQKcqBS3orq5GKXYJgLWytls4EJ+fBn4fAzZvh3DUwpkAq+z+2E8qDsNtRrEWTpEBz5peSh/b2Pul3dUE36JcGf9gmovbj9fDS3vCxsgGvJ1Pz2BRFAcBa0qqryKwqJ6OqgsxK3/96lleaM8maOaXVhqZilyD8fBPcVQin5MFLo2BiprgkQ8DCcvhGHmyaCntq4Kq1kJEE7+2Hp3dEClWZhac9ydoWeGFv5LH8U3YqdIrSgWmGADVmO/cv4Jkaq0xNoyItg/K0TPmfnhn9eXomabWteyVRsevk/GWbVBIpC0po7VelkP2lFFu+sb/0jAtZOD4HzlgBr+yFg9Il6fvJBgodK4rSSnQSAapIzWBvdh6FPfrVv12U5ZWp6YSS4i+Lcs5k6NbwZk1GA1Q6cIDKjmqpQ3lUV6mK4idkIekLeXxkNhyZI3UpH9wm818bpkjHgT21cNhi2OIppVVfGS6lnWjOB+J+oN7/De23o/SGc8fX3uPpRALUkNDEWtdYAWpLyg+GjLSGt6sPDVDphIQsTFoEu2qkDc4tA+pu847Hvbg/CLcOlMLIoZD0Szt9uTRLdKumeFGhawJ+MYn1Jnov3hYxud3XBInMbnWXefcVIPyBhZx17vUpmpCFCDdZc5dZ33Pv8fzjjLVfd707/pBvbP59eMdfG2VbF/943WXe8f1v7D4Bcv9XlJNR7TyvjCIs1c5yr/BUt60FFK8V1JEFqC2pCUFGK+5fxa6deWYX/GqzFGX+ft/IdZUh+c3P2gd7+khNSpc15XDyyvDztAD8qRBOyoOTusHJKyKvHx1G3PwD8QtDNMsk1j6inZj39f71/gu4/zXenAqvuLgXefeiH+08olV58Y/BFbWQb3k8+3LH4Bcc7z5ifcjuuYYIn5crovVt6+J9X0KIAAWrIoWmMoZlU+0IT5Vvu+oGLKLqegTIb9mk+kQkPeN/y4qzcins1o+KdM+2GTFEKy28r8o0R4D836Gmduz1vj7aa6Mta4yV3tAx/a/1jqO9LWqH8lppJ9ZaqBuznd2YXWZDeUjuOmoODS8vC0rX7mOWSt7a6HSYNxHSA7CsHCYsCn9XuyfLNWxXLfRJghk50lanRfFeVOsTJL/QeP8gfLGFSJHxd5v0H8O1kCC+Zl3JvjHHeo1x9ttQOTNve+pYFzz/RSmJ6OLWUkQIZgwLKJpFU12PMPmXN0WA0qJYNrGso0Ysj2oBeW9AvJ+L13IMEv7eBX2v8X5G9d0s+L+H3u90rBudaAnS3u+0+52LZQH79xfrxsY9V//Nmvd7H/A9xvMa742du9z73HvO3uO1sHt82wTok9XwdvWhbswOzIws+GA/jM4Qgbt+I6ypgNklEhnpfi83V0vT1GlZcEmPyO/Zntrw823BOIQuljUUTcBi/WCgbpO4aILlP543Uc+7vf+CEc3qiSVY0e5QXYEJ+raLdn71CZ1f5N3HftGLJs41cQhQteOSq3H+RxOaWP8dYXJFKEKAUtKiu9KiiNL/LKD0flGtJK/F87//6fUIUKz3ujXwfif8VrwXv3XqEk9yp98ijudmyyugxvNX33fYKygQ/bcSax/1na87bv9j/8dWn3cg2n7c1xsi3dYNufnd1/goLweaKXb1oWLXzrw/TtyV6QF4ZLv0gvPid0V+VQqLSyO3+d81zvvj8n6RvR1RvdaJu1PvQWK5AaP9EGo820H4wuF9vfdH57eivK5B/12yuw4a/gFC5IUhlvvSEGkBVXsExv1f7bNwvHND7nqvYMWwjKIJUFVKanTLJTW6RfM/F1xaFNebK0CpdZdXpqYTcpuD1XeB9BLwbOe+7/4bGe97GyT8eXo/v3hdbt7PKNocnv/YeLaFyAut/9yizV96x9aQVRLNTV7lHDPaDQ6+5X683g2/d8B97vc++G+cgp7H0fbfFNx9eq8PsYi2zrUK/dcX//be9yjWbz8AAU0qjx9jzAjgn0APoBi4zFq7vF0HFYPyoHTU7p8qgSbnrYbqWF8QoMwjJJX+H63/h+cNaghGWY/ndf4LhfslDHle6/7Iq5x1SYiwWSJFyX836b0b9R/X/eIHgJAlraSKzJBHOGqc/+UVYWFyBaXGmfup9mwXbc6ougkC5LdsfEJUnJFLYV6/SBdbRiYVKRmUJ2eG54bSMyL2UZGWQSiQFCko7gXb6Trxv/c5mmB4xch9vTewxfs6742Gd/tYQuTu1/2+RNvO7/5z19V6Xu/uy8W9uvgvht59+MXS7x0gynN3H/7AGe/3yr+tS5Lvubv/WO97NEvL+1n4f4P+u1PvGKMJhvem1CtqXkvS77Hw3gRGCwYKUvfz9hNL2Pw3mP4bYj9+z4n/ptP9Xvk9KO730vP5JDVVtOMkocQO+BvwsLX2cWPM2cDfgZntPCZAoiovXA19UuGRoTBwHhQ5AjYy3SN0fred84W1sdw1rggmEXnhCFK/iyaAXGTdoBfv3XoKIkDVVWSW+qLbXEvHFZpqR5C8QuS65NxouFqfy646LGCuCEUIUHIUAfJbQqlhYSrOyKUwt1+Eq60iJYPyTJ/7LTWTimTneYoEMYTcVu7+mwKvBeFeALyWq/u+VVPXinYvQt5t3JsHdxv/vt3lXsvXvdEAsaKDvtdC5J20d1vX6vZfHP3zV96xVzvbuLm93ou633UbzRXmtWBcjG+ZX1C8IuoXKq+VH8tF7f3Moll59bn3vO9LtJuKWMTrxoTI9y/WvqJ5Qdx13uAo/368LtVo83Gue9E7fx1NvGJ5ebyfebT3rb6izd73NJ5pgpAYAK1JwgSoGGN6AauBHtbaWmOMAbYBM6y1G2O9riUCVP5eCMEi2FIKGRZ2AFtToDYE+dlQEYK5+2FxfXd49eG+LmRJq62KFB6vJVPusWRqolg6VeWR4dp+11xjBcgRjQhBSs2IePy/eZ/UzEgBSo1iRaU5ApSUFOkSiRf/3W1L4J0vacj1Fc1qgfgujk3Ff8GO5aLzL/dbMtFcxvG+/35XVrRje7f1p1r4ieUadc8vmifB+P6U8PfWbyXGCqiB+K5N7rYtnC3xUV84ckTz9nGgBKgMAAqttbUA1lprjNkMDAQ2ttZBzef1J6LWOMsnVpQzwxt+3ZgouMa44DyiEbEuJYPizFy25fWtI05R9+H+T/IIUEvgCpLfXQPR50Mac+FqDVHxu19ibeP979KaIhftGH5RqW9uyvsavzXm31dDxPv+QPwBIdFe77VADHUvtipykbjvo9c1Hcsl7HojvN+ZWHO30VzNTcVzg1pbU++WzSaRxA7i+MkaY64DrnOf5+TkNOuAu07uSY99Rf97XpWSGnalxUpE9QhMcXYu21L7xt7O/Z8SwwLynnl9F53GthTwuyDiuXDGQ30pAF5x8wuIXsgaT7Rfg39ZrM+0KZZ1W2F8j/W7Ej+xLPxY75m73Osebcnvhmc/b+6FY1tot1EPlWBuzDVA97Z0Y854vYB1gXCYd0wLyPr+GkNDbh93//WJXTz7iLU/f4pBU3DdT955AL9bqj6LTi9g8RHtffUGcHi3q28fHQlvsEe0eTy/5adEp7FC5+L9PbbG++u4we3Xmr+rA8KNaa3daYxZAFwEPA6cBWysT+hagtmnRn1fFUVRlA5Ewoidw9XA48aYG4H9wKXtPB5FURSlA5BQYmetXUUHSTVQFEVROg71ZUooiqIoSkKgYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsJjrLUNb5XAGGOqgF3N3E0WUNoCw+mo6Pl1fhL9HPX8Oj8tcY49rbVp0VYc8GLXEhhjCqy1+e09jtZCz6/zk+jnqOfX+Wntc1Q3pqIoipLwqNgpiqIoCY+KXctwb3sPoJXR8+v8JPo56vl1flr1HHXOTlEURUl41LJTFEVREh4VO0VRFCXhUbFrBsaYEcaYWcaY1caYr4wxY9p7TM3FGLPRGLPSGLPQ+TvPWd7LGPOOMWaNMWapMebw9h5rPBhj/uyckzXGjPMsj3k+xphMY8wzxpi1zmd7ZvuMvmHqOb+PjDHrPZ/jjz3rOtP5pRtjXnHGudD5zAY76xLlM6zvHBPlc3zXGLPYOYdPjTGTnOVt9xlaa/WviX/AB8BlzuOzgS/ae0wtcE4bgXFRlj8G/Np5PA3YBCS393jjOJ8jgHz/edV3PsDNwOPO4yHAdiCvvc+lkef3EXBKjNd0pvNLB04iHF/wfeDdBPsM6zvHRPkccz2PvwnMb+vPsN3fhM76B/QCij0fjHE+jMHtPbZmnlcssStFqhO4z78Cjmrv8Tb1vOo7H2AZMM2z7nmcm5qO+tdIset05+cZ61RgbSJ+hjHOMeE+R+BSYG5bf4bqxmw6A4BCa20tgJVPYzMwsF1H1TI8ZYxZYox51BjT0xjTHQhYa71l1TbSSc81jvMZiNxhRlvXmbjb+RyfM8YM9SzvzOf3Q+D1BP8Mfwi87nmeEJ+jMeZfxpgtwG+AS9v6M1Sxax7+vA3TLqNoWY6w1k4EpgBFwD+d5Yl2rg2dj61nXWfgYmvtaGAC8Cnwhm99pzs/Y8yNwAjgl86ihPsMo5xjwnyO1tpLrLUDgJuAu93Fvs1a7TNUsWs6W4B8Y0wygDHGINbe5nYdVTOx1m52/tcA9wFfs9YWARhjeno2HUQnPdc4zmczMDjGuk6BtXaL899aax8Ahjp30tAJz88Ycz1wJvANa215In6G/nOExPscAay1/wSOdp+31WeoYtdErLU7gQXARc6is4CN1tqN7TaoZmKM6WKMyfUs+hZyjgAvANc6200D+gCftekAW5b6zse7bghwJPBaO4yxSRhjko0xvT3PzwJ2uAJBJzs/Y8x1yHfxeGttsWdVwnyG0c4xUT5HY0xXY0w/z/MzEK/RHtryM2zvycrO/AeMAr4AVgNzgbHtPaZmns9QRNwWA0uAV3ECboDewLvAGmTi+Mj2Hm+c5/QgUADUIgFE7sR/zPMBugDPAWudz/bs9j6PxpyfM/65zme4CHgfmNhJzy8fcWWtAxY6f18m2GcY9RwT5XNEPF5fec7jPWBSW3+GWi5MURRFSXjUjakoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2ilIPxph+xphfu1Xam7iPo5yuBEe14Lg+MsZ81FL7ay7GmGuMMZdFWe6e+9nN3P/NxpjlxphWu2YZY243xsxvzWMo7Yd+qIpSP/2AW4BJzdjHfGCm8z9RuQa4rDV27CQk3wDcbK0NtcYxHP6AVNe/tBWPobQTKnbKAYcxJqMtj2et3W+tnW2t3d+Wx00g/g/pMPJSax7EWrsPeBL4uVP+T0kgVOyUTofjVrTGmMnGmJeMMfuNMfuMMU/66uy5zWjfMMacaYxZYIypRCw1jDHjjDGvGmP2GmMqncaSl3peexQwx3n6D+eY1hjza882U40xrxlj9jj7WGCMOdc3hjpuTGPM48aYUmPMcGPMW87jLcaYe4wxaU18X1KNMTcZab5bZYzZZYz5Rz3vyYmO267Cec23o+zzcGPMF865bXVcfVc45zPY3R8wFjjS8x5t9O0qxRhzhzGm0Pm83jPGjIrnnIDvAE/7rTpjzC3GmC+d936/cy7f8QuVMeY8I81DtznnusIYc6cxpkuUQz4BjMRTu1FJDJLbewCK0gxeRnpcPYRcbG8HxhhjDrFSyNplCjAaaS2yAShzLrSzgJ1IS5UipM7p48aY3tbauxC34+XAP5zXvunsrwDAGHM08A5S2um7wD7gfOA5Y0ymtfbxBsafgtT6+ztwD9KI9VfOfm5rzBvhzDO9CnwNuMs5t0HArcBHxpip1toKz0smOse8E9gBXAH83Riz1lr7ibPPCcB/kVJNlwLlznleRCRnAC86477GWVbl2+a3wOfOcboCv0da9Yy21gbrObVDgO7Ah1HWDQb+Rrg48AzgfqA/ke/fCOAtpLB5GXAQ8DNgOnCMb5/zkB5rJyPNmZVEob3rpumf/jX2D/g1UkvwXt/yC5zlF3qWbUTqRo70bfsMUAkM8C1/C7kg5jjPpzr7vCzKOFYggpjsW/46UIj06gI4ytnHUZ5tHneWneN77ZvAyjjeg4+AjzzPz3f2d6ZvO3f83/O9JxXAQM+ydETwH/Isex658PfwLAsgNQwtnkbFwFLveDzL3XN/07f8HGf5jAbO8wZnu94NbBdAbt5/BezG6fodZTvjbHeEs98JUbb5DJjd3t9z/WvZP3VjKp2Zp3zPn0eEze+CWmytXe1bdgzwvnVaqHh4HMhEAkpiYowZjlgITznPk90/RDD7IoXC68MS2aQTpAj3oAZeF41TkHmt131jWYgUiD7Kt/1C67RzArDWViIWnPfYRwIfWGt3e7YLIe9zY/FXq1/s/G/oXPsh79Nu/wpjzDGOO3QfEARqEIuuO9DLs91QY8zTxpjtnu0+dlaPjnLMnYh1qCQQ6sZUOjPbvU+stbXGmCLkYudlW5TXdo+xvNCzvj7c1it/cP6i0aOBfZQ7IuOlCrGyGktvIBeojnMsRVG2qQK8wTvdERenn2jLGsJ/PNfN2VCwUAZQY32uTmPMdKRa/kfAlYhruRr4JtL4NMPZLgtpelqJNA1djbhjByABL9GOXxnHuJROhoqd0pnpA2x1nziWTHfqXlijtfYoQqwvP27frTqWhA93/e+IHSW4qoF9tCS7kXM6Mcb6kibss4iwqHvp04R9NZXdQKoxpou1tsyz/HzEQjvFe8NgjPmm7/XHIJ/pUdbajz3b5dZzzG40/PkrnQwVO6UzcyESUOByLvKd/iiO174PnGGM6WetLfQsvwS585/tPI9qgVhrVxlj1iD9xW5swthbmjcQAUiy1n7ZQvv8GDjJGNPDdWU6gTDnRNnWbxW2FCud/8MIuz5BbmBqEbckztgygIt9r3dvdPwBM1fXc8yhyBykkkCo2CmdmTONMbVIxKAbjbmI+OaUbkXmuT40xtyGdE2+EInCu8FKzhVIQ80K4EJjzAokYKPQEcirgbeNMf9B5vq2IlbBaGCKtTaaKLQWzzrjf8sY8yekWWYN0hj0aOBVa+3LjdznHcCpwPvGmDuQ9+G7SFNNAG8qwBLgfGPMecB6oNJau6SpJ+PhI+f/DCLF7k3gOuBpY8zDiEV/PXVFbRawF3jIGHMr8p5ciESj1sEY0x2J3ry/BcaudCA0QEXpzJyJBIm8hAQmvA583Voba97qf1hrVwGHIq7GB4FXgHHA5dbauz3blQPfRi6m7yJ5d1c56z5EwteLkbD294C/Asc5j9sMZ07rNCTE/0wkLeMV4OfIHFSjhcdauwg4HhG5fwEPI5GYf3E22efZ/BbEEnwEEVp/4E2TcAKIPgVO9y3/APlcxjvHugNJf7jTt10RcgNTjiSMP4bcsJwX45CnI4LYlCAcpQOjncqVToeT1H0L0NMbKai0DcaYd5G0g5FtdLyzgOeAQdbarQ1t38xjfQpsttZe2JrHUdoedWMqihITY8y9wAJgC+KivRCx9r7ThsN4CbGofwF8v7UOYow5ApiG1sZMSFTsFEWpjyTERdwHCfZYDlxsrX2yrQZgrbXGmCuB04wxAdt6xaC7A5dYa9e30v6VdkTdmIqiKErCowEqiqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsKjYqcoiqIkPCp2iqIoSsLz//iTtbuxgC2iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 480x560 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Density dot plot displays correlation between protein length and MS score (sum of unique peptides per study)\n",
    "figure(figsize=(6, 7), dpi=80)\n",
    "plt.scatter(x, y,c=z,s=2,cmap=new_cmap)\n",
    "zz = np.polyfit(x, y, 1)\n",
    "p = np.poly1d(zz)\n",
    "p1_y=np.min(p(x))\n",
    "p2_y=np.max(p(x))\n",
    "x_values = [0, 300]\n",
    "y_values = [p1_y, p2_y]\n",
    "plt.plot(x_values, y_values, 'r', linestyle=\"-\",linewidth=1);\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.ylabel(\"MS score\",size=15);\n",
    "plt.xlabel(\"protein length (aa)\",size=15);\n",
    "plt.savefig(\"ms_protlen5.png\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b043353",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select pseudogenic proteins with protein lengths < 300 aa\n",
    "OP_df_altprot_ms0_psdg_lngstorf_len300=OP_df_altprot_ms0_psdg_lngstorf.loc[OP_df_altprot_ms0_psdg_lngstorf['protein length (a.a.)']<300,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b01579d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Percent of pseudogenic proteins with protein length < 300 aa = 0.9671824519639517%'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Percent of pseudogenic proteins with protein length < 300 aa = {len(OP_df_altprot_ms0_psdg_lngstorf_len300)/len(OP_df_altprot_ms0_psdg_lngstorf)}%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87317ec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lera\\AppData\\Local\\Temp\\ipykernel_15516\\3668338568.py:2: RuntimeWarning: divide by zero encountered in log\n",
      "  my_dict3 = {'Canonical proteins': np.log(list(OP_df_refprot_ms0_len300['MS score'])), 'Pseudogenic proteins': list(np.log(list(OP_df_altprot_ms0_psdg_lngstorf_len300['MS score'])))}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot shows MS score of canonical and pseudogenic proteins of lengths <300 aa. Outliers are hidden for canonical proteins\n",
    "my_dict3 = {'Canonical proteins': np.log(list(OP_df_refprot_ms0_len300['MS score'])), 'Pseudogenic proteins': list(np.log(list(OP_df_altprot_ms0_psdg_lngstorf_len300['MS score'])))}\n",
    "my_dict3['Pseudogenic proteins'].sort()\n",
    "my_dict3['Pseudogenic proteins']=my_dict3['Pseudogenic proteins'][8971:] #remove -inf so the data can be plotted\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(my_dict3.values(),labels=my_dict3.keys(),showfliers=False,medianprops = dict(color = \"mediumblue\", linewidth = 1.5)) #outliers are hidden\n",
    "plt.ylabel(\"MS score (log)\",size=15);\n",
    "ax.tick_params(axis='x', labelsize=15)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(6, 7)\n",
    "plt.savefig(\"ms_refalt5.png\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f05aea37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save main objects\n",
    "#OP_df.to_pickle(\"./OP16_df.pkl\")\n",
    "#OP_df_refprot_ms0.to_pickle(\"./OP16_df_refprot_ms0.pkl\")\n",
    "#OP_df_altprot_ms0.to_pickle(\"./OP16_df_altprot_ms0.pkl\")\n",
    "#MART_df.to_pickle(\"./MART95_df.pkl\")\n",
    "#MART_df_psdg.to_pickle(\"./MART95_df_psdg.pkl\")\n",
    "#OP_df_altprot_ms0_psdg.to_pickle(\"./OP16_df_altprot_ms0_psdg.pkl\")\n",
    "#OP_df_refprot_ms0_len300.to_pickle(\"./OP16_df_refprot_ms0_len300.pkl\")\n",
    "#OP_df_altprot_ms0_psdg_lngstorf.to_pickle(\"./OP_df_altprot_ms0_psdg_lngstorf\")\n",
    "#OP_df_altprot_ms0_psdg_lngstorf_len300.to_pickle(\"./OP_df_altprot_ms0_psdg_lngstorf_len300\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b82ad44",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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 },
 "nbformat": 4,
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