schistosome_orthologue_analysis4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statistics\n",
    "from IPython.display import Image, display, clear_output\n",
    "\n",
    "from utils.statistics import cov\n",
    "\n",
    "def dev_from_med_pc(arr: list, el: int):\n",
    "       return (el - np.median(arr)) / el * 100\n",
    "\n",
    "\n",
    "def dev_from_med(arr: list, el: int):\n",
    "       return (el - np.median(arr))\n",
    "\n",
    "def zscore(arr: list, el: int):\n",
    "       return (el - np.mean(arr)) / np.std(arr)\n",
    "\n",
    "\n",
    "def preproc_tsv(path):\n",
    "       df = pd.read_csv(path, delimiter=\"\\t\", header=0, index_col=0)\n",
    "       selected_transcripts = df[\"selected_transcripts\"].str.split(\",\").map(lambda x: list(map(str.strip, x)))\n",
    "       for hog, li in selected_transcripts.items():\n",
    "              idx = li.index(df.loc[hog, \"worst_transcript\"])\n",
    "              df.loc[hog, \"prot_lengt_dev_pc\"] = dev_from_med_pc(list(map(int, df.loc[hog, \"protein_lengths\"].split(\",\"))), int(df.loc[hog, \"protein_lengths\"].split(\",\")[idx]))\n",
    "              df.loc[hog, \"exon_count_dev\"] = dev_from_med(list(map(int, df.loc[hog, \"exon_counts\"].split(\",\"))), int(df.loc[hog, \"exon_counts\"].split(\",\")[idx]))\n",
    "              df.loc[hog, \"prot_lengt_cov\"] = cov(list(map(int, df.loc[hog, \"protein_lengths\"].split(\",\"))))\n",
    "              df.loc[hog, \"exon_count_cov\"] = cov(list(map(int, df.loc[hog, \"exon_counts\"].split(\",\"))))\n",
    "              df.loc[hog, \"prot_lengt_zscore\"] = zscore(list(map(int, df.loc[hog, \"protein_lengths\"].split(\",\"))), int(df.loc[hog, \"protein_lengths\"].split(\",\")[idx]))\n",
    "       return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n",
      "/tmp/ipykernel_108300/1258931625.py:9: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  return (el - np.median(arr)) / el * 100\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selected_transcripts</th>\n",
       "      <th>exon_counts</th>\n",
       "      <th>protein_lengths</th>\n",
       "      <th>gene_counts</th>\n",
       "      <th>transcript_counts</th>\n",
       "      <th>worst_transcript</th>\n",
       "      <th>worst_pair</th>\n",
       "      <th>blast_pident</th>\n",
       "      <th>align_pident</th>\n",
       "      <th>prot_lengt_dev_pc</th>\n",
       "      <th>exon_count_dev</th>\n",
       "      <th>prot_lengt_cov</th>\n",
       "      <th>exon_count_cov</th>\n",
       "      <th>prot_lengt_zscore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOG</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N0.HOG0000000</th>\n",
       "      <td>SBOV2_26620.1, SCUR1_89610.1, SGUI1_74730.1, S...</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 0, 11</td>\n",
       "      <td>1006, 525, 210, 579, 90, 1006, 127, 0, 317</td>\n",
       "      <td>13, 30, 40, 42, 21, 18, 2, 7, 1</td>\n",
       "      <td>13, 30, 40, 42, 21, 18, 2, 7, 1</td>\n",
       "      <td>EWB00_002233</td>\n",
       "      <td>SRDH2_30130.1, EWB00_002233</td>\n",
       "      <td>73.9</td>\n",
       "      <td>4.572565</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.883339</td>\n",
       "      <td>1.695582</td>\n",
       "      <td>-0.313250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004882</th>\n",
       "      <td>SBOV2_71970.1, SCUR1_71990.1, SGUI1_73020.1, S...</td>\n",
       "      <td>7, 9, 7, 7, 7, 9, 7, 0, 7</td>\n",
       "      <td>364, 401, 361, 370, 364, 412, 358, 0, 358</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>SRDH2_72900.1</td>\n",
       "      <td>SRDH2_72900.1, EWB00_004970.2</td>\n",
       "      <td>72.5</td>\n",
       "      <td>85.679612</td>\n",
       "      <td>11.650485</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.379611</td>\n",
       "      <td>0.396863</td>\n",
       "      <td>0.673271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004881</th>\n",
       "      <td>SBOV2_71960.3, SCUR1_71980.1, SGUI1_73010.1, S...</td>\n",
       "      <td>7, 7, 7, 7, 7, 7, 7, 0, 5</td>\n",
       "      <td>357, 326, 320, 334, 351, 320, 321, 0, 287</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>EWB00_004971.1</td>\n",
       "      <td>SBOV2_71960.3, EWB00_004971.1</td>\n",
       "      <td>57.3</td>\n",
       "      <td>63.025210</td>\n",
       "      <td>-11.846690</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.381377</td>\n",
       "      <td>0.390868</td>\n",
       "      <td>-0.035083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004880</th>\n",
       "      <td>SBOV2_71950.1, SCUR1_71970.1, SGUI1_73000.1, S...</td>\n",
       "      <td>3, 3, 3, 3, 3, 3, 3, 0, 3</td>\n",
       "      <td>572, 574, 575, 568, 573, 591, 583, 0, 580</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>SRDH2_72920.1</td>\n",
       "      <td>SHAE2_77160.1, SRDH2_72920.1</td>\n",
       "      <td>74.1</td>\n",
       "      <td>99.323181</td>\n",
       "      <td>2.876481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375238</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.430486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004879</th>\n",
       "      <td>SBOV2_71940.1, SCUR1_71960.1, SGUI1_72990.1, S...</td>\n",
       "      <td>13, 13, 13, 13, 13, 13, 13, 0, 8</td>\n",
       "      <td>1032, 1032, 1031, 1032, 1032, 1036, 1036, 0, 392</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>EWB00_004973</td>\n",
       "      <td>SBOV2_71940.1, EWB00_004973</td>\n",
       "      <td>74.0</td>\n",
       "      <td>39.437984</td>\n",
       "      <td>-163.265306</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.450857</td>\n",
       "      <td>0.404009</td>\n",
       "      <td>-1.263763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            selected_transcripts  \\\n",
       "HOG                                                                \n",
       "N0.HOG0000000  SBOV2_26620.1, SCUR1_89610.1, SGUI1_74730.1, S...   \n",
       "N0.HOG0004882  SBOV2_71970.1, SCUR1_71990.1, SGUI1_73020.1, S...   \n",
       "N0.HOG0004881  SBOV2_71960.3, SCUR1_71980.1, SGUI1_73010.1, S...   \n",
       "N0.HOG0004880  SBOV2_71950.1, SCUR1_71970.1, SGUI1_73000.1, S...   \n",
       "N0.HOG0004879  SBOV2_71940.1, SCUR1_71960.1, SGUI1_72990.1, S...   \n",
       "\n",
       "                                    exon_counts  \\\n",
       "HOG                                               \n",
       "N0.HOG0000000        1, 1, 1, 1, 1, 1, 1, 0, 11   \n",
       "N0.HOG0004882         7, 9, 7, 7, 7, 9, 7, 0, 7   \n",
       "N0.HOG0004881         7, 7, 7, 7, 7, 7, 7, 0, 5   \n",
       "N0.HOG0004880         3, 3, 3, 3, 3, 3, 3, 0, 3   \n",
       "N0.HOG0004879  13, 13, 13, 13, 13, 13, 13, 0, 8   \n",
       "\n",
       "                                                protein_lengths  \\\n",
       "HOG                                                               \n",
       "N0.HOG0000000        1006, 525, 210, 579, 90, 1006, 127, 0, 317   \n",
       "N0.HOG0004882         364, 401, 361, 370, 364, 412, 358, 0, 358   \n",
       "N0.HOG0004881         357, 326, 320, 334, 351, 320, 321, 0, 287   \n",
       "N0.HOG0004880         572, 574, 575, 568, 573, 591, 583, 0, 580   \n",
       "N0.HOG0004879  1032, 1032, 1031, 1032, 1032, 1036, 1036, 0, 392   \n",
       "\n",
       "                                   gene_counts  \\\n",
       "HOG                                              \n",
       "N0.HOG0000000  13, 30, 40, 42, 21, 18, 2, 7, 1   \n",
       "N0.HOG0004882        1, 1, 1, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0004881        1, 1, 1, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0004880        1, 1, 1, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0004879        1, 1, 1, 1, 1, 1, 1, 1, 1   \n",
       "\n",
       "                             transcript_counts worst_transcript  \\\n",
       "HOG                                                               \n",
       "N0.HOG0000000  13, 30, 40, 42, 21, 18, 2, 7, 1     EWB00_002233   \n",
       "N0.HOG0004882        1, 1, 1, 1, 1, 1, 1, 1, 1    SRDH2_72900.1   \n",
       "N0.HOG0004881        1, 1, 1, 1, 1, 1, 1, 1, 1   EWB00_004971.1   \n",
       "N0.HOG0004880        1, 1, 1, 1, 1, 1, 1, 1, 1    SRDH2_72920.1   \n",
       "N0.HOG0004879        1, 1, 1, 1, 1, 1, 1, 1, 1     EWB00_004973   \n",
       "\n",
       "                                  worst_pair  blast_pident  align_pident  \\\n",
       "HOG                                                                        \n",
       "N0.HOG0000000    SRDH2_30130.1, EWB00_002233          73.9      4.572565   \n",
       "N0.HOG0004882  SRDH2_72900.1, EWB00_004970.2          72.5     85.679612   \n",
       "N0.HOG0004881  SBOV2_71960.3, EWB00_004971.1          57.3     63.025210   \n",
       "N0.HOG0004880   SHAE2_77160.1, SRDH2_72920.1          74.1     99.323181   \n",
       "N0.HOG0004879    SBOV2_71940.1, EWB00_004973          74.0     39.437984   \n",
       "\n",
       "               prot_lengt_dev_pc  exon_count_dev  prot_lengt_cov  \\\n",
       "HOG                                                                \n",
       "N0.HOG0000000           0.000000            10.0        0.883339   \n",
       "N0.HOG0004882          11.650485             2.0        0.379611   \n",
       "N0.HOG0004881         -11.846690            -2.0        0.381377   \n",
       "N0.HOG0004880           2.876481             0.0        0.375238   \n",
       "N0.HOG0004879        -163.265306            -5.0        0.450857   \n",
       "\n",
       "               exon_count_cov  prot_lengt_zscore  \n",
       "HOG                                               \n",
       "N0.HOG0000000        1.695582          -0.313250  \n",
       "N0.HOG0004882        0.396863           0.673271  \n",
       "N0.HOG0004881        0.390868          -0.035083  \n",
       "N0.HOG0004880        0.375000           0.430486  \n",
       "N0.HOG0004879        0.404009          -1.263763  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = preproc_tsv(\"data/schistosome_orthogroups/Results_Jul22/table_2024_10_09_16_28_50.tsv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "candidates = df1[(df1[\"align_pident\"] < 50) & (df1[\"blast_pident\"] > 95)]\n",
    "sman_candidates = candidates[candidates[\"worst_transcript\"].str.startswith(\"Smp\")]\n",
    "shae_candidates = candidates[candidates[\"worst_transcript\"].str.startswith(\"SHAE2\")]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/schistosome_orthogroups/shae_candidates.txt\", \"w\") as f:\n",
    "    f.write(\"\\n\".join(shae_candidates.index.to_list()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The impact of filtering for lower align_pident and 95%+ blast_pident is that we see greater protein length deviation for the \"problem\" orthologue, ~~but no impact on exon count deviation~~. This makes sense because if there is lower identity in global alignment between any two sequences, then it stands to reason that their protein lengths will be different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inf\n",
      "inf\n",
      "---\n",
      "7.455357142857143\n",
      "2.3164309359012942\n"
     ]
    }
   ],
   "source": [
    "print(candidates[\"prot_lengt_dev_pc\"].abs().mean())\n",
    "print(df1[\"prot_lengt_dev_pc\"].abs().mean())\n",
    "print(\"---\")\n",
    "print(candidates[\"exon_count_dev\"].abs().mean())\n",
    "print(df1[\"exon_count_dev\"].abs().mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188, 14)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[(df1[\"align_pident\"] < 50) & (df1[\"blast_pident\"] > 95)].shape\n",
    "# df1.shape\n",
    "\n",
    "candidates[(candidates[\"prot_lengt_zscore\"] < -2) ].sort_values(\"prot_lengt_dev_pc\", ascending=False).shape#.hist([\"prot_lengt_dev_pc\", \"exon_count_dev\"])\n",
    "# candidates[candidates[\"zscore\"] > 2.5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N0.HOG0002604\n",
      "SHAE2_102780.1 - 21.0 - 100.0\n",
      "SBOV2_34110.1, SHAE2_102780.1\n",
      "prot length deviation: -376%\n",
      "exon count deviation: -1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for idx, row in shae_candidates.iterrows():\n",
    "    clear_output(wait=False)\n",
    "    hog = row.name\n",
    "    print(hog)\n",
    "    print(\" - \".join((row[\"worst_transcript\"], str(row[\"align_pident\"]), str(row[\"blast_pident\"]))))\n",
    "    print(row[\"worst_pair\"])\n",
    "    print(\"prot length deviation: \" + \"%+d\" % round(row[\"prot_lengt_dev_pc\"]) + \"%\")\n",
    "    print(\"exon count deviation: \" + \"%+d\" % row[\"exon_count_dev\"])\n",
    "    display(Image(f\"plots/Results_Jul22/{hog}.png\", width=600))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/schistosome_orthogroups/sman_candidate_transcript_ids.txt\", \"w\") as f:\n",
    "    f.write(\"\\n\".join(sman_candidates[\"worst_transcript\"].to_list()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_117176/665755333.py:7: 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",
      "  test_df[\"pident_diff\"] = (test_df[\"blast_pident\"] - test_df[\"align_pident\"]).abs()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'r = 0.76')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8/BASEsLZFhwcjAcPHgAAfH19AQBZWVmcfbKysmSPVTdr1izk5ubK/j18+FAHLSeEEEKM04KDKQidd4QTCH3zXjvcW9LHLAMhwMR7hiIiInDr1i3Ottu3b6N+/foAgKCgIPj6+iI2NhatW7cGUDnsde7cOYwfP17uMe3t7WFvb55vNiGEEKLIxfsvMGjjWc6210N88M177cxqSEwekw6Gpk+fjk6dOuHLL7/EO++8g/Pnz+Pbb7/Ft99+CwAQiUSYNm0avvjiCzRu3BhBQUGYM2cO/P39MWDAAMM2nhBCCFGgQsJwPv05nuaXwNvVAR2CPGFtpZuApLisAp2XHkd2YRlne8Ks7vAVO+jknMbGpIOhV155Bfv378esWbOwYMECBAUFYfXq1Rg2bJhsn08++QSFhYUYO3YscnJy0LlzZ8TExMDBwTLeYEIIIaYlJjkD8w+mICP333p4fmIHzO0bgl4t/LR6rjXHUrHq2G3OtlXvtsJbbepq9TzGzqTrDOkD1RkihBCiLzHJGRi/MwnVb8zSPqGNw9tqJSBKeZKH3mu5NfrCGnjixw/CdNYDpW9C7t8m3TNECCGEmIsKCcP8gyk1AiEAYKgMiOYfTEGPEF+1A5bSlxV4Y/Vp3H1WyNl+6uPXUM/LSa1jmgMKhgghhBAjcD79OWdorDoGICO3BOfTnyO8oZfg4285k46Ff6Rwti0c0ALvhdUXfCxzY5BgqLS0FOfOncP9+/dRVFSE2rVro02bNggKCjJEcyyKPpPyCCGE8Pc0n9+amXz3k7r7TwEiV5zkbAvxc8OBSRGwtTbpCjtao9dgKC4uDmvWrMHBgwdRXl4OsVgMR0dHPH/+HKWlpWjQoAHGjh2LcePGwdXVVZ9Nswj6TMojhBAijLcrv4k9fPd7WSHBfzbF4/LDHM72I9O7ookP3WOr0ltI2K9fP7z77rsIDAzEkSNHkJ+fj+zsbDx69AhFRUVITU3FZ599htjYWDRp0gRHjx7VV9MsgjQpr3oXbGZuCcbvTEJMcoaBWkYIIQQAOgR5wk/sAEV99SJUfoHtEOSp8lg/JT5Eo0//4gRCM3o1w70lfSgQkkNvPUN9+vTBL7/8Altb+Qu6NWjQAA0aNMCIESOQkpKCjAzLvTlreyhLH0l5hBBCNGNtJcLcviEYvzMJIoDzmS39ZJ7bN0Tp5/TjnGJELDnO2VbH3RGxH3aDg6211ttsLmhqvQr6nlqvi6Gs+LRsDNmcoHK/3WPC1ErKI4QQoj3q3AckEoZR2xNx8vY/nO2/T4pAaF13XTbXaJnU1Prk5GScPHkSFRUViIiIQLt27QzdJINRVF9COpSlbn0JXSXlEUII0b5eLfzQI8SX9wjBoasZmLgribNtwqsN8UmvZvporlkwaDC0fv16LFiwAN26dUN5eTnmzJmDTz75BJ9++qkhm2UQuhzK0nZSHiGEmDJTmFVrbSVS2VP/T34pXll0jLPN1d4G8bO7w8Xe4H0dJkWvV+vhw4cICAiQ/bxu3Tpcv34dtWrVAgDEx8ejX79+FhkM6bK+hDQpLzO3RG6wJQLgyzMpjxBCTJk5zKpljGHqnsv4/coTzvY9Y8MQ1oBSHdSh1wIDUVFRWLNmDaRpSl5eXoiJiUFpaSny8/Nx7Ngx1K5dW59NMhq6HMqSJuUBqDFLgW9SHiFEvgoJQ3xaNg5cfoz4tGxUSCgN01iZw6zaE7eeImjWn5xAaHhYPdxb0ocCIQ3otWcoMTERM2fORMeOHWWry7/33nv473//C5FIhODgYOzYsUOfTTIauh7K6tXCDxuHt63xjcjXxL4REWJMzKGXwVKY+qza3KJytFpwpMb2y5/3gLuTnQFaZF70Ggy5ublhw4YNOHv2LEaOHInIyEicPn0aFRUVqKiogLu7uz6bY1T0MZQlNCmPEKKYriY8EN3Q9VIXuvTZb9ewM+EBZ9u2Ua/gtabeGh/bFPKn9MEgGVadOnXChQsXsHjxYrRp0wYrV65Enz59DNEUo6GN+hJ8z2Nsf+iEmBpT72WwRKY4q/bc3Wy8+y23LErfVv5YO7g1RCLNf6+oZ/Nfes0ZevnyJTZs2IDJkydj+/btmD17Ng4ePIgVK1bg7bffRlZWlj6bY3SkQ1m+Yu5QmK/Ygb5lEmJEhPQyEONgSrNqC0pfovnnMTUCofOfdsfXQ9poLRAy9fwpbRIcDC1YsABFRUU1thcXF2PBggVKnzt69GisW7cOzs7O2LZtG6ZPn44mTZrg+PHj6NWrF8LDw7Fx40ahTTIrvVr44cyMSOweE4Y1g1tj95gwnJkRSYEQIUbEFHsZLJ02l7rQpWUxN9Fi7mEUllXItq0b2gb3lvTRWqCmqmcTqOzZtKTJAIIrUFtbWyMjIwPe3tyxyuzsbHh7e6OiokLBMwF3d3fEx8cjODgYRUVFaNmyJdLS0mSPP336FNOmTcOuXbsEvgzd0XcFakKI8aOq7qZJ2hsCyE9FMGQP/NVHOei3Lo6zrVuT2tg28hVYaXmo1VJ+f3VagZoxJreL7sqVK/D0VB5R+/j44MiRI2jYsCGOHz8OLy/uRfb29jaqQIgQXaGkRdNGtbtMkzHOqi0pr0D3FSfxOKeYs/3MjNdQ18NJJ+ekns2aeAdDHh4eEIlEEIlEaNKkCScgqqioQEFBAcaNG6f0GOvWrcOwYcMQHR0NPz8//PTTT+q3nBATRUmLpk9fEx6I9hnTrNqNf6dhacxNzralg1ri3Vfq6fS8ppQ/pS+8h8l27NgBxhjef/99rF69GmKxWPaYnZ0dAgMDER4ervI4jDE8e/bMZIor0jAZ0SZF07GNoZueCEeBLVHH7ax8vL7qFGdbqwB3/DIuHDbWup/XVCFh6Lz0uMqezTMzIk06oBdy/xacM3Ty5El06tQJtra2GjXSVFAwRLRF+gGkaBaSuXwAWRoa8iR8lVdI0PfrM7iZmc/ZHvthNzSs7aLXthhz/pS26DRnqFu3bpBIJLh9+zaePn0KiUTCebxr165yn9erVy/MmzcPYWFhSo+fn5+PDRs2wMXFBRMnThTaPEKMlikXfSOKUe0uwsfOhPv47LdkzrbP+gTjgy4NDNIeY8yfMiTBwVBCQgKGDh2K+/fvo3qnkkgkUjib7O2338agQYMgFovRt29ftG/fHv7+/nBwcMCLFy+QkpKCM2fO4M8//0SfPn2wfPly9V4RIUaKkhYJsTwPsovQdfkJzrYGtZzx17QusLexNlCrKhlT/pShCQ6Gxo0bh/bt2+PQoUPw8/PjXfxp9OjRGD58OPbt24e9e/fi22+/RW5uLoDKICokJAQ9e/ZEYmIigoODhTaLEKNHSYuEmCZ1hkIrJAzDvktAwl1u4c1DUzqjub9YwbP0j3o2KwnOGXJ2dsaVK1fQqFEjjU+em5uL4uJieHl5GW0OEuUMEW2xlKRFQsyJOknyv116jGl7L3O2TYtqjGlRTXTZVFKNkPu34LT1jh074s6dO2o3riqxWAxfX1+jDYQI0SbpdGwANarg0nRsQoyP0CUrMnNLEDjzECcQ8nS2Q8qCnhQIGTnBw2STJ0/Ghx9+iMzMTLRs2bJGIBMaGqq1xhFibihpkRDTIGQxXisR8L8fLuJICnd9zV/Gh6NdfSq8aQoED5NZWdXsTBKJRLLK1MqW4zBFNExGdIGmYxNi3PguWfHh602w4shtzrZREYGY27e5rppGeNLp1Pr09HS1G0YIqURJi4QYN76zOqsGQjZWIiR93gNuDpT6YWoEB0P169fXRTsIIYQQoyF0VucPozugS2PTWFmB1KRW3e8ffvgBERER8Pf3x/379wEAq1evxoEDB3gfIycnB9999x1mzZqF588rpx4mJSXh8ePH6jSJEEII0RrpYryqBq8Hta2D9MW9KRAycYKDoY0bNyI6Ohq9e/dGTk6OLEfI3d0dq1ev5nWMq1evokmTJli6dCm++uor5OTkAAB+/fVXzJo1S2iTCNG7CglDfFo2Dlx+jPi0bFRIBKXeEUKMXNXZn4os/09LrHinNe96e8R4CQ6Gvv76a2zevBmffvoprK3/rZ7Zvn17XLt2jdcxoqOjMXLkSKSmpsLB4d+uyN69e+PUqVNKnkmI4cUkZ6Dz0uMYsjkBU/dcxpDNCei89HiNabaEENPWq4Ufugd719ju4WSLTcPb4u32ul1dnuiPWgnUbdq0qbHd3t4ehYWFvI6RmJiIb775psb2OnXqIDMzU2iTCNEbRavOS+uOmMPihoQQIOnBCwzccJazrWUdN8x6IxgdG3jR7E8zIzgYCgoKwuXLl2skUsfExPBeRsPe3h55eXk1tt++fRu1a9O4KzFOQuqO0AclIaapuKwCXZYdx7OCMs72+FmR8BM7GqhVRNcEB0PR0dGYOHEiSkpKwBjD+fPnsXv3bixevBjfffcdr2P069cPCxYswE8//QSgsk7RgwcPMGPGDAwaNEhokwjRC1p1nhDztjY2FSuPcmsGrXq3Fd5qU9dALSL6IjgY+uCDD+Do6IjPPvsMRUVFGDp0KPz9/bFmzRoMHjyY1zFWrFiB//znP/D29kZxcTG6deuGzMxMhIeHY9GiRYJfBCH6QKvOE2KeUp7koffa05xtHYM8sWtMGPXyWgjBwRAADBs2DMOGDUNRUREKCgrg7V0zwUwZsViMo0eP4syZM7h69SoKCgrQtm1bREVFCTrOvHnzMH/+fM62pk2b4ubNmwCAkpISfPjhh9izZw9KS0vRs2dPbNiwAT4+PoLOQwhAq84TYm7KXkrQa80p3P2Hm+968uNXUd/L2UCtIoagVjAk5eTkBCcnJ7Wf37lzZ3Tu3FmTJqB58+Y4duyY7Gcbm39f0vTp03Ho0CHs27cPYrEYkyZNwsCBAxEXF6fROYllktYdUbXqfIcgWouIEGO39Uw6FvyRwtm2sH9zvBceaJgGEYPiFQy1adOGdx2FpKQkudvXrl3Lu1FTpkzhva+NjQ18fX1rbM/NzcWWLVuwa9cuREZGAgC2bduG4OBgJCQkICwsjPc5CAH+rTsyfmcSRAAnIKJV5wkxDXf/KUDkipOcbc18XXFwcmfYWqtVh5iYAV7B0IABA2T/LykpwYYNGxASEoLw8HAAQEJCAq5fv44JEyYoPMaqVat4NUgkEgkKhlJTU+Hv7w8HBweEh4dj8eLFqFevHi5evIjy8nLO0FuzZs1Qr149xMfHKwyGSktLUVpaKvtZ3qw3Yrlo1XlCTNPLCgne/iYelx7kcLYfmd4VTXxcDdMoYjR4BUNz586V/f+DDz7AlClTsHDhwhr7PHz4UOExdLHAa8eOHbF9+3Y0bdoUGRkZmD9/Prp06YLk5GRkZmbCzs4O7u7unOf4+PgorWW0ePHiGnlIhFTVq4UfeoT40qrzhJiIfRce4uOfr3K2fdKrKSa82shALSLGRsQYE7SOgFgsxoULF9C4cWPO9tTUVLRv3x65ublabaAQOTk5qF+/PlauXAlHR0eMGjWK08sDAB06dMBrr72GpUuXyj2GvJ6hgIAA5Obmws3NTaftJ4QQoj2Pc4oRseQ4Z5u/2AHHP3oVDrbWCp5FzEVeXh7EYjGv+7fgBGpHR0fExcXVCIbi4uI4S2tUFx0dzfscK1euFNosAJXrozVp0gR37txBjx49UFZWhpycHE7vUFZWltwcIyl7e3vY29urdX5CCCGGJ5EwvL8jEX/f+oez/cDECLQKcDdMo4hRExwMTZs2DePHj0dSUhI6dOgAADh37hy2bt2KOXPmKHzepUuXOD8nJSXh5cuXaNq0KYDK6tPW1tZo166d0CbJFBQUIC0tDe+99x7atWsHW1tbxMbGygo53rp1Cw8ePJDlOhFCjF+FhNGQJOHtz2sZmPAjdyLP+FcbYkavZgZqETEFgoOhmTNnokGDBlizZg127twJAAgODsa2bdvwzjvvKHzeiRMnZP9fuXIlXF1dsWPHDnh4eAAAXrx4gVGjRqFLly682/LRRx+hb9++qF+/Pp48eYK5c+fC2toaQ4YMgVgsxujRoxEdHQ1PT0+4ublh8uTJCA8Pp5lkxKjRzf9fMckZNZLV/YwsWZ3eL350fZ3+yS/FK4uOcbY521nj3KdRcLHXqIoMsQCCc4a0oU6dOjhy5AiaN2/O2Z6cnIzXX38dT5484XWcwYMH49SpU8jOzkbt2rXRuXNnLFq0CA0bNgTwb9HF3bt3c4ouKhsmq07ImCMhmjKFm7++KFoUV3r7NIZFcen94keX14kxhml7L+PAZe59Y8/YMIQ1oGVxLJmQ+7dBgiFXV1ccPHgQr776Kmf7iRMn0K9fP+Tn5+u7SQpRMET0xRRu/vpSIWHovPS4wrXgpAUuz8yINFgvDL1f/OjyOp28/Q9GbD3P2Ta0Yz18+VZLtY5HzIvWE6g9PT1x+/Zt1KpVCx4eHkoLMD5//lzl8d566y2MGjUKK1as4OQdffzxxxg4cCCfJhFiViokDPMPpsitbM1QeeOYfzAFPUJ8LWIIxtgXxaX3ix9dXafconK0WnCkxvbLn/eAu5Od2u0llotXMLRq1Sq4ulYWpVq9erXGJ920aRM++ugjDB06FOXl5ZUNsbHB6NGjsXz5co2PT4ipMfabv74Z+6K49H7xo4vrNOe3ZPyQcJ+zbdvIV/BaM2FrZBJSFa9gaMSIEXL/ry4nJyds2LABy5cvR1paGgCgYcOGcHamhfGIZTL2m7++GfuiuPR+8aPN63Q+/Tne+Saes61vK3+sHdya93JRhCiiVop9RUUF9u/fjxs3bgAAQkJC0L9/f84iqXxkZGQgIyMDXbt2haOjIxhj9EtNLJKx3/z1zdgXxaX3ix9tXKfC0pcI+zIW+aUvOdvPz+4ObzfLvr5EewSvSnf9+nU0adIEI0aMwP79+7F//36MGDECjRs3RnJyMq9jZGdno3v37mjSpAl69+6NjIwMAMDo0aPx4YcfCm0SISZPevNX9FVAhMrZN4a6+fNRIWGIT8vGgcuPEZ+WjQqJ+nMzpIviAqhxTYxhUVxzeL/0QdPrtPzwTTSfe5gTCK0b2gb3lvShQIholeBg6IMPPkDz5s3x6NEjJCUlISkpCQ8fPkRoaCjGjh3L6xjTp0+Hra0tHjx4ACcnJ9n2d999FzExMUKbRIjJM/abvyoxyRnovPQ4hmxOwNQ9lzFkcwI6Lz2OmOQMtY8pXRTXV8y96fmKHQw+U8vU3y99Ufc6XXuUi8CZh7D+RJpsW5fGtXD3y954M9Rfhy0mlkrw1HpHR0dcuHBBbo2gV155BcXFxSqP4evri8OHD6NVq1ZwdXXFlStX0KBBA9y9exehoaEoKCgQ9ip0iKbWE30yxbo1up5ibsxFDU3x/TIEvteppLwC3VecxOMc7n3kzIzXUNfDCYQIodO1yZo0aYKsrKwawdDTp0/RqBG/FYALCws5PUJSz58/p3XBiEXr1cIPPUJ8jfbmX50+pphbW4mMdkaWqb1fhsLnOm06mYYlf93kPG/JwJYY3KGevptLLJDgYGjx4sWYMmUK5s2bJ1vWIiEhAQsWLMDSpUuRl5cn21dRJNalSxd8//33WLhwIQBAJBJBIpFg2bJleO2119R5HYSYDWO++VdHU8xN6/0yJEXXKTUrHz1WneJsaxXgjl/GhcPGWnAmByFqERwMvfnmmwCAd955RzbzSzrS1rdvX9nPIpEIFRUVco+xbNkydO/eHRcuXEBZWRk++eQTXL9+Hc+fP0dcXJxaL4QQon80xZyoq7xCgv7r4pCSkcfZfiy6Gxp5uxioVcRSCQ6Gqi64qq4WLVrg9u3b+Prrr+Hq6oqCggIMHDgQEydOhJ8fjbMTYipoijlRx86E+/jsN+7s48/6BOODLg0M1CJi6QyyNpkpoQRqQhSTriGmqh6QIdcQI8bjQXYRui7nfqEO9HLC4eldYW9jbaBWEXMl5P5tsAHZ06dPY/jw4ejUqRMeP34MAPjhhx9w5swZQzWJECIQTTEnfEgkDEO+TagRCP0xuTP+/vg1CoSIwRkkGPrll1/Qs2dPODo6IikpCaWlpQCA3NxcfPnll4ZoEiFETcZcD4gY3oHLj9Fg9p+Iv5st2zale2PcW9IHLeqIDdgyQv5lkGGyNm3aYPr06fjvf//LqTN06dIlvPHGG8jMzNR3kxSiYTJC+DHmekDqMLfXo29ZeSXo+GUsZ5unsx3OzHgNTnZqrQRFiCA6rTOkDbdu3ULXrl1rbBeLxcjJydF/gwhRgm6K/JjTFHNTLKZoLL+njDGM35mEmOvcL7U/jwtH+0DLXp6EGC+DBEO+vr64c+cOAgMDOdvPnDmDBg1oNgExHqZ4UySaUVRROzO3BON3Jhnl0J82f081CaqOpmRhzPcXONtGRQRibt/mCp5BiHEQHAxlZWXho48+QmxsLJ4+fYrqo2yKagtVNWbMGEydOhVbt26FSCTCkydPEB8fj48++ghz5swR2iRCdMIUb4pEM/qoqK1t2vw9VTeoel5YhrYLj3K2WVuJkDSnB8SOtrxfCyGGIjgYGjlyJB48eIA5c+bAz89PVnhRiJkzZ0IikaB79+4oKipC165dYW9vj48++giTJ08WfDxCtM0Ub4pEc6ZWUVubv6fqBlUzfr6KvRcecrb9MLoDujSuLei1EGJIgoOhM2fO4PTp02jdurXaJxWJRPj000/x8ccf486dOygoKEBISAhcXKjqKDEOpnZTJNphahW1tfV7qk5QdfbOMwz97hxn30Ft6+Krt0PV+pJMiCEJDoYCAgJqDI2py87ODiEhIVo5FjENxpLkqYqp3RSJdphaRW1t/Z4KCapa1HFDu4XHUFYh4exz4bMo1HKhhbaJaRIcDK1evRozZ87EN998UyMBmq/CwkIsWbJElnckkXD/qO7evavWcYlxM6VkZFO7KRLt6BDkCT+xg8qK2h2CjGNWlLZ+T/kGVZtPp+H4zX842755rx16Nvfl9XxlTOWLEjFPgoOhd999F0VFRWjYsCGcnJxga8tNjnv+/LnKY3zwwQc4efIk3nvvPbXzjohpMbVkZFO7KRLtkFbUHr8zCSKA894bY0Vtbf2e8g2qqgZCPUJ88O177bTy+W1KX5SIeRJcdHHHjh1KHx8xYoTKY7i7u+PQoUOIiIgQcmqDoKKLmpOuX6WoG95Y16+SBnCA/JuisQVwRHtM6easjd9TVWvMVRc/KxJ+Yke12ludoi9K9HdGNCXk/m2QCtRBQUH4888/ERwcrO9TC0bBkObi07IxZHOCyv12jwkzumRkU7opEu0ypWEbbfyeKgqqqlr5TisMbFtX0+bKmOoXJWIatF6BOi8vT3agvLw8pfvyCRgWLlyIzz//HDt27ICTkxOfJhATZsrJyL1a+KFHiK/J3BSJ9phSRW1t/J5K15j77LdkPCso4zzW2NsFMdO6av33nmZtEmPBKxjy8PBARkYGvL294e7uLneMmDEGkUjEq+jiihUrkJaWBh8fHwQGBtbIO0pKSuLZfGIKTD0Z2ZRuisRyafp7WvZSguWHb9UIhGI/7IaGtXVT9sSUvygR88IrGDp+/Dg8PSsT8E6cOKHxSQcMGKDxMYjpoGRkQozb9rh0zDuYwtm2sH9zvBceqNPzmvoXJWI+DJIzZEooZ0g7KBmZEOOT/qwQr331N2dbM19XHJzcGbbWVjo/v6rEbcoZIpoQcv/W/W87Ifg3H8FXzP2G5yt2oECIyFRIGOLTsnHg8mPEp2WjQkLf1XShQsIwaOPZGoHQ4WldETOtq14CIeDfUgbAv1+MpIyxlAExX9QzpAL1DGmXKc3QIfpFM/f0Y9+Fh/j456ucbR/3bIqJrzUyUIvM672nzzjjYfRT600JBUPE3BjjhzXVmtG9JznF6LTkOGebn9gBJz56FQ621gZq1b+M8fdSKHMK6swBBUNaRMEQMSfG+GFNtWZ0SyJhGL0jESducZfRODAxAq0C3A3TKDNEAb3x0XnO0MuXL3Hs2DF88803yM/PBwA8efIEBQUF6hyOELNmLHkw0g/r6kGHdEmUmOQMg7RLSK0ZIsxf1zLQYPafnEBoXLeGuLekDwVCWlQhYZh/MEVuErh02/yDKZQDZ8QEr012//599OrVCw8ePEBpaSl69OgBV1dXLF26FKWlpdi0aZPc50VHR/M+x8qVK4U2ixCjZCw9Mao+rEWo/LDuEeKr994XvjVk4u48M+khFH36J78Uryw6xtnmZGeN859GwcVe8Mc+UYGKR5o+wX8VU6dORfv27XHlyhV4ef37pr711lsYM2aMwuddunSJ83NSUhJevnyJpk2bAgBu374Na2trtGvXTmiTiAUyhfwCY1qclu+H9fa4dNRytdfrNeVbQ2bdiTuy/xt6aM9YMcYQ/dMV7L/0mLPdGJe6MSdUPNL0CQ6GTp8+jbNnz8LOzo6zPTAwEI8fP1bwLG6xxpUrV8LV1RU7duyAh4cHAODFixcYNWoUunTpIrRJxMIYS2+LMsbWE8P3Q3jhoRuy/+vrmqoqyimPIQJKY3fy9j8YsfU8Z9uQDvWweGBLA7XItAn5wkXFI02f4JwhiUQid8mNR48ewdXVldcxVqxYgcWLF8sCIaByyY8vvvgCK1asENokmSVLlkAkEmHatGmybSUlJZg4cSK8vLzg4uKCQYMGISsrS+1zmCpjyVvRlLHmvVRnbHkw6nwI6+uaKqs1owjlYfwrt6gcgTMP1QiELs3pQYHQ/xP6+ReTnIHOS49jyOYETN1zGUM2J6Dz0uMK/xakAb2i318RKr9cUJV94yU4GHr99dexevVq2c8ikQgFBQWYO3cuevfuzesYeXl5+Oeff2ps/+eff2QJ2UIlJibim2++QWhoKGf79OnTcfDgQezbtw8nT57EkydPMHDgQLXOYaqE/mEbK1NKUjS2bnNVH9bysP//N3v/NZS9lOioZZUUFeVUhhKrgc8PJKPVgiOcbVtHtse9JX3g4Wyn4FmWRejnnzpfuKh4pOkTHAytWLECcXFxCAkJQUlJCYYOHSobIlu6dCmvY7z11lsYNWoUfv31Vzx69AiPHj3CL7/8gtGjR6sVqBQUFGDYsGHYvHkzp7cpNzcXW7ZswcqVKxEZGYl27dph27ZtOHv2LBISEgSfxxSZSk8KH8bW26KMsXWbq9P7IvW8sBxhi2N1/rvSq4UfzsyIxO4xYVgzuDUmvdaQ1/OkAaW59H7ykXjvOQJnHsL38fdl2/qE+iF9cW9ENvMxYMuMi9DPP02+cFGVfdMmOGeobt26uHLlCvbu3YsrV66goKAAo0ePxrBhw+Do6MjrGJs2bcJHH32EoUOHory8vLIhNjYYPXo0li9fLrRJmDhxIvr06YOoqCh88cUXsu0XL15EeXk5oqKiZNuaNWuGevXqIT4+HmFhYTWOVVpaitLSUtnPeXl5gttjLIwtb0VTxtbboowxLk4r/bCunm/Fx/PCMr3k6FRdeT0+LRvrTqSpfI63q4NJ5JFpQ2HpS4QtjkV+yUvO9vOzu8PbjfJRqlLn80/TWWG9WvihR4iv0U/uIDWpNcfSxsYGw4YNw7Bhw9Q6qZOTEzZs2IDly5cjLa3yw65hw4ZwdnYWfKw9e/YgKSkJiYmJNR7LzMyEnZ0d3N3dOdt9fHyQmZkp93iLFy/G/PnzBbfDGJnbdE9j621RRtoTM35nEkSQvzitIbrNq39YP8sv5SRNq6LP4JlvQPmisBQTd10yill7urTiyC18ffwOZ9vXQ9qgbyt/A7XIuKnz+aeNL1xVA3piOgQPky1evBhbt26tsX3r1q28h8mknJ2dERoaitDQULUCoYcPH2Lq1Kn48ccf4eCgnRvgrFmzkJubK/v38OFDrRzXEEypJ4UPU0tSNNZuc+mHdf/WdTAyIoh3LpG+hyH55GHM6ROChYdumEQembqSH+cicOYhTiDUpXEt3P2yNwVCSqjz+WdKX7iIdgnuGfrmm2+wa9euGtubN2+OwYMHY8aMGSqPUVhYiCVLliA2NhZPnz6FRMJNzrx79y6vtly8eBFPnz5F27ZtZdsqKipw6tQprFu3DocPH0ZZWRlycnI4vUNZWVnw9fWVe0x7e3vY29vzOr+xM7c/bG31tuizRpGxd5tXvaZ86TN4VjS05/v/Q2BiRzuz6v2sqqS8AlErT+LRi2LO9tOfvIYATycDtcp0qPP5Z4zD20Q/BAdDmZmZ8POr+Y22du3ayMjgl2D5wQcf4OTJk3jvvffg5+cHkUi9G0P37t1x7do1zrZRo0ahWbNmmDFjBgICAmBra4vY2FgMGjQIAHDr1i08ePAA4eHhap3TlJjjH7aqm6Oq3hZD5JYYe7e59JrO3n8NzwvLVe6v7+BZWUB54LLi2mZVmUrvp9S3p9Lw5Z83OduWDGyJwR3qGahFpkedzz9jHd4muic4GAoICEBcXByCgoI42+Pi4uDvz6/L9q+//sKhQ4cQEREh9PQcrq6uaNGiBWebs7MzvLy8ZNtHjx6N6OhoeHp6ws3NDZMnT0Z4eLjc5GlzY65/2Or2thhTRWhj06uFHyKb+SBscSyeF5bJ3ceQwbOigNLcej/vPM1H1MpTnG2hdcX4dXwn2FirtZSk1plC9XdA/c8/Tb9wGQNTeY+MieBgaMyYMZg2bRrKy8sRGRkJAIiNjcUnn3yCDz/8kNcxPDw84Ompnw/UVatWwcrKCoMGDUJpaSl69uyJDRs26OXcxsAc/rDlEdrbYm4z63TBzsYKX77VQjZkZgrBs7n0fpZXSDBgfRyuP+HOXj0W3Q2NvF042wx5ozO1WXvqfv4Z+/C2Mqb2HhkLEWNMUGYhYwwzZ87E2rVrUVZW+Q3SwcEBM2bMwOeff87rGDt37sSBAwewY8cOODkZ99h3Xl4exGIxcnNz4ebmZujmqM3SvynEp2VjyGbVtaVoDSfT+zCV9vgB8gM4Y+/x23XuAWbv5w73f9o7GGO6NqixryHfG0U9q6ZwnS3l88+U3yNdEHL/FhwMSRUUFODGjRtwdHRE48aNBSUdt2nTBmlpaWCMITAwELa2tpzHk5L4J3PqmrkEQ5buwOXHmLrnssr91gxujf6t6+i+QUbO1G4ephbAAcDD50XosuwEZ1t9Lyccmd4V9jbWNfY35I2uQsLQeelxhcnq0h64MzMiFf6emNrvlKnRxntkboTcv9WqMwQALi4ueOWVV9R67oABA9Q9LSFqMbfcEl0z9qTv6kxpWEMiYRi+5RzOpmVztv8xuTNa1BHLfY6hh3k1rVlmisGqqTG3unL6JjgY0sa0+Llz5wo9LSEaMZfcEnOgqx4CUwjg5PVQTuneGNE9mih9nqFvdJrULKOJC/phbnXl9E1wMKStafGE6JOpzKwz96EEQ/YQGPLaZuWVoOOXsZxtHk62iJsZCSc71R/Dhr7RqduzaqgeLXP/O5KHer81IzgY0sa0+IqKCqxatQo//fQTHjx4IEvElnr+3PALbRLzY+wz68x5KKFCwrDueCpWHUut8Zg+eggMdW0ZY5jwYxL+SuYu//PzuHC0D+TfC2noG526PauG6NEy578jZaj3WzOCC1doY1r8/PnzsXLlSrz77rvIzc1FdHQ0Bg4cCCsrK8ybN0+jYxOiTPWV0XePCcOZGZEG/5AUurq2KYlJzkDEkuNyAyFA98tm6OraVkgY4tOyceDyY8SnZddo+7GULATN+pMTCI3sFIh7S/oICoQAwy9Fw2dpFHk9q/ru0TLnvyNV1H2PSCXBs8m0MS2+YcOGWLt2Lfr06QNXV1dcvnxZti0hIUHuch+GQrPJiK6Z8ywQRfkiimi7tIGurq2y3oeOQV5os/Ao9zwi4PLnr8PF3kbt4RtjKCEgtNdFnyUtzPnvSAhL7RmTR6ezyVasWIG0tDT4+PioPS0+MzMTLVu2BFA5Ky03NxcA8Oabb2LOnDlCm0SISTN0cqyuKMsXUUTbOS+6uLbKEoLHyVnj7fv3O6Brk9oa36SMYZhX6Kw9fQ7dmOvfkVCmNLPSmAgOhrQxLb5u3brIyMhAvXr10LBhQxw5cgRt27ZFYmKi2SySSghfhk6O1RVVNyd5tJ3zou1rqyohuKqBbetgxdutIBKJtDajyhhudEJm7elz4oK5/h2pwxRmVhobwcGQNqbFv/XWW4iNjUXHjh0xefJkDB8+HFu2bMGDBw8wffp0jY9PiCkxdHKsrgi56egquVPb15ZvgLdpeDv0auELQPszqkztRqevHi1z/Tsi+qFW0cWcnBz8/PPPSEtLw8cffwxPT08kJSXBx8cHdeqort67ZMkS2f/fffdd1K9fH2fPnkXjxo3Rt29fdZpEiMkSMpRgSlOGhd50dJHcqe1hGr4BXmJ6tiwYouEb/fRo0WwqognBwdDVq1cRFRUFsViMe/fuYcyYMfD09MSvv/6KBw8e4PvvvxfciLCwMItYRZ4YL0MGGXyHEo6mZJpUYqSqm5OUr5s95vVrrpPXoO1hGr4B3v7LjzG7T+Vxafimkq57tEyllhgxToKn1kdHR2PkyJFITU2Fg8O/Hwy9e/fGqVOntNo4QvQhJjkDnZcex5DNCZi65zKGbE5A56XH9ToNVzqU4Cvm3mx9xQ7YOLwtAJjclGFlU32lpkc1QdzM7joN5lRdW77nLi6rwJQ9l3jt+7ywHOfTK+ul0fCN/mjrvSaWR/DUerFYjKSkJDRs2BCurq64cuUKGjRogPv376Np06YoKTGvbzc0td68GXqV5+o9Uu3qe+Di/RecHioAJj1l2Fim+mrS+7fueCq+OnJb0Pmki/5Kp3yrGr4x1vfPFJnScDLRHZ1Orbe3t0deXl6N7bdv30bt2rWFHo4QgzH04pfKgoT+rf/NvYtPyzbpnBNjmAEFKB6mUXbjvJmZh16rT3P2b+rjiltZ+SrPJ+3poeEb/TO1JHNieIKDoX79+mHBggX46aefAAAikQgPHjzAjBkzMGjQIKXPPXXqFDp16gQbG7XytgnRKkMmtgqZam0OOSfGenNSFJB+2rsZ1sTeQerTAs7+f3/0KgI8nXj11FVN1DWGGkGEEMXUKrr4n//8B97e3iguLka3bt2QmZmJ8PBwLFq0SOlzX3vtNWRkZMDb21vtBhOiLYYKMoT2SFHOiW4oCkgzckswafdlzrb5/ZpjRKdA2c/Snh6Af0+PtnrIaAiIEO0THAyJxWIcPXoUcXFxuHLlCgoKCtC2bVtERUWpfK7A9CRCdMpQQYbQHimaMqx9fKtjN/FxwR+Tu8DOhjvXRN2eHk17yIwl/4oQcyMoGCovL4ejoyMuX76MiIgItVauF4noGwzhR9ffgLUVZAhtJ9+eprg7z2TH0lfOiaX0OvAtnji/X4sagZCUvnOhtFXFmhBSk6BgyNbWFvXq1UNFRYXaJxw5cqTKJTd+/fVXtY9PjJeQG60+vgFrI8hQp518e5rWnbiDX5IeyY6l65wTS+p10NYQqSY9PUL+Hgyd7E+IuRM8TPbpp59i9uzZ+OGHH+DpKbxb3tXVFY6OjoKfR0ybkButPr8BaxJkqNtOvsUI5R1LVz0RQl6LOfQeWfPsodZVHpbQwJOqWBOiW4LrDLVp0wZ37txBeXk56tevD2dnZ87jylatt7KyQmZmpkklUFOdIc0JqeUjrcmi75o6Qm/wmrZTek2Amgt8Cj2WpoS8FlOrgl0dYwwf7LiA2JtPle6ny2uuTm2rA5cfY+qeyyqPLa1tRAjRcZ0hTVatp3whyyO0e99Q34CFDndo2k5FPVLqHEtTfF/LuuOpWH0s1WRzVmKSMzBuZ80va/qs/aPucBfNKCREt/S6aj3NJjNNmgyLCA0aTKWmjjbaKR32WnX0FtadSFN5rLg7/+hkaIrva9kWd88kc1aeFZSi/RfHONscba1x/tPuiLvzTK+1f9QNomlGISG6pddV60+cOKFWnhExHE2TaoUGDabyDVhb7bS2EiGiUW1ewVDVfbQ5NMX3teQUlyt8TB85K0KDcsYYon+6gv2XHnO27xrTEZ0a1gKg/xlh6gbRVMWaEN0SvFDr1atX0aRJEyxduhRfffUVcnJyAFTOAJs1a5bS59rZ2SEmJoaz7fvvv0dQUBC8vb0xduxYlJaWCm0S0RFpboMmi4MKDRqk34AVfaSLUBkIGPobsDbbqepY8qh6DyokDPFp2Thw+THi07JRIVHcK8vntbg72fJql6567IQupnvq9j8ImvUnJxAa0iEA95b0kQVCgP6TwTUJomkRUkJ0R3DPkHTV+mXLlsHV1VW2vXfv3hg6dKjS5y5YsACvvvoq3nzzTQDAtWvXMHr0aIwcORLBwcFYvnw5/P39MW/ePKHNIlqmram8Qrv3TeUbsDbbqexYiih7D4T25knPLy+fRnquUZ2CsOqY6oVKddFjJ2SmW25xOVrNP1LjGJfm9ICHs12N4+o7GVzT4S5jWeeNEHMjuGcoMTER//vf/2psr1OnDjIzM5U+9/Lly+jevbvs5z179qBjx47YvHkzoqOjsXbtWtmaZ8SwhOQ2KCO90QKo0fOgKGgwlW/A2mynomMpI+890EZvnjyNvV0M0mOnKigHKgPCCgnDvN+v1wiEtoxoj3tL+sgNhHRxnVRR5+9B3jHCG3qhf+s6CG/oRYEQIVqg11XrX7x4AR8fH9nPJ0+exBtvvCH7+ZVXXsHDhw+FNonogDYTmdWp5WMq34C12c7qx0rNKsC6E3dUPk/6Hqjbmyd9niIiAAsPpWBOn2BM3HVJrz12fIPyhrP/5Gzv09IP64a2kTuD1dAFDGnRVkKMj15Xrffx8UF6ejoCAgJQVlaGpKQkzJ8/X/Z4fn4+bG355SYQ3dJ2IrM6QYOxrnRenTbbWfVY8WnZvIIh6Xug7kwlvs/zcLbX+01cnRyk87O7w9tN8e+lMRQwNJVgnxBLoddV63v37o2ZM2di6dKl+O233+Dk5IQuXbrIHr969SoaNmwo/FUQrdPFVF5TCW6MhdD3QN3ePCHP69+6jk5u4ooSmYXkIPEtOGgs5Rvo74EQ46HXVesXLlyIgQMHolu3bnBxccGOHTtgZ/fvWP7WrVvx+uuvC20S0QFTSWQ2Z0LfA3V784Q+T9s3cWWJzD1CfFUuXWJnY4Xr83rCVsGCqtWZSvkGS2AOS7sQ88BrOQ5PT0/cvn0btWrVwvvvv481a9ZwZpIJlZubCxcXF1hbW3O2P3/+HK6urkY1VGbpy3FY0uKdxorveyBdVkNVT1L1JSbUfZ428FmaAoDCmW4AsElgsrohXy/5F322EF0Tcv/mFQy5uLjg6tWraNCgAaytrZGZmakyWVooxhhiYmKwZcsW/Pzzz1o9tiYsPRgCdPftjb4V8sf3Wila80zZuleaPE8TfNdEG9kpEIv/ulnjcU1unIZ4veRf6qzPRohQWg+GevTogaysLLRr1w47duzAu+++q3Dl+a1btwpqbHp6OrZu3Yrt27fjn3/+QVRUFP744w9Bx9AlCoZ0g74V6o6611bf70l8WjaGbE4Q9JygWs6YHNkIfmJHjYNn+h00DEMtxkwsj9YXat25cydWrVqFtLTK5QByc3NRUqJ+cmFpaSl+/vlnbNmyBWfOnEFFRQW++uorjB49mgIOCyCkiJ6+mUNvlbozlYx1aQqpY9Fd0chb/eH56mhGl2EYw2w+QqrjFQz5+PhgyZIlAICgoCD88MMP8PIS/kt68eJFbNmyBbt370ajRo3w3nvvYffu3ahbty569uxJgZAFMHSNF2XMqadA3SRnfc5w4pugPKxjPSx6q6VO2kAzuvTPWGbzEVIVr+kXnp6eePbsGQDgtdde48wAE6Jjx46wt7dHQkICEhMTMWXKFE4RRqE2btyI0NBQuLm5wc3NDeHh4fjrr79kj5eUlGDixInw8vKCi4sLBg0ahKysLLXPRzSnrcrW2maoisSWjM+abL5u9ljQv4Xe2kR0j2bzEWPEKxgqKyuTVZ3esWOH2kNk3bt3x5YtW7BgwQLExMSAR7qSUnXr1sWSJUtw8eJFXLhwAZGRkejfvz+uX78OAJg+fToOHjyIffv24eTJk3jy5AkGDhyo0TmJZozxW6GQJR+I9lhbiTDnzWCFU+ZFAOb1a07DVmbGVBZjJpaF1zBZeHg4BgwYgHbt2oExhilTpqiVQH348GE8fPgQ27Ztw/jx41FcXIx3330XAOSWzVelb9++nJ8XLVqEjRs3IiEhAXXr1sWWLVuwa9cuREZGAgC2bduG4OBgJCQkICwsTPD5iOaM8VuhueUwmEre0+9XnmDK7ktyHzPV4UlzoOvfH6phRoyR4ARqkUikUQJ1QEAAPv/8c3z++ec4evQotm3bBhsbG/Tv3x//+c9/8J///Adt27YVfNyKigrs27cPhYWFCA8Px8WLF1FeXs4pBtmsWTPUq1cP8fHxCoOh0tJSlJaWyn6Wtw4bUZ8uKltryhh7q9RlCnlPT/NK0OHLWM42saMtVr/bGnkl5UYdwJk7ff3+0PpsxNjwmlpfVVBQEC5cuKBWArUiL168wM6dO7F161ZcvXoVFRUVvJ977do1hIeHo6SkBC4uLti1axd69+6NXbt2YdSoUZzABgA6dOiA1157DUuXLpV7vHnz5nHWS5OyxKn1uvqGqGmNF03bVf35EsYw7LtzKp+3e0yYUfcMqVu7RV89SYwxTNyVhD+vZXK27xsXjlcCaUjE0AxR+8dUejGJadJ6nSF9SkpKEtQzVFZWhgcPHiA3Nxc///wzvvvuO5w8eRKXL19WKxiS1zMUEBBgccGQrr8hGqoWjrzn+7rZo+SlBLlF5SZbkVho7RbpTehYSib2X36M54Xlsn110RMQeyMLo3dc4GwbEV4f8yk52ihQ7R9ijrReZ2jt2rUYO3YsHBwcsHbtWqX7TpkyhX9Lq7l69SrCwsJQVlbG+zl2dnZo1KgRAKBdu3ZITEzEmjVr8O6776KsrAw5OTlwd3eX7Z+VlQVfX1+Fx7O3t4e9vb3ar0FbDPmNSdE3xIzcEozbmSRb/kCTNqpT40XT+kSKnp+VVyrbZqo5DELynnKLy2oEhFUpup7qvN8vCsvQZuHRGtuvzH0dYkfjWXbH0plb3hwhQvEKhlatWoVhw4bBwcEBq1atUrifSCTSKBhijAkaIpNHIpGgtLQU7dq1g62tLWJjYzFo0CAAwK1bt/DgwQOEh4drdA5dM2Teh7KZVVIf/nQFEgnDwkM3NGqjkBovmtYn4vN8sZMtHGyskZnHzWGY0ycEYkc7HLj82Gi78vnmMx1LycTWuHtK319511Od38kZv1zF3sSHnG073u+Abk20u5QP0Zw55c0Rog5ewVB6errc/xvarFmz8MYbb6BevXrIz8/Hrl278Pfff+Pw4cMQi8UYPXo0oqOj4enpCTc3N0yePBnh4eFGPZPM0NWZVX1DBIDCsgpM2FVzFlDVNmq7sq+m31z5PD+nqBw/jm4LKyuRrN0vCkux8JBxJyQD/Gff7b/8WGkgJFW9J0nI7+TZtGcYurlmDpavmz2Ky17yaifRL2Oc5UmIPvEKhozV06dP8d///hcZGRkQi8UIDQ3F4cOH0aNHDwCVPVpWVlYYNGgQSktL0bNnT2zYsMHArVbMGKoza/LNT9rGmb9ew7zfUzg9LJoGEJp+c+X7/GeFpejfug6AysB04q5LRrlsSHXSWXrKAj4vZztkF/IfggaAzNxiLDt8i9fvZFHZS7yy6BhKyiVyj5WVV2p0141UMsZZnoToE69gKDo6mvcBV65cqfAxVdPU8/PzeZ8HALZs2aL0cQcHB6xfvx7r168XdFxDqJAwbI9LN/i4vabf/KQ9LEA5Z7umAYSm31yFPt8YAlMhrK1E6NfKD9+cUtxz2zpAjNib/wg67vPCMl6/k9P2XMLBq8qrdBvjdSOVqPYPsXS8gqFLl7hDIklJSXj58iWaNm0KALh9+zasra3Rrl07pcdxd3dXWlyRMaZW8UVTJy8fQxldjtt3CPKEk601iso1y92qTtWNUFVyrqbfXIU+39QSSiskDL9fUR6MXHqYw/t40uvh6cJvMoGqQEjK2K4b+RfV/iGWjFcwdOLECdn/V65cCVdXV+zYsQMeHh4AKusEjRo1Cl26dOF9HFJJUY6QMur23vCZDWRtJULvln74OemRWudQRtGNkE9yrqbfXIU+3xQSSqu+n8/yS1UG088Ly+HpbIcXhWW8ft/m9q1MHBdiXt8QzDuYonI/SsQ1TurM8iTEHAjOGVqxYgWOHDkiC4QAwMPDA1988QVef/11fPjhhwqf261bN/Vaaab4zNyqSpNxeyGzgb4c2BK/XHoEXVWgqnojFJIwruk3VyHPN/aEUqG9iVIDWvtjW9y9GgFhVVV/Lw5eeQKRCCp/F5b9JxTvtA9AfFo2r3ZQIq7xEjLLkxBzITgYysvLwz//1Mw7+OeffwTn/ACVQ2MnTpxAcXExOnXqxAmyzB2fmVtSmozbC52hZmdjhbFdgpTmn2hCk7wcPt9clfWA8f3mq4+EUnXrNKnTmyjVI8QXHYI8awRSXs526N/aX/a4tZUIi/9M4fU7sGFoG/QO9QdAibiEENMkOBh66623MGrUKKxYsQIdOnQAAJw7dw4ff/yxyhXhc3JyMHXqVCQlJSEsLAwrVqxA7969cfbsWQCAt7c3jhw5gtDQUDVeimmpkDDE3XnGe391x+3VTQSe1TsEALD5dDq0tVi7tvJyrK1E6BDkKQskzqc/l93A+Q65qfrmW3VYTRFNEkrVrSUltDexKk9nW9l1UhUQ/nn1icpASARgfZVACKBEXEKIaRIcDG3atAkfffQRhg4divLyyhlDNjY2GD16NJYvX670uR999BHi4+MxYsQIHDx4EL169QJjDPHx8bCyssInn3yCTz/9FAcPHlTv1ZgIoUMcc/oEY2REkFo3EE0SgWf1DsGHrzfDJz9fwW+Xnwg+d1XazMtRFEj0a+WHb0+la20qfK8WfhjbNahGQGglAsZ0CVI7oVSTWlJCehOre6t1Hdm1VxYQVkgYPjuQrPJ4DICHc80Ea0rEJYSYGsHBkJOTEzZs2IDly5cjLS0NANCwYUM4OzurfO5ff/2FXbt2oVu3bhg5ciQCAgJw/PhxdOzYEQCwdOlS9OvXT2iTTIqQIQ5pT4q6gRCgeSKwnY0V3n2lnsbBkLbycpQFEop6MvhO6a4+bPWisExucMUY8O2pdLSp56G3njopTRKPI4N9eO13Pv05Z60yZRS1hxJxCSGmRO2ii87OzoKHs7KystCkSRMAQJ06deDg4ICAgADZ4/Xq1ZObj2Qu1Bni0HRIQRuJwKryQJRxd7LF+iFtEdbQS+O8HFWBhDKqpnTL622yEsk/rib1cjSdsq9J4vGEHy/i/YggTIpsrDQg/HT/Nd7HVNYeocutUOBECDEUK32eTCKRwNraWvaztbU1p66QudcYEjrEMbar+kMxUtKAQ9GVFaFyiElZQqs0D0S6vxDvtq+LiMa15N7YlB1X3rCaJkNEUvJ6MqS9TdWPrSxXqmrQoun5heyn6v1UJrf4JVYdS0W7L44iJrlmXaBfLj5Cw9l/4u6zQl7H83K200oidExyBjovPY4hmxMwdc9lDNmcgM5Lj8ttIyGE6IJegyEA+O6777B27VqsXbsWL1++xPbt22U/f/fdd/pujl4JHeL4/UoGKjTMXpYGHIp6XgB+vU/SPBBfsbCeCVWvQdFxfcUONXJntFGbpnpPhiYJyeq0iW/PTmpWPuLTsmtcO00CU6mconKM35kkCzYycosROPMQPtx3RbYPn06Zhf1baNx7oygQleZPUUBECNEHva5NVq9ePWzevFn2s6+vL3744Yca+5groUMc2qzU6+5k+//LZPxL7GSLJQNb8u59kuaBrDp6C+tOpPF6Dp/XwDe/RJMhIkVTujXtbRLSpgoJg0TC4O5oi5xi5Tk5606kYd2JNLkzzBQlKPuJHfBO+wCsiU1V2RYGYN7v17HvwiPE3nzKeWz/hE7IyitRmtvmbGcNKw2/SpnakieEEPOl12Do3r17+jyd0VEn90bT3hBlCdu5RfySZKuythIholFt3sEQwO818MkvUTd3SVkPmLrXV2i9HHWLJCqaYaYogPzjKv9E98y8UmTm/RsIje3aALN7B8t+3ji8LWb+eq1GEA0ARWUVGi+6ampLnhBCzJda3+1++OEHREREwN/fH/fv3wcArF69GgcOHNBq48xN1SEOvjTpDeEzBDT/YIrgoThpUMKXtqoNq8oxEgH4X9egGm2TN+SmSduk5x78Sj38cfWJ3OGsqhQNBfEhPaq890kaQPZvXQfh/5+grs7rsbexwrV5r3MCIaCyQKODjbXc5yhrF1+msOQJIcQyCA6GNm7ciOjoaPTu3Rs5OTmoqKhc0NPd3R2rV6/WdvvMjixHxk35TYtPYrMqQr55C8E3qNPGa6hOVY7RrN4hODMjErvHhGHN4NbYPSYMZ2ZEKuy94JOQXH2Ext3JFmInW6w6dltlwi+fgNTFXn7AISXkfRIaqH7WJxi3vngDrg62nO0VEobtcenIzNP+74+UsS95QgixHIKHyb7++mts3rwZAwYMwJIlS2Tb27dvj48++kirjTNX0iGOdcdTsepYzfwObVXq1eU3714t/LBJyTCKLqsNq8oxEjKlWxrYjVNSaXrdkDbwcLbH0/wS3HtWKPc9UzScxScnqaC0gldb+Q43qno9Uu5Otgj2dUOFhHHeI6FDeur23NDSHYQQYyG4Zyg9PR1t2rSpsd3e3h6Fhfym5JLKm9bUqCbYNLytoGEdIXT9zbtXCz9c/KwHpkc1gbsjt2dBW6+hQsIQn5aNA5cfc4aj5A0R6YrV/5/rzVB/7El8KHcfRcNG2hziUfY+Vb1OYkc7bBjaBm6Oyr/r5BSVY9iWc5xeLXWG9NT9/RFaWoEQQnRFcM9QUFAQLl++jPr163O2x8TEIDg4WMGziCK6rNSrj2/elUFdY0yKbKT116Du+l1CVEgY5v1+XeHjVWc0qZPwyzdQ8HS2xYvCcrXeJ3nXycnOGkVl/HqcpL1a64e2xcJD/MsMaOP3h5buIIQYA8HBUHR0NCZOnIiSkhIwxnD+/Hns3r0bixcvFlQnSCKR4M6dO3j69CkkEgnnsa5duwptlkkTMqwj9Lj6WjRT269Bk/W7hFh3/A4y80oVPl41wFFn2JFvQDqnTzAm7rok+H1SdJ2qBkJvtPDFsI71MGnXJblT+qXT2OccSEZ2YRmv16jN3x9dfCGgitaEECEEB0MffPABHB0d8dlnn6GoqAhDhw6Fv78/1qxZg8GDB/M6RkJCAoYOHYr79++DMe7HuEgkkiVlE82Z4jdvTerPVL0J1nKxBxjwrLBU7g0xJjkDq47d5tWmYymZiArx5bVv1d4gvgFprxZ+2GglEvQ+8UnO9na1x7qhbXE+/bnS2kYM4B0IqWqXOrQZTOujR5EQYl5ErHo0IkBRUREKCgrg7e0t6HmtW7dGkyZNMH/+fPj5+dVYhkMsFqvbJK3Ly8uDWCxGbm4u3NzcDN0ctcn7pgzAKL89x6dlY8jmBJX77R4TxrmBqkr89RM7YE6fEHg42yEztxgLD93Ac54BgKezLRb0bYEpey8pXKZD2stzZkZkjevI9wYtL5h7WlCK5wWl8HS2g6/YUfY+xaU+w7At51S2ffeYMDzNL8HUPZd5vVZV5vQJ1mjxYF1S1FMmbam2ehQJIcZPyP1bcM9QZGQkfv31V7i7u8PJyQlOTk6ykw4YMADHjx9XeYzU1FT8/PPPaNSokdDTEzVV/+ZtzN+e1RmOUlZcUiojtwQTdqmeZSXP88JyTNpzSeV+ioaN+A4FSd+nmOQMfLTvitzAzk/sgH6t/PDjOfnJ3NVJz8cHn9wlYw2EqKI1IURdgmeT/f333ygrq/ltuqSkBKdPn+Z1jI4dO+LOnTtCT020xNjXgxI6C07T9cW0QQRgavdG6KFkKI3vDDhVM7oyckvwzal0FJS+5NU2aeDFZ8HeL/q3kP1c/XHAuGd36aquFiHE/PHuGbp69ars/ykpKcjMzJT9XFFRgZiYGNSpU4fXsSZPnowPP/wQmZmZaNmyJWxtudOyQ0ND+TbLLOgz2dNYvj0re81CZ8FpYzV7TTEAq2PvYO+FRxr1rmkzsKt6nXSZu2QsqKI1IURdvIOh1q1bQyQSQSQSITIyssbjjo6O+Prrr3kda9CgQQCA999/X7ZNJBKBMWZxCdQxyRmY93sKp9Kvr5sD5vXTzY3HGNaDUjVEJ3QWnDHd3DSd7abtwK7qdeKbTK/Lcg+6RBWtCSHq4h0MpaengzGGBg0a4Pz586hdu7bsMTs7O3h7e8PaWvmyAlWPRSqDAnmVgjPzSjBuZxI26SDZ09DfnvlOmRcyC86Ybm6a9q5p67q7O9piyaCWNX5/hOYumRKqaE0IURfvYEhaZLF6TSB1VC/YaIkqJAwzf72mdJ9Zv17T+nCVIb89Cx2i43vjVnc1ez6sRMDozkH442oG7+Nr0rumreu+flhbRDSqJfcxUwx0+NBnXS1CiHkRPJvs+++/V/r4f//7X17H+eGHH7Bp0yakp6cjPj4e9evXx+rVqxEUFIT+/fsLbZbJSbibLXdNr6peFJUj4W62wpuaOnT97VlZLpA6Q3R8btzKboJ8Odtbo1DOGmGMAd+dTsfYrkH49lS6oOOr08vTIcgTtV3t8E8+/5o/VUnfv7AG/IMdcypQaIp1tQghhic4GJo6dSrn5/LychQVFcHOzg5OTk68gqGNGzfi888/x7Rp07Bo0SJZjpC7uztWr15tEcFQfFo27/20GQxp8u1Z1U1TVS6QOkN0fG/Uim6CqkivgaJqW9Ieq9+vZGD90DZYeOgG7+PXcrbn3Q4AkEgYRm47r1EgBAjr/TDmEgvqMtWcJ0KI4QgOhl68eFFjW2pqKsaPH4+PP/6Y1zG+/ppWvuffv6D9CePqfHtWddPkkwskdIhO6I26+k2wegXqF4WlNYIZkagyEFK2jpe0x8rD2R5nZkQiIS0bE3clKa3oDADRP13G/P7NVRZVfFZYirSnBVh7nFtuwsXeWu6K9tI6Q79fydCo90NfS54YgrkOBRJCdEOjCtRVXbhwAcOHD8fNmzdV7uvo6IibN2+ifv36cHV1xZUrV9CgQQOkpqYiNDQUxcXF2miSVuiqAjXf6sFTuzfC9B5NFT6uyRAH3+eqquqrqsdEOnRz8uPX0G35CZVDdGdmROJoSqZOKglLX/OxlExsibsn6LlrBrdG/9aV5SOk1wRQHa5KE+FVVciWcnOwQfys7nCwtcb59OfIzCuRW4Fa0/e+89LjKt8zedW0CSHEFOi0ArXCA9nY4MmTJ7z2DQqile/DGnrB3clWZd7Qmtg7CPZzU6u3RhU+3575JD1/diAZzwuVr3uVkVuCi/df8BqiA6CVtcnkBQjWViJ0CPLE9L2Xlb5uear2bEl71+b9fl3pQq8AMPPXa5BIgIm7lFfIllr2n1A421f+aSp7fzTp/TCGEguEWDJzytUzB4KDod9//53zM2MMGRkZWLduHSIiIngdQ1sr35syaysRvhzQktfyEPJu/Poa4uBz01QWCFX1NL8E/VvXUTlEF5+WrdaNmm9wuO54KqeukyqKksp7tfCDs50N3tt6Xunzc4rK8dmBZF6BEN9p+Zp+kBq6xAIhlswcc/VMneBgaMCAAZyfRSIRateujcjISKxYsYLXMbSx8r058HC247Vf9Ru/qt4aQHtVpLV5M5T2rKhKcNXm2mTVg8PKlepTebdZVVLyuXR+ifB8F4Tl0yOjjQ9SKlBIiGGYc66eKRMcDGmjzhAADBs2DMOGDUNRkXor35sDIYFG1X35VCnW1hCHNm6G8npWlA3xaHNtsqrDapHNfDD/YIqgtqtOStZNt7ai3w1tfZBSgUJC9M9YlkMiNQleqFVbXr58iWPHjuGHH36Ao6MjAODJkycoKCgwVJP0TkigUXVfvkHU0ZQq68dJGOLTsnHg8mPEp2WjQsIvb17VAp+qqDPdm++ionzXJpP2tuw4m857Wry7oy1+/KAjzsyIVBpc6CqfRt7vBt8eQT7vrbTEAmCai7ISYopoMWHjxatnKDo6mvcBV65cqXKf+/fvo1evXnjw4AFKS0vRo0cPuLq6YunSpSgtLcWmTZt4n8+UdQjyhK+bvcoEXL9q39D5BlEHLj/Bp31CcDQlU+1hFU0LGqpT7E5Xa5MJGR5bMqil3PpO1afGSyQMTnbWSqfmC6GsR0bbSc9UoJAQ/aJcPePFKxi6dOkSr4OJRPy+RU6dOhXt27fHlStX4OX174f2W2+9hTFjxvA6BgAsXrwYv/76K27evAlHR0d06tQJS5cuRdOm/05FLykpwYcffog9e/agtLQUPXv2xIYNG+Dj48P7PLpibSXCvH7N5a5PVlX1b+gdgjzh6WyrMnE5u7AM647fwepjtzUaVlG3oCEAfPWfVohoLLxopKJzih1tMSoiED1CfGXb+AaHfAOW6VFNeM/e42ts1yBsPlW5Jp+igFJVj4wuPkipQCEh+kO5esaLVzB04sQJrZ709OnTOHv2LOzsuAnEgYGBePz4Me/jnDx5EhMnTsQrr7yCly9fYvbs2Xj99deRkpICZ2dnAMD06dNx6NAh7Nu3D2KxGJMmTcLAgQMRFxen1dekrl4t/LBpeFvM/PVajWn2Hk62WDyw5mKb1lYivNW6Dq86Odvi0rUyPl39ppmalY91J9JUnv9ZofJeLz7nXHf8DrbFpSOnuBw5xeVYdSwVexIfynovtLk2ma+bPSZFNqqxXVGujiot67jh90mdIRKJ0Laeh9JgSlWPjK4+SKlAISH6Qbl6xkujOkOPHj0CANStW1fQ8yQSiWwJjurHc3V15X2cmJgYzs/bt2+Ht7c3Ll68iK5duyI3NxdbtmzBrl27EBkZCQDYtm0bgoODkZCQgLCwMEHt1hXpTT8hLRvxd58BqLw5hTXwUhikRIX48gqGlFVJFjqsUvWmGZ+WzSsY0vQbztGUTF49W5quTQZUfhDN69dcbu0iRbk6ypz+5DUEeDrJflZVIVtVjwx9kBJi2mgxYeMlOIFaIpFgwYIFEIvFqF+/PurXrw93d3csXLiQ90yz119/HatXr5b9LBKJUFBQgLlz56J3795CmySTm5sLAPD0rLwZXLx4EeXl5YiKipLt06xZM9SrVw/x8fFqn0cbqic0A0BE41r4qGczfNSzKSIa1eJ1Y1SWZOzuaMurLdWHVfgkWwtNclaHkIRh6bCar5gbfPG9Bu6OtgqHDPnM3qtu95gwTiAkJQ0o+7eug4hGtRDRuBb6t66D8IaKA9+qz6WkZ0JMm6LPKl+xA02rNyDBPUOffvoptmzZgiVLlsiKLJ45cwbz5s1DSUkJFi1apPIYK1asQM+ePRESEoKSkhIMHToUqampqFWrFnbv3i38VaAySJs2bRoiIiLQokULAEBmZibs7Ozg7u7O2dfHxweZmZlyjgKUlpaitPTfoZ28vDy12qOMNurE8PmGMSoikFfScNXeG75t08c3HKEJw/LyXySMYdh3qpc9WT+srcIFcdVJZtRVAiQlPRNi+ihXz/gIDoZ27NiB7777Dv369ZNtCw0NRZ06dTBhwgRewVDdunVx5coV7NmzB1evXkVBQQFGjx6NYcOGyabZCzVx4kQkJyfjzJkzaj1favHixZg/f75Gx1BGmwW3VN0Ye4T4Yk/iQ97DKkLbpusbM9+A4lhKpmz4rnr+S4WE8RpaCmugeJhQnaE+XSZA0gcpIaaPcvWMi+Bg6Pnz52jWrFmN7c2aNcPz5/xrI9jY2GD48OFCTy/XpEmT8Mcff+DUqVOc/CVfX1+UlZUhJyeH0zuUlZUFX19fOUcCZs2axSklkJeXh4CAAK20UxcFt1TdGPn23qjbNl3emPkGFFvi7uGVIE+5wZc2erBeCEgC11feDn2QEkKI9gjOGWrVqhXWrVtXY/u6devQqlUrXseoV68e/vvf/2LLli24e/eu0CbIMMYwadIk7N+/H8ePH0dQUBDn8Xbt2sHW1haxsbGybbdu3cKDBw8QHh4u95j29vZwc3Pj/NMWXRXcqpqHUj33hM/4dIWEYXuc8oKEytqm7PyaEFLwUVmxQU3G6CskDHN/v86rvZS3Qwghpklwz9CyZcvQp08fHDt2TBZQxMfH4+HDh/jzzz95HePLL7/EqVOnsHTpUowZMwZ16tRBt27d0K1bN7z66qto3Lgxr+NMnDgRu3btwoEDB+Dq6irLAxKLxXB0dIRYLMbo0aMRHR0NT09PuLm5YfLkyQgPDzfITDJDFdxS1nsjtHaOPouBSXt1VNVhAlTPilOnB6tCwtBn7Wn8U8BvXTFlw4OaLqxKK1wTQojuiBhjgmciP3nyBOvXr8fNmzcBAMHBwZgwYQL8/f0FNyAjIwMnT57EH3/8gb179yqcdi+PoiKP27Ztw8iRIwH8W3Rx9+7dnKKLiobJqsvLy4NYLEZubq7GvUTxadkYsjlB5X4/ftBRYTKvNqlTO2f3mDC9D88sOHgdW3mUEVgzuDX6t66jlXP+mvQI0T9d4b3/pNcaYXqPJnIDFE0T5mmFa0IIEU7I/VutYEgbioqKcObMGfz99984ceIELl26hODgYLz66qtYtWqVIZoklzaDoQoJQ+elx1UWB/R1s8e8fs11eqOTtoVvj5A0F+bMjEi990jwDSJ3jwlDhyBPjXpQMnKLEb74uOA2KgoSFQWc0hapGqbT9PnEfFFvISHKCbl/Cx4mi4mJgYuLCzp37gwAWL9+PTZv3oyQkBCsX78eHh4eKo/RqVMnTvAzc+ZMdO3alddzTRnfYZ+svFLZ7C1dJScLqZ0jPducPiEG+fDlW2zwRWFZjQCPbw8KYwxjvr+IYzeyBLVNWcK0pgnztMI1UYR6CwnRLsEJ1B9//LGs9s61a9cQHR2N3r17Iz09nfeCrjdv3oSzszOaNWuGZs2aITg42OwDISGkN79Zv15DxJJYDNmcgKl7LmPI5gR0XnocMckZGp9DSO6Pr9gBY7sGYeGhFJ20RRVlxQaByuv1ZqgfJu5KqhHgScsCKGtnTHImgmb9qVYgBChOmE5Iy9YoYZ5WuCbySHsL1fldJ4TIJzgYSk9PR0hI5Y3pl19+Qd++ffHll19i/fr1+Ouvv3gdIzs7G8ePH0dYWBgOHz6MiIgI1KlTB0OHDsXmzZuFNslkSL/p88EAvCgqr7GivbY+8PhOW5/TJxhz+gTj21M1Z5sJaQufqtbKKJoRJrXljOI12AD5s82yC0oROPMQxu28KKgtUspmo8UkZ2DiLtWJ30BlYFohYYhLfYavDt/EV4dvIe7OM2TmFvN+PrEMQqqyE0L4EzxMZmdnh6KiIgDAsWPH8N///hdA5RIYfKs1i0QihIaGIjQ0FJMnT8bFixexbt06/Pjjj9i7d6+gletNiTrLOlSnaHikQsIErW3Gd+jpvfBAdFt+gvdQjbw8hqMpmWp36Ze9lOCH+Hu4/7wI9T2dMLNXM0zde7nGfso++6tXqmaM4aN9V/FL0iPOfp/2DsaiP28obQ9QmSwd0aiWwmFCoYnp954Vot0XRzkL9a47cQcu9vz+PGmFa8shtCo7IYQfwcFQ586dER0djYiICJw/fx579+4FANy+fZv3gq1JSUn4+++/8ffff+PMmTPIz89Hy5YtMXnyZHTr1k1ok0wG32/6qlT/wItJzqix6v26E3fg7mSLJXJWvQf4FyO8eP8F7w/f3OKyGkGPu5Mtp11SqipuV0gYpu1Jwh9XMzVeif7fcxbj21N38WW1gGfwKwFYMigUBy4/5nWcxj4uCm80Qhd19XCyVbhkSkHpS6XPpYVZLY+hynMQYu4EB0Pr1q3DhAkT8PPPP2Pjxo2oU6dyKvNff/2FXr168TpGhw4d0KZNG3Tr1g1jxoxB165dIRaLhTbF5Dwv5Fevhq+n+SWISc5QmJCdU1SOcTuTsElBwMFnOQ2+AcLRlExsi7tXIwiQFwgByhOAY5Iz8OFPV1BYxq/EAl/T5UyV/+o/ofhP+8oK43x7WJTtJ7T3r+wlv8WNq9OkwCPNQjJd2vgdJYTUJDgYqlevHv74448a24VMh3/+/LlWKzubCk8Xe60er5azPT7cd1nlfvKG1KreDE9+/Bou3n8h9+bI90P1t8tPBPfgyOvSV6f2kbpEAD7++SpcHGzQq4Uf76FDZT0xQr+R8w34PJ1t8bywvMrPdljYv4XgmUOmNAuJgraatPE7SgipSXAwBAAVFRXYv38/btyoHG4IDg7GgAEDYGPD73CtW7dGYmIivLy4Qw05OTlo27atRkt0GDNfN+19W3N3tAVEqJFgLU/1ITVFN0N5BQv5fPh6ONtq1OslDSCEDjFpSnqeT/cnI7KZD+xsrDRex0xX38j7t/LHgStPZAFRdmEZFh5KgZUVeAcx2lwkWNdMKWjTJ22stUcIqUnwbLLr16+jcePGGDFiBPbv34/9+/dj5MiRaNy4MZKTk3kd4969e3KrTJeWluLxY37DMqZIGlhow6iIQDwr4L+A6NP8Evx59QnG8ZiSW3Xm1/n055jTJxhAzWnt0p/f0rDqszSA0EaCuTqyC8sQtvgYYpIzNFrHDBC2npqHE//vItvO3uf0DAHCZ/OZyiwkmjqunKa/o4SQmgT3DH3wwQdo0aIFLl68KKsN9OLFC4wcORJjx47F2bNnFT73999/l/3/8OHDnDyhiooKxMbGIjAwUGiTTIaQtbaU8XCyxaTIxoLqy6T/U4i1x+Un6lbN35FIgIWHan4jH9s1CL9fyZCbWyR2tMMWHstlyOPpbIuMnGJsOX0X6dmFah1DG54XlnN6R9Qtdln1m7sqDCKIHW2QW6w8UdpKJH+2nJDCi6YyC4kKTfKjye8oIaQmwcHQ5cuXceHCBU6RRA8PDyxatAivvPKK0ucOGDAAQOXU+hEjRnAes7W1RWBgIFasWCG0SRZFBGDxwJawthKhQ5AnfN3sVQ6VeTjZYnWs/EBISnoznCCnNk5mbgm+PZWO9UPbwsPZrsaHb4WEKR1KU+Z5YTmi9/FfA0zXqt5o1Q0KFCWmV5dbVM7regkpG6CIqcxCMpWgzRho8jtKCOESPEzWpEkTZGXVrNT79OlTNGrUSOlzJRIJJBIJ6tWrh6dPn8p+lkgkKC0txa1bt/Dmm28KbZLJEFJ0UR4XexuMigiE2NEOFRIGaysR5vVrrvJ5mg58SJ+/8FAKOgR5on/rOghv+G8NI1UVok2FNis692rhhzMzIvHjBx0hdrRVeD4RKoNVd8ea30s8nGzxfkQgr/OpCmJMZRaSqQRthBDzwqtnqGoxxcWLF2PKlCmYN28ewsLCAAAJCQlYsGABli5dyuuk6enpsv+XlJTAwcEypoFqmhNTUPoSW+PuYWvcPc5irpuGt61RZwgA3B1tEBXsi5+rFRdUh6pv5D1CfDEtqgm2xaUjp1j+dHpTIeRGq2zGk7WVCFYiEXKVXA9ppfEfP+gIMNQomnk+/Tm28hiCVBXEmMosJFMJ2ggh5oVXMOTu7g6R6N/v/IwxvPPOO7Jt0oXv+/btKzcxujqJRIJFixZh06ZNyMrKwu3bt9GgQQPMmTMHgYGBGD16tDqvxehp89tsZl4pp4ZQjxBfTgVqGysR9iQ+1EogVNWxlMwawZC8mT/VZ7oYivS3dmzXIPx47qHKQoYA/xstnxlPfN/zZwWl6N+6DiIa1+Js11YQYyqzkEwlaCOEmBdewdCJEye0etIvvvgCO3bswLJlyzhLb7Ro0QKrV68222BIF99mZ/56TZbjEtG4FiIa19JprZ4tcffwSpCn7Gav6FzaOvfrIT44ksJ/AVV3R1tOz5Q0ybtHiC9+u/QYqibg+fG80fKdpq5pT4c2gxg+RTYNzVSCNkKIeeEVDPFdIoPv1Prvv/8e3377Lbp3745x48bJtrdq1Qo3b97kdQxT1K6+h8KZQerKKSrH1jPp8HazRy0Xe0gqGGb+ck1wMOLuZAsHGytk5ZWqfK40yVj6f132ADXxcREUDK0f2hZWVqIaw1bxadnIylddC2nwK/VU3miFzHjSRk+HNoMYU5iFZApBGyHEvKhVdLGq/Px87N69G9999x0uXrzIa5js8ePHcpOtJRIJystNO99EmYv3X2g1EJLis7ioKjlF5Zge1VjhOllVVU0y1mVdID+xAxrVdhW0PwC5N3m+w1WBtZxU7iN0xpM2ejq0GcSYwiwkUwjaCCHmQ+1g6NSpU9iyZQt++eUX+Pv7Y+DAgVi/fj2v54aEhOD06dOoX78+Z/vPP/+MNm3aqNsko2fsM2ACaznj/YhAXgm7+ngtHk62mPbTZd77F5dXYNiWc7Kfq+bvaDMxV+iMJ231dJhCEKNNlvZ6CSGGIygYyszMxPbt27Flyxbk5eXhnXfeQWlpKX777TeEhITwPs7nn3+OESNG4PHjx5BIJPj1119x69YtfP/993LXPTMXxj4DxtvVAT1CfLUye0kR6ff6qBBvxN54KrenzMXeGgWlFUjJyK/xXHkda8721igsragxm65q/k6PEF+tJeaqE1hRTwchhBgv3nWG+vbti6ZNm+Lq1atYvXo1njx5gq+//lqtk/bv3x8HDx7EsWPH4OzsjM8//xw3btzAwYMH0aNHD7WOaQoqiyQaX0Akwr+Jw6qWk5C3rxC+/1/N+liK/EAIAApK/x1qdXOwwfX5PbFJzvID7o62mNa9MVzt5cf0VZeZAKCwFpLQxFwh16gqaU9H9TpNhBBCDEvEpPPiVbCxscGUKVMwfvx4NG7cWLbd1tYWV65cEdQzZEry8vIgFouRm5sLNzc3jY8Xk5yh8XIc2iS9HVetLn3vWRFWH7sNQH6OS9X1j4S8njl9gvFeeCC6LjuBzDx+Q02bqpxLXk2f8+nPMWRzgsrj7B4TpnKhWmXDVdXP/aKwFBN3XQKg+hoRQgjRPyH3b97DZGfOnMGWLVvQrl07BAcH47333sPgwYM1auiFCxdw40Zl8m9ISAjatWun0fGIcL5iB/Rr5VdjPTJ3p8qqyVWHnuTluPRq4YcNQ9tg0u5LCnt6pENQIyOCsO54Ku9ACABm77+G4nIJfN0qg5/qOSTq5O8IHa5SFEApW6+NAiFCCDEdvHuGpAoLC7F3715s3boV58+fR0VFBVauXIn3338frq78Zv48evQIQ4YMQVxcHNzd3QEAOTk56NSpE/bs2YO6desKfiG6os2eoQoJQ+elxzWegfVp72bYcuYesvKErwVW1aTXGiLEzw0Td11SeJw3WviiYW1nhDeohTAlQzt/Xs2Qu65Z1Z4SABr1isnrwYlPyxbUMySUonpC8nrUKA+IEEKEUVbFX1NC7t+Cg6Gqbt26hS1btuCHH35ATk4OevTowVmZXpFevXohJycHO3bsQNOmTWXHGjVqFNzc3BATE6Nuk7ROm8EQ3xu3KmsGt4a9jZVsZXR138AfP+iIj/Zd4RWc8RlKUjYE1SPEV+NAUN4QlDTAVJUYfWZGpOA/MFXBqybHJoQQS6du2gJfQu7fghdqrapp06ZYtmwZHj16hN27d/N+3smTJ7Fx40ZZICQ91tdff41Tp05p0iSjpq3p6N6uDrLp2tWTivmQJviC8a8TJJ2ZFZOcoXAf6eKku8eEYc3g1tg9JgxnZkSiVws/jddlA7gJ0RX/PyanbJFYIYnRFRKG+LRsHLj8GPFp2bJvK3zrCRFCCOFP2ute/TOWz71GFzQuuggA1tbWGDBgAAYMGMBr/4CAALnFFSsqKuDv76+NJhklbUyt93G1k81Sqp7/UsvFHmDAs8JS3HtWiFXHUuVOR2cABr8SICg4q15ZWVFwUbU2jDSgePC8EJ/9xq86OZ92VF8wVtM6Poq+nbzRwpdXm4y9fhQhhBgTIVX89dXrrpVgSKjly5dj8uTJWL9+Pdq3bw+gMpl66tSp+OqrrwzRJL2QTsnWpIekS+PasLYS1RhnfaOFHy7ef4GnBZU/vxnqj6a+rjVu8lKrjqXC09lO0LlVrVxflbwAQ5uqByDq1vFRtsYYn3pLgPHXjyKEEGMitIq/PugtGPLw8JCtcg9UJmJ37NgRNjaVTXj58iVsbGzw/vvv8+5hMjXSIR1Nkoid7G3kBhrV1zyTjruemRGJdcfvYNX/T5Wv6kWh6rW65FHVE6KP8gHyAhChFYv5fDsRKVlLjlZQJ4QQ4YTOAtYHvQVDq1ev1tepjNqlBy80en5RaYXcnozqN2zpuOv6oW2xJ/GB3GOpm3gtDUSq9061q++BU7f+0WkgpM0AhM+3E+n0AlpBnRBCtEObyyNpi96CoREjRujrVEar7KUEm0+nq/18EYCjNzJ5BTHSno05B5KRzaMHyNPZFs8LVS+SK62srOthMHm0HYDw/dbxapNauPo4l3N9qJ4QIYSoR5oyoo3lkbTFIDlDluqH+HsarVrPAOQWvxS0P59ACAA+6x2Chy+K8c2pNBSVVSjcr7i8AstibuDbU+ka1ThSh7YDEL7fOv6+/QwA4OlshwGt/dEjxJfqCRFCiJqkKSPjdyYZTa87BUN6dP95kaGboNDZtGz8kvRIZYCTW1SOb06p37sl1BstfNGrha8sIRqorNekjQJdqr6dVPeisAzb4u5RIEQIIRrSdBawtlEwpEf1PZ0M3QSFjqbwH37Tp3Pp2Vg3tC2srURaL9Cl7NuJPIaa8kkIIeZI3VnAuqBR0UUizHvhgTDW+2duCf/hN316XliO8+nPdVagS2jxSiq0SAgh2iOdBdy/dR2EK1nySdcoGNIjOxsrjOkSZOhm1ODuaGvoJih1+HqG0inwALcqtVBVK2f/N7w+r+doMuVTXrVrQgghhkPDZHo2q3cI9iY+RI6ARGh1eDnb8U6eHhURiFXHUnXaHk38mvQYeUp6rrRRoKtqjaLv4++r3F/dKZ+6XouHEEKIcNQzpGeL/0zRaSA06bVG2D0mDPGzusNP7FBjva6qrETAhqFtMCmyscp9dcXVwVrlPsoCoaq0UaBLmlSt6FpI13VTZ8qnsa3FQwghpJJJB0OnTp1C37594e/vD5FIhN9++43zOGMMn3/+Ofz8/ODo6IioqCikphquB6TspQTfalBniI/GPi4Ib+gFOxsrhQuYSq0b0ha9Q/2VLnYqhKKFUhXxEzvgP23ranBGLm0U6NLWwq/Vqap2DWg21EcIIUR9Jh0MFRYWolWrVli/fr3cx5ctW4a1a9di06ZNOHfuHJydndGzZ0+UlBhmYc0dZ+/JKhrrStWAQFFysJ/YAZuGt0XvUL8a+7o4qD9yyvelif7/39y+IXi9Ob+hIU9nO5301sij6Lr5ih2wcXhbtYazhKzFQwghRL9MOmfojTfewBtvvCH3McYYVq9ejc8++wz9+/cHAHz//ffw8fHBb7/9hsGDB+uzqQCAxHvZOju2ooqdPUJ84Wpvi/i7zwBU5sWENaiZsf/oRZHcZTQcbUUoLlcvglP0LHcnWywe2BK9WvihQsJ4VSKd0ycEE3fVbJ+uCnRpe8qnMa7FQwghpJJJB0PKpKenIzMzE1FRUbJtYrEYHTt2RHx8vMJgqLS0FKWlpbKf8/LytNYmR1vV+TGaqB4QyEvW/SXpkSxZt+ylBN+fTceyw7dRViGRe0x1AyFlGGPoEeILgH8lUgAQO9kip4i7ZEjVwErbhC78qowxrsVDCCGkkkkPkymTmZkJAPDx8eFs9/HxkT0mz+LFiyEWi2X/AgICtNam5v5irR2rurFdgzgBgapk3THfJ6LpZ3/hiz9vKgyEdCWn+CUS7v7bS6ZqWAoAxu9MqhEIAcALOduMkS4TswkhhGjGbIMhdc2aNQu5ubmyfw8fPtTasWu52GvtWNX9fiVDlnyrKlmXATia8lTv1aSrik/jDhlWrfWzZnBr7B4ThjMzItEjxFfhawH+rQht7InHukrMJoQQojmzDYZ8fSuHYbKysjjbs7KyZI/JY29vDzc3N84/beFb90cdVZNvVSXrGoeawYu8SqTmlHisi8RsQgghmjPbnKGgoCD4+voiNjYWrVu3BlCZ/3Pu3DmMHz/eIG3KLixVvZMGpMm3ppCEG96gFq/9zC3x2JjW4iGEEFLJpIOhgoIC3LlzR/Zzeno6Ll++DE9PT9SrVw/Tpk3DF198gcaNGyMoKAhz5syBv78/BgwYYJD2PtHxqvXS5FtjT8J1srNGGM/EZHNMPNZmYjYhhBDNmXQwdOHCBbz22muyn6OjowEAI0aMwPbt2/HJJ5+gsLAQY8eORU5ODjp37oyYmBg4OBjmxnk3W3fBkJUIePH/PU/SZF1jHSqzt+E/Oit9Laqm3lPiMSGEEHWJGNN1GUDTlpeXB7FYjNzcXI3zh95cexrJT7Q3Vb86EYCNw9vC0c4GI7ae19l5tGH3mDDevSPSmXGA/Kn32sq3qZAwGr4ihBAzIeT+bdI9Q6bGV2yP5Ce6Oz4D5BZO1DYnO2sUlVVodAwhOT7SxOPqNZN8tbjAKS2gSgghlouCIT3q3swbx278o7fzvdO+LhYPDJX1dqRmFWDdiTuqn6iECICdjZXGwZDQHB9dJh5Le56qd5FKazLRTC9CCDFvFAzpUUpGvl7PdyQlC5HNMmU38vi0bI2DIQYgp6gcznbWKFQzIKrMbxJeZkAXiceqajJJ6xj1CPGlITNCCDFTZltnyDjp92aaU1SO8TuTEJOcAUB1FWQh1A2EAEDCgIm7/m2XIZlTHSNCCCHqoWBIjwK9nPR+Tgbg0/3JKHspUVoF2RA0rRxdIWGIT8vGgcuPEZ+WrdaxzK2OESGEEOEoGNKjoR3rq/U8dydbjc6bXViGsMXHEJOcobAKsvQc+gqSNO1xiUnOQOelxzFkcwKm7rmMIZsT0HnpccG9TeZYx4gQQogwlDOkRxfuCb/xbxjaBj1CfNH4s780OvfzwnJOMrC8ZOSjKZk1ZlTpmjo9LtpMeKY6RoQQQqhnSI9+vih80dfvEx5oHAhVJR2akrcOWNXFUrs3q621cyojtMdFVcIzIGz4jRZQJYQQQsGQHj3OKRb8nIS72ap34onP0JS1lQgdgjx1PvNNhMo6PkJ7XHSR8GwsC6hqIweKEEKIcDRMpkd+GuSdiB1tMfiVuvj2VDoAeWu+8xd35x+ltXoS7mbrdKhMkx4XXSU8G3oBVSr6SAghhkPBkB4VvVR/OnpecTm+PZWOsV2D8PuVDG4lZjd75BSXo6RcwutY606kyf5f/YYbk5yBmb9cU7ud1dnbWMHDyQ6ZedqpHK3LhGdDLaBKRR8JIcSwKBjSo+Kyl2o/V3qj/PniY5yZEYnLD3NkPRgSxjDsu3NqHbfqDReA3JuyJia82gjjX22Ii/dfaKXHxdwSnqnoIyGEGB7lDOlR6UvNw4zswjK0WXAECXef4Y3/7y04fD1T7eNVTTqe9/t1XoGQNN/nx9EdsWZwa0yPagIfV3u5+646dhvdlp9AbnEZJ1lbXeaW8ExFHwkhxPCoZ0iPmvi44OKDHI2PU/JSgjWxd7AmVrOlNaSkN1wh5vYNQUTjWrKfJ0U2wrrjqVh1LLXGvtWHezRdHV4fC7fqCxV9JIQQw6NgSI9srE2/I87dyRZLBraUG3DsSZRfOqDqcI9EwrDw0A2NE4UNnfCsLVT0kRBCDM/0784mpE2Ah6GboLH1Q+Qn8/Id7pmw61KN/aQ9R0KrR8urlWRKKiQMEsbg7qi4wri6JQgIIYTwRz1DeuTlYqf3c7o72iKnuJzXfg62VsjKK1WamBymYLaVJsM4lpgoLG8qfXWmmANFCCGmiHqG9OhIivqJzkK1r++OH97vgJGdAnntH1pXjDda+CoMhADlN2VNh3FUJQqbU0FC6VR6VXla+i76SAghlop6hvTofnaR3s514X4O3tt6Hk521rz2P5X6DKdSnwEArERA1ViDT2KyqinvfMnrYTKngoTKptJLuTvZYv2QtggzwaE/QggxRdQzpEf2Nvq/3EVlwgs9SgOhkZ3qY/eYMJyZEaky6OAz5Z2P6j1MinpR1M0zMjRVuVUAkFNUDisrEQVChBCiJxQM6ZGdNb9eGmOxI/4+jqRk4nz6c17DUsrW+NowtC38xA4KAyN5icLaXpRVHdoenqOp9IQQYnxomEyPEu+ZVuE8xoBtcfewLe4e72EpZVPerawqK1yLwF1bTVFOkpCChLpYRkMXw3M0lZ4QQowP9Qzplekm/WYIGJZSNOVd6OrwhuxF0dXwnDS3SkgPGSGEEN2iniE9quvugGeFqqe5GzNNp78LKZZoqF4UXa4XJs2tEtJDRgghRLeoZ0iP2pv4t31trZPFt1iioXpRdL1emNAeMkIIIbpFPUN69KzAsL1Cs99ohtpuDniQXYTVx26rPWjHZ1hK0/XHAMP1ouhjeM5clhMhhBBzQMGQHvm6GTYp9su/bsoSgOUtdMqXqmEpbSYeG2JRVn0Nz0l7yAghhBgWBUN6VFD60tBN4Kwgf2ZGJM6nP8eR65nYdvYer+erGpaSJh5X73WqvnK9EPruRVFVQFK6NAklORNCiHmgnCE9EhnBCEjV+jwAEN7QC3P7Ncem4ZV1gFTp18pPYRCiy7pA+lyUlU8BSUpyJoQQ80HBkB7V83Q2dBMAyE8A7tXCD2dmRGL3mDB0b1Zb4XO/PZWucFq5rhOP9YmSnAkhxHLQMJkeNfF2MXQTOKonAFtbidAhyBPRP11W+jxF08rNrboyJTkTQohloGBIj+LSnhm6CRyejnaIT8vm3Og1qfpsjtWVKcmZEELMHwVDenT4eqahm8AxYvt5zur0fmIH9G7hy+u58np3KPGYEEKIKaKcIT36J6/Y0E3gqJ7HnJlbgi1x93g9V17vjrWVCHP6hCgMhABKPCaEEGJ8KBjSozLDz6xXShrEKJv1pqzqc0xyBhYeSpH7PEo8JoQQYqwoGNIjfcdCns62mPJaQ8HPY0pmvjPI791RtLCpVM/mPhA72tWYVl8hYYhPy8aBy48Rn5at1rR7QgghRBOUM2TGnheW4/uEB1o9pruTLXqEcPOKlNUXktp+9j62n73PqUStzUrVhBBCiLospmdo/fr1CAwMhIODAzp27Ijz588bukl6kVOs3fXQcorKa9QJUjUDrSppJerFf6bI7UmSPq6olhEhhBCibRYRDO3duxfR0dGYO3cukpKS0KpVK/Ts2RNPnz41dNNMUvWZZELqBrH//7f5dLpOKlUTQgghQllEMLRy5UqMGTMGo0aNQkhICDZt2gQnJyds3brV0E0zSdVnkqlTN0hZnGNKlaoJIYSYPrMPhsrKynDx4kVERUXJtllZWSEqKgrx8fE19i8tLUVeXh7nn7lwd7TV+BjyZpJJ6wtpe8K8qVSqJoQQYtrMPhh69uwZKioq4OPjw9nu4+ODzMyaRRAXL14MsVgs+xcQEKCvpurc+qFtsXtMGCa91kjtY8ibSaZsYVNNmFKlakIIIabL7IMhoWbNmoXc3FzZv4cPHxq6SVrh5WyHsIZeCG/ohek9mqjVkzM9qrHCWV6KFjZVxEqkOHBSVsuIEEII0TazD4Zq1aoFa2trZGVlcbZnZWXB17fm0hP29vZwc3Pj/NOWno0MV3l5Yf8Wsh4ddXpy/MQOmBTZWOk+vVr8u/L96IhAufuI/v/fmC5Bcs9PlaoJIYTom9kHQ3Z2dmjXrh1iY2Nl2yQSCWJjYxEeHq7XtnzzQW+9nk/qf12D0DuU26PDtydHGrzwDU6kC5vO6dscm4a3hV+140srUc/qHSL3/FSpmhBCiL6JGFNWb9g87N27FyNGjMA333yDDh06YPXq1fjpp59w8+bNGrlE1eXl5UEsFiM3N1drvUSBMw8Jfo6znTUYgKKyCs52DydbLB7YEgBqFDD0dLbFF/1boHeov8LjVkgYzqc/x9P8Etx7VoTd5x8gM097RRCrHt/btXLoq2pQpepxQgghRB1C7t8WEQwBwLp167B8+XJkZmaidevWWLt2LTp27KjyeboIhgDgf9/9icN3uJc+0MsRHo62cLSzgZeLPaxEQB0PJ0Q0qoWwBl4AgIS0bMTffQagsgcmrIGXLHjQRmBBwQkhhBBzQMGQFukqGCKEEEKI7gi5f5t9zhAhhBBCiDIUDBFCCCHEolEwRAghhBCLRsEQIYQQQiwaBUOEEEIIsWgUDBFCCCHEolEwRAghhBCLRsEQIYQQQiwaBUOEEEIIsWg2hm6AsZMW6M7LyzNwSwghhBDCl/S+zWehDQqGVMjPzwcABAQEGLglhBBCCBEqPz8fYrFY6T60NpkKEokET548gaurK0Qi7S5YmpeXh4CAADx8+JDWPVMDXT/N0TXUDF0/zdE11BxdQ/kYY8jPz4e/vz+srJRnBVHPkApWVlaoW7euTs/h5uZGv8AaoOunObqGmqHrpzm6hpqja1iTqh4hKUqgJoQQQohFo2CIEEIIIRaNgiEDsre3x9y5c2Fvb2/oppgkun6ao2uoGbp+mqNrqDm6hpqjBGpCCCGEWDTqGSKEEEKIRaNgiBBCCCEWjYIhQgghhFg0CoYIIYQQYtEoGDKQ9evXIzAwEA4ODujYsSPOnz9v6CYZpcWLF+OVV16Bq6srvL29MWDAANy6dYuzT0lJCSZOnAgvLy+4uLhg0KBByMrKMlCLjd+SJUsgEokwbdo02Ta6hqo9fvwYw4cPh5eXFxwdHdGyZUtcuHBB9jhjDJ9//jn8/Pzg6OiIqKgopKamGrDFxqOiogJz5sxBUFAQHB0d0bBhQyxcuJCzZhRdP65Tp06hb9++8Pf3h0gkwm+//cZ5nM/1ev78OYYNGwY3Nze4u7tj9OjRKCgo0OOrMB0UDBnA3r17ER0djblz5yIpKQmtWrVCz5498fTpU0M3zeicPHkSEydOREJCAo4ePYry8nK8/vrrKCwslO0zffp0HDx4EPv27cPJkyfx5MkTDBw40ICtNl6JiYn45ptvEBoaytlO11C5Fy9eICIiAra2tvjrr7+QkpKCFStWwMPDQ7bPsmXLsHbtWmzatAnnzp2Ds7MzevbsiZKSEgO23DgsXboUGzduxLp163Djxg0sXboUy5Ytw9dffy3bh64fV2FhIVq1aoX169fLfZzP9Ro2bBiuX7+Oo0eP4o8//sCpU6cwduxYfb0E08KI3nXo0IFNnDhR9nNFRQXz9/dnixcvNmCrTMPTp08ZAHby5EnGGGM5OTnM1taW7du3T7bPjRs3GAAWHx9vqGYapfz8fNa4cWN29OhR1q1bNzZ16lTGGF1DPmbMmME6d+6s8HGJRMJ8fX3Z8uXLZdtycnKYvb092717tz6aaNT69OnD3n//fc62gQMHsmHDhjHG6PqpAoDt379f9jOf65WSksIAsMTERNk+f/31FxOJROzx48d6a7upoJ4hPSsrK8PFixcRFRUl22ZlZYWoqCjEx8cbsGWmITc3FwDg6ekJALh48SLKy8s517NZs2aoV68eXc9qJk6ciD59+nCuFUDXkI/ff/8d7du3x9tvvw1vb2+0adMGmzdvlj2enp6OzMxMzjUUi8Xo2LEjXUMAnTp1QmxsLG7fvg0AuHLlCs6cOYM33ngDAF0/ofhcr/j4eLi7u6N9+/ayfaKiomBlZYVz587pvc3GjhZq1bNnz56hoqICPj4+nO0+Pj64efOmgVplGiQSCaZNm4aIiAi0aNECAJCZmQk7Ozu4u7tz9vXx8UFmZqYBWmmc9uzZg6SkJCQmJtZ4jK6hanfv3sXGjRsRHR2N2bNnIzExEVOmTIGdnR1GjBghu07y/q7pGgIzZ85EXl4emjVrBmtra1RUVGDRokUYNmwYAND1E4jP9crMzIS3tzfncRsbG3h6etI1lYOCIWIyJk6ciOTkZJw5c8bQTTEpDx8+xNSpU3H06FE4ODgYujkmSSKRoH379vjyyy8BAG3atEFycjI2bdqEESNGGLh1xu+nn37Cjz/+iF27dqF58+a4fPkypk2bBn9/f7p+xCjQMJme1apVC9bW1jVm6mRlZcHX19dArTJ+kyZNwh9//IETJ06gbt26su2+vr4oKytDTk4OZ3+6nv+6ePEinj59irZt28LGxgY2NjY4efIk1q5dCxsbG/j4+NA1VMHPzw8hISGcbcHBwXjw4AEAyK4T/V3L9/HHH2PmzJkYPHgwWrZsiffeew/Tp0/H4sWLAdD1E4rP9fL19a0xKefly5d4/vw5XVM5KBjSMzs7O7Rr1w6xsbGybRKJBLGxsQgPDzdgy4wTYwyTJk3C/v37cfz4cQQFBXEeb9euHWxtbTnX89atW3jw4AFdz//XvXt3XLt2DZcvX5b9a9++PYYNGyb7P11D5SIiImqUdLh9+zbq168PAAgKCoKvry/nGubl5eHcuXN0DQEUFRXByop7u7G2toZEIgFA108oPtcrPDwcOTk5uHjxomyf48ePQyKRoGPHjnpvs9EzdAa3JdqzZw+zt7dn27dvZykpKWzs2LHM3d2dZWZmGrppRmf8+PFMLBazv//+m2VkZMj+FRUVyfYZN24cq1evHjt+/Di7cOECCw8PZ+Hh4QZstfGrOpuMMbqGqpw/f57Z2NiwRYsWsdTUVPbjjz8yJycntnPnTtk+S5YsYe7u7uzAgQPs6tWrrH///iwoKIgVFxcbsOXGYcSIEaxOnTrsjz/+YOnp6ezXX39ltWrVYp988olsH7p+XPn5+ezSpUvs0qVLDABbuXIlu3TpErt//z5jjN/16tWrF2vTpg07d+4cO3PmDGvcuDEbMmSIoV6SUaNgyEC+/vprVq9ePWZnZ8c6dOjAEhISDN0kowRA7r9t27bJ9ikuLmYTJkxgHh4ezMnJib311lssIyPDcI02AdWDIbqGqh08eJC1aNGC2dvbs2bNmrFvv/2W87hEImFz5sxhPj4+zN7ennXv3p3dunXLQK01Lnl5eWzq1KmsXr16zMHBgTVo0IB9+umnrLS0VLYPXT+uEydOyP3sGzFiBGOM3/XKzs5mQ4YMYS4uLszNzY2NGjWK5efnG+DVGD8RY1VKgBJCCCGEWBjKGSKEEEKIRaNgiBBCCCEWjYIhQgghhFg0CoYIIYQQYtEoGCKEEEKIRaNgiBBCCCEWjYIhQgghhFg0CoYIUeHvv/+GSCSqsXaXNr366quYNm2azo5vagIDA7F69WqDH0Nbtm/fDnd3d42Po4vfxZEjR2LAgAFGcxw+tmzZgtdff12jY2zatAl9+/bVUouIqaNgiBAA8fHxsLa2Rp8+fQzdFF7u3bsHkUiEy5cvG7opSqkbkCQmJmLs2LHab5AeGFMQxseaNWuwfft23vsr+t0Tehx1lZSUYM6cOZg7d65s29GjR9GkSRO4ubnhvffeQ1lZmeyx3NxcNGnSBPfv3+cc5/3330dSUhJOnz6t8zYT40fBECGo/KY5efJknDp1Ck+ePDF0c4xe1ZuNLtSuXRtOTk46PQepJBaLtdJrpa3jqPLzzz/Dzc0NERERACoXuh46dCjGjRuH+Ph4XLhwAd9++61s/5kzZ2LcuHGyRXWl7OzsMHToUKxdu1bnbSbGj4IhYvEKCgqwd+9ejB8/Hn369FH47TYuLg6hoaFwcHBAWFgYkpOTZY/dv38fffv2hYeHB5ydndG8eXP8+eefssdPnjyJDh06wN7eHn5+fpg5cyZevnypsE0ikQi//fYbZ5u7u7usbUFBQQCANm3aQCQS4dVXX5Xt99133yE4OBgODg5o1qwZNmzYoPT1v/rqq5g0aRImTZoEsViMWrVqYc6cOai6Uk9gYCAWLlyI//73v3Bzc5P12vzyyy9o3rw57O3tERgYiBUrVnCOe//+fUyfPh0ikQgikUj22JkzZ9ClSxc4OjoiICAAU6ZMQWFhIed8VXtXRCIRvvvuO7z11ltwcnJC48aN8fvvvyt9XdXl5OTggw8+QO3ateHm5obIyEhcuXJF9vi8efPQunVr/PDDDwgMDIRYLMbgwYORn58v2yc/Px/Dhg2Ds7Mz/Pz8sGrVKs4Qp7LXDACHDx9GcHAwXFxc0KtXL2RkZCht859//okmTZrA0dERr732Gu7du1djH2XXcvbs2XJXKG/VqhUWLFgAoObwVkxMDDp37gx3d3d4eXnhzTffRFpamuxxRb971Y9TWlqKKVOmwNvbGw4ODujcuTMSExNlj0uH/GJjY9G+fXs4OTmhU6dOuHXrltJrsmfPHs7w1rNnz/Ds2TNMmDABzZs3R79+/XDjxg0AwNmzZ5GYmIipU6fKPVbfvn3x+++/o7i4WOk5iQUw8NpohBjcli1bWPv27RljlYtxNmzYkEkkEtnj0gUTg4OD2ZEjR9jVq1fZm2++yQIDA1lZWRljjLE+ffqwHj16sKtXr7K0tDR28OBBdvLkScYYY48ePWJOTk5swoQJ7MaNG2z//v2sVq1abO7cubJzVF84FQDbv38/p51isVi2QO358+cZAHbs2DGWkZHBsrOzGWOM7dy5k/n5+bFffvmF3b17l/3yyy/M09OTbd++XeHr79atG3NxcWFTp05lN2/eZDt37mROTk6chUjr16/P3Nzc2FdffcXu3LnD7ty5wy5cuMCsrKzYggUL2K1bt9i2bduYo6OjrI3Z2dmsbt26bMGCBSwjI0O28OudO3eYs7MzW7VqFbt9+zaLi4tjbdq0YSNHjuScb9WqVZzrUbduXbZr1y6WmprKpkyZwlxcXGSvW57qx4iKimJ9+/ZliYmJ7Pbt2+zDDz9kXl5esmPMnTuXubi4sIEDB7Jr166xU6dOMV9fXzZ79mzZMT744ANWv359duzYMXbt2jX21ltvMVdXV9l7p+g1b9u2jdna2rKoqCiWmJjILl68yIKDg9nQoUMVtv/BgwfM3t6eRUdHy94XHx8fBoC9ePGC17VMTk5mANidO3dkx5VuS01NZYxVrijfv39/2eM///wz++WXX1hqaiq7dOkS69u3L2vZsiWrqKhgjCn+3at+nClTpjB/f3/2559/suvXr7MRI0YwDw8P2f7Sv6uOHTuyv//+m12/fp116dKFderUSeE1Yazy72DPnj2ynyUSCfPz82MHDhxghYWFLDw8nG3atImVlZWx0NBQduHCBYXHKiwsZFZWVuzEiRNKz0nMHwVDxOJ16tSJrV69mjHGWHl5OatVqxbnw1H6oV31Azg7O5s5OjqyvXv3MsYYa9myJZs3b57c48+ePZs1bdqUE2CtX7+eubi4yG4wQoOh9PR0BoBdunSJs0/Dhg3Zrl27ONsWLlzIwsPDFb7+bt26seDgYE77ZsyYwYKDg2U/169fnw0YMIDzvKFDh7IePXpwtn388ccsJCSE87yqAQljjI0ePZqNHTuWs+306dPMysqKFRcXy30eAPbZZ5/Jfi4oKGAA2F9//aXwdVU9xunTp5mbmxsrKSnh7NOwYUP2zTffMMYqgyEnJyeWl5fHeT0dO3ZkjFWuvG5ra8v27dsnezwnJ4c5OTlx3jt5r3nbtm01gpL169czHx8fhe2fNWsW51oyVvm+VA2G+FzLVq1asQULFnCOK31NjNUMYqr7559/GAB27do1xpji372qxykoKGC2trbsxx9/lD1eVlbG/P392bJlyxhj//5dHTt2TLbPoUOHGABZ26t78eIFA8BOnTpV4zW3b9+eBQYGsgkTJrCysjK2YMECNnXqVJacnMw6derEmjRpwr7++usax/Tw8FD6ZYFYBhomIxbt1q1bOH/+PIYMGQIAsLGxwbvvvostW7bU2Dc8PFz2f09PTzRt2lTWHT9lyhR88cUXiIiIwNy5c3H16lXZvjdu3EB4eDhnyCQiIgIFBQV49OiR1l5LYWEh0tLSMHr0aLi4uMj+ffHFF5xhDnnCwsI47QsPD0dqaioqKipk29q3b895zo0bN2R5G1IRERE1nlfdlStXsH37dk4be/bsCYlEgvT0dIXPCw0Nlf3f2dkZbm5uePr0qdLXVfWcBQUF8PLy4pw3PT2dc20CAwPh6uoq+9nPz092jrt376K8vBwdOnSQPS4Wi9G0aVNebXByckLDhg3lHlueGzdu1Bjiqvo7KH1dqq7lsGHDsGvXLgAAYwy7d+/GsGHDFJ43NTUVQ4YMQYMGDeDm5obAwEAAwIMHD3i9TgBIS0tDeXk55/fD1tYWHTp0kP3NSFV9X/38/ABA4XWRDmc5ODhwtkuH4NLT07F+/Xqkp6fj+++/xxdffIH33nsPY8eOxenTp7FgwQLO3yYAODo6oqioiPdrI+bJxtANIMSQtmzZgpcvX8Lf31+2jTEGe3t7rFu3DmKxmNdxPvjgA/Ts2ROHDh3CkSNHsHjxYqxYsQKTJ09Wq10ikYiTswMA5eXlSp9TUFAAANi8eXONm6i1tbVa7ajK2dlZ42MAle383//+hylTptR4rF69egqfZ2try/lZJBJBIpHwPqefnx/+/vvvGo9VTfrV5ByqyDt29fdYKD7XcsiQIZgxYwaSkpJQXFyMhw8f4t1331V4zL59+6J+/frYvHkz/P39IZFI0KJFC50lzVe9LtKAXNE19/LygkgkwosXL5Qe83//+x9WrFgBiUSCS5cu4e2334aTkxO6deuGkydPcgKw58+fo3bt2lp4JcSUUTBELNbLly/x/fffY8WKFTVqlgwYMAC7d+/GuHHjZNsSEhJkN5gXL17g9u3bCA4Olj0eEBCAcePGYdy4cZg1axY2b96MyZMnIzg4GL/88gsYY7IP+7i4OLi6uqJu3bpy21a7dm1Ocm1qairn26udnR0AcHpgfHx84O/vj7t37yr95i/PuXPnOD8nJCSgcePGSoOo4OBgxMXFcbbFxcWhSZMmsufZ2dnV6CVq27YtUlJS0KhRI0Ft1ETbtm2RmZkJGxsbWU+HUA0aNICtrS0SExNlvwe5ubm4ffs2unbtKttP3mtWR3BwcI0k8YSEBM7PfK5l3bp10a1bN/z4448oLi5Gjx494O3tLXff7Oxs3Lp1C5s3b0aXLl0AVCZoVyXvd6+6hg0bws7ODnFxcbJZXOXl5UhMTNSonpadnR1CQkKQkpKisM7Qli1b4OnpiX79+smCJukXifLyck6709LSUFJSgjZt2qjdJmIeaJiMWKw//vgDL168wOjRo9GiRQvOv0GDBtUYKluwYAFiY2ORnJyMkSNHolatWrLZM9OmTcPhw4eRnp6OpKQknDhxQhYoTZgwAQ8fPsTkyZNx8+ZNHDhwAHPnzkV0dDSsrOT/CUZGRmLdunW4dOkSLly4gHHjxnG+QXt7e8PR0RExMTHIyspCbm4uAGD+/PlYvHgx1q5di9u3b+PatWvYtm0bVq5cqfRaPHjwANHR0bh16xZ2796Nr7/+WuEMHKkPP/wQsbGxWLhwIW7fvo0dO3Zg3bp1+Oijj2T7BAYG4tSpU3j8+DGePXsGAJgxYwbOnj2LSZMm4fLly0hNTcWBAwcwadIkpefTRFRUFMLDwzFgwAAcOXIE9+7dw9mzZ/Hpp5/iwoULvI7h6uqKESNG4OOPP8aJEydw/fp1jB49GlZWVpwhRnmvWR3jxo1DamoqPv74Y9y6dQu7du2qMdOR77UcNmwY9uzZg3379ikNlD08PODl5YVvv/0Wd+7cwfHjxxEdHc3ZR9HvXlXOzs4YP348Pv74Y8TExCAlJQVjxoxBUVERRo8erfY1AYCePXvWCNCknj59ii+++AJff/217PUEBwdj9erViI+PR2xsLGfo7vTp02jQoAFn+JJYKINmLBFiQG+++Sbr3bu33MfOnTvHALArV67IEj0PHjzImjdvzuzs7FiHDh3YlStXZPtPmjSJNWzYkNnb27PatWuz9957jz179kz2+N9//81eeeUVZmdnx3x9fdmMGTNYeXm57PHqCdSPHz9mr7/+OnN2dmaNGzdmf/75JyeBmjHGNm/ezAICApiVlRXr1q2bbPuPP/7IWrduzezs7JiHhwfr2rUr+/XXXxVeh27durEJEyawcePGMTc3N+bh4cFmz57NSaiWlxTMWOXMo5CQEGZra8vq1avHli9fznk8Pj6ehYaGMnt7e1b14+b8+fOsR48ezMXFhTk7O7PQ0FC2aNEiheeDioRyeaofIy8vj02ePJn5+/szW1tbFhAQwIYNG8YePHjAGKtMoG7VqhXnGKtWrWL169fnHGPo0KHMycmJ+fr6spUrV7IOHTqwmTNnKn3N27ZtY2KxmHPs/fv3M1UfwQcPHmSNGjVi9vb2rEuXLmzr1q2cBGrGVF9LxioTj+3t7ZmTkxPLz8/nPFY9gfro0aMsODiY2dvbs9DQUPb333/XuP7yfveqH6e4uJhNnjyZ1apVi9nb27OIiAh2/vx52ePSv6uqr+XSpUsMAEtPT1d4Ta5fv84cHR1ZTk5OjccGDx5cI0n63LlzrFmzZszT05PNnz+f89jrr7/OFi9erPBcxHKIGNNw0JoQYtJeffVVtG7d2qSqJhuLwsJC1KlTBytWrNC4x4Pw9/bbb6Nt27aYNWuW2se4fv06IiMjcfv2bd65gcR80TAZIYTwdOnSJezevRtpaWlISkqSDTn179/fwC2zLMuXL4eLi4tGx8jIyMD3339PgRABQAnUhBAiyFdffYVbt27Bzs4O7dq1w+nTp1GrVi1DN8uiBAYGqj1TUyoqKkpLrSHmgIbJCCGEEGLRaJiMEEIIIRaNgiFCCCGEWDQKhgghhBBi0SgYIoQQQohFo2CIEEIIIRaNgiFCCCGEWDQKhgghhBBi0SgYIoQQQohFo2CIEEIIIRbt/wCGsdK5AKCiGwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "# import numpy.ma as ma\n",
    "\n",
    "test_df = df1[(~df1[\"align_pident\"].isna()) & (~df1[\"blast_pident\"].isna())]\n",
    "# test_df = candidates\n",
    "test_df[\"pident_diff\"] = (test_df[\"blast_pident\"] - test_df[\"align_pident\"]).abs()\n",
    "\n",
    "x = test_df[\"prot_lengt_dev_pc\"].abs().round(2)\n",
    "y = test_df[\"pident_diff\"].round(2)\n",
    "coeff = np.corrcoef(x, y)\n",
    "# coeff = ma.corrcoef(ma.masked_invalid(x), ma.masked_invalid(y))\n",
    "b, m = polyfit(x, y, 1)\n",
    "plt.scatter(x=x, y=y)\n",
    "plt.plot(x, b + m * x, '-')\n",
    "plt.xlabel(\"Absolute protein length deviation (%)\")\n",
    "plt.ylabel(\"Absolute difference in pident \\n between BLAST and needle (%)\")\n",
    "plt.title(f\"r = {round(coeff[1][0], 2)}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}