schistosome_orthologue_analysis3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\n",
      "/tmp/ipykernel_31848/1258931625.py:16: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return (el - np.mean(arr)) / np.std(arr)\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.HOG0004466</th>\n",
       "      <td>SBOV2_89180.2, SCUR1_87700.1, SGUI1_89400.3, S...</td>\n",
       "      <td>6, 4, 5, 6, 6, 6, 6, 4</td>\n",
       "      <td>1577, 852, 1187, 1581, 1578, 1569, 1581, 996</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>2, 1, 4, 1, 2, 1, 1, 1</td>\n",
       "      <td>SCUR1_87700.1</td>\n",
       "      <td>SCUR1_87700.1, SRDH2_91810.1</td>\n",
       "      <td>92.1</td>\n",
       "      <td>52.326322</td>\n",
       "      <td>-84.624413</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.224293</td>\n",
       "      <td>0.170442</td>\n",
       "      <td>-1.791562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005082</th>\n",
       "      <td>SBOV2_91980.2, SCUR1_91350.1, SGUI1_92980.1, S...</td>\n",
       "      <td>7, 7, 8, 8, 7, 8, 8, 7</td>\n",
       "      <td>2570, 2688, 2927, 2943, 2923, 2904, 2966, 2593</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>2, 4, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>SBOV2_91980.2</td>\n",
       "      <td>SBOV2_91980.2, SGUI1_92980.1</td>\n",
       "      <td>93.9</td>\n",
       "      <td>87.427400</td>\n",
       "      <td>-13.365759</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.059572</td>\n",
       "      <td>0.071270</td>\n",
       "      <td>-1.557489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005081</th>\n",
       "      <td>SBOV2_91830.1, SCUR1_91510.1, SGUI1_93140.1, S...</td>\n",
       "      <td>18, 16, 16, 16, 16, 16, 16, 15</td>\n",
       "      <td>698, 714, 714, 714, 714, 728, 728, 586</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 2, 1, 3, 1, 2</td>\n",
       "      <td>EWB00_007421.1</td>\n",
       "      <td>SRDH2_96130.2, EWB00_007421.1</td>\n",
       "      <td>65.3</td>\n",
       "      <td>75.274725</td>\n",
       "      <td>-21.843003</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.066945</td>\n",
       "      <td>0.051753</td>\n",
       "      <td>-2.591115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005080</th>\n",
       "      <td>SBOV2_9170.1, SCUR1_9290.1, SGUI1_8670.1, SHAE...</td>\n",
       "      <td>4, 4, 4, 4, 4, 4, 4, 4</td>\n",
       "      <td>380, 378, 380, 380, 380, 376, 376, 383</td>\n",
       "      <td>1, 1, 2, 1, 1, 1, 1, 1</td>\n",
       "      <td>4, 1, 2, 1, 1, 1, 1, 1</td>\n",
       "      <td>SBOV2_9170.1</td>\n",
       "      <td>SBOV2_9170.1, SCUR1_9290.1</td>\n",
       "      <td>99.2</td>\n",
       "      <td>99.736842</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005079</th>\n",
       "      <td>SBOV2_91540.1, SCUR1_91750.1, SGUI1_93390.1, S...</td>\n",
       "      <td>8, 8, 8, 8, 8, 8, 8, 8</td>\n",
       "      <td>898, 901, 898, 895, 811, 901, 879, 825</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 2, 1, 1, 3, 1, 2</td>\n",
       "      <td>SINT2_90980.1</td>\n",
       "      <td>SINT2_90980.1, Smp_175640.1</td>\n",
       "      <td>89.6</td>\n",
       "      <td>86.689420</td>\n",
       "      <td>-10.542540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041863</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.894834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            selected_transcripts  \\\n",
       "HOG                                                                \n",
       "N0.HOG0004466  SBOV2_89180.2, SCUR1_87700.1, SGUI1_89400.3, S...   \n",
       "N0.HOG0005082  SBOV2_91980.2, SCUR1_91350.1, SGUI1_92980.1, S...   \n",
       "N0.HOG0005081  SBOV2_91830.1, SCUR1_91510.1, SGUI1_93140.1, S...   \n",
       "N0.HOG0005080  SBOV2_9170.1, SCUR1_9290.1, SGUI1_8670.1, SHAE...   \n",
       "N0.HOG0005079  SBOV2_91540.1, SCUR1_91750.1, SGUI1_93390.1, S...   \n",
       "\n",
       "                                  exon_counts  \\\n",
       "HOG                                             \n",
       "N0.HOG0004466          6, 4, 5, 6, 6, 6, 6, 4   \n",
       "N0.HOG0005082          7, 7, 8, 8, 7, 8, 8, 7   \n",
       "N0.HOG0005081  18, 16, 16, 16, 16, 16, 16, 15   \n",
       "N0.HOG0005080          4, 4, 4, 4, 4, 4, 4, 4   \n",
       "N0.HOG0005079          8, 8, 8, 8, 8, 8, 8, 8   \n",
       "\n",
       "                                              protein_lengths  \\\n",
       "HOG                                                             \n",
       "N0.HOG0004466    1577, 852, 1187, 1581, 1578, 1569, 1581, 996   \n",
       "N0.HOG0005082  2570, 2688, 2927, 2943, 2923, 2904, 2966, 2593   \n",
       "N0.HOG0005081          698, 714, 714, 714, 714, 728, 728, 586   \n",
       "N0.HOG0005080          380, 378, 380, 380, 380, 376, 376, 383   \n",
       "N0.HOG0005079          898, 901, 898, 895, 811, 901, 879, 825   \n",
       "\n",
       "                          gene_counts       transcript_counts  \\\n",
       "HOG                                                             \n",
       "N0.HOG0004466  1, 1, 1, 1, 1, 1, 1, 1  2, 1, 4, 1, 2, 1, 1, 1   \n",
       "N0.HOG0005082  1, 1, 1, 1, 1, 1, 1, 1  2, 4, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0005081  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 1, 2, 1, 3, 1, 2   \n",
       "N0.HOG0005080  1, 1, 2, 1, 1, 1, 1, 1  4, 1, 2, 1, 1, 1, 1, 1   \n",
       "N0.HOG0005079  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 2, 1, 1, 3, 1, 2   \n",
       "\n",
       "              worst_transcript                     worst_pair  blast_pident  \\\n",
       "HOG                                                                           \n",
       "N0.HOG0004466    SCUR1_87700.1   SCUR1_87700.1, SRDH2_91810.1          92.1   \n",
       "N0.HOG0005082    SBOV2_91980.2   SBOV2_91980.2, SGUI1_92980.1          93.9   \n",
       "N0.HOG0005081   EWB00_007421.1  SRDH2_96130.2, EWB00_007421.1          65.3   \n",
       "N0.HOG0005080     SBOV2_9170.1     SBOV2_9170.1, SCUR1_9290.1          99.2   \n",
       "N0.HOG0005079    SINT2_90980.1    SINT2_90980.1, Smp_175640.1          89.6   \n",
       "\n",
       "               align_pident  prot_lengt_dev_pc  exon_count_dev  \\\n",
       "HOG                                                              \n",
       "N0.HOG0004466     52.326322         -84.624413            -2.0   \n",
       "N0.HOG0005082     87.427400         -13.365759            -0.5   \n",
       "N0.HOG0005081     75.274725         -21.843003            -1.0   \n",
       "N0.HOG0005080     99.736842           0.000000             0.0   \n",
       "N0.HOG0005079     86.689420         -10.542540             0.0   \n",
       "\n",
       "               prot_lengt_cov  exon_count_cov  prot_lengt_zscore  \n",
       "HOG                                                               \n",
       "N0.HOG0004466        0.224293        0.170442          -1.791562  \n",
       "N0.HOG0005082        0.059572        0.071270          -1.557489  \n",
       "N0.HOG0005081        0.066945        0.051753          -2.591115  \n",
       "N0.HOG0005080        0.006216        0.000000           0.396934  \n",
       "N0.HOG0005079        0.041863        0.000000          -1.894834  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = preproc_tsv(\"data/schistosome_orthogroups/Results_May10/table_2024_07_09_15_19_48.tsv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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.HOG0005095</th>\n",
       "      <td>SBOV2_93720.1, SCUR1_93360.1, SGUI1_95060.2, S...</td>\n",
       "      <td>6, 6, 5, 6, 7, 2, 6, 7</td>\n",
       "      <td>148, 148, 112, 148, 145, 64, 148, 198</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 2, 2, 1, 1, 2, 2</td>\n",
       "      <td>SRDH2_98060.1</td>\n",
       "      <td>SBOV2_93720.1, SRDH2_98060.1</td>\n",
       "      <td>97.6</td>\n",
       "      <td>28.378378</td>\n",
       "      <td>-131.250000</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.274801</td>\n",
       "      <td>0.284087</td>\n",
       "      <td>-2.097446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005007</th>\n",
       "      <td>SBOV2_81950.2, SCUR1_81960.1, SGUI1_83310.1, S...</td>\n",
       "      <td>9, 10, 9, 9, 9, 9, 9, 9</td>\n",
       "      <td>281, 4099, 269, 287, 287, 282, 282, 295</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>5, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>SCUR1_81960.1</td>\n",
       "      <td>SCUR1_81960.1, SGUI1_83310.1</td>\n",
       "      <td>95.7</td>\n",
       "      <td>6.172237</td>\n",
       "      <td>93.059283</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.774520</td>\n",
       "      <td>0.038746</td>\n",
       "      <td>2.645712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005207</th>\n",
       "      <td>SBOV2_19290.1, SCUR1_21290.1, SGUI1_20930.1, S...</td>\n",
       "      <td>13, 13, 13, 5, 13, 10, 13, 13</td>\n",
       "      <td>647, 647, 647, 296, 647, 537, 647, 646</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 3, 1, 2</td>\n",
       "      <td>SHAE2_23450.1</td>\n",
       "      <td>SBOV2_19290.1, SHAE2_23450.1</td>\n",
       "      <td>99.3</td>\n",
       "      <td>43.894900</td>\n",
       "      <td>-118.581081</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.211402</td>\n",
       "      <td>0.247345</td>\n",
       "      <td>-2.516663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005202</th>\n",
       "      <td>SBOV2_18710.2, SCUR1_19270.1, SGUI1_18870.1, S...</td>\n",
       "      <td>4, 5, 5, 5, 5, 3, 3, 5</td>\n",
       "      <td>202, 237, 237, 222, 237, 121, 121, 238</td>\n",
       "      <td>1, 1, 1, 1, 1, 2, 2, 1</td>\n",
       "      <td>2, 1, 1, 1, 1, 2, 2, 1</td>\n",
       "      <td>SRDH2_19700.1</td>\n",
       "      <td>SHAE2_21430.1, SRDH2_19700.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.189189</td>\n",
       "      <td>-89.669421</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.254607</td>\n",
       "      <td>0.209400</td>\n",
       "      <td>-1.682120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005235</th>\n",
       "      <td>SBOV2_21580.1, SCUR1_22110.1, SGUI1_21760.1, S...</td>\n",
       "      <td>5, 5, 1, 5, 5, 5, 5, 5</td>\n",
       "      <td>548, 548, 159, 547, 548, 546, 546, 547</td>\n",
       "      <td>1, 1, 1, 1, 2, 1, 1, 1</td>\n",
       "      <td>2, 1, 1, 1, 3, 1, 1, 1</td>\n",
       "      <td>SGUI1_21760.1</td>\n",
       "      <td>SBOV2_21580.1, SGUI1_21760.1</td>\n",
       "      <td>96.2</td>\n",
       "      <td>28.832117</td>\n",
       "      <td>-244.025157</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.275220</td>\n",
       "      <td>0.314270</td>\n",
       "      <td>-2.645703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005229</th>\n",
       "      <td>SBOV2_20850.1, SCUR1_19760.1, SGUI1_19360.1, S...</td>\n",
       "      <td>6, 9, 14, 14, 8, 12, 9, 9</td>\n",
       "      <td>1048, 1476, 2018, 2018, 1463, 2062, 1471, 1472</td>\n",
       "      <td>1, 2, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>2, 3, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>SBOV2_20850.1</td>\n",
       "      <td>SBOV2_20850.1, SGUI1_19360.1</td>\n",
       "      <td>97.9</td>\n",
       "      <td>49.058474</td>\n",
       "      <td>-40.648855</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.223614</td>\n",
       "      <td>0.286432</td>\n",
       "      <td>-1.704161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0008213</th>\n",
       "      <td>SBOV2_61800.1, SCUR1_62070.1, SGUI1_62680.1, S...</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>646, 646, 645, 646, 646, 582, 325, 644</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>Smp_326080.1</td>\n",
       "      <td>SRDH2_63830.1, Smp_326080.1</td>\n",
       "      <td>97.7</td>\n",
       "      <td>45.017182</td>\n",
       "      <td>-98.615385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.187998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.593416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005159</th>\n",
       "      <td>SBOV2_13140.1, SCUR1_13530.1, SGUI1_12700.1, S...</td>\n",
       "      <td>5, 5, 5, 5, 3, 5, 5, 5</td>\n",
       "      <td>629, 630, 630, 633, 215, 623, 625, 615</td>\n",
       "      <td>1, 1, 1, 1, 2, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 3, 2, 1, 1, 1</td>\n",
       "      <td>SINT2_12760.1</td>\n",
       "      <td>SHAE2_15250.3, SINT2_12760.1</td>\n",
       "      <td>97.7</td>\n",
       "      <td>33.807267</td>\n",
       "      <td>-191.627907</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.253165</td>\n",
       "      <td>0.148865</td>\n",
       "      <td>-2.643791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005165</th>\n",
       "      <td>SBOV2_13480.1, SCUR1_13820.1, SGUI1_13180.1, S...</td>\n",
       "      <td>2, 1, 2, 2, 1, 1, 2, 1</td>\n",
       "      <td>798, 583, 798, 798, 340, 522, 790, 344</td>\n",
       "      <td>1, 1, 1, 1, 2, 1, 1, 2</td>\n",
       "      <td>1, 1, 1, 1, 3, 1, 1, 2</td>\n",
       "      <td>SINT2_13080.2</td>\n",
       "      <td>SBOV2_13480.1, SINT2_13080.2</td>\n",
       "      <td>96.8</td>\n",
       "      <td>42.606516</td>\n",
       "      <td>-101.911765</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.327139</td>\n",
       "      <td>0.356348</td>\n",
       "      <td>-1.480495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004850</th>\n",
       "      <td>SBOV2_59290.1, SCUR1_59620.1, SGUI1_60180.1, S...</td>\n",
       "      <td>4, 8, 7, 13, 13, 13, 13, 13</td>\n",
       "      <td>308, 885, 1143, 1393, 1398, 1381, 1368, 1332</td>\n",
       "      <td>2, 2, 2, 1, 1, 1, 1, 1</td>\n",
       "      <td>2, 2, 2, 2, 1, 1, 1, 1</td>\n",
       "      <td>SBOV2_59290.1</td>\n",
       "      <td>SBOV2_59290.1, SINT2_58540.1</td>\n",
       "      <td>96.7</td>\n",
       "      <td>21.959943</td>\n",
       "      <td>-338.311688</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0.333881</td>\n",
       "      <td>0.345267</td>\n",
       "      <td>-2.345071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0008262</th>\n",
       "      <td>SBOV2_66250.1, SCUR1_66320.1, SGUI1_67000.1, S...</td>\n",
       "      <td>2, 2, 2, 4, 2, 2, 2, 4</td>\n",
       "      <td>173, 173, 173, 359, 173, 173, 173, 260</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>SHAE2_70880.1</td>\n",
       "      <td>SBOV2_66250.1, SHAE2_70880.1</td>\n",
       "      <td>97.2</td>\n",
       "      <td>40.111421</td>\n",
       "      <td>51.810585</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.330734</td>\n",
       "      <td>0.370328</td>\n",
       "      <td>2.370124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004771</th>\n",
       "      <td>SBOV2_46560.1, SCUR1_47290.2, SGUI1_47160.1, S...</td>\n",
       "      <td>4, 9, 9, 9, 9, 9, 9, 8</td>\n",
       "      <td>105, 590, 578, 581, 573, 562, 562, 395</td>\n",
       "      <td>2, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>2, 3, 1, 1, 1, 1, 1, 2</td>\n",
       "      <td>SBOV2_46560.1</td>\n",
       "      <td>SBOV2_46560.1, SCUR1_47290.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>17.796610</td>\n",
       "      <td>-440.476190</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.343071</td>\n",
       "      <td>0.212430</td>\n",
       "      <td>-2.452766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004788</th>\n",
       "      <td>SBOV2_49560.1, SCUR1_50240.1, SGUI1_50340.1, S...</td>\n",
       "      <td>2, 3, 2, 2, 2, 2, 2, 2</td>\n",
       "      <td>105, 179, 105, 89, 102, 105, 105, 105</td>\n",
       "      <td>1, 1, 1, 2, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 5, 1, 1, 1, 1</td>\n",
       "      <td>SCUR1_50240.1</td>\n",
       "      <td>SCUR1_50240.1, SHAE2_53460.3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>41.340782</td>\n",
       "      <td>41.340782</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.247410</td>\n",
       "      <td>0.166378</td>\n",
       "      <td>2.592570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004941</th>\n",
       "      <td>SBOV2_72170.1, SCUR1_72150.1, SGUI1_73220.1, S...</td>\n",
       "      <td>9, 9, 2, 9, 9, 9, 9, 9</td>\n",
       "      <td>1013, 1004, 347, 1009, 1010, 1016, 1014, 1012</td>\n",
       "      <td>1, 1, 2, 1, 1, 2, 1, 1</td>\n",
       "      <td>1, 1, 2, 1, 1, 4, 1, 1</td>\n",
       "      <td>SGUI1_73220.1</td>\n",
       "      <td>SBOV2_72170.1, SGUI1_73220.1</td>\n",
       "      <td>98.2</td>\n",
       "      <td>33.267522</td>\n",
       "      <td>-191.354467</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.253024</td>\n",
       "      <td>0.304600</td>\n",
       "      <td>-2.645433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004940</th>\n",
       "      <td>SBOV2_72160.1, SCUR1_72140.1, SGUI1_73210.1, S...</td>\n",
       "      <td>7, 3, 7, 7, 8, 8, 7, 7</td>\n",
       "      <td>629, 262, 630, 629, 908, 640, 633, 621</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 2, 2, 2, 1, 2</td>\n",
       "      <td>SCUR1_72140.1</td>\n",
       "      <td>SCUR1_72140.1, SINT2_71240.2</td>\n",
       "      <td>97.2</td>\n",
       "      <td>23.237885</td>\n",
       "      <td>-140.267176</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.281099</td>\n",
       "      <td>0.234243</td>\n",
       "      <td>-2.193385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004972</th>\n",
       "      <td>SBOV2_7640.1, SCUR1_7660.1, SGUI1_7280.1, SHAE...</td>\n",
       "      <td>12, 12, 12, 12, 7, 11, 11, 12</td>\n",
       "      <td>1119, 1134, 1119, 1119, 460, 1102, 1102, 1049</td>\n",
       "      <td>1, 1, 1, 1, 2, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 2, 3, 2, 1</td>\n",
       "      <td>SINT2_7490.1</td>\n",
       "      <td>SCUR1_7660.1, SINT2_7490.1</td>\n",
       "      <td>96.7</td>\n",
       "      <td>39.506173</td>\n",
       "      <td>-141.413043</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.224204</td>\n",
       "      <td>0.155226</td>\n",
       "      <td>-2.629358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004966</th>\n",
       "      <td>SBOV2_75200.3, SCUR1_75540.2, SGUI1_76570.1, S...</td>\n",
       "      <td>5, 4, 5, 5, 5, 5, 5, 4</td>\n",
       "      <td>136, 144, 183, 183, 183, 181, 181, 160</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>3, 2, 2, 1, 1, 1, 1, 1</td>\n",
       "      <td>SCUR1_75540.2</td>\n",
       "      <td>SBOV2_75200.3, SCUR1_75540.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.888889</td>\n",
       "      <td>-25.694444</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.115668</td>\n",
       "      <td>0.097455</td>\n",
       "      <td>-1.361386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0004895</th>\n",
       "      <td>SBOV2_65820.1, SCUR1_65770.1, SGUI1_69820.1, S...</td>\n",
       "      <td>9, 12, 4, 11, 14, 9, 6, 6</td>\n",
       "      <td>317, 515, 122, 354, 608, 328, 292, 233</td>\n",
       "      <td>2, 1, 1, 2, 1, 2, 2, 1</td>\n",
       "      <td>2, 1, 1, 2, 1, 2, 2, 1</td>\n",
       "      <td>SGUI1_69820.1</td>\n",
       "      <td>SGUI1_69820.1, SINT2_64620.1</td>\n",
       "      <td>97.2</td>\n",
       "      <td>17.434211</td>\n",
       "      <td>-164.344262</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.442619</td>\n",
       "      <td>0.382992</td>\n",
       "      <td>-1.563952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005618</th>\n",
       "      <td>SBOV2_70430.1, SCUR1_70340.1, SGUI1_71430.1, S...</td>\n",
       "      <td>7, 2, 7, 7, 7, 7, 7, 7</td>\n",
       "      <td>433, 75, 433, 433, 433, 433, 433, 433</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 1, 1, 4, 1, 1</td>\n",
       "      <td>SCUR1_70340.1</td>\n",
       "      <td>SBOV2_70430.1, SCUR1_70340.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>17.090069</td>\n",
       "      <td>-477.333333</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.326007</td>\n",
       "      <td>0.277297</td>\n",
       "      <td>-2.645751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N0.HOG0005587</th>\n",
       "      <td>SBOV2_66320.1, SCUR1_66390.1, SGUI1_67080.1, S...</td>\n",
       "      <td>7, 7, 7, 7, 7, 3, 7, 7</td>\n",
       "      <td>293, 293, 293, 236, 293, 137, 293, 293</td>\n",
       "      <td>1, 1, 1, 1, 1, 1, 1, 1</td>\n",
       "      <td>1, 1, 1, 3, 1, 1, 1, 2</td>\n",
       "      <td>SRDH2_69440.1</td>\n",
       "      <td>SHAE2_70950.1, SRDH2_69440.1</td>\n",
       "      <td>96.2</td>\n",
       "      <td>33.050847</td>\n",
       "      <td>-113.868613</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.210047</td>\n",
       "      <td>0.217571</td>\n",
       "      <td>-2.471928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            selected_transcripts  \\\n",
       "HOG                                                                \n",
       "N0.HOG0005095  SBOV2_93720.1, SCUR1_93360.1, SGUI1_95060.2, S...   \n",
       "N0.HOG0005007  SBOV2_81950.2, SCUR1_81960.1, SGUI1_83310.1, S...   \n",
       "N0.HOG0005207  SBOV2_19290.1, SCUR1_21290.1, SGUI1_20930.1, S...   \n",
       "N0.HOG0005202  SBOV2_18710.2, SCUR1_19270.1, SGUI1_18870.1, S...   \n",
       "N0.HOG0005235  SBOV2_21580.1, SCUR1_22110.1, SGUI1_21760.1, S...   \n",
       "N0.HOG0005229  SBOV2_20850.1, SCUR1_19760.1, SGUI1_19360.1, S...   \n",
       "N0.HOG0008213  SBOV2_61800.1, SCUR1_62070.1, SGUI1_62680.1, S...   \n",
       "N0.HOG0005159  SBOV2_13140.1, SCUR1_13530.1, SGUI1_12700.1, S...   \n",
       "N0.HOG0005165  SBOV2_13480.1, SCUR1_13820.1, SGUI1_13180.1, S...   \n",
       "N0.HOG0004850  SBOV2_59290.1, SCUR1_59620.1, SGUI1_60180.1, S...   \n",
       "N0.HOG0008262  SBOV2_66250.1, SCUR1_66320.1, SGUI1_67000.1, S...   \n",
       "N0.HOG0004771  SBOV2_46560.1, SCUR1_47290.2, SGUI1_47160.1, S...   \n",
       "N0.HOG0004788  SBOV2_49560.1, SCUR1_50240.1, SGUI1_50340.1, S...   \n",
       "N0.HOG0004941  SBOV2_72170.1, SCUR1_72150.1, SGUI1_73220.1, S...   \n",
       "N0.HOG0004940  SBOV2_72160.1, SCUR1_72140.1, SGUI1_73210.1, S...   \n",
       "N0.HOG0004972  SBOV2_7640.1, SCUR1_7660.1, SGUI1_7280.1, SHAE...   \n",
       "N0.HOG0004966  SBOV2_75200.3, SCUR1_75540.2, SGUI1_76570.1, S...   \n",
       "N0.HOG0004895  SBOV2_65820.1, SCUR1_65770.1, SGUI1_69820.1, S...   \n",
       "N0.HOG0005618  SBOV2_70430.1, SCUR1_70340.1, SGUI1_71430.1, S...   \n",
       "N0.HOG0005587  SBOV2_66320.1, SCUR1_66390.1, SGUI1_67080.1, S...   \n",
       "\n",
       "                                 exon_counts  \\\n",
       "HOG                                            \n",
       "N0.HOG0005095         6, 6, 5, 6, 7, 2, 6, 7   \n",
       "N0.HOG0005007        9, 10, 9, 9, 9, 9, 9, 9   \n",
       "N0.HOG0005207  13, 13, 13, 5, 13, 10, 13, 13   \n",
       "N0.HOG0005202         4, 5, 5, 5, 5, 3, 3, 5   \n",
       "N0.HOG0005235         5, 5, 1, 5, 5, 5, 5, 5   \n",
       "N0.HOG0005229      6, 9, 14, 14, 8, 12, 9, 9   \n",
       "N0.HOG0008213         1, 1, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0005159         5, 5, 5, 5, 3, 5, 5, 5   \n",
       "N0.HOG0005165         2, 1, 2, 2, 1, 1, 2, 1   \n",
       "N0.HOG0004850    4, 8, 7, 13, 13, 13, 13, 13   \n",
       "N0.HOG0008262         2, 2, 2, 4, 2, 2, 2, 4   \n",
       "N0.HOG0004771         4, 9, 9, 9, 9, 9, 9, 8   \n",
       "N0.HOG0004788         2, 3, 2, 2, 2, 2, 2, 2   \n",
       "N0.HOG0004941         9, 9, 2, 9, 9, 9, 9, 9   \n",
       "N0.HOG0004940         7, 3, 7, 7, 8, 8, 7, 7   \n",
       "N0.HOG0004972  12, 12, 12, 12, 7, 11, 11, 12   \n",
       "N0.HOG0004966         5, 4, 5, 5, 5, 5, 5, 4   \n",
       "N0.HOG0004895      9, 12, 4, 11, 14, 9, 6, 6   \n",
       "N0.HOG0005618         7, 2, 7, 7, 7, 7, 7, 7   \n",
       "N0.HOG0005587         7, 7, 7, 7, 7, 3, 7, 7   \n",
       "\n",
       "                                              protein_lengths  \\\n",
       "HOG                                                             \n",
       "N0.HOG0005095           148, 148, 112, 148, 145, 64, 148, 198   \n",
       "N0.HOG0005007         281, 4099, 269, 287, 287, 282, 282, 295   \n",
       "N0.HOG0005207          647, 647, 647, 296, 647, 537, 647, 646   \n",
       "N0.HOG0005202          202, 237, 237, 222, 237, 121, 121, 238   \n",
       "N0.HOG0005235          548, 548, 159, 547, 548, 546, 546, 547   \n",
       "N0.HOG0005229  1048, 1476, 2018, 2018, 1463, 2062, 1471, 1472   \n",
       "N0.HOG0008213          646, 646, 645, 646, 646, 582, 325, 644   \n",
       "N0.HOG0005159          629, 630, 630, 633, 215, 623, 625, 615   \n",
       "N0.HOG0005165          798, 583, 798, 798, 340, 522, 790, 344   \n",
       "N0.HOG0004850    308, 885, 1143, 1393, 1398, 1381, 1368, 1332   \n",
       "N0.HOG0008262          173, 173, 173, 359, 173, 173, 173, 260   \n",
       "N0.HOG0004771          105, 590, 578, 581, 573, 562, 562, 395   \n",
       "N0.HOG0004788           105, 179, 105, 89, 102, 105, 105, 105   \n",
       "N0.HOG0004941   1013, 1004, 347, 1009, 1010, 1016, 1014, 1012   \n",
       "N0.HOG0004940          629, 262, 630, 629, 908, 640, 633, 621   \n",
       "N0.HOG0004972   1119, 1134, 1119, 1119, 460, 1102, 1102, 1049   \n",
       "N0.HOG0004966          136, 144, 183, 183, 183, 181, 181, 160   \n",
       "N0.HOG0004895          317, 515, 122, 354, 608, 328, 292, 233   \n",
       "N0.HOG0005618           433, 75, 433, 433, 433, 433, 433, 433   \n",
       "N0.HOG0005587          293, 293, 293, 236, 293, 137, 293, 293   \n",
       "\n",
       "                          gene_counts       transcript_counts  \\\n",
       "HOG                                                             \n",
       "N0.HOG0005095  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 2, 2, 1, 1, 2, 2   \n",
       "N0.HOG0005007  1, 1, 1, 1, 1, 1, 1, 1  5, 1, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0005207  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 1, 1, 1, 3, 1, 2   \n",
       "N0.HOG0005202  1, 1, 1, 1, 1, 2, 2, 1  2, 1, 1, 1, 1, 2, 2, 1   \n",
       "N0.HOG0005235  1, 1, 1, 1, 2, 1, 1, 1  2, 1, 1, 1, 3, 1, 1, 1   \n",
       "N0.HOG0005229  1, 2, 1, 1, 1, 1, 1, 1  2, 3, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0008213  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0005159  1, 1, 1, 1, 2, 1, 1, 1  1, 1, 1, 3, 2, 1, 1, 1   \n",
       "N0.HOG0005165  1, 1, 1, 1, 2, 1, 1, 2  1, 1, 1, 1, 3, 1, 1, 2   \n",
       "N0.HOG0004850  2, 2, 2, 1, 1, 1, 1, 1  2, 2, 2, 2, 1, 1, 1, 1   \n",
       "N0.HOG0008262  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 1, 1, 1, 1, 1, 1   \n",
       "N0.HOG0004771  2, 1, 1, 1, 1, 1, 1, 1  2, 3, 1, 1, 1, 1, 1, 2   \n",
       "N0.HOG0004788  1, 1, 1, 2, 1, 1, 1, 1  1, 1, 1, 5, 1, 1, 1, 1   \n",
       "N0.HOG0004941  1, 1, 2, 1, 1, 2, 1, 1  1, 1, 2, 1, 1, 4, 1, 1   \n",
       "N0.HOG0004940  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 1, 2, 2, 2, 1, 2   \n",
       "N0.HOG0004972  1, 1, 1, 1, 2, 1, 1, 1  1, 1, 1, 1, 2, 3, 2, 1   \n",
       "N0.HOG0004966  1, 1, 1, 1, 1, 1, 1, 1  3, 2, 2, 1, 1, 1, 1, 1   \n",
       "N0.HOG0004895  2, 1, 1, 2, 1, 2, 2, 1  2, 1, 1, 2, 1, 2, 2, 1   \n",
       "N0.HOG0005618  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 1, 1, 1, 4, 1, 1   \n",
       "N0.HOG0005587  1, 1, 1, 1, 1, 1, 1, 1  1, 1, 1, 3, 1, 1, 1, 2   \n",
       "\n",
       "              worst_transcript                    worst_pair  blast_pident  \\\n",
       "HOG                                                                          \n",
       "N0.HOG0005095    SRDH2_98060.1  SBOV2_93720.1, SRDH2_98060.1          97.6   \n",
       "N0.HOG0005007    SCUR1_81960.1  SCUR1_81960.1, SGUI1_83310.1          95.7   \n",
       "N0.HOG0005207    SHAE2_23450.1  SBOV2_19290.1, SHAE2_23450.1          99.3   \n",
       "N0.HOG0005202    SRDH2_19700.1  SHAE2_21430.1, SRDH2_19700.1         100.0   \n",
       "N0.HOG0005235    SGUI1_21760.1  SBOV2_21580.1, SGUI1_21760.1          96.2   \n",
       "N0.HOG0005229    SBOV2_20850.1  SBOV2_20850.1, SGUI1_19360.1          97.9   \n",
       "N0.HOG0008213     Smp_326080.1   SRDH2_63830.1, Smp_326080.1          97.7   \n",
       "N0.HOG0005159    SINT2_12760.1  SHAE2_15250.3, SINT2_12760.1          97.7   \n",
       "N0.HOG0005165    SINT2_13080.2  SBOV2_13480.1, SINT2_13080.2          96.8   \n",
       "N0.HOG0004850    SBOV2_59290.1  SBOV2_59290.1, SINT2_58540.1          96.7   \n",
       "N0.HOG0008262    SHAE2_70880.1  SBOV2_66250.1, SHAE2_70880.1          97.2   \n",
       "N0.HOG0004771    SBOV2_46560.1  SBOV2_46560.1, SCUR1_47290.2         100.0   \n",
       "N0.HOG0004788    SCUR1_50240.1  SCUR1_50240.1, SHAE2_53460.3         100.0   \n",
       "N0.HOG0004941    SGUI1_73220.1  SBOV2_72170.1, SGUI1_73220.1          98.2   \n",
       "N0.HOG0004940    SCUR1_72140.1  SCUR1_72140.1, SINT2_71240.2          97.2   \n",
       "N0.HOG0004972     SINT2_7490.1    SCUR1_7660.1, SINT2_7490.1          96.7   \n",
       "N0.HOG0004966    SCUR1_75540.2  SBOV2_75200.3, SCUR1_75540.2         100.0   \n",
       "N0.HOG0004895    SGUI1_69820.1  SGUI1_69820.1, SINT2_64620.1          97.2   \n",
       "N0.HOG0005618    SCUR1_70340.1  SBOV2_70430.1, SCUR1_70340.1         100.0   \n",
       "N0.HOG0005587    SRDH2_69440.1  SHAE2_70950.1, SRDH2_69440.1          96.2   \n",
       "\n",
       "               align_pident  prot_lengt_dev_pc  exon_count_dev  \\\n",
       "HOG                                                              \n",
       "N0.HOG0005095     28.378378        -131.250000            -4.0   \n",
       "N0.HOG0005007      6.172237          93.059283             1.0   \n",
       "N0.HOG0005207     43.894900        -118.581081            -8.0   \n",
       "N0.HOG0005202     39.189189         -89.669421            -2.0   \n",
       "N0.HOG0005235     28.832117        -244.025157            -4.0   \n",
       "N0.HOG0005229     49.058474         -40.648855            -3.0   \n",
       "N0.HOG0008213     45.017182         -98.615385             0.0   \n",
       "N0.HOG0005159     33.807267        -191.627907            -2.0   \n",
       "N0.HOG0005165     42.606516        -101.911765            -0.5   \n",
       "N0.HOG0004850     21.959943        -338.311688            -9.0   \n",
       "N0.HOG0008262     40.111421          51.810585             2.0   \n",
       "N0.HOG0004771     17.796610        -440.476190            -5.0   \n",
       "N0.HOG0004788     41.340782          41.340782             1.0   \n",
       "N0.HOG0004941     33.267522        -191.354467            -7.0   \n",
       "N0.HOG0004940     23.237885        -140.267176            -4.0   \n",
       "N0.HOG0004972     39.506173        -141.413043            -5.0   \n",
       "N0.HOG0004966     38.888889         -25.694444            -1.0   \n",
       "N0.HOG0004895     17.434211        -164.344262            -5.0   \n",
       "N0.HOG0005618     17.090069        -477.333333            -5.0   \n",
       "N0.HOG0005587     33.050847        -113.868613            -4.0   \n",
       "\n",
       "               prot_lengt_cov  exon_count_cov  prot_lengt_zscore  \n",
       "HOG                                                               \n",
       "N0.HOG0005095        0.274801        0.284087          -2.097446  \n",
       "N0.HOG0005007        1.774520        0.038746           2.645712  \n",
       "N0.HOG0005207        0.211402        0.247345          -2.516663  \n",
       "N0.HOG0005202        0.254607        0.209400          -1.682120  \n",
       "N0.HOG0005235        0.275220        0.314270          -2.645703  \n",
       "N0.HOG0005229        0.223614        0.286432          -1.704161  \n",
       "N0.HOG0008213        0.187998        0.000000          -2.593416  \n",
       "N0.HOG0005159        0.253165        0.148865          -2.643791  \n",
       "N0.HOG0005165        0.327139        0.356348          -1.480495  \n",
       "N0.HOG0004850        0.333881        0.345267          -2.345071  \n",
       "N0.HOG0008262        0.330734        0.370328           2.370124  \n",
       "N0.HOG0004771        0.343071        0.212430          -2.452766  \n",
       "N0.HOG0004788        0.247410        0.166378           2.592570  \n",
       "N0.HOG0004941        0.253024        0.304600          -2.645433  \n",
       "N0.HOG0004940        0.281099        0.234243          -2.193385  \n",
       "N0.HOG0004972        0.224204        0.155226          -2.629358  \n",
       "N0.HOG0004966        0.115668        0.097455          -1.361386  \n",
       "N0.HOG0004895        0.442619        0.382992          -1.563952  \n",
       "N0.HOG0005618        0.326007        0.277297          -2.645751  \n",
       "N0.HOG0005587        0.210047        0.217571          -2.471928  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = df1[(df1[\"align_pident\"] < 50) & (df1[\"blast_pident\"] > 95)]\n",
    "candidates.head(20)"
   ]
  },
  {
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172.15831761176025\n",
      "44.97051849980446\n",
      "---\n",
      "5.6253561253561255\n",
      "2.1521931115690314\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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N0.HOG0008518\n",
      "SCUR1_85140.1 - 56.55 - 96.7\n",
      "SCUR1_85140.1, SRDH2_88900.1\n",
      "prot length deviation: -20%\n",
      "exon count deviation: +0\n"
     ]
    },
    {
     "data": {
      "image/png": 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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 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_May10/{hog}.png\", width=600))\n",
    "    print()"
   ]
  },
  {
   "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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",
      "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
}