motif_PLM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse par classification des PLM et de l'expression de leur gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mise en place des données et séparation entre expression negative et positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            1101  1104   29   30  1977  1973  1980  1976  1978  1974  ...  \\\n",
      "Unnamed: 0                                                            ...   \n",
      "AT1G01010    0.0   0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "AT1G01030    0.0   0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "AT1G01040    0.0   0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "AT1G01050    0.0   0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "AT1G01060    0.0   0.0  1.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "...          ...   ...  ...  ...   ...   ...   ...   ...   ...   ...  ...   \n",
      "AT5G67550    0.0   0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "AT5G67560    0.0   0.0  0.0 -1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "AT5G67590    0.0   0.0  0.0  0.0   0.0   0.0   0.0   0.0   1.0   0.0  ...   \n",
      "AT5G67620    0.0   0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "AT5G67630    0.0   0.0  0.0  1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "\n",
      "            1563  796  797  799  800  798  801  1296  1677  1672  \n",
      "Unnamed: 0                                                        \n",
      "AT1G01010    0.0  0.0  0.0  0.0  0.0  0.0  0.0   1.0   1.0   1.0  \n",
      "AT1G01030    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
      "AT1G01040    0.0  1.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
      "AT1G01050    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
      "AT1G01060    0.0  0.0  0.0  0.0  0.0 -1.0  0.0   0.0  -1.0  -1.0  \n",
      "...          ...  ...  ...  ...  ...  ...  ...   ...   ...   ...  \n",
      "AT5G67550    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
      "AT5G67560    0.0  0.0  0.0  0.0  0.0  0.0 -1.0   0.0   0.0   0.0  \n",
      "AT5G67590   -1.0 -1.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
      "AT5G67620    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
      "AT5G67630    0.0  0.0  0.0  0.0  0.0 -1.0  0.0   1.0   0.0   0.0  \n",
      "\n",
      "[10769 rows x 387 columns]\n"
     ]
    }
   ],
   "source": [
    "complete_data = pd.read_csv(\"data/expression_final.csv\", index_col=0)\n",
    "print(complete_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Info_allswap = pd.read_table(\"data/Info-allswap.txt\")\n",
    "Info_allswap[\"uniq_name\"] = Info_allswap[['StressName', 'swap_name']].agg('__'.join, axis=1)\n",
    "\n",
    "replace_col = {str(k): Info_allswap[Info_allswap[\"swap_id\"] == k][\"uniq_name\"].values.tolist()[0] for k in Info_allswap[\"swap_id\"]}\n",
    "\n",
    "complete_data = complete_data.rename(replace_col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1  \\\n",
      "Unnamed: 0                                                       \n",
      "AT1G01010                                               False    \n",
      "AT1G01030                                               False    \n",
      "AT1G01040                                               False    \n",
      "AT1G01050                                               False    \n",
      "AT1G01060                                               False    \n",
      "...                                                       ...    \n",
      "AT5G67550                                               False    \n",
      "AT5G67560                                               False    \n",
      "AT5G67590                                               False    \n",
      "AT5G67620                                               False    \n",
      "AT5G67630                                               False    \n",
      "\n",
      "            DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2  \\\n",
      "Unnamed: 0                                                       \n",
      "AT1G01010                                               False    \n",
      "AT1G01030                                               False    \n",
      "AT1G01040                                               False    \n",
      "AT1G01050                                               False    \n",
      "AT1G01060                                               False    \n",
      "...                                                       ...    \n",
      "AT5G67550                                               False    \n",
      "AT5G67560                                               False    \n",
      "AT5G67590                                               False    \n",
      "AT5G67620                                               False    \n",
      "AT5G67630                                               False    \n",
      "\n",
      "            DROUGHT__Col0_stress / Col0  \\\n",
      "Unnamed: 0                                \n",
      "AT1G01010                         False   \n",
      "AT1G01030                         False   \n",
      "AT1G01040                         False   \n",
      "AT1G01050                         False   \n",
      "AT1G01060                          True   \n",
      "...                                 ...   \n",
      "AT5G67550                         False   \n",
      "AT5G67560                         False   \n",
      "AT5G67590                         False   \n",
      "AT5G67620                         False   \n",
      "AT5G67630                         False   \n",
      "\n",
      "            DROUGHT__35S_HAHB4_stress / 35S_HAHB4  \\\n",
      "Unnamed: 0                                          \n",
      "AT1G01010                                   False   \n",
      "AT1G01030                                   False   \n",
      "AT1G01040                                   False   \n",
      "AT1G01050                                   False   \n",
      "AT1G01060                                   False   \n",
      "...                                           ...   \n",
      "AT5G67550                                   False   \n",
      "AT5G67560                                   False   \n",
      "AT5G67590                                   False   \n",
      "AT5G67620                                   False   \n",
      "AT5G67630                                    True   \n",
      "\n",
      "            DROUGHT__Col0_M_1 / Col0_control_1  \\\n",
      "Unnamed: 0                                       \n",
      "AT1G01010                                False   \n",
      "AT1G01030                                False   \n",
      "AT1G01040                                False   \n",
      "AT1G01050                                False   \n",
      "AT1G01060                                False   \n",
      "...                                        ...   \n",
      "AT5G67550                                False   \n",
      "AT5G67560                                False   \n",
      "AT5G67590                                False   \n",
      "AT5G67620                                False   \n",
      "AT5G67630                                False   \n",
      "\n",
      "            DROUGHT__Col0_M_2 / Col0_control_2  DROUGHT__W498_M_1 / Col0_M_1  \\\n",
      "Unnamed: 0                                                                     \n",
      "AT1G01010                                False                         False   \n",
      "AT1G01030                                False                         False   \n",
      "AT1G01040                                False                         False   \n",
      "AT1G01050                                False                         False   \n",
      "AT1G01060                                False                         False   \n",
      "...                                        ...                           ...   \n",
      "AT5G67550                                False                         False   \n",
      "AT5G67560                                False                         False   \n",
      "AT5G67590                                False                         False   \n",
      "AT5G67620                                False                         False   \n",
      "AT5G67630                                False                         False   \n",
      "\n",
      "            DROUGHT__W498_M_2 / Col0_M_2  DROUGHT__W498_M_1 / W498_control_1  \\\n",
      "Unnamed: 0                                                                     \n",
      "AT1G01010                          False                               False   \n",
      "AT1G01030                          False                               False   \n",
      "AT1G01040                          False                               False   \n",
      "AT1G01050                          False                               False   \n",
      "AT1G01060                          False                               False   \n",
      "...                                  ...                                 ...   \n",
      "AT5G67550                          False                               False   \n",
      "AT5G67560                          False                               False   \n",
      "AT5G67590                          False                                True   \n",
      "AT5G67620                          False                               False   \n",
      "AT5G67630                          False                               False   \n",
      "\n",
      "            DROUGHT__W498_M_2 / W498_control_2  ...  \\\n",
      "Unnamed: 0                                      ...   \n",
      "AT1G01010                                False  ...   \n",
      "AT1G01030                                False  ...   \n",
      "AT1G01040                                False  ...   \n",
      "AT1G01050                                False  ...   \n",
      "AT1G01060                                False  ...   \n",
      "...                                        ...  ...   \n",
      "AT5G67550                                False  ...   \n",
      "AT5G67560                                False  ...   \n",
      "AT5G67590                                False  ...   \n",
      "AT5G67620                                False  ...   \n",
      "AT5G67630                                False  ...   \n",
      "\n",
      "            VIRUS__LMV_Bl1_2 / Bl1_Control_2  VIRUS__CaMV_Col0 / Col0_Control  \\\n",
      "Unnamed: 0                                                                      \n",
      "AT1G01010                              False                            False   \n",
      "AT1G01030                              False                            False   \n",
      "AT1G01040                              False                             True   \n",
      "AT1G01050                              False                            False   \n",
      "AT1G01060                              False                            False   \n",
      "...                                      ...                              ...   \n",
      "AT5G67550                              False                            False   \n",
      "AT5G67560                              False                            False   \n",
      "AT5G67590                              False                            False   \n",
      "AT5G67620                              False                            False   \n",
      "AT5G67630                              False                            False   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl3 / CaMV_Col0  \\\n",
      "Unnamed: 0                                      \n",
      "AT1G01010                               False   \n",
      "AT1G01030                               False   \n",
      "AT1G01040                               False   \n",
      "AT1G01050                               False   \n",
      "AT1G01060                               False   \n",
      "...                                       ...   \n",
      "AT5G67550                               False   \n",
      "AT5G67560                               False   \n",
      "AT5G67590                               False   \n",
      "AT5G67620                               False   \n",
      "AT5G67630                               False   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl4 / CaMV_Col0  \\\n",
      "Unnamed: 0                                      \n",
      "AT1G01010                               False   \n",
      "AT1G01030                               False   \n",
      "AT1G01040                               False   \n",
      "AT1G01050                               False   \n",
      "AT1G01060                               False   \n",
      "...                                       ...   \n",
      "AT5G67550                               False   \n",
      "AT5G67560                               False   \n",
      "AT5G67590                               False   \n",
      "AT5G67620                               False   \n",
      "AT5G67630                               False   \n",
      "\n",
      "            VIRUS__CaMV_dcl3_dcl4 / CaMV_Col0  \\\n",
      "Unnamed: 0                                      \n",
      "AT1G01010                               False   \n",
      "AT1G01030                               False   \n",
      "AT1G01040                               False   \n",
      "AT1G01050                               False   \n",
      "AT1G01060                               False   \n",
      "...                                       ...   \n",
      "AT5G67550                               False   \n",
      "AT5G67560                               False   \n",
      "AT5G67590                               False   \n",
      "AT5G67620                               False   \n",
      "AT5G67630                               False   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0  \\\n",
      "Unnamed: 0                                           \n",
      "AT1G01010                                    False   \n",
      "AT1G01030                                    False   \n",
      "AT1G01040                                    False   \n",
      "AT1G01050                                    False   \n",
      "AT1G01060                                    False   \n",
      "...                                            ...   \n",
      "AT5G67550                                    False   \n",
      "AT5G67560                                    False   \n",
      "AT5G67590                                    False   \n",
      "AT5G67620                                    False   \n",
      "AT5G67630                                    False   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Control  \\\n",
      "Unnamed: 0                                                        \n",
      "AT1G01010                                               False     \n",
      "AT1G01030                                               False     \n",
      "AT1G01040                                               False     \n",
      "AT1G01050                                               False     \n",
      "AT1G01060                                               False     \n",
      "...                                                       ...     \n",
      "AT5G67550                                               False     \n",
      "AT5G67560                                               False     \n",
      "AT5G67590                                               False     \n",
      "AT5G67620                                               False     \n",
      "AT5G67630                                               False     \n",
      "\n",
      "            VIRUS__TCV_G1_Col0_2 / G1_Col0_2  \\\n",
      "Unnamed: 0                                     \n",
      "AT1G01010                               True   \n",
      "AT1G01030                              False   \n",
      "AT1G01040                              False   \n",
      "AT1G01050                              False   \n",
      "AT1G01060                              False   \n",
      "...                                      ...   \n",
      "AT5G67550                              False   \n",
      "AT5G67560                              False   \n",
      "AT5G67590                              False   \n",
      "AT5G67620                              False   \n",
      "AT5G67630                               True   \n",
      "\n",
      "            VIRUS__TCV_G11_Col0_1 / G11_Col0_1  \\\n",
      "Unnamed: 0                                       \n",
      "AT1G01010                                 True   \n",
      "AT1G01030                                False   \n",
      "AT1G01040                                False   \n",
      "AT1G01050                                False   \n",
      "AT1G01060                                False   \n",
      "...                                        ...   \n",
      "AT5G67550                                False   \n",
      "AT5G67560                                False   \n",
      "AT5G67590                                False   \n",
      "AT5G67620                                False   \n",
      "AT5G67630                                False   \n",
      "\n",
      "            VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3_dcl4_1  \n",
      "Unnamed: 0                                                          \n",
      "AT1G01010                                                True       \n",
      "AT1G01030                                               False       \n",
      "AT1G01040                                               False       \n",
      "AT1G01050                                               False       \n",
      "AT1G01060                                               False       \n",
      "...                                                       ...       \n",
      "AT5G67550                                               False       \n",
      "AT5G67560                                               False       \n",
      "AT5G67590                                               False       \n",
      "AT5G67620                                               False       \n",
      "AT5G67630                                               False       \n",
      "\n",
      "[10769 rows x 387 columns]\n"
     ]
    }
   ],
   "source": [
    "plus_data = np.where(complete_data == 1, True, False)\n",
    "plus_data = pd.DataFrame(plus_data, index=complete_data.index, columns=complete_data.columns)\n",
    "print(plus_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1  \\\n",
      "Unnamed: 0                                                       \n",
      "AT1G01010                                               False    \n",
      "AT1G01030                                               False    \n",
      "AT1G01040                                               False    \n",
      "AT1G01050                                               False    \n",
      "AT1G01060                                               False    \n",
      "...                                                       ...    \n",
      "AT5G67550                                               False    \n",
      "AT5G67560                                               False    \n",
      "AT5G67590                                               False    \n",
      "AT5G67620                                               False    \n",
      "AT5G67630                                               False    \n",
      "\n",
      "            DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2  \\\n",
      "Unnamed: 0                                                       \n",
      "AT1G01010                                               False    \n",
      "AT1G01030                                               False    \n",
      "AT1G01040                                               False    \n",
      "AT1G01050                                               False    \n",
      "AT1G01060                                               False    \n",
      "...                                                       ...    \n",
      "AT5G67550                                               False    \n",
      "AT5G67560                                               False    \n",
      "AT5G67590                                               False    \n",
      "AT5G67620                                               False    \n",
      "AT5G67630                                               False    \n",
      "\n",
      "            DROUGHT__Col0_stress / Col0  \\\n",
      "Unnamed: 0                                \n",
      "AT1G01010                         False   \n",
      "AT1G01030                         False   \n",
      "AT1G01040                         False   \n",
      "AT1G01050                         False   \n",
      "AT1G01060                         False   \n",
      "...                                 ...   \n",
      "AT5G67550                         False   \n",
      "AT5G67560                         False   \n",
      "AT5G67590                         False   \n",
      "AT5G67620                         False   \n",
      "AT5G67630                         False   \n",
      "\n",
      "            DROUGHT__35S_HAHB4_stress / 35S_HAHB4  \\\n",
      "Unnamed: 0                                          \n",
      "AT1G01010                                   False   \n",
      "AT1G01030                                   False   \n",
      "AT1G01040                                   False   \n",
      "AT1G01050                                   False   \n",
      "AT1G01060                                   False   \n",
      "...                                           ...   \n",
      "AT5G67550                                   False   \n",
      "AT5G67560                                    True   \n",
      "AT5G67590                                   False   \n",
      "AT5G67620                                   False   \n",
      "AT5G67630                                   False   \n",
      "\n",
      "            DROUGHT__Col0_M_1 / Col0_control_1  \\\n",
      "Unnamed: 0                                       \n",
      "AT1G01010                                False   \n",
      "AT1G01030                                False   \n",
      "AT1G01040                                False   \n",
      "AT1G01050                                False   \n",
      "AT1G01060                                False   \n",
      "...                                        ...   \n",
      "AT5G67550                                False   \n",
      "AT5G67560                                False   \n",
      "AT5G67590                                False   \n",
      "AT5G67620                                False   \n",
      "AT5G67630                                False   \n",
      "\n",
      "            DROUGHT__Col0_M_2 / Col0_control_2  DROUGHT__W498_M_1 / Col0_M_1  \\\n",
      "Unnamed: 0                                                                     \n",
      "AT1G01010                                False                         False   \n",
      "AT1G01030                                False                         False   \n",
      "AT1G01040                                False                         False   \n",
      "AT1G01050                                False                         False   \n",
      "AT1G01060                                False                         False   \n",
      "...                                        ...                           ...   \n",
      "AT5G67550                                False                         False   \n",
      "AT5G67560                                False                         False   \n",
      "AT5G67590                                False                         False   \n",
      "AT5G67620                                False                         False   \n",
      "AT5G67630                                False                         False   \n",
      "\n",
      "            DROUGHT__W498_M_2 / Col0_M_2  DROUGHT__W498_M_1 / W498_control_1  \\\n",
      "Unnamed: 0                                                                     \n",
      "AT1G01010                          False                               False   \n",
      "AT1G01030                          False                               False   \n",
      "AT1G01040                          False                               False   \n",
      "AT1G01050                          False                               False   \n",
      "AT1G01060                          False                               False   \n",
      "...                                  ...                                 ...   \n",
      "AT5G67550                          False                               False   \n",
      "AT5G67560                          False                               False   \n",
      "AT5G67590                          False                               False   \n",
      "AT5G67620                          False                               False   \n",
      "AT5G67630                          False                               False   \n",
      "\n",
      "            DROUGHT__W498_M_2 / W498_control_2  ...  \\\n",
      "Unnamed: 0                                      ...   \n",
      "AT1G01010                                False  ...   \n",
      "AT1G01030                                False  ...   \n",
      "AT1G01040                                False  ...   \n",
      "AT1G01050                                False  ...   \n",
      "AT1G01060                                False  ...   \n",
      "...                                        ...  ...   \n",
      "AT5G67550                                False  ...   \n",
      "AT5G67560                                False  ...   \n",
      "AT5G67590                                False  ...   \n",
      "AT5G67620                                False  ...   \n",
      "AT5G67630                                False  ...   \n",
      "\n",
      "            VIRUS__LMV_Bl1_2 / Bl1_Control_2  VIRUS__CaMV_Col0 / Col0_Control  \\\n",
      "Unnamed: 0                                                                      \n",
      "AT1G01010                              False                            False   \n",
      "AT1G01030                              False                            False   \n",
      "AT1G01040                              False                            False   \n",
      "AT1G01050                              False                            False   \n",
      "AT1G01060                              False                            False   \n",
      "...                                      ...                              ...   \n",
      "AT5G67550                              False                            False   \n",
      "AT5G67560                              False                            False   \n",
      "AT5G67590                               True                             True   \n",
      "AT5G67620                              False                            False   \n",
      "AT5G67630                              False                            False   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl3 / CaMV_Col0  \\\n",
      "Unnamed: 0                                      \n",
      "AT1G01010                               False   \n",
      "AT1G01030                               False   \n",
      "AT1G01040                               False   \n",
      "AT1G01050                               False   \n",
      "AT1G01060                               False   \n",
      "...                                       ...   \n",
      "AT5G67550                               False   \n",
      "AT5G67560                               False   \n",
      "AT5G67590                               False   \n",
      "AT5G67620                               False   \n",
      "AT5G67630                               False   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl4 / CaMV_Col0  \\\n",
      "Unnamed: 0                                      \n",
      "AT1G01010                               False   \n",
      "AT1G01030                               False   \n",
      "AT1G01040                               False   \n",
      "AT1G01050                               False   \n",
      "AT1G01060                               False   \n",
      "...                                       ...   \n",
      "AT5G67550                               False   \n",
      "AT5G67560                               False   \n",
      "AT5G67590                               False   \n",
      "AT5G67620                               False   \n",
      "AT5G67630                               False   \n",
      "\n",
      "            VIRUS__CaMV_dcl3_dcl4 / CaMV_Col0  \\\n",
      "Unnamed: 0                                      \n",
      "AT1G01010                               False   \n",
      "AT1G01030                               False   \n",
      "AT1G01040                               False   \n",
      "AT1G01050                               False   \n",
      "AT1G01060                               False   \n",
      "...                                       ...   \n",
      "AT5G67550                               False   \n",
      "AT5G67560                               False   \n",
      "AT5G67590                               False   \n",
      "AT5G67620                               False   \n",
      "AT5G67630                               False   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0  \\\n",
      "Unnamed: 0                                           \n",
      "AT1G01010                                    False   \n",
      "AT1G01030                                    False   \n",
      "AT1G01040                                    False   \n",
      "AT1G01050                                    False   \n",
      "AT1G01060                                     True   \n",
      "...                                            ...   \n",
      "AT5G67550                                    False   \n",
      "AT5G67560                                    False   \n",
      "AT5G67590                                    False   \n",
      "AT5G67620                                    False   \n",
      "AT5G67630                                     True   \n",
      "\n",
      "            VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Control  \\\n",
      "Unnamed: 0                                                        \n",
      "AT1G01010                                               False     \n",
      "AT1G01030                                               False     \n",
      "AT1G01040                                               False     \n",
      "AT1G01050                                               False     \n",
      "AT1G01060                                               False     \n",
      "...                                                       ...     \n",
      "AT5G67550                                               False     \n",
      "AT5G67560                                                True     \n",
      "AT5G67590                                               False     \n",
      "AT5G67620                                               False     \n",
      "AT5G67630                                               False     \n",
      "\n",
      "            VIRUS__TCV_G1_Col0_2 / G1_Col0_2  \\\n",
      "Unnamed: 0                                     \n",
      "AT1G01010                              False   \n",
      "AT1G01030                              False   \n",
      "AT1G01040                              False   \n",
      "AT1G01050                              False   \n",
      "AT1G01060                              False   \n",
      "...                                      ...   \n",
      "AT5G67550                              False   \n",
      "AT5G67560                              False   \n",
      "AT5G67590                              False   \n",
      "AT5G67620                              False   \n",
      "AT5G67630                              False   \n",
      "\n",
      "            VIRUS__TCV_G11_Col0_1 / G11_Col0_1  \\\n",
      "Unnamed: 0                                       \n",
      "AT1G01010                                False   \n",
      "AT1G01030                                False   \n",
      "AT1G01040                                False   \n",
      "AT1G01050                                False   \n",
      "AT1G01060                                 True   \n",
      "...                                        ...   \n",
      "AT5G67550                                False   \n",
      "AT5G67560                                False   \n",
      "AT5G67590                                False   \n",
      "AT5G67620                                False   \n",
      "AT5G67630                                False   \n",
      "\n",
      "            VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3_dcl4_1  \n",
      "Unnamed: 0                                                          \n",
      "AT1G01010                                               False       \n",
      "AT1G01030                                               False       \n",
      "AT1G01040                                               False       \n",
      "AT1G01050                                               False       \n",
      "AT1G01060                                                True       \n",
      "...                                                       ...       \n",
      "AT5G67550                                               False       \n",
      "AT5G67560                                               False       \n",
      "AT5G67590                                               False       \n",
      "AT5G67620                                               False       \n",
      "AT5G67630                                               False       \n",
      "\n",
      "[10769 rows x 387 columns]\n"
     ]
    }
   ],
   "source": [
    "minus_data = np.where(complete_data == -1, True, False)\n",
    "minus_data = pd.DataFrame(minus_data, index=complete_data.index, columns=complete_data.columns)\n",
    "print(minus_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nombre de PLM par gene et combinaison de gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMCAATAATTRW_-317</th>\n",
       "      <th>TGTAAAGT_-280</th>\n",
       "      <th>CCAATGT_-275</th>\n",
       "      <th>GGATA_-263</th>\n",
       "      <th>TAACAAA_-261</th>\n",
       "      <th>HDTTAACAGAAWW_-260</th>\n",
       "      <th>AWTTAAWT_-239</th>\n",
       "      <th>RTTTTTR_-229</th>\n",
       "      <th>TATCCA_-226</th>\n",
       "      <th>GRWAAW_-212</th>\n",
       "      <th>...</th>\n",
       "      <th>SAGATCYRR_295</th>\n",
       "      <th>DYCACCGACAHH_312</th>\n",
       "      <th>GTGGWWHG_319</th>\n",
       "      <th>AGATCCAA_326</th>\n",
       "      <th>HYRGATCYRD_334</th>\n",
       "      <th>ATGTCGGYRR_344</th>\n",
       "      <th>YAGATCTR_353</th>\n",
       "      <th>CTGACY_367</th>\n",
       "      <th>ACNGCT_426</th>\n",
       "      <th>AGCAGC_444</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT1G01010</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT1G01030</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT1G01040</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT1G01050</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT1G01060</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT5G67550</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT5G67560</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT5G67590</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT5G67620</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT5G67630</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10769 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           WMCAATAATTRW_-317  TGTAAAGT_-280  CCAATGT_-275  GGATA_-263  \\\n",
       "AT1G01010              False          False         False       False   \n",
       "AT1G01030              False          False         False       False   \n",
       "AT1G01040              False          False         False       False   \n",
       "AT1G01050              False          False         False       False   \n",
       "AT1G01060              False          False         False       False   \n",
       "...                      ...            ...           ...         ...   \n",
       "AT5G67550              False          False         False       False   \n",
       "AT5G67560              False          False         False       False   \n",
       "AT5G67590              False          False         False       False   \n",
       "AT5G67620              False          False         False       False   \n",
       "AT5G67630              False          False         False       False   \n",
       "\n",
       "           TAACAAA_-261  HDTTAACAGAAWW_-260  AWTTAAWT_-239  RTTTTTR_-229  \\\n",
       "AT1G01010         False               False          False         False   \n",
       "AT1G01030         False               False          False         False   \n",
       "AT1G01040         False               False          False         False   \n",
       "AT1G01050         False               False          False         False   \n",
       "AT1G01060         False               False          False         False   \n",
       "...                 ...                 ...            ...           ...   \n",
       "AT5G67550         False               False          False         False   \n",
       "AT5G67560         False               False          False         False   \n",
       "AT5G67590         False               False          False         False   \n",
       "AT5G67620         False               False          False          True   \n",
       "AT5G67630         False               False          False         False   \n",
       "\n",
       "           TATCCA_-226  GRWAAW_-212  ...  SAGATCYRR_295  DYCACCGACAHH_312  \\\n",
       "AT1G01010        False        False  ...          False             False   \n",
       "AT1G01030        False         True  ...          False             False   \n",
       "AT1G01040        False         True  ...          False             False   \n",
       "AT1G01050        False         True  ...          False             False   \n",
       "AT1G01060        False         True  ...          False             False   \n",
       "...                ...          ...  ...            ...               ...   \n",
       "AT5G67550        False         True  ...          False             False   \n",
       "AT5G67560        False        False  ...          False             False   \n",
       "AT5G67590        False         True  ...          False             False   \n",
       "AT5G67620        False        False  ...          False             False   \n",
       "AT5G67630        False        False  ...          False             False   \n",
       "\n",
       "           GTGGWWHG_319  AGATCCAA_326  HYRGATCYRD_334  ATGTCGGYRR_344  \\\n",
       "AT1G01010         False         False           False           False   \n",
       "AT1G01030         False         False           False           False   \n",
       "AT1G01040         False         False           False           False   \n",
       "AT1G01050         False         False           False           False   \n",
       "AT1G01060         False         False           False           False   \n",
       "...                 ...           ...             ...             ...   \n",
       "AT5G67550         False         False           False           False   \n",
       "AT5G67560         False         False           False           False   \n",
       "AT5G67590         False         False           False           False   \n",
       "AT5G67620         False         False           False           False   \n",
       "AT5G67630         False         False           False           False   \n",
       "\n",
       "           YAGATCTR_353  CTGACY_367  ACNGCT_426  AGCAGC_444  \n",
       "AT1G01010         False       False       False       False  \n",
       "AT1G01030         False       False       False       False  \n",
       "AT1G01040         False       False       False       False  \n",
       "AT1G01050         False       False       False       False  \n",
       "AT1G01060         False       False        True        True  \n",
       "...                 ...         ...         ...         ...  \n",
       "AT5G67550         False       False        True       False  \n",
       "AT5G67560         False        True       False        True  \n",
       "AT5G67590         False        True        True       False  \n",
       "AT5G67620         False        True       False        True  \n",
       "AT5G67630         False       False        True       False  \n",
       "\n",
       "[10769 rows x 178 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PLM_final = pd.read_csv(\"data/PLM_final.csv\", index_col=0)\n",
    "\n",
    "PLM_final = pd.DataFrame(np.where(PLM_final == 1, True, False), index=PLM_final.index, columns=PLM_final.columns)\n",
    "\n",
    "PLM_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "motif_plus_cond_data = {i:{k: 0 for k in PLM_final.columns.tolist()} for i in plus_data.columns.to_list()}\n",
    "motif_minus_cond_data = {i:{k: 0 for k in PLM_final.columns.tolist()} for i in minus_data.columns.to_list()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "motif_plus_cond_data = {i:{k: 0 for k in PLM_final.columns.tolist()} for i in plus_data.columns.to_list()}\n",
    "motif_minus_cond_data = {i:{k: 0 for k in PLM_final.columns.tolist()} for i in minus_data.columns.to_list()}\n",
    "PLM_final_t = PLM_final.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "i=0\n",
    "for cond in plus_data.columns:\n",
    "    gene_x = plus_data[cond][plus_data[cond]].index.to_list()\n",
    "    i +=1\n",
    "    print(i, \" / \", len(plus_data.columns))\n",
    "    for gene in gene_x:\n",
    "        plm_x = PLM_final_t[gene][PLM_final_t[gene]].index\n",
    "        for plm in plm_x:\n",
    "            motif_plus_cond_data[cond][plm] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "i=0\n",
    "for cond in minus_data.columns:\n",
    "    gene_x = minus_data[cond][minus_data[cond]].index.to_list()\n",
    "    i +=1\n",
    "    print(i, \" / \", len(minus_data.columns))\n",
    "    for gene in gene_x:\n",
    "        plm_x = PLM_final_t[gene][PLM_final_t[gene]].index\n",
    "        for plm in plm_x:\n",
    "            motif_minus_cond_data[cond][plm] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMCAATAATTRW_-317</th>\n",
       "      <th>TGTAAAGT_-280</th>\n",
       "      <th>CCAATGT_-275</th>\n",
       "      <th>GGATA_-263</th>\n",
       "      <th>TAACAAA_-261</th>\n",
       "      <th>HDTTAACAGAAWW_-260</th>\n",
       "      <th>AWTTAAWT_-239</th>\n",
       "      <th>RTTTTTR_-229</th>\n",
       "      <th>TATCCA_-226</th>\n",
       "      <th>GRWAAW_-212</th>\n",
       "      <th>...</th>\n",
       "      <th>SAGATCYRR_295</th>\n",
       "      <th>DYCACCGACAHH_312</th>\n",
       "      <th>GTGGWWHG_319</th>\n",
       "      <th>AGATCCAA_326</th>\n",
       "      <th>HYRGATCYRD_334</th>\n",
       "      <th>ATGTCGGYRR_344</th>\n",
       "      <th>YAGATCTR_353</th>\n",
       "      <th>CTGACY_367</th>\n",
       "      <th>ACNGCT_426</th>\n",
       "      <th>AGCAGC_444</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_stress / Col0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>192</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__35S_HAHB4_stress / 35S_HAHB4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>610</td>\n",
       "      <td>...</td>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>82</td>\n",
       "      <td>355</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_M_1 / Col0_control_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>649</td>\n",
       "      <td>...</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>133</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>79</td>\n",
       "      <td>342</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>609</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>82</td>\n",
       "      <td>324</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Control</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>389</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>216</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G1_Col0_2 / G1_Col0_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>729</td>\n",
       "      <td>...</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>154</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>92</td>\n",
       "      <td>360</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_Col0_1 / G11_Col0_1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>417</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>63</td>\n",
       "      <td>220</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3_dcl4_1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>99</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>258</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    WMCAATAATTRW_-317  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1                  0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2                  0   \n",
       "DROUGHT__Col0_stress / Col0                                         0   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                               0   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                                  0   \n",
       "...                                                               ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                              0   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...                  0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                    0   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                                  2   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...                  1   \n",
       "\n",
       "                                                    TGTAAAGT_-280  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1              0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2              0   \n",
       "DROUGHT__Col0_stress / Col0                                     0   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                           0   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                              0   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                          0   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...              0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                0   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                              0   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...              0   \n",
       "\n",
       "                                                    CCAATGT_-275  GGATA_-263  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             0           0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             0           1   \n",
       "DROUGHT__Col0_stress / Col0                                    2           2   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          6           9   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             2           8   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         2           8   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             2           5   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               3           7   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             0           5   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             1           8   \n",
       "\n",
       "                                                    TAACAAA_-261  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             1   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             5   \n",
       "DROUGHT__Col0_stress / Col0                                   14   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         29   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            28   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        23   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            16   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              34   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            17   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            19   \n",
       "\n",
       "                                                    HDTTAACAGAAWW_-260  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1                   0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2                   0   \n",
       "DROUGHT__Col0_stress / Col0                                          3   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                                1   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                                   2   \n",
       "...                                                                ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                               0   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...                   0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                     0   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                                   0   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...                   1   \n",
       "\n",
       "                                                    AWTTAAWT_-239  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1              3   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2              2   \n",
       "DROUGHT__Col0_stress / Col0                                    22   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          43   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             43   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         30   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             31   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               60   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             28   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             39   \n",
       "\n",
       "                                                    RTTTTTR_-229  TATCCA_-226  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             2            0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             2            0   \n",
       "DROUGHT__Col0_stress / Col0                                   31            6   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         50            7   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            42            5   \n",
       "...                                                          ...          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        51            1   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            28            2   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              46            5   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            24            5   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            44            6   \n",
       "\n",
       "                                                    GRWAAW_-212  ...  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1           65  ...   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2           62  ...   \n",
       "DROUGHT__Col0_stress / Col0                                 360  ...   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       610  ...   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          649  ...   \n",
       "...                                                         ...  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      609  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          389  ...   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            729  ...   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          417  ...   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          546  ...   \n",
       "\n",
       "                                                    SAGATCYRR_295  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             11   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             11   \n",
       "DROUGHT__Col0_stress / Col0                                    59   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         101   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            121   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        123   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             81   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              135   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             85   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             94   \n",
       "\n",
       "                                                    DYCACCGACAHH_312  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1                 0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2                 0   \n",
       "DROUGHT__Col0_stress / Col0                                        1   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                              2   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                                 1   \n",
       "...                                                              ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                             2   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...                 0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                   1   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                                 2   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...                 1   \n",
       "\n",
       "                                                    GTGGWWHG_319  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             0   \n",
       "DROUGHT__Col0_stress / Col0                                    2   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          2   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             2   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         1   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             2   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               2   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             0   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             1   \n",
       "\n",
       "                                                    AGATCCAA_326  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             2   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             2   \n",
       "DROUGHT__Col0_stress / Col0                                    9   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         12   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            15   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        13   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            13   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              17   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             7   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             9   \n",
       "\n",
       "                                                    HYRGATCYRD_334  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1               7   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2              11   \n",
       "DROUGHT__Col0_stress / Col0                                     67   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          103   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             133   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         140   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             102   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               154   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                              85   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...              99   \n",
       "\n",
       "                                                    ATGTCGGYRR_344  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1               0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2               0   \n",
       "DROUGHT__Col0_stress / Col0                                      1   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                            3   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                               4   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                           1   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...               1   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                 4   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                               1   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...               3   \n",
       "\n",
       "                                                    YAGATCTR_353  CTGACY_367  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             3           3   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             5           6   \n",
       "DROUGHT__Col0_stress / Col0                                   24          36   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         41          82   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            55          79   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        70          82   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            52          52   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              56          92   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            41          63   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            40          60   \n",
       "\n",
       "                                                    ACNGCT_426  AGCAGC_444  \n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          41          15  \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          29          14  \n",
       "DROUGHT__Col0_stress / Col0                                192          97  \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                      355         191  \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                         342         166  \n",
       "...                                                        ...         ...  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                     324         200  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...         216         103  \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                           360         184  \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                         220         121  \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...         258         143  \n",
       "\n",
       "[387 rows x 178 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motif_plus_cond_data = pd.DataFrame(motif_plus_cond_data).T\n",
    "motif_plus_cond_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>WMCAATAATTRW_-317</th>\n",
       "      <th>TGTAAAGT_-280</th>\n",
       "      <th>CCAATGT_-275</th>\n",
       "      <th>GGATA_-263</th>\n",
       "      <th>TAACAAA_-261</th>\n",
       "      <th>HDTTAACAGAAWW_-260</th>\n",
       "      <th>AWTTAAWT_-239</th>\n",
       "      <th>RTTTTTR_-229</th>\n",
       "      <th>TATCCA_-226</th>\n",
       "      <th>GRWAAW_-212</th>\n",
       "      <th>...</th>\n",
       "      <th>SAGATCYRR_295</th>\n",
       "      <th>DYCACCGACAHH_312</th>\n",
       "      <th>GTGGWWHG_319</th>\n",
       "      <th>AGATCCAA_326</th>\n",
       "      <th>HYRGATCYRD_334</th>\n",
       "      <th>ATGTCGGYRR_344</th>\n",
       "      <th>YAGATCTR_353</th>\n",
       "      <th>CTGACY_367</th>\n",
       "      <th>ACNGCT_426</th>\n",
       "      <th>AGCAGC_444</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_stress / Col0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>517</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>62</td>\n",
       "      <td>271</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__35S_HAHB4_stress / 35S_HAHB4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>587</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>311</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_M_1 / Col0_control_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>513</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>77</td>\n",
       "      <td>302</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>507</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>51</td>\n",
       "      <td>277</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Control</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>367</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>234</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G1_Col0_2 / G1_Col0_2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>821</td>\n",
       "      <td>...</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>99</td>\n",
       "      <td>447</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_Col0_1 / G11_Col0_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>574</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>74</td>\n",
       "      <td>288</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3_dcl4_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>711</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>93</td>\n",
       "      <td>405</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    WMCAATAATTRW_-317  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1                  0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2                  0   \n",
       "DROUGHT__Col0_stress / Col0                                         1   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                               2   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                                  0   \n",
       "...                                                               ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                              0   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...                  0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                    1   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                                  0   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...                  0   \n",
       "\n",
       "                                                    TGTAAAGT_-280  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1              0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2              0   \n",
       "DROUGHT__Col0_stress / Col0                                     2   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                           1   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                              0   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                          0   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...              0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                0   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                              0   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...              0   \n",
       "\n",
       "                                                    CCAATGT_-275  GGATA_-263  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             0           0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             0           0   \n",
       "DROUGHT__Col0_stress / Col0                                    4           8   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          4           7   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             3           6   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         2           8   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             0           4   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               6          11   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             3           7   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             2           5   \n",
       "\n",
       "                                                    TAACAAA_-261  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             1   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             1   \n",
       "DROUGHT__Col0_stress / Col0                                   18   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         25   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            23   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        24   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             7   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              36   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            32   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            30   \n",
       "\n",
       "                                                    HDTTAACAGAAWW_-260  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1                   0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2                   0   \n",
       "DROUGHT__Col0_stress / Col0                                          1   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                                1   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                                   0   \n",
       "...                                                                ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                               3   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...                   0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                     0   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                                   2   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...                   3   \n",
       "\n",
       "                                                    AWTTAAWT_-239  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1              1   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2              0   \n",
       "DROUGHT__Col0_stress / Col0                                    28   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          39   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             27   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         41   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             25   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               56   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             35   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             28   \n",
       "\n",
       "                                                    RTTTTTR_-229  TATCCA_-226  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             3            0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             0            1   \n",
       "DROUGHT__Col0_stress / Col0                                   31            5   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         39            3   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            32            4   \n",
       "...                                                          ...          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        37            3   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            29            6   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              54            8   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            37            4   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            39            6   \n",
       "\n",
       "                                                    GRWAAW_-212  ...  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1           31  ...   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2           20  ...   \n",
       "DROUGHT__Col0_stress / Col0                                 517  ...   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       587  ...   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          513  ...   \n",
       "...                                                         ...  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      507  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          367  ...   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            821  ...   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          574  ...   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          711  ...   \n",
       "\n",
       "                                                    SAGATCYRR_295  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1              5   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2              1   \n",
       "DROUGHT__Col0_stress / Col0                                    95   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         113   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             82   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         87   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             71   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              106   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             84   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             95   \n",
       "\n",
       "                                                    DYCACCGACAHH_312  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1                 0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2                 0   \n",
       "DROUGHT__Col0_stress / Col0                                        0   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                              0   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                                 1   \n",
       "...                                                              ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                             0   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...                 0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                   0   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                                 1   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...                 0   \n",
       "\n",
       "                                                    GTGGWWHG_319  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             0   \n",
       "DROUGHT__Col0_stress / Col0                                    1   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          3   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                             4   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                         1   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             1   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               3   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                             3   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             2   \n",
       "\n",
       "                                                    AGATCCAA_326  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             0   \n",
       "DROUGHT__Col0_stress / Col0                                   13   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          7   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            14   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        14   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            13   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              19   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            14   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            20   \n",
       "\n",
       "                                                    HYRGATCYRD_334  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1               5   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2               1   \n",
       "DROUGHT__Col0_stress / Col0                                     89   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                          115   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                              77   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                          81   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...              48   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                               110   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                              94   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...             101   \n",
       "\n",
       "                                                    ATGTCGGYRR_344  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1               0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2               0   \n",
       "DROUGHT__Col0_stress / Col0                                      3   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                            1   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                               2   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                           2   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...               2   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                 2   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                               3   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...               4   \n",
       "\n",
       "                                                    YAGATCTR_353  CTGACY_367  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             4           3   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             0           3   \n",
       "DROUGHT__Col0_stress / Col0                                   52          62   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         58          64   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            38          77   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        33          51   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            22          61   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              48          99   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            29          74   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            43          93   \n",
       "\n",
       "                                                    ACNGCT_426  AGCAGC_444  \n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          15           7  \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          10           2  \n",
       "DROUGHT__Col0_stress / Col0                                271         152  \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                      311         171  \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                         302         149  \n",
       "...                                                        ...         ...  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                     277         148  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...         234         123  \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                           447         246  \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                         288         177  \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...         405         207  \n",
       "\n",
       "[387 rows x 178 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motif_minus_cond_data = pd.DataFrame(motif_minus_cond_data).T\n",
    "motif_minus_cond_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reunion 10/01/2025 :\n",
    "- Diviser le nombre de fois vue le plm par gene et par condition par diviser par le nombre de fois total qu'on a vu dans toute la population de gene\n",
    "- Faire une recherche de motif fréquent en faisant un threshold choisis sur la fréquence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_PLM_gene = np.sum(PLM_final, 0)\n",
    "\n",
    "motif_minus_cond_freq = motif_minus_cond_data / sum_PLM_gene\n",
    "motif_plus_cond_freq = motif_plus_cond_data / sum_PLM_gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMCAATAATTRW_-317</th>\n",
       "      <th>TGTAAAGT_-280</th>\n",
       "      <th>CCAATGT_-275</th>\n",
       "      <th>GGATA_-263</th>\n",
       "      <th>TAACAAA_-261</th>\n",
       "      <th>HDTTAACAGAAWW_-260</th>\n",
       "      <th>AWTTAAWT_-239</th>\n",
       "      <th>RTTTTTR_-229</th>\n",
       "      <th>TATCCA_-226</th>\n",
       "      <th>GRWAAW_-212</th>\n",
       "      <th>...</th>\n",
       "      <th>SAGATCYRR_295</th>\n",
       "      <th>DYCACCGACAHH_312</th>\n",
       "      <th>GTGGWWHG_319</th>\n",
       "      <th>AGATCCAA_326</th>\n",
       "      <th>HYRGATCYRD_334</th>\n",
       "      <th>ATGTCGGYRR_344</th>\n",
       "      <th>YAGATCTR_353</th>\n",
       "      <th>CTGACY_367</th>\n",
       "      <th>ACNGCT_426</th>\n",
       "      <th>AGCAGC_444</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.003257</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.004868</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005042</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>0.009964</td>\n",
       "      <td>0.006793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>0.013774</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.003257</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.007650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.006466</td>\n",
       "      <td>0.007047</td>\n",
       "      <td>0.006341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_stress / Col0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.038567</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.042969</td>\n",
       "      <td>0.050489</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.046452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042477</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>0.046592</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.040336</td>\n",
       "      <td>0.038793</td>\n",
       "      <td>0.046659</td>\n",
       "      <td>0.043931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__35S_HAHB4_stress / 35S_HAHB4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>0.079890</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.083984</td>\n",
       "      <td>0.081433</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>0.078710</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072714</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.071627</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.068908</td>\n",
       "      <td>0.088362</td>\n",
       "      <td>0.086270</td>\n",
       "      <td>0.086504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_M_1 / Col0_control_1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.077135</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.083984</td>\n",
       "      <td>0.068404</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.083742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087113</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.086207</td>\n",
       "      <td>0.092490</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.092437</td>\n",
       "      <td>0.085129</td>\n",
       "      <td>0.083111</td>\n",
       "      <td>0.075181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.063361</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058594</td>\n",
       "      <td>0.083062</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.078581</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088553</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.074713</td>\n",
       "      <td>0.097357</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.088362</td>\n",
       "      <td>0.078736</td>\n",
       "      <td>0.090580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Control</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.052083</td>\n",
       "      <td>0.044077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060547</td>\n",
       "      <td>0.045603</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.050194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058315</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.074713</td>\n",
       "      <td>0.070932</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.087395</td>\n",
       "      <td>0.056034</td>\n",
       "      <td>0.052491</td>\n",
       "      <td>0.046649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G1_Col0_2 / G1_Col0_2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.072917</td>\n",
       "      <td>0.093664</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117188</td>\n",
       "      <td>0.074919</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.094065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097192</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.097701</td>\n",
       "      <td>0.107093</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.094118</td>\n",
       "      <td>0.099138</td>\n",
       "      <td>0.087485</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_Col0_1 / G11_Col0_1</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.052083</td>\n",
       "      <td>0.046832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054688</td>\n",
       "      <td>0.039088</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.053806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061195</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040230</td>\n",
       "      <td>0.059110</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.068908</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>0.053463</td>\n",
       "      <td>0.054801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3_dcl4_1</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.052342</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.076172</td>\n",
       "      <td>0.071661</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.070452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067675</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>0.068846</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.067227</td>\n",
       "      <td>0.064655</td>\n",
       "      <td>0.062697</td>\n",
       "      <td>0.064764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    WMCAATAATTRW_-317  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1           0.000000   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2           0.000000   \n",
       "DROUGHT__Col0_stress / Col0                                  0.000000   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                        0.000000   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                           0.000000   \n",
       "...                                                               ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                       0.000000   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...           0.000000   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                             0.000000   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                           0.222222   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...           0.111111   \n",
       "\n",
       "                                                    TGTAAAGT_-280  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1            0.0   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2            0.0   \n",
       "DROUGHT__Col0_stress / Col0                                   0.0   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         0.0   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            0.0   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        0.0   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            0.0   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              0.0   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            0.0   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            0.0   \n",
       "\n",
       "                                                    CCAATGT_-275  GGATA_-263  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          0.00    0.000000   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          0.00    0.010417   \n",
       "DROUGHT__Col0_stress / Col0                                 0.08    0.020833   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       0.24    0.093750   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          0.08    0.083333   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      0.08    0.083333   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          0.08    0.052083   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            0.12    0.072917   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          0.00    0.052083   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          0.04    0.083333   \n",
       "\n",
       "                                                    TAACAAA_-261  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1      0.002755   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2      0.013774   \n",
       "DROUGHT__Col0_stress / Col0                             0.038567   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                   0.079890   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                      0.077135   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                  0.063361   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...      0.044077   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                        0.093664   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                      0.046832   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...      0.052342   \n",
       "\n",
       "                                                    HDTTAACAGAAWW_-260  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1            0.000000   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2            0.000000   \n",
       "DROUGHT__Col0_stress / Col0                                   0.333333   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         0.111111   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            0.222222   \n",
       "...                                                                ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        0.000000   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            0.000000   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              0.000000   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            0.000000   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            0.111111   \n",
       "\n",
       "                                                    AWTTAAWT_-239  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1       0.005859   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2       0.003906   \n",
       "DROUGHT__Col0_stress / Col0                              0.042969   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                    0.083984   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                       0.083984   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                   0.058594   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...       0.060547   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                         0.117188   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                       0.054688   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...       0.076172   \n",
       "\n",
       "                                                    RTTTTTR_-229  TATCCA_-226  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1      0.003257     0.000000   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2      0.003257     0.000000   \n",
       "DROUGHT__Col0_stress / Col0                             0.050489     0.115385   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                   0.081433     0.134615   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                      0.068404     0.096154   \n",
       "...                                                          ...          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                  0.083062     0.019231   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...      0.045603     0.038462   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                        0.074919     0.096154   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                      0.039088     0.096154   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...      0.071661     0.115385   \n",
       "\n",
       "                                                    GRWAAW_-212  ...  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1     0.008387  ...   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2     0.008000  ...   \n",
       "DROUGHT__Col0_stress / Col0                            0.046452  ...   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                  0.078710  ...   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                     0.083742  ...   \n",
       "...                                                         ...  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                 0.078581  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...     0.050194  ...   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                       0.094065  ...   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                     0.053806  ...   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...     0.070452  ...   \n",
       "\n",
       "                                                    SAGATCYRR_295  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1       0.007919   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2       0.007919   \n",
       "DROUGHT__Col0_stress / Col0                              0.042477   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                    0.072714   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                       0.087113   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                   0.088553   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...       0.058315   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                         0.097192   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                       0.061195   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...       0.067675   \n",
       "\n",
       "                                                    DYCACCGACAHH_312  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          0.000000   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          0.000000   \n",
       "DROUGHT__Col0_stress / Col0                                 0.090909   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       0.181818   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          0.090909   \n",
       "...                                                              ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      0.181818   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          0.000000   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            0.090909   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          0.181818   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          0.090909   \n",
       "\n",
       "                                                    GTGGWWHG_319  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1      0.000000   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2      0.000000   \n",
       "DROUGHT__Col0_stress / Col0                             0.055556   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                   0.055556   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                      0.055556   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                  0.027778   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...      0.055556   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                        0.055556   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                      0.000000   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...      0.027778   \n",
       "\n",
       "                                                    AGATCCAA_326  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1      0.011494   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2      0.011494   \n",
       "DROUGHT__Col0_stress / Col0                             0.051724   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                   0.068966   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                      0.086207   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                  0.074713   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...      0.074713   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                        0.097701   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                      0.040230   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...      0.051724   \n",
       "\n",
       "                                                    HYRGATCYRD_334  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1        0.004868   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2        0.007650   \n",
       "DROUGHT__Col0_stress / Col0                               0.046592   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                     0.071627   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                        0.092490   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                    0.097357   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...        0.070932   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                          0.107093   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                        0.059110   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...        0.068846   \n",
       "\n",
       "                                                    ATGTCGGYRR_344  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1        0.000000   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2        0.000000   \n",
       "DROUGHT__Col0_stress / Col0                               0.038462   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                     0.115385   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                        0.153846   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                    0.038462   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...        0.038462   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                          0.153846   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                        0.038462   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...        0.115385   \n",
       "\n",
       "                                                    YAGATCTR_353  CTGACY_367  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1      0.005042    0.003233   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2      0.008403    0.006466   \n",
       "DROUGHT__Col0_stress / Col0                             0.040336    0.038793   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                   0.068908    0.088362   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                      0.092437    0.085129   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                  0.117647    0.088362   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...      0.087395    0.056034   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                        0.094118    0.099138   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                      0.068908    0.067888   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...      0.067227    0.064655   \n",
       "\n",
       "                                                    ACNGCT_426  AGCAGC_444  \n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1    0.009964    0.006793  \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2    0.007047    0.006341  \n",
       "DROUGHT__Col0_stress / Col0                           0.046659    0.043931  \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                 0.086270    0.086504  \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                    0.083111    0.075181  \n",
       "...                                                        ...         ...  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                0.078736    0.090580  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...    0.052491    0.046649  \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                      0.087485    0.083333  \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                    0.053463    0.054801  \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...    0.062697    0.064764  \n",
       "\n",
       "[387 rows x 178 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motif_plus_cond_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parcours des table au dessus pour voir les valeurs que l'on a afin de préparer la recherche de motif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>168</th>\n",
       "      <th>169</th>\n",
       "      <th>170</th>\n",
       "      <th>171</th>\n",
       "      <th>172</th>\n",
       "      <th>173</th>\n",
       "      <th>174</th>\n",
       "      <th>175</th>\n",
       "      <th>176</th>\n",
       "      <th>177</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.022107</td>\n",
       "      <td>0.017571</td>\n",
       "      <td>0.042997</td>\n",
       "      <td>0.037172</td>\n",
       "      <td>0.034944</td>\n",
       "      <td>0.047373</td>\n",
       "      <td>0.036610</td>\n",
       "      <td>0.030827</td>\n",
       "      <td>0.050487</td>\n",
       "      <td>0.035241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033874</td>\n",
       "      <td>0.032182</td>\n",
       "      <td>0.037468</td>\n",
       "      <td>0.033488</td>\n",
       "      <td>0.034855</td>\n",
       "      <td>0.046015</td>\n",
       "      <td>0.036645</td>\n",
       "      <td>0.036950</td>\n",
       "      <td>0.035319</td>\n",
       "      <td>0.035285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.052887</td>\n",
       "      <td>0.060157</td>\n",
       "      <td>0.057612</td>\n",
       "      <td>0.041925</td>\n",
       "      <td>0.037564</td>\n",
       "      <td>0.077164</td>\n",
       "      <td>0.038883</td>\n",
       "      <td>0.034900</td>\n",
       "      <td>0.055201</td>\n",
       "      <td>0.036870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036747</td>\n",
       "      <td>0.066085</td>\n",
       "      <td>0.045840</td>\n",
       "      <td>0.038991</td>\n",
       "      <td>0.037620</td>\n",
       "      <td>0.060104</td>\n",
       "      <td>0.041137</td>\n",
       "      <td>0.038605</td>\n",
       "      <td>0.037285</td>\n",
       "      <td>0.038316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.022107  0.017571  0.042997  0.037172  0.034944  0.047373  0.036610   \n",
       "1  0.052887  0.060157  0.057612  0.041925  0.037564  0.077164  0.038883   \n",
       "\n",
       "        7         8         9    ...       168       169       170       171  \\\n",
       "0  0.030827  0.050487  0.035241  ...  0.033874  0.032182  0.037468  0.033488   \n",
       "1  0.034900  0.055201  0.036870  ...  0.036747  0.066085  0.045840  0.038991   \n",
       "\n",
       "        172       173       174       175       176       177  \n",
       "0  0.034855  0.046015  0.036645  0.036950  0.035319  0.035285  \n",
       "1  0.037620  0.060104  0.041137  0.038605  0.037285  0.038316  \n",
       "\n",
       "[2 rows x 178 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.array([motif_plus_cond_freq.apply(np.mean),motif_plus_cond_freq.apply(np.std)]), )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mise en place d'une recherche par motif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import fpgrowth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va d'abord observer le nombre de True que l'on obtiens selon le treshhold choisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\seiko\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "c:\\Users\\seiko\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "c:\\Users\\seiko\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0\n",
      "count    101.000000\n",
      "mean    2850.445545\n",
      "std     8604.044353\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%       11.000000\n",
      "75%      345.000000\n",
      "max    53077.000000\n"
     ]
    }
   ],
   "source": [
    "nb_True_cond = []\n",
    "nb_True_plm = []\n",
    "tresh_list = [i/100 for i in range(0,101,1)]\n",
    "\n",
    "for n in tresh_list:\n",
    "    data = motif_plus_cond_freq > n\n",
    "    nb_True_cond.append(np.sum(np.sum(data, 1)))\n",
    "    nb_True_plm.append(np.sum(np.sum(data,0)))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(tresh_list,nb_True_cond)\n",
    "plt.show()\n",
    "\n",
    "print(pd.DataFrame(nb_True_cond).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0\n",
      "count    101.000000\n",
      "mean    2850.445545\n",
      "std     8604.044353\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%       11.000000\n",
      "75%      345.000000\n",
      "max    53077.000000\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(tresh_list,nb_True_plm)\n",
    "plt.show()\n",
    "\n",
    "print(pd.DataFrame(nb_True_plm).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0\n",
      "count    101.000000\n",
      "mean    2652.623762\n",
      "std     8026.223787\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        9.000000\n",
      "75%      289.000000\n",
      "max    50732.000000\n"
     ]
    }
   ],
   "source": [
    "nb_True_cond = []\n",
    "nb_True_plm = []\n",
    "tresh_list = [i/100 for i in range(0,101,1)]\n",
    "\n",
    "for n in tresh_list:\n",
    "    data = motif_minus_cond_freq > n\n",
    "    nb_True_cond.append(np.sum(np.sum(data, 1)))\n",
    "    nb_True_plm.append(np.sum(np.sum(data,0)))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(tresh_list,nb_True_cond)\n",
    "plt.show()\n",
    "\n",
    "print(pd.DataFrame(nb_True_cond).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0\n",
      "count    101.000000\n",
      "mean    2652.623762\n",
      "std     8026.223787\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        9.000000\n",
      "75%      289.000000\n",
      "max    50732.000000\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(tresh_list,nb_True_plm)\n",
    "plt.show()\n",
    "\n",
    "print(pd.DataFrame(nb_True_plm).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMCAATAATTRW_-317</th>\n",
       "      <th>TGTAAAGT_-280</th>\n",
       "      <th>CCAATGT_-275</th>\n",
       "      <th>GGATA_-263</th>\n",
       "      <th>TAACAAA_-261</th>\n",
       "      <th>HDTTAACAGAAWW_-260</th>\n",
       "      <th>AWTTAAWT_-239</th>\n",
       "      <th>RTTTTTR_-229</th>\n",
       "      <th>TATCCA_-226</th>\n",
       "      <th>GRWAAW_-212</th>\n",
       "      <th>...</th>\n",
       "      <th>SAGATCYRR_295</th>\n",
       "      <th>DYCACCGACAHH_312</th>\n",
       "      <th>GTGGWWHG_319</th>\n",
       "      <th>AGATCCAA_326</th>\n",
       "      <th>HYRGATCYRD_334</th>\n",
       "      <th>ATGTCGGYRR_344</th>\n",
       "      <th>YAGATCTR_353</th>\n",
       "      <th>CTGACY_367</th>\n",
       "      <th>ACNGCT_426</th>\n",
       "      <th>AGCAGC_444</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_stress / Col0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__35S_HAHB4_stress / 35S_HAHB4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROUGHT__Col0_M_1 / Col0_control_1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Control</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G1_Col0_2 / G1_Col0_2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_Col0_1 / G11_Col0_1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3_dcl4_1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    WMCAATAATTRW_-317  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1              False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2              False   \n",
       "DROUGHT__Col0_stress / Col0                                     False   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                           False   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                              False   \n",
       "...                                                               ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                          False   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...              False   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                False   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                               True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...               True   \n",
       "\n",
       "                                                    TGTAAAGT_-280  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          False   \n",
       "DROUGHT__Col0_stress / Col0                                 False   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       False   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          False   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      False   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          False   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            False   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          False   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          False   \n",
       "\n",
       "                                                    CCAATGT_-275  GGATA_-263  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1         False       False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2         False        True   \n",
       "DROUGHT__Col0_stress / Col0                                 True        True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       True        True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          True        True   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      True        True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          True        True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            True        True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                         False        True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          True        True   \n",
       "\n",
       "                                                    TAACAAA_-261  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          True   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          True   \n",
       "DROUGHT__Col0_stress / Col0                                 True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          True   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          True   \n",
       "\n",
       "                                                    HDTTAACAGAAWW_-260  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1               False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2               False   \n",
       "DROUGHT__Col0_stress / Col0                                       True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                             True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                                True   \n",
       "...                                                                ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                           False   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...               False   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                 False   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                               False   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...                True   \n",
       "\n",
       "                                                    AWTTAAWT_-239  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1           True   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2           True   \n",
       "DROUGHT__Col0_stress / Col0                                  True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                        True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                           True   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                       True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...           True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                             True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                           True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...           True   \n",
       "\n",
       "                                                    RTTTTTR_-229  TATCCA_-226  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          True        False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          True        False   \n",
       "DROUGHT__Col0_stress / Col0                                 True         True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       True         True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          True         True   \n",
       "...                                                          ...          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      True         True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          True         True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            True         True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          True         True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          True         True   \n",
       "\n",
       "                                                    GRWAAW_-212  ...  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1         True  ...   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2         True  ...   \n",
       "DROUGHT__Col0_stress / Col0                                True  ...   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                      True  ...   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                         True  ...   \n",
       "...                                                         ...  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                     True  ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...         True  ...   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                           True  ...   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                         True  ...   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...         True  ...   \n",
       "\n",
       "                                                    SAGATCYRR_295  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1           True   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2           True   \n",
       "DROUGHT__Col0_stress / Col0                                  True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                        True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                           True   \n",
       "...                                                           ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                       True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...           True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                             True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                           True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...           True   \n",
       "\n",
       "                                                    DYCACCGACAHH_312  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1             False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2             False   \n",
       "DROUGHT__Col0_stress / Col0                                     True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                           True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                              True   \n",
       "...                                                              ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                          True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...             False   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                                True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                              True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...              True   \n",
       "\n",
       "                                                    GTGGWWHG_319  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1         False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2         False   \n",
       "DROUGHT__Col0_stress / Col0                                 True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          True   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                         False   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          True   \n",
       "\n",
       "                                                    AGATCCAA_326  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          True   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          True   \n",
       "DROUGHT__Col0_stress / Col0                                 True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          True   \n",
       "...                                                          ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          True   \n",
       "\n",
       "                                                    HYRGATCYRD_334  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1            True   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2            True   \n",
       "DROUGHT__Col0_stress / Col0                                   True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            True   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            True   \n",
       "\n",
       "                                                    ATGTCGGYRR_344  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1           False   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2           False   \n",
       "DROUGHT__Col0_stress / Col0                                   True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                         True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                            True   \n",
       "...                                                            ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                        True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...            True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                              True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                            True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...            True   \n",
       "\n",
       "                                                    YAGATCTR_353  CTGACY_367  \\\n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1          True        True   \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2          True        True   \n",
       "DROUGHT__Col0_stress / Col0                                 True        True   \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                       True        True   \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                          True        True   \n",
       "...                                                          ...         ...   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                      True        True   \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...          True        True   \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                            True        True   \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                          True        True   \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...          True        True   \n",
       "\n",
       "                                                    ACNGCT_426  AGCAGC_444  \n",
       "DROUGHT__Total_RNA_Drought_1 / Total_RNA_Control_1        True        True  \n",
       "DROUGHT__Total_RNA_Drought_2 / Total_RNA_Control_2        True        True  \n",
       "DROUGHT__Col0_stress / Col0                               True        True  \n",
       "DROUGHT__35S_HAHB4_stress / 35S_HAHB4                     True        True  \n",
       "DROUGHT__Col0_M_1 / Col0_control_1                        True        True  \n",
       "...                                                        ...         ...  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / CaMV_Col0                    True        True  \n",
       "VIRUS__CaMV_dcl2_dcl3_dcl4 / dcl2_dcl3_dcl4_Con...        True        True  \n",
       "VIRUS__TCV_G1_Col0_2 / G1_Col0_2                          True        True  \n",
       "VIRUS__TCV_G11_Col0_1 / G11_Col0_1                        True        True  \n",
       "VIRUS__TCV_G11_dcl2_dcl3_dcl4_1 / G11_dcl2_dcl3...        True        True  \n",
       "\n",
       "[387 rows x 178 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motif_plus_cond_freq > 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fpgrowth_2(data, min_support,tresh, max_length = None):\n",
    "    data = data > tresh\n",
    "    return fpgrowth(data, min_support=min_support, max_len = max_length, use_colnames=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [support, itemsets]\n",
       "Index: []"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tresh = Plus c'est haut, plus le seuil de fréquence est exigeant, moins il y a de valeurs\n",
    "# min_support = Plus c'est bas, plus la fréquence des pattern pris en compte est basse \n",
    "\n",
    "fpgrowth_2(motif_plus_cond_freq, min_support=0.5, tresh = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va essayé avec les motifs de taille 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.325581</td>\n",
       "      <td>(HDTTAACAGAAWW_-260)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.312661</td>\n",
       "      <td>(CCACGTGC_-120)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.297158</td>\n",
       "      <td>(ATGTCGGC_-190)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.255814</td>\n",
       "      <td>(CACGTGGCAYW_-72)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.219638</td>\n",
       "      <td>(TACGTGTC_-94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0.054264</td>\n",
       "      <td>(VAWAAAGTDR_-146)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.054264</td>\n",
       "      <td>(WTAATTAAWW_-96)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.051680</td>\n",
       "      <td>(YTGTCWC_-2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0.051680</td>\n",
       "      <td>(CCDYCKCCGCCGYYD_234)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.041344</td>\n",
       "      <td>(YGRCGGCGGMGRHGG_213)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      support               itemsets\n",
       "1    0.325581   (HDTTAACAGAAWW_-260)\n",
       "2    0.312661        (CCACGTGC_-120)\n",
       "62   0.297158        (ATGTCGGC_-190)\n",
       "0    0.255814      (CACGTGGCAYW_-72)\n",
       "19   0.219638         (TACGTGTC_-94)\n",
       "..        ...                    ...\n",
       "170  0.054264      (VAWAAAGTDR_-146)\n",
       "122  0.054264       (WTAATTAAWW_-96)\n",
       "171  0.051680           (YTGTCWC_-2)\n",
       "172  0.051680  (CCDYCKCCGCCGYYD_234)\n",
       "173  0.041344  (YGRCGGCGGMGRHGG_213)\n",
       "\n",
       "[178 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = fpgrowth_2(motif_plus_cond_freq, min_support=0.0001, tresh = t, max_length=1).sort_values(by = 'support', ascending = False)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(res[\"support\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Faisons varier le treshold de 0 et de 1 en prenant tous les motifs de taille 1 en compte en regardant le nombre de motifs garder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tresh_result = []\n",
    "tresh_list = [i/100 for i in range(0,101,1)]\n",
    "\n",
    "for t in tresh_list:\n",
    "    tresh_result.append(len(fpgrowth_2(motif_plus_cond_freq, min_support=0.0001, tresh = t, max_length=1)[\"support\"]))"
   ]
  },
  {
   "cell_type": "code",
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     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "d = {\"Treshold\" : tresh_list, \"number_motifs\" : tresh_result}\n",
    "df = pd.DataFrame(data = d)\n",
    "\n",
    "fig = px.line(df, x=\"Treshold\", y=\"number_motifs\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va regarder plus en détail où se passe la perte de donnée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.325581</td>\n",
       "      <td>(HDTTAACAGAAWW_-260)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.312661</td>\n",
       "      <td>(CCACGTGC_-120)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.297158</td>\n",
       "      <td>(ATGTCGGC_-190)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.255814</td>\n",
       "      <td>(CACGTGGCAYW_-72)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.219638</td>\n",
       "      <td>(TACGTGTC_-94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0.054264</td>\n",
       "      <td>(VAWAAAGTDR_-146)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.054264</td>\n",
       "      <td>(WTAATTAAWW_-96)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.051680</td>\n",
       "      <td>(YTGTCWC_-2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0.051680</td>\n",
       "      <td>(CCDYCKCCGCCGYYD_234)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.041344</td>\n",
       "      <td>(YGRCGGCGGMGRHGG_213)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      support               itemsets\n",
       "1    0.325581   (HDTTAACAGAAWW_-260)\n",
       "2    0.312661        (CCACGTGC_-120)\n",
       "62   0.297158        (ATGTCGGC_-190)\n",
       "0    0.255814      (CACGTGGCAYW_-72)\n",
       "19   0.219638         (TACGTGTC_-94)\n",
       "..        ...                    ...\n",
       "170  0.054264      (VAWAAAGTDR_-146)\n",
       "122  0.054264       (WTAATTAAWW_-96)\n",
       "171  0.051680           (YTGTCWC_-2)\n",
       "172  0.051680  (CCDYCKCCGCCGYYD_234)\n",
       "173  0.041344  (YGRCGGCGGMGRHGG_213)\n",
       "\n",
       "[178 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>itemsets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(AAAGYV_-116)</th>\n",
       "      <td>0.987080</td>\n",
       "      <td>(AAAGYV_-116)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(ACGT_-73)</th>\n",
       "      <td>0.987080</td>\n",
       "      <td>(ACGT_-73)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(GCCAC_-62)</th>\n",
       "      <td>0.987080</td>\n",
       "      <td>(GCCAC_-62)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(TGAC_-78)</th>\n",
       "      <td>0.987080</td>\n",
       "      <td>(TGAC_-78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(YTCANTYY_-3)</th>\n",
       "      <td>0.987080</td>\n",
       "      <td>(YTCANTYY_-3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(KGTCCCAT_-93)</th>\n",
       "      <td>0.134367</td>\n",
       "      <td>(KGTCCCAT_-93)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(TGTAAAGT_-280)</th>\n",
       "      <td>0.082687</td>\n",
       "      <td>(TGTAAAGT_-280)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(WMCAATAATTRW_-317)</th>\n",
       "      <td>0.170543</td>\n",
       "      <td>(WMCAATAATTRW_-317)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(AADRGAATMTTYTT_-102)</th>\n",
       "      <td>0.139535</td>\n",
       "      <td>(AADRGAATMTTYTT_-102)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(GGGSCCCAC_-100)</th>\n",
       "      <td>0.067183</td>\n",
       "      <td>(GGGSCCCAC_-100)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        support               itemsets\n",
       "itemsets                                              \n",
       "(AAAGYV_-116)          0.987080          (AAAGYV_-116)\n",
       "(ACGT_-73)             0.987080             (ACGT_-73)\n",
       "(GCCAC_-62)            0.987080            (GCCAC_-62)\n",
       "(TGAC_-78)             0.987080             (TGAC_-78)\n",
       "(YTCANTYY_-3)          0.987080          (YTCANTYY_-3)\n",
       "...                         ...                    ...\n",
       "(KGTCCCAT_-93)         0.134367         (KGTCCCAT_-93)\n",
       "(TGTAAAGT_-280)        0.082687        (TGTAAAGT_-280)\n",
       "(WMCAATAATTRW_-317)    0.170543    (WMCAATAATTRW_-317)\n",
       "(AADRGAATMTTYTT_-102)  0.139535  (AADRGAATMTTYTT_-102)\n",
       "(GGGSCCCAC_-100)       0.067183       (GGGSCCCAC_-100)\n",
       "\n",
       "[178 rows x 2 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "C:\\Users\\seiko\\AppData\\Local\\Temp\\ipykernel_13708\\2680906437.py:10: FutureWarning:\n",
      "\n",
      "ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "\n",
      "c:\\Users\\seiko\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:86: FutureWarning:\n",
      "\n",
      "The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tresh_vs_supp = pd.DataFrame(np.zeros((178,101)), index=motif_plus_cond_freq.columns, columns=tresh_list)\n",
    "tresh_list = [i/100 for i in range(0,101,1)]\n",
    "for t in tresh_list:\n",
    "    res1 = fpgrowth_2(motif_plus_cond_freq, min_support=0.0001, tresh = t, max_length=1)\n",
    "    itm_set = list(map(list, res1[\"itemsets\"]))\n",
    "    for it in range(len(itm_set)):\n",
    "        itm_set[it] = itm_set[it][0]\n",
    "    res1.index = itm_set\n",
    "    for itm in itm_set:\n",
    "        tresh_vs_supp[t][itm] = res1[\"support\"][itm]\n",
    "\n",
    "tresh_vs_supp_rappart = tresh_vs_supp / np.sum(tresh_vs_supp)\n",
    "tresh_vs_supp_rappart = tresh_vs_supp_rappart.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species = list(map(str, tresh_vs_supp_rappart.columns.to_list()))\n",
    "weight_counts = tresh_vs_supp_rappart.T.to_dict()\n",
    "width = 0.5\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bottom = np.zeros(len(tresh_vs_supp_rappart.columns))\n",
    "\n",
    "for boolean, weight_count in weight_counts.items():\n",
    "    weight_count = list(weight_count.values())\n",
    "    p = ax.bar(species, weight_count, width, label=boolean, bottom=bottom)\n",
    "    bottom += weight_count\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A interpréter pour choisir un treshold et interpréter les donnéee complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "tresh_vs_supp = pd.DataFrame(np.zeros((178,101)), index=motif_plus_cond_freq.columns, columns=tresh_list)\n",
    "for t in tresh_list:\n",
    "    data = motif_plus_cond_freq > t\n",
    "    summ = np.sum(data,0)\n",
    "    tresh_vs_supp[t] = summ\n",
    "\n",
    "tresh_vs_supp_rappart = tresh_vs_supp / np.sum(tresh_vs_supp)\n",
    "tresh_vs_supp_rappart = tresh_vs_supp_rappart.fillna(0)\n",
    "\n",
    "tresh_vs_supp_freq = tresh_vs_supp / np.sum(tresh_vs_supp[0])\n",
    "tresh_vs_supp_freq = tresh_vs_supp_freq.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.bar(list(map(str, tresh_vs_supp_freq.columns.to_list())), np.sum(tresh_vs_supp_freq,0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SN5IuToD01gfRCUrk9Cw4s9nWXgAt75djKQHSYy0hhJD2QOjFiK3QsLF6mFwwmZy0TX3rcRym65JURdUDX00Cwa/gVAmZXHBPd+WYBAtTctPD25PmAAB6Hv0c6NvSNoQQfwm9GDGRrI5G831+oVs9JC4Yk1tEUgtFt/hIFgmUYBInEmFxZ/nnAPyfgJfWPm913DHLx17/BkMIIeS4hF6MBCk0dMuDpCqqpCKspO6JpK6HSXjowamSsvKS1F6T20hSV0QXNZmMM9n6r1Xu69qbmv69Z9evARwTVLap2YQQEmZCL0ZM6BOKrQtGgsQSYZOSK6nrYTqvpM6Ivk8iKkxl5YNCkjJswhEbjtBor5YSuoQIIdkGxYhPSGp/mFwwOhIXjCQd2SSgdAuGqS9T9oxOsoUEm4safYx/6/XT454327G1ounvxfyX/uXYxsVtHBQhhLQDQi9GJJN2kIGnmSzYJUn/lYzP5DrR40HSGUBq25duTdItJRIctw1AywMhhEgJvRjxC0ngqf/xIECyaq8msSSJWdGDUU1Cw694kOOd18/juAIvIYRkL6EXIyarhz5pS1wwtkhcMBK3jKQeiC6OJC4ZE35Vrc22Mu7HCz5lcCohhARD6MWIX6JCUojMr0X5JLELpuuymfwlwiOdwam2abs2SASeBD3jhhBCiJfQixETugVB4oKRYHLBSNBdMJLsHtP4dDFkmmx1944pRVgXH/u33nbcawhytd10ChRCALviaYSQ5IRejEgyXCTYFiKTrP4rQRcRksnfJGp0i4pJsOjiwxQfEpQVRpK261d8CN0yhBCSHkIvRiRIXDASUon9AJIvuGdb00TiEtItKqaUXJvgVAkLK2cefZWZuh6ZLM2fSf5W+VyzrYnaPn372D5CCPELihEDNkJDkgVjwuQGsUG3aJisMroYMk2++rVnck0XKbZFzlKFabuEEBIMoRcjknRbExIXjA2SeBBJ0K1ECPkdvBtEPAghhJD2T+jFiAldaNjEfpj2meJB/MrY0C0YpvHoVhhJkKuJoFJyTRaO9VP/CgBYenQ7Fyw1hBBCUoNixBKblFwJkmqvkqBbyUJ5JvTr8svqYWsFyWQQaVhjSNLNjPK8Vrdt2xBCcofQixFJ7Q9JSq6kKqopONWvydYme0aypkwuQGsJIYTkNqEXI7bYTNoS4SGp9mqKc5HUEAmKIGM/dNdNkGXdbVYjJoQQ0nasxMjq1atx5513YufOnRg8eDBWrVqFceNaSRf92c9wxx134M9//jPKyspwxhln4Lvf/S46d+5sPXC/MFk9dKHhV0quiaBcAbar9uoFzSRr09jCeBBCCCGAhRh5+OGHsXjxYqxevRpjx47F2rVrMWXKFGzZsgW9evVq0f73v/89Zs+ejZUrV2L69OnYsWMH5s+fj3nz5uHXv/61oYfcwCYlV1IfRFLt1a+y8qZj/HIb6evemARdthUVe+uRHwI4Zm3StwkhhARDymLkrrvuwty5czFv3jwAwKpVq7BhwwasWbMGK1asaNH+5ZdfRk1NDRYtajJ59+nTB1dccQXuuOOONg7dHyRWD0lVVElKrgmJC0YSDyKpIaL3Zbp2v2p26Kv/mgqaSfqitYQQQto/KYmRgwcP4tVXX8WyZcs8+ydNmoQXX3zReMyYMWNw/fXX48knn8SUKVOwe/du/PKXv8S0acn98I2NjWhsbHS39+3bl8ow24zE6mETk2GKB0nnGjeSvnJx8g8yjoQQQkjwpCRG9uzZg8OHD6OiosKzv6KiArt2mZ9vx4wZg5/97GeYNWsWDhw4gEOHDuGss87C97///aT9rFixAjfffHMqQwsUySq5kpRcCZI1biQ1RCSr9kpEl6R+ih77AbS0eqSrSipJH5J0W3IMyWJ6XHCPhBWrANZIJOLZVkq12OewZcsWLFq0CDfccAMmT56MnTt34hvf+Abmz5+PdevWGY9Zvnw5liw5NuHt27cP1dXVNkM9LraFyGziHUzxIH4VENPHLLFwmK5TIlj0IFe/Yj9MFo6grB49C848+ioz6+AQQgg5RkpipEuXLojFYi2sILt3725hLXFYsWIFxo4di2984xsAgGHDhqG4uBjjxo3Drbfeiu7du7c4JpFIIJFIpDK0QLGdbG3KrUvWuLFd2Ve3sOir75rQYz+AlvfDNiMoF11ChBBC/CclMZKfn48RI0Zg48aNmDlzprt/48aNmDFjhvGYzz77DPG4t5tYLAagyaKSafwqRCYRHpL6IBIkYzZZXHRRIxmzSWjo+/xa4yadOIveccG7YKErhxAiIWU3zZIlS3DRRRdh5MiRqK+vx3333Ydt27Zh/vz5AJpcLDt27MADDzwAAJg+fTouu+wyrFmzxnXTLF68GKNGjUJVVZW/VxMQtvEgNk/+khgNE5Lx6OexXZtGIj70c9MKQgghJBkpi5FZs2bh448/xi233IKdO3diyJAhePLJJ9G7d28AwM6dO7Ft27Gn9Dlz5mD//v34wQ9+gKVLl6K8vBwTJ07E7bffnqyLtCIpDiZBsl6MpD6IbRqxpC8JNtaTXMSmTgwhhJBgsApgXbBgARYsWGD8v/Xr17fYd9VVV+Gqq66y6SprScUtk0p9EBOSiVMfj6mgmX4ev4JnafUghBDSFrg2jQF9YreNB9GR1AeRlJWXVHI1iRy/1quRiA9JFgzrgxBCCAEoRkQTuwnJhCxxlfjlLpCMR+LekcSV2JBtwiOdCwkSQghpndCLEQm28SA6EmuFpKy8XwXNTAveZXsshc0aPIQQQrIbihEDEmuAJN7CJjjVNnNHF0OmJ39JFVRTXZFsYmnt81bH6UXO/CrUpheAy0UkVX8JISRIQi9G0hkPYsKvSq6S89q4XEyiS3e5+OWC8cvqweqqhBCSW4RejJiQWD1s3DKSNW5M4kiysq8+kdtWRdWPS+cTsmmNG0IIIe2f0IuRIONBJNiKhuP1L4krMY3Zr7oiNlYOiZXIr/tFCCEkewi9GDEhER+SyVYPBpVMtiZxpLtXJOOTuGSCDFad+kd/ao7oLpc5Ba8c3Z6W0nkoYgghJHuhGBEgiQeRrP5rCk6VuGAkbiOJsJAEueoixjaOwyZ11i/BYDqPLmIoTlKDAa2EkCAJvRiRxF9kGyZxpE/+EgFjm1EiuT/6uU3H2JSeN6Uj69haT/zAya4BsiPDZtB5lzXbYoVcQkh2EnoxYoskODU1t0xyF4y+zxTrIenLJshVsnCfRJyYzqOn6fpVYK3rrgbRPkIIIdlB6MWIbXCqTbxFkIvO2WTB2BZP0/dJgncXVs48+ip5uq3JmqNfl22gcLYXcyOEkDATejFiwmbCs139V5+ATS4YSfE0yUJ5kuuSiBjdJRTkRC8RcOlyq2WbC4YQQtoLoRcjQQan6kjSiCWYXEJBrZxrCkTVhZdttVddrJniQeZveqzpxYThSc+ju3toBSGEkNwi9GLEhF+TmSRTRhIPou+zDTzVr8uvxfVMbXQRYRIn6yYtOvpKHmgapNDIptLutMIQQsIExYgASXCqX/EgJiuMZAKWFDSTpNv65fLQrRwmC4dkzJLsGQn6tdsIIUIIIcEQejFiO2lLkFgebCZ/iUvIhI1FxRT3km01OrJtPDZkk1WGEELSTejFiARJcKpfZeVNVhhdHJmsMPq5TdYUfcym7BVJWXl9n0kM6OcxWTj0fRILkEQoStq89cgPAbReh0XCqurPjttGFxrpdMFIFnkkhJBME3oxYnKL+LW8vCRTRpLhoo/HJHxsLCy2ZeX1fSbB4ldsh+T+BFXkzMZaIREnfsGCZoSQ9kLoxYgE2zLuEmzWr5Fgsg5I4lr08ZisJ/o+iTtKEphri19xJdlEOkUNIYRkmtCLEZNbxK8YBEmmjI5J+CRzrwDJhY9fosaEfl2S0vMmdOEjca+YrssvUZNJ0ik+/lb53NFX9NsQQrKD0IsRCSaLgs1KuiYkE7Dev6kvvyZkfTwmq4PuEjK18aswml/uniDFGSGEkLYRejFimqT8StOVVE71K0VYn7RN1h2/6orowiedlgmJ1YrCgxBCcovQixHbyqmS4FQdk0tI0pcuIiR9+SWoghQauvXEJCL8SrNuDzD9lxDSXgm9GDHhV2n1VGJEPmiljSQmI5OTtl8puZnGj7gN20Bm/T2WnIdpu4SQ9gLFiABJGXcJEveB7QSTyrnbOnFl29ovkvH4EZRsEit+3VNCCAkzoRcjJneGzQRjsoJIJkmJO8UmK8fWuqNP2rbCQxdHJrGk7/Mri6k9VGQlhJAwEXoxIsFkBZEEp0pW9vUrK0cyAevntl2HRr+uIF0wDEYlhJD2T+jFiKSMu1/YBpVKhE9Q5EKsR7aju3dY0IwQQryEXoyYSGZBAFLLlLGJ4/ArK0fiajK5e/zKwpEgseZI2lAwEUJIbhN6MWIbMGqT8mprhZH0JSmMJmH+pseaXkwYbnV8MvyK41g3adHRV6mtQ2NzXLZZMLJtPIQQ4hehFyMmJLEUkjVlbOI4JPgVRyEJTpUEntpee1DYChZCCCGZIfRiRCIGbAt/2SymJ8nKkRRqa6+0h1TaIOuDHFvJl6v4EkJyh9CLERO6ILDNlJFgm9GiY5PKa4q1yOQKuJJ4FdP4dMvMG2trAXgn+rce+WGLfYQQQrIDihGf8GsxPYnw8SsrJ9vSZv2qfGtLe7C6EEJILhJ6MWLKXkm2tD2QWqaMDZJMGRN+TaQ2Linb+BAbUSUZXzoX7pNAkUMIIa0TejESJJK0XcnEaSN8TO4fv4SGRHzoQaSmoFLdEsJJu3V4fwgh7RWKEQOS1WT1Cdmv4mm2fdlk5WS6bLo+uZriQSTWk3RdBxemI4SQYAi9GAnSpC9ZUyaoReds+/JrPMlWoW0+iUsEVFBxJPfWXw0AWMhSaYQQknFCL0ZMSNwiNmm7EkxZOUH1FWS1Vd3KYbJ66K6kbIv1yEUkrhzbgniEEBIUoRcjknVegkzbtSllnlpWzrFr0vuytTroIsZW1EjEB+MkCCGk/RN6MWKLX2m7uhXGr7Rd2/Va9OMkgacmUaMLDVurRybrnqTTykBrBSEkzIRejJhSafWAyCCf/CX4FTehCx+/rA60Xhwj20RFto0nnbw9aQ4AoCer0RKS9YRejNji12q7fmWC6MLHrzooJqEx8On1TS9ayYLxC4mg41o0hBCS21CMGNAtIX6l7Uow9WUjfEzowkfy1Gxq41cJe9I2wmz1IIS0L0IvRvyyIEhSaU19+ZUpk2wxvQ9aOcYkKvQxSoRHprNgTGvR5DoUGoSQMBF6MWIiWWVQ4NiEl87JQlKvRBKw6lcqb6bFByGEkPZF6MWIbdquBJuVff0SORLhkYvrvPjFqurPMtY3rR6EEOIl9GLEhB6jIYmbkNQrMWGTKSNJETadV9KXXwG1QVWWNVVOba+CiRBCwgLFSIAEVUPEL4ISDMQLLSGEENI6oRcjEoEgWW3XNm03qBRhU0puOuuB6C4qk/DR91EcEUJIOAm9GDER1JOs7Wq7YXFD2FaNJYQQktuEXozY1hCRpO3qSNJ2JcJDkiJsEjn6PtPkH+TiednO8YQh3S2EEBIMoRcjJvxaTTYVF1Br8SA27gvTeW1iT2zdK7pgMgkfG0vI33r9NIXWhBBCcgGKEUuCSts1ZcpI+vJr/RrJefTxmERF5fhNAIBdbRqNnRAzZdy0B2iZIYS0V0IvRkyuE90Sks60XVv04FSTNUeyfo1+nkzHcfjVvy5Q0rkiLyGEkNYJvRixRZK2K8mUkWTl+JUinO1IXDDpFHiEEELSQ+jFiOTp1yQQso3UXEDJrTs21gCThUUiGhj/QQghBKAYMWITpyBJ27VdTE+vISLJALKt2WET5Cqpp0KrByGEkGRQjFhiuyqujiQ41a+sHB3JNZjEkV9uI4qP3GHk5FL3dVuDkgkhRCf0YsQUx6ELBEldD0kNEdtAWB1J0K1tKq1+HpNg8Wv9GkIIIQSgGBER5Mq+EiuDbkFIZ5aHaTz6PomFw9YKwrgSQghp/4RejNgWNJMIBEmmTFBI3CtBEtQ6OLbnDYuoYToyISQXsRIjq1evxp133omdO3di8ODBWLVqFcaNSx6r0NjYiFtuuQUPPvggdu3ahZ49e+L666/HpZdeaj3wILGZtCUL3JmQBKdKUoRtAlZzIdbDpi+T8GB8CiGEZC8pi5GHH34YixcvxurVqzF27FisXbsWU6ZMwZYtW9CrVy/jMeeddx4++ugjrFu3Dv369cPu3btx6NChNg/eDyRxHJIaIq2fW75+jS1+FQfTx5zJwm22UHgQQkhukbIYueuuuzB37lzMmzcPALBq1Sps2LABa9aswYoVK1q0f+qpp7Bp0ya899576NSpEwCgpqambaMOGJsATb/WrzEFp0pShHUk15DpSdsv8dFey78TQkhYSEmMHDx4EK+++iqWLVvm2T9p0iS8+OKLxmN+85vfYOTIkbjjjjvw05/+FMXFxTjrrLPw7W9/G4WFhcZjGhsb0djY6G7v27cvlWG2meQBmkAqQaQ269eYsEnbNaELFJMYsLGwBBUfQgghJBykJEb27NmDw4cPo6KiwrO/oqICu3aZqw+89957+P3vf4+CggL8+te/xp49e7BgwQL8z//8D+6//37jMStWrMDNN9+cytCskQSVmqwVukDwa/0a28X0sh0KlvbH25PmAAB6Hv3sOtvN9xFCiASrANZIJOLZVkq12Odw5MgRRCIR/OxnP0NZWRmAJlfPV7/6Vdxzzz1G68jy5cuxZMkxV8S+fftQXV1tM9SM4ldxMIlbxqYvifDJtIjIdP+EEEKCJyUx0qVLF8RisRZWkN27d7ewljh0794dPXr0cIUIANTW1kIphf/+7/9G//79WxyTSCSQSCRSGZqv+JW2K4nbkGTK6PiVImxy/0iCbCkQCCGE+ElKYiQ/Px8jRozAxo0bMXPmTHf/xo0bMWPGDOMxY8eOxS9+8Qv87//+L0pKSgAA77zzDqLRKHr27NmGofuDafK1EQgSJGvK+IUpODWdq/1mst6FSSxRQBFCSPaSsptmyZIluOiiizBy5EjU19fjvvvuw7Zt2zB//nwATS6WHTt24IEHHgAAXHDBBfj2t7+NSy65BDfffDP27NmDb3zjG7j00kuTBrCmE9ugUr/SdoNaTM+EJHsmLKXeWRyMEEKyh5TFyKxZs/Dxxx/jlltuwc6dOzFkyBA8+eST6N27NwBg586d2Lbt2GRZUlKCjRs34qqrrsLIkSPRuXNnnHfeebj11lv9uwqfKd26OeVjJAvcSQJhTQSV4WKbjkwIIYT4iVUA64IFC7BgwQLj/61fv77FvkGDBmHjxo02XQWOxHVhmrT9KjKmE2SmjESg2LhybK0M+nG25wlLqXdCCGmvhH5tGr/iOCQL3JmwsU5IrDB0Q5BsIFn6L1N/CSHNCb0YMblOdIFgslbYxIikUigNOGZ1actaOc3dP3r/NuvZmM6TTiiyCCGk/RF6MSJBkkpru8CdX/hV2j3TJeIJIYSEj9CLEVOwqsRi4FfWiU2mjG2Krk1Z+VywRLQHAfWjgn8DANzks0glhJBcIPRixIQenBpk2q5f6FYYSV9BBeGayAVRQwghJDNQjFgiSduVYCMIbBfuy8VU3mwXMVNqm7LK3uSKwYQQYk3oxYhkgk7naruSTJkg8atSqV8rDacTCgtCCMkMoRcjJiRumUyutpvaKsLJ+7F1P0lYP/WvR8eTfrLNmqKLHCc+BGCMCCGEABQjRoHgV8CozWq7JrItQFMXPo9/8gUAYGHSI3KDc94/5/iNfDiGEEKIl9CLEdsVcINabdckfPS+UllFuHk/urXENiNo6h+9oshkhQnS6mJDtllLCCGEHCP0YsSELghsq7SmM0U4nehCxyR80nld2bQir+OSAY65ZWg9IYSQ1qEY8Qn/XTJAsngUiRXG1JcuGtIZGEsIIYQkI/RixDRp6zEapoDRVEq7pyM3w7a0uw36Pcs2l0xQmKwehBBC2k7oxYht2q6ObaaMpF6JHjCaSjryB+KRNGETW2Hqqz1YXWxSfemSSR3JYno2bQghuUPoxYhtwKhfNURsMAkfiRVGt2BIsnRM1+lXPAjremQHIyeXuq93aft2GdoTQojfhF6M+IXtaruSeiU2lVNNIiuooFJbK4iNeyeTAiZIqwezfQghYYZiRIApYDTbSqsHFbehp/ECMvExf9NjTS8mDE/aRhdHEqGRzvgUulwIISQ9hF6M2K7zomO72m5Q9UpMLphk8SnZkBILyIRGLqZCE0IIaZ3QixFbgspeMVlcMlmvxK+MIInVw3QN6XTL3ISV7qsgoUuGEEK8hF6M+J29AiS3hARphQkKU1+6C8bkkpFcl8Tds3/rbcdt0x4nd65fQwgJE6EXI7aks4aIpC/JxJ5ta9zY0B5ShgkhhHgJvRiRZK9IaohIJsnUVttN3pckRdiveBBb949+XRILSy4KjXENPz366qakbdqj5YYQQvwk9GIkSPyyRKTTLSOpn6JPrpLJ1q+YFkmWjgRTLMq/NXQBAFA6EEJIeqEYESCpIWKKB2nLarvN+5Jk5dgInyCf2P1KfQ7KWpJtabs2RcYYV0IIaS+EXoyYUmnb62q7EteNXlfEJAZ064nJmqLfQ79qk2QbA59e3/QiS9KjCSEkFwm9GJEgcZOY4kF0bIWPjskKk050q4fJClJ7/ofpGg4hhJAch2LEgF/ZK36l7aYSHNvWgNV0ZglJ7k86rSVvPfJDAMEXgTOtBUMIIWEm9GLE9FSvCwK/Frgz9aVP/n71ZRsPol+7SQzo1hyJdScXXDALK2cefbUXQMsiaMe2j+0jhBDSdkIvRvzCr5V9TejBqRKXUJDoAsrWJePX/ckmjqX6Ao5g4Qq4hBDSOqEXI6a6HpLgVJvsFb/qldjiVwpuUGvIZFs9Dj3V19luvq89CipCCEk3oRcjEiQCwXZl31SCY1uzgthM5EFOpEGJKsl1pnM9G0IIIW0n9GLEVNfDr4BRPZZCstquX9gKDb9EhG45ktRYkYxZ0iadReJ03FRfgOm+hBAiJPRiRIIklda2yJfuzpD05Zc7QzLmdFZXleBXMTXJCr1VC/KbXrylbTfbRwghpO2EXozYPkWnc7Vdm74kk7ZNjRMTJmuKbjkyWTT07BUTNteeiwXpCCEkzIRejJhSaf3KXvGrXolfSFwcksDc5NacthVgMwmoTAaIfrj6IACgVttuvi/bYcl4QkguEHox4hd+r7YLpCZ8kq/+m9zqYRJLEouGhNKtm1Nqn4zkKygnvy6TwJMEtd6z69cAgIVJW6SPbMssIoSQIAm9GJG4GEz4FUjpl1VBgj6x27qNJNYcScl43cIicdtIapqYCqzt33qbp41khd5sW3fGsXLQwkEIaW+EXoxIkDylSjJlbF0yyRama74v2eq/QWTtJHNjpZJZJMWvuBY/+pJkyrDUOyGEpE7oxYjtonOSAFFJIKVfa8pI0CdbvwI9JWLNZNGQiDP9OIlFw8ScgleOvpqW4pGtc+f8WwEAS309KyGEhIvQixET+uRqskTo+FXbQlKPwySEdKFhGo/EWqL3LxFdJpfVvfVXAwAWttKbbmFJZxZMuhbFy0ZYnp4Qkm1QjFhiE0hpa4VJ3nfy/iUTu63bSLfmSBYblGA7HlZcJYSQ3Cb0YsSUvaI/6afikgH8f7K3zYwJqm+/an/oosYUzFs5fhOAY0/xJreWjfAxBcvqqbxcd4YQQtJD6MWILZLJX1KvxMYlZBssK5m09QnYdJ02k7St1UO/h113NbRoowsdv9KKdSTvDSGEkNQJvRiRxGhkerVdifCxsQ5I0ool55VYbkx96aIhncG8JrItlfd43Dv+bPf1TRkbBSGEtJ3QixETksk1qMXY/Fp7xYRuQbCd/P1KI9bPY7J62BDkPSSEEOI/FCMCbFfb1Sd7kxXGxuVhEkJ+lWgPaiK3FT561oskS8ckJoNy3QQF65UQQsJE6MWIbRyAzWq7EkwuIb+sMEG5kmzHJxEWkr706zLVNJn6x+Ar3BJCCLEj9GLEhO52CHK1XRtLhClzR5+QJZYI0/gkhdFsqqKa+vJLZJnKv+tIysgze4YQQjJD6MWIbY2MZJM/kFwA2GbKpLMYmCT+wyZGRDLRm9JtbawnkiqtkkXxcjH2hOvXEEJykdCLEUmmTHr6l/ctcbeYLBE22TN+uXZME7tENOjjsS0H7we5KE4IISQXCL0YkeBXQTPbyUwiCGwCViXWCtN5g8okCtICpIsYiQj89qxOAFq3nhBCCGk7oRcjtm4RfZK2zZSxiU8xBcvqMSKmvtNZx0OvnGpCH8+6SYuO/s+xxex0EfHG2lrPMYQQQnKf0IsRCX65KmxdQrZpuscjSLeDPmZJWflMFz07Ho6lBKC1hBBC/CT0YkSSKmrCr4lT71/iEpKUlTcJDZs1ZWyvUz9Oks1iGp9NVpBkRV59HRpCCCGZI/RiRIJtDZFklUoB/4un2WCyVvgVt+HXGCUiS+IOkwiUbMcp/35TRkdBCCH+E3oxYrJEtCUYFGhbnQqTVUYysesTsk2Wji2S+BCTyNHHbBIVkutilgshhOQ2oRcjEiRuERMSQZDOGiKSFXl1MSQpo24Sb3pfpuDUoLCpTUIIISRzhF6MBFlHQ8c2UyaZBaH5Pp2gVvo1IYnrCDI4VRd9puvSC6qZVuilhYUQQjJD6MWICb8mTokgSKcYkrTRrRymY3S3jF/xIaa+JGOWBMeGFSfOBGCsCSEke6EYMaBPrpIaIpK0XUl8isklpE/IfllhgkQfo+ke6m1Mbi19n00pekIIIdlN6MWIX6vtmkinIJDEp0jaSKxC+sJ0psBTv8aj49c95aJ4hBCSPYRejJiQxGjYxBeYXDKSyV8yadtYDCTBs+nMypFgWpsmk+vVhIUgRTshhIRejEhW25WQztV2U3EJNe/bRrCYjrGJEbEpjW/L97aOO9oXIYSQXMBKjKxevRp33nkndu7cicGDB2PVqlUYN+745u4XXngB48ePx5AhQ/DGG2/YdO07konVZAWxsRhIni5N49EnaZPwkbgvJCvy+hWMKkGyLk86BQoXxiOEkMyQshh5+OGHsXjxYqxevRpjx47F2rVrMWXKFGzZsgW9evVKetzevXsxe/ZsnHbaafjoo4/aNOigsXHB2JaVD2ryt83S0S0YpuvS25jul01qscmaYxMjYpuVQwghJDOkLEbuuusuzJ07F/PmzQMArFq1Chs2bMCaNWuwYsWKpMddccUVuOCCCxCLxfDYY4+12kdjYyMaGxvd7X379qU6TDF+VU71C9N49AnZryBOvxbgM1mJ9m+9zdNm6h9b9qGnCJsElKS6q17qXZKVQwghJHtISYwcPHgQr776KpYtW+bZP2nSJLz44otJj/vxj3+Mv/zlL3jwwQdx6623HrefFStW4Oabb05laL6iT1ymGA39yd92gTu/0K0KtkIjkxYE05j1faZiZYQQQnKblMTInj17cPjwYVRUVHj2V1RUYNcu87Prn//8ZyxbtgzPP/884nFZd8uXL8eSJccmxX379qG6ujqVoYqxnXxTq6aaXBBIXB65iMRN5JdlhhBCSG5jFcAaiUQ820qpFvsA4PDhw7jgggtw8803Y8CAAeLzJxIJJBIJm6H5giRgVMc2PkQiPnSrh8QKIzmPbaXZXBRMH64+CACozfA4CCGEtCQlMdKlSxfEYrEWVpDdu3e3sJYAwP79+7F582a8/vrruPLKKwEAR44cgVIK8XgcTz/9NCZOzE17uyRt12ayl6TtmtDFkK3QsClWZgpW1a0eQVZO1RfGk5SHz0VBRQgh7ZWUxEh+fj5GjBiBjRs3YubMme7+jRs3YsaMGS3ad+jQAW++6Z1+Vq9ejWeeeQa//OUv0adPH8th+4dp8g8qYNQUnCqZ/HWhYZspIzkuWSzMB60cY5NWbIupXol+bhZBI4SQ3CJlN82SJUtw0UUXYeTIkaivr8d9992Hbdu2Yf78+QCa4j127NiBBx54ANFoFEOGDPEc361bNxQUFLTYn2voE7uphohN2q7EJWRCt0RI+pa0kQgYiWAJypIkJSyBr87CeDdldBSEEJIaKYuRWbNm4eOPP8Ytt9yCnTt3YsiQIXjyySfRu3dvAMDOnTuxbVtmF2lrK37FaOjY1uPQhYZtaW79PBIrgyTIVCJYbGNqwiIiCCEkzFgFsC5YsAALFiww/t/69etbPfamm27CTTfdZNNtIPhliTCl7dosxmbrztCtCn4tBGeyVnDlXEIIIX4S+rVpMrOyLpAsPsVEkO4LG2zuWZBpvJIYEV2csfQ7IYRkD6EXIyYkAaO6QJDEX0isMLbF0/T+TS4hfcySjBLTdfkV/+GXNeeNtbWe89C1QwghuUXoxYhf8SAmwpI+amv1kAi49loUjhBCyDFCL0ZMSAJG9YnUdo0biZVB78s0iUusCvpxQa7Xks7Vf9sjtkHKfvXF6riEkHQSejFiW7PDBkmsRTrHY0JfmM4kcjI9Rh3dWuJX8C4hhJD0EHoxIsE2U8Yvy4Pel8kKo0/Ipr718wSZFSO5P36JBq7ISwghuU3oxYjJRK0HQEpcDpIaIqb4FEnxtHRC8zwhhJB0E3oxYks6Aylt+pJYPUxuI0nGTbIaK6lkzkjR+7+z/HMAqafkMvCVEEKyl9CLEZMLxgbbGiKS4mk6polV4qqwmZD9coH4FdCabfEqmSTTVjRCCPGL0IsRSWaKJFNGUkMk24Jl/cIvoWFrvZCs0ksIISR7Cb0YMaFPiraWCAkS94WkL7+CUW2ERTqDVU3oFVjpkiGEkNyCYsQSffI3WSL0GiISs7pEDEhcQqb6Jbqo0dN4sxFmyhBCSPsn9GLEtliZX6RSRj4Vq4etSyid6b8SCwZdMIQQ0v4JvRiRuGBsg1N1JMGpJnGk92WKT5FUctWFRZBpvH65SiTXJYEL4xFCSPYSejHiF5IaIn5hk6XjZ19BrSK8sHLm0Vd73X2Se8iF8QghJLcJvRgxWT10C4IkU8YWvxaCk0zaEhGj9++XZUJCOvsihBCSPYRejNiiT5wmMaBbJ/xKgTVZYfwqRCYJGM22RfB0QUeXDCGE5BYUIwZ0C0KQNTuyPQ3VJLJsrDmmNrpbhgXNWoel+gkh7ZXQixFbF4xk4rSJrTC5jSRWGB2T9UI/T7ZZOAghhIST0IsRE/qkbXKL6EhqiJgyZWzqaJj6kgifdFoeTMGoOowRIYQQAlCMZB0mS42NiDBVPNXN/KY2fqX/SoQG3TKZge4eQki2EXoxIokHkQSnmmqI6G6QIOND6HLJDJzYCSGk7YRejKSzPogJSVl5m5V9bfErWJdWD0IIIVJCL0Zs0WM0JKv/+lXJ1YQkw0Ufs6mNxL2S7RlAhBBCcguKEQO6JUISnBokEuFjAxehI4QQkg2EXoy0V3eCSWi017iSoCw1jAchhJD0EHoxIkESnGpK29UnSUlNE0kasaQvW4ISZ5JU3/ZApq1ohBCSi1CMGAhqITgTkhgNiUVD4nIxpfIeD8m9YE0RQgghbSH0YsT2SdamJLopWFW3RPhlmdCzdIKEQqN16O4hhJDWCb0YMaFbImxjLfwKEPVrZV8dk2AJanG/9hCbQxeMF91qFmTKOSGkfRN6MWL7Axp80CSQbMIzpQhL6pXYIHHtZFpo3Fn+OQCu0ksIIblK6MWIBFPAqI5EIJiCUyUmfL+Ej34eW8GSbSnBfomhdLlTaGEhhBAvFCMGbAI9JaSy0i/QNlO3KY5DFxG2sR61539oPzBCCCFEI/RiRBITYbJM6BO7KW1XYnmQZKv41ZdfSERMUFYGBoMSQkj7I/RixEQmy53bBozqAkFihTG1kYijoGJEKDT8hy4hQkguEHoxIokHMSEJGNUFgmliaK9VUUlqpHMxxDDzxtpaAMfuqb4tbUMI8ZfQixEJpuDUoDCJI4nwkVgrJLVHSrdulg3UBySWEFpLCCGk/UMxYkCSLaILAlOmjC4Q/A5Obd6XDifx1vHr/qSzWi8hhLRXQi9GJPEhpoBRv6qOSjJ3/ApO1c+TzrRiQgghJBmhFyMmbEqpm9wkNk/ftpO/pC+WbU8NWj0IISQ9hF6MSOJBbGM0dEzBqhLxYZMpQ3KbdMbuEEJIpgm9GDEhiQfR8SuFMp3BskFCqwIhhBApoRcjQcaD6BOyKVNGEixrYwkxiQFaVPwn2ywYDFwmhOQioRcjEiTxIOmsC2GywvhliQh+AcDsnST1e5htQiMXYc0OQogEihEDflkQbAqamSw1QfVNV0pqSAQni5URQkjqhF6MSNJm/bJEmKwOkswdiVVBIj5srBMLK2cefbVXfAxAqwIhhBA5oRcjkuBUCSYxYLP6bzoXvJMIGKYDE0IICZrQixETflkidPzKlPHLFWAjltoLdFERQkj2EHoxIokPkUz+tgvuSdKIJROnLizSKTRsg1P166JASI1Mx6forji65gghtoRejJhIZ2aKjRsknSv9+lVZtj1gO9lSZBFCSOtQjAjwKx5Ekiljmvz9Eh+SSVFS94Qkh9YBQghJndCLEVOmjM3kb7KCBDWx27qEJNSe/6Gv5yOEEEKOR+jFiATT5G9THMx2jRuJFUYyHl1kmY5h9gwhhJB0E3ox4lcQoClTxmb1X7/WuDEJjfaQPcP4C0IIaX+EXoyYkEzaNi4YU6ZMtgWDBrV+jUlESLIx2qP4sBXALFdPCGmvUIwIkLhA/CrjLiGo9WNsaY+CgRBCSPoIvRgxBav6NdnrMSJ+1TTJNNkuPrLN2kQLBiGEtE7oxYgE23gQv4JBJeJI4jbSz5PpNF59PJm2+OiiIdPjIYSQsBB6MWLKlLGZpG3XlNGtDLY1RXRxJLkGmwBbE3zyJ9nIG2trAWSvBY8QcozQixFbJGXcdfzKlDFZavTxSIRGti3KJ8FW+ATluvHLepLtri9CCAmS0IuRIINTJTEi+iTtV0GzdAoNCdmWVpyL1pxcHDMhhEgIvRgxkc54EL/KytuMR3JMkMGguivJ5FpK5zo86YKighBCvIRejJhcHjoSK4NkQTnbTBl9kpaMJ8hKqjaTqa07I9ssKiQ17px/KwBgaZJtQggBgKjNQatXr0afPn1QUFCAESNG4Pnnn0/a9le/+hW+8pWvoGvXrujQoQPq6+uxYcMG6wGngzff33bcCf/A5B6BFQizQTKeic8u1MRXcBR0XBJINorkvM8/d5FH+OUCpVs302JCCAktKVtGHn74YSxevBirV6/G2LFjsXbtWkyZMgVbtmxBr169WrR/7rnn8JWvfAXf+c53UF5ejh//+MeYPn06/vCHP+BLX/qSLxfRFiTxIH4Fp0pcDqaJVncbScZjQj+PxJpDjpHOxRCzjVyof0MIyV1SFiN33XUX5s6di3nz5gEAVq1ahQ0bNmDNmjVYsWJFi/arVq3ybH/nO9/B448/jieeeCKpGGlsbERjY6O7vW/fvlSH2Sb8iqWQZEhIrAc2wagmoZFtQa26OPIr1ZgQQkhukZIYOXjwIF599VUsW7bMs3/SpEl48cUXRec4cuQI9u/fj06dOiVts2LFCtx8882pDC1Q/HLH+JUpYyIoi4ZtyqkkOFWCRKwFde1htnpI3vdk8SDN9xFCiISUxMiePXtw+PBhVFRUePZXVFRg165donN873vfw6efforzzjsvaZvly5djyZJjk9C+fftQXV2dylDF+GUtCNKMrVtqbMVRkEGtNuzfelur237COh6EEJK9WGXTRCIRz7ZSqsU+Ew899BBuuukmPP744+jWrVvSdolEAolEwmZovqBP9qZ4EMnkJglITG5BOLZPgl+TbTonbf0+S0RWtlsrWEKeEEJSJyUx0qVLF8RisRZWkN27d7ewlug8/PDDmDt3Ln7xi1/g9NNPT32kAWEbDKrjVz2MIFf/zabsn2zkeELCVigSQghpnZTESH5+PkaMGIGNGzdi5syZ7v6NGzdixowZSY976KGHcOmll+Khhx7CtGnT7EebJiQxCDaVUyWZMiYklhqdTGfF+BWcqltC/Apy9cuCMWHNQwAAmZOSEEKIiZTdNEuWLMFFF12EkSNHor6+Hvfddx+2bduG+fPnA2iK99ixYwceeOABAE1CZPbs2fiXf/kXjB492rWqFBYWoqyszMdLCQ5TPIhfxbj0mBVJDEs60yzbYwVUKRQahBCSHlIWI7NmzcLHH3+MW265BTt37sSQIUPw5JNPonfv3gCAnTt3Ytu2YxPq2rVrcejQISxcuBALFx4zc1988cVYv35926+gjZhcFzZxE6Yn7XTGD+iiQXINJutJMotPqqLrb71+6tm2DRS2sYS0h+JhzEwhhIQJqwDWBQsWYMGCBcb/0wXGs88+a9NFVuGXdcAUc6BnuNjGsPhlqbE5j0R0VY7fBKDtVgZd5ADMlMl13lhbC+DY+6dv27YhhOQOoV+bRhJ/YYoHyWTWhEQctRf3ikl8ZArHbQMkF1UMciWEkNQJvRgx4ZeZP1nwJZDc7eBXiXaThSPZwn2pPkkyfZUQQoifhF6MSIJBJZOv6YnYJlPGhC4ass1Sk+21P4gXurUIIdlG6MWIidTEh9zqkW1rw4QJ3dplElB+iCqTKyfbLUkmS6C+rz0EBRNCspfQi5EgYyv8Eh/6GG1Xj9WfiNtLXAkhhJDcJvRiRFKsTOKCkQgPU6aMHschCaiVIHka9ysDJ9tW2812SwTJff4p8iiAY9YvfTvZPkKImdCLEVsrgwTJwnS6tUJSYM02Y8P/FOXsiBGhC4EQQnKb0IsRCaZ4EN0SIqkPEuTaMJksk55N6bdSTPeLFVcJISQzhF6M2GbB+IVurTBZL3SBIBFHJuuFX24ZCZleG+d45KKAIoSQ9kroxYgEUzyI7oIxWT3awyq5QcZfZLtgaQ+kcx0jQgixhWLEQFApuKbgVN1aYQqo1a0cEnEUJEFZjtqrONHfP647QwghXkIvRiT1QUzxILrVwyQ05m96rGlzwnAA5qdUG8uDZDy2giHbglP9ghk2x2DALyEk2wi9GDFhY2WQmMMl2SymSTMoS4TtBG0Tb9FerR6EEELaTujFiMQlY4r90K0etthYIiSxKKbrshEfuWAp0a/LrzH7JaAkWTqO66atbhubUu8mt5Ff48k29OsyXaekDSHEX0IvRkxIXDA6JquHvs+2wJpNDIttDEm2FTAjhBDS/gm9GJFURTW5YGwKiKXTBSOxnpgsCO1h/Zx0CqqwuJ8YdEsICZLQixETNmu4mKweNnU9TAG1fmXKSNwXleM3ATjmUpBM7Nk2IbOGCCGE5BahFyPrJi1qtjUNQGriwxEaJqtH8lgGoC0umHSS7eOT4kcciV/rBqUTZs4QQnKB0IsRSRaMyeohCQa1mQBNk7/E5RKUdUK3lEgZ+PT6phcstEUIIeQ4hF6MmKwgEquHjmTxOr9cMH49obeXYFWJ6Gsv10oIIe2R0IsRExL3SlAToGTBPVv08di6YCRWmMc/+QIAsDBpC9JWbINKmapKCMk2Qi9GJOXXTegTu6SSq8QFk871bExWmXQGo771yA8BHHOP6dvS8UhEnx7UajpvtgXi2sAYEUJILhJ6MRJkmXBdfNhaPfyaJPXxBCl8bOJlbINMmT1DCCG5TejFiMkFI7F66BN7OrNOTEG3ksqb6VxMT8c0Pl18TDn7uwBaH197sF7YQvcKIaS9EnoxInGv2KJP/iZLhM3kKqn2asLGEiIZnyRzxqZIXC5SsGHHsY02LhVACCFhIfRixISN1cOvwFO/MmUkIsIvK4OpuFvt+R962qQzoDXM1hOdMFdOpSWJkNwh9GLE1r0icXn4FZMhccHYVHv1C1PcjeT+6IKFEEJIOAm9GDGhT6QSq0dqhcmAtjy12xZh88tiYLMyrCk4NZMxLIQQQrKH0IsRW/eKX1VRJRO7Hm8RZAaQBH08fpRaz1XmPn03AOADVpolhBBrQi9GTEhqf+hCwy+rh6Q8vW0RtlT6b00chbWgWdWC/GMbb2VuHK3BOAlCSC5CMWIgnUJDtzJIytOb0NORbVwpyfo/HpLYD0nari3pDNYlhBDiP6EXI0EW/rIVBDoSt4weiGubSqsLH0l9EMZ+EEIIaQuhFyMSq4ekyJjEvWJr9dDxa8E9U30QXfiEpT6ICdctc9Ql41h3AAovQgjxk9CLERN+WTRsJnLbtXKSnae5yPHruiRrwaRzjZ1s4qr31zTbmpaxceQSepyLKe7Fpg0hJHcIvRiRWDRMSGI9dCQpuTYuGROm8+hjNAkWXfiYglX3b73tuP23hxiNIONcCCGEHCP0YsSEjdAwEVQhMr+qvWY6RViCaSXfMMCy8oSQMEExYokuNExWDx3T5K9bIiQL90kwuXZssnLSWUPE5NrR+9fjOPzE5tzH3DJylwyFBiGEeAm9GJEElUqEhgm/LA82JetNAsYvF5COX/EhJteO7iqh64QQQtofoRcjfgkNyUQvsXr4tYqwRFRISrRLrDImEfHG2loArbtXMhnkarKCBCV0XEsIrSCEEGIk9GJEgq2FQ5/sTUIjlRWBP2iljT6xm46xcblIglVtybUgV2bKEEJIMFCMGEjNypE81sMG21WEbSZ2m1gUAFg3adHRV/5OyBJril/Q3UMIIdlD6MWIrXtFgj7Zm4SGTbEyW/eGZDw255bULwlKwASJTXAqIYSQ1Am9GDEhcWcEFevhV9puKn0178fGwlK6dbNV/34VYWsPMK6EEBJmQi9Gggwq1S0PtkJDt1bYLtxn6wIKClsRczyCTP8lhBDiP6EXI34JDduJ3q+MEomVwcYlZHKvmNa0yXYkAoVuGUIIyQyhFyMm/BIafsWDSFwnNlVibYWQTWXZdAanSsjFGBZCCGmvhF6M+CU0JC4YW6GhY7ueTnJ3z7G+czGOI9nquh80a9Mes2e+ce+3jm1M+G3mBkIIIW0k9GJEgkloSKwKNpYHSTyIpGps6+eWW1hyUZxIyKSlhvVKCCHES+jFiEloBJVuaxt4aiMI/HLBBBVk6ifZZPVYP/Wv7mu/l7J3LSG0ghBC2hmhFyOmSVvfZzuxSywRNkLDtoS9H31LcSZlvydkCdkWD8LAWEIIaZ3QixFbdKEhsXpIYj1MbWyCU4MKes1VghIoQYkuunIIIWGCYsSAjdCwRRcEtvEgEnRLiF/nnfrHzK4tIxEafsSI+OWCCdKVQwghuUjoxUiQcRx6m0xbIoLq33aNm2zHL6tHJl1WhBCSC4RejJjQRYSte0WCbp3wKx7EVJhMYgmRrNUjsYT4Ve3Vxr2SbTVNgmJp7fOZHgIhhPhC6MWIrYiQuFf0fX4JDRMSESHBZl2eTOOX+PDDgmErwiRBrhQfhJD2SujFiIRMu1d0JMLD1gqiC410WkFykTBfOyGE+EXoxYjEomFCMtn75RaRtJFYNGyOyTYriF9ZMTZWkHQKj3RaQUyVXFnThBCSTkIvRiRI3CuSNv4LDyDZSsOSc9sKjXROypmM/2ivVg8KDUJItkExYkC3aJgEQjotGjZWD1PfNudJ54QssVbYihPJuTMpPoKyhHD9GkJILhB6MWKyaNgEg9paNGzaOBYNILlVwyQ8MulyMd2foFJes014+LUKtARaPQghuUjoxYhtFopfFg2JQEiniAhqory3/moAwMJWrsLUt+T9yaY6HpLVmwkhhHgJvRgxoYuIdFo0JG1Mk7a+TyJgMh0TofdvWqBQImKOd95k+1LFr0UVCSGEeAm9GJGICBPptGhIJlLJpJhJ8bF/620t9tlM5CYriF/XpY+HQoMQQtKDlRhZvXo17rzzTuzcuRODBw/GqlWrMG5c8vTVTZs2YcmSJfjTn/6EqqoqXHvttZg/f771oNONXxYNEzbxBBJXQKatHkFN5EEJD0IIIZkjZTHy8MMPY/HixVi9ejXGjh2LtWvXYsqUKdiyZQt69erVov3777+PqVOn4rLLLsODDz6IF154AQsWLEDXrl1xzjnnGHpILyYREZRFwzSRJnsaN+37ANlBOi0INlYh03iy7R4SQgg5Rspi5K677sLcuXMxb948AMCqVauwYcMGrFmzBitWrGjR/t5770WvXr2watUqAEBtbS02b96M7373u0nFSGNjIxobG93tvXv3AgD27duX6nCPy+HPD7uvnfO/8NZfjdvN9+1944am7Zne7eb7huz9EQDgv/Z5t5vvO9L4mee8zrZpXypt0KicBm7bZOP5rxTbJBvPvgDaZNN4bN+bFu+Fs23a18Y2B774wtO3s23al0qbI5/+b8trP7rPpo3nvoekzaefHmnRRt9n08bZzlQbQlrD+ZwopVpvqFKgsbFRxWIx9atf/cqzf9GiRaqhocF4zLhx49SiRYs8+371q1+peDyuDh48aDzmxhtvVAD4xz/+8Y9//ONfO/jbvn17q/oiihTYs2cPDh8+jIqKCs/+iooK7Nq1y3jMrl27jO0PHTqEPXv2GI9Zvnw59u7d6/79/e9/x1/+8hd88sknnv1+/W3fvh0AsH37dtE227BNNrTJ9vGxDdvws5ubbfz8++STT7B9+3ZUVVWhNawCWCORiGdbKdVi3/Ham/Y7JBIJJBIJz77y8nKLkaZGhw4d0KFDB/E227BNNrTJ9vGxDdtkY99sc/w2flFWVnbcNilZRrp06YJYLNbCCrJ79+4W1g+HyspKY/t4PI7OnTun0j0hhBBC2iEpiZH8/HyMGDECGzdu9OzfuHEjxowZYzymvr6+Rfunn34aI0eORF5eXorDJYQQQkh7IyUxAgBLlizBj370I9x///3YunUrrrnmGmzbts2tG7J8+XLMnj3bbT9//nz89a9/xZIlS7B161bcf//9WLduHb7+9a/7dxVtJJFI4MYbb3RdQ8fbZhu2yYY22T4+tmEbfnZzs00miCh1vHyblqxevRp33HEHdu7ciSFDhmDlypVoaGgAAMyZMwcffPABnn32Wbf9pk2bcM0117hFz6677rqcKnpGCCGEkOCwEiOEEEIIIX6RspuGEEIIIcRPKEYIIYQQklEoRgghhBCSUShGCCGEEJJZRIvS5DizZs1S8XhcAVCFhYXq+9//vrrnnntUTU2NSiQS6qSTTlLXXHONu92zZ0+Vn5/v1tSPRqNqwIABqlu3bu6+RCLhnjMajapoNKoKCgpUJBJx28RiMc+26Tymv0cffVQlEolW2xQUFKjy8nL3/M37iUQiKi8vT3Xs2FGVl5e7fefl5alYLOYZv3MNzcfonCsajap4PO45T7K/mTNnqry8vJTGrP9FIhFVWlrquY/NxxOLxVQ0GnWvoflxzV9Ho9EW5zH9NTQ0qGg02uqYe/furbp165b0PE5f8Xhc5eXlicbnjLF5m1gspkpLS487nuN9Lpz7HI/HVSwWU7FYTFVWVqoOHTp4xvDcc8+pJUuWuNvl5eWqtLTUc10lJSWe/oqKijzfi7y8PPXcc8+pqVOntjqeK664QvTZcMbc2nuWTX/5+fme+1xRUdHi/TvhhBNUx44d3e3CwkLPvXA+D80/B926dfN8L+PxuOrTp89xPxvH+47G4/FWv3/Z+Od8Np0x679XAFSPHj1Uly5dPL8TznfQub/NP7em+x6JRFR1dbXq0qVLq+O5/fbbW5xL/0skEp4x678H0WhUVVdXq44dO7ptmv82O78hej/Nfwud8zrnaW08l112mfG+Nf+rra1VAwYMUAUFBapHjx6qrq7O7T8/P19deeWVgc/T7V6MLFq0SAFQs2fPVk888YQaPny4+6H+4Q9/qLZs2aKmTJmiAKjbbrtN/eQnP3E/nLFYTNXX13vetCVLlqjzzz/fbQNAjRw50tNmzpw5qnfv3p7zOG2cY4qLi90fj1gsph555BH13e9+1/OhOeWUU9SZZ57p+fBNnz7d09d1112nvvzlL3v2dezYUeXl5anu3bsrAKp79+5q/PjxqmvXru4PWv/+/dWECRPcD3LXrl3dSdf5UvTv319FIhFXPEWjUVVUVOR+QAcMGKDmzZunfv/736vCwkIFQJWWlqpRo0Z5Jr+TTz65xZidic/pz/kRcP79+te/rvr27ev263xhnOsDoC655BJVXFzs+QFy2jhjLikpcX/88/Pz1aOPPqquuuoqFY1G3TGPGzdOVVdXu+O78MIL1TnnnOO57ytWrFBDhw51911wwQVq0KBB7jXE43E1d+5clZeXp0pKShTQNHk7P5LOD0z//v0913D55ZerhoYGz2ejoqLCve6CggL1m9/8Ri1dulRFIhFVVlbmfuZisZgqKChQANTFF1/sfo6d8SxevFgVFha69y8SibjX7NyTuro61bt3b1VTU+O2KSoqUsXFxW6bsrIyFYlE1JAhQ9xtp31zkZ9IJFR+fr4qKipSPXr0UA0NDaqgoMC9t926dVPdunVzr6+0tNT9njjCd/HixZ7vwJw5c9zvgCOev/vd77pCyrnPPXr08Pyojxw50jPpL1iwQM2cOdMzOfTp00dVVVW543/qqafUvffeqwC4+6dOnarOPfdc9zyXXnqpuuKKK1qM2bmvANT48ePVxRdf3OLz88///M+uuCssLFT9+vVzjzvnnHPUpEmTPBPXJZdcoubPn+9uFxYWqiFDhrj355577lH/8R//oR5//HF338iRI9UNN9zg9hOLxdTixYtbfP+af+ZisZiaPHmy+30BoK6//nr1ve99zyMOp0+fruLxuOrcubMCoObPn69OOukkz71wfp+c97ikpMS9l/F4XD311FPq1VdfVWVlZe61T5gwQX3zm9/0TN7N319nzM5322mj/+5ee+216tJLL/Xsmzhxoue9mTdvnjrhhBM855k4caLnmJKSEtWpUyf3/jz++OPqscce8/xGNDQ0qK9+9aue8+h9X3fddaqystLTZsCAAZ7v+rXXXqtOPfVUz77Ro0d7xNLcuXM9v8HN5yWnTUlJiftbGI/H1WOPPabWrFnjCoqKigrPPT3vvPPUyy+/rO644w73vD/72c/U+++/7z5c9O3bV23YsEH90z/9kwKg7rzzzkDn6nbvplm3bh3q6urwk5/8BGeeeSZef/11RCIRdOrUCfPmzUNtbS0+/vhjlJeX45NPPsE999yD/Px8JBIJDBs2DC+++KJbtr68vBz5+fno1auXu1ZOXl4e/umf/gknnngiIpEIysvLUVlZiXPPPdcdw7Bhw/DKK6+goKAASinEYjE0NDSguLjYPcdrr72GpUuXorKyEgBQW1uL559/HnV1dW5fnTt3xsSJExGNRhGNRlFeXo5IJIL//u//xsSJExGJRBCNRtG7d29MnToVBw8eRHl5OeLxOIYPH47du3cjPz8fkUgE3bt3xzPPPIP+/fujvLwcBQUFOP/881FSUoJoNIri4mJMnToVw4cPRzweRywWQ1lZGcrKyhCPx9G3b198/vnnKC4uxptvvokDBw6gtrYWJ510Eurr67F3715EIhHk5eUhkUggLy8PpaWl7phLSkowceJE9/787W9/QzQaxSeffOKO+S9/+Qvq6uoQjUaRl5eHyy+/HHV1dSgqKkJ5eTkqKiqwd+9eRKNRKKUwbNgwbNmyBQUFBfj8888Ri8Uwbtw4jBo1CkDT2guvvPIK7r77blRUVODzzz9HbW0tnnvuOWzbtg0FBQWIRCJ49913ccopp3jev2XLlmHPnj3o0KEDYrEYevXqha1btyKRSEAphaFDh+JHP/oRzjzzTCQSCfcarr/+epx99tnuebZu3Yp4PI4vvvgC5eXl6NSpEzZt2oSysjIopVBdXY0zzjgDZWVliMViqKmpwYsvvoh+/fqhsLAQn376Kerq6vDKK6/grLPOQnFxMaLRKN5++23s378fQNOaT0OHDsXKlSvdexeJRNCjRw+MGjUK0WgUX3zxBSoqKnDuueeivLwc3bp1Q15eHvLz8zFu3Dj069fPXRRz0aJFiEQi+Oyzz5CXl4fBgwfj4osvRkFBAQ4dOoQOHTpgwIABiEajKCwsRCwWQ+/evVFaWoojR47gww8/RJcuXfC1r30N3bt3R+/evd3348iRpiXp8/Pz3TE7a1Z16NABlZWVWLp0KQDgyJEjGDp0KJYuXYrp06cDgPt5dO5xLBbDsGHD8PLLLyMej7ttOnTogEWLFgEADh8+jOrqajQ0NKCoqAgAUF1djWeffRZvv/02AOCTTz5BXV0dfve732HBggUAgGg0ii1btuBPf/oTYrGYZ8yHDh1yx1xfX4/zzz8f8Xgc0WjU/fycdNJJOHToEMrKypCXl4eJEyfi4MGDKCsrQ//+/fHkk08iPz8fADB06FDcf//9mDFjhvuennrqqaisrIRSCtFoFNu3b8eXv/xlbNy4EYcOHcKAAQPwyiuvoL6+3v0sd+7cGb17927x/fvzn//s+f596Utfcq+xvLwchw8fRt++fXHkyBHk5+cjLy8PXbt2xeHDh9GzZ0+Ul5ejvLwc119/vXvc0KFDcemllwKA+zs3btw4d72T3r1749lnn8W2bduwb98+9zfjmWeewZe//GX3Ojt37oxPPvkEAFBcXOyO+b333kMkEkEkEnF/U0tLS93f5mg0itraWvf3Oi8vD9OnT8ekSZPcNl26dMHll1/u+W7/27/9m7t2ij7mWCyGl156CTNmzEDHjh3d3+ZNmzbh5JNPdvvq3Lkzhg4dimg06s4DkUgEH330EUpLS90xv/3224jFYlBKuWNubGzESSed5I551qxZqKysRDweR3l5Obp27Yq+fft6fudefPFFxONxRCIRd8zRaNQ9x8svv4z58+ejoKAAsVgM27dvx/e+9z0AQN++fbFjxw6cfPLJyMvLQzQaRUlJCS644ALU1NRg06ZNKCoqQlVVFSZNmoQHH3wQNTU1uP322xEogUqdDLN//35XfTo0Nja6T1POdiwWU9OmTVMNDQ2qQ4cOaujQoSoSiai6ujqllHKfuiZPnqwaGhrUuHHj3KfQeDyuVq5c6ar2KVOmqIaGBjVmzBhXtdbW1qr/9//+n6tKu3Xr5vbl7MvPz1fTpk1zn2iGDh2qunXrpoqKitynaefJaPDgwQqAGjNmjGpoaFDl5eWep+nhw4e7T3PTpk1TZWVl6uqrr1aNjY3umBoaGjzXXlZWpr72ta+pRCLhPh3/wz/8gyosLFSRSERVVFSosrIy1b17dxWJRFRJSYmKRCKqpqbGtXwMGzZM5eXlqU6dOqmbb77ZHU/nzp1d9W8ac11dnfriiy/ce+G8F4cPH1Y9e/Z07/P3vvc91bNnTxWNRt02n3zyiXtcXV2devrpp93tiooK970A4D65T5061X2SHDZsmOrWrZvH6lJWVqYGDx7sPlX06dNHKaVcq8TAgQNVQ0OD+vnPf+7p+7XXXlPdunVzx5dIJNTSpUtdi1NdXZ16/vnn3Sca5xrWrFnjOc+ZZ56p4vG4e5/z8/NVeXm5O+aioiLVt29fVVxcrKLRqKqpqVFlZWUe919dXZ36y1/+4nF3nXLKKWr8+PFum9raWnXjjTeqoUOHqmg0qsrKylQikVCTJ09273ttba1atmyZO+aysjI1btw4ddFFF3nOc+KJJ7qflWg06lpInHtYWFiounbt6roy8vPzVYcOHTwWMmfMznmdP+dpHc2eZJ0nOscy5HxHnTbjx493LUIFBQUqLy9P9evXz9OmsrLSfWpPZlIvLi5WZ5xxhvt/kUhEJRIJ97U+Zucz0txdF4/HVf/+/d3fFcfi53zX+/Tpo/Lz8z1P7J07d1ZVVVUeS2R+fr7Ky8vzWHyaWy4cy2c0Gm3x2+K8n8m+f926dXPP63wuzzzzTHXaaae5lra6ujp12mmneX4vzzjjDPcpvq6uzvNU73z/HCug81nu1q2bOz7n+1dSUuJef1FRkSotLXUtx86Ym1s46urq1Jtvvum+x1/+8pdVQ0ODeuKJJ9w28Xhc3XLLLa5F02nzyCOPeM7zi1/8wt0uLy9XDQ0NrtXTcZecfvrp7metpqZGde3aVfXq1cs9rqioSA0aNMh9/50xO+Nw+lJKuWN25pOTTjpJjR492jOfON9lZz5xLPqtzSeOZaT5fOK8p82tK126dFF5eXnq4MGDasiQIa518oUXXlBKKRWLxVRRUZFasWKFO2+ecsopCoA6ePBgYPN1uxYjr776qgKg7r33Xnffjh073A9Z8+3LL79cDRgwQOXl5alx48YpAKq6uloppVyz5DnnnKMGDBig+vfv75quYrGYWrlypfujduGFF6oBAwaosrIy94fE+eFxPiznnnuuGjBggCoqKnLPXVVV5XENjBw5Um3evFlVVla6Hyjnx+3OO+/0/IA2/xGMRCLqhBNOcL84l19+uSosLFRXX321e62OGGl+7YWFhWrYsGHuj5bT38KFCxXQ5OYqLCxUNTU16qSTTlJXXnmlSiQSnh/4+vp6ddJJJ6kzzjijhf/6t7/9bdIxV1dXq1GjRrn7nffijjvu8Liypk+f7m47berr61v4UZ0f+dmzZ6sBAwaoQYMGqVgspubOnauqq6tdd4Mz5s2bN6uzzz5bAU0CxvnxaD4JJvO56jFBX//6190fJ+f/nX3OtnPdzf+ax2ZEIhHVu3dv9dOf/tTzA9O8n0gk4r7HzScn3XTt/ACNHz9eDRgwwDUTO/f9xhtvdCcBJ7bqlFNOcT9r1dXVauTIke71N/fFNz+PI0Y6dOigLrzwQlVdXe25Z8uXL1fnnHOOe9zQoUPd/3dM4vqYR48erSoqKtzvlvP+fO1rX3O3/+Ef/kHFYjH1pS99ydPGEezJxlxRUaEGDhyogCZXzKxZs1S3bt08YmT27Nnq9NNPd7fr6upaiBX9c5Cfn28c8+9+9zv3Pe7Vq5eKxWKuG7WoqEj16tVLTZs2zXPO5n05bZx7VVhYqCorK9Xtt9/utiktLVXr169Xffr08XxWmt9T0/ev+YOM893q27evisVirlB24rWcbadNJBJp8f5NmjTJvX9Omz59+qgrr7xSVVVVecZQX1+vnnrqKY/b1Lnu6667rsWYk8VrNP+sDRs2zDie5m30GCbH7ajfc1OsU3OhqcfxOL9jpric6upq9Y1vfMPdnjhxohowYICaM2eO56Hhmmuucb/bU6dOVQMGDHA/q83H74zhtNNOUwMGDPAI8qqqKo/7adKkSeqVV17xnMP5TXbEYvMYwoKCAvXOO++ow4cPq6efftq97x9++GFg83W7d9MAcM2+0n36v621cVDNCtn+/e9/x/79+3HqqacCALp164b/83/+Dz799FMATWsBRCIRxONxlJSUAAAKCwvxyCOPuOc499xzMWLECJSWlmLo0KEAmszUQ4YMwYEDBwAAo0aNQk1NDX784x+jsbHRHcf27dsxa9as490WD4cOHcLbb7+NdevWAWgy9Y0aNQr33HMPAKCgoMAdZ3V1NSorKxGNRjF16lTXHD579myUlpZi4MCBrkkcaDJhfv755wCAcePGtRjz9u3b8R//8R+uqRIA9u/fj5tuuglr1qxxr2vDhg3uNgDs3LkTL730Er7zne8AaDLHO2ZPZ8z79+/Hu+++i1GjRqFPnz44dOgQ3nvvPdekOXv2bLzzzjt46qmnAAAff/wxDh8+jEgkgmuuuQYA0KlTJ7z22mu46667PPestLTUfd8rKytx77334kc/+hEAYMaMGYjFYjj33HPdfaWlpSgsLHRdE2eeeSa6d++OoqIid8yVlZUYPXo0PvzwQ8RiMcyfPx81NTXo1q2b2++3vvUtrF271t0eNGgQotEoOnfujM2bN7vn+dd//Vfs3r0bQNOSDO+88w7+/Oc/u8dFIhEcOXIEO3bscO8fAPznf/4nunbt6r4Pb775JmKxGADgq1/9Knr16uW5D5FIBEopNDY24vbbb8egQYNQVFTk+U7U1NSgrq7OPU8ikcCRI0dQVlaGvXv3umP+1a9+5X63Xn75ZXz00UcoKSlxP3+JRAKrV6/G6aefDqBpJfFIJILZs2e7+xobG/Hhhx/i5JNP9ozZMZ075xk9ejQqKioQi8UwdOhQdOzYEX379nXHPHbsWGzcuBH9+/cHAPeaampq3EU+nTE713rw4EF89NFHrpsBAOLxOKZOnYoTTjgBANDQ0IBIJILCwkIAwGeffYbPPvsML774ott/RUUFNm3a5N6vzz77DAUFBe5vx+eff44OHTrg2muvdb9/Q4YMwcUXX4x+/fq5n2+lFIYMGYKLLroIADBmzJgW37+PPvoIADy/Gfv27UN5eTnOOOMMAE2/PYWFhe528+N++tOfAgBKSkpQVFSEp59+GkDT92/fvn3Iz8/HkCFDUFlZiYMHD6JTp07udc2ePRuvvfYaOnbsiCVLlgBocqPFYjEMHz4cwLHfjEWLFuHgwYNu/507d3bfzxEjRqCmpgZr167Fn/70JwBNrqMrrrjC/cyPGDECHTt2RFFREQ4fPux+fi6++GJ88MEHAICRI0eipqYGo0ePxueff45u3bqhpqYGTz75pNvvjBkz8Nprr2Ht2rWez3jv3r3dFeqdMX//+993/3/79u2488470alTJwDAv//7v+Odd97Bhg0bMHDgQPc+33///e779X//7//FO++8g7fffhs1NTXuuQoKCtxreOaZZ/DOO+/g448/dt1WH374Id566y33/jzzzDMYPXo0ALhu/+nTpyMej2Py5MkAgNtvvx2vvfYaKioqcOjQIQwcOBD5+fm48sorMW3aNADmOdE3ApM5WUAm3DQDBw50n9ScNrW1ter11183Knrnb+TIkUop5Srkr3/960oppcaNG6dGjBjhtlu1apWrkh0TnlJK/fjHP3aV9fDhw9XSpUsVIHPTOCbJ888/321TVFSkLrjggqRPgZGjmS9XX321+9R17bXXqvHjx6urr75aPf7442775m4r5z43HzMAdcMNN7hj/tKXvqSi0aj67W9/6xnzvHnz3DE7T54rV65020QiEXX33XcnHbPz729/+1t3zNOnT1eFhYXq17/+tecp57LLLnPdO87noLGx0TWDn3zyyerIkSNuUJvT5sYbb3TveyKRUCtXrnT3RaNR9corr3jaOAGAprECTYGpDQ0NHjOs83l23h/HXNy3b183sK2urk598MEHrX7msu2vd+/ens/F5MmT3fvsXKNzn2+66aYW99nZ59zn5m3acp8dK2jz+6x/Nrp27eoZ8+zZs13rWK9evZRSyg3uPPXUU90sPqd/Z8xOm379+h33N2PYsGFKKeU+2fbo0UMppdTs2bM9WSGrVq1yzfynnnqq8fvX/Ddj6tSpqqCgQC1evPi4vxn/+I//6LbJy8tz71Wye51IJNTixYs9vxn9+vVTixcv9vxmjB8/vsVvRp8+fVzLaP/+/dWRI0dcq84pp5yiGhoa1IUXXuhxj6xcudK1bvXr10/FYjH3dxOAGjBgwHHv8/Dhw5VSyrUaLFy4UCml1IUXXuixsq5atcq1yJ122mkt7nN1dbU6cOCA+/t9xhlnqJNPPlkdOnTI/d1y3NHO9oABA1QsFlMvv/yyu69///5q8+bNrY75xBNPVIcOHVIdOnRQkUhEHThwQG3fvl0BcOel4uJiddddd6kTTjhB5efnq8OHD7tzzqRJk1RBQYHatm2bOnLkiGs5DtJN064tIyUlJSguLsbvfvc7d58TwOkoyPz8fIwYMQIvvPACxowZg0GDBuHtt99GIpFwVzF0LBF/+MMfMGbMGNTX1+Pll192g6kAuG3ffvttXHDBBfjKV77itikoKMCgQYPcJ88OHTpg7ty56NevH+LxOBKJBCZMmOAGfQFwlXh9fT22bNnijv/CCy/Exx9/jLy8PLz00ksYM2aMp38AUErhscceQ/fu3fHCCy+4wVhOYK4z5vz8fFRVVeGtt95CRUUFKioqPG06duyImTNnori42A0m7Nu3LyZMmIA+ffqgvLzcDaYDgN/+9rfuGN5//333dW1tLQ4cOIDKykr3PgPAQw89BKDJcvStb30Ljz32GMrLy/H666/j7LPPxrRp0/Doo4+692Xw4MHIz89HeXk5PvzwQ5x66qlYvHix26ampgaXXXYZZs6c6d6P0047DTNmzEBtbS0ikQhmzJiBqVOnesb885//HGeffbYboAYAK1aswIEDB9z3x7lfffr0wZEjRzB+/HgcOnQIf/vb3zz33wlsfOGFF9x9L730EgCgX79+GDlypKfNxIkT8eabb6K2thZAk5XjrLPOQp8+fVBVVeV+5saMGeN+Dp3P89///ncATU9cTlBwLBZDJBJBIpFAjx49XOtOcXExZs6c6T55R6NR9O/f37W89O/fH6WlpSguLsbw4cPde9GrV68WbS6++GJMnjwZhYWFiEQiqKmpQe/evVFQUICVK1eipqYGl156KQYNGuT21bdvX8yfP999KnY+g507d0anTp3coF/nXkYiEfzhD3/AhAkTcOjQIdd649zTN998E6WlpZ77/Oijj3ruc/M2zn2uqqry3OeuXbti1KhRnvv82WefIRKJuPf5v/7rvxCJRDz3ec+ePYhGo27fAwYMANBkzZkwYQJGjx6Nzz77DMAxi1PPnj0BAK+//joSiYRrKQGA888/HyNHjnTbFBYWYtCgQbjhhhvcNhdffLEbKO98tgG4T8xOAPPYsWPdzwbQ9Juxb98+AMAbb7xh/M345je/6f5mPPfcczhw4ADmzp3rBq8CwOjRoz2/Gc5xTpsvvvgC11xzjfub4QQHT5gwARMmTED37t3R2NiISy+91P3+PfLII3j33Xcxd+5cz2/Grbfe2uI3o7Gx0T2uuLgYhw4dcq/zj3/8o/veOeN1cIKL3333XVxwwQUoLCx02+Tn52PQoEHo0qULgKY5Y+7cuejevbv7Haivr4dSyr3vGzduBNBkrXKsS859/uKLLwAAmzdvbnGfO3fujGg0infeeQfRaNT9rMRiMfzyl790g1+j0Sh++ctfoqioCO+88w4uuOACnHzyyW6bkpISDB48GJ07d0YsFnPnkxkzZqC8vBzFxcWYNGkSotGoG8ifl5fn+fzl5eXh0KFDmD17Nj766COccMIJ7r2tr693rUtVVVU4dOgQnnnmGVRVVbW4t74SmMzJEpzU3jlz5qgnnnjC9S3H43G1bt0619cNQN1xxx1uai/Q5D8cO3asR3EuXbpUXX311Z59zf3TQFPKW/N0W1Ma2tKlS92nl0gkotasWdOir6985Ssev18kElFz5szxtFm2bJlavny556k+Ho+rRCLhBgf26NFDTZkyRfXs2dN9QunevbsnYK6yslKdcMIJnpoazpOg84TXo0cPVVpaqhKJhIpGo6pfv35uMJ7zV15ergYPHuwZz8knn+zxKy9btsxj7YnFYqqmpsbjn/3Wt76lvvnNb3rOfeqpp7p+dgBq8eLFnnsWi8XUWWed5TnmW9/6lict7sEHH1SzZs3ytJkyZYr7tICjT1CbNm3ypA3fdtttnmuYNm2aJyYoHo+rSy65REWjUTc2oLi42HNMPB5308Kda7300ks9KYvxeNx9XwoLC1VBQYG677773PfCeTpzjnG2TzjhBDVhwgTPeZYsWeLxe8+fP9+Ng3Hqzpx11lmqY8eO7pN9SUmJe4+dwEXnKcm5lqVLl7qWAeeJ2Aksrq6uVkVFRep3v/udqqurUwDcz6FTK8N5uh00aJDne+GMuXk9hcmTJ3s+F/F43H2vnO+tE/DY/HPgfG8c33vzdHvnPI7F06lJs2bNGvez4ryHzjHNg02bx6c4Y3b6AZqeeJungcbjcXXttdd6xlhUVOSJt7riiivU3Xff7YlvWbRokSdWYOnSpW6gIwD1k5/8RP3oRz/y3J/TTz/dY9GMRCLq2muv9Xwfnd+M5jEUgwYN8rz31dXV6o033nDfQ+e9aP5bV1VVpS677DLPb8B5552nAG882MCBA1VxcbGKRCJq4MCB6pJLLvFYpTp27KhuuOEGT7zGunXrPAHZy5YtUyeeeKK7HY1GXauI83fdddepuXPnevaNGjXKc38WLlzoCQY1pfZed911nu/tunXrWtTSmTRpkhvU6dznZcuWee7psmXL1KxZs1rUbXJ+GwCoK6+8Up1yyimeVN7KykrP/bnkkkvc3+7I0VRs5/PlfO6uvPJK9/coGo2qlStXemKHpk6d6gZ9A01xWqeccoobJxaPx9VDDz2k3nvvPXXDDTcooCmO5oEHHnCtYD/5yU8CnavbvRhRqqnomfNBLywsVHfffbe65557PDUOFi9erHr37u1mAegBSE42QPN9qf5FjtZ5SHaegoICNXjwYNW/f3/Pj0fzD3PzHytTIGS2/+XamCORyHELITnBpMcrhiTpq6CgoEVQnJPBYQqma+5+av6D5hTjcv7PcS85x5144onqnnvuSfn9cAIZgabPo3MeZ2yOgOnatavq27evKxSa9+38gFZUVHgmoOZjTvbn1DNp6/vavP6Kfv9bK9Dm3GPnWP0+N28bjUbdei2pFHPTi1tFo1HPg4Spj169enkmwmSfA9P7HYvF3Mys4uJiFYvFVOfOnVP+POfl5bnZeM3P03zcJSUlatKkSWrq1KktAk2d8U+YMMFTp+l4n1Eng096f5P95efnJz1PIpFQVVVVqnv37sbic5L73Py9cX7Lm39nU/lzPiPNz6P30bVrVzVy5Mik72MsFlMzZsxQM2bMUL1793YTH7p27epmSwJNwql5Zk1QRJRqFoFDCCGEEJJm2nXMCCGEEEKyH4oRQgghhGQUihFCCCGEZBSKEUIIIYRkFIoRQgghhGQUihFCCCGEZBSKEUIIIYRkFIoRQgghhGQUihFCCCGEZBSKEUIIIYRkFIoRQgghhGSU/w9sk3ZrTDTMvQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species = list(map(str, tresh_vs_supp_rappart.columns.to_list()))\n",
    "weight_counts = tresh_vs_supp_rappart.T.to_dict()\n",
    "width = 0.5\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bottom = np.zeros(len(tresh_vs_supp_rappart.columns))\n",
    "\n",
    "for boolean, weight_count in weight_counts.items():\n",
    "    weight_count = list(weight_count.values())\n",
    "    p = ax.bar(species, weight_count, width, label=boolean, bottom=bottom)\n",
    "    bottom += weight_count\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [support, itemsets]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpgrowth_2(motif_plus_cond_freq, min_support=0.5, tresh = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "motif_plus_cond_data_new = pd.DataFrame(motif_plus_cond_data).T\n",
    "motif_plus_cond_data_new[\"1cond\"] = motif_plus_cond_data_new.index\n",
    "motif_plus_cond_data_new['1cond'] = motif_plus_cond_data_new['1cond'].apply(lambda s: s.split('__')[0])\n",
    "motif_plus_cond_data_new = motif_plus_cond_data_new.reset_index(drop=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}