Circadian_qPCR_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "* [Library Import](#library)\n",
    "* [Data Import](#data_import)\n",
    "* [Check data integrity](#data_check)\n",
    "    * [No-template controls (NTC's)](#ntc)\n",
    "    * [Failed samples](#fail)  \n",
    "    * [Outlier identification with reference genes](#outliers)\n",
    "    * [Check reference genes](#ref_genes)\n",
    "    * [Target genes](#target_genes)\n",
    "        * [Calculate ΔCq & 2^-ΔCq](#delta_Cq)\n",
    "        * [Plot genes](#target_plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library import<a class=\"anchor\" id=\"library\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import re\n",
    "import os\n",
    "\n",
    "import warnings\n",
    "from pandas.core.common import SettingWithCopyWarning\n",
    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
    "\n",
    "# Import plot libraries\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as pp\n",
    "import seaborn as sb\n",
    "\n",
    "# Set custom design for plots\n",
    "%matplotlib inline\n",
    "IPython_default = pp.rcParams.copy()\n",
    "\n",
    "mpl.rcParams['font.family'] = 'Arial'\n",
    "params = {'figure.figsize': (14, 12),\n",
    "         'axes.labelsize': 20,\n",
    "         'axes.titlesize': 36,\n",
    "         'xtick.labelsize':20,\n",
    "         'ytick.labelsize':18,\n",
    "         'legend.fontsize': 20,\n",
    "         'legend.title_fontsize': 'xx-large',\n",
    "         'axes.facecolor': '#FFFFFF',\n",
    "         'axes.axisbelow': 'True',\n",
    "         'axes.grid': 'False',\n",
    "#          'axes.prop_cycle': colors,\n",
    "         'lines.linewidth': 2,\n",
    "         'axes.xmargin' : 0.01}\n",
    "mpl.rcParams.update(params)\n",
    "pp.style.use('ggplot')\n",
    "\n",
    "\n",
    "# Statistics\n",
    "from scipy import stats\n",
    "import researchpy as rp\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import scikit_posthocs as sp\n",
    "import statsmodels.stats.multicomp as mc\n",
    "from scipy.stats.mstats import gmean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading in the data <a class=\"anchor\" id=\"data_import\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each plate imported as individual csv file.\n",
    "\n",
    "**Notes:**\n",
    "- Imported as CSV files in Norwegian, so delimiter is \";\" and decimal is \",\" whereas some files were edited on a US computer, so decimal is still \",\" but the delimiter is no longer \";\".\n",
    "- Group 1 contains accessions (N2 and I1), run in 2020\n",
    "- Group 2 contains clonse (N13 and I4), run in 2021\n",
    "\n",
    "- Plates 48, 49, 50 (PRR5 for group 2) failed, re-done in 2022 as plates 64, 65, 66"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biological Replicate 1 <a class=\"anchor\" id=\"Biological_Replicate_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Group 1\n",
    "LHY_1 = pd.read_csv(Path(\"plate1_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "GAPDH_1 = pd.read_csv(Path(\"plate2_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRR9_1 = pd.read_csv(Path(\"plate4_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRDX_1 = pd.read_csv(Path(\"plate5_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "MSI1_1 = pd.read_csv(Path(\"plate6_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1) \n",
    "LUX_1 = pd.read_csv(Path(\"plate19_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)\n",
    "PRR7_1 = pd.read_csv(Path(\"plate21_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)\n",
    "PRR5_1 = pd.read_csv(Path(\"plate24_Cq_Results.csv\"), decimal = \",\", index_col=1)\n",
    "RVE8_1 = pd.read_csv(Path(\"plate27_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)\n",
    "TOC1_1 = pd.read_csv(Path(\"plate30_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)\n",
    "\n",
    "# Group 2\n",
    "LHY_4 = pd.read_csv(Path(\"plate33_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "GAPDH_4 = pd.read_csv(Path(\"plate60_Cq_Results.csv\"), decimal=\",\", index_col=1)\n",
    "PRR9_4 = pd.read_csv(Path(\"plate39_Cq_Results.csv\"), decimal=\",\", index_col=1)\n",
    "PRDX_4 = pd.read_csv(Path(\"plate57_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "MSI1_4 = pd.read_csv(Path(\"plate45_Cq_Results.csv\"), decimal=\",\",index_col=1) \n",
    "LUX_4 = pd.read_csv(Path(\"plate54_Cq_Results.csv\"), decimal=\",\",index_col=1)\n",
    "PRR7_4 = pd.read_csv(Path(\"plate42_Cq_Results.csv\"), decimal=\",\",index_col=1)\n",
    "PRR5_4 = pd.read_csv(Path(\"plate64_Cq_Results.csv\"), delimiter=\";\",index_col=1)\n",
    "RVE8_4 = pd.read_csv(Path(\"plate36_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)\n",
    "TOC1_4 = pd.read_csv(Path(\"plate51_Cq_Results.csv\"), decimal=\",\",index_col=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biological Replicate 2 <a class=\"anchor\" id=\"Biological_Replicate_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Group 1\n",
    "LHY_2 = pd.read_csv(Path(\"plate7_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "GAPDH_2 = pd.read_csv(Path(\"plate8_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRR9_2 = pd.read_csv(Path(\"plate10_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRDX_2 = pd.read_csv(Path(\"plate11_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "MSI1_2 = pd.read_csv(Path(\"plate12_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "LUX_2 = pd.read_csv(Path(\"plate20_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRR7_2 = pd.read_csv(Path(\"plate22_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRR5_2 = pd.read_csv(Path(\"plate25_Cq_Results.csv\"), decimal = \",\", index_col=1)\n",
    "RVE8_2 = pd.read_csv(Path(\"plate28_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "TOC1_2 = pd.read_csv(Path(\"plate31_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "\n",
    "# Group 2\n",
    "LHY_5 = pd.read_csv(Path(\"plate34_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "GAPDH_5 = pd.read_csv(Path(\"plate61_Cq_Results.csv\"), decimal=\",\", index_col=1)\n",
    "PRR9_5 = pd.read_csv(Path(\"plate40_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRDX_5 = pd.read_csv(Path(\"plate58_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "MSI1_5 = pd.read_csv(Path(\"plate46_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1) \n",
    "LUX_5 = pd.read_csv(Path(\"plate55_Cq_Results.csv\"), decimal=\",\",index_col=1)\n",
    "PRR7_5 = pd.read_csv(Path(\"plate43_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)\n",
    "PRR5_5 = pd.read_csv(Path(\"plate65_Cq_Results.csv\"), delimiter=\";\",index_col=1)\n",
    "RVE8_5 = pd.read_csv(Path(\"plate37_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)\n",
    "TOC1_5 = pd.read_csv(Path(\"plate52_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biological Replicate 3 <a class=\"anchor\" id=\"Biological_Replicate_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group 1\n",
    "LHY_3 = pd.read_csv(Path(\"plate13_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "GAPDH_3 = pd.read_csv(Path(\"plate14_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "LUX_3 = pd.read_csv(Path(\"plate15_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRR9_3 = pd.read_csv(Path(\"plate16_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRDX_3 = pd.read_csv(Path(\"plate17_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "MSI1_3 = pd.read_csv(Path(\"plate18_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRR7_3 = pd.read_csv(Path(\"plate23_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "PRR5_3 = pd.read_csv(Path(\"plate26_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "RVE8_3 = pd.read_csv(Path(\"plate29_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "TOC1_3 = pd.read_csv(Path(\"plate32_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "\n",
    "# Group 2\n",
    "LHY_6 = pd.read_csv(Path(\"plate35_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "GAPDH_6 = pd.read_csv(Path(\"plate62_Cq_Results.csv\"), decimal=\",\", index_col=1)\n",
    "PRR9_6 = pd.read_csv(Path(\"plate41_Cq_Results.csv\"), decimal=\",\", index_col=1)\n",
    "PRR9_extra_6 = pd.read_csv(Path(\"plate50_Cq_Results.csv\"), decimal=\",\", index_col=1)\n",
    "PRDX_6 = pd.read_csv(Path(\"plate59_Cq_Results.csv\"), delimiter=\";\", decimal=\",\", index_col=1)\n",
    "MSI1_6 = pd.read_csv(Path(\"plate47_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1) \n",
    "LUX_6 = pd.read_csv(Path(\"plate56_Cq_Results.csv\"), decimal=\",\",index_col=1)\n",
    "PRR7_6 = pd.read_csv(Path(\"plate44_Cq_Results.csv\"), decimal=\",\",index_col=1)\n",
    "PRR5_6 = pd.read_csv(Path(\"plate66_Cq_Results.csv\"),delimiter=\";\",index_col=1)\n",
    "RVE8_6 = pd.read_csv(Path(\"plate38_Cq_Results.csv\"), decimal=\",\",index_col=1)\n",
    "TOC1_6 = pd.read_csv(Path(\"plate53_Cq_Results.csv\"), delimiter=\";\", decimal=\",\",index_col=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_br_1 = [LHY_1, GAPDH_1, PRR9_1, PRDX_1, MSI1_1, LUX_1, PRR7_1, PRR5_1, RVE8_1, TOC1_1, LHY_4, RVE8_4, PRR9_4, PRR7_4, PRR5_4, MSI1_4, TOC1_4, LUX_4, GAPDH_4, PRDX_4]\n",
    "\n",
    "genes_br_2 = [LHY_2, GAPDH_2, PRR9_2, PRDX_2, MSI1_2, LUX_2, PRR7_2, PRR5_2, RVE8_2, TOC1_2, LHY_5, RVE8_5, PRR9_5, PRR7_5, PRR5_5, MSI1_5, TOC1_5, LUX_5, PRDX_5, GAPDH_5]\n",
    "\n",
    "genes_br_3 = [LHY_3, GAPDH_3, LUX_3, PRR9_3, PRDX_3, MSI1_3, PRR7_3, PRR5_3, RVE8_3, TOC1_3, LHY_6, RVE8_6, PRR9_6, PRR7_6, PRR5_6, MSI1_6, TOC1_6, LUX_6, PRDX_6, GAPDH_6, PRR9_extra_6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "br_1 = pd.concat(genes_br_1)\n",
    "br_2 = pd.concat(genes_br_2)\n",
    "br_3 = pd.concat(genes_br_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataframe cleanup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Drop all unneccessary columns\n",
    "* Replace Sample \"NaN\" with \"NTC\"\n",
    "* Replace Cq \"NaN\" with 0\n",
    "* Replace \"GADPH\" with \"GAPDH\" (typo)\n",
    "* Change Target \"IRC\" from \"GAPDH\" to \"IRC\" for IRC samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "br_1.reset_index(inplace = True)\n",
    "br_1.drop(['Well', 'Unnamed: 0',\n",
    " 'Fluor',\n",
    " 'Content',\n",
    " 'Biological Set Name',\n",
    " 'Cq Mean',\n",
    " 'Cq Std. Dev',\n",
    " 'Starting Quantity (SQ)',\n",
    " 'Log Starting Quantity',\n",
    " 'SQ Mean',\n",
    " 'SQ Std. Dev',\n",
    " 'Set Point',\n",
    " 'Well Note'], axis=1, inplace = True)\n",
    "\n",
    "# replace sample NaN name's with NTC\n",
    "br_1[\"Sample\"] = br_1[\"Sample\"].replace(np.nan, \"NTC\") \n",
    "br_1[\"Sample\"] = br_1[\"Sample\"].replace(\"BLANK\", \"NTC\") \n",
    "\n",
    "# replace Cq NaN values with 0\n",
    "br_1[\"Cq\"] = br_1[\"Cq\"].replace(np.nan, 0) \n",
    "\n",
    " # rename misnamed target's\n",
    "br_1[\"Target\"].replace({\"GADPH\":\"GAPDH\", \"qMSI1\":\"MSI1\"}, inplace=True)\n",
    "\n",
    "# set replicate number for future merged dataframe\n",
    "br_1['Replicate'] = '1' \n",
    "\n",
    "# Set order of columns\n",
    "br_1 = br_1[['Sample', 'Replicate', 'Target', 'Cq']]\n",
    "\n",
    "#rename IRC targets for separate use\n",
    "br_1.loc[br_1[\"Sample\"].str.contains(\"IRC1\"), \"Target\" ] = \"IRC1\"\n",
    "br_1.loc[br_1[\"Sample\"].str.contains(\"IRC2\"), \"Target\" ] = \"IRC2\"\n",
    "br_1.loc[br_1[\"Sample\"].str.contains(\"IRC3\"), \"Target\" ] = \"IRC3\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>28.967996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>RVE8</td>\n",
       "      <td>28.439062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>25.922230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR7</td>\n",
       "      <td>26.273794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR5</td>\n",
       "      <td>24.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>29.318290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>28.393389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>LUX</td>\n",
       "      <td>29.706691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>22.861219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRDX</td>\n",
       "      <td>22.761280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample Replicate Target         Cq\n",
       "854   I4_3B         1    LHY  28.967996\n",
       "935   I4_3B         1   RVE8  28.439062\n",
       "1015  I4_3B         1   PRR9  25.922230\n",
       "1095  I4_3B         1   PRR7  26.273794\n",
       "1175  I4_3B         1   PRR5  24.710000\n",
       "1252  I4_3B         1   MSI1  29.318290\n",
       "1284  I4_3B         1   MSI1   0.000000\n",
       "1331  I4_3B         1   TOC1  28.393389\n",
       "1410  I4_3B         1    LUX  29.706691\n",
       "1489  I4_3B         1  GAPDH  22.861219\n",
       "1564  I4_3B         1   PRDX  22.761280"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "br_1.loc[br_1['Sample'] == 'I4_3B'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plate mistakes: Re-did I4_3B_1 MSI1 (plate 45) in well G7, delete the other cell\n",
    "br_1.drop(1206, axis=0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "br_2.reset_index(inplace = True)\n",
    "br_2.drop(['Well', 'Unnamed: 0',\n",
    " 'Fluor',\n",
    " 'Content',\n",
    " 'Biological Set Name',\n",
    " 'Cq Mean',\n",
    " 'Cq Std. Dev',\n",
    " 'Starting Quantity (SQ)',\n",
    " 'Log Starting Quantity',\n",
    " 'SQ Mean',\n",
    " 'SQ Std. Dev',\n",
    " 'Set Point',\n",
    " 'Well Note'], axis=1, inplace = True)\n",
    "\n",
    "# Same transformations as br_1\n",
    "br_2[\"Sample\"] = br_2[\"Sample\"].replace(np.nan, \"NTC\")\n",
    "br_2[\"Sample\"] = br_2[\"Sample\"].replace(\"BLANK\", \"NTC\")\n",
    "br_2[\"Cq\"] = br_2[\"Cq\"].replace(np.nan, 0)\n",
    "br_2[\"Target\"].replace({\"GADPH\":\"GAPDH\", \"qMSI1\":\"MSI1\"}, inplace=True)\n",
    "br_2['Replicate'] = '2'\n",
    "br_2 = br_2[['Sample', 'Replicate', 'Target', 'Cq']]\n",
    "br_2.loc[br_2[\"Sample\"].str.contains(\"IRC1\"), \"Target\" ] = \"IRC1\"\n",
    "br_2.loc[br_2[\"Sample\"].str.contains(\"IRC2\"), \"Target\" ] = \"IRC2\"\n",
    "br_2.loc[br_2[\"Sample\"].str.contains(\"IRC3\"), \"Target\" ] = \"IRC3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sample         I4_2B_1\n",
       "Replicate            2\n",
       "Target             NaN\n",
       "Cq           26.357008\n",
       "Name: 850, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "br_2.loc[850, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plate mistakes: Dont need this sample, accidental re-make\n",
    "br_2.drop(850, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "br_3.reset_index(inplace = True)\n",
    "br_3.drop(['Well', 'Unnamed: 0',\n",
    " 'Fluor',\n",
    " 'Content',\n",
    " 'Biological Set Name',\n",
    " 'Cq Mean',\n",
    " 'Cq Std. Dev',\n",
    " 'Starting Quantity (SQ)',\n",
    " 'Log Starting Quantity',\n",
    " 'SQ Mean',\n",
    " 'SQ Std. Dev',\n",
    " 'Set Point',\n",
    " 'Well Note'], axis=1, inplace = True)\n",
    "\n",
    "# Same transformations as br_1\n",
    "br_3[\"Sample\"] = br_3[\"Sample\"].replace(np.nan, \"NTC\")\n",
    "br_3[\"Sample\"] = br_3[\"Sample\"].replace(\"BLANK\", \"NTC\")\n",
    "br_3[\"Cq\"] = br_3[\"Cq\"].replace(np.nan, 0)\n",
    "br_3[\"Target\"].replace({\"GADPH\":\"GAPDH\", \"qMSI1\":\"MSI1\"}, inplace=True)\n",
    "br_3['Replicate'] = '3'\n",
    "br_3 = br_3[['Sample', 'Replicate', 'Target', 'Cq']]\n",
    "br_3.loc[br_3[\"Sample\"].str.contains(\"IRC1\"), \"Target\" ] = \"IRC1\"\n",
    "br_3.loc[br_3[\"Sample\"].str.contains(\"IRC2\"), \"Target\" ] = \"IRC2\"\n",
    "br_3.loc[br_3[\"Sample\"].str.contains(\"IRC3\"), \"Target\" ] = \"IRC3\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group all data in one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "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>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N2_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>23.972973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2_2A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>22.608713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2_1B</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>22.410477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2_2B</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>23.546714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2_3A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>21.134845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4723</th>\n",
       "      <td>I4_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>30.051217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4724</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.440899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4725</th>\n",
       "      <td>IRC1_50</td>\n",
       "      <td>3</td>\n",
       "      <td>IRC1</td>\n",
       "      <td>21.016340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4726</th>\n",
       "      <td>IRC2_50</td>\n",
       "      <td>3</td>\n",
       "      <td>IRC2</td>\n",
       "      <td>21.970216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4727</th>\n",
       "      <td>IRC3_50</td>\n",
       "      <td>3</td>\n",
       "      <td>IRC3</td>\n",
       "      <td>22.780957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4728 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Replicate Target         Cq\n",
       "0       N2_1A         1    LHY  23.972973\n",
       "1       N2_2A         1    LHY  22.608713\n",
       "2       N2_1B         1    LHY  22.410477\n",
       "3       N2_2B         1    LHY  23.546714\n",
       "4       N2_3A         1    LHY  21.134845\n",
       "...       ...       ...    ...        ...\n",
       "4723   I4_13D         3   PRR9  30.051217\n",
       "4724      NTC         3   PRR9  39.440899\n",
       "4725  IRC1_50         3   IRC1  21.016340\n",
       "4726  IRC2_50         3   IRC2  21.970216\n",
       "4727  IRC3_50         3   IRC3  22.780957\n",
       "\n",
       "[4728 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replicates = [br_1, br_2, br_3]\n",
    "data = pd.concat(replicates, ignore_index = True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pull out IRC's for separate use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[~data.Sample.str.contains('IRC')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set column names for sample ID variables using dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N2_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>23.972973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2_2A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>22.608713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2_1B</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>22.410477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2_2B</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>23.546714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2_3A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>21.134845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4720</th>\n",
       "      <td>I4_13A</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>32.218594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4721</th>\n",
       "      <td>I4_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>33.073067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4722</th>\n",
       "      <td>I4_13C</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>28.913361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4723</th>\n",
       "      <td>I4_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>30.051217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4724</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.440899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4565 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sample Replicate Target         Cq\n",
       "0      N2_1A         1    LHY  23.972973\n",
       "1      N2_2A         1    LHY  22.608713\n",
       "2      N2_1B         1    LHY  22.410477\n",
       "3      N2_2B         1    LHY  23.546714\n",
       "4      N2_3A         1    LHY  21.134845\n",
       "...      ...       ...    ...        ...\n",
       "4720  I4_13A         3   PRR9  32.218594\n",
       "4721  I4_13B         3   PRR9  33.073067\n",
       "4722  I4_13C         3   PRR9  28.913361\n",
       "4723  I4_13D         3   PRR9  30.051217\n",
       "4724     NTC         3   PRR9  39.440899\n",
       "\n",
       "[4565 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleID_treatment = {\"A\": \"Arctic\", \n",
    "                      \"B\": \"Mid-latitude\", \n",
    "                      \"C\": \"D-Arctic\", \n",
    "                      \"D\": \"D-Mid-latitude\"}\n",
    "\n",
    "sampleID_zeitgeber = {\"_2\": \"4\", \"_3\": \"8\", \n",
    "                      \"_4\": \"12\", \"_5\": \"16\", \"_6\": \"20\", \n",
    "                      \"_7\": \"24\", \"_8\": \"28\", \"_9\": \"32\", \n",
    "                      \"_10\": \"36\", \"_11\": \"40\", \"_12\": \"44\", \n",
    "                      \"_13\": \"48\", \"_1\": \"0\"}\n",
    "\n",
    "zt_to_24 = {\"48\": \"24\", \"44\": \"20\", \"40\": \"16\", \"36\": \"12\", \n",
    "            \"32\": \"8\", \"28\": \"4\", \"24\": \"0\",\"20\": \"20\",\n",
    "            \"16\": \"16\", \"12\": \"12\", \"8\": \"8\",\"4\": \"4\",  \"0\": \"0\"}\n",
    "\n",
    "sampleID_accession = {\"I1\": \"IT1\", \"I4\": \"IT4\", \"N2\": \"NOR2\", \"N13\": \"NOR13\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "treatment_pattern = '(' + '|'.join(list(sampleID_treatment)) + ')'\n",
    "zt_pattern = '(' + '|'.join(list(sampleID_zeitgeber)) + ')'\n",
    "zt_to_24_pattern = '(' + '|'.join(list(zt_to_24)) + ')'\n",
    "accession_pattern = '(' + '|'.join(list(sampleID_accession)) + ')'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Treatment'] = data.Sample.str.extract(treatment_pattern).squeeze().map(sampleID_treatment)\n",
    "data['Zeitgeber'] = data.Sample.str.extract(zt_pattern).squeeze().map(sampleID_zeitgeber)\n",
    "data['ZT_24'] = data.Zeitgeber.str.extract(zt_to_24_pattern).squeeze().map(zt_to_24)\n",
    "data['Accession'] = data.Sample.str.extract(accession_pattern).squeeze().map(sampleID_accession)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "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>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N2_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>23.972973</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2_2A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>22.608713</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2_1B</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>22.410477</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2_2B</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>23.546714</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2_3A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>21.134845</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4720</th>\n",
       "      <td>I4_13A</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>32.218594</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4721</th>\n",
       "      <td>I4_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>33.073067</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4722</th>\n",
       "      <td>I4_13C</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>28.913361</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4723</th>\n",
       "      <td>I4_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>30.051217</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4724</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.440899</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4565 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sample Replicate Target         Cq       Treatment Zeitgeber ZT_24  \\\n",
       "0      N2_1A         1    LHY  23.972973          Arctic         0     0   \n",
       "1      N2_2A         1    LHY  22.608713          Arctic         4     4   \n",
       "2      N2_1B         1    LHY  22.410477    Mid-latitude         0     0   \n",
       "3      N2_2B         1    LHY  23.546714    Mid-latitude         4     4   \n",
       "4      N2_3A         1    LHY  21.134845          Arctic         8     8   \n",
       "...      ...       ...    ...        ...             ...       ...   ...   \n",
       "4720  I4_13A         3   PRR9  32.218594          Arctic        48    24   \n",
       "4721  I4_13B         3   PRR9  33.073067    Mid-latitude        48    24   \n",
       "4722  I4_13C         3   PRR9  28.913361        D-Arctic        48    24   \n",
       "4723  I4_13D         3   PRR9  30.051217  D-Mid-latitude        48    24   \n",
       "4724     NTC         3   PRR9  39.440899        D-Arctic       NaN   NaN   \n",
       "\n",
       "     Accession  \n",
       "0         NOR2  \n",
       "1         NOR2  \n",
       "2         NOR2  \n",
       "3         NOR2  \n",
       "4         NOR2  \n",
       "...        ...  \n",
       "4720       IT4  \n",
       "4721       IT4  \n",
       "4722       IT4  \n",
       "4723       IT4  \n",
       "4724       NaN  \n",
       "\n",
       "[4565 rows x 8 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cq</th>\n",
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       "      <th>count</th>\n",
       "      <td>4565.000000</td>\n",
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       "      <td>26.510188</td>\n",
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       "      <th>std</th>\n",
       "      <td>6.086401</td>\n",
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       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.549344</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.444216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.451303</td>\n",
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       "      <th>max</th>\n",
       "      <td>41.931212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Cq\n",
       "count  4565.000000\n",
       "mean     26.510188\n",
       "std       6.086401\n",
       "min       0.000000\n",
       "25%      24.549344\n",
       "50%      27.444216\n",
       "75%      29.451303\n",
       "max      41.931212"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check data integrity <a class=\"anchor\" id=\"data_check\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No-template controls (NTC) <a class=\"anchor\" id=\"ntc\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check NTC > 0\n",
    "\n",
    "(Validate further by melt curves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>NTC</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.105059</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>NTC</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.139342</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>NTC</td>\n",
       "      <td>1</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>36.279197</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1573</th>\n",
       "      <td>NTC</td>\n",
       "      <td>1</td>\n",
       "      <td>PRDX</td>\n",
       "      <td>37.749753</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>NTC</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.559262</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>39.274383</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4043</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>RVE8</td>\n",
       "      <td>26.898251</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4238</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR7</td>\n",
       "      <td>41.931212</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4396</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>39.286813</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4636</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>PRDX</td>\n",
       "      <td>39.010386</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4724</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.440899</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample Replicate Target         Cq Treatment Zeitgeber ZT_24 Accession\n",
       "1025    NTC         1   PRR9  35.105059  D-Arctic       NaN   NaN       NaN\n",
       "1048    NTC         1   PRR9  39.139342  D-Arctic       NaN   NaN       NaN\n",
       "1340    NTC         1   TOC1  36.279197  D-Arctic       NaN   NaN       NaN\n",
       "1573    NTC         1   PRDX  37.749753  D-Arctic       NaN   NaN       NaN\n",
       "1793    NTC         2   PRR9  36.559262  D-Arctic       NaN   NaN       NaN\n",
       "3610    NTC         3   MSI1  39.274383  D-Arctic       NaN   NaN       NaN\n",
       "4043    NTC         3   RVE8  26.898251  D-Arctic       NaN   NaN       NaN\n",
       "4238    NTC         3   PRR7  41.931212  D-Arctic       NaN   NaN       NaN\n",
       "4396    NTC         3   MSI1  39.286813  D-Arctic       NaN   NaN       NaN\n",
       "4636    NTC         3   PRDX  39.010386  D-Arctic       NaN   NaN       NaN\n",
       "4724    NTC         3   PRR9  39.440899  D-Arctic       NaN   NaN       NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[(data['Sample']=='NTC') & (data['Cq'] > 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cq's > 35 likely primer-dimers.\n",
    "\n",
    "RVE8 NTC was mislabelled - Sample was re-done on a new plate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>37.544769</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>N13_12B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>38.411122</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR13</td>\n",
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       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>N13_13A</td>\n",
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       "      <td>PRR9</td>\n",
       "      <td>35.956793</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>N13_13B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.491828</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>I4_1B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.748392</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>I4_6B</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>37.093610</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>NTC</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.105059</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>NTC</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.139342</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>NTC</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.559262</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1806</th>\n",
       "      <td>I1_7B</td>\n",
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       "      <td>PRR9</td>\n",
       "      <td>38.188996</td>\n",
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       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
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       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>I1_13B</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.934719</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
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       "    <tr>\n",
       "      <th>2531</th>\n",
       "      <td>N13_1B</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.279059</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2541</th>\n",
       "      <td>N13_6B</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.711538</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2546</th>\n",
       "      <td>N13_8C</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.659552</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>N13_12B</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.211548</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>N13_12D</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.254870</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>N13_13A</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.389923</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>N13_13B</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.707281</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>I4_6B</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.319164</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>I4_7B</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.503114</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2596</th>\n",
       "      <td>I4_11C</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.985538</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>I4_12D</td>\n",
       "      <td>2</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.106091</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>N2_6B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.259944</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3405</th>\n",
       "      <td>N2_12B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.148129</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3409</th>\n",
       "      <td>N2_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.035648</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423</th>\n",
       "      <td>I1_6B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.795134</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3447</th>\n",
       "      <td>I1_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.079640</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4088</th>\n",
       "      <td>N13_1A</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.749391</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4089</th>\n",
       "      <td>N13_1B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.185294</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4099</th>\n",
       "      <td>N13_6B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.541579</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4101</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.987019</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4123</th>\n",
       "      <td>N13_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>36.197659</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4137</th>\n",
       "      <td>I4_6B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.372852</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4150</th>\n",
       "      <td>I4_10C</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.079119</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4717</th>\n",
       "      <td>I4_12B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>35.365836</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4724</th>\n",
       "      <td>NTC</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>39.440899</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Replicate Target         Cq       Treatment Zeitgeber ZT_24  \\\n",
       "236    I1_13B         1   PRR9  35.379026    Mid-latitude        48    24   \n",
       "237    I1_13C         1   PRR9  35.879865        D-Arctic        48    24   \n",
       "972    N13_1A         1   PRR9  35.173178          Arctic         0     0   \n",
       "983    N13_6B         1   PRR9  35.098362    Mid-latitude        20    20   \n",
       "985    N13_7B         1   PRR9  37.544769    Mid-latitude        24     0   \n",
       "1003  N13_12B         1   PRR9  38.411122    Mid-latitude        44    20   \n",
       "1006  N13_13A         1   PRR9  35.956793          Arctic        48    24   \n",
       "1007  N13_13B         1   PRR9  35.491828    Mid-latitude        48    24   \n",
       "1011    I4_1B         1   PRR9  36.748392    Mid-latitude         0     0   \n",
       "1021    I4_6B         1   PRR9  37.093610    Mid-latitude        20    20   \n",
       "1025      NTC         1   PRR9  35.105059        D-Arctic       NaN   NaN   \n",
       "1048      NTC         1   PRR9  39.139342        D-Arctic       NaN   NaN   \n",
       "1793      NTC         2   PRR9  36.559262        D-Arctic       NaN   NaN   \n",
       "1806    I1_7B         2   PRR9  38.188996    Mid-latitude        24     0   \n",
       "1827   I1_13B         2   PRR9  35.934719    Mid-latitude        48    24   \n",
       "2531   N13_1B         2   PRR9  36.279059    Mid-latitude         0     0   \n",
       "2541   N13_6B         2   PRR9  36.711538    Mid-latitude        20    20   \n",
       "2546   N13_8C         2   PRR9  35.659552        D-Arctic        28     4   \n",
       "2561  N13_12B         2   PRR9  36.211548    Mid-latitude        44    20   \n",
       "2563  N13_12D         2   PRR9  35.254870  D-Mid-latitude        44    20   \n",
       "2564  N13_13A         2   PRR9  35.389923          Arctic        48    24   \n",
       "2565  N13_13B         2   PRR9  35.707281    Mid-latitude        48    24   \n",
       "2579    I4_6B         2   PRR9  36.319164    Mid-latitude        20    20   \n",
       "2581    I4_7B         2   PRR9  36.503114    Mid-latitude        24     0   \n",
       "2596   I4_11C         2   PRR9  35.985538        D-Arctic        40    16   \n",
       "2601   I4_12D         2   PRR9  35.106091  D-Mid-latitude        44    20   \n",
       "3385    N2_6B         3   PRR9  35.259944    Mid-latitude        20    20   \n",
       "3405   N2_12B         3   PRR9  35.148129    Mid-latitude        44    20   \n",
       "3409   N2_13B         3   PRR9  35.035648    Mid-latitude        48    24   \n",
       "3423    I1_6B         3   PRR9  35.795134    Mid-latitude        20    20   \n",
       "3447   I1_13B         3   PRR9  36.079640    Mid-latitude        48    24   \n",
       "4088   N13_1A         3   PRR9  35.749391          Arctic         0     0   \n",
       "4089   N13_1B         3   PRR9  36.185294    Mid-latitude         0     0   \n",
       "4099   N13_6B         3   PRR9  35.541579    Mid-latitude        20    20   \n",
       "4101   N13_7B         3   PRR9  39.987019    Mid-latitude        24     0   \n",
       "4123  N13_13B         3   PRR9  36.197659    Mid-latitude        48    24   \n",
       "4137    I4_6B         3   PRR9  35.372852    Mid-latitude        20    20   \n",
       "4150   I4_10C         3   PRR9  35.079119        D-Arctic        36    12   \n",
       "4717   I4_12B         3   PRR9  35.365836    Mid-latitude        44    20   \n",
       "4724      NTC         3   PRR9  39.440899        D-Arctic       NaN   NaN   \n",
       "\n",
       "     Accession  \n",
       "236        IT1  \n",
       "237        IT1  \n",
       "972      NOR13  \n",
       "983      NOR13  \n",
       "985      NOR13  \n",
       "1003     NOR13  \n",
       "1006     NOR13  \n",
       "1007     NOR13  \n",
       "1011       IT4  \n",
       "1021       IT4  \n",
       "1025       NaN  \n",
       "1048       NaN  \n",
       "1793       NaN  \n",
       "1806       IT1  \n",
       "1827       IT1  \n",
       "2531     NOR13  \n",
       "2541     NOR13  \n",
       "2546     NOR13  \n",
       "2561     NOR13  \n",
       "2563     NOR13  \n",
       "2564     NOR13  \n",
       "2565     NOR13  \n",
       "2579       IT4  \n",
       "2581       IT4  \n",
       "2596       IT4  \n",
       "2601       IT4  \n",
       "3385      NOR2  \n",
       "3405      NOR2  \n",
       "3409      NOR2  \n",
       "3423       IT1  \n",
       "3447       IT1  \n",
       "4088     NOR13  \n",
       "4089     NOR13  \n",
       "4099     NOR13  \n",
       "4101     NOR13  \n",
       "4123     NOR13  \n",
       "4137       IT4  \n",
       "4150       IT4  \n",
       "4717       IT4  \n",
       "4724       NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[(data['Target']=='PRR9') & (data['Cq'] > 35)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>I1_8A</td>\n",
       "      <td>1</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>39.310573</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>NTC</td>\n",
       "      <td>1</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>36.279197</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>I4_9A</td>\n",
       "      <td>1</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>35.146323</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>I1_1B</td>\n",
       "      <td>2</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>37.606885</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>2</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>38.787735</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>N13_9B</td>\n",
       "      <td>2</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>35.605565</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4405</th>\n",
       "      <td>N13_3B</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>36.447834</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4462</th>\n",
       "      <td>I4_10C</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>37.166448</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4463</th>\n",
       "      <td>I4_10D</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>36.885063</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4464</th>\n",
       "      <td>I4_11A</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>36.641713</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4470</th>\n",
       "      <td>I4_12C</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>37.933138</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4471</th>\n",
       "      <td>I4_12D</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>37.390478</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>I4_13A</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>37.684287</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4473</th>\n",
       "      <td>I4_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>38.376442</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4474</th>\n",
       "      <td>I4_13C</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>39.026467</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4475</th>\n",
       "      <td>I4_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>37.307220</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sample Replicate Target         Cq       Treatment Zeitgeber ZT_24  \\\n",
       "782    I1_8A         1   TOC1  39.310573          Arctic        28     4   \n",
       "1340     NTC         1   TOC1  36.279197        D-Arctic       NaN   NaN   \n",
       "1343   I4_9A         1   TOC1  35.146323          Arctic        32     8   \n",
       "2333   I1_1B         2   TOC1  37.606885    Mid-latitude         0     0   \n",
       "2851  N13_7B         2   TOC1  38.787735    Mid-latitude        24     0   \n",
       "2857  N13_9B         2   TOC1  35.605565    Mid-latitude        32     8   \n",
       "4405  N13_3B         3   TOC1  36.447834    Mid-latitude         8     8   \n",
       "4462  I4_10C         3   TOC1  37.166448        D-Arctic        36    12   \n",
       "4463  I4_10D         3   TOC1  36.885063  D-Mid-latitude        36    12   \n",
       "4464  I4_11A         3   TOC1  36.641713          Arctic        40    16   \n",
       "4470  I4_12C         3   TOC1  37.933138        D-Arctic        44    20   \n",
       "4471  I4_12D         3   TOC1  37.390478  D-Mid-latitude        44    20   \n",
       "4472  I4_13A         3   TOC1  37.684287          Arctic        48    24   \n",
       "4473  I4_13B         3   TOC1  38.376442    Mid-latitude        48    24   \n",
       "4474  I4_13C         3   TOC1  39.026467        D-Arctic        48    24   \n",
       "4475  I4_13D         3   TOC1  37.307220  D-Mid-latitude        48    24   \n",
       "\n",
       "     Accession  \n",
       "782        IT1  \n",
       "1340       NaN  \n",
       "1343       IT4  \n",
       "2333       IT1  \n",
       "2851     NOR13  \n",
       "2857     NOR13  \n",
       "4405     NOR13  \n",
       "4462       IT4  \n",
       "4463       IT4  \n",
       "4464       IT4  \n",
       "4470       IT4  \n",
       "4471       IT4  \n",
       "4472       IT4  \n",
       "4473       IT4  \n",
       "4474       IT4  \n",
       "4475       IT4  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[(data['Target']=='TOC1') & (data['Cq'] > 35)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop NTC's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.Sample != 'NTC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Failed wells <a class=\"anchor\" id=\"fail\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "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>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>I1_13C</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>I1_12B</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3283</th>\n",
       "      <td>I1_12A</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>I1_13C</td>\n",
       "      <td>1</td>\n",
       "      <td>LUX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>I1_12D</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>N2_11C</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>N2_11D</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3803</th>\n",
       "      <td>N2_11D</td>\n",
       "      <td>3</td>\n",
       "      <td>RVE8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>N2_13B</td>\n",
       "      <td>1</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>N2_11D</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sample Replicate Target   Cq       Treatment Zeitgeber ZT_24 Accession\n",
       "156   I1_13C         1  GAPDH  0.0        D-Arctic        48    24       IT1\n",
       "1733  I1_12B         2  GAPDH  0.0    Mid-latitude        44    20       IT1\n",
       "3283  I1_12A         3  GAPDH  0.0          Arctic        44    20       IT1\n",
       "480   I1_13C         1    LUX  0.0        D-Arctic        48    24       IT1\n",
       "3605  I1_12D         3   MSI1  0.0  D-Mid-latitude        44    20       IT1\n",
       "...      ...       ...    ...  ...             ...       ...   ...       ...\n",
       "3642  N2_11C         3   PRR7  0.0        D-Arctic        40    16      NOR2\n",
       "3643  N2_11D         3   PRR7  0.0  D-Mid-latitude        40    16      NOR2\n",
       "3803  N2_11D         3   RVE8  0.0  D-Mid-latitude        40    16      NOR2\n",
       "764   N2_13B         1   TOC1  0.0    Mid-latitude        48    24      NOR2\n",
       "3880  N2_11D         3   TOC1  0.0  D-Mid-latitude        40    16      NOR2\n",
       "\n",
       "[71 rows x 8 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "failed_wells = data.loc[data['Cq'] == 0]\n",
    "failed_wells.sort_values(['Accession', 'Target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(failed_wells['Target'], hue = failed_wells['Accession'])\n",
    "pp.legend(loc='upper right', title = 'Accession', fontsize=16)\n",
    "# pp.savefig('Circadian_gene_results_figures/Failed_wells.jpg', dpi = 300)\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc5262ba090>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(failed_wells['Accession'], hue = failed_wells['Target'])\n",
    "pp.legend(bbox_to_anchor=(1.05, 0.75), loc=2, borderaxespad=0., title = 'Gene', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify samples where reference gene failed (nothing to compare to, inhibits downstream analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "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>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>I1_13C</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>I1_12B</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3283</th>\n",
       "      <td>I1_12A</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>I1_12D</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4651</th>\n",
       "      <td>N13_6B</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4652</th>\n",
       "      <td>N13_7A</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4716</th>\n",
       "      <td>I4_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sample Replicate Target   Cq       Treatment Zeitgeber ZT_24 Accession\n",
       "156   I1_13C         1  GAPDH  0.0        D-Arctic        48    24       IT1\n",
       "1283   I4_3B         1   MSI1  0.0    Mid-latitude         8     8       IT4\n",
       "1733  I1_12B         2  GAPDH  0.0    Mid-latitude        44    20       IT1\n",
       "3283  I1_12A         3  GAPDH  0.0          Arctic        44    20       IT1\n",
       "3605  I1_12D         3   MSI1  0.0  D-Mid-latitude        44    20       IT1\n",
       "4651  N13_6B         3  GAPDH  0.0    Mid-latitude        20    20     NOR13\n",
       "4652  N13_7A         3  GAPDH  0.0          Arctic        24     0     NOR13\n",
       "4716  I4_13D         3  GAPDH  0.0  D-Mid-latitude        48    24       IT4"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "failed_ref = failed_wells.loc[(failed_wells['Target'] == 'MSI1') | (failed_wells['Target'] == 'GAPDH')]\n",
    "failed_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_samps = failed_ref[['Sample', 'Replicate']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop failed wells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.Cq != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify outliers using reference genes <a class=\"anchor\" id=\"outliers\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref_genes = data[(data['Target']=='GAPDH') | (data['Target']=='MSI1')]\n",
    "\n",
    "ref_genes.groupby('Target').boxplot(column='Cq')\n",
    "# pp.savefig('Circadian_gene_results_figures/Ref_gene_outliers.jpg', dpi = 300)\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>N2_5A</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>31.675457</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>I1_8A</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>34.160538</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>I1_11B</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>31.056825</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>I1_13C</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>39.121616</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>I1_13D</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>35.847011</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4379</th>\n",
       "      <td>I4_9D</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>31.007316</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4382</th>\n",
       "      <td>I4_10C</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>34.421100</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4645</th>\n",
       "      <td>N13_3B</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>31.662452</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4677</th>\n",
       "      <td>N13_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>33.375033</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4703</th>\n",
       "      <td>I4_10C</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>31.847880</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Replicate Target         Cq       Treatment Zeitgeber ZT_24  \\\n",
       "332     N2_5A         1   MSI1  31.675457          Arctic        16    16   \n",
       "377     I1_8A         1   MSI1  34.160538          Arctic        28     4   \n",
       "390    I1_11B         1   MSI1  31.056825    Mid-latitude        40    16   \n",
       "399    I1_13C         1   MSI1  39.121616        D-Arctic        48    24   \n",
       "400    I1_13D         1   MSI1  35.847011  D-Mid-latitude        48    24   \n",
       "...       ...       ...    ...        ...             ...       ...   ...   \n",
       "4379    I4_9D         3   MSI1  31.007316  D-Mid-latitude        32     8   \n",
       "4382   I4_10C         3   MSI1  34.421100        D-Arctic        36    12   \n",
       "4645   N13_3B         3  GAPDH  31.662452    Mid-latitude         8     8   \n",
       "4677  N13_13D         3  GAPDH  33.375033  D-Mid-latitude        48    24   \n",
       "4703   I4_10C         3  GAPDH  31.847880        D-Arctic        36    12   \n",
       "\n",
       "     Accession  \n",
       "332       NOR2  \n",
       "377        IT1  \n",
       "390        IT1  \n",
       "399        IT1  \n",
       "400        IT1  \n",
       "...        ...  \n",
       "4379       IT4  \n",
       "4382       IT4  \n",
       "4645     NOR13  \n",
       "4677     NOR13  \n",
       "4703       IT4  \n",
       "\n",
       "[75 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_genes.loc[ref_genes['Cq'] > 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Cq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GAPDH</th>\n",
       "      <td>440.0</td>\n",
       "      <td>22.591800</td>\n",
       "      <td>2.217914</td>\n",
       "      <td>18.114577</td>\n",
       "      <td>21.423183</td>\n",
       "      <td>22.265899</td>\n",
       "      <td>23.191409</td>\n",
       "      <td>39.619334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSI1</th>\n",
       "      <td>445.0</td>\n",
       "      <td>28.892576</td>\n",
       "      <td>1.695956</td>\n",
       "      <td>25.503145</td>\n",
       "      <td>27.960188</td>\n",
       "      <td>28.663441</td>\n",
       "      <td>29.391472</td>\n",
       "      <td>39.503811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Cq                                                        \\\n",
       "        count       mean       std        min        25%        50%   \n",
       "Target                                                                \n",
       "GAPDH   440.0  22.591800  2.217914  18.114577  21.423183  22.265899   \n",
       "MSI1    445.0  28.892576  1.695956  25.503145  27.960188  28.663441   \n",
       "\n",
       "                              \n",
       "              75%        max  \n",
       "Target                        \n",
       "GAPDH   23.191409  39.619334  \n",
       "MSI1    29.391472  39.503811  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bygene = ref_genes.groupby(by='Target')\n",
    "bygene.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "GAPDH = data[data['Target']=='GAPDH']\n",
    "MSI1 = data[data['Target']=='MSI1'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IQR method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def locate_outlier_IQR(df):\n",
    "    Q1=df['Cq'].quantile(0.25)\n",
    "    Q3=df['Cq'].quantile(0.75)\n",
    "    IQR=Q3-Q1\n",
    "    outliers = df.loc[(df['Cq']>(Q3+1.5*IQR))]\n",
    "    return (outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>N2_5A</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>28.186665</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>I1_8A</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>28.984997</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>I4_9A</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>30.084437</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>I4_10B</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>26.242289</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>I4_11A</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>30.940667</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>I4_12D</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>29.104890</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>I1_1A</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>30.201841</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>I1_1B</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>32.805041</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>I1_7B</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>29.436695</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3075</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>39.619334</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>N13_9B</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>30.606742</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3120</th>\n",
       "      <td>I4_10A</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>31.638719</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3126</th>\n",
       "      <td>I4_11C</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>29.363604</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127</th>\n",
       "      <td>I4_11D</td>\n",
       "      <td>2</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>30.206548</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4645</th>\n",
       "      <td>N13_3B</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>31.662452</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4653</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>28.127702</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4655</th>\n",
       "      <td>N13_8B</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>27.030444</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4666</th>\n",
       "      <td>N13_11A</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>28.042069</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4677</th>\n",
       "      <td>N13_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>33.375033</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4703</th>\n",
       "      <td>I4_10C</td>\n",
       "      <td>3</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>31.847880</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Replicate Target         Cq       Treatment Zeitgeber ZT_24  \\\n",
       "89      N2_5A         1  GAPDH  28.186665          Arctic        16    16   \n",
       "134     I1_8A         1  GAPDH  28.984997          Arctic        28     4   \n",
       "1501    I4_9A         1  GAPDH  30.084437          Arctic        32     8   \n",
       "1506   I4_10B         1  GAPDH  26.242289    Mid-latitude        36    12   \n",
       "1509   I4_11A         1  GAPDH  30.940667          Arctic        40    16   \n",
       "1515   I4_12D         1  GAPDH  29.104890  D-Mid-latitude        44    20   \n",
       "1704    I1_1A         2  GAPDH  30.201841          Arctic         0     0   \n",
       "1705    I1_1B         2  GAPDH  32.805041    Mid-latitude         0     0   \n",
       "1716    I1_7B         2  GAPDH  29.436695    Mid-latitude        24     0   \n",
       "3075   N13_7B         2  GAPDH  39.619334    Mid-latitude        24     0   \n",
       "3081   N13_9B         2  GAPDH  30.606742    Mid-latitude        32     8   \n",
       "3120   I4_10A         2  GAPDH  31.638719          Arctic        36    12   \n",
       "3126   I4_11C         2  GAPDH  29.363604        D-Arctic        40    16   \n",
       "3127   I4_11D         2  GAPDH  30.206548  D-Mid-latitude        40    16   \n",
       "4645   N13_3B         3  GAPDH  31.662452    Mid-latitude         8     8   \n",
       "4653   N13_7B         3  GAPDH  28.127702    Mid-latitude        24     0   \n",
       "4655   N13_8B         3  GAPDH  27.030444    Mid-latitude        28     4   \n",
       "4666  N13_11A         3  GAPDH  28.042069          Arctic        40    16   \n",
       "4677  N13_13D         3  GAPDH  33.375033  D-Mid-latitude        48    24   \n",
       "4703   I4_10C         3  GAPDH  31.847880        D-Arctic        36    12   \n",
       "\n",
       "     Accession  \n",
       "89        NOR2  \n",
       "134        IT1  \n",
       "1501       IT4  \n",
       "1506       IT4  \n",
       "1509       IT4  \n",
       "1515       IT4  \n",
       "1704       IT1  \n",
       "1705       IT1  \n",
       "1716       IT1  \n",
       "3075     NOR13  \n",
       "3081     NOR13  \n",
       "3120       IT4  \n",
       "3126       IT4  \n",
       "3127       IT4  \n",
       "4645     NOR13  \n",
       "4653     NOR13  \n",
       "4655     NOR13  \n",
       "4666     NOR13  \n",
       "4677     NOR13  \n",
       "4703       IT4  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GAPDH_outliers = locate_outlier_IQR(GAPDH)\n",
    "GAPDH_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>N2_5A</td>\n",
       "      <td>1</td>\n",
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       "      <th>377</th>\n",
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       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
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       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>39.121616</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>I1_13D</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>35.847011</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>I4_2A</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>32.391904</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>I4_9A</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>36.163812</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>I4_10B</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>33.028914</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>I4_11A</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>32.896792</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>I4_12C</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>32.523775</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>I1_1B</td>\n",
       "      <td>2</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>37.285711</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>I1_7B</td>\n",
       "      <td>2</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>38.120442</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2775</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>2</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>39.503811</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2781</th>\n",
       "      <td>N13_9B</td>\n",
       "      <td>2</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>34.394276</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>I4_10A</td>\n",
       "      <td>2</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>32.595807</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2826</th>\n",
       "      <td>I4_11C</td>\n",
       "      <td>2</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>33.297525</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2827</th>\n",
       "      <td>I4_11D</td>\n",
       "      <td>2</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>32.998120</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3539</th>\n",
       "      <td>N2_4A</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>31.627026</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>N2_4B</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>33.062104</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4325</th>\n",
       "      <td>N13_3B</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>34.767060</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>31.986785</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4357</th>\n",
       "      <td>N13_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>33.412304</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4382</th>\n",
       "      <td>I4_10C</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>34.421100</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Replicate Target         Cq       Treatment Zeitgeber ZT_24  \\\n",
       "332     N2_5A         1   MSI1  31.675457          Arctic        16    16   \n",
       "377     I1_8A         1   MSI1  34.160538          Arctic        28     4   \n",
       "399    I1_13C         1   MSI1  39.121616        D-Arctic        48    24   \n",
       "400    I1_13D         1   MSI1  35.847011  D-Mid-latitude        48    24   \n",
       "1248    I4_2A         1   MSI1  32.391904          Arctic         4     4   \n",
       "1263    I4_9A         1   MSI1  36.163812          Arctic        32     8   \n",
       "1268   I4_10B         1   MSI1  33.028914    Mid-latitude        36    12   \n",
       "1271   I4_11A         1   MSI1  32.896792          Arctic        40    16   \n",
       "1277   I4_12C         1   MSI1  32.523775        D-Arctic        44    20   \n",
       "1949    I1_1B         2   MSI1  37.285711    Mid-latitude         0     0   \n",
       "1960    I1_7B         2   MSI1  38.120442    Mid-latitude        24     0   \n",
       "2775   N13_7B         2   MSI1  39.503811    Mid-latitude        24     0   \n",
       "2781   N13_9B         2   MSI1  34.394276    Mid-latitude        32     8   \n",
       "2820   I4_10A         2   MSI1  32.595807          Arctic        36    12   \n",
       "2826   I4_11C         2   MSI1  33.297525        D-Arctic        40    16   \n",
       "2827   I4_11D         2   MSI1  32.998120  D-Mid-latitude        40    16   \n",
       "3539    N2_4A         3   MSI1  31.627026          Arctic        12    12   \n",
       "3540    N2_4B         3   MSI1  33.062104    Mid-latitude        12    12   \n",
       "4325   N13_3B         3   MSI1  34.767060    Mid-latitude         8     8   \n",
       "4333   N13_7B         3   MSI1  31.986785    Mid-latitude        24     0   \n",
       "4357  N13_13D         3   MSI1  33.412304  D-Mid-latitude        48    24   \n",
       "4382   I4_10C         3   MSI1  34.421100        D-Arctic        36    12   \n",
       "\n",
       "     Accession  \n",
       "332       NOR2  \n",
       "377        IT1  \n",
       "399        IT1  \n",
       "400        IT1  \n",
       "1248       IT4  \n",
       "1263       IT4  \n",
       "1268       IT4  \n",
       "1271       IT4  \n",
       "1277       IT4  \n",
       "1949       IT1  \n",
       "1960       IT1  \n",
       "2775     NOR13  \n",
       "2781     NOR13  \n",
       "2820       IT4  \n",
       "2826       IT4  \n",
       "2827       IT4  \n",
       "3539      NOR2  \n",
       "3540      NOR2  \n",
       "4325     NOR13  \n",
       "4333     NOR13  \n",
       "4357     NOR13  \n",
       "4382       IT4  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSI1_outliers = locate_outlier_IQR(MSI1)\n",
    "MSI1_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples to be dropped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers + samples where reference gene failed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N2_5A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I1_8A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I4_9A</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I4_10B</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>I4_11A</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>I4_12D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I1_1A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I1_1B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I1_7B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>N13_9B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>I4_10A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>I4_11C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>I4_11D</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>N13_3B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>N13_7B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>N13_8B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>N13_11A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>N13_13D</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>I4_10C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>I1_13C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>I1_13D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>I4_2A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>I4_12C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>N2_4A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>N2_4B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>I4_3B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>I1_12B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>I1_12A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>I1_12D</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>N13_6B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>N13_7A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>I4_13D</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>I4_7B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample Replicate\n",
       "0     N2_5A         1\n",
       "1     I1_8A         1\n",
       "2     I4_9A         1\n",
       "3    I4_10B         1\n",
       "4    I4_11A         1\n",
       "5    I4_12D         1\n",
       "6     I1_1A         2\n",
       "7     I1_1B         2\n",
       "8     I1_7B         2\n",
       "9    N13_7B         2\n",
       "10   N13_9B         2\n",
       "11   I4_10A         2\n",
       "12   I4_11C         2\n",
       "13   I4_11D         2\n",
       "14   N13_3B         3\n",
       "15   N13_7B         3\n",
       "16   N13_8B         3\n",
       "17  N13_11A         3\n",
       "18  N13_13D         3\n",
       "19   I4_10C         3\n",
       "20   I1_13C         1\n",
       "21   I1_13D         1\n",
       "22    I4_2A         1\n",
       "23   I4_12C         1\n",
       "24    N2_4A         3\n",
       "25    N2_4B         3\n",
       "26    I4_3B         1\n",
       "27   I1_12B         2\n",
       "28   I1_12A         3\n",
       "29   I1_12D         3\n",
       "30   N13_6B         3\n",
       "31   N13_7A         3\n",
       "32   I4_13D         3\n",
       "33    I4_7B         2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "High_GAPDH = GAPDH_outliers[['Sample', 'Replicate']]\n",
    "High_MSI1 = MSI1_outliers[['Sample', 'Replicate']]\n",
    "\n",
    "drop_refs = pd.concat([High_GAPDH, High_MSI1]).drop_duplicates().reset_index(drop=True)\n",
    "\n",
    "\n",
    "# Add samples where reference genes failed, nothing to use for relative expression\n",
    "drop_samples = drop_refs.append(drop_samps).drop_duplicates().reset_index(drop=True)\n",
    "\n",
    "drop_samples.loc[drop_samples.shape[0]] = ['I4_7B', '2']\n",
    "drop_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge dataframe with dropped samples to identify which are being dropped\n",
    "data_update = pd.merge(data,drop_samples, on=['Sample', 'Replicate'], \n",
    "                 indicator=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "      <th>Sample_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>LHY</td>\n",
       "      <td>32.630286</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>N2_5A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>GAPDH</td>\n",
       "      <td>28.186665</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>N2_5A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>PRR9</td>\n",
       "      <td>31.193318</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>N2_5A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>PRDX</td>\n",
       "      <td>29.231520</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>N2_5A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>MSI1</td>\n",
       "      <td>31.675457</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>N2_5A1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4393</th>\n",
       "      <td>MSI1</td>\n",
       "      <td>33.412304</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>N13_13D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4394</th>\n",
       "      <td>TOC1</td>\n",
       "      <td>33.681369</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>N13_13D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4395</th>\n",
       "      <td>LUX</td>\n",
       "      <td>34.526111</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>N13_13D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4396</th>\n",
       "      <td>PRDX</td>\n",
       "      <td>24.903024</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>N13_13D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4397</th>\n",
       "      <td>GAPDH</td>\n",
       "      <td>33.375033</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>N13_13D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>295 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Target         Cq       Treatment Zeitgeber ZT_24 Accession Sample_ID\n",
       "79      LHY  32.630286          Arctic        16    16      NOR2    N2_5A1\n",
       "80    GAPDH  28.186665          Arctic        16    16      NOR2    N2_5A1\n",
       "81     PRR9  31.193318          Arctic        16    16      NOR2    N2_5A1\n",
       "82     PRDX  29.231520          Arctic        16    16      NOR2    N2_5A1\n",
       "83     MSI1  31.675457          Arctic        16    16      NOR2    N2_5A1\n",
       "...     ...        ...             ...       ...   ...       ...       ...\n",
       "4393   MSI1  33.412304  D-Mid-latitude        48    24     NOR13  N13_13D3\n",
       "4394   TOC1  33.681369  D-Mid-latitude        48    24     NOR13  N13_13D3\n",
       "4395    LUX  34.526111  D-Mid-latitude        48    24     NOR13  N13_13D3\n",
       "4396   PRDX  24.903024  D-Mid-latitude        48    24     NOR13  N13_13D3\n",
       "4397  GAPDH  33.375033  D-Mid-latitude        48    24     NOR13  N13_13D3\n",
       "\n",
       "[295 rows x 7 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped = data_update[data_update['_merge']=='both']\n",
    "dropped['Sample_ID'] = dropped['Sample'] + dropped['Replicate']\n",
    "dropped.drop(columns = ['Sample', 'Replicate', '_merge'], inplace = True)\n",
    "dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/corinefaehn/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py:303: FutureWarning: Dropping invalid columns in SeriesGroupBy.agg is deprecated. In a future version, a TypeError will be raised. Before calling .agg, select only columns which should be valid for the aggregating function.\n",
      "  results[key] = self.aggregate(func)\n"
     ]
    },
    {
     "data": {
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       "      <th colspan=\"6\" halign=\"left\">Cq</th>\n",
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       "    <tr>\n",
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       "      <th>N</th>\n",
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       "      <th>Interval</th>\n",
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       "      <th>Sample_ID</th>\n",
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       "      <th>I1_12A3</th>\n",
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       "      <td>0.646609</td>\n",
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       "      <td>1.111477</td>\n",
       "      <td>26.172683</td>\n",
       "      <td>30.529673</td>\n",
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       "      <th>I1_12D3</th>\n",
       "      <td>9</td>\n",
       "      <td>28.496006</td>\n",
       "      <td>2.417693</td>\n",
       "      <td>0.805898</td>\n",
       "      <td>26.916446</td>\n",
       "      <td>30.075565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I1_13C1</th>\n",
       "      <td>7</td>\n",
       "      <td>37.628873</td>\n",
       "      <td>1.688175</td>\n",
       "      <td>0.638070</td>\n",
       "      <td>36.378255</td>\n",
       "      <td>38.879490</td>\n",
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       "      <th>I1_13D1</th>\n",
       "      <td>10</td>\n",
       "      <td>28.960332</td>\n",
       "      <td>3.159424</td>\n",
       "      <td>0.999098</td>\n",
       "      <td>27.002101</td>\n",
       "      <td>30.918564</td>\n",
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       "      <th>I1_1A2</th>\n",
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       "      <td>2.394863</td>\n",
       "      <td>0.757322</td>\n",
       "      <td>31.133930</td>\n",
       "      <td>34.102633</td>\n",
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       "      <th>I1_1B2</th>\n",
       "      <td>7</td>\n",
       "      <td>35.933996</td>\n",
       "      <td>2.177923</td>\n",
       "      <td>0.823178</td>\n",
       "      <td>34.320568</td>\n",
       "      <td>37.547424</td>\n",
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       "    <tr>\n",
       "      <th>I1_7B2</th>\n",
       "      <td>8</td>\n",
       "      <td>34.939494</td>\n",
       "      <td>3.892940</td>\n",
       "      <td>1.376362</td>\n",
       "      <td>32.241825</td>\n",
       "      <td>37.637164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I1_8A1</th>\n",
       "      <td>10</td>\n",
       "      <td>33.679895</td>\n",
       "      <td>3.429013</td>\n",
       "      <td>1.084349</td>\n",
       "      <td>31.554571</td>\n",
       "      <td>35.805219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4_10A2</th>\n",
       "      <td>10</td>\n",
       "      <td>34.122593</td>\n",
       "      <td>2.714333</td>\n",
       "      <td>0.858348</td>\n",
       "      <td>32.440232</td>\n",
       "      <td>35.804955</td>\n",
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       "    <tr>\n",
       "      <th>I4_10B1</th>\n",
       "      <td>9</td>\n",
       "      <td>30.654534</td>\n",
       "      <td>3.156749</td>\n",
       "      <td>1.052250</td>\n",
       "      <td>28.592124</td>\n",
       "      <td>32.716943</td>\n",
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       "    <tr>\n",
       "      <th>I4_10C3</th>\n",
       "      <td>8</td>\n",
       "      <td>35.350287</td>\n",
       "      <td>2.098452</td>\n",
       "      <td>0.741915</td>\n",
       "      <td>33.896134</td>\n",
       "      <td>36.804440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4_11A1</th>\n",
       "      <td>9</td>\n",
       "      <td>33.817742</td>\n",
       "      <td>2.482582</td>\n",
       "      <td>0.827527</td>\n",
       "      <td>32.195789</td>\n",
       "      <td>35.439696</td>\n",
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       "    <tr>\n",
       "      <th>I4_11C2</th>\n",
       "      <td>8</td>\n",
       "      <td>34.100580</td>\n",
       "      <td>2.187461</td>\n",
       "      <td>0.773384</td>\n",
       "      <td>32.584747</td>\n",
       "      <td>35.616413</td>\n",
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       "      <th>I4_11D2</th>\n",
       "      <td>9</td>\n",
       "      <td>34.660529</td>\n",
       "      <td>2.723060</td>\n",
       "      <td>0.907687</td>\n",
       "      <td>32.881463</td>\n",
       "      <td>36.439595</td>\n",
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       "      <th>I4_12C1</th>\n",
       "      <td>10</td>\n",
       "      <td>32.323457</td>\n",
       "      <td>4.320134</td>\n",
       "      <td>1.366146</td>\n",
       "      <td>29.645810</td>\n",
       "      <td>35.001104</td>\n",
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       "    <tr>\n",
       "      <th>I4_12D1</th>\n",
       "      <td>10</td>\n",
       "      <td>28.647517</td>\n",
       "      <td>2.546294</td>\n",
       "      <td>0.805209</td>\n",
       "      <td>27.069308</td>\n",
       "      <td>30.225726</td>\n",
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       "      <th>I4_13D3</th>\n",
       "      <td>7</td>\n",
       "      <td>29.540075</td>\n",
       "      <td>3.997002</td>\n",
       "      <td>1.510725</td>\n",
       "      <td>26.579055</td>\n",
       "      <td>32.501095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4_2A1</th>\n",
       "      <td>10</td>\n",
       "      <td>30.580345</td>\n",
       "      <td>3.112007</td>\n",
       "      <td>0.984103</td>\n",
       "      <td>28.651504</td>\n",
       "      <td>32.509187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4_3B1</th>\n",
       "      <td>10</td>\n",
       "      <td>26.735395</td>\n",
       "      <td>2.622875</td>\n",
       "      <td>0.829426</td>\n",
       "      <td>25.109720</td>\n",
       "      <td>28.361070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4_7B2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.930073</td>\n",
       "      <td>7.881470</td>\n",
       "      <td>5.573041</td>\n",
       "      <td>20.006912</td>\n",
       "      <td>41.853233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4_9A1</th>\n",
       "      <td>9</td>\n",
       "      <td>33.463723</td>\n",
       "      <td>2.204209</td>\n",
       "      <td>0.734736</td>\n",
       "      <td>32.023640</td>\n",
       "      <td>34.903806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_11A3</th>\n",
       "      <td>10</td>\n",
       "      <td>28.974233</td>\n",
       "      <td>2.162030</td>\n",
       "      <td>0.683694</td>\n",
       "      <td>27.634193</td>\n",
       "      <td>30.314273</td>\n",
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       "    <tr>\n",
       "      <th>N13_13D3</th>\n",
       "      <td>8</td>\n",
       "      <td>32.718088</td>\n",
       "      <td>3.251500</td>\n",
       "      <td>1.149579</td>\n",
       "      <td>30.464913</td>\n",
       "      <td>34.971263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_3B3</th>\n",
       "      <td>9</td>\n",
       "      <td>32.209879</td>\n",
       "      <td>4.286928</td>\n",
       "      <td>1.428976</td>\n",
       "      <td>29.409086</td>\n",
       "      <td>35.010672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_6B3</th>\n",
       "      <td>9</td>\n",
       "      <td>29.660173</td>\n",
       "      <td>3.257571</td>\n",
       "      <td>1.085857</td>\n",
       "      <td>27.531893</td>\n",
       "      <td>31.788453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_7A3</th>\n",
       "      <td>9</td>\n",
       "      <td>28.115319</td>\n",
       "      <td>2.828308</td>\n",
       "      <td>0.942769</td>\n",
       "      <td>26.267492</td>\n",
       "      <td>29.963147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_7B2</th>\n",
       "      <td>3</td>\n",
       "      <td>39.303627</td>\n",
       "      <td>0.450494</td>\n",
       "      <td>0.260093</td>\n",
       "      <td>38.793845</td>\n",
       "      <td>39.813408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_7B3</th>\n",
       "      <td>10</td>\n",
       "      <td>32.721179</td>\n",
       "      <td>4.648605</td>\n",
       "      <td>1.470018</td>\n",
       "      <td>29.839943</td>\n",
       "      <td>35.602414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_8B3</th>\n",
       "      <td>10</td>\n",
       "      <td>27.322082</td>\n",
       "      <td>2.467566</td>\n",
       "      <td>0.780313</td>\n",
       "      <td>25.792669</td>\n",
       "      <td>28.851495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N13_9B2</th>\n",
       "      <td>9</td>\n",
       "      <td>33.614686</td>\n",
       "      <td>2.353645</td>\n",
       "      <td>0.784548</td>\n",
       "      <td>32.076971</td>\n",
       "      <td>35.152401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N2_4A3</th>\n",
       "      <td>9</td>\n",
       "      <td>28.359069</td>\n",
       "      <td>2.154798</td>\n",
       "      <td>0.718266</td>\n",
       "      <td>26.951267</td>\n",
       "      <td>29.766870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N2_4B3</th>\n",
       "      <td>9</td>\n",
       "      <td>31.405527</td>\n",
       "      <td>2.812454</td>\n",
       "      <td>0.937485</td>\n",
       "      <td>29.568057</td>\n",
       "      <td>33.242997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N2_5A1</th>\n",
       "      <td>10</td>\n",
       "      <td>32.094960</td>\n",
       "      <td>2.201548</td>\n",
       "      <td>0.696191</td>\n",
       "      <td>30.730426</td>\n",
       "      <td>33.459493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Cq                                                     \n",
       "            N       Mean        SD        SE  95% Conf.   Interval\n",
       "Sample_ID                                                         \n",
       "I1_12A3     9  27.590546  1.939826  0.646609  26.323193  28.857899\n",
       "I1_12B2     9  28.351178  3.334431  1.111477  26.172683  30.529673\n",
       "I1_12D3     9  28.496006  2.417693  0.805898  26.916446  30.075565\n",
       "I1_13C1     7  37.628873  1.688175  0.638070  36.378255  38.879490\n",
       "I1_13D1    10  28.960332  3.159424  0.999098  27.002101  30.918564\n",
       "I1_1A2     10  32.618282  2.394863  0.757322  31.133930  34.102633\n",
       "I1_1B2      7  35.933996  2.177923  0.823178  34.320568  37.547424\n",
       "I1_7B2      8  34.939494  3.892940  1.376362  32.241825  37.637164\n",
       "I1_8A1     10  33.679895  3.429013  1.084349  31.554571  35.805219\n",
       "I4_10A2    10  34.122593  2.714333  0.858348  32.440232  35.804955\n",
       "I4_10B1     9  30.654534  3.156749  1.052250  28.592124  32.716943\n",
       "I4_10C3     8  35.350287  2.098452  0.741915  33.896134  36.804440\n",
       "I4_11A1     9  33.817742  2.482582  0.827527  32.195789  35.439696\n",
       "I4_11C2     8  34.100580  2.187461  0.773384  32.584747  35.616413\n",
       "I4_11D2     9  34.660529  2.723060  0.907687  32.881463  36.439595\n",
       "I4_12C1    10  32.323457  4.320134  1.366146  29.645810  35.001104\n",
       "I4_12D1    10  28.647517  2.546294  0.805209  27.069308  30.225726\n",
       "I4_13D3     7  29.540075  3.997002  1.510725  26.579055  32.501095\n",
       "I4_2A1     10  30.580345  3.112007  0.984103  28.651504  32.509187\n",
       "I4_3B1     10  26.735395  2.622875  0.829426  25.109720  28.361070\n",
       "I4_7B2      2  30.930073  7.881470  5.573041  20.006912  41.853233\n",
       "I4_9A1      9  33.463723  2.204209  0.734736  32.023640  34.903806\n",
       "N13_11A3   10  28.974233  2.162030  0.683694  27.634193  30.314273\n",
       "N13_13D3    8  32.718088  3.251500  1.149579  30.464913  34.971263\n",
       "N13_3B3     9  32.209879  4.286928  1.428976  29.409086  35.010672\n",
       "N13_6B3     9  29.660173  3.257571  1.085857  27.531893  31.788453\n",
       "N13_7A3     9  28.115319  2.828308  0.942769  26.267492  29.963147\n",
       "N13_7B2     3  39.303627  0.450494  0.260093  38.793845  39.813408\n",
       "N13_7B3    10  32.721179  4.648605  1.470018  29.839943  35.602414\n",
       "N13_8B3    10  27.322082  2.467566  0.780313  25.792669  28.851495\n",
       "N13_9B2     9  33.614686  2.353645  0.784548  32.076971  35.152401\n",
       "N2_4A3      9  28.359069  2.154798  0.718266  26.951267  29.766870\n",
       "N2_4B3      9  31.405527  2.812454  0.937485  29.568057  33.242997\n",
       "N2_5A1     10  32.094960  2.201548  0.696191  30.730426  33.459493"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_grouped = dropped.groupby('Sample_ID')\n",
    "rp.summary_cont(dropped_grouped)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove samples from dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "remove_index = data_update[data_update['_merge']=='both'].index\n",
    "data_update.drop(remove_index, inplace = True)\n",
    "data_update.drop('_merge', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify any duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th>Target</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">N2_1A</th>\n",
       "      <th>LHY</th>\n",
       "      <th>1</th>\n",
       "      <td>23.972973</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPDH</th>\n",
       "      <th>1</th>\n",
       "      <td>21.430556</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR9</th>\n",
       "      <th>1</th>\n",
       "      <td>32.956244</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRDX</th>\n",
       "      <th>1</th>\n",
       "      <td>24.378550</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSI1</th>\n",
       "      <th>1</th>\n",
       "      <td>28.762125</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Cq Treatment Zeitgeber ZT_24 Accession\n",
       "Sample Target Replicate                                               \n",
       "N2_1A  LHY    1          23.972973    Arctic         0     0      NOR2\n",
       "       GAPDH  1          21.430556    Arctic         0     0      NOR2\n",
       "       PRR9   1          32.956244    Arctic         0     0      NOR2\n",
       "       PRDX   1          24.378550    Arctic         0     0      NOR2\n",
       "       MSI1   1          28.762125    Arctic         0     0      NOR2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_update.set_index(['Sample', 'Target', 'Replicate'], drop=True, inplace = True)\n",
    "data_update.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th>Target</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">I1_1A</th>\n",
       "      <th>PRR5</th>\n",
       "      <th>1</th>\n",
       "      <td>30.272990</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR7</th>\n",
       "      <th>3</th>\n",
       "      <td>27.708206</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Cq Treatment Zeitgeber ZT_24 Accession\n",
       "Sample Target Replicate                                               \n",
       "I1_1A  PRR5   1          30.272990    Arctic         0     0       IT1\n",
       "       PRR7   3          27.708206    Arctic         0     0       IT1"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_update[data_update.index.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_update.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check reference genes <a class=\"anchor\" id=\"ref_genes\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "GAPDH = data_update[(data_update['Target']=='GAPDH')]\n",
    "MSI1 = data_update[(data_update['Target']=='MSI1')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pp.figure(figsize=(20,14))\n",
    "\n",
    "ax1 = sb.boxplot(x='Accession', y='Cq', color='black', data = GAPDH)\n",
    "ax2 = sb.boxplot(x='Accession', y='Cq', hue='Treatment', data = GAPDH)\n",
    "\n",
    "pp.ylim(18, 26)\n",
    "ax2.legend(bbox_to_anchor=(1.0, 0.75), loc=2, borderaxespad=0., title = 'Treatment')\n",
    "\n",
    "pp.title('GAPDH Cq values by Accession', fontsize = '28')\n",
    "# pp.savefig('Circadian_gene_results_figures/GAPDH_avg_Cq.jpg', dpi = 300)\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pp.figure(figsize=(20,14))\n",
    "\n",
    "ax1 = sb.boxplot(x='Accession', y='Cq', color='black', data = MSI1)\n",
    "\n",
    "ax2 = sb.boxplot(x='Accession', y='Cq', hue='Treatment', data = MSI1)\n",
    "\n",
    "pp.ylim(24,32)\n",
    "ax2.legend(bbox_to_anchor=(1.0, 0.75), loc=2, borderaxespad=0., title = 'Treatment')\n",
    "\n",
    "pp.title('MSI1 Cq values by Accession', fontsize = '28')\n",
    "# pp.savefig('Circadian_gene_results_figures/MSI1_avg_Cq.jpg', dpi = 300)\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference gene statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAPDH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "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>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(Treatment)</th>\n",
       "      <td>39.710695</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.255864</td>\n",
       "      <td>1.101411e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(Accession)</th>\n",
       "      <td>197.704865</td>\n",
       "      <td>3.0</td>\n",
       "      <td>61.017414</td>\n",
       "      <td>1.843929e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(Treatment):C(Accession)</th>\n",
       "      <td>28.423058</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.924058</td>\n",
       "      <td>2.283622e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>428.778311</td>\n",
       "      <td>397.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               sum_sq     df          F        PR(>F)\n",
       "C(Treatment)                39.710695    3.0  12.255864  1.101411e-07\n",
       "C(Accession)               197.704865    3.0  61.017414  1.843929e-32\n",
       "C(Treatment):C(Accession)   28.423058    9.0   2.924058  2.283622e-03\n",
       "Residual                   428.778311  397.0        NaN           NaN"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GAPDH_formula = 'Cq ~ C(Treatment) + C(Accession) + C(Treatment)*C(Accession)'\n",
    "GAPDH_model = ols(GAPDH_formula, GAPDH).fit()\n",
    "GAPDH_anova_table = sm.stats.anova_lm(GAPDH_model, typ=2)\n",
    "GAPDH_anova_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9861940145492554, pvalue=0.0005769907147623599)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assumption Check - Normality \n",
    "GAPDH_res = GAPDH_model.resid\n",
    "stats.shapiro(GAPDH_model.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc525f62ad0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GAPDH['Cq'].hist(figsize=(5,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assumption Check - Residuals Plot\n",
    "fig = pp.figure(figsize= (10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "stats.probplot(GAPDH_res, plot=pp,rvalue=True)\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>1.2142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8170</td>\n",
       "      <td>1.6114</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>-0.7012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0928</td>\n",
       "      <td>-0.3096</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0.9326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5325</td>\n",
       "      <td>1.3328</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>-0.9828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.3774</td>\n",
       "      <td>-0.5882</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>-1.9155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.3080</td>\n",
       "      <td>-1.5229</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group1 group2  meandiff  p-adj   lower   upper  reject\n",
       "1    IT1  NOR13    1.2142    0.0  0.8170  1.6114    True\n",
       "2    IT1   NOR2   -0.7012    0.0 -1.0928 -0.3096    True\n",
       "3    IT4  NOR13    0.9326    0.0  0.5325  1.3328    True\n",
       "4    IT4   NOR2   -0.9828    0.0 -1.3774 -0.5882    True\n",
       "5  NOR13   NOR2   -1.9155    0.0 -2.3080 -1.5229    True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GAPDH_treatment_comp = mc.MultiComparison(GAPDH['Cq'], GAPDH['Accession'])\n",
    "GAPDH_t_results = GAPDH_treatment_comp.tukeyhsd()\n",
    "GAPDH_t_table = pd.DataFrame(data=GAPDH_t_results._results_table.data[1:], columns=GAPDH_t_results._results_table.data[0])\n",
    "GAPDH_t_sig = GAPDH_t_table.loc[GAPDH_t_table['p-adj'] < 0.05, :]\n",
    "GAPDH_t_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IT1_Arctic</td>\n",
       "      <td>IT4_D-Mid-latitude</td>\n",
       "      <td>1.2412</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.1399</td>\n",
       "      <td>2.3425</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IT1_Arctic</td>\n",
       "      <td>NOR13_Arctic</td>\n",
       "      <td>1.5008</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6561</td>\n",
       "      <td>2.3454</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IT1_Arctic</td>\n",
       "      <td>NOR13_D-Arctic</td>\n",
       "      <td>1.6384</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5838</td>\n",
       "      <td>2.6930</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IT1_Arctic</td>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>1.8402</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7856</td>\n",
       "      <td>2.8948</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IT1_Arctic</td>\n",
       "      <td>NOR13_Mid-latitude</td>\n",
       "      <td>1.5087</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6380</td>\n",
       "      <td>2.3793</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>IT1_D-Arctic</td>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>1.3104</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>0.0812</td>\n",
       "      <td>2.5396</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>IT1_D-Arctic</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-1.3147</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>-2.3741</td>\n",
       "      <td>-0.2553</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>IT1_D-Arctic</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-1.3097</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>-2.3554</td>\n",
       "      <td>-0.2641</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>IT1_D-Mid-latitude</td>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>1.3670</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.1188</td>\n",
       "      <td>2.6153</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IT1_D-Mid-latitude</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-1.2581</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>-2.3396</td>\n",
       "      <td>-0.1766</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>IT1_D-Mid-latitude</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-1.2531</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>-2.3212</td>\n",
       "      <td>-0.1851</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>IT1_Mid-latitude</td>\n",
       "      <td>NOR13_Arctic</td>\n",
       "      <td>0.8719</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>1.7289</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>IT1_Mid-latitude</td>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>1.2113</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.1468</td>\n",
       "      <td>2.2758</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>IT1_Mid-latitude</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-1.4138</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.2768</td>\n",
       "      <td>-0.5509</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>IT1_Mid-latitude</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-1.4089</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.2549</td>\n",
       "      <td>-0.5629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>IT4_Arctic</td>\n",
       "      <td>NOR13_Arctic</td>\n",
       "      <td>1.1285</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.2778</td>\n",
       "      <td>1.9791</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>IT4_Arctic</td>\n",
       "      <td>NOR13_D-Arctic</td>\n",
       "      <td>1.2661</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.2066</td>\n",
       "      <td>2.3255</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>IT4_Arctic</td>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>1.4679</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.4084</td>\n",
       "      <td>2.5273</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>IT4_Arctic</td>\n",
       "      <td>NOR13_Mid-latitude</td>\n",
       "      <td>1.1364</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.2598</td>\n",
       "      <td>2.0129</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>IT4_Arctic</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-1.1573</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-2.0139</td>\n",
       "      <td>-0.3006</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>IT4_Arctic</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-1.1523</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-1.9919</td>\n",
       "      <td>-0.3127</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>IT4_D-Arctic</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-1.6708</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.7768</td>\n",
       "      <td>-0.5649</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>IT4_D-Arctic</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-1.6659</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.7587</td>\n",
       "      <td>-0.5731</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>IT4_D-Mid-latitude</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-2.0262</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-3.1321</td>\n",
       "      <td>-0.9202</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>IT4_D-Mid-latitude</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-2.0212</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-3.1140</td>\n",
       "      <td>-0.9284</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>IT4_Mid-latitude</td>\n",
       "      <td>NOR13_Arctic</td>\n",
       "      <td>0.9272</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0765</td>\n",
       "      <td>1.7779</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>IT4_Mid-latitude</td>\n",
       "      <td>NOR13_D-Arctic</td>\n",
       "      <td>1.0648</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>2.1242</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>IT4_Mid-latitude</td>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>1.2666</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>2.3260</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>IT4_Mid-latitude</td>\n",
       "      <td>NOR13_Mid-latitude</td>\n",
       "      <td>0.9351</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>0.0586</td>\n",
       "      <td>1.8116</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>IT4_Mid-latitude</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-1.3585</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.2152</td>\n",
       "      <td>-0.5019</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>IT4_Mid-latitude</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-1.3536</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.1932</td>\n",
       "      <td>-0.5140</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>NOR13_Arctic</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-2.2857</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-3.1364</td>\n",
       "      <td>-1.4350</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>NOR13_Arctic</td>\n",
       "      <td>NOR2_D-Arctic</td>\n",
       "      <td>-1.2520</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>-2.3066</td>\n",
       "      <td>-0.1974</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NOR13_Arctic</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-2.2808</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-3.1143</td>\n",
       "      <td>-1.4473</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>NOR13_D-Arctic</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-2.4233</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-3.4828</td>\n",
       "      <td>-1.3639</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>NOR13_D-Arctic</td>\n",
       "      <td>NOR2_D-Arctic</td>\n",
       "      <td>-1.3896</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>-2.6188</td>\n",
       "      <td>-0.1604</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>NOR13_D-Arctic</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-2.4184</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-3.4640</td>\n",
       "      <td>-1.3727</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-2.6251</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-3.6846</td>\n",
       "      <td>-1.5657</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>NOR2_D-Arctic</td>\n",
       "      <td>-1.5914</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>-2.8206</td>\n",
       "      <td>-0.3622</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>NOR13_D-Mid-latitude</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-2.6202</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-3.6658</td>\n",
       "      <td>-1.5745</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>NOR13_Mid-latitude</td>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>-2.2936</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-3.1701</td>\n",
       "      <td>-1.4171</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>NOR13_Mid-latitude</td>\n",
       "      <td>NOR2_D-Arctic</td>\n",
       "      <td>-1.2599</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>-2.3355</td>\n",
       "      <td>-0.1844</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>NOR13_Mid-latitude</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-2.2887</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-3.1485</td>\n",
       "      <td>-1.4288</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>NOR2_Arctic</td>\n",
       "      <td>NOR2_D-Mid-latitude</td>\n",
       "      <td>1.7267</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6873</td>\n",
       "      <td>2.7662</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>NOR2_D-Mid-latitude</td>\n",
       "      <td>NOR2_Mid-latitude</td>\n",
       "      <td>-1.7218</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.7472</td>\n",
       "      <td>-0.6963</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   group1                group2  meandiff   p-adj   lower  \\\n",
       "5              IT1_Arctic    IT4_D-Mid-latitude    1.2412  0.0113  0.1399   \n",
       "7              IT1_Arctic          NOR13_Arctic    1.5008  0.0000  0.6561   \n",
       "8              IT1_Arctic        NOR13_D-Arctic    1.6384  0.0000  0.5838   \n",
       "9              IT1_Arctic  NOR13_D-Mid-latitude    1.8402  0.0000  0.7856   \n",
       "10             IT1_Arctic    NOR13_Mid-latitude    1.5087  0.0000  0.6380   \n",
       "23           IT1_D-Arctic  NOR13_D-Mid-latitude    1.3104  0.0238  0.0812   \n",
       "25           IT1_D-Arctic           NOR2_Arctic   -1.3147  0.0025 -2.3741   \n",
       "28           IT1_D-Arctic     NOR2_Mid-latitude   -1.3097  0.0021 -2.3554   \n",
       "36     IT1_D-Mid-latitude  NOR13_D-Mid-latitude    1.3670  0.0168  0.1188   \n",
       "38     IT1_D-Mid-latitude           NOR2_Arctic   -1.2581  0.0071 -2.3396   \n",
       "41     IT1_D-Mid-latitude     NOR2_Mid-latitude   -1.2531  0.0062 -2.3212   \n",
       "46       IT1_Mid-latitude          NOR13_Arctic    0.8719  0.0414  0.0149   \n",
       "48       IT1_Mid-latitude  NOR13_D-Mid-latitude    1.2113  0.0099  0.1468   \n",
       "50       IT1_Mid-latitude           NOR2_Arctic   -1.4138  0.0000 -2.2768   \n",
       "53       IT1_Mid-latitude     NOR2_Mid-latitude   -1.4089  0.0000 -2.2549   \n",
       "57             IT4_Arctic          NOR13_Arctic    1.1285  0.0007  0.2778   \n",
       "58             IT4_Arctic        NOR13_D-Arctic    1.2661  0.0046  0.2066   \n",
       "59             IT4_Arctic  NOR13_D-Mid-latitude    1.4679  0.0003  0.4084   \n",
       "60             IT4_Arctic    NOR13_Mid-latitude    1.1364  0.0011  0.2598   \n",
       "61             IT4_Arctic           NOR2_Arctic   -1.1573  0.0005 -2.0139   \n",
       "64             IT4_Arctic     NOR2_Mid-latitude   -1.1523  0.0003 -1.9919   \n",
       "71           IT4_D-Arctic           NOR2_Arctic   -1.6708  0.0000 -2.7768   \n",
       "74           IT4_D-Arctic     NOR2_Mid-latitude   -1.6659  0.0000 -2.7587   \n",
       "80     IT4_D-Mid-latitude           NOR2_Arctic   -2.0262  0.0000 -3.1321   \n",
       "83     IT4_D-Mid-latitude     NOR2_Mid-latitude   -2.0212  0.0000 -3.1140   \n",
       "84       IT4_Mid-latitude          NOR13_Arctic    0.9272  0.0179  0.0765   \n",
       "85       IT4_Mid-latitude        NOR13_D-Arctic    1.0648  0.0473  0.0054   \n",
       "86       IT4_Mid-latitude  NOR13_D-Mid-latitude    1.2666  0.0046  0.2072   \n",
       "87       IT4_Mid-latitude    NOR13_Mid-latitude    0.9351  0.0236  0.0586   \n",
       "88       IT4_Mid-latitude           NOR2_Arctic   -1.3585  0.0000 -2.2152   \n",
       "91       IT4_Mid-latitude     NOR2_Mid-latitude   -1.3536  0.0000 -2.1932   \n",
       "95           NOR13_Arctic           NOR2_Arctic   -2.2857 -0.0000 -3.1364   \n",
       "96           NOR13_Arctic         NOR2_D-Arctic   -1.2520  0.0052 -2.3066   \n",
       "98           NOR13_Arctic     NOR2_Mid-latitude   -2.2808 -0.0000 -3.1143   \n",
       "101        NOR13_D-Arctic           NOR2_Arctic   -2.4233  0.0000 -3.4828   \n",
       "102        NOR13_D-Arctic         NOR2_D-Arctic   -1.3896  0.0108 -2.6188   \n",
       "104        NOR13_D-Arctic     NOR2_Mid-latitude   -2.4184  0.0000 -3.4640   \n",
       "106  NOR13_D-Mid-latitude           NOR2_Arctic   -2.6251 -0.0000 -3.6846   \n",
       "107  NOR13_D-Mid-latitude         NOR2_D-Arctic   -1.5914  0.0011 -2.8206   \n",
       "109  NOR13_D-Mid-latitude     NOR2_Mid-latitude   -2.6202 -0.0000 -3.6658   \n",
       "110    NOR13_Mid-latitude           NOR2_Arctic   -2.2936 -0.0000 -3.1701   \n",
       "111    NOR13_Mid-latitude         NOR2_D-Arctic   -1.2599  0.0064 -2.3355   \n",
       "113    NOR13_Mid-latitude     NOR2_Mid-latitude   -2.2887 -0.0000 -3.1485   \n",
       "115           NOR2_Arctic   NOR2_D-Mid-latitude    1.7267  0.0000  0.6873   \n",
       "119   NOR2_D-Mid-latitude     NOR2_Mid-latitude   -1.7218  0.0000 -2.7472   \n",
       "\n",
       "      upper  reject  \n",
       "5    2.3425    True  \n",
       "7    2.3454    True  \n",
       "8    2.6930    True  \n",
       "9    2.8948    True  \n",
       "10   2.3793    True  \n",
       "23   2.5396    True  \n",
       "25  -0.2553    True  \n",
       "28  -0.2641    True  \n",
       "36   2.6153    True  \n",
       "38  -0.1766    True  \n",
       "41  -0.1851    True  \n",
       "46   1.7289    True  \n",
       "48   2.2758    True  \n",
       "50  -0.5509    True  \n",
       "53  -0.5629    True  \n",
       "57   1.9791    True  \n",
       "58   2.3255    True  \n",
       "59   2.5273    True  \n",
       "60   2.0129    True  \n",
       "61  -0.3006    True  \n",
       "64  -0.3127    True  \n",
       "71  -0.5649    True  \n",
       "74  -0.5731    True  \n",
       "80  -0.9202    True  \n",
       "83  -0.9284    True  \n",
       "84   1.7779    True  \n",
       "85   2.1242    True  \n",
       "86   2.3260    True  \n",
       "87   1.8116    True  \n",
       "88  -0.5019    True  \n",
       "91  -0.5140    True  \n",
       "95  -1.4350    True  \n",
       "96  -0.1974    True  \n",
       "98  -1.4473    True  \n",
       "101 -1.3639    True  \n",
       "102 -0.1604    True  \n",
       "104 -1.3727    True  \n",
       "106 -1.5657    True  \n",
       "107 -0.3622    True  \n",
       "109 -1.5745    True  \n",
       "110 -1.4171    True  \n",
       "111 -0.1844    True  \n",
       "113 -1.4288    True  \n",
       "115  2.7662    True  \n",
       "119 -0.6963    True  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GAPDH_groups = GAPDH.Accession.astype(str) + '_' + GAPDH.Treatment.astype(str)\n",
    "\n",
    "GAPDH_comp = mc.MultiComparison(GAPDH['Cq'], GAPDH_groups)\n",
    "GAPDH_results = GAPDH_comp.tukeyhsd()\n",
    "GAPDH_table = pd.DataFrame(data=GAPDH_results._results_table.data[1:], columns=GAPDH_results._results_table.data[0])\n",
    "GAPDH_sig = GAPDH_table.loc[GAPDH_table['p-adj'] < 0.05, :]\n",
    "GAPDH_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Significant differences within N2 (Italy - D-Italy and Norway - D-Italy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MSI1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(Treatment)</th>\n",
       "      <td>15.210326</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.299082</td>\n",
       "      <td>3.494101e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(Accession)</th>\n",
       "      <td>121.824509</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.451425</td>\n",
       "      <td>1.207006e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(Treatment):C(Accession)</th>\n",
       "      <td>6.668586</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.920558</td>\n",
       "      <td>5.070086e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>319.543866</td>\n",
       "      <td>397.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               sum_sq     df          F        PR(>F)\n",
       "C(Treatment)                15.210326    3.0   6.299082  3.494101e-04\n",
       "C(Accession)               121.824509    3.0  50.451425  1.207006e-27\n",
       "C(Treatment):C(Accession)    6.668586    9.0   0.920558  5.070086e-01\n",
       "Residual                   319.543866  397.0        NaN           NaN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSI1_formula = 'Cq ~ C(Treatment) + C(Accession) + C(Treatment)*C(Accession)'\n",
    "MSI1_model = ols(MSI1_formula, MSI1).fit()\n",
    "MSI1_anova_table = sm.stats.anova_lm(MSI1_model, typ=2)\n",
    "MSI1_anova_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9911720156669617, pvalue=0.014466281980276108)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assumption Check - Normality \n",
    "MSI1_res = MSI1_model.resid\n",
    "stats.shapiro(MSI1_model.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5282a3cd0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MSI1['Cq'].hist(figsize=(5,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assumption Check - Residuals Plot\n",
    "fig = pp.figure(figsize= (10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "stats.probplot(MSI1_res, plot=pp,rvalue=True)\n",
    "pp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>0.3369</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.6678</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0.9647</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6354</td>\n",
       "      <td>1.2939</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>-0.5184</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.8430</td>\n",
       "      <td>-0.1938</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0.6277</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>0.9594</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>-0.8554</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1.1824</td>\n",
       "      <td>-0.5283</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>-1.4831</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1.8085</td>\n",
       "      <td>-1.1577</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group1 group2  meandiff   p-adj   lower   upper  reject\n",
       "0    IT1    IT4    0.3369  0.0441  0.0061  0.6678    True\n",
       "1    IT1  NOR13    0.9647  0.0000  0.6354  1.2939    True\n",
       "2    IT1   NOR2   -0.5184  0.0003 -0.8430 -0.1938    True\n",
       "3    IT4  NOR13    0.6277  0.0000  0.2960  0.9594    True\n",
       "4    IT4   NOR2   -0.8554  0.0000 -1.1824 -0.5283    True\n",
       "5  NOR13   NOR2   -1.4831  0.0000 -1.8085 -1.1577    True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSI1_treatment_comp = mc.MultiComparison(MSI1['Cq'], MSI1['Accession'])\n",
    "MSI1_t_results = MSI1_treatment_comp.tukeyhsd()\n",
    "MSI1_t_table = pd.DataFrame(data=MSI1_t_results._results_table.data[1:], columns=MSI1_t_results._results_table.data[0])\n",
    "MSI1_t_sig = MSI1_t_table.loc[MSI1_t_table['p-adj'] < 0.05, :]\n",
    "MSI1_t_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IT1 Arctic</td>\n",
       "      <td>IT4 D-Mid-latitude</td>\n",
       "      <td>0.9704</td>\n",
       "      <td>0.0399</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>1.9212</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IT1 Arctic</td>\n",
       "      <td>NOR13 Arctic</td>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.1483</td>\n",
       "      <td>1.6067</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IT1 Arctic</td>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>1.3603</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4499</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IT1 Arctic</td>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>1.7054</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7949</td>\n",
       "      <td>2.6158</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IT1 Arctic</td>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>1.2152</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4636</td>\n",
       "      <td>1.9669</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>IT1 D-Arctic</td>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>1.2768</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.2157</td>\n",
       "      <td>2.3380</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>IT1 D-Arctic</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-0.9175</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>-1.8202</td>\n",
       "      <td>-0.0148</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IT1 D-Mid-latitude</td>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>1.1582</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>2.2358</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>IT1 D-Mid-latitude</td>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>1.5032</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.4256</td>\n",
       "      <td>2.5808</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>IT1 D-Mid-latitude</td>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>1.0131</td>\n",
       "      <td>0.0229</td>\n",
       "      <td>0.0659</td>\n",
       "      <td>1.9604</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>IT1 Mid-latitude</td>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>0.9510</td>\n",
       "      <td>0.0341</td>\n",
       "      <td>0.0321</td>\n",
       "      <td>1.8700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>IT1 Mid-latitude</td>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>1.2961</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>2.2150</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>IT1 Mid-latitude</td>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>0.8059</td>\n",
       "      <td>0.0263</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>1.5679</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>IT1 Mid-latitude</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-0.7878</td>\n",
       "      <td>0.0263</td>\n",
       "      <td>-1.5328</td>\n",
       "      <td>-0.0429</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>IT1 Mid-latitude</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-0.8983</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>-1.6286</td>\n",
       "      <td>-0.1680</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>IT4 Arctic</td>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>0.9740</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.0594</td>\n",
       "      <td>1.8885</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>IT4 Arctic</td>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>1.3190</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.4044</td>\n",
       "      <td>2.2336</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>IT4 Arctic</td>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>0.8288</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.0722</td>\n",
       "      <td>1.5855</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>IT4 Arctic</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-0.7649</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>-1.5044</td>\n",
       "      <td>-0.0254</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>IT4 Arctic</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-0.8754</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>-1.6002</td>\n",
       "      <td>-0.1506</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>IT4 D-Arctic</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-1.1069</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>-2.0616</td>\n",
       "      <td>-0.1521</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>IT4 D-Arctic</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-1.2173</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>-2.1607</td>\n",
       "      <td>-0.2740</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>IT4 D-Mid-latitude</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-1.3490</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>-2.3037</td>\n",
       "      <td>-0.3942</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>IT4 D-Mid-latitude</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-1.4594</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.4028</td>\n",
       "      <td>-0.5161</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>IT4 Mid-latitude</td>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>1.1793</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.2647</td>\n",
       "      <td>2.0939</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>IT4 Mid-latitude</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-0.9046</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>-1.6441</td>\n",
       "      <td>-0.1651</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>IT4 Mid-latitude</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-1.0151</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>-1.7399</td>\n",
       "      <td>-0.2903</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>NOR13 Arctic</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-1.2560</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1.9904</td>\n",
       "      <td>-0.5216</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NOR13 Arctic</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-1.3665</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.0860</td>\n",
       "      <td>-0.6469</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-1.7389</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.6535</td>\n",
       "      <td>-0.8243</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>NOR2 D-Arctic</td>\n",
       "      <td>-1.2921</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>-2.3532</td>\n",
       "      <td>-0.2310</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>NOR2 D-Mid-latitude</td>\n",
       "      <td>-1.3435</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>-2.3898</td>\n",
       "      <td>-0.2972</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>NOR13 D-Arctic</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-1.8493</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.7520</td>\n",
       "      <td>-0.9466</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-2.0839</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.9985</td>\n",
       "      <td>-1.1693</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>NOR2 D-Arctic</td>\n",
       "      <td>-1.6371</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.6982</td>\n",
       "      <td>-0.5760</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>NOR2 D-Mid-latitude</td>\n",
       "      <td>-1.6885</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.7348</td>\n",
       "      <td>-0.6423</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>NOR13 D-Mid-latitude</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-2.1943</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-3.0970</td>\n",
       "      <td>-1.2916</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>NOR2 Arctic</td>\n",
       "      <td>-1.5938</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.3504</td>\n",
       "      <td>-0.8371</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>NOR2 D-Arctic</td>\n",
       "      <td>-1.1470</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>-2.0755</td>\n",
       "      <td>-0.2185</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>NOR2 D-Mid-latitude</td>\n",
       "      <td>-1.1984</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>-2.1099</td>\n",
       "      <td>-0.2869</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>NOR13 Mid-latitude</td>\n",
       "      <td>NOR2 Mid-latitude</td>\n",
       "      <td>-1.7042</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.4465</td>\n",
       "      <td>-0.9619</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   group1                group2  meandiff   p-adj   lower  \\\n",
       "5              IT1 Arctic    IT4 D-Mid-latitude    0.9704  0.0399  0.0197   \n",
       "7              IT1 Arctic          NOR13 Arctic    0.8775  0.0041  0.1483   \n",
       "8              IT1 Arctic        NOR13 D-Arctic    1.3603  0.0000  0.4499   \n",
       "9              IT1 Arctic  NOR13 D-Mid-latitude    1.7054  0.0000  0.7949   \n",
       "10             IT1 Arctic    NOR13 Mid-latitude    1.2152  0.0000  0.4636   \n",
       "23           IT1 D-Arctic  NOR13 D-Mid-latitude    1.2768  0.0042  0.2157   \n",
       "28           IT1 D-Arctic     NOR2 Mid-latitude   -0.9175  0.0419 -1.8202   \n",
       "35     IT1 D-Mid-latitude        NOR13 D-Arctic    1.1582  0.0215  0.0806   \n",
       "36     IT1 D-Mid-latitude  NOR13 D-Mid-latitude    1.5032  0.0002  0.4256   \n",
       "37     IT1 D-Mid-latitude    NOR13 Mid-latitude    1.0131  0.0229  0.0659   \n",
       "47       IT1 Mid-latitude        NOR13 D-Arctic    0.9510  0.0341  0.0321   \n",
       "48       IT1 Mid-latitude  NOR13 D-Mid-latitude    1.2961  0.0002  0.3771   \n",
       "49       IT1 Mid-latitude    NOR13 Mid-latitude    0.8059  0.0263  0.0440   \n",
       "50       IT1 Mid-latitude           NOR2 Arctic   -0.7878  0.0263 -1.5328   \n",
       "53       IT1 Mid-latitude     NOR2 Mid-latitude   -0.8983  0.0029 -1.6286   \n",
       "58             IT4 Arctic        NOR13 D-Arctic    0.9740  0.0241  0.0594   \n",
       "59             IT4 Arctic  NOR13 D-Mid-latitude    1.3190  0.0001  0.4044   \n",
       "60             IT4 Arctic    NOR13 Mid-latitude    0.8288  0.0168  0.0722   \n",
       "61             IT4 Arctic           NOR2 Arctic   -0.7649  0.0343 -1.5044   \n",
       "64             IT4 Arctic     NOR2 Mid-latitude   -0.8754  0.0039 -1.6002   \n",
       "71           IT4 D-Arctic           NOR2 Arctic   -1.1069  0.0075 -2.0616   \n",
       "74           IT4 D-Arctic     NOR2 Mid-latitude   -1.2173  0.0012 -2.1607   \n",
       "80     IT4 D-Mid-latitude           NOR2 Arctic   -1.3490  0.0002 -2.3037   \n",
       "83     IT4 D-Mid-latitude     NOR2 Mid-latitude   -1.4594  0.0000 -2.4028   \n",
       "86       IT4 Mid-latitude  NOR13 D-Mid-latitude    1.1793  0.0012  0.2647   \n",
       "88       IT4 Mid-latitude           NOR2 Arctic   -0.9046  0.0032 -1.6441   \n",
       "91       IT4 Mid-latitude     NOR2 Mid-latitude   -1.0151  0.0002 -1.7399   \n",
       "95           NOR13 Arctic           NOR2 Arctic   -1.2560  0.0000 -1.9904   \n",
       "98           NOR13 Arctic     NOR2 Mid-latitude   -1.3665  0.0000 -2.0860   \n",
       "101        NOR13 D-Arctic           NOR2 Arctic   -1.7389  0.0000 -2.6535   \n",
       "102        NOR13 D-Arctic         NOR2 D-Arctic   -1.2921  0.0034 -2.3532   \n",
       "103        NOR13 D-Arctic   NOR2 D-Mid-latitude   -1.3435  0.0013 -2.3898   \n",
       "104        NOR13 D-Arctic     NOR2 Mid-latitude   -1.8493  0.0000 -2.7520   \n",
       "106  NOR13 D-Mid-latitude           NOR2 Arctic   -2.0839  0.0000 -2.9985   \n",
       "107  NOR13 D-Mid-latitude         NOR2 D-Arctic   -1.6371  0.0000 -2.6982   \n",
       "108  NOR13 D-Mid-latitude   NOR2 D-Mid-latitude   -1.6885  0.0000 -2.7348   \n",
       "109  NOR13 D-Mid-latitude     NOR2 Mid-latitude   -2.1943 -0.0000 -3.0970   \n",
       "110    NOR13 Mid-latitude           NOR2 Arctic   -1.5938  0.0000 -2.3504   \n",
       "111    NOR13 Mid-latitude         NOR2 D-Arctic   -1.1470  0.0027 -2.0755   \n",
       "112    NOR13 Mid-latitude   NOR2 D-Mid-latitude   -1.1984  0.0008 -2.1099   \n",
       "113    NOR13 Mid-latitude     NOR2 Mid-latitude   -1.7042  0.0000 -2.4465   \n",
       "\n",
       "      upper  reject  \n",
       "5    1.9212    True  \n",
       "7    1.6067    True  \n",
       "8    2.2708    True  \n",
       "9    2.6158    True  \n",
       "10   1.9669    True  \n",
       "23   2.3380    True  \n",
       "28  -0.0148    True  \n",
       "35   2.2358    True  \n",
       "36   2.5808    True  \n",
       "37   1.9604    True  \n",
       "47   1.8700    True  \n",
       "48   2.2150    True  \n",
       "49   1.5679    True  \n",
       "50  -0.0429    True  \n",
       "53  -0.1680    True  \n",
       "58   1.8885    True  \n",
       "59   2.2336    True  \n",
       "60   1.5855    True  \n",
       "61  -0.0254    True  \n",
       "64  -0.1506    True  \n",
       "71  -0.1521    True  \n",
       "74  -0.2740    True  \n",
       "80  -0.3942    True  \n",
       "83  -0.5161    True  \n",
       "86   2.0939    True  \n",
       "88  -0.1651    True  \n",
       "91  -0.2903    True  \n",
       "95  -0.5216    True  \n",
       "98  -0.6469    True  \n",
       "101 -0.8243    True  \n",
       "102 -0.2310    True  \n",
       "103 -0.2972    True  \n",
       "104 -0.9466    True  \n",
       "106 -1.1693    True  \n",
       "107 -0.5760    True  \n",
       "108 -0.6423    True  \n",
       "109 -1.2916    True  \n",
       "110 -0.8371    True  \n",
       "111 -0.2185    True  \n",
       "112 -0.2869    True  \n",
       "113 -0.9619    True  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSI1_groups = MSI1.Accession.astype(str) + ' ' + MSI1.Treatment.astype(str)\n",
    "\n",
    "MSI1_comp = mc.MultiComparison(MSI1['Cq'], MSI1_groups)\n",
    "MSI1_results = MSI1_comp.tukeyhsd()\n",
    "MSI1_table = pd.DataFrame(data=MSI1_results._results_table.data[1:], columns=MSI1_results._results_table.data[0])\n",
    "MSI1_sig = MSI1_table.loc[MSI1_table['p-adj'] < 0.05, :]\n",
    "MSI1_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No significant differences within accessions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">N2_1A</th>\n",
       "      <th>1</th>\n",
       "      <td>LHY</td>\n",
       "      <td>23.972973</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAPDH</td>\n",
       "      <td>21.430556</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRR9</td>\n",
       "      <td>32.956244</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRDX</td>\n",
       "      <td>24.378550</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSI1</td>\n",
       "      <td>28.762125</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOR2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">I4_2B</th>\n",
       "      <th>3</th>\n",
       "      <td>MSI1</td>\n",
       "      <td>28.408805</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TOC1</td>\n",
       "      <td>28.897120</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LUX</td>\n",
       "      <td>31.606425</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PRDX</td>\n",
       "      <td>23.439644</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAPDH</td>\n",
       "      <td>21.144145</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>IT4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4103 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Target         Cq     Treatment Zeitgeber ZT_24 Accession\n",
       "Sample Replicate                                                          \n",
       "N2_1A  1            LHY  23.972973        Arctic         0     0      NOR2\n",
       "       1          GAPDH  21.430556        Arctic         0     0      NOR2\n",
       "       1           PRR9  32.956244        Arctic         0     0      NOR2\n",
       "       1           PRDX  24.378550        Arctic         0     0      NOR2\n",
       "       1           MSI1  28.762125        Arctic         0     0      NOR2\n",
       "...                 ...        ...           ...       ...   ...       ...\n",
       "I4_2B  3           MSI1  28.408805  Mid-latitude         4     4       IT4\n",
       "       3           TOC1  28.897120  Mid-latitude         4     4       IT4\n",
       "       3            LUX  31.606425  Mid-latitude         4     4       IT4\n",
       "       3           PRDX  23.439644  Mid-latitude         4     4       IT4\n",
       "       3          GAPDH  21.144145  Mid-latitude         4     4       IT4\n",
       "\n",
       "[4103 rows x 6 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data_update.copy()\n",
    "data2.set_index([\"Sample\", 'Replicate'], drop=True, inplace = True)\n",
    "data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming final datafrmae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate ΔCq & 2 <sup>-ΔCq <a class=\"anchor\" id=\"delta_Cq\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate delta Cq from geometric mean of two reference genes: MSI1 and GAPDH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ΔCq "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target gene (Cq) - Reference gene (geometric mean of Cq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_delta_Cq_geom(df, ref1, ref2):\n",
    "    \n",
    "    '''Determines delta Cq of each sample based on reference gene geometric mean \n",
    "    \n",
    "    df: Dataframe with index: sample, columns: Target, Cq\n",
    "    \n",
    "    ref: reference gene to use for calculation. Must be a string\n",
    "    \n",
    "    '''\n",
    "    ref_gene = df[(df['Target']==ref1) | (df['Target']==ref2)]\n",
    "\n",
    "    ref_gene.reset_index(inplace=True)\n",
    "    ref_gene.set_index(['Sample', 'Replicate', 'Target'], inplace = True)\n",
    "    ref_gene = ref_gene.unstack(level=2)\n",
    "\n",
    "    # Get geometric mean of reference genes\n",
    "    ref_gene[(       'Cq_mean', 'gmean')] = gmean([ref_gene[(       'Cq', ref1)], \n",
    "                                                            ref_gene[(       'Cq',  ref2)]])\n",
    "    ref_gene = ref_gene.stack(level=1)\n",
    "    ref_gene.reset_index(inplace=True)\n",
    "    ref_gene.set_index(['Sample', 'Replicate'], inplace = True)\n",
    "    \n",
    "    # Subtract geometric mean from target Cq values\n",
    "    df.sort_index(inplace=True)\n",
    "    d2 = (df.Cq - ref_gene.loc[ref_gene['Target']=='gmean']['Cq_mean'])\n",
    "    df['delta_Cq'] = d2.values\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "      <th>delta_Cq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">I1_10A</th>\n",
       "      <th>1</th>\n",
       "      <td>LHY</td>\n",
       "      <td>24.282946</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT1</td>\n",
       "      <td>0.378490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAPDH</td>\n",
       "      <td>21.278777</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT1</td>\n",
       "      <td>-2.625678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRR9</td>\n",
       "      <td>24.430283</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT1</td>\n",
       "      <td>0.525827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRDX</td>\n",
       "      <td>22.831522</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT1</td>\n",
       "      <td>-1.072934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSI1</td>\n",
       "      <td>26.854128</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>IT1</td>\n",
       "      <td>2.949672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">N2_9D</th>\n",
       "      <th>3</th>\n",
       "      <td>MSI1</td>\n",
       "      <td>28.106467</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>3.788659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PRR7</td>\n",
       "      <td>27.157224</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>2.839416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PRR5</td>\n",
       "      <td>27.408761</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>3.090953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RVE8</td>\n",
       "      <td>24.546936</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>0.229128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TOC1</td>\n",
       "      <td>28.409097</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4.091289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4103 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Target         Cq       Treatment Zeitgeber ZT_24 Accession  \\\n",
       "Sample Replicate                                                               \n",
       "I1_10A 1            LHY  24.282946          Arctic        36    12       IT1   \n",
       "       1          GAPDH  21.278777          Arctic        36    12       IT1   \n",
       "       1           PRR9  24.430283          Arctic        36    12       IT1   \n",
       "       1           PRDX  22.831522          Arctic        36    12       IT1   \n",
       "       1           MSI1  26.854128          Arctic        36    12       IT1   \n",
       "...                 ...        ...             ...       ...   ...       ...   \n",
       "N2_9D  3           MSI1  28.106467  D-Mid-latitude        32     8      NOR2   \n",
       "       3           PRR7  27.157224  D-Mid-latitude        32     8      NOR2   \n",
       "       3           PRR5  27.408761  D-Mid-latitude        32     8      NOR2   \n",
       "       3           RVE8  24.546936  D-Mid-latitude        32     8      NOR2   \n",
       "       3           TOC1  28.409097  D-Mid-latitude        32     8      NOR2   \n",
       "\n",
       "                  delta_Cq  \n",
       "Sample Replicate            \n",
       "I1_10A 1          0.378490  \n",
       "       1         -2.625678  \n",
       "       1          0.525827  \n",
       "       1         -1.072934  \n",
       "       1          2.949672  \n",
       "...                    ...  \n",
       "N2_9D  3          3.788659  \n",
       "       3          2.839416  \n",
       "       3          3.090953  \n",
       "       3          0.229128  \n",
       "       3          4.091289  \n",
       "\n",
       "[4103 rows x 7 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_delta_Cq_geom(data2, 'GAPDH', 'MSI1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2<sup/>-ΔCq "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['2delta_Cq'] = data2['delta_Cq'].apply(lambda x: 2 ** -(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set zeitgeber and zt_24 as index, 0-48 and 0-24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.reset_index(inplace=True)\n",
    "data2.Zeitgeber = pd.to_numeric(data2.Zeitgeber)\n",
    "data2.ZT_24 = pd.to_numeric(data2.ZT_24)\n",
    "data2.sort_values([\"Zeitgeber\", 'ZT_24', 'Sample', 'Replicate'], inplace = True)\n",
    "data2.set_index('Zeitgeber', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "      <th>delta_Cq</th>\n",
       "      <th>2delta_Cq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeitgeber</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I1_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>LHY</td>\n",
       "      <td>21.915832</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "      <td>-2.688863</td>\n",
       "      <td>6.448049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I1_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>GAPDH</td>\n",
       "      <td>21.834811</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "      <td>-2.769884</td>\n",
       "      <td>6.820532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I1_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>PRR9</td>\n",
       "      <td>32.813400</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8.208705</td>\n",
       "      <td>0.003380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I1_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>PRDX</td>\n",
       "      <td>22.368548</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "      <td>-2.236147</td>\n",
       "      <td>4.711370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I1_1A</td>\n",
       "      <td>1</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>27.725956</td>\n",
       "      <td>Arctic</td>\n",
       "      <td>0</td>\n",
       "      <td>IT1</td>\n",
       "      <td>3.121262</td>\n",
       "      <td>0.114923</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>N2_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>MSI1</td>\n",
       "      <td>28.323940</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>3.854101</td>\n",
       "      <td>0.069151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>N2_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR7</td>\n",
       "      <td>25.474186</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>1.004346</td>\n",
       "      <td>0.498496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>N2_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR5</td>\n",
       "      <td>26.425477</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>1.955638</td>\n",
       "      <td>0.257807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>N2_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>RVE8</td>\n",
       "      <td>25.277144</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>0.807304</td>\n",
       "      <td>0.571449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>N2_13D</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>27.535871</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>3.066031</td>\n",
       "      <td>0.119408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4103 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sample Replicate Target         Cq       Treatment  ZT_24  \\\n",
       "Zeitgeber                                                              \n",
       "0           I1_1A         1    LHY  21.915832          Arctic      0   \n",
       "0           I1_1A         1  GAPDH  21.834811          Arctic      0   \n",
       "0           I1_1A         1   PRR9  32.813400          Arctic      0   \n",
       "0           I1_1A         1   PRDX  22.368548          Arctic      0   \n",
       "0           I1_1A         1   MSI1  27.725956          Arctic      0   \n",
       "...           ...       ...    ...        ...             ...    ...   \n",
       "48         N2_13D         3   MSI1  28.323940  D-Mid-latitude     24   \n",
       "48         N2_13D         3   PRR7  25.474186  D-Mid-latitude     24   \n",
       "48         N2_13D         3   PRR5  26.425477  D-Mid-latitude     24   \n",
       "48         N2_13D         3   RVE8  25.277144  D-Mid-latitude     24   \n",
       "48         N2_13D         3   TOC1  27.535871  D-Mid-latitude     24   \n",
       "\n",
       "          Accession  delta_Cq  2delta_Cq  \n",
       "Zeitgeber                                 \n",
       "0               IT1 -2.688863   6.448049  \n",
       "0               IT1 -2.769884   6.820532  \n",
       "0               IT1  8.208705   0.003380  \n",
       "0               IT1 -2.236147   4.711370  \n",
       "0               IT1  3.121262   0.114923  \n",
       "...             ...       ...        ...  \n",
       "48             NOR2  3.854101   0.069151  \n",
       "48             NOR2  1.004346   0.498496  \n",
       "48             NOR2  1.955638   0.257807  \n",
       "48             NOR2  0.807304   0.571449  \n",
       "48             NOR2  3.066031   0.119408  \n",
       "\n",
       "[4103 rows x 9 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save new dataframe to excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.to_excel('qPCR_data_final.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze target data <a class=\"anchor\" id=\"target_genes\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop Reference genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data2[data2.Target != 'GAPDH']\n",
    "data2 = data2[data2.Target != 'MSI1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data2.drop(data2[data2['Target'] == 'PRDX'].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that replicates have similar values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
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      "text/plain": [
       "              N       Mean        SD        SE  95% Conf.   Interval\n",
       "Replicate                                                           \n",
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       "3          1088  27.606644  3.096829  0.093886  27.422542  27.790746"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp.summary_cont(data2.groupby(\"Replicate\")['Cq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "              N      Mean        SD        SE  95% Conf.  Interval\n",
       "Replicate                                                         \n",
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       "2          1073  1.450699  4.006642  0.122315   1.210849  1.690549\n",
       "3          1088  0.895117  1.911788  0.057960   0.781464  1.008770"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp.summary_cont(data2.groupby(\"Replicate\")['2delta_Cq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/corinefaehn/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py:303: FutureWarning: Dropping invalid columns in SeriesGroupBy.agg is deprecated. In a future version, a TypeError will be raised. Before calling .agg, select only columns which should be valid for the aggregating function.\n",
      "  results[key] = self.aggregate(func)\n"
     ]
    },
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       "      <td>5.175607</td>\n",
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       "      <td>0.211009</td>\n",
       "      <td>28.177693</td>\n",
       "      <td>29.004847</td>\n",
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       "      <td>11.838384</td>\n",
       "      <td>7.535910</td>\n",
       "      <td>0.757387</td>\n",
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       "      <td>2.174088</td>\n",
       "      <td>0.218504</td>\n",
       "      <td>2.860089</td>\n",
       "      <td>3.716625</td>\n",
       "      <td>99</td>\n",
       "      <td>0.251244</td>\n",
       "      <td>0.291706</td>\n",
       "      <td>0.029318</td>\n",
       "      <td>0.193781</td>\n",
       "      <td>0.308706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>102</td>\n",
       "      <td>28.977947</td>\n",
       "      <td>2.659654</td>\n",
       "      <td>0.263345</td>\n",
       "      <td>28.461791</td>\n",
       "      <td>29.494103</td>\n",
       "      <td>102</td>\n",
       "      <td>12.352941</td>\n",
       "      <td>7.188615</td>\n",
       "      <td>0.711779</td>\n",
       "      <td>...</td>\n",
       "      <td>2.416728</td>\n",
       "      <td>0.239292</td>\n",
       "      <td>2.395980</td>\n",
       "      <td>3.334003</td>\n",
       "      <td>102</td>\n",
       "      <td>0.397494</td>\n",
       "      <td>0.535779</td>\n",
       "      <td>0.053050</td>\n",
       "      <td>0.293516</td>\n",
       "      <td>0.501472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>108</td>\n",
       "      <td>24.168503</td>\n",
       "      <td>2.370455</td>\n",
       "      <td>0.228097</td>\n",
       "      <td>23.721433</td>\n",
       "      <td>24.615573</td>\n",
       "      <td>108</td>\n",
       "      <td>12.037037</td>\n",
       "      <td>7.587405</td>\n",
       "      <td>0.730098</td>\n",
       "      <td>...</td>\n",
       "      <td>2.046604</td>\n",
       "      <td>0.196935</td>\n",
       "      <td>-0.583350</td>\n",
       "      <td>0.188633</td>\n",
       "      <td>108</td>\n",
       "      <td>2.708433</td>\n",
       "      <td>3.896690</td>\n",
       "      <td>0.374959</td>\n",
       "      <td>1.973513</td>\n",
       "      <td>3.443352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">LUX</th>\n",
       "      <th>IT1</th>\n",
       "      <td>102</td>\n",
       "      <td>30.329341</td>\n",
       "      <td>2.001780</td>\n",
       "      <td>0.198206</td>\n",
       "      <td>29.940858</td>\n",
       "      <td>30.717825</td>\n",
       "      <td>102</td>\n",
       "      <td>12.078431</td>\n",
       "      <td>7.229870</td>\n",
       "      <td>0.715864</td>\n",
       "      <td>...</td>\n",
       "      <td>1.859456</td>\n",
       "      <td>0.184114</td>\n",
       "      <td>4.958483</td>\n",
       "      <td>5.680208</td>\n",
       "      <td>102</td>\n",
       "      <td>0.047989</td>\n",
       "      <td>0.050984</td>\n",
       "      <td>0.005048</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.057883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>99</td>\n",
       "      <td>30.228211</td>\n",
       "      <td>1.901009</td>\n",
       "      <td>0.191059</td>\n",
       "      <td>29.853736</td>\n",
       "      <td>30.602686</td>\n",
       "      <td>99</td>\n",
       "      <td>11.878788</td>\n",
       "      <td>7.590640</td>\n",
       "      <td>0.762888</td>\n",
       "      <td>...</td>\n",
       "      <td>1.728853</td>\n",
       "      <td>0.173756</td>\n",
       "      <td>4.587523</td>\n",
       "      <td>5.268648</td>\n",
       "      <td>99</td>\n",
       "      <td>0.060872</td>\n",
       "      <td>0.065067</td>\n",
       "      <td>0.006540</td>\n",
       "      <td>0.048055</td>\n",
       "      <td>0.073690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>102</td>\n",
       "      <td>30.852069</td>\n",
       "      <td>1.955160</td>\n",
       "      <td>0.193590</td>\n",
       "      <td>30.472633</td>\n",
       "      <td>31.231505</td>\n",
       "      <td>102</td>\n",
       "      <td>12.352941</td>\n",
       "      <td>7.188615</td>\n",
       "      <td>0.711779</td>\n",
       "      <td>...</td>\n",
       "      <td>1.959406</td>\n",
       "      <td>0.194010</td>\n",
       "      <td>4.358853</td>\n",
       "      <td>5.119373</td>\n",
       "      <td>102</td>\n",
       "      <td>0.078105</td>\n",
       "      <td>0.089500</td>\n",
       "      <td>0.008862</td>\n",
       "      <td>0.060736</td>\n",
       "      <td>0.095474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>107</td>\n",
       "      <td>29.470304</td>\n",
       "      <td>1.700710</td>\n",
       "      <td>0.164414</td>\n",
       "      <td>29.148053</td>\n",
       "      <td>29.792555</td>\n",
       "      <td>107</td>\n",
       "      <td>12.112150</td>\n",
       "      <td>7.582660</td>\n",
       "      <td>0.733043</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863305</td>\n",
       "      <td>0.180133</td>\n",
       "      <td>4.742647</td>\n",
       "      <td>5.448767</td>\n",
       "      <td>107</td>\n",
       "      <td>0.056851</td>\n",
       "      <td>0.060691</td>\n",
       "      <td>0.005867</td>\n",
       "      <td>0.045351</td>\n",
       "      <td>0.068351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">PRDX</th>\n",
       "      <th>IT1</th>\n",
       "      <td>100</td>\n",
       "      <td>24.016242</td>\n",
       "      <td>1.432181</td>\n",
       "      <td>0.143218</td>\n",
       "      <td>23.735535</td>\n",
       "      <td>24.296950</td>\n",
       "      <td>100</td>\n",
       "      <td>11.680000</td>\n",
       "      <td>7.001991</td>\n",
       "      <td>0.700199</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939925</td>\n",
       "      <td>0.093992</td>\n",
       "      <td>-1.149804</td>\n",
       "      <td>-0.781353</td>\n",
       "      <td>100</td>\n",
       "      <td>2.323488</td>\n",
       "      <td>1.263334</td>\n",
       "      <td>0.126333</td>\n",
       "      <td>2.075875</td>\n",
       "      <td>2.571102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>98</td>\n",
       "      <td>22.855051</td>\n",
       "      <td>1.295014</td>\n",
       "      <td>0.130816</td>\n",
       "      <td>22.598651</td>\n",
       "      <td>23.111450</td>\n",
       "      <td>98</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>7.472286</td>\n",
       "      <td>0.754815</td>\n",
       "      <td>...</td>\n",
       "      <td>1.216879</td>\n",
       "      <td>0.122923</td>\n",
       "      <td>-2.706243</td>\n",
       "      <td>-2.224383</td>\n",
       "      <td>98</td>\n",
       "      <td>7.460695</td>\n",
       "      <td>5.492924</td>\n",
       "      <td>0.554869</td>\n",
       "      <td>6.373151</td>\n",
       "      <td>8.548238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>102</td>\n",
       "      <td>23.237817</td>\n",
       "      <td>1.616581</td>\n",
       "      <td>0.160065</td>\n",
       "      <td>22.924089</td>\n",
       "      <td>23.551546</td>\n",
       "      <td>102</td>\n",
       "      <td>12.352941</td>\n",
       "      <td>7.188615</td>\n",
       "      <td>0.711779</td>\n",
       "      <td>...</td>\n",
       "      <td>1.346343</td>\n",
       "      <td>0.133308</td>\n",
       "      <td>-3.136421</td>\n",
       "      <td>-2.613855</td>\n",
       "      <td>102</td>\n",
       "      <td>10.261060</td>\n",
       "      <td>7.254740</td>\n",
       "      <td>0.718326</td>\n",
       "      <td>8.853140</td>\n",
       "      <td>11.668979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>108</td>\n",
       "      <td>24.016023</td>\n",
       "      <td>1.598738</td>\n",
       "      <td>0.153839</td>\n",
       "      <td>23.714499</td>\n",
       "      <td>24.317547</td>\n",
       "      <td>108</td>\n",
       "      <td>12.037037</td>\n",
       "      <td>7.587405</td>\n",
       "      <td>0.730098</td>\n",
       "      <td>...</td>\n",
       "      <td>1.048920</td>\n",
       "      <td>0.100932</td>\n",
       "      <td>-0.547666</td>\n",
       "      <td>-0.152011</td>\n",
       "      <td>108</td>\n",
       "      <td>1.623529</td>\n",
       "      <td>1.169554</td>\n",
       "      <td>0.112540</td>\n",
       "      <td>1.402950</td>\n",
       "      <td>1.844108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">PRR5</th>\n",
       "      <th>IT1</th>\n",
       "      <td>102</td>\n",
       "      <td>27.853363</td>\n",
       "      <td>2.673265</td>\n",
       "      <td>0.264693</td>\n",
       "      <td>27.334566</td>\n",
       "      <td>28.372161</td>\n",
       "      <td>102</td>\n",
       "      <td>12.039216</td>\n",
       "      <td>7.241139</td>\n",
       "      <td>0.716980</td>\n",
       "      <td>...</td>\n",
       "      <td>2.254861</td>\n",
       "      <td>0.223265</td>\n",
       "      <td>2.435558</td>\n",
       "      <td>3.310755</td>\n",
       "      <td>102</td>\n",
       "      <td>0.315313</td>\n",
       "      <td>0.361082</td>\n",
       "      <td>0.035752</td>\n",
       "      <td>0.245238</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>96</td>\n",
       "      <td>26.294375</td>\n",
       "      <td>2.164963</td>\n",
       "      <td>0.220961</td>\n",
       "      <td>25.861292</td>\n",
       "      <td>26.727458</td>\n",
       "      <td>96</td>\n",
       "      <td>11.958333</td>\n",
       "      <td>7.622566</td>\n",
       "      <td>0.777975</td>\n",
       "      <td>...</td>\n",
       "      <td>2.014653</td>\n",
       "      <td>0.205620</td>\n",
       "      <td>0.618853</td>\n",
       "      <td>1.424882</td>\n",
       "      <td>96</td>\n",
       "      <td>1.036240</td>\n",
       "      <td>1.122856</td>\n",
       "      <td>0.114601</td>\n",
       "      <td>0.811622</td>\n",
       "      <td>1.260858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>102</td>\n",
       "      <td>26.974902</td>\n",
       "      <td>2.359507</td>\n",
       "      <td>0.233626</td>\n",
       "      <td>26.516995</td>\n",
       "      <td>27.432809</td>\n",
       "      <td>102</td>\n",
       "      <td>12.352941</td>\n",
       "      <td>7.188615</td>\n",
       "      <td>0.711779</td>\n",
       "      <td>...</td>\n",
       "      <td>2.305359</td>\n",
       "      <td>0.228265</td>\n",
       "      <td>0.414548</td>\n",
       "      <td>1.309345</td>\n",
       "      <td>102</td>\n",
       "      <td>1.264390</td>\n",
       "      <td>1.390444</td>\n",
       "      <td>0.137674</td>\n",
       "      <td>0.994548</td>\n",
       "      <td>1.534232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>105</td>\n",
       "      <td>27.865146</td>\n",
       "      <td>2.344641</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>27.416671</td>\n",
       "      <td>28.313620</td>\n",
       "      <td>105</td>\n",
       "      <td>12.076190</td>\n",
       "      <td>7.585025</td>\n",
       "      <td>0.740223</td>\n",
       "      <td>...</td>\n",
       "      <td>2.400827</td>\n",
       "      <td>0.234297</td>\n",
       "      <td>3.055431</td>\n",
       "      <td>3.973874</td>\n",
       "      <td>105</td>\n",
       "      <td>0.219953</td>\n",
       "      <td>0.266995</td>\n",
       "      <td>0.026056</td>\n",
       "      <td>0.168883</td>\n",
       "      <td>0.271023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">PRR7</th>\n",
       "      <th>IT1</th>\n",
       "      <td>103</td>\n",
       "      <td>26.903628</td>\n",
       "      <td>2.039366</td>\n",
       "      <td>0.200945</td>\n",
       "      <td>26.509777</td>\n",
       "      <td>27.297480</td>\n",
       "      <td>103</td>\n",
       "      <td>11.961165</td>\n",
       "      <td>7.292115</td>\n",
       "      <td>0.718513</td>\n",
       "      <td>...</td>\n",
       "      <td>1.624920</td>\n",
       "      <td>0.160108</td>\n",
       "      <td>1.583765</td>\n",
       "      <td>2.211389</td>\n",
       "      <td>103</td>\n",
       "      <td>0.437518</td>\n",
       "      <td>0.392444</td>\n",
       "      <td>0.038669</td>\n",
       "      <td>0.361727</td>\n",
       "      <td>0.513308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>100</td>\n",
       "      <td>27.439924</td>\n",
       "      <td>1.672807</td>\n",
       "      <td>0.167281</td>\n",
       "      <td>27.112054</td>\n",
       "      <td>27.767795</td>\n",
       "      <td>100</td>\n",
       "      <td>11.960000</td>\n",
       "      <td>7.595746</td>\n",
       "      <td>0.759575</td>\n",
       "      <td>...</td>\n",
       "      <td>1.496891</td>\n",
       "      <td>0.149689</td>\n",
       "      <td>1.836607</td>\n",
       "      <td>2.423389</td>\n",
       "      <td>100</td>\n",
       "      <td>0.347146</td>\n",
       "      <td>0.290136</td>\n",
       "      <td>0.029014</td>\n",
       "      <td>0.290279</td>\n",
       "      <td>0.404013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>102</td>\n",
       "      <td>27.989742</td>\n",
       "      <td>1.694600</td>\n",
       "      <td>0.167790</td>\n",
       "      <td>27.660872</td>\n",
       "      <td>28.318611</td>\n",
       "      <td>102</td>\n",
       "      <td>12.352941</td>\n",
       "      <td>7.188615</td>\n",
       "      <td>0.711779</td>\n",
       "      <td>...</td>\n",
       "      <td>1.669683</td>\n",
       "      <td>0.165323</td>\n",
       "      <td>1.552753</td>\n",
       "      <td>2.200820</td>\n",
       "      <td>102</td>\n",
       "      <td>0.451044</td>\n",
       "      <td>0.392481</td>\n",
       "      <td>0.038861</td>\n",
       "      <td>0.374876</td>\n",
       "      <td>0.527212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>105</td>\n",
       "      <td>26.760981</td>\n",
       "      <td>1.709189</td>\n",
       "      <td>0.166800</td>\n",
       "      <td>26.434054</td>\n",
       "      <td>27.087909</td>\n",
       "      <td>105</td>\n",
       "      <td>12.076190</td>\n",
       "      <td>7.585025</td>\n",
       "      <td>0.740223</td>\n",
       "      <td>...</td>\n",
       "      <td>1.832029</td>\n",
       "      <td>0.178788</td>\n",
       "      <td>2.060053</td>\n",
       "      <td>2.760900</td>\n",
       "      <td>105</td>\n",
       "      <td>0.349225</td>\n",
       "      <td>0.404793</td>\n",
       "      <td>0.039504</td>\n",
       "      <td>0.271798</td>\n",
       "      <td>0.426652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">PRR9</th>\n",
       "      <th>IT1</th>\n",
       "      <td>102</td>\n",
       "      <td>28.840286</td>\n",
       "      <td>3.622535</td>\n",
       "      <td>0.358684</td>\n",
       "      <td>28.137265</td>\n",
       "      <td>29.543307</td>\n",
       "      <td>102</td>\n",
       "      <td>11.960784</td>\n",
       "      <td>7.197251</td>\n",
       "      <td>0.712634</td>\n",
       "      <td>...</td>\n",
       "      <td>3.146517</td>\n",
       "      <td>0.311552</td>\n",
       "      <td>3.244922</td>\n",
       "      <td>4.466204</td>\n",
       "      <td>102</td>\n",
       "      <td>0.371172</td>\n",
       "      <td>0.632317</td>\n",
       "      <td>0.062609</td>\n",
       "      <td>0.248459</td>\n",
       "      <td>0.493885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>100</td>\n",
       "      <td>30.285367</td>\n",
       "      <td>3.190874</td>\n",
       "      <td>0.319087</td>\n",
       "      <td>29.659955</td>\n",
       "      <td>30.910778</td>\n",
       "      <td>100</td>\n",
       "      <td>11.960000</td>\n",
       "      <td>7.595746</td>\n",
       "      <td>0.759575</td>\n",
       "      <td>...</td>\n",
       "      <td>3.078206</td>\n",
       "      <td>0.307821</td>\n",
       "      <td>4.372112</td>\n",
       "      <td>5.578769</td>\n",
       "      <td>100</td>\n",
       "      <td>0.177439</td>\n",
       "      <td>0.311559</td>\n",
       "      <td>0.031156</td>\n",
       "      <td>0.116373</td>\n",
       "      <td>0.238504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>102</td>\n",
       "      <td>30.725234</td>\n",
       "      <td>3.476356</td>\n",
       "      <td>0.344211</td>\n",
       "      <td>30.050581</td>\n",
       "      <td>31.399886</td>\n",
       "      <td>102</td>\n",
       "      <td>12.352941</td>\n",
       "      <td>7.188615</td>\n",
       "      <td>0.711779</td>\n",
       "      <td>...</td>\n",
       "      <td>3.357975</td>\n",
       "      <td>0.332489</td>\n",
       "      <td>3.960600</td>\n",
       "      <td>5.263957</td>\n",
       "      <td>102</td>\n",
       "      <td>0.246508</td>\n",
       "      <td>0.422887</td>\n",
       "      <td>0.041872</td>\n",
       "      <td>0.164439</td>\n",
       "      <td>0.328578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>108</td>\n",
       "      <td>29.066346</td>\n",
       "      <td>3.472071</td>\n",
       "      <td>0.334100</td>\n",
       "      <td>28.411509</td>\n",
       "      <td>29.721182</td>\n",
       "      <td>108</td>\n",
       "      <td>12.037037</td>\n",
       "      <td>7.587405</td>\n",
       "      <td>0.730098</td>\n",
       "      <td>...</td>\n",
       "      <td>3.330381</td>\n",
       "      <td>0.320466</td>\n",
       "      <td>4.072371</td>\n",
       "      <td>5.328598</td>\n",
       "      <td>108</td>\n",
       "      <td>0.248082</td>\n",
       "      <td>0.425961</td>\n",
       "      <td>0.040988</td>\n",
       "      <td>0.167745</td>\n",
       "      <td>0.328419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">RVE8</th>\n",
       "      <th>IT1</th>\n",
       "      <td>103</td>\n",
       "      <td>27.441949</td>\n",
       "      <td>2.399291</td>\n",
       "      <td>0.236409</td>\n",
       "      <td>26.978587</td>\n",
       "      <td>27.905311</td>\n",
       "      <td>103</td>\n",
       "      <td>12.038835</td>\n",
       "      <td>7.205557</td>\n",
       "      <td>0.709985</td>\n",
       "      <td>...</td>\n",
       "      <td>2.291959</td>\n",
       "      <td>0.225833</td>\n",
       "      <td>2.001623</td>\n",
       "      <td>2.886890</td>\n",
       "      <td>103</td>\n",
       "      <td>0.450470</td>\n",
       "      <td>0.553848</td>\n",
       "      <td>0.054572</td>\n",
       "      <td>0.343508</td>\n",
       "      <td>0.557431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>100</td>\n",
       "      <td>27.860502</td>\n",
       "      <td>1.852829</td>\n",
       "      <td>0.185283</td>\n",
       "      <td>27.497348</td>\n",
       "      <td>28.223657</td>\n",
       "      <td>100</td>\n",
       "      <td>11.960000</td>\n",
       "      <td>7.595746</td>\n",
       "      <td>0.759575</td>\n",
       "      <td>...</td>\n",
       "      <td>2.014502</td>\n",
       "      <td>0.201450</td>\n",
       "      <td>2.155734</td>\n",
       "      <td>2.945418</td>\n",
       "      <td>100</td>\n",
       "      <td>0.358837</td>\n",
       "      <td>0.372364</td>\n",
       "      <td>0.037236</td>\n",
       "      <td>0.285854</td>\n",
       "      <td>0.431820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>101</td>\n",
       "      <td>28.422419</td>\n",
       "      <td>2.556616</td>\n",
       "      <td>0.254393</td>\n",
       "      <td>27.923809</td>\n",
       "      <td>28.921029</td>\n",
       "      <td>101</td>\n",
       "      <td>12.356436</td>\n",
       "      <td>7.224381</td>\n",
       "      <td>0.718853</td>\n",
       "      <td>...</td>\n",
       "      <td>2.382420</td>\n",
       "      <td>0.237060</td>\n",
       "      <td>1.856217</td>\n",
       "      <td>2.785490</td>\n",
       "      <td>101</td>\n",
       "      <td>0.511125</td>\n",
       "      <td>0.593726</td>\n",
       "      <td>0.059078</td>\n",
       "      <td>0.395332</td>\n",
       "      <td>0.626918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>107</td>\n",
       "      <td>27.336855</td>\n",
       "      <td>2.270137</td>\n",
       "      <td>0.219462</td>\n",
       "      <td>26.906708</td>\n",
       "      <td>27.767001</td>\n",
       "      <td>107</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.613295</td>\n",
       "      <td>0.736005</td>\n",
       "      <td>...</td>\n",
       "      <td>2.072418</td>\n",
       "      <td>0.200348</td>\n",
       "      <td>2.591977</td>\n",
       "      <td>3.377342</td>\n",
       "      <td>107</td>\n",
       "      <td>0.290243</td>\n",
       "      <td>0.341137</td>\n",
       "      <td>0.032979</td>\n",
       "      <td>0.225604</td>\n",
       "      <td>0.354881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">TOC1</th>\n",
       "      <th>IT1</th>\n",
       "      <td>102</td>\n",
       "      <td>28.503983</td>\n",
       "      <td>1.224559</td>\n",
       "      <td>0.121249</td>\n",
       "      <td>28.266334</td>\n",
       "      <td>28.741632</td>\n",
       "      <td>102</td>\n",
       "      <td>11.921569</td>\n",
       "      <td>7.141687</td>\n",
       "      <td>0.707132</td>\n",
       "      <td>...</td>\n",
       "      <td>1.093621</td>\n",
       "      <td>0.108285</td>\n",
       "      <td>3.310506</td>\n",
       "      <td>3.734982</td>\n",
       "      <td>102</td>\n",
       "      <td>0.121317</td>\n",
       "      <td>0.180900</td>\n",
       "      <td>0.017912</td>\n",
       "      <td>0.086210</td>\n",
       "      <td>0.156424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>99</td>\n",
       "      <td>28.519162</td>\n",
       "      <td>2.765632</td>\n",
       "      <td>0.277957</td>\n",
       "      <td>27.974368</td>\n",
       "      <td>29.063957</td>\n",
       "      <td>99</td>\n",
       "      <td>12.080808</td>\n",
       "      <td>7.537223</td>\n",
       "      <td>0.757519</td>\n",
       "      <td>...</td>\n",
       "      <td>2.767639</td>\n",
       "      <td>0.278158</td>\n",
       "      <td>2.664294</td>\n",
       "      <td>3.754674</td>\n",
       "      <td>99</td>\n",
       "      <td>0.190111</td>\n",
       "      <td>0.109931</td>\n",
       "      <td>0.011048</td>\n",
       "      <td>0.168456</td>\n",
       "      <td>0.211766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>102</td>\n",
       "      <td>28.257652</td>\n",
       "      <td>1.051363</td>\n",
       "      <td>0.104100</td>\n",
       "      <td>28.053615</td>\n",
       "      <td>28.461689</td>\n",
       "      <td>102</td>\n",
       "      <td>12.352941</td>\n",
       "      <td>7.188615</td>\n",
       "      <td>0.711779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983673</td>\n",
       "      <td>0.097398</td>\n",
       "      <td>1.953796</td>\n",
       "      <td>2.335597</td>\n",
       "      <td>102</td>\n",
       "      <td>0.281638</td>\n",
       "      <td>0.194694</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>0.243854</td>\n",
       "      <td>0.319422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>106</td>\n",
       "      <td>27.886986</td>\n",
       "      <td>1.115142</td>\n",
       "      <td>0.108312</td>\n",
       "      <td>27.674694</td>\n",
       "      <td>28.099278</td>\n",
       "      <td>106</td>\n",
       "      <td>11.886792</td>\n",
       "      <td>7.558434</td>\n",
       "      <td>0.734140</td>\n",
       "      <td>...</td>\n",
       "      <td>1.091182</td>\n",
       "      <td>0.105985</td>\n",
       "      <td>3.308611</td>\n",
       "      <td>3.724072</td>\n",
       "      <td>106</td>\n",
       "      <td>0.112637</td>\n",
       "      <td>0.081613</td>\n",
       "      <td>0.007927</td>\n",
       "      <td>0.097100</td>\n",
       "      <td>0.128174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Cq                                                       \\\n",
       "                    N       Mean        SD        SE  95% Conf.   Interval   \n",
       "Target Accession                                                             \n",
       "LHY    IT1        103  24.399956  2.581632  0.254376  23.901380  24.898533   \n",
       "       IT4         99  28.591270  2.099511  0.211009  28.177693  29.004847   \n",
       "       NOR13      102  28.977947  2.659654  0.263345  28.461791  29.494103   \n",
       "       NOR2       108  24.168503  2.370455  0.228097  23.721433  24.615573   \n",
       "LUX    IT1        102  30.329341  2.001780  0.198206  29.940858  30.717825   \n",
       "       IT4         99  30.228211  1.901009  0.191059  29.853736  30.602686   \n",
       "       NOR13      102  30.852069  1.955160  0.193590  30.472633  31.231505   \n",
       "       NOR2       107  29.470304  1.700710  0.164414  29.148053  29.792555   \n",
       "PRDX   IT1        100  24.016242  1.432181  0.143218  23.735535  24.296950   \n",
       "       IT4         98  22.855051  1.295014  0.130816  22.598651  23.111450   \n",
       "       NOR13      102  23.237817  1.616581  0.160065  22.924089  23.551546   \n",
       "       NOR2       108  24.016023  1.598738  0.153839  23.714499  24.317547   \n",
       "PRR5   IT1        102  27.853363  2.673265  0.264693  27.334566  28.372161   \n",
       "       IT4         96  26.294375  2.164963  0.220961  25.861292  26.727458   \n",
       "       NOR13      102  26.974902  2.359507  0.233626  26.516995  27.432809   \n",
       "       NOR2       105  27.865146  2.344641  0.228814  27.416671  28.313620   \n",
       "PRR7   IT1        103  26.903628  2.039366  0.200945  26.509777  27.297480   \n",
       "       IT4        100  27.439924  1.672807  0.167281  27.112054  27.767795   \n",
       "       NOR13      102  27.989742  1.694600  0.167790  27.660872  28.318611   \n",
       "       NOR2       105  26.760981  1.709189  0.166800  26.434054  27.087909   \n",
       "PRR9   IT1        102  28.840286  3.622535  0.358684  28.137265  29.543307   \n",
       "       IT4        100  30.285367  3.190874  0.319087  29.659955  30.910778   \n",
       "       NOR13      102  30.725234  3.476356  0.344211  30.050581  31.399886   \n",
       "       NOR2       108  29.066346  3.472071  0.334100  28.411509  29.721182   \n",
       "RVE8   IT1        103  27.441949  2.399291  0.236409  26.978587  27.905311   \n",
       "       IT4        100  27.860502  1.852829  0.185283  27.497348  28.223657   \n",
       "       NOR13      101  28.422419  2.556616  0.254393  27.923809  28.921029   \n",
       "       NOR2       107  27.336855  2.270137  0.219462  26.906708  27.767001   \n",
       "TOC1   IT1        102  28.503983  1.224559  0.121249  28.266334  28.741632   \n",
       "       IT4         99  28.519162  2.765632  0.277957  27.974368  29.063957   \n",
       "       NOR13      102  28.257652  1.051363  0.104100  28.053615  28.461689   \n",
       "       NOR2       106  27.886986  1.115142  0.108312  27.674694  28.099278   \n",
       "\n",
       "                 ZT_24                                 ...  delta_Cq  \\\n",
       "                     N       Mean        SD        SE  ...        SD   \n",
       "Target Accession                                       ...             \n",
       "LHY    IT1         103  12.038835  7.205557  0.709985  ...  2.326325   \n",
       "       IT4          99  11.838384  7.535910  0.757387  ...  2.174088   \n",
       "       NOR13       102  12.352941  7.188615  0.711779  ...  2.416728   \n",
       "       NOR2        108  12.037037  7.587405  0.730098  ...  2.046604   \n",
       "LUX    IT1         102  12.078431  7.229870  0.715864  ...  1.859456   \n",
       "       IT4          99  11.878788  7.590640  0.762888  ...  1.728853   \n",
       "       NOR13       102  12.352941  7.188615  0.711779  ...  1.959406   \n",
       "       NOR2        107  12.112150  7.582660  0.733043  ...  1.863305   \n",
       "PRDX   IT1         100  11.680000  7.001991  0.700199  ...  0.939925   \n",
       "       IT4          98  11.714286  7.472286  0.754815  ...  1.216879   \n",
       "       NOR13       102  12.352941  7.188615  0.711779  ...  1.346343   \n",
       "       NOR2        108  12.037037  7.587405  0.730098  ...  1.048920   \n",
       "PRR5   IT1         102  12.039216  7.241139  0.716980  ...  2.254861   \n",
       "       IT4          96  11.958333  7.622566  0.777975  ...  2.014653   \n",
       "       NOR13       102  12.352941  7.188615  0.711779  ...  2.305359   \n",
       "       NOR2        105  12.076190  7.585025  0.740223  ...  2.400827   \n",
       "PRR7   IT1         103  11.961165  7.292115  0.718513  ...  1.624920   \n",
       "       IT4         100  11.960000  7.595746  0.759575  ...  1.496891   \n",
       "       NOR13       102  12.352941  7.188615  0.711779  ...  1.669683   \n",
       "       NOR2        105  12.076190  7.585025  0.740223  ...  1.832029   \n",
       "PRR9   IT1         102  11.960784  7.197251  0.712634  ...  3.146517   \n",
       "       IT4         100  11.960000  7.595746  0.759575  ...  3.078206   \n",
       "       NOR13       102  12.352941  7.188615  0.711779  ...  3.357975   \n",
       "       NOR2        108  12.037037  7.587405  0.730098  ...  3.330381   \n",
       "RVE8   IT1         103  12.038835  7.205557  0.709985  ...  2.291959   \n",
       "       IT4         100  11.960000  7.595746  0.759575  ...  2.014502   \n",
       "       NOR13       101  12.356436  7.224381  0.718853  ...  2.382420   \n",
       "       NOR2        107  12.000000  7.613295  0.736005  ...  2.072418   \n",
       "TOC1   IT1         102  11.921569  7.141687  0.707132  ...  1.093621   \n",
       "       IT4          99  12.080808  7.537223  0.757519  ...  2.767639   \n",
       "       NOR13       102  12.352941  7.188615  0.711779  ...  0.983673   \n",
       "       NOR2        106  11.886792  7.558434  0.734140  ...  1.091182   \n",
       "\n",
       "                                               2delta_Cq                       \\\n",
       "                        SE 95% Conf.  Interval         N       Mean        SD   \n",
       "Target Accession                                                                \n",
       "LHY    IT1        0.229220 -1.047007 -0.148466       103   4.154126  5.289237   \n",
       "       IT4        0.218504  2.860089  3.716625        99   0.251244  0.291706   \n",
       "       NOR13      0.239292  2.395980  3.334003       102   0.397494  0.535779   \n",
       "       NOR2       0.196935 -0.583350  0.188633       108   2.708433  3.896690   \n",
       "LUX    IT1        0.184114  4.958483  5.680208       102   0.047989  0.050984   \n",
       "       IT4        0.173756  4.587523  5.268648        99   0.060872  0.065067   \n",
       "       NOR13      0.194010  4.358853  5.119373       102   0.078105  0.089500   \n",
       "       NOR2       0.180133  4.742647  5.448767       107   0.056851  0.060691   \n",
       "PRDX   IT1        0.093992 -1.149804 -0.781353       100   2.323488  1.263334   \n",
       "       IT4        0.122923 -2.706243 -2.224383        98   7.460695  5.492924   \n",
       "       NOR13      0.133308 -3.136421 -2.613855       102  10.261060  7.254740   \n",
       "       NOR2       0.100932 -0.547666 -0.152011       108   1.623529  1.169554   \n",
       "PRR5   IT1        0.223265  2.435558  3.310755       102   0.315313  0.361082   \n",
       "       IT4        0.205620  0.618853  1.424882        96   1.036240  1.122856   \n",
       "       NOR13      0.228265  0.414548  1.309345       102   1.264390  1.390444   \n",
       "       NOR2       0.234297  3.055431  3.973874       105   0.219953  0.266995   \n",
       "PRR7   IT1        0.160108  1.583765  2.211389       103   0.437518  0.392444   \n",
       "       IT4        0.149689  1.836607  2.423389       100   0.347146  0.290136   \n",
       "       NOR13      0.165323  1.552753  2.200820       102   0.451044  0.392481   \n",
       "       NOR2       0.178788  2.060053  2.760900       105   0.349225  0.404793   \n",
       "PRR9   IT1        0.311552  3.244922  4.466204       102   0.371172  0.632317   \n",
       "       IT4        0.307821  4.372112  5.578769       100   0.177439  0.311559   \n",
       "       NOR13      0.332489  3.960600  5.263957       102   0.246508  0.422887   \n",
       "       NOR2       0.320466  4.072371  5.328598       108   0.248082  0.425961   \n",
       "RVE8   IT1        0.225833  2.001623  2.886890       103   0.450470  0.553848   \n",
       "       IT4        0.201450  2.155734  2.945418       100   0.358837  0.372364   \n",
       "       NOR13      0.237060  1.856217  2.785490       101   0.511125  0.593726   \n",
       "       NOR2       0.200348  2.591977  3.377342       107   0.290243  0.341137   \n",
       "TOC1   IT1        0.108285  3.310506  3.734982       102   0.121317  0.180900   \n",
       "       IT4        0.278158  2.664294  3.754674        99   0.190111  0.109931   \n",
       "       NOR13      0.097398  1.953796  2.335597       102   0.281638  0.194694   \n",
       "       NOR2       0.105985  3.308611  3.724072       106   0.112637  0.081613   \n",
       "\n",
       "                                                 \n",
       "                        SE 95% Conf.   Interval  \n",
       "Target Accession                                 \n",
       "LHY    IT1        0.521164  3.132644   5.175607  \n",
       "       IT4        0.029318  0.193781   0.308706  \n",
       "       NOR13      0.053050  0.293516   0.501472  \n",
       "       NOR2       0.374959  1.973513   3.443352  \n",
       "LUX    IT1        0.005048  0.038094   0.057883  \n",
       "       IT4        0.006540  0.048055   0.073690  \n",
       "       NOR13      0.008862  0.060736   0.095474  \n",
       "       NOR2       0.005867  0.045351   0.068351  \n",
       "PRDX   IT1        0.126333  2.075875   2.571102  \n",
       "       IT4        0.554869  6.373151   8.548238  \n",
       "       NOR13      0.718326  8.853140  11.668979  \n",
       "       NOR2       0.112540  1.402950   1.844108  \n",
       "PRR5   IT1        0.035752  0.245238   0.385388  \n",
       "       IT4        0.114601  0.811622   1.260858  \n",
       "       NOR13      0.137674  0.994548   1.534232  \n",
       "       NOR2       0.026056  0.168883   0.271023  \n",
       "PRR7   IT1        0.038669  0.361727   0.513308  \n",
       "       IT4        0.029014  0.290279   0.404013  \n",
       "       NOR13      0.038861  0.374876   0.527212  \n",
       "       NOR2       0.039504  0.271798   0.426652  \n",
       "PRR9   IT1        0.062609  0.248459   0.493885  \n",
       "       IT4        0.031156  0.116373   0.238504  \n",
       "       NOR13      0.041872  0.164439   0.328578  \n",
       "       NOR2       0.040988  0.167745   0.328419  \n",
       "RVE8   IT1        0.054572  0.343508   0.557431  \n",
       "       IT4        0.037236  0.285854   0.431820  \n",
       "       NOR13      0.059078  0.395332   0.626918  \n",
       "       NOR2       0.032979  0.225604   0.354881  \n",
       "TOC1   IT1        0.017912  0.086210   0.156424  \n",
       "       IT4        0.011048  0.168456   0.211766  \n",
       "       NOR13      0.019278  0.243854   0.319422  \n",
       "       NOR2       0.007927  0.097100   0.128174  \n",
       "\n",
       "[32 rows x 24 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Target_groups = rp.summary_cont(data2.groupby(['Target', 'Accession']))\n",
    "Target_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5273d9a10>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data=data2, x='Accession', y='Cq', hue ='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc52767eb50>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data=data2, x='Target', y='Cq', hue ='Accession')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5264162d0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data=data2, x='Target', y='2delta_Cq', hue ='Accession')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>SE</th>\n",
       "      <th>95% Conf.</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LHY</th>\n",
       "      <td>412</td>\n",
       "      <td>26.479806</td>\n",
       "      <td>3.319157</td>\n",
       "      <td>0.163523</td>\n",
       "      <td>26.158911</td>\n",
       "      <td>26.800701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUX</th>\n",
       "      <td>410</td>\n",
       "      <td>30.210779</td>\n",
       "      <td>1.949007</td>\n",
       "      <td>0.096255</td>\n",
       "      <td>30.021889</td>\n",
       "      <td>30.399668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRDX</th>\n",
       "      <td>408</td>\n",
       "      <td>23.542664</td>\n",
       "      <td>1.571877</td>\n",
       "      <td>0.077820</td>\n",
       "      <td>23.389951</td>\n",
       "      <td>23.695378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR5</th>\n",
       "      <td>405</td>\n",
       "      <td>27.265638</td>\n",
       "      <td>2.474236</td>\n",
       "      <td>0.122946</td>\n",
       "      <td>27.024366</td>\n",
       "      <td>27.506910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR7</th>\n",
       "      <td>410</td>\n",
       "      <td>27.268105</td>\n",
       "      <td>1.844745</td>\n",
       "      <td>0.091105</td>\n",
       "      <td>27.089320</td>\n",
       "      <td>27.446889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR9</th>\n",
       "      <td>412</td>\n",
       "      <td>29.716954</td>\n",
       "      <td>3.523989</td>\n",
       "      <td>0.173614</td>\n",
       "      <td>29.376256</td>\n",
       "      <td>30.057652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RVE8</th>\n",
       "      <td>411</td>\n",
       "      <td>27.757369</td>\n",
       "      <td>2.317401</td>\n",
       "      <td>0.114309</td>\n",
       "      <td>27.533051</td>\n",
       "      <td>27.981688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC1</th>\n",
       "      <td>409</td>\n",
       "      <td>28.286319</td>\n",
       "      <td>1.693724</td>\n",
       "      <td>0.083749</td>\n",
       "      <td>28.121969</td>\n",
       "      <td>28.450668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          N       Mean        SD        SE  95% Conf.   Interval\n",
       "Target                                                          \n",
       "LHY     412  26.479806  3.319157  0.163523  26.158911  26.800701\n",
       "LUX     410  30.210779  1.949007  0.096255  30.021889  30.399668\n",
       "PRDX    408  23.542664  1.571877  0.077820  23.389951  23.695378\n",
       "PRR5    405  27.265638  2.474236  0.122946  27.024366  27.506910\n",
       "PRR7    410  27.268105  1.844745  0.091105  27.089320  27.446889\n",
       "PRR9    412  29.716954  3.523989  0.173614  29.376256  30.057652\n",
       "RVE8    411  27.757369  2.317401  0.114309  27.533051  27.981688\n",
       "TOC1    409  28.286319  1.693724  0.083749  28.121969  28.450668"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp.summary_cont(data2.groupby(\"Target\")['Cq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>SE</th>\n",
       "      <th>95% Conf.</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LHY</th>\n",
       "      <td>412</td>\n",
       "      <td>1.907289</td>\n",
       "      <td>3.694179</td>\n",
       "      <td>0.181999</td>\n",
       "      <td>1.550137</td>\n",
       "      <td>2.264441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUX</th>\n",
       "      <td>410</td>\n",
       "      <td>0.060905</td>\n",
       "      <td>0.068620</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.054254</td>\n",
       "      <td>0.067555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRDX</th>\n",
       "      <td>408</td>\n",
       "      <td>5.356535</td>\n",
       "      <td>5.835421</td>\n",
       "      <td>0.288896</td>\n",
       "      <td>4.789603</td>\n",
       "      <td>5.923467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR5</th>\n",
       "      <td>405</td>\n",
       "      <td>0.700503</td>\n",
       "      <td>1.017496</td>\n",
       "      <td>0.050560</td>\n",
       "      <td>0.601283</td>\n",
       "      <td>0.799723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR7</th>\n",
       "      <td>410</td>\n",
       "      <td>0.396229</td>\n",
       "      <td>0.375266</td>\n",
       "      <td>0.018533</td>\n",
       "      <td>0.359860</td>\n",
       "      <td>0.432599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRR9</th>\n",
       "      <td>412</td>\n",
       "      <td>0.261020</td>\n",
       "      <td>0.466504</td>\n",
       "      <td>0.022983</td>\n",
       "      <td>0.215918</td>\n",
       "      <td>0.306121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RVE8</th>\n",
       "      <td>411</td>\n",
       "      <td>0.401367</td>\n",
       "      <td>0.482800</td>\n",
       "      <td>0.023815</td>\n",
       "      <td>0.354633</td>\n",
       "      <td>0.448100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC1</th>\n",
       "      <td>409</td>\n",
       "      <td>0.175701</td>\n",
       "      <td>0.163493</td>\n",
       "      <td>0.008084</td>\n",
       "      <td>0.159837</td>\n",
       "      <td>0.191566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          N      Mean        SD        SE  95% Conf.  Interval\n",
       "Target                                                        \n",
       "LHY     412  1.907289  3.694179  0.181999   1.550137  2.264441\n",
       "LUX     410  0.060905  0.068620  0.003389   0.054254  0.067555\n",
       "PRDX    408  5.356535  5.835421  0.288896   4.789603  5.923467\n",
       "PRR5    405  0.700503  1.017496  0.050560   0.601283  0.799723\n",
       "PRR7    410  0.396229  0.375266  0.018533   0.359860  0.432599\n",
       "PRR9    412  0.261020  0.466504  0.022983   0.215918  0.306121\n",
       "RVE8    411  0.401367  0.482800  0.023815   0.354633  0.448100\n",
       "TOC1    409  0.175701  0.163493  0.008084   0.159837  0.191566"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp.summary_cont(data2.groupby(\"Target\")['2delta_Cq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>SE</th>\n",
       "      <th>95% Conf.</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accession</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT1</th>\n",
       "      <td>817</td>\n",
       "      <td>27.290288</td>\n",
       "      <td>3.095199</td>\n",
       "      <td>0.108287</td>\n",
       "      <td>27.077915</td>\n",
       "      <td>27.502662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT4</th>\n",
       "      <td>791</td>\n",
       "      <td>27.773906</td>\n",
       "      <td>3.124002</td>\n",
       "      <td>0.111077</td>\n",
       "      <td>27.556058</td>\n",
       "      <td>27.991755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR13</th>\n",
       "      <td>815</td>\n",
       "      <td>28.179425</td>\n",
       "      <td>3.194439</td>\n",
       "      <td>0.111896</td>\n",
       "      <td>27.959973</td>\n",
       "      <td>28.398876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOR2</th>\n",
       "      <td>854</td>\n",
       "      <td>27.064665</td>\n",
       "      <td>2.893537</td>\n",
       "      <td>0.099015</td>\n",
       "      <td>26.870482</td>\n",
       "      <td>27.258848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             N       Mean        SD        SE  95% Conf.   Interval\n",
       "Accession                                                          \n",
       "IT1        817  27.290288  3.095199  0.108287  27.077915  27.502662\n",
       "IT4        791  27.773906  3.124002  0.111077  27.556058  27.991755\n",
       "NOR13      815  28.179425  3.194439  0.111896  27.959973  28.398876\n",
       "NOR2       854  27.064665  2.893537  0.099015  26.870482  27.258848"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(rp.summary_cont(data2.groupby(\"Accession\")['Cq']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Zeitgeber</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cq</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>ZT_24</th>\n",
       "      <th>Accession</th>\n",
       "      <th>delta_Cq</th>\n",
       "      <th>2delta_Cq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3224</th>\n",
       "      <td>48</td>\n",
       "      <td>N2_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRDX</td>\n",
       "      <td>23.085818</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>-0.650046</td>\n",
       "      <td>1.569218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3225</th>\n",
       "      <td>48</td>\n",
       "      <td>N2_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR7</td>\n",
       "      <td>30.241095</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>6.505232</td>\n",
       "      <td>0.011009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3226</th>\n",
       "      <td>48</td>\n",
       "      <td>N2_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>PRR5</td>\n",
       "      <td>32.032950</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8.297087</td>\n",
       "      <td>0.003179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3227</th>\n",
       "      <td>48</td>\n",
       "      <td>N2_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>RVE8</td>\n",
       "      <td>24.831301</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>1.095437</td>\n",
       "      <td>0.467994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3228</th>\n",
       "      <td>48</td>\n",
       "      <td>N2_13B</td>\n",
       "      <td>3</td>\n",
       "      <td>TOC1</td>\n",
       "      <td>28.286258</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>24</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4.550394</td>\n",
       "      <td>0.042677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Zeitgeber  Sample Replicate Target         Cq     Treatment  ZT_24  \\\n",
       "3224         48  N2_13B         3   PRDX  23.085818  Mid-latitude     24   \n",
       "3225         48  N2_13B         3   PRR7  30.241095  Mid-latitude     24   \n",
       "3226         48  N2_13B         3   PRR5  32.032950  Mid-latitude     24   \n",
       "3227         48  N2_13B         3   RVE8  24.831301  Mid-latitude     24   \n",
       "3228         48  N2_13B         3   TOC1  28.286258  Mid-latitude     24   \n",
       "\n",
       "     Accession  delta_Cq  2delta_Cq  \n",
       "3224      NOR2 -0.650046   1.569218  \n",
       "3225      NOR2  6.505232   0.011009  \n",
       "3226      NOR2  8.297087   0.003179  \n",
       "3227      NOR2  1.095437   0.467994  \n",
       "3228      NOR2  4.550394   0.042677  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data2.sort_values(by = ['Treatment', 'ZT_24'], ascending = (True, True)) #, inplace = True\n",
    "data2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_gene_24h(df, target_gene): \n",
    "    \n",
    "    fig, ax = pp.subplots(2, 2, figsize = (18, 14), sharex = True, sharey = True) \n",
    "    gene_data = df.loc[df['Target']==target_gene] \n",
    "    \n",
    "    ## MID-LATITUDE PHOTOPERIOD \n",
    "    ml_dark = gene_data[gene_data['Treatment'] == 'D-Mid-latitude'] \n",
    "    ml_dark_groups = ml_dark.groupby('Treatment')\n",
    "    \n",
    "    for name, group in ml_dark_groups:\n",
    "        ml_dark_result = group.groupby(['ZT_24', 'Accession']).agg({'2delta_Cq' : ['mean', 'std']})\n",
    "        ml_dark_x = ml_dark_result.index.levels[0] # Zeitgebers for x-axis\n",
    "        ml_dark_accessions = ml_dark_result.index.levels[1] # Accessions for separate y-values\n",
    "    \n",
    "        for accession in ml_dark_accessions:\n",
    "            ml_dark_avg_data = ml_dark_result.loc[[(zeitgeber, accession) for zeitgeber in ml_dark_x], ('2delta_Cq', 'mean')] \n",
    "            ml_dark_err_data = ml_dark_result.loc[[(zeitgeber, accession) for zeitgeber in ml_dark_x], ('2delta_Cq',  'std')] \n",
    "            \n",
    "            ax[0,1].errorbar(ml_dark_x, ml_dark_avg_data, ml_dark_err_data, fmt = '-o',\n",
    "                                  lw = 3, label = str(accession))\n",
    "\n",
    "            ax[0,1].set_ylim(0, gene_data['2delta_Cq'].max())\n",
    " \n",
    "        \n",
    "    ml = gene_data[gene_data['Treatment'] == 'Mid-latitude'] \n",
    "    ml_groups = ml.groupby('Treatment')\n",
    "    \n",
    "    for name, group in ml_groups:\n",
    "        ml_result = group.groupby(['ZT_24', 'Accession']).agg({'2delta_Cq' : ['mean', 'std']})\n",
    "        ml_x = ml_result.index.levels[0] # Zeitgebers for x-axis\n",
    "        ml_accessions = ml_result.index.levels[1] # Accessions for separate y-values\n",
    "        \n",
    "        for accession in ml_accessions:\n",
    "            ml_avg_data = ml_result.loc[[(zeitgeber, accession) for zeitgeber in ml_x], ('2delta_Cq', 'mean')] \n",
    "            ml_err_data = ml_result.loc[[(zeitgeber, accession) for zeitgeber in ml_x], ('2delta_Cq',  'std')]\n",
    "           \n",
    "            ax[0,0].errorbar(ml_x, ml_avg_data, ml_err_data, fmt = '-o',\n",
    "                                  lw = 3, label = str(accession))\n",
    "\n",
    "            ax[0,0].set_ylim(0, gene_data['2delta_Cq'].max())\n",
    "            ax[0,0].set_ylabel(\"Relative Expression\", fontsize = '20') \n",
    "\n",
    "    ## ARCTIC PHOTOPERIOD \n",
    "    ar_dark = gene_data[gene_data['Treatment'] == 'D-Arctic'] \n",
    "    ar_dark_groups = ar_dark.groupby('Treatment')\n",
    "    \n",
    "    for name, group in ar_dark_groups:\n",
    "        ar_dark_result = group.groupby(['ZT_24', 'Accession']).agg({'2delta_Cq' : ['mean', 'std']})\n",
    "        ar_dark_x = ar_dark_result.index.levels[0] # Zeitgebers for x-axis\n",
    "        ar_dark_accessions = ar_dark_result.index.levels[1] # Accessions for separate y-values        \n",
    "        \n",
    "        for accession in ar_dark_accessions:\n",
    "            ar_dark_avg_data = ar_dark_result.loc[[(zeitgeber, accession) for zeitgeber in ar_dark_x], ('2delta_Cq', 'mean')] \n",
    "            ar_dark_err_data = ar_dark_result.loc[[(zeitgeber, accession) for zeitgeber in ar_dark_x], ('2delta_Cq',  'std')]\n",
    "\n",
    "            ax[1,1].errorbar(ar_dark_x, ar_dark_avg_data, ar_dark_err_data, fmt = '-o',\n",
    "                                  lw = 3, label = str(accession)) \n",
    "            ax[1,1].set_xticks(ar_dark_x)\n",
    "            ax[1,1].set_xlabel(\"Zeitgeber\", fontsize = '20')\n",
    "            ax[1,1].set_ylim(0, gene_data['2delta_Cq'].max())\n",
    "    \n",
    "    ar = gene_data[gene_data['Treatment'] == 'Arctic'] \n",
    "    ar_groups = ar.groupby('Treatment')\n",
    "    \n",
    "    for name, group in ar_groups:\n",
    "        ar_result = group.groupby(['ZT_24', 'Accession']).agg({'2delta_Cq' : ['mean', 'std']})\n",
    "        ar_x = ar_result.index.levels[0] # Zeitgebers for x-axis\n",
    "        ar_accessions = ar_result.index.levels[1] # Accessions for separate y-values        \n",
    "        \n",
    "        for accession in ar_accessions:\n",
    "            ar_avg_data = ar_result.loc[[(zeitgeber, accession) for zeitgeber in ar_x], ('2delta_Cq', 'mean')] \n",
    "            ar_err_data = ar_result.loc[[(zeitgeber, accession) for zeitgeber in ar_x], ('2delta_Cq',  'std')]\n",
    "    \n",
    "            ax[1,0].errorbar(ar_x, ar_avg_data, ar_err_data, fmt = '-o',\n",
    "                                  lw = 3, label = str(accession))   \n",
    "            ax[1,0].set_xticks(ar_x)\n",
    "            ax[1,0].set_xlabel(\"Zeitgeber\", fontsize = '20')\n",
    "            ax[1,0].set_ylim(0, gene_data['2delta_Cq'].max())\n",
    "            ax[1,0].set_ylabel(\"Relative Expression\", fontsize = '20') \n",
    "     \n",
    "    \n",
    "    handles, labels = ax[1,1].get_legend_handles_labels()\n",
    "    ax[1,1].legend(handles[:], labels[:], \n",
    "               borderaxespad=0., bbox_to_anchor=(1.05, 1.25), \n",
    "               title='Accession', loc = 2, fontsize = '18', title_fontsize = '20')    \n",
    "    fig.suptitle(t = target_gene, fontsize = '36')\n",
    "    pp.subplots_adjust(hspace=.0, wspace = .05)\n",
    "    pp.savefig(target_gene + \"24h_expression.png\", dpi=300, bbox_inches='tight')\n",
    "#     return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Significant values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tukey_btw_treatment_24h(df, target_gene):\n",
    "    \n",
    "    '''At which ZT (24h period) do the indivual accessions differ between the treatments'''\n",
    "    \n",
    "    gene_data = df[df['Target']==target_gene]\n",
    "    \n",
    "    # IT1\n",
    "    IT1_data = gene_data[gene_data['Accession'] == 'IT1']\n",
    "    IT1_groups = IT1_data.ZT_24.astype(str) + '_' + IT1_data.Treatment.astype(str)\n",
    "\n",
    "    IT1_comp = mc.MultiComparison(IT1_data['2delta_Cq'], IT1_groups)\n",
    "    IT1_results = IT1_comp.tukeyhsd()\n",
    "    IT1_table = pd.DataFrame(data = IT1_results._results_table.data[1:], columns = IT1_results._results_table.data[0])\n",
    "    IT1_sig = IT1_table.loc[IT1_table['p-adj'] < 0.05, :]\n",
    "    \n",
    "    IT1_sig['ZT_24'] = IT1_sig['group1'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    IT1_sig['ZT_24_2'] = IT1_sig['group2'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    IT1_sig['Treatment_1'] = IT1_sig['group1'].str.extract(r'([^_]*$)', expand = False)\n",
    "    IT1_sig['Treatment_2'] = IT1_sig['group2'].str.extract(r'([^_]*$)', expand = False)\n",
    "    \n",
    "    IT1_sig.drop(['group1', 'group2'], axis = 1, inplace =True)\n",
    "    \n",
    "    IT1_Final = IT1_sig.loc[(IT1_sig['ZT_24'] == IT1_sig['ZT_24_2'])]\n",
    "    \n",
    "    IT1_Final.drop(['ZT_24_2', 'reject'], axis = 1, inplace =True)\n",
    "    IT1_Final['Accession'] = 'IT1'\n",
    "    IT1_Final = IT1_Final.drop(IT1_Final[(IT1_Final['Treatment_1'] == 'D-Arctic') & (IT1_Final['Treatment_2'] == 'Mid-latitude')].index)\n",
    "    IT1_Final = IT1_Final.drop(IT1_Final[(IT1_Final['Treatment_1'] == 'Mid-latitude') & (IT1_Final['Treatment_2'] == 'D-Arctic')].index)\n",
    "    IT1_Final = IT1_Final.drop(IT1_Final[(IT1_Final['Treatment_1'] == 'D-Mid-latitude') & (IT1_Final['Treatment_2'] == 'Arctic')].index)\n",
    "    IT1_Final = IT1_Final.drop(IT1_Final[(IT1_Final['Treatment_1'] == 'Arctic') & (IT1_Final['Treatment_2'] == 'D-Mid-latitude')].index)\n",
    "\n",
    "    IT1_Final.rename(columns = {'ZT_24' : 'ZT'}, inplace = True)\n",
    "    IT1_Final = IT1_Final[['Treatment_1', 'Treatment_2', 'Accession', 'ZT', 'meandiff', 'lower', 'upper', 'p-adj']]\n",
    "\n",
    "\n",
    "    # IT4\n",
    "    IT4_data = gene_data[gene_data['Accession'] == 'IT4']\n",
    "    IT4_groups = IT4_data.ZT_24.astype(str) + '_' + IT4_data.Treatment.astype(str)\n",
    "\n",
    "    IT4_comp = mc.MultiComparison(IT4_data['2delta_Cq'], IT4_groups)\n",
    "    IT4_results = IT4_comp.tukeyhsd()\n",
    "    IT4_table = pd.DataFrame(data = IT4_results._results_table.data[1:], columns = IT4_results._results_table.data[0])\n",
    "    IT4_sig = IT4_table.loc[IT4_table['p-adj'] < 0.05, :]\n",
    "    \n",
    "    IT4_sig['ZT_24'] = IT4_sig['group1'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    IT4_sig['ZT_24_2'] = IT4_sig['group2'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    IT4_sig['Treatment_1'] = IT4_sig['group1'].str.extract(r'([^_]*$)', expand = False)\n",
    "    IT4_sig['Treatment_2'] = IT4_sig['group2'].str.extract(r'([^_]*$)', expand = False)\n",
    "    \n",
    "    IT4_sig.drop(['group1', 'group2'], axis = 1, inplace =True)\n",
    "    \n",
    "    IT4_Final = IT4_sig.loc[(IT4_sig['ZT_24'] == IT4_sig['ZT_24_2'])]\n",
    "\n",
    "    IT4_Final.drop(['ZT_24_2', 'reject'], axis = 1, inplace =True)\n",
    "    IT4_Final['Accession'] = 'IT4'\n",
    "    IT4_Final = IT4_Final.drop(IT4_Final[(IT4_Final['Treatment_1'] == 'D-Arctic') & (IT4_Final['Treatment_2'] == 'Mid-latitude')].index)\n",
    "    IT4_Final = IT4_Final.drop(IT4_Final[(IT4_Final['Treatment_1'] == 'Mid-latitude') & (IT4_Final['Treatment_2'] == 'D-Arctic')].index)\n",
    "    IT4_Final = IT4_Final.drop(IT4_Final[(IT4_Final['Treatment_1'] == 'D-Mid-latitude') & (IT4_Final['Treatment_2'] == 'Arctic')].index)\n",
    "    IT4_Final = IT4_Final.drop(IT4_Final[(IT4_Final['Treatment_1'] == 'Arctic') & (IT4_Final['Treatment_2'] == 'D-Mid-latitude')].index)\n",
    "\n",
    "    IT4_Final.rename(columns = {'ZT_24' : 'ZT'}, inplace = True)\n",
    "    IT4_Final = IT4_Final[['Treatment_1', 'Treatment_2', 'Accession', 'ZT', 'meandiff', 'lower', 'upper', 'p-adj']]\n",
    "\n",
    "    \n",
    "    # NOR2\n",
    "    NOR2_data = gene_data[gene_data['Accession'] == 'NOR2']\n",
    "    NOR2_groups = NOR2_data.ZT_24.astype(str) + '_' + NOR2_data.Treatment.astype(str)\n",
    "\n",
    "    NOR2_comp = mc.MultiComparison(NOR2_data['2delta_Cq'], NOR2_groups)\n",
    "    NOR2_results = NOR2_comp.tukeyhsd()\n",
    "    NOR2_table = pd.DataFrame(data = NOR2_results._results_table.data[1:], columns = NOR2_results._results_table.data[0])\n",
    "    NOR2_sig = NOR2_table.loc[NOR2_table['p-adj'] < 0.05, :]\n",
    "    \n",
    "    NOR2_sig['ZT_24'] = NOR2_sig['group1'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    NOR2_sig['ZT_24_2'] = NOR2_sig['group2'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    NOR2_sig['Treatment_1'] = NOR2_sig['group1'].str.extract(r'([^_]*$)', expand = False)\n",
    "    NOR2_sig['Treatment_2'] = NOR2_sig['group2'].str.extract(r'([^_]*$)', expand = False)\n",
    "    \n",
    "    NOR2_sig.drop(['group1', 'group2'], axis = 1, inplace =True)\n",
    "    \n",
    "    NOR2_Final = NOR2_sig.loc[(NOR2_sig['ZT_24'] == NOR2_sig['ZT_24_2'])]\n",
    "\n",
    "    NOR2_Final.drop(['ZT_24_2', 'reject'], axis = 1, inplace =True)\n",
    "    NOR2_Final['Accession'] = 'NOR2'\n",
    "    NOR2_Final = NOR2_Final.drop(NOR2_Final[(NOR2_Final['Treatment_1'] == 'D-Arctic') & (NOR2_Final['Treatment_2'] == 'Mid-latitude')].index)\n",
    "    NOR2_Final = NOR2_Final.drop(NOR2_Final[(NOR2_Final['Treatment_1'] == 'Mid-latitude') & (NOR2_Final['Treatment_2'] == 'D-Arctic')].index)\n",
    "    NOR2_Final = NOR2_Final.drop(NOR2_Final[(NOR2_Final['Treatment_1'] == 'D-Mid-latitude') & (NOR2_Final['Treatment_2'] == 'Arctic')].index)\n",
    "    NOR2_Final = NOR2_Final.drop(NOR2_Final[(NOR2_Final['Treatment_1'] == 'Arctic') & (NOR2_Final['Treatment_2'] == 'D-Mid-latitude')].index)\n",
    "\n",
    "    NOR2_Final.rename(columns = {'ZT_24' : 'ZT'}, inplace = True)\n",
    "    NOR2_Final = NOR2_Final[['Treatment_1', 'Treatment_2', 'Accession', 'ZT', 'meandiff', 'lower', 'upper', 'p-adj']]\n",
    "    \n",
    "    \n",
    "    # NOR13\n",
    "    NOR13_data = gene_data[gene_data['Accession'] == 'NOR13']\n",
    "    NOR13_groups = NOR13_data.ZT_24.astype(str) + '_' + NOR13_data.Treatment.astype(str)\n",
    "\n",
    "    NOR13_comp = mc.MultiComparison(NOR13_data['2delta_Cq'], NOR13_groups)\n",
    "    NOR13_results = NOR13_comp.tukeyhsd()\n",
    "    NOR13_table = pd.DataFrame(data = NOR13_results._results_table.data[1:], columns = NOR13_results._results_table.data[0])\n",
    "    NOR13_sig = NOR13_table.loc[NOR13_table['p-adj'] < 0.05, :]\n",
    "    \n",
    "    NOR13_sig['ZT_24'] = NOR13_sig['group1'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    NOR13_sig['ZT_24_2'] = NOR13_sig['group2'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    NOR13_sig['Treatment_1'] = NOR13_sig['group1'].str.extract(r'([^_]*$)', expand = False)\n",
    "    NOR13_sig['Treatment_2'] = NOR13_sig['group2'].str.extract(r'([^_]*$)', expand = False)\n",
    "    \n",
    "    NOR13_sig.drop(['group1', 'group2'], axis = 1, inplace =True)\n",
    "    \n",
    "    NOR13_Final = NOR13_sig.loc[(NOR13_sig['ZT_24'] == NOR13_sig['ZT_24_2'])]\n",
    "\n",
    "    NOR13_Final.drop(['ZT_24_2', 'reject'], axis = 1, inplace =True)\n",
    "    NOR13_Final['Accession'] = 'NOR13'\n",
    "    NOR13_Final = NOR13_Final.drop(NOR13_Final[(NOR13_Final['Treatment_1'] == 'D-Arctic') & (NOR13_Final['Treatment_2'] == 'Mid-latitude')].index)\n",
    "    NOR13_Final = NOR13_Final.drop(NOR13_Final[(NOR13_Final['Treatment_1'] == 'Mid-latitude') & (NOR13_Final['Treatment_2'] == 'D-Arctic')].index)\n",
    "    NOR13_Final = NOR13_Final.drop(NOR13_Final[(NOR13_Final['Treatment_1'] == 'D-Mid-latitude') & (NOR13_Final['Treatment_2'] == 'Arctic')].index)\n",
    "    NOR13_Final = NOR13_Final.drop(NOR13_Final[(NOR13_Final['Treatment_1'] == 'Arctic') & (NOR13_Final['Treatment_2'] == 'D-Mid-latitude')].index)\n",
    "\n",
    "    NOR13_Final.rename(columns = {'ZT_24' : 'ZT'}, inplace = True)\n",
    "    NOR13_Final = NOR13_Final[['Treatment_1', 'Treatment_2', 'Accession', 'ZT', 'meandiff', 'lower', 'upper', 'p-adj']]\n",
    "    \n",
    "\n",
    "    Sig_list = [IT1_Final, IT4_Final, NOR2_Final, NOR13_Final]\n",
    "    All_sig_values = pd.concat(Sig_list)\n",
    "    All_sig_values.sort_values(by = ['Treatment_1', 'Treatment_2', 'ZT'], ascending = [True, False, True], inplace = True)\n",
    "    All_sig_values.to_excel(target_gene + \"_significant_24h_btw_treatments.xlsx\")\n",
    "                              \n",
    "    print('Significant results between treatments for ' + target_gene)\n",
    "    display(All_sig_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tukey_btw_accs_24h(df, target_gene):\n",
    "    \n",
    "    '''Within each treatment, at which ZT (24h period) do the accessions have different expression'''\n",
    "    \n",
    "    gene_data = df[df['Target']==target_gene]\n",
    "    \n",
    "    ar_data = gene_data[gene_data['Treatment'] == 'Arctic']\n",
    "    ml_data = gene_data[gene_data['Treatment'] == 'Mid-latitude']\n",
    "\n",
    "    \n",
    "    # Arctic photoperiod  \n",
    "    ar_groups =  ar_data.ZT_24.astype(str)+ '_' + ar_data.Accession.astype(str)\n",
    "\n",
    "    ar_comp = mc.MultiComparison(ar_data['2delta_Cq'], ar_groups)\n",
    "    ar_results = ar_comp.tukeyhsd()\n",
    "    ar_table = pd.DataFrame(data = ar_results._results_table.data[1:], columns = ar_results._results_table.data[0])\n",
    "    ar_sig = ar_table.loc[ar_table['p-adj'] < 0.05, :]\n",
    "    \n",
    "    ar_sig['ZT_24'] = ar_sig['group1'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    ar_sig['ZT_24_2'] = ar_sig['group2'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    ar_sig['Accession_1'] = ar_sig['group1'].str.extract(r'([^_]*$)', expand = False)\n",
    "    ar_sig['Accession_2'] = ar_sig['group2'].str.extract(r'([^_]*$)', expand = False)\n",
    "    \n",
    "    ar_sig.drop(['group1', 'group2'], axis = 1, inplace =True)\n",
    "    \n",
    "    ar_final = ar_sig.loc[(ar_sig['ZT_24'] == ar_sig['ZT_24_2'])]\n",
    "    \n",
    "    ar_final.drop(['ZT_24_2', 'reject'], axis = 1, inplace =True)\n",
    "    ar_final['Treatment'] = 'Arctic'\n",
    "    ar_final.rename(columns = {'ZT_24' : 'ZT'}, inplace = True)\n",
    "    ar_final = ar_final[['Treatment', 'Accession_1', 'Accession_2', 'ZT', 'meandiff', 'lower', 'upper', 'p-adj']]\n",
    "    \n",
    "    \n",
    "     # Mid-latitude photoperiod\n",
    "    ml_groups =   ml_data.ZT_24.astype(str) + '_' + ml_data.Accession.astype(str) \n",
    "\n",
    "    comp = mc.MultiComparison(ml_data['2delta_Cq'], ml_groups)\n",
    "    results = comp.tukeyhsd()\n",
    "    table = pd.DataFrame(data = results._results_table.data[1:], columns = results._results_table.data[0])\n",
    "    ml_sig = table.loc[table['p-adj'] < 0.05, :]\n",
    "    \n",
    "    ml_sig['ZT_24'] = ml_sig['group1'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    ml_sig['ZT_24_2'] = ml_sig['group2'].str.extract(r'(^[^_]+)', expand = False)\n",
    "    ml_sig['Accession_1'] = ml_sig['group1'].str.extract(r'([^_]*$)', expand = False)\n",
    "    ml_sig['Accession_2'] = ml_sig['group2'].str.extract(r'([^_]*$)', expand = False)\n",
    "    \n",
    "    ml_sig.drop(['group1', 'group2'], axis = 1, inplace =True)\n",
    "    \n",
    "    ml_final = ml_sig.loc[(ml_sig['ZT_24'] == ml_sig['ZT_24_2'])]\n",
    "    \n",
    "    ml_final.drop(['ZT_24_2', 'reject'], axis = 1, inplace =True)\n",
    "    ml_final['Treatment'] = 'Mid-latitude'\n",
    "    ml_final.rename(columns = {'ZT_24' : 'ZT'}, inplace = True)\n",
    "    ml_final = ml_final[['Treatment', 'Accession_1', 'Accession_2', 'ZT', 'meandiff', 'lower', 'upper', 'p-adj']]\n",
    "\n",
    "    \n",
    "    Sig_list = [ar_final, ml_final]\n",
    "    All_sig_values = pd.concat(Sig_list)\n",
    "    All_sig_values.sort_values(by = ['Treatment', 'ZT', 'Accession_1', 'Accession_2'], ascending = [True, True, True, True], inplace = True)\n",
    "    All_sig_values.to_excel(target_gene + \"_significant_24h_btw_accessions.xlsx\")\n",
    "    \n",
    "    print('Significant results between accessions for ' + target_gene)\n",
    "    display(All_sig_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot target genes 2<sup>-ΔCq <a class=\"anchor\" id=\"target_plots\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LHY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gene_24h(data2, 'LHY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between treatments for LHY\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment_1</th>\n",
       "      <th>Treatment_2</th>\n",
       "      <th>Accession</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2339</td>\n",
       "      <td>1.4618</td>\n",
       "      <td>13.0061</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4801</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.7130</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3135</td>\n",
       "      <td>0.5888</td>\n",
       "      <td>2.0382</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>24</td>\n",
       "      <td>8.8444</td>\n",
       "      <td>1.8188</td>\n",
       "      <td>15.8700</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>24</td>\n",
       "      <td>0.4572</td>\n",
       "      <td>0.1432</td>\n",
       "      <td>0.7711</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>24</td>\n",
       "      <td>0.8481</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>1.6849</td>\n",
       "      <td>0.0431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>4</td>\n",
       "      <td>6.9469</td>\n",
       "      <td>1.5049</td>\n",
       "      <td>12.3890</td>\n",
       "      <td>0.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>4</td>\n",
       "      <td>7.8170</td>\n",
       "      <td>1.5331</td>\n",
       "      <td>14.1010</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3799</td>\n",
       "      <td>0.0991</td>\n",
       "      <td>0.6607</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0413</td>\n",
       "      <td>1.3543</td>\n",
       "      <td>14.7282</td>\n",
       "      <td>0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8</td>\n",
       "      <td>7.9922</td>\n",
       "      <td>1.9078</td>\n",
       "      <td>14.0765</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.2972</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.5780</td>\n",
       "      <td>0.0259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8</td>\n",
       "      <td>-8.5755</td>\n",
       "      <td>-15.6011</td>\n",
       "      <td>-1.5499</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.3645</td>\n",
       "      <td>-0.6784</td>\n",
       "      <td>-0.0505</td>\n",
       "      <td>0.0073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>24</td>\n",
       "      <td>10.0648</td>\n",
       "      <td>2.2099</td>\n",
       "      <td>17.9197</td>\n",
       "      <td>0.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>24</td>\n",
       "      <td>0.6339</td>\n",
       "      <td>0.2829</td>\n",
       "      <td>0.9849</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0207</td>\n",
       "      <td>0.0851</td>\n",
       "      <td>1.9564</td>\n",
       "      <td>0.0174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.3076</td>\n",
       "      <td>-0.5884</td>\n",
       "      <td>-0.0268</td>\n",
       "      <td>0.0166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>-11.1301</td>\n",
       "      <td>-17.8170</td>\n",
       "      <td>-4.4431</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment_1     Treatment_2 Accession  ZT  meandiff    lower    upper  \\\n",
       "0            Arctic    Mid-latitude       IT1   0    7.2339   1.4618  13.0061   \n",
       "0            Arctic    Mid-latitude       IT4   0    0.4801   0.2473   0.7130   \n",
       "0            Arctic    Mid-latitude     NOR13   0    1.3135   0.5888   2.0382   \n",
       "261          Arctic    Mid-latitude       IT1  24    8.8444   1.8188  15.8700   \n",
       "261          Arctic    Mid-latitude       IT4  24    0.4572   0.1432   0.7711   \n",
       "261          Arctic    Mid-latitude     NOR13  24    0.8481   0.0113   1.6849   \n",
       "299          Arctic    Mid-latitude       IT1   4    6.9469   1.5049  12.3890   \n",
       "297          Arctic        D-Arctic       IT1   4    7.8170   1.5331  14.1010   \n",
       "297          Arctic        D-Arctic       IT4   4    0.3799   0.0991   0.6607   \n",
       "297          Arctic        D-Arctic      NOR2   4    8.0413   1.3543  14.7282   \n",
       "319          Arctic        D-Arctic       IT1   8    7.9922   1.9078  14.0765   \n",
       "319          Arctic        D-Arctic       IT4   8    0.2972   0.0164   0.5780   \n",
       "322        D-Arctic  D-Mid-latitude       IT1   8   -8.5755 -15.6011  -1.5499   \n",
       "322        D-Arctic  D-Mid-latitude       IT4   8   -0.3645  -0.6784  -0.0505   \n",
       "280  D-Mid-latitude    Mid-latitude       IT1  24   10.0648   2.2099  17.9197   \n",
       "280  D-Mid-latitude    Mid-latitude       IT4  24    0.6339   0.2829   0.9849   \n",
       "280  D-Mid-latitude    Mid-latitude     NOR13  24    1.0207   0.0851   1.9564   \n",
       "310  D-Mid-latitude    Mid-latitude       IT4   4   -0.3076  -0.5884  -0.0268   \n",
       "324  D-Mid-latitude    Mid-latitude      NOR2   8  -11.1301 -17.8170  -4.4431   \n",
       "\n",
       "      p-adj  \n",
       "0    0.0021  \n",
       "0    0.0000  \n",
       "0    0.0000  \n",
       "261  0.0019  \n",
       "261  0.0001  \n",
       "261  0.0431  \n",
       "299  0.0015  \n",
       "297  0.0024  \n",
       "297  0.0005  \n",
       "297  0.0041  \n",
       "319  0.0009  \n",
       "319  0.0259  \n",
       "322  0.0033  \n",
       "322  0.0073  \n",
       "280  0.0014  \n",
       "280  0.0000  \n",
       "280  0.0174  \n",
       "310  0.0166  \n",
       "324  0.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_treatment_24h(data2, 'LHY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between accessions for LHY\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Accession_1</th>\n",
       "      <th>Accession_2</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.9925</td>\n",
       "      <td>-6.9684</td>\n",
       "      <td>-3.0167</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.7461</td>\n",
       "      <td>-6.9350</td>\n",
       "      <td>-2.5572</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.1513</td>\n",
       "      <td>-5.2150</td>\n",
       "      <td>-1.0876</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>24</td>\n",
       "      <td>-3.0854</td>\n",
       "      <td>-5.7497</td>\n",
       "      <td>-0.4212</td>\n",
       "      <td>0.0066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>24</td>\n",
       "      <td>-3.0305</td>\n",
       "      <td>-5.6948</td>\n",
       "      <td>-0.3663</td>\n",
       "      <td>0.0088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>4</td>\n",
       "      <td>-7.3028</td>\n",
       "      <td>-9.3665</td>\n",
       "      <td>-5.2391</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.8521</td>\n",
       "      <td>-8.8280</td>\n",
       "      <td>-4.8763</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.0236</td>\n",
       "      <td>-5.9995</td>\n",
       "      <td>-2.0478</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.2792</td>\n",
       "      <td>1.3033</td>\n",
       "      <td>5.2551</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.8285</td>\n",
       "      <td>0.9446</td>\n",
       "      <td>4.7124</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>-3.0224</td>\n",
       "      <td>-4.9983</td>\n",
       "      <td>-1.0465</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>8</td>\n",
       "      <td>-2.8411</td>\n",
       "      <td>-4.7250</td>\n",
       "      <td>-0.9572</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>2.1614</td>\n",
       "      <td>0.1855</td>\n",
       "      <td>4.1373</td>\n",
       "      <td>0.0157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>1.9801</td>\n",
       "      <td>0.0962</td>\n",
       "      <td>3.8640</td>\n",
       "      <td>0.0271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>0</td>\n",
       "      <td>-11.7463</td>\n",
       "      <td>-15.5029</td>\n",
       "      <td>-7.9898</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0</td>\n",
       "      <td>-10.6665</td>\n",
       "      <td>-14.6262</td>\n",
       "      <td>-6.7068</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.7271</td>\n",
       "      <td>-11.3418</td>\n",
       "      <td>-4.1123</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0193</td>\n",
       "      <td>0.6284</td>\n",
       "      <td>7.4102</td>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>24</td>\n",
       "      <td>-11.4727</td>\n",
       "      <td>-16.0449</td>\n",
       "      <td>-6.9004</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>24</td>\n",
       "      <td>-11.0268</td>\n",
       "      <td>-15.5991</td>\n",
       "      <td>-6.4545</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>24</td>\n",
       "      <td>7.1948</td>\n",
       "      <td>2.6225</td>\n",
       "      <td>11.7670</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>24</td>\n",
       "      <td>6.7489</td>\n",
       "      <td>2.1766</td>\n",
       "      <td>11.3212</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>4</td>\n",
       "      <td>-14.3567</td>\n",
       "      <td>-17.8984</td>\n",
       "      <td>-10.8150</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>4</td>\n",
       "      <td>-14.2072</td>\n",
       "      <td>-17.9638</td>\n",
       "      <td>-10.4507</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>-10.7523</td>\n",
       "      <td>-14.1432</td>\n",
       "      <td>-7.3614</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.6044</td>\n",
       "      <td>0.2135</td>\n",
       "      <td>6.9953</td>\n",
       "      <td>0.0234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment Accession_1 Accession_2  ZT  meandiff    lower    upper  \\\n",
       "0          Arctic         IT1         IT4   0   -4.9925  -6.9684  -3.0167   \n",
       "1          Arctic         IT1       NOR13   0   -4.7461  -6.9350  -2.5572   \n",
       "2          Arctic         IT1        NOR2   0   -3.1513  -5.2150  -1.0876   \n",
       "312        Arctic         IT1         IT4  24   -3.0854  -5.7497  -0.4212   \n",
       "313        Arctic         IT1       NOR13  24   -3.0305  -5.6948  -0.3663   \n",
       "350        Arctic         IT1         IT4   4   -7.3028  -9.3665  -5.2391   \n",
       "351        Arctic         IT1       NOR13   4   -6.8521  -8.8280  -4.8763   \n",
       "352        Arctic         IT1        NOR2   4   -4.0236  -5.9995  -2.0478   \n",
       "358        Arctic         IT4        NOR2   4    3.2792   1.3033   5.2551   \n",
       "363        Arctic       NOR13        NOR2   4    2.8285   0.9446   4.7124   \n",
       "372        Arctic         IT1         IT4   8   -3.0224  -4.9983  -1.0465   \n",
       "373        Arctic         IT1       NOR13   8   -2.8411  -4.7250  -0.9572   \n",
       "376        Arctic         IT4        NOR2   8    2.1614   0.1855   4.1373   \n",
       "377        Arctic       NOR13        NOR2   8    1.9801   0.0962   3.8640   \n",
       "0    Mid-latitude         IT1         IT4   0  -11.7463 -15.5029  -7.9898   \n",
       "1    Mid-latitude         IT1       NOR13   0  -10.6665 -14.6262  -6.7068   \n",
       "2    Mid-latitude         IT1        NOR2   0   -7.7271 -11.3418  -4.1123   \n",
       "28   Mid-latitude         IT4        NOR2   0    4.0193   0.6284   7.4102   \n",
       "312  Mid-latitude         IT1         IT4  24  -11.4727 -16.0449  -6.9004   \n",
       "313  Mid-latitude         IT1       NOR13  24  -11.0268 -15.5991  -6.4545   \n",
       "324  Mid-latitude         IT4        NOR2  24    7.1948   2.6225  11.7670   \n",
       "333  Mid-latitude       NOR13        NOR2  24    6.7489   2.1766  11.3212   \n",
       "350  Mid-latitude         IT1         IT4   4  -14.3567 -17.8984 -10.8150   \n",
       "351  Mid-latitude         IT1       NOR13   4  -14.2072 -17.9638 -10.4507   \n",
       "352  Mid-latitude         IT1        NOR2   4  -10.7523 -14.1432  -7.3614   \n",
       "358  Mid-latitude         IT4        NOR2   4    3.6044   0.2135   6.9953   \n",
       "\n",
       "      p-adj  \n",
       "0    0.0000  \n",
       "1    0.0000  \n",
       "2    0.0000  \n",
       "312  0.0066  \n",
       "313  0.0088  \n",
       "350  0.0000  \n",
       "351  0.0000  \n",
       "352  0.0000  \n",
       "358  0.0000  \n",
       "363  0.0000  \n",
       "372  0.0000  \n",
       "373  0.0000  \n",
       "376  0.0157  \n",
       "377  0.0271  \n",
       "0   -0.0000  \n",
       "1   -0.0000  \n",
       "2    0.0000  \n",
       "28   0.0046  \n",
       "312  0.0000  \n",
       "313  0.0000  \n",
       "324  0.0000  \n",
       "333  0.0000  \n",
       "350 -0.0000  \n",
       "351 -0.0000  \n",
       "352 -0.0000  \n",
       "358  0.0234  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_accs_24h(data2, 'LHY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RVE8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gene_24h(data2, 'RVE8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between treatments for RVE8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        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>Treatment_1</th>\n",
       "      <th>Treatment_2</th>\n",
       "      <th>Accession</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5285</td>\n",
       "      <td>0.2154</td>\n",
       "      <td>0.8417</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3016</td>\n",
       "      <td>0.4685</td>\n",
       "      <td>2.1348</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.7637</td>\n",
       "      <td>-1.3298</td>\n",
       "      <td>-0.1976</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment_1   Treatment_2 Accession ZT  meandiff   lower   upper  \\\n",
       "0            Arctic  Mid-latitude       IT4  0    0.5285  0.2154  0.8417   \n",
       "0            Arctic  Mid-latitude     NOR13  0    1.3016  0.4685  2.1348   \n",
       "324  D-Mid-latitude  Mid-latitude      NOR2  8   -0.7637 -1.3298 -0.1976   \n",
       "\n",
       "      p-adj  \n",
       "0    0.0000  \n",
       "0    0.0000  \n",
       "324  0.0005  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_treatment_24h(data2, 'RVE8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between accessions for RVE8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Accession_1</th>\n",
       "      <th>Accession_2</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.5753</td>\n",
       "      <td>-1.1239</td>\n",
       "      <td>-0.0266</td>\n",
       "      <td>0.0280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.6742</td>\n",
       "      <td>-1.1973</td>\n",
       "      <td>-0.1512</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.5248</td>\n",
       "      <td>-1.0479</td>\n",
       "      <td>-0.0018</td>\n",
       "      <td>0.0481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7421</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>1.4803</td>\n",
       "      <td>0.0471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.2014</td>\n",
       "      <td>-1.9118</td>\n",
       "      <td>-0.4910</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.7260</td>\n",
       "      <td>-1.4364</td>\n",
       "      <td>-0.0156</td>\n",
       "      <td>0.0386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment Accession_1 Accession_2 ZT  meandiff   lower   upper   p-adj\n",
       "352        Arctic         IT1        NOR2  4   -0.5753 -1.1239 -0.0266  0.0280\n",
       "363        Arctic       NOR13        NOR2  4   -0.6742 -1.1973 -0.1512  0.0010\n",
       "377        Arctic       NOR13        NOR2  8   -0.5248 -1.0479 -0.0018  0.0481\n",
       "27   Mid-latitude         IT4       NOR13  0    0.7421  0.0038  1.4803  0.0471\n",
       "53   Mid-latitude       NOR13        NOR2  0   -1.2014 -1.9118 -0.4910  0.0000\n",
       "363  Mid-latitude       NOR13        NOR2  4   -0.7260 -1.4364 -0.0156  0.0386"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_accs_24h(data2, 'RVE8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PRR9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gene_24h(data2, 'PRR9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between treatments for PRR9\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment_1</th>\n",
       "      <th>Treatment_2</th>\n",
       "      <th>Accession</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7305</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>1.4469</td>\n",
       "      <td>0.0402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3548</td>\n",
       "      <td>0.0738</td>\n",
       "      <td>0.6359</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7477</td>\n",
       "      <td>0.2797</td>\n",
       "      <td>1.2158</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>4</td>\n",
       "      <td>0.5164</td>\n",
       "      <td>0.0743</td>\n",
       "      <td>0.9584</td>\n",
       "      <td>0.0066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8</td>\n",
       "      <td>1.2346</td>\n",
       "      <td>0.5806</td>\n",
       "      <td>1.8885</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.7840</td>\n",
       "      <td>0.5029</td>\n",
       "      <td>1.0651</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.6919</td>\n",
       "      <td>0.2238</td>\n",
       "      <td>1.1599</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0307</td>\n",
       "      <td>0.5887</td>\n",
       "      <td>1.4728</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.3874</td>\n",
       "      <td>-0.7592</td>\n",
       "      <td>-0.0155</td>\n",
       "      <td>0.0313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.6479</td>\n",
       "      <td>-1.1321</td>\n",
       "      <td>-0.1636</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1.0017</td>\n",
       "      <td>-1.8026</td>\n",
       "      <td>-0.2007</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.4277</td>\n",
       "      <td>-0.7523</td>\n",
       "      <td>-0.1031</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.6101</td>\n",
       "      <td>-1.1833</td>\n",
       "      <td>-0.0368</td>\n",
       "      <td>0.0239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.6733</td>\n",
       "      <td>-1.1576</td>\n",
       "      <td>-0.1891</td>\n",
       "      <td>0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4372</td>\n",
       "      <td>0.1127</td>\n",
       "      <td>0.7618</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.8164</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>1.3896</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>4</td>\n",
       "      <td>0.6232</td>\n",
       "      <td>0.1001</td>\n",
       "      <td>1.1462</td>\n",
       "      <td>0.0049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0756</td>\n",
       "      <td>1.2747</td>\n",
       "      <td>2.8766</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.1383</td>\n",
       "      <td>0.8137</td>\n",
       "      <td>1.4628</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.9383</td>\n",
       "      <td>0.3651</td>\n",
       "      <td>1.5116</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>8</td>\n",
       "      <td>1.6343</td>\n",
       "      <td>1.1112</td>\n",
       "      <td>2.1573</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment_1   Treatment_2 Accession  ZT  meandiff   lower   upper  \\\n",
       "299          Arctic  Mid-latitude       IT1   4    0.7305  0.0141  1.4469   \n",
       "299          Arctic  Mid-latitude       IT4   4    0.3548  0.0738  0.6359   \n",
       "299          Arctic  Mid-latitude      NOR2   4    0.7477  0.2797  1.2158   \n",
       "299          Arctic  Mid-latitude     NOR13   4    0.5164  0.0743  0.9584   \n",
       "321          Arctic  Mid-latitude       IT1   8    1.2346  0.5806  1.8885   \n",
       "321          Arctic  Mid-latitude       IT4   8    0.7840  0.5029  1.0651   \n",
       "321          Arctic  Mid-latitude      NOR2   8    0.6919  0.2238  1.1599   \n",
       "321          Arctic  Mid-latitude     NOR13   8    1.0307  0.5887  1.4728   \n",
       "49           Arctic      D-Arctic       IT4  12   -0.3874 -0.7592 -0.0155   \n",
       "49           Arctic      D-Arctic     NOR13  12   -0.6479 -1.1321 -0.1636   \n",
       "319          Arctic      D-Arctic       IT1   8   -1.0017 -1.8026 -0.2007   \n",
       "319          Arctic      D-Arctic       IT4   8   -0.4277 -0.7523 -0.1031   \n",
       "319          Arctic      D-Arctic      NOR2   8   -0.6101 -1.1833 -0.0368   \n",
       "319          Arctic      D-Arctic     NOR13   8   -0.6733 -1.1576 -0.1891   \n",
       "310  D-Mid-latitude  Mid-latitude       IT4   4    0.4372  0.1127  0.7618   \n",
       "310  D-Mid-latitude  Mid-latitude      NOR2   4    0.8164  0.2431  1.3896   \n",
       "310  D-Mid-latitude  Mid-latitude     NOR13   4    0.6232  0.1001  1.1462   \n",
       "324  D-Mid-latitude  Mid-latitude       IT1   8    2.0756  1.2747  2.8766   \n",
       "324  D-Mid-latitude  Mid-latitude       IT4   8    1.1383  0.8137  1.4628   \n",
       "324  D-Mid-latitude  Mid-latitude      NOR2   8    0.9383  0.3651  1.5116   \n",
       "324  D-Mid-latitude  Mid-latitude     NOR13   8    1.6343  1.1112  2.1573   \n",
       "\n",
       "      p-adj  \n",
       "299  0.0402  \n",
       "299  0.0019  \n",
       "299  0.0000  \n",
       "299  0.0066  \n",
       "321  0.0000  \n",
       "321  0.0000  \n",
       "321  0.0001  \n",
       "321  0.0000  \n",
       "49   0.0313  \n",
       "49   0.0006  \n",
       "319  0.0022  \n",
       "319  0.0009  \n",
       "319  0.0239  \n",
       "319  0.0003  \n",
       "310  0.0006  \n",
       "310  0.0002  \n",
       "310  0.0049  \n",
       "324  0.0000  \n",
       "324 -0.0000  \n",
       "324  0.0000  \n",
       "324  0.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_treatment_24h(data2, 'PRR9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between accessions for PRR9\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>Treatment</th>\n",
       "      <th>Accession_1</th>\n",
       "      <th>Accession_2</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.5936</td>\n",
       "      <td>-0.9958</td>\n",
       "      <td>-0.1914</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>-1.0441</td>\n",
       "      <td>-1.6999</td>\n",
       "      <td>-0.3884</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.8869</td>\n",
       "      <td>-1.5121</td>\n",
       "      <td>-0.2616</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment Accession_1 Accession_2 ZT  meandiff   lower   upper   p-adj\n",
       "372        Arctic         IT1         IT4  8   -0.5936 -0.9958 -0.1914  0.0000\n",
       "372  Mid-latitude         IT1         IT4  8   -1.0441 -1.6999 -0.3884  0.0000\n",
       "374  Mid-latitude         IT1        NOR2  8   -0.8869 -1.5121 -0.2616  0.0001"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_accs_24h(data2, 'PRR9')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PRR7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vuzDF8syZM7WOnT59WmFhYQRjAHyWw2Gq5LxD589V6fzZi7/sNvM7vcLr3X7FFnid8nPK1b5DgAxL/TNzAQAAAOBq16hgrD5Nvch9WFiYoqOj69wBMysrS3FxcU1aDwA0F7vNcTH8+jYIKz7vkOlw46KGoe1fWtUi3KLuCcGK7UhABgAAAODaVG8wNnv27Mu+6OTJky/7XFf1799fy5cvV15enmJjYyVJu3fvVn5+vu69916v3x8AmlJVVe1ZYMXnvj8LrGGWqnK1LMlVy5IchVedkT0yRkdD+snh953ZvqYpfbt+Y8l5h3Zutipz77cBWacAWQjIAAAAAFxD6g3Gtm7d2pR1NKigoEAZGRnq0aOH2rZtK0kaMWKE0tPTNWPGDP3whz9UeXm5PvvsM8XFxWnQoEHNXDEAXB7TNGW3mbVmgZWcd8h0PQNTSFmhWpYcU8uSYwovPqaWJTkK87PJkjhQxuBUqWtPGRaLWq7epf15LWUPilSw7ZS6Hv1MtpDWyu44TJX+zvUcS4sd2vWVVQf3WtQ9IUix1wcSkAEAAAC4JtQbjL355ptNWUeD9u/fr3nz5mnChAnVwVh4eLimT5+u9957T0uWLFFQUJCSkpI0btw4BQQENHPFAHBpVVWmis85Z36dO+tcGP/8uSqV211PwPxUpZa242pZlOkMwUqOqWVJrgIqrc4OIaEy+qXISBov9bpJhp9fjfM7Du2rj+fMkSS1KLfpjuJvpPxz6pK9Qlm9xyir3WBVVjr7lpY4tGtLmTL32tWtV5A6dg6UxY+ADAAAAMDVq95grE2bNk1ZR7W5c+fWaktLS1NaWlqt9piYGE2ZMqUJqgKAy2eapmxltWeBlRY3bhZYaJhFLVs4FF6apxbZ2xSesVGh1gIZ+t5FAgJlJN4qIzlVujFRRoBrG6OUBAbLMmGKHLN/rcDKUsXvelddEo8o+7YJOppZrooK532spQ7t3lamg/ts6tYrWB27BMqPgAwAAADAVajRi+/n5uZq3bp1ys7OVklJiWbOnKnt27erpKREgwYNksVSzzZnAOADKitNlZyrqrkj5DmHKsobMQvMXwpv5afwCOevlqGVapG9Q/7b1kl7d0pVlXWc5Ccl9JORnCqjb7KM4MvbFMXoliDjx0/KfO+PkqSA7evVPSZGcT8cq6yDdh3OsFe/lzKrqW+2XwzIOsURkAEAAAC4ujQqGPvkk0+0ZMkSORw1tzvbt2+fPv/8c3311VeaNGmS/P09stklAFyxTNNUmbWOWWAlDn1/AldDwlpYLgZgrSxqFeGnkDCLVFkp7dkuc+0GmV9/JZWX1z7ZMKT43jKSB8m4eaCMFuEeeW+W2+6UIzdL5pp/ON/rPxbJP/Z6dU8cqC7dg5R1yBmQXXjk01Zmas+OMh3ab1PXnsG6Pi5Qfv4EZAAAAACufC4nWJs3b9aiRYsUHx+vMWPGaOfOnfrnP/8pSbrzzjuVk5Oj7du3a9WqVRo+fLjXCgaAplZZ4VwLrOYssCpVVrh+Df8AOQOw784EC/eTf8DFAMmsqpIydsvcki5zx2aprLTui3WJd4Zht9wmI6K1m++ubsYDj8nMz5H2fy1Jcsz/nSzR7eXfsYu69QpW5+5Byj5k16EDNQOyvTsvBGRBur5rkPwJyAAAAABcwVwOxj7//HO1bdtWL774ogICAnTgwIHqY+3atdOUKVM0efJkrVu3jmAMwFXJNE1ZSx3fhl8OnT9XpeILs8BcZThngbWK8FPL7wRhIaGGDKN2SGQ6HNKRA84wbNtGqfhc3deN6eR8TDJpkIzo9pf5Dl1n+PnJ8vPn5Xh1snTyuFRul+PNl2X51RsywiPk7284Z4d1C1LOYWdAZrc5AzK7zdS+XTYd2m9X155B6tw1qEYACAAAAABXCpeDsezsbN1555317vhosVjUt29frVmzxmPFAYC3VFSY1btAfncWWF3Ld9UnIND4dhbYxcchW4T7XXKWlGma0rGjzjBs6wbpdGHdHaPaOsOw5FQZsdc34t15hhHWUpaJv3aGY7Yy6XShHH+aJcukl2T4O/8u8Pc3FNcjWNd3DVLOkXIdOmCTrcwZkJXbTe3/2qbDB+yK6xGkLt0IyAAAAABcWVwOxvz8/GSz2RrsU1payuL7AK4opmmqtMRRI/wqPuuQtdT1WWCGIYW1rD0LLDik7llg9dZyIk/m1g0yt6RLJ3Lr7tQqUsYt3+4o2SW+Udf3BqN9R1memCzHmy9Lpikd3Cfz729L456qUZufv6Eu8UHq1DVQx46U6+D+mgHZgd01A7KAQAIyAAAAAM3P5WCsa9eu2rZtm/77v/9bYWFhtY6fPXtW27ZtU1xcnEcLBICG5Gbba7w+sLtMQSGW6iCs+FyVqqpcv15gkPG9tcAsahHud9m7LZqnC2Vu/Y8zDMs5XHen0BYyEgc6w7D4G2RY/C7rXt5i9EmScd/DMj96T5JkblgldewiY/A9tfr6+Rnq3D1IHeMClZtVroP7bCqzOgOyinJTGd/YdORCQNY9UAGB/GcKAAAAgObjcjA2atQovfTSS3rxxRc1ZswYnTvnXAensLBQhw8f1qJFi1RSUqIf/vCHXisWAL4rN9uur7eW1Wg7uN9eT++aDENqEX7xEcgLQVhQcONmgdXFLD4nc9tGmVvTpYP76u4UFCyjb39nGJbQt/rRxCuV8YP7pNwsmV+tlySZi/8is10HGb1uqrO/n5+h67sGqWPnQOVml+vgPnv1LL2KClMZe2w6nGFTl+5BiosPUmAQARkAAACApudyMNa7d2/97Gc/0/z58/Xb3/62un3ixImSnGuMjRs3Tn379vV8lQBQh327bHK4MBssKPjiLLCWEX5qFeGnFi0tslzmLLC6mNZSmbs2O2eG7f9actTxqKa/v3TjLTKSUp2zsIKCPHZ/bzMMQ3p4osyCfCnroORwyPH2a7K8MLvBzQAsfoY6xQWpQ+dA5WVX6OA+W/VmBpUV0sF9dh3NtKtz9yDF9QhSEAEZAAAAgCbkcjAmSUOGDFG/fv2Unp6uI0eOyGq1Kjg4WJ06dVJqaqratWvnrToBoJrpMHVw/8VdEOuS0Df4O7PAvBO2mOV2afdWObakS99sdyY932exSD1vci6g32+AjNDaj6JfLYzAIFkmvCDHK89K505LpcXOnSqnvC4jJLTBcy0WQx27BCr2+gDl51Qoc59NpcXfBmSV0qH9dh09aFfnbkHq2iPIa58ZAAAAAHxXo4IxSYqMjNTIkSPrPW61WhUa2vA/kADgcpVZHdq5uVSnCuufKhYSaqhrj2Cv3N+srJT273LuKLnzK8leVnfHbgnOMCxxoIzwCK/U0hyM61rLMmGKHK+/4AwCjx+T453fyjLhBRkubL5isRjq0DlQsZ0ClJ9bocy9NpWcdwZkVZXS4QN2ZR206/pvA7LgEAIyAAAAAN7jUjBWXl6uysrKSwZeGzdu1Hvvvac///nPHikOAL7reG65vt5apory+meK+flJPft4NhQzHVXO3Ri3pMvcvkkqLa67Y6c4Zxh2yyAZrdt4tIYriRHXQ8a4p2S++3tnw9dbZH76voxR41y/hsVQbKdAxXQM0PFvA7Lic98GZFXSkQy7sg7ZdX3XIHXrSUAGAAAAwDsaDMYyMjL0f//3f8rMzJQktWnTRqNHj1ZaWlqNfkVFRfrLX/6iXbt2ea1QAL6rqtLU3l1lyj5cfrHRkOITghUaZmjXlouztvokhajD9e6v3WWappR1UOaWDTK3bZDOnq67Y7tY55phyYNktOvg9n2vFpaBd8iRly1z1ceSJHP5B3J06CxL0qBGXccwDMV0DFT7DgE6kecMyM6fdQZkjirpaKZd2Yfs6hQXqG69ghUSSkAGAAAAwHPqDcYOHjyo6dOnq6qqSn5+fgoNDVVhYaHeeustWa1WDR8+XJK0evVqLVy4UHa7XSEhIRo7dmyTFQ/g2nf+bJV2fFmq4vMXF7MPDjV084AwtW7j/CPsu8GYu6GYmZfjnBm2NV0qPFF3p8goGUmDnDtKdoxzexfLq5Vx/8My87OlPTskSeaCP8iMbi/j+m6Nv5ZhqH2HQLWLDVBBfqUy99p07ozzcVmHQ8o6VK6cI+Xq2MUZkIWGEZABAAAAcF+9wdinn36qqqoq3X///brvvvvk7++vI0eO6He/+50++OAD3Xnnnfrb3/6mlStXSpKSk5P12GOP6brrrmuy4gFcu0zTVPbhcu3dVVZj58n2HQLUJylEgYGeC0bMwhPfhmEbpLzsuju1bCXjlludYVhcT5fW07rWGRY/WZ6YLMfM56QTeVJ5uRxzX5XlV2/IaHV5fxcYhqF2sQFqG+Ovk8edAdnZ0xcDsuzD5co5Wq6OnQPVPSFIoWF+nnxLAAAAjZaxp0zHQk/Jai1Tj94hzV0OgEaqNxg7dOiQ4uPjNWbMmOq2uLg4Pfroo/rNb36jt99+Wxs2bFCrVq30+OOPKzk5uUkKBnDtK7c79PXWMp3Iu7jLo8VP6t0vRJ3iAj0yQ8s8e1rmtv/I3JIuHc2su1NImIybB8hISpV69pHhRwjzfUZoC1me+rUcr06WykqlM0VyvDVTlmdfkREQcPnXNQy1jQlQdHt/FZ5wBmRnTjkDMtMh5Rwp17Gj5erwbUAW1oLPBgAANI/MvXZJdkkiGAOuQvUGY8XFxUpJSanV3rNnT0nShg0b1KtXLz3zzDNq1aqV9yoE4FOKTlZq5+ZS2couLrAf3sqim1PC1LKVe+GHWVosc/smZxiWuUcy61jEPzBQRp9k58yw3jfLCAh0656+wGgXK8vPnpNjzgxnanX4gMz/myc98r9uh5iGYSi6fYDatPNXUYEzIDtd9G1AZkrHjpYrN6tcsdcHqHtCsFq0JCADAAAA4Lp6g7HKykqFhYXVar+wM2Xr1q31wgsvKDCQfzQCcJ/DYSpzr00H99lrtHfpHqheN4XIz+/yAhbTVibz6y3OMGzvTqmqsnYnPz/phpudO0relCwjmP/payyj980yHnhU5tJ3JEnmpjVSx84yho7wzPUNQ23aBSiqrb9OnXQGZKcKLwZkuVkVys2uUGwnZ0DWMpyADAAAAMClNbgrZUMGDBhAKAbAI6ylVdrxpbX6UTlJCgg01Dc5VO1iG/84nllRIe3Z7lw3bPcWqby8difDkOJ7O8OwxIEywlq68xYgyRh6r5Sb5QzFJJlL35XZvpOMG/p57h6Goai2AYpqG6Cik5U6uM+mooJvw05TysuuUF52hWI6BSg+IdjtWYYAAAAArm2XHYxdmDkGAO7IzynX19usqry4nJiiov3Vb0CogkMuvcC9Y/M6pf3nPYXYTqk8IFxV2R2k3Gzneld16RLvDMNuuU1GRKSH3gUkZ2ilH0+QWZAnHT4gmQ45/vyaLFNmy2gX6/H7RUX7Kyq6hU4XVipzn02FJy7OBszPqVB+ToXad3QGZOERBGQAAAAAarvsYAwA3FFZaWrPjjIdO3pxNpdhSD1uDFa3HkEyLJd+dNKxeZ3MhW8qtMJ5jaCK89LBfbU7xl4vI2mQMxBr085j7wG1GQEBsjw5RY5XnpXOFEnWUjnmvuwMx0JrP57vCZFt/DXg9hY6U+QMyE4evxiQHT9WoePHKtSuQ4DiE4LU6jr+2gMAAABwUYP/Qti7d+9lHRs9evTlVwTgmnfuTKW2f2lVabGjui00zKKbU0J1XWvXggvz+DGZf5srVdTxmKQktWknIynVGYbFdvJE2XCR0eo6WZ56QY7f/NL5+ZzIk+Mvs2X5n1/LsHhv5tZ1Uf7qn9pCZ0871yAryL8YkJ3IrdCJ3Aq1jfFX/A3BiogkIAMAAABwiWBs37592revjtkXlzhGMAagLqZp6mimXft32+S4mIkptlOAbkwMVUDgpWeJmUcz5fjiQ2nXV3XvKvktyytvu70jIi6fcX03GT/5H5l/fcPZsGe7zGULZYx+1Ov3joj0V/KgFjp3plKZe+06kXfxOd2C/EoV5Jcour0zIHM1iAUAAABwbar3XwRPPvlkU9YB4Bpntzm0a4u1xmNufv7SjTeHqkPngAZDLNM0pf275PjiI+nA7kvfLLINodgVwNL/djnysmV+8aEkyVz5sRyxnWVJGdwk9291nb+SbvPXuTNVOrjPpuO5FwOyk8crdfJ4idq0cwZkkVEEZAAA35Sbba/1usP1Qc1UDQA0vXr/JZCWltaEZQC4lhWeqNDOr6yy2y7O8Gp1nZ9uTglVi5b1P1pnOqqknZudgVj2ododOsZJx4+pxsr9gUEyRo3zZPlwgzHyxzLzc6Svt0iSzIVvymwXK6NLfJPV0Oo6P91ya5jOn3UGZPnHLv68/H/23jw8yuu8+/+cZ2Y0m/YVIQESIAkksYPBgA02TuLd8RpncRYnbVInb9+2adKmTa/3Tesmad7kl7SNkzRp3Tq2Y+N4d4zteAMbA2ZftCJACxJC22gZzT7znN8fjzSj0QjMog04n+vSNTPnPMuZkUbPeb7nvr931+kwXacHyc4zBLKsHCWQKRQKheLKobU5wOE9vri24ddKHFMoFFcK6g5AoVBMGHpEUlfl53hd/ErkvDIrCxbZ0ExjR3XJcAi5ayvy9eehoy2+U9MM37Ab70YUzEHftRXPk0ZVSp8tC+dnv4C2ZuMEvSPF+SI0De3Lf4X+g29FRUz9ke+jffcniPSsSR1LarqJFWudlA4YAllbSwiGtNrujjDdHYNk5ZoprbCSnWuZ1LEpFAqFQjEV1B7yE4nEt0UiUH3AT35hEqYzzNUUCoXickIJYwqFYkLwuCPs2+mlvzc227LaBEuvcpCbP7boIP0+5Pt/RP7xRejrie+0JCHW34D4+J2I7Lxos7ZmI1ubl0Zf37YmfXzfiOKiEXYH2je+a1Sq9A5Cv8sQx771fUTS5K9Gp6SaWL7GSWl5hIZaP23NoahdXU9nmJ2dYTJzTJRW2MjONau0XIVCoVBclvT3hvH7xvZrDQYkr7/QT2a2mexc4yct04R2DlXDFQqF4lJDCWMKhWLcOdkU5Mg+L5GYnRg5M8wsW+3AatMStpeDA8h3/oB851XwuOM77U7EdTcjNt2KSM2Y4JErJgqRm4/2tb9B/9n/AV2HpgajquiDfzllwlNyqollq4cFsgCtTcGoQObqirBrq4eMLEMgy5mhBDKFQqGYzugv/45BhwPd60W7/TNTPZxpjZSSlhNBqvb7zrqdHhmOqDYmdGYzZOaYyc4zk51rITVdU9dGhUJxWaCEMYVCMW6EQpIj+7y0Ncc8nIQGCxfbmFtqTZg8SVc38s0Xke+9AcH4dEtS0xEfuwNx7Y0Ih3Myhq+YYMTCJYhPfQX51K8BkLu2QmER4hN3Tem4nCkmll7loLTcSkNtgJONMYGstyfCh+95SM80BLLcfCWQKRQKxXREvvI0nuEXShg7I+Gw5PDe+LnaaIQAS1Li1CwcHi5eEwb8WJKEEU2WZ/w4k5VQplAoLk2UMKZQTAN27dqFw+HA6/WyZs2aqR7OBdHXE2bfLi/eQT3a5kzWWH61g/TM+H818nQr8vXnkLu2ERdWBpAzw0iXXLcJYUmajKErJhFx3S3Q2oR8/48AyOceQ86cjVi0copHBo5kE0tWOSgpt3Gs1k9LYxA59Ofc54qw+30PaRmGQJY3UwlkCoVCobi0cPdH2LvDw+BAbK6WkqZRWGSh9lBMBVu62k7hHCs+r053Z5iejjBdnSH83vi0y1BQ0t4ailZ9ttljQllWrgWHMzFLQKFQKKYjShhTKKYBu3fvjj6/1IQxKSXH6wLUHfFHo2wAZhUnUbnMjtkSEw9kU4NRYfLATuI2BiNy6Ma7ESvXI0xnrlSpuLQRQsBnvopsb4VjNSAl+m9+jPad/4fInzXVwwPA4dRYvHKEQHYiiD50D9HfG2HPdg+p6SZKK6zMKFAm/QqFQqGY/rQ2BTm81xtntD+rOInK5Xa0vdvI2z6ikNGcL8CcjdgdGrOKkphVlISUEu+gIZR1d4Tp7gwTDMTP5fw+SWtziNbmEODDmayRNRxRlmse005DoVAopgNKGFMoFBeM36dz4ENv1HsCwGyBxSscFMwxor2klFB3GP21Z6H2UOJB5pej3XwPVK5QEThXCMJsQfuzvzXM+F1d4POi//xhtL/7CcKZPNXDi2J3aCxa4WD+QhvH6/w0nwiiD91QDPRF2PuBF5s9fp/W5oAqb69QKBSKaUMkIqk+4KP5eDDapplg0XI7s2ZG0F98DPnmSziGLnAOfw/ytz8nAphGVPkWQuBMMeFMMTFnnhUpJe7+YaEsRE9XmPCo7EzPoI5nMEjLCePcKWnaUESZhawcE5YkJZQpFIrpgRLGFArFBdFxKsTB3d641cKMLBPL1zhwJJuQug4HdxkRYk0NiQdYtBLtpnsQJeWTOGrFaOrr6xNel5WVTfh5RWo62tf/Hv1f/sYwMelsR//1j9D+/P9Mu4hBu0OjcvmwQBag6XggKpD5R/kWH95jNChxTKFQKBRTjWfQWMQZ6IuFiTktAZaHtpHyu13oLSeIegaMJBSER3+GfngPlJQbc7WZcxBaTMgSQpCabiI13cTcUiu6LhnojdDdGaarI4yrOxy9Vg7j7tdx9wdpbAiCgPQMUzT1MiPbjNmsFkgVCsXUoIQxhUJxXkQiktpDPmNSM4L5C62UVdoQehj9g3eRrz8Hp9vidxYaYtU1iJvuQhQWj8t4tjX283S4k0F0ktFIbRRsKE4bl2Nf7tTX1/P222/HtQ2/nhRxbPZctAf/Av1X/2I01BxEPvvfiE99ZcLPfSHY7BoVy+zMX2jleH2A43WBhG0iEag77FfCmEKhUCimlPbWIAc/9BAOx8Sm/NO7WFT7KOaI/6MPIHXknvdhz/tIALsT5i9EDAtlc0oQlpidgKYJ0rPMpGeZmb/QmC/29UTo7gzR3RmmtycSr8FJw7+zzxXhWF0AoRkLrNm5FkMoyzShmZRQplAoJgcljCkUinPGPRBh/04PA32xmY3NLli22kFWegT5zivob74Iru74Hc0WxPobDFP9nBnjNp5tjf088uFpAsaUjUF0/m3XadoGAmwsTifVZsJpURWSzsT27dsJh+OLH4TDYXbs2DEpwhiAWLEOcev9yD88DYB862X0gjlo6z82Kee/EKw2jfIl9jGFMQDfKHNihUKhUCgmGiklnG4jcrSauhYnjUmVgDH/EXqYhUefZE7r28TNiIQATSPOeOxM+DxwZC/yyF5j1mVJguISxPwKRMlCmLcQYXdENzeZBFm5ZrJyzZRhVMN0dYWjHmX9vfHnlDq4uiK4uiIcrQaTCTJzzNGIsrR0E0JT8zmFQjExKGFMoVB8JFJKTjYGqdrvi5s75c00s6RSx7L9OfR3XoFBd/yOdgdi402ITbcj0jLGdUwRXfKbfR0EIvEiRFiXbK5ysbnKBYBZg1SrmTSbiVSriTSrmVSbiTSraehx5GszyUka2mUspEUiERobG6mqqsLj8Yy5jdvtHrN9ohC33Y9sa4IDuwCQT/wSOaMAMX96p9naHeKMIljX6RA5M5Qxv0KhUCgmBqlH4GQTsqEK2VADDTX4giYOLPo6fbyn3MMAACAASURBVOkl0e3svi6WHfk56QONYDJD0XxEaQWipALmLUAe3ot8/BHD1mCYJCvcfA/C5kA2VENDDQz0xQ8gFISj1cij1YZQJjSYVYQoqTAiyuaXx839zGZBbr6F3Hzj2hgM6vR0hukZEsrcA/EpnZEIdJ0O03XaWMCzWAyhbVgoS05VC58KhWL8UMKYQqE4K6GgzqG9PtpPxhxVNQ3KS3Vm122GzW8gA6NC8lPSEDfcjth4M8LhHNfxhHXJ1sZ+nq3uwR0YwxcjYXtw+cK4fOGP3BZAE5BijQlladYhQc1mihfYhvpSrCZMl8AKZl9fH9XV1dTU1ODz+c66raZp9PX1kZ6ePiljE5qG9uBfov/w29DWDJEw+i9+gPbd/w+RmTMpY7gQFiy2cXiPb8yF9t3ve1i13hm9AVAoFAqF4mKQoSA0NiAbqpHHauBYbZzRZVfmIg4u+xqhpJRoW27PIRYHtmO97mpEyVeguBSRFJ/qL9ZsRAc8T46oSvnZL6ANG+9vus2IRutqHxLgqo3HzvZRA9Sh5QSy5QTy7VeGBpBviGTDYllOflTMSkrSyC9MIr/QKNbk9+n0dA1VvOwI4/XEz/FCIcnpthCn24z5qNUWL5Q5nEooUygUF44SxhQKxRlxdYfZv9MTFxWT7NBZ1vsqKb98ASKjxKasXMQn7kKs25Qw8bpYQhGdt47383xND52es4tcZg0y7RYGAmH84fNLa9Ml9Psj9Psj0B/8yO0FkJykxUS0oSi0kQJaTFgzxDXLJHlmhMNhjh8/TlVVFW1tbWNuI4QwJrwj0HWdzZs3c+ONNzJnzpzJGCrCZkf7xneNSpWDA+DuR3/k+2jf/iHCOj39uoZ9xA7sit2YmC0QDoGuw57tHlauc5I3U4ljCoVCoTg/pN8Lx+qQDTXIhipobCCh7CMgETTMvZNjxbcbUVuAQKdslpd5d61Fs2z4yHNpazaytXlp9PVta+IXxoQQkDsTkTsT1t1gnLfPBcdqkMdqjaiyk02JRv6d7cjOdvjgbSOqLC1jyKdsSCgrLEJoRsEdm12jYHYSBbMNoczriRgi2VBEWcAfP1cJ+CWnWkKcajE+E7tDkJ1niQplNruqeKlQKM4dJYwpFIoEpC5pqA1wtNrPSM1kVqCa8nd/hikyylupYA7ixrsNY/1xrigYCOv88Vgfz9e4EqK+kkwQ0WFkNqXVJPj66hlRA/5AWGcgYAhdA4Hw0GOEfn+Y/kAkrm/AH8ET+ugotJFIwB3UcQeDjC09JeK0aKSOjkBLENFifVbz+U3uenp6qK6upq6uDr8/0WDX6XRSXl5ORUUF7e3tvPHGG9G+YaEsEAjw8ssvs27dOpYtWzYpq7AiOw/ta3+L/tN/MHIoWo4jH/s3+JO/nrarwIVzrHHC2IaPp7BjqwefRzfEsQ88rLjaEV0RVygUCoViLKS7HxpqhoSwajhTxcgRBLLncLDya/SYC6JtVptgxdWpZOVmTuh4RXomrFyPWLneGL/XAyeGhLxjNXDiaKKQ198L+3Yg9+0whDKbHeYtiAllxaUIi3G9dDhNzJ5rYvZcK1JKBt06PcNCWWeYUDBeKPN5DduPk43GomZyikZ2njkaVZZkVUKZQqE4M0oYUygUcfi8Ogd2eejpiuWHmXU/i4/8mhlde+M3nrcA7aZ7YdGKuBLe4zKOkM5rDb28WOsyordGkGo1cceCTG4uS2dP6yC/3tERrUr5p6vz4qpSWs0aOWaNHOe5Re2EIiOFNENAi3s9JKD1ByIM+MO4g+cnpAF4QjqekE67O3HldyxsZjEqjXNIOBuKUEu3mXGYdPpPNdHcUEdnx+mEYwghKCoqoqKigqKiIrSh31dqamqcMHbvvffy6quv4vF4kFKyfft2Ojs72bRpExbLxEc+ibJKxKe/inziFwBGRayCOYhb7pvwc48HjmQTa69LZue7g3g9OlKHfTu8LL8aZs5S4phCoVAoDKSrC3m0ekgMq4b2kx+9U+5MRGkFlJTjyq5kf60tLpIqO8/M8jUOrLbJF4GEwwmVKxCVKwCQoRA0NwwJfUOpn75R3qZ+H1QfQFYfMIQysxmKSozKl/PLjegyRzJCCFJSTaSkmigqMYSygb5INJqspyuckMQw6NYZdAdpOmYIZanpJrLzDJEsK8eM2TI9F9wUCsXUoIQxhUIRpb01yKE9vrhVuIzeOpZW/Qf2QE9sw8oVaDfdY0zOxhlPMMKrR3t5ua4XdyBeEEu3mbizPJMbSzKwDUVRbShOY2B3bLwjRbELwWLSyHJoZDnOTQSK6BJ3wBDK4kW0EdFpQyJafyCCOxBBP8+ihf6wxB8O0elJFNJSwgPM9LcyI3gas0xMMY1YHIicYpwz5xJITeWYbqKrzRNN+TzS4WF7+jUENBs23c8Sn53777+fLVu20N5u+IccPXoUl8vFrbfeSmpq6vkN/gLQNtyI3tqE3LoFAPniE8iC2Yilayb83OOBw6mx9npDHPMM6kgJ+3d6kZJoiohCoVAorhyklNDRFi+E9XSefSchjFTD4WiqkgpEWgZSSo7XBag76I+zQiitsFJabps2lRuFxWIY8M8vh5uGigWcaokWCpAN1dDnit8pHIZjtUZ6Js8Zn0HBnNj7n1+OyMhCCEFahpm0DDPzykDXJX0uQyjr6Qjj6g6jj1q3HOiLMNAX4UR9ACEgPTMmlGVkmzFNks2FQqGYnihhTKFQEAlLqg/6aD4+wlNL6pSceIF5Ta+gSR2Ehli5DnHTPYhZxeM+Bncgwiv1Lv5Q34tnVBRWlt3MXRWZfGxe+nmnFU40Jk2QbjeTbjcDH+2FpUvJYFQsMyLQxhLQRkaljSq8iUkPMyPYzkx/K6mRxAqSOoKupFxOWQtxWTIhIKAxAHSd4U3YAfCb7Dzy4Wm+vnoGd911F9u2baOqqgqA7u5unn76aW666SZmzZp1Ph/RBSE+9RVk+0moP2K8p//8Kdp3foQomBzPs4vF7jDEsR3vDuJxD4lju7xIHQqLlDimUCgU483TRR+LPv/sFI4DRlaMrI5VdXT3n32n4YqRJRXGwuO8BQhHctwmwYDOwd1eOk7FFsKSrIJlaxzkTvNKyEIzQWExorAYrrvFEPW6O4zPZ9in7PQoUwopobUJ2doE724xosqy8+IN/fMK0DRBZraZzGwzlBvz2t6emD9ZnysSZw0iJfT2ROjtidBQE0DTIDPbTFaemZxcM2mZJrRpIjAqFIrJQQljCsUVzkBfhP07BnG7YzMGm6+bpVW/IrP/KJjNiLUfR3ziTkRu/rifv98f5qVaF68e7cMfjhfEcp0W7qnI4vq5qVhM00sQu1A0IUi1mUm1meEcgtuklHhCOv2+MC2nTtFUX0d3WyNydM4A4Dc7OWUtoDUpn5B2YYb1gYjkv/Z1sqE4jeuvv56cnBy2bduGruv4/X5efPFFrrnmGpYsWTKhvl/CbEb76t+gf/+b0N0BAR/6zx9G+/ufIJInPmptPLDZNSOtcusggwM6SDjwoRE5NqtYiWMKhUIxnjwzhcKYDIWgqSEmhB2vA5/37DslWWP+WqUVUFR61mIzfT1h9u6IL4iUkWVixVondselN0cSQkDODETODFi7CQA50GcY+g+nX548QULoV3cHsrsDdr5rCGUpafGG/rPmYjKbDCP+PAssgnBIxipedoYZ6IvPSNB1ot5l9RgZnZk55mhEWWq6adp6nSoUivFBCWMKxRWKlJKmGjc1VSF0Yob5Mzp2s6j2USwmaYhhN9xhGKyOMy5fmBdreni9oY/AqJComSmGILahOA3zFb5iFwgEOFZXR1VVFS6XK6HfZDIxf/58KisrmTlzJgC+sD4i4myUL9qICLWGnkRjfoD+QIR/fPckX1qey6JFi8jKymLLli14vV6klLz33nt0dXVx3XXXYTZP3GVEpKQalSp/8G0I+KC7A/1X/4L2F99DTOB5x5OR4pi735jcH9xtfI6z507PapsKhUKhODvS74Xj9TEhbCyj+dE4kqGkPCaEzZp7TtcyKSVNx4JUH/TFefHPLbOycLHtsopsEqnpsHwtYvlaYOhzPlEfE8pO1ENoVMVwdz8c2IU8sMsQyqx2mFdmpF2WlENxGWarlbyZlmiV6EBAp6czJpR53PHiWzgMne1hOtuNRUhLkohWu8zONeNM0ZRQNo7ou7YiX3icjt5uyMhG3PkA2pqNUz0sxRXGpXFnoVAoxpVAZw+H3u2gg5kwJIppkQAV9U9Q6D6IdutdiI03I5zJZz/QBdDlCfF8TQ9vHusnNMpsa1ZaEvdWZLF+Tiqmy2iid75IKWlra6O6uppjx44RiUQStsnKyqKyspKysjJsNltcn8NiwmExMSPl7Of5ygvH6PImRp4B7Dvl4UB7IzeVpHP/4jzuv/9+Xn31VTo6OgCora3F5XJx8803k5LyESe6CETBHLSv/CX6I983GuqPIJ/5T8RnvjZh5xxvrDaNq69LZtdWT3SV+tAeH7oORfOVOKZQKBTTHekeGIpkqjZ8wsaKZBpNeiaipAJKK4zH/FnnXagoHJIc2uPl1MmY6Ga2wNKrroxqx8LmgPJliPJlAMhwCJqPI4eiymioAe9g/E4BH9QcRNYcNIQykxnmzIsZ+peUY3WmMHNWUrQojs87JJR1hunuCMVF5QGEgpL21hDtrcbvwWaPCWVZuRZc3fGiaGtzgMI56vp+LkR2vgOP/yImeLq6kI8/gg5KHFNMKkoYUyiuIGTnKbrf+ICDcgV+68xoe4q7hWUnnyJlwzrEum+cNZT/QukYDPJsdQ/vnOhnVMYkxRlW7q3M4upZKWhX8Aqc1+ultraW6upq+vr6EvotFgslJSVUVlaSl5d30auVDyzN4ZEPT8dF7GmCaHEAXcKrR/vY2jTA/Yuyuf2Td7H9va3U1tYC0NHRwebNm7n55puj0WoTgVi6BnHHZ5EvPQmAfHcLekER2oYbJ+yc443VqnH1Rie7tnno7zXEsSP7fEgJxSVq8qxQKBTTCenqMoSXo9XnVzGypDwmhGVf3HV6oC/C3h2euGim1HQTK9c5cCabzrLn5YswW4z003kL4BN3IXUd2k8O+bjVIo9Vg6s7fqdI2Ig6O1GPfOMFo23m7DifMntmDoVFSRQWJSGlxOvRo9Fk3R1hgoF4oczvk7Q2h2htDgG+hHEe3mO0XSnimNR1o8Koxw1ejyFWetxI7yB4PEPtg0OvB4f6hx7HSjkOBpAvPA5KGFNMIkoYUyiuAGTLcSKvPc/R3jyOF90KIrZiWdSzgwVLHZi/+E8Tkp7WNhDk2eputjYOJFRjLMmycV9lFqsKkq/YkHQpJS0tLVRVVdHY2Ig+xgp0bm4ulZWVlJSUYB1H0XK4gucv3j+Bf6gq5UPXzGVWmpVH93dypMOYrHiCOv+1r5PXjvbyxWWruTYnh/fff9+YPHq9PP/882zYsIFFixaN29hGI265zzDg3fcBAPKp/0DmFyJKKyfsnONNklVjzUYnH27z0OcyxLGq/T6kLplbZvuIvRUKhUIxEUQrRo4Uws6lYmRBkRGFVFphVF8cR9uJlhMBjuz3oY8IGJ8zL4mKZXZVPXEEQtOMqpUFc2DjzQDIns5owQPZUDO2qHmqBXmqBba9bkSVZebECWWO/FnMmWdlzjwrUkrc/fqQB1mIns7wR2bNRiJweK8PgSAr14zNPv094KSUEPAbYtWwcOUZErNGCVpy+Hm03UNcnu94MFrgVCgmGCWMKRSXKVJKOFqN/vqzeI+1cLDyz+grLon2WyJels52kXfvjecd2n8utPQF+H1VD9tbEgWxhTl27qvMYlm+84oVxNxudzQ6zO1OrCyZlJREWVkZlZWV5OTkTNg4NhSnceiV90e8NtIV/mnTLHa3DvLo/k5ODxozwFPuEN9/7xRLZmRy+w23sP/9t/D7/ei6zrvvvktXVxcbNmzAZBr/lWwhBHzpfyO72qHlBEQi6L/8oWHGn5037uebKJKSNNZsSObD9wbp7THueKoP+tElzF+gxDGFQqGYaKQeMRZajlYPpeNVn0PFSBMUlRi+VaUVhtm7Y/ztJsJhSdV+HycbYz5aJjMsXumgcM7lnzo5HoisXERWLqy5DhhKgz0+wtC/5bihXI3E1YX8cBt8uM0QypJTYF7M0D9l9jxS063MLbUidUl/XyQaUdZ1emxLikjYqEYN4EzRyMox/MkmWiiToWBCVJYhZLmN6K1oNFfseVTcGqOw05SRmT3VI1BcYShhTKG4zJC6Dof3oL/+HByv41TeaqrWPEzY7Ihuk5XsZ9l1edgd45/+dsLl55mqHnaeTBR7FuU5uK8yi0V5jnEVxHoHD454tXHcjjve6LpOU1MTVVVVNDc3G+LlKPLz86msrGT+/PlYLFNXel0IwepZKSyf6eTVo708c6QHT8hYDTx02suRDrhhwSYyT36Iq8dY1auqqqKnp4ebb74Zp9M5/mOy2tAe+nv0f/4r4yZmcAD9kX9G+5t/Qdjs436+icKSJKLimKvbmJzXHvIjdSgpV+KYQqFQjCcyFILmhpgQdrz2/CpGDhm4T4TNxEgG3RH2fuCJFmoBSE7VWLnOSUrqlZk6OR6IlFRYugaxdA0AMuCPGfofqzEqiAYD8TsNuuHQbuSh3YZQlmSFuTFD/7S5ZaQvtDN/Ibz5Sj9+b+J8biQet47HHaTlhCF4fpRQJsNh8HmGoraM9EQ5LGKNSE9MiObyDkIwONYQJh6rHZxOcKSAMxkcToQzxSg64XCC02gXjuShfuNRP7wHnvhl/O8gyYq484GpeR+KKxYljCkUlwkyHEbufR/52nNwqoWwlkTNwi/TWrAhuo0QkrJFduaXpSHG2dz+aLePZ6p62NM2mNC3LN/JpyqzWJjrGGPPi6fPc3jEq40Tco6Lob+/n5qaGmpqavB4PAn9NpuNBQsWUFFRQVZW1hSM8MxYTBqfXJjFdcVpPHW4mzeO9aFLw3/sjydDJJuXsTHvKL6OZgDa29t5+umnueWWW5gxY8a4j0dk5aA99B30H3/XWNlsbUL/739F++q3JyTycaIwWwSrr01m93YPPZ3GCm3dET9SQmmFEscUCoXifNB3beVXOx8lO9BHjzWNiG0TwmQ2hLDGo4mVDEczsmJkSTnMnjep1Y9PtQQ5uMcbF7BTOMfCopUOzOYrM7J+ohBWGyxcgli4BBgSoU6eiEWUHauBwYH4nYIBqDuMrDtsCGWaZvyNlJSzIG8Nh70z4iq8a+jkzrQQCuj09uroevzvcLRQ5gi5yPKcILO3jqyuw9gGTk/kR3BmLEkx0WpIuIoKWSPaDcHLOdSeAnbnBX9fTFdfjy40+n/7C1JCftwWG2kPPKSM9xWTjhLGFIpLHBkMID94yzAUHfLE6E+Zw8HKh/A486PbOZway692kJE1vl/7mk4vm6t6ONieKPhcVZjMvRVZlGZfOtE840UkEuHEiRNUVVVx8uTYpr2FhYVUVlYyd+5czJM4Ab8Q0mxmvnbVDG4qzeDR/Z3R3/dgWPAHWUpFhoMZvXWAxOPx8Oyzz3L99ddTXl4+7mMR88sRn/sz5GP/bjTs34H8w2bE7Z8e93NNJGaL4KprnOzZ7qG7w7gbqq/yo+uSskrbFZtmrFAoFOeDvmsr8rc/J3dI/MoJ9MPrz3PWGJ7hipHDQtjM2VOyuBKJSGoO+mg6FhPuNA0ql9uZPTdJXQcmAWE2Q3EporgUPv5JI5r/dGu8T9lozzldh6YGZFMDM3kJmXc19fPvxW/LwubvoezY7yl4aycAEc1Cf+pcejIW4MpYSG/afHRTfFqs15KJNz2Tk+kroRgcntNk9dYaQllfHbZA77m/IZMpKmoZjykIhzP63IjqSh4RzRUTvoRlatJ1tTUbeXx3bJH7z5UoppgCpvedmEKhOCPSO4h8dwvy7Vei3hgSaJr1CepL7kPXYml4BbONVUeLZXwmWFJKjnQYglhVR2I6wtrZKdxbkcXczCsv8qW3t5fq6mpqa2vx+RIrFTkcDhYuXEhFRQXp6elTMMKLY066lf97XSH7Tnl4dH8nbQNBEIJqMYv2FDtLPEfQ9BC6rvPWW2/R1dXF+vXrx913TFv/MfTWJuPvH5CvPIUsmINYsXZczzPRmM2Cq9Y72fOBJ+pT0lATQEpYsEiJYwqFQnE2pJTIp3/90RFhuflD5uqVxmPOjCn//+r1RNi3wxstxgLgSNZYudZBWoa6RZsqhBCQPwuRPwuuNapfS1e3IZQdGxLK2prj9ino2ElBx84xj2fSQ2T21ZPZVw+NL52bUOacgdc5g5OFhk+aI9xLln6aLM1Flt2LzWmOpSqOSEvEkQxWNXdQKC6EafFft7Ozk9/+9rdUV1cDsGLFCj7/+c+Tmpp61v2+853vcPz48YT21atX881vfnNCxqpQTDWyvxf55kvIba8ZpZGHCFhSOLz4IboyKqJtJjMsWu6gsMgyLhdJKSUH2j1sPtJDXXe86KMJWD8nlXsrspidfmWUpx4mHA7T0NBAdXU1p06dGnObOXPmUFlZSVFR0YSY008mQghWFiSzNN/J6w29PHW4m8Ggjispm12m1Sx2HyQ5YqTUHjp0iO7ubm666SYcjvFNpRX3PmhUlao9BID+6E/RcvMRs4rH9TwTjcksWLXeyd4PPHS2G+LYsdoAug7lS9QEV6FQKMZCurrRH/t3w1/pDIg//bZROXIcK0aOBx2nQhz40EsoGItrm1FoYekqB5Yk9T9/uiEysxGrN8Bqw55EetxwrA7ZUI184/kz72h3xItWjmTMzmSyHMlkOZPBOYBua6BPZNITTKNn0ErvgInRBcq95gy8ZHASIATOsEaWzUxWpuFRZndcOlYSCsV0ZcqFMbfbzfe+9z3C4TB33HEHuq7z8ssv09zczA9+8IMzphdJKWlra2PVqlWsXr06rm8iK7gpFFOF7GxHvvECcsfbjK4T3TVnLYdLHyQgYytOaRkmll/tIDnl4kUYKSW72wZ55kgPx1z+uD5NwMbiNO6pyKIg9cqqmNTd3U1VVRX19fUEAoGE/uTkZMrLy6moqCAlJWUKRjixmDXBrWWZbChKY/ORbrYc7cVncrA37SrKB6vIDRqpB21tbWzevJlbbrmF3NzccTu/MJnQvvpt9O//NXS2QzCA/vOHjUqVqZdWNJ7JJFi5zsm+HR46Thni2In6AFKXVCyzK3FMoVAohpBSIndtRT71a8Og/Exk5qCtWj95AzsHdF1Sf8TPsbrYnEEIKF9qp7hEpU5eKghnCixZhViyisie98HVlbhRZg6mf/mvjzyWBmQP/YCRXtvXE6Gny6h62dsTRh9VRNMzqOMZHGHmn6yRlWsmK0cJZQrFhTLlwtgf/vAHenp6+PGPf0xhYSEA8+fP5+GHH2br1q3ccMMNY+7X1dWF3+9n1apVXHvttZM5ZIViUpEnG5GvPYvc+wHI+CUkfWYRR1c/xAn3DEaaacwrs7JgkQ3NdHETLF1Kdra4eaaqh6a+eOHHrMGmuencXZFJXvKVI4gFg0GOHj1KdXU1HR0dCf1CCIqLi6msrGT27Nlol5Ah/IWSYjXxlZV53Fiazv/s72RPm4cjyUso8jUy13cMgbEI8uyzz7Jp0ybKysrG7dzCmYL2je8a4pjfB64u9F/9EO2v/glhnrqqnheCySRYudbJvl1eTrca4ndjQxApDb8ZdcOkUCiudORAH/oTv4ADu+I7NBNx6sE0rGrn9+ns2+nB1RUbp80hWHm1k4zsKb8lU1wg4s4HkI8/Mm5VFU0mYYhcuWZKK4aEMleEns4wPZ1hXEooUygmhCn/L7xjxw4qKiqiohjA4sWLmTlzJjt27DijMDZsZl1QUDAp41QoJht5tBr99efgyN7EzuJSvJs+zYH+Evp7Y2KZ1SZYepWD3PyLEwQiuuT95gF+X9VD60C8b4dFE3x8fhp3lmeR47y0hIcLRUpJZ2cnVVVVHD16lFAolLBNamoqFRUVlJeX43Q6p2CUU09hqpXvbpzFwXYPj+7rpEnMxW1OoXLwCGYZJhwO88Ybb9DV1cXatWvHTTQU+bPQ/uSv0X/+MEhpmOX+7j/gga9fcmKSZhKsuNrB/l1e2k8af2dNx4LoOixeqcQxheJyZteuXTgcDrxeL2vWrJnq4Uw75P6dhig25KsKQM4MtC/+b6Sri44njKqU3dZ08j734LSqatfVEWL/Ti/BQGwVMzffzNLVDqxWJVpcymhrNqIDnicfw+7vwWfLwvnZL4zb35/JJAyBK8cMFyiUOZI1sodEMiWUKRRjM6XC2ODgIB0dHQmpkADFxcXs37//jPuOFsb8fj8225Vn9K249NF3beWBuu3REsWRZ5KhsR6O1SZuXL4U7aZ7aLWWcWS/j0g4JorlzDCzbLUDq+3CL3ZhXbK1sZ9nq3tod8eLP1aT4KbSDO5YmEmmfco19UkhEAhQV1dHdXU13d3dCf2apjFv3jwqKyspLCxUosUQS/Od/PTmIv54rI/fHTaxx+Rg8cABnLpRqGH//v2c6ujk9ltuHrf/22LxKsSdn0c+/xgA8v0/wqxixHW3jMvxJxNNEyxf4+Cg8NLWYnwPW04YkWNLVtoRmvo7UyguR3bv3h19roSxGNIziHz618hdW+PaxcabEHd/EWGzI4CvHZ8R7XtpzYLJHeQZkFLSUBOgvmqEDYWAskobJQutat5wmaCt2cgL1bHXn59AUfZChDLvoE7LYJCWRiWUKRRnYkrvbl0uFwCZmYmGmBkZGfh8Prxe75iGzSdPnsRut/Pb3/6WHTt24Pf7ycvL4/7772fdunUTPnaFYjzQd21FPv4IqSEj/Do15Ic3X4zfSAhYfjXajXcTLpjPoX1e2ppjxvdCg4WLbcwtvfAJViii8/aJfp6rdtHpiRfE7GaNW8oyuH1BBmm2y18Qk1LS3t5OVVUVx44dIxwOJ2yTkZFBRUUFCxcuNZmFZAAAIABJREFUxG63T8Eopz8mzRBSrylK5fdVPbxWm0TZQBU5IcOH43RbK//5299x1x23MjNvfHzHxI13QWsTcvc2AOTTv0HOKEQsXDIux59MNE2wbLUDIby0NhvfyZONQaQuWXqVQ4ljCoXiikBW7TcM9vt6Yo3pWWhf/HNExbJo07bG/rj9tjX2s6E4bbKGOSYBv86BD73RisNgRPYvX+MgO+/KiLi/kujzHB7xauOknVcJZQrF+DCld7l+v7F6YrUmVrBLSkqKbjOWMNba2orP58Pj8fCNb3wDj8fDli1b+Nd//VcikYjyHVNcEsgXHo/3JBiJyYy4+jrEJ+5EzCiktyfM/jfceD2xKDFnisbyNQ7SMy/sqxwI67x5vI/nq130+OIFIGeSxm1lGdxalkmK9dKuongu+Hw+amtrqa6upre3N6HfZDJRUlJCZWUl+fn5apX3HElOMvGl5bncWJLO/+xP58TRQ8z1nQBA9w+y+ZnfU7x8PbevXYx2kZ+pEAK+8A1kRxs0HwNdR/+PH6H93Y8Rufnj8XYmFaGJIRHMx8mhyWtrcwgpvSxd7UBT4phCobhMkX4f8vf/jXzv9bh2seY6xKf/BOFIjrZta+zn5x+ejtvu33edpqnXz5L8ZIQwCgVpCDRhXCuMR9CGn2MsSGgYbdF9os+H+4z9R/Yb+4uh10a/qzvMvh0e/L5Y6mRWjonlVzux2aef6LCtsZ+nw50MopOMRmqjmHJhUXFhnFUo6wrj6j43oWzYnyxbCWWKK4QpFcb0oVq0Z7vBPJMHzaZNm9B1nRtvvDHatm7dOr75zW/y+OOPs379+oR933rrLd566y0AfvjDH5KdnR3XbzabE9oU5476/M6PwOG99I1VxWaI7F89iyk7FyklR/b3sX93X5z3fsmCFFZfk4Ml6fwvVt5ghBePtPPU/jZc3vgIsTSbmU8tK+DuJfkkWy+9CLHz+RvUdZ3Gxkb27dtHbW0tkUgkYZsZM2awYsUKFi9efEVEh03Udzg7G35SPJP9rcX85tUPyGjbi5kIJhmhZd82ftB8mi/dfSuLCy5+Ih75h5/g+taD6L094HEjfvVDMn/4azTHRHm/9UWfTcTnt+kmyY6tXRytGQCgrSWExRJhw8fyLrrAxnRhMq8fZ5sLqOvYxaM+w/HhSv4MgzUHGfi3h9E7TkXbRGo6qX/2N9jWbIjbNhTR+c9njxGMyPh2XfJ8bS/P1yYudE00lcLBVVpK3GJPFR6qu73wqsCkDQlpmogJdppxP2SKCncxoc0Q52J9pgThbsQxBbHjjjiOECJOyBvZd7LPx4HWfoZnQIPo/PzD0+gWG3ctnolJLcKcN9Pt+5uXByw0nkciku4OP+2nfJxu89HZ7icy6vvjHdTxDgaji3IpqWZmFNiNn5l2klMmNuJxun1+iiuDKb3rHb7JDAaDCX3DbWfyn/n4xz+e0JaUlMQ111zDs88+S2trK7Nnz47rv+GGG+LM/Ed7BmVnZ4/pI6Q4N9Tnd27IlhPozz8G1QfOvFFmDr1o+E92cuBDL90dsWguswUWr3RQMNtE/4DrvM7tDUV4tb6Xl+p6cQfiRaB0m4k7yzP5xPwM7BYNv7sPv/u8Dj8tOJe/QY/HQ01NDTU1NfT39yf0WywWSktLqaysJDc3FyEEHo8Hj+csZeEvEyb6OzzbBv/3zqt47VAeNTvewhYxfMdSuuv51aM9pJSv5QsrCshNvphJl4Cv/S38v7+DcIjIyUa6fvT3aA/9HWKCq4RO1OdXWikIBJJoPm5cG5uOD+L3B1hxteOyEMfGun7MnDlzQs51trmAuo5dPOozHB+uxM9QhoLIF59AvvmSUUhlmOVXIz73EIMpaQwOfS4RXbKtaYCnj3QzEEi0PJgKkhBcq6VRpMXuXfxSZ6veR6tMvNeZzgQjkp9ta+Tn7zeS5bCQ47SQ6zQPPRo/OU4L2Q4LlsvgGjTeTPfvrykJCougsMhKJJL0kRFl7oEw7gE3DbXGjYHDqUXTLiciomzk5zdRcwGFYjRTKowNq8FjpS25XC6cTud5GzOnpRnRBsNpmgrFdEF2dyBfehL54bb4Cd9ohko8d5wKcXB3fAWjjCwTy9c4cCSfX2rjYCDCK/UuXqnvxRPU4/qy7GbuqsjkY/PSsZov31BpXddpaWmhqqqKxsZG5Bi/g7y8PCoqKigtLY2mcyvGH5MmuHVZEetKPs1TL2wh3NsOQHaoG8+Rt/hmyzI+vmg2d1dk4rBcWBqvmFuGeODryP/+mdFwaDfypd8h7vzceL2NSUUIwaIVdjQNGhuMG6zTbSH27vCwYq0Tk7oxUSgUlzCyqQH90Z9B+8lYo8OJ+PRXEas3RLNLdCn5oNnNU0e6aRs4u9hk0QQLc+1IafiH6pKhH4lkxPOhx+F+yRhtUqIz4rmMf56BiY0inVQRu7XqlEHeifQxiH7GMU53wjp0DIboGEysxg1GCmqG3ZwgnI18tFsu37nlMPX19Qmvy8rKpmg050dc6iXnmHrp0fE2xiLKokLZUPqlw3n5/84Vlx9TKow5nU5yc3NpbGxM6GtqamLu3Llj7udyuXj44YdZu3Yt99xzT1xfW1sbALm542PmrFBcLHJwALnl98h3X4WRRu5CQ6y9HllYzMDzj0erUiZ/7uvUJ62i8f346KSSciulFbbz8hXq94d5ua6XV+t78YXjJ2a5TjN3V2SxaW4aFtPlewFzu91UV1dTU1PD4OBgQr/VaqWsrIzKykoVuj3JZCQ7+dpn7+atbdupO3IQAKfuZVnfLrYe8PD28Xw+tzSH64rTLiiVQ1t7PXpbM/KPLwAgtzyDXjgHbdU14/o+JgshBBXLjKqUJ+oNb8KOU2H2fuBh5ToljikUiksPGQ4hX/09csszoI+Yp1QsQ/vCnyMysoztpGR32yC/O9RNU1+8N6vVZAg4I7PBrCbB11fPmHCfLCklzceDVB/wxQ2/qCSJjy9K5XMie0iEk0NCWkyMiwwJdGOKcGMIeMP7R/uJP2bcc0aKfvEC4GhR8IlDXQwGE8U7AZxlGdd4/4DLF8blC1N/hiCplCSNnFFiWezRTIrVdEn7ttbV1fH222/Htb355ps0NTVRWFiIxWI544/ZbMZkml7vf7RQpg8JZd1dQ2b+SihTXKZMuYHQ6tWr2bJlC21tbRQUFABw+PBhTp06xe233z7mPpmZmXi9Xt5++21uvvnmqDl/d3c327Zto6KigvT09El7DwrFWMhgAPn2K8jXngPfqBS8JVeh3fl5RMFs6uvreWPB+qEOwayTqZhHhNzb7EZ1uvOpYOTyhXmp1sVrR3sJjPINyE+xcE9FFhuL0zBfpr4RkUiEpqYmqqqqaG5uHnObmTNnUlFRQUlJCWbzlP8rvGLRNI2PX3ctRQUz+OObb6JHIphlhCXugxwPu/n3nWFere/lyyvyqMxLLMTyUYi7P4881QxV+wGQ//OvyNx8xJz54/1WJgUhBOVLbGgaHKs1bg4728Ps2e5h1TonJvPl+Z1WKBSXH7KtGf3Rn0LLiVij1Ya470HENZ9ACIGUkkOnvTxxqIuGnvhsEIdF446Fmdy+IIM9rYP84v0T+DUbNt3PQ2vnTrgoFg5JDu/z0tYci6Yym2HJVQ5mzrp0os4dFo1HPjwdN18cFhZXz0qhyxOiyxOi0xOiyxMeejRe9/rC6B+hnrmDOu5ggBO9YxebsplFnFgWe24m12khw26+6OI8E4HL5aKuro59+/YlZCHouk59fX1CJNlYaJqG2WzGYrGQlJQUfX6hP2azmaSkpHET3DSTIDPHTGaOGcqVUKa4fJnyu8E77riD9957j3/8x3/ktttuIxgM8vLLLzN37lyuucZY1e/o6IiGpObl5QHw4IMP8uMf/5h/+Id/YNOmTfh8Pl5//XU0TePLX/7yVL4lxRWOjESQO95GvvxUfHlxgLllaHd/AVFaCRih1m+9NXKVSXLy9Aek2DqwWfNIzzBRWGKjf9DEgEegadpQNaTYz8i2gaDOOyf62XHSQ1gHkxDYAYlgRnIStyzIZM2sVMwmjVDAT3iMYww/v1QYPel4+eWX6ezsxOv1Jmxrs9lYuHAhFRUVZGZmTtYQFedAaWkpGRkZ/OEPf8DtNjws5vmOkxJxU0Mlf/9WC1fPSuYLy3LJTzn3Gw6hmdD+5K/Rf/AtON0GwSD6I99H+/ufINIyJurtTChCCBYssiEENNQYNxpdp8Ps3u5h1XonZiWOKRSKaYzUI8g/voh86cn4SPqScrQv/QUiZwYA1Z1enjzURXWnL25/q0lw24JMPrkwVjV7Q3Eah155P7rNhuJlE/oe3P0R9u7wMDgQi7RKTddYsdZJcsqlVcl7WED89Y6OaFXKP12dF22flWZlVpp1zH3DuqTHO4ZoNmi0dXvDhD9COfOHJSf7g5zsHzs11qxBtmOkaBafsjmZPmder5ejR49SV1dHZ2fnuBxT13WCwSDBYHBcvWyFEBcksg0LayMfR28z3kKZqztEYfZdmDUnYd1Da3OAwjlj/80pFBPFlAtjqampfO973+Oxxx5j8+bNWK1WVq1axQMPPIDFYkTI1NbW8otf/IKHHnooKoxdddVVfOtb3+KFF17gySefJCkpifLycj7zmc9EI88UislESgmH96A/91i8RwZAXgHaXQ/AsqvjRKdtW98nEhltGqvj9h/F7T9KVz80NJ3/WFaO1dgHda1Qd47HOJP4NtbrscQ1YEzB7XyO+1GvXS4XLS0tceNuakr8wGbNmkVFRQVz585V0WHTmJycHO6//35ee+01WltbAcgNduLo/5DDKcvYeRL2tHm4rSyDeyuzcCad282HcCSjff276N//ayN6s7cb/Zc/QPvmPyMsE1tZaaIwxDE7miaorzKiKLo7wux+38NV652YLUocUygU0w/ZecrwEjs+YjZitiDu+jxi020ITaOhx8eTh7o50B4vElg0wY2l6dxTnkW6fequ5a1NQQ7v9TKykPXsuUlULrNfslG7G4rTGNgt416fC2ZNkJecRF7y2AtWupT0+sJxolks+ixEpyeMP3x2D7awDqcHQ5w+i89Zpt0cF2kWfZ5sPNouwkM3HA5z4sQJ6urqaG5uHtOjdiwsFgslJSWEQiFCoRDBYJBwOBx9Pfyj6xPjQSeljJ5jvDmb4GZOtiAiJsIhE0G/Cb9PQ2BGCAuaMCOEGX/QTG+fmRMNFoQw4w200uc5QkT3YNKcbN+2jPUbKpQ4pphUpsUd4syZM/nOd75zxv6NGzeycePGhPZVq1axatWqCRyZQnFuyON16M/9DzTUxHekpiNu+zRi/ccQIwQZt9vNe9t24g8kRjVNF6SU0Yt/JBL5iK2nH06nMxodNlyUQzH9sdvtfPKTn2T79u0cPGj4jiVHPKzq30VV8mJcSdm8UOvinRP9fGZJNh+bl35O/mNiRgHan34L/d/+EaQOx+uQT/wCvvjnl1SE5GhKK4zIsbojhjjW0xnmw/cGWX1tshLHFArFtEHqOnLba8hn/weCI1Lq5sxH+/JfIvJn0dTr53eHu/mwNd4P1CTghnnp3Lcoi2zH1C1mRCKSqv0+Wk7EIps0Eyxe4WBW8aWTOjmZaEKQ5bCQ5bCwIMee0C+lZDCojxLLYo+dnnBCFfWEYwA9vjA9vjB13b4xt0mxmqKRZmN5naUkxWdLSClpa2ujrq6OY8eOEQwmRrNpmkZxcTEpKSlUVVURHhH9aDabuf7668/JgD8SiSSIZaN/wuHwGYW1M/1M5Nw9HA4TDofx+cb+vC+GiO6hq28nuz4Q3DNnYiM/FYqRTAthTKG4VJGnW9FfeBz274zvsNoRN96JuOEOhC02EQgEAry3bQ919YeQ8swXLIGJ+SVzo+KUrutxz32hCJ2DIQb8oaHtJUJKQGI3C9KsGlaTiO4z1jGAhLaRYtilzpe+9KVo5Jri0kLTNK699lpycnJ45513iEQiWGSYpe79HHeU0Gwroj8Q4Ze7O9hytI8Hl+eyNN/5kccVlcsR934J+cx/ASB3vA2zihA33DHRb2lCKSm3ITSoPWSIY67uCLu2GeKYJUmJYwqFYmqRPV3oj/0b1B6KNZpMiFs/hbjpXk55Ijy1vY3tze44s3dNwIaiVO5flM2M80ihnwg8gxH2fuBloC82d3OmaKxc6yQ1/dJKnZxOCCFIsZpIsZqYm2kbcxt/WDfEssHQiKizWASayxf+yCIB7kAEdyDCcdeZfM40cp1m8kw+0j1tCFcLEf/Yi9czZ85kwYIFzJ8/H5vNGHNeXh5vvPFGdJtNmzadc1VKk8mEyWSKHmu80HV93MW24f0mGkmEDtd+QAljislDCWMKxQUg+1zIV55Gbv9jfBUlkwmx4SbELfchUmMFIEKhMDu2H6a6Zi/hiH/U0eLr/ghM5Gev5aabEi8Gjb1+nqnqYWeLG2kFRkQYV+Y5+FRlFovyHBcdATOWWDbW67EEt4vpP99j7N27d8xVvJSUFCWKXQYsXLiQzMxMXn31VQYHBxHAfG8D2XKQA/aF6MJMc1+A//POSVYVJPOl5bkUpJ795knccDucbETufAcA+cx/I/NnIyou7cnX/AVG5FjNQeP/S2+PIY6t2eDEkqS+CwqFYvKRUiJ3vIPc/BvwjRAZCuagPfgXdGbOYvPuTt5t7E8wcF83O4VPL84+o7fVZNLeGuTgbi/hERlpM2dbWLLSoSJzJwGbWWN2mpXZZ/hbCEVG+pwlFgjo9oY4W7amRQ+Q4z5NXnc7qZEBxtrUa3IwmFyIKWcOZKQT1i10tPnJcUbIdVpos+TxQfo10eIPS5JmcG6y2MShaRpWqxWrdXy/Q8MpmsPC2miR7XzFtv7+/jHPE9HHz29NoTgXlDCmUJwH0udFvvE88s2X4lMBALHqGsQnP4fIzY+2hUM6u3fVc7jqQ4Khgbjtk8yZzClYRX+fhx73gWhefVbqMtasK4/btqHHxzNVPewelV4AsDTfyacqsyjPPf+KfWditFfYdCUlJYW33347IXx97dq1UzgqxXiSl5fH/fffz5YtWzh16hQA6b52Pmbyscu2mH5prLDuaRtk/6lBbi7L4P7KbJKtY6/gCyHggYeQHW1woh6kjv7rH6F958eIGZe2P+W8MhuaJqjab6Q29Lki7NzqYc0GJ0nW6f1dVigUlxeyvxf98Ufg0O5Yo9AQn7iT3o/dx+/r+nnzgxMJgsWqgmQ+szj7jNFDk4kekdQe9nPiaGy+p2lQsczOnHlJl3Qa/uWExSSYkZJ0xqjCYZ+zkQUCOgd89J8+iexuxunrQowRcxYUFjqSZnDams+AOQ2EgF6gd2whB5ORIeI32fnpjnaeONRJms2CJgQmAZom0IRRGEsTRpqpSTMeR7abtBH9YkS/JhK2HeuYZzpGwrnOMp7E844ep4ZmtpJksWIXsXFpgvP+XvzmN4/i8yXe39jtyed1HIXiYlHCmEJxDshwCLntDeSrm8E96oK4YLFRabKoJNoUDOoc2HuSw0d24gvEV60xm5yUzl/F2vUVOJwmWpsD7P4gN1qJ5ap1OVGzydpOL5urehIMaMGYPN5XmUVpdqJnw5XCcJj6hYavKy4NHA4Hd955J++99x5HjhwBIDzYx7rQbgJFq3mn24oEIhJeqetl64l+Pr04h0+UpGMew39MWJLQ/uw76P/8TaNyrNeD/sjDhjjm+OiUzOlMcYkVIeDIPkMc6+8dEsc2OrEqcUyhUEwCcu929Cd/CYPuWGNuPu7P/QXPezN5bUsLwUi8ELF0hoPPLMmhbJrMaXxenX07PPT2xFIn7U6NlWsdpGeq26dLiWGfs0y7mWR/K4FTdXQdO4ZzDFN6oZmw5xQQzpiNJymbiC+CHAxB8PwM8iXQ6YnQ6bn0PHovlgRRTxsh+o0Q9Ib7rba5zPZVoxH7rHRMZC+4tCP5FZce6j+7QnEWpK4j925HvvgEdJ2O7ywsQrv7C1CxPLo64vfpVB3s5NCRD/H4m+M214SF+fOWce2G5TicsVWtwjlWnn/p+ejrO2f/Lw6f9vBMVQ9HOhL9Da6elcJ9lVnTYjV1OlBWVhYnjClR7PLEZDJx3XXXkZOTw9atW9F1nUDAjzj6Ht9YvoZ3PDlUdxlphO6gzq/3drDlaC8PLs9lRUHiqqNIz0T7+t+h/+g7EArC6Tb03/wY7X99F6Fd2n4xRfMNcezwXkMcG+iLsPPdQa7emIzVpsQxhUIxMUiPG/m7/0Dufi+u3bPxDl5ecDOv/P/s3Xl0HOd55/tv9b5ibTQWggQBkAQkUiRFiSS4k6KkUJsVWbYkW5HiOHMmM4mTGc/cmeRm4ms79ok9Z5ybnNyxk+NkHEuJrcXWEkm0RJmUKHOnRFIiRQogsRILATR29L7Ue/9ooIEmGtwAEgD5fM7BQXd1VXV1Yem3f/W+z3tyiHC8P+2x2wrs/M6KApYVTl+v96nqvhDj+OEgsehYeFdYYmLlWgcWGZo+5/T29lJbW0tdXR1+/8SeSTBWN2zx4sUZhx6GYpNNEBCnbpKC/7cqXSV76SXHclxB3WJDEX6nTmWoHpseJmyw0WBfxKc9bh67zscqxHgSjAkxCfXZJ+ivPAct9ekP5BUkh0yu3YI2MtTQP5Sg9tQgpz/7iMHgWUirUGCgvOx2tm2vweWa2PD7oGkwVZfArKJ89mYjHcPpV7EMGmxckMUXl+WzIGfm620IMVOWLVtGfn4+O3fuJBgMopTizLFDbK2u5sH1d/P8yX66RqZ0bxuK8pd727iz2MlXV3kn/O1oCxej/e4fo/7pr5MLPj2GevV5tC/83g1+VdOvrNKKwaDx8dFkuD48qHNwJByz2eWDnRBieqlTH6E/979hsC+1LOQp4Vf3fY3Xey0EatN721fm2fidFR7uLHbOmiGJSlfUnQ5z7szY0ElNg9uW26ioss6a4xSXFwgEOHv2LLW1tfh8vozr5OTkUF1dTVVV1WVnL7ebDSzIsWZsg/+71+rxBScWpM+xGfnzLaXouiIxEhbpChK6SoVHCTV6e3T5pdbNvFxXJPejj9uXSu5L1xn3HCP7uOTxTPYcY9teat1rnb6ry1ZCl60kbZmW4ZwKcT1JMCbERVRrE/orP4XTJ9IfcLjQHvoi2raH0MzJHl8DvXHqzgQ4e+4kA4FPUSo90JpXUsk92zeQm5tDJh80DfLDI51ERuoSxDRrWihm0GBreRZfWOq5bFFxIW4VxcXFPPXUU+zcuZOuri4Aamtr8fb18f0dD7C3Pc7Ln/YSGilec+JCgP/0qyZ+a1EOX17uIcs29tZnWLsFvb0F9fYvAVC7XkMvXYihZtuNf2HTbH65BU2DE0eDoMA/lAzH1m+TcEwIMT1UKIj6xU9Q+95NLYsYTOza8Cyv2m9n6ILO+IuFZdlWvrzCw9pS17QGTXV1dRPuX00P8nBI5/jhIL3dYx/GbXaNVeuc5BfIx6W5IBaL0djYSG1tLefPn884y7rNZmPJkiVUV1dTWFg4Lb+Dz6wsSLblxw0Ptho1vrrKO2uGBt8o+vgQTr8ouEstTw/w/p/d5+kPTxxy6nHI3524seQ3TogRqrcb9frPUEf2wvg3U7MFbfsjaA88juZwoZTC1xnj3GdhmlvO0u8/QUJPH/LoyS9i2z2bKC4uJhOlFPV9Yf7+aFfaG+l49y/K5vHb82d8inIhZiOXy8Xjjz/O3r17OXPmDADd3d288ouXefDBB7nncxX8/GQPv24YSDXA3j43wG+ah3jyDg8PLsnFbEw2iLXf/h1Ux/lUkWj13P9GFc5DK18yY69vupQutKAZ4MThIEpBYFjn4Ht+1m1zYXdIOCaEuHaq9iT6T/8OepO1VGOakd3lW/hlxf30xw1pdZlK3Ga+tLyADQvcGDPUfryq51UKv9/PwMAA/f39NDY20tramrbO7t27icfjLF269LL76+mOc/xQgEh4rD3mKTSxqsZxSw0/P66PDTN8hMwXdGcbXddpb2+ntraW+vp6YhnqhhmNRsrLy6murqasrAyjcXrLJWwpT/Y2+/HBLvzouDDw79cWppbfSkaL8IMGV3iaf2+VN2Ow+MzKgutyjEJMRoIxcctT/iHUr36Ben8njJvdEM2Atv4etM99CS2vAKUrOlqj1H8WobOzlT7/MaIX1crIcuewafMGKioqMl6F8gVifNA8xPuNg7QNRS95XH+0NnOoJoRIMplMbN++nYKCAvbt24eu64RCIV577TU2b97Mf1xzBw8uyeH/HO/mZGcyvA7EdH5yvJt3zvXzlTu9rCl1oRkMGH7/v6B/77/BhVaIx9B/+FcY/uKv0XLyZ/hVTt28BcmeY8cPjYRj/rFwzOG8dT70CSGmh4pGUK8+j9rzJgAJzcDewlW8vORhfAYHjGtKFThMPLXcw7by7KsKxJRSBINBBgYGMn4lEpcuap5IJNizZw8ff/wxhYWFFBUVUVRURF5eXmrGbaUU9bURak+F08aALVlqY8ntVrQpBnhzzfhgbLbr6elJ1Q0LBCZOUAUwb948qqurWbRoUca6YdNpS3k2Q0dV2n1xZUbP1Y/2NRI22LDpYf5wfYWcQ3HDSTAmblkqGkHteSs5hCp00ZvqijUYHnsGbV4ZiYSirSFCQ22E/v5e+vzHCEU70la32ezU1Kxl6dKlE65EhWI6h1qHeb9pkFOdwSsaf18g3YeFuCKaprFixQry8/N5++23CYVC6LrO3r178fl8bNmyhb+8Zz5H2/389Hh3aqhyx3CMv/pNO8sLHfz+XV4W5jowfO0vkjNVBv0w2JcMx/7bX6FZ5n5dv5L5yXDs2KEgSodgYHRYpROHc25PNiCEuHFUYx36P/9tcsISNA56l/Ni+Q467OkXEXLtJp5Yls99ldmYjZkDeKUU4XB40vArU++fq9Xb20tvb2+qZ7HZbKawsJACjxf/QC5hfy4mY7L+q8WqsarGQUGRecrPK6ZfIBC94S93AAAgAElEQVSgrq6O2tpaenp6Mq6Tm5ubqhuWlZV1g49QXKst5dl88ua+cfdlRkpx48mnb3HLUXoCdfA91L/9HAZ60x8sX4LhC19BW7KMWEzR8lmYxrMRAoEA/f4T+MMNaasbjSZWrbqTu+66C4tlbMhjQlec6gryftMgh84PZxwuaTNpVORaOdcbIaZL92EhpqK0tJSnnnqKt956K1Vo9/Tp0/T29vLQQw+xttTNqmIXb5/r58VTPQRGhvic7Ary9bebubcym6eXF5D9H/4U/W+/CboOzedQ//JD+OrXb4qiy8WlFlZv0PjoQABdh1BA58B7yZpjTpeEY0KIyal4DPXmi6i3X0EpnaOepbyw8H7Ou9J7t2dZjTy+NI8HFudiNSUDsUgkMmn4FYlEMj3dZdlsNnJycsjNzaWhoYFo9NK98EfFYjHa2tpoa2tLLTManLhdBVQtnEdMLyYe92IyyUek2SAWi9HQ0EBtbS2tra0Z64bZ7fZU3TCv13tTvF8LIW48+a8vbhlKKTj5Ifqrz0PH+fQHC+dheOwZWLWOaETReDJEc32EaCTKQPA0Q4EzKMa67Wuaxm233UZNTQ0ulyu1/PxAhPebBvmgaYje0MTZVDRgRZGDbRXZ1Mx3YzMZ+KBpULoPCzEN3G43X/jCF3jvvfdShZg7Ozt58cUXefDBBykuLuZz1XlsXZjFC6d6eOfcWP2xd+sH2dc8zBPLSnnoiX+H6cUfA6AO74XShWi/9fkZfGXTp7DEzOqNTj7cnwzHwkGVGlbpcks4JoSYSLU2of/kb1BtzXycu4Sfl/8WDVnz09ZxG3UeKDOzMlcnONzIb94fC79CodA1Pa/FYiEnJyfjl81mS623YMEC9uzZQ3xcOQyTycSWLVvIzs6mq6uLzs5OOjs7Mw67S+gBBoYCHDnSzJEjYDAY8Hg8aUMwc3JybvrA5YnmX4+7Vz1jx6HrOm1tbdTW1tLQ0DBp3bCKigqqq6tZsGDBtNcNE0LceiQYE7cE1VCbnGny3Jn0B7Jy0B75EtrG+wiGNRqOhWhtipJI6AyHztLvP4muwmmblJWVsWHDBjweDwAD4Tj7mod4v2mIhr70dUfNz7ZwT3k2m8uz8DjSu+hL92Ehpo/ZbOb++++noKCAAwcOoJQiEAjwyiuvsG3bNpYuXUqWzcQfrC7igSW5/POxbo5fSH5QCsV1nvvYxzvO23h20+9Qs+9f0QD1ynOokgVod9w9sy9umniLzazZ5OTo/gB6AsKhsXDMnSUfLoQQSSqRQL3zCurNFzntms8LK/6AFnchdj3IglAzjkQAlx4kRwujoiF6u2HPVT6HyWSaNPyy2+1XFEaNzj65a9eu1LLt27enlpeWlhKLKT75MEhr8wCRWA+RmI9ovIdooo9EIv1Cpq7rdHd3093dzalTpwCwWq0UFRWlhWXjw7mbwVNpwdgf3/Dnn211w4QQtxYJxsRNTXW2ob/2r3D8YPoDVjvajsfQ7n2UoZCFhg/DtLfGULoiGGml33+cWGIobROPx8OmTZuYP38+0YTOgZZkGHa8w0+miSWzrUY2L8xiW0U2FbnWm/5KoxCzhaZprFq1Co/HwzvvvEM4HEbXdfbs2YPP52PTpk0YjUYWZFv55j3zOdbu5yfHu1MTYnQFYvwv43JuX/df+L1TL1Hpb0f/xx9g+L//F1rx/Ms8+9xQUGRm7SYnR/cFSCQgElYcet/Puq0u3NkSjglxq0okEgwPD9PfVE//7p20RKDh9h0kDFCkN1A82DBhm8vVTjUajWRnZ2cMv5xO57S0j6qqqtKCsdFQDGCwP8GxgwECfh2T0YnJ6KSkuIK71zuw2pN1yMb3Kuvv75+w/0gkQktLCy0tLallOTk5aUGZx+ORnktXye/3c/bs2SuqG1ZdXY3b7b7BR3jl5uKsnkKIMRKMiZuSGuxHvfkCat+7yVpBo4xGtM074KEn6Yu4qD8Sxtc5DEA46qPP/xGRmC9tXy6Xi3Xr1lFVVUVdT5gfHelkf8sQgZjOxcwGjTWlLraVZ3NniRPTLTajkRCzyYIFC3jyySd566236O1N1hM8efIkPT09PPjggzgcyYLLd81zsaLYya5zA7xw0sfwSP2xM9Yi/vtdf8LWzo94umkXef/7uxj+/K/RnK5Jn3Mu8RSaWbvZxZF9fhLxZDh2cCQcy8qRD3dC3Kx0Xcfv92es+TU0NIQ+2m6y5oIVLIRhYpMnjaZpk4ZfLpcrNRPkjXa+McKp4yH0cZNYLlxk4faVdozGZBvN6/Xi9Xq54447AAiHw3R1daWFZeHwxBEBo+dsdOi+0WjE6/WmhWVut1sujF4kGo2m1Q3LxG63U1VVRXV1NQUFBXPiHC5ZasXhcBAMBmf6UIQQ10CCMXFTUeEgatdrqHdfh2h6QVdt9SZ49Gm64wXUfximvzd5ZScWH6LPf4JgpCVtfYvFwurVqymquI19rUH+9s0mOv2ZZ0i6rcDOtvJsNixw47LKB0oxt6xZs+ambcxlZ2fzxBNP8Otf/5r6+noAOjo6ePHFF3n44Yfxer0AmAwaD1XlsmVhFi992sPOun4SCpSm8X7xag55l/PY+ff53I//Gvuf/AXaTdIrIN9romaziyO/8ROPQzQyGo45yc6VJoIQc9XoMPJM4dfg4CCJROLyO8nA6XKTlzsWeuXm5pKTk4Pb7Z5VvaXiccWnx0K0No8V5TeaYMVqB/MWWC6xZbKwf1lZGWVlZUDyXA4ODtLZ2ZkKy3w+31iAOCKRSHDhwgUuXLiQWuZwONKGYBYWFqZN1nSr0HWd1tbWVN2w8fXgRhmNRiorK1N1w2YqTL1WX1pegMfjmbTnmxBidpNWr7gpqHgM9ZtdqLdeguHB9Aer7oDHvkK7YQH1xyP4h5J1CxJ6mAH/SYZCZxl/KdRgMFC9dBnRwmp+2RbhTO1FhfpHFLnMbC3PYmt5NsXuW6+RI24eNTU1N3Vjzmw288ADD3Ds2DEOHkwOq/b7/fziF79g+/btVFePFRl2WY38/l2F7Ficy09PdHO0LRmgh41WXijfwa/D/Tz78ptsfurROXEF+0rkFZio2eLi8G/8xGMQiyoO7Q1Qs8VJTp40E4SYrZRShEKhScOvTEXLr0RYsxIyOgiOfC0s8vDA8vksKfHMidkazcYs9u8eZnhwrG3nzjZw13rnNdVR1DQtFQSOvl/E43F8Pl9aWDY0NDRh22AwSGNjI42Njall+fn5ab3K8vLy5lwIdCWUUml1wya7+FZaWkp1dTWVlZVSN0wIMWNm/7ubEJegdB117ADqtX8BX2f6g6UL0X/7K5y33U7j6QjhkVmRdBVnKFjLYOAUukpvNObPW0hHdhX/0AmxjoEJz+c0G9hYlsW28iyqC66sKKwQYuZpmsbdd9+dqjsWjUZJJBK8++67+Hw+NmzYkPbBZF6Whf+xpZRPOgP8n2PdtAwke6D22HL5f/Vcdr5ykt/fuoQqj32mXtK0yvWYWLfFxeEPAsRiaiQc81OzxUVuvjQVhLjeRofjjb8/WicrEonQ39+fFnqN3o9Go5l2d1l2uz05u6PVRkv7AKcs8/Cb3ASNdnQt+Te/Zp6D/7jCy8Lc2V9kvq0lQqnn85gMToC0UKx0oZk77nJgMk1fm81kMlFcXExxcXFqWTAYTAvKOjs7M4aTvb299Pb2cuZMckIos9lMYWFhWljmdDqn7VhvNL/fT11dHbW1takyBhfLy8ujurqaqqqqWV03TAhx65DWrpizVO1J9F/+FFrq0x/IKyD2yO/SnH03zfUxYtFkXQilFP5wIwOBE8QT6VetDFkeTlsX0xF2Qzi9jKxBg7tKnGwrz2Z1qQuL8ea7qifErWLhwoWpumOjBZZPnDhBT08PO3bswG5PD7pWFDn5mwcW8uv6fn525DxDhuQHxLqIlf++q4Vqj43z8ShBdFwYyGrS2FKefcNf13TIyTdRs9WZDMeiingMDu/1s3aLizyPNBeEuF7q6urYsyd9Psd3332XQ4cOEY1GM9a3uhJWq3XSGR9Dysgv95zkpT4Lsaz0v++V2fB0TRlL5kjw39YS4ZMPQ5iNF9V/1GDF3Xbml1tuyIVMh8NBRUUFFRUVQHL4YH9/f1pY1tvbi1Lp7cxYLEZbWxttbW2pZW63O20IptfrndW99a6kbpjD4WDJkiVzqm6YEOLWMXv/wwoxCdXWhP7Kc/Dp8fQHHC7CD/wOjZ7NtDbHSbSPXUUNRTroDxwnEutL2yRqdvKZbTE9pgLQ09+gK/OsbCvPZlNZFjl2+VOZzW7mGlli+uXm5vLEE0/w7rvv0tTUBEBraysvvfQSDz/8MB6PJ219o0Fjx5I8NhZb+cVP/423cpYTNyT/J9T2jH1g9aPzwyPJnqtzNhzLM7Fuq4vDH/iJRhTxOBz+wM/azS7yC+T/oBDTKZFI0NbWxnvvvTeh5pJSKuPQvIuZzWays7NTtb7Gf9lstgnhgz+S4KVPLrDz7ABhzQHjrvXdziBPb6liWensn1FP1xWDfQl8XXHOnQmjZ5gcwGrVWFAxc0PzDAYD+fn55Ofns3TpUiAZIHV3d6eFZYFAYMK2w8PDDA8Pc+7cudS+PB5PWliWk5Mzo+GSruucP3+eurq6SeuGmUwmKisrqaqqmpN1w4QQtw5p5Yo5Q/V2o/7tZ6jDe2H81TazBf89X6axeDvt7Qo1OPbGHI31Mxg6jj/UnravqGam0VFJh7UUpY29SefZTam6YWU5UudgrrjZa2SJ6We1Wnn44Yc5cuQIR48eBWBoaIiXX36Z++67j8WLF0/YxuV28pUvbef+//Udni/ezOGC5RPWiSQU//hRF3fNc+GyzJ5C1FcjO9fIuq0uDu1NhmOJOBz5wM+azU48XvNMH54Qc1osFqOlpYWGhgaampquaCik0WhMC79GZ3/Mzc3F4XBcUTgSjCV4s7af1z/1EdQ10MY+AiwKXuDpFQXcuXrNrO7FE/Qn6O6M09OV/IrF1CXXj4Qv/fhMsFgslJaWUlpaCoyMZvD7U0Mvu7q66O7unhAy6bpOd3c33d3dqWU2m23CEEyb7foOe1VK4fP5qK2t5ezZs5NekJw/f36qbtitONmAEGLukWBMzHoqMIz61S9Q7+2E+LhaDZqB/o1P0DhvB109BmgbawDFE0ECsU/oG6wHxpYnMHDevpAW20ISIz0+rEaNdfPdbKvI5o5CB0bD7G0UCiGmj6Zp1NTUUFBQwLvvvkssFiMej/P222/j8/moqamZcHVb8xRS8tX/wH//m2/wec8dkOFD5HBU53dfqeeuEiebyrJYU+rCappbV8mzcoysv8fFoff9RMKKRAKO/CbAmo1OCookHBPiaoTDYZqammhoaKClpeWKZ4S02+089dRTuFyuaw6sInGdX53t55XTvQxHdWBsP2X+Dr5s72bN049gcMy+mlaxmKK3O46vM4avM07An6Fb2CXYHbO/PadpGm63G7fbnbogk0gk6O3tTetVNjr0f7xwOExLSwstLWOzqufk5KR6lRUXF5Ofnz8ts4UODw+n6ob19fVlXCc/P5/q6mqWLFkidcPEVZPRH2KmSTAmZi0VjaD2vIV6+5cQGutmrgDf3V+gccGD9A2ZYFwnIV2PEuUzuvo+RdcTadtcsM6j0V5JxGhDA5YXOthWkU3NfBcO89zs2SGEmLrKykqeeOIJ3nrrLQYHk7PafvTRR/h8Pnbs2DFhliytahnal/6AgvoBfLbcjPuM64ojbX6OtPmxmTTWzHOzaaGbO4tdmI2z/8MagDvLyPptyZ5j4ZBCT8DR/QFWb3DiLZZwTIhLCQQCNDQ00NDQQHt7O3qmsX5AVlYWOTk5tLe3pwVmJpOJzZs3X3PAEEvovFs/yC9O99IfSu99VBL08VTXATZ+7l6My++5pv1fD+OHR3Z3xhjoTaAu0enLZtcoKDJjMCpam2KMa/ZhNEL18tk/aUAmRqMRr9eL1+tNLQuHw6mQbPR7ptpzoxM01NbWpu1r/BBMt9t9RUFrJBJJ1Q0bX/9sPIfDQVVVFdXV1Xg8nlnd41DMbjL6Q8w0CcbErKP0BOrQ+6h/+zn0j/1z1DUDF5Y9SmPZQwyHLTCu9IZSOrq5nrbuE+ixSNr+esz51DuWEDC5mZdl4Z7ybLaUZ1HglA92Qoik/Px8nnrqKd55553U1feWlhZeeuklHnroIfLz89PWN2zZwdOn/4m/N68hYhwbJmLU4+SZwKePvb2G44rftAzxm5YhnBYD6+a72VSWNSd6qLpGwrGD74+FYx/uD3D3BieFJfI/VIjxBgYGaGxspL6+ns7OzknXy8vLo7KyksrKylQR8rq6Onbt2pVaZ/v27alZKa9GQle83zTIiyd78AXTAzFvqI8nWn7N1gUuTF//IzTnzPfquZrhkUYj5HtNFBSaKCgy48oypIKYPE+Eowd8mAxO4nqAO2sKKC27eUpi2Gw2ysrKKCsrA5JDGgcHB9OGYPp8vgkBbCKR4MKFC1y4cCG1zOFwpIZejg7FbGpq4kDVBvxmG/ZEjOyXX6anp+eSdcOqq6uZP3++1A0TQtwUJBgTs4ZSCk5+hP7qc9BxPrU8YbDQWvUwTWUPEopbIO0CmSJqbaOt80O0qD9tf8NGN+ccS4i7vWwtSw6VXJQ3sRCtEEJAsu7YI488wuHDh/noo4+A5Afdl19+mfvvv5/Kysq09Te3HALTeX5W8QA91hw8kQGebnybzd0f03FbDfvL1rNfK6ItNLZNIKqzu2GQ3Q2D5NiMbCjLYlOZm2qPfdb+b3K6x4ZVhoIKXYcPDwS4e72TonkSjolbl1KK3t5e6uvraWxsvGRPh8LCwlQYlps7sadpVVVVWjB2taFYQlfsbxnixVM9dAzH0h7LiwzyhZY9bB/+DOuX/wDt7o1Xte/pdLXDI7NzjRQUJcOwXI8J4yQ9bkvLrLy3u37c/dJpPe7ZRtO01CQL1dXVAMTjcXw+X1pYlmkCh2AwSGNjI42NjekPWJKzkIZMFkIZgl2pGyaEuJlJMCZmBdVYh/7KT+Hs6dSymMlBS+XDNC/4LaLKDOMuWmlG6DN00t71Ic5YP+ObSWGDjSbnIsoqFvMfK3JYVTJ3hi4JIWaWwWBg/fr1eDwedu/eTTweJxaLsXPnTtasWcPatWvHAqy+HjbjY3P3xxP2U/LZYZ747DBfBJoLFnFgyT3sty+kOz72tjsQTrCzrp+ddf14nSY2lmWxqSyL8lzrrAvJnC4j6+9xc+h9P8GAjtLhowMB7lrvoLhUPiCJW4dSis7OztQwydHh1xfTNI158+ZRWVlJRUXFdau5pJTicJufn3/i4/xgeiH/rKifx8+/z/0dh7DesQrDf/k7tOzMw7+vl2sdHllQZMLjNWG1XXlvpFzXymk44rnLZDJRXFxMcXFxalkwGEwFZaNhWSwWu8Re0o3WDauqqsLlcl2PwxZCiFlBgjExo1RnO/rr/wLHDqaWha25NJU/xPnSe0hgGl87H2VUnEv4GOg5QUG0i/GlYuOaiUD+YlbduZL/UJFHllXqhgkhrs2SJUvIzc1l586dqSvuR48epaenh/vuuy9ZdyzPA32+S+5HA8p99ZT76nkaOJu1gP0VmzmYU00/Y4FSdyDOq2f6ePVMH6VZFjaVZbFpYRbzsmZP6ORwGlh/T3JYZdCvoxQcOxhkVQ2ULJg9xynEdEskErS3t9PQ0EBjYyOBQCDjekajkfnz57No0SLKy8ux2+3X7ZiUUhzvCPCzkz009KXXmnLGgjza+gEPtR/AbjGhPftHaOvvuWGB+3QNjxRT53A4qKiooKKiAkjObtnf358WlF2qp+PTTz99ow5VCCFmlARjYkaooX7Umy+ifrMLRuoh+B1FNC58mPbiDSgtPdSKGHSOR3qJD56mJNxKwbi0TEfDMW8x925aR4U3+4a+DiHEzaugoIAnn3ySd955h9bWVgAaGxt5+eWXefjhh8l+7BnUv/wQouPqGlqs8IWvoNkd8Olx1JmPYTjZo0QDqobOU/Xxv/IVNM7kVLC/aBWHvMvxG8Zq4bQNRXnhVA8vnOqhItfKpoXJnmSzoS6i3WFIFeQPDI+EY4eD6ApKyyQcEzePeDxOS0sLDQ0NNDU1EYlEMq5nNpspLy+noqKChQsX3pAhZqe6Avzskx4+84XSltviER5u28ejbb/BGQ/DbSsw/O6foOUXXNfjuV7DI8X0MxgM5Ofnk5+fz9KlSwH4yU9+gt/vn7CuzCwphLiVSDAmpoX+xs/xOxzowSCGz3150vVUOIja9Trq169DJHmFcyCrnMayh+j0roaLrhL2qzgnE4NEQ2cpCzVhVhcVkp1fzm9t20RuTs70vyghxC3Pbrfz6KOPcuDAAU6cOAFAf38/L730Ejt27GDBM3/E4PM/wh0LM2y2kf3MH2Ko2ZrcuGYbStehtQl1+jjq9Alo+AwSCYwo7hho4I6BBv5d3at8kreE/d4VHPUsIzyumH9jf4TGfh/PnfBR7bGzeWEWGxa4ybHP3Nv3+HDMP6SDghNHgigd5pdLOCbmrkgkQlNTEw0NDbS0tGQsPA7JQugVFRVUVlYyf/58TKYb8/dY1xPiZ5/4+KQzmLbcosd5oO0Av926l+xYACxWtC//AdqWB9CuQ2H0Gzk8Ulx/GzZsYM+ePWm/7yaTifXr18/gUQkhxI0lwZiYFurNF0kNLMgQjKl4HLVvF+rNF2F4EAX05C2lceHD9OYtnbB+l4ryccJPLNJMZbAem54+TKCoqJhNmzam1VEQQojrwWAwsGnTJgoKCtizZw+JRIJoNMobb7zB+vXrOVg9Vsj6T0ZDsRGawQBllWhllfDgF1HhINSeQp0+gTp9HHydmFWCu3s/4+7ez4gYzBzLr2afdyXH86uJGcZ6idX2hKjtCfFPx7q4o9DB5oVZ1JS6cc3AsHGbfSwcGx5MhmMfHw2ilGJBxc0zE5y4+Y0WIm9oaKC1tXXCrH6jXC5Xqnh+SUnJDZ2Jr7EvzM9P+viwPX0IpwnFfR1HeLx5N3nRkSLrldUYfu8/oxWWTOsxyPDIm9foRA8HDx5keHgYt9vN+vXrr2lWVCGEmKskGBtR92mIVkcvwWCIqmXXrybErUYphfroAOr1f4HuCyg0Or2raVj4MENZ5RPWP6+H+UQPEI11sihwlqzEcNrjOTk5bNiwgYqKCmlkCSFuqOrqavLy8njrrbdSw04OHjyYtk5dXd0lP0xoNgesXIu2ci0AqvvCWEhWewprJMR63ynW+04RMNo46lnKfu8KPslbjD4yxFxX8ElnkE86g/y9oZNVJS42lWWxptSFzXTjPqxbbQbWbXVxeK+focFkmPDJhyGUgrJKCcfE7DU0NJQqnt/R0THperm5uakwzOv13vB2R+tghBdO9nDgfHpbyKDBNv85vnjql3jD/cmFJhPao0+j3f/baIaph+VzcXjkkqVWHA4HwWDw8iuLNFVVVVRVVeHxeC5Zc0wIIW5WEoyNOHs6AiTrR0gwdvVeXHhf6vZomU5Vdwr9lz+F5nMkDGba522lsexBgo6itG11pWhQYU7qAaLxfhYFz+KJ9aatY7fbWbt2LUuXLsVolKL6QoiZ4fV6eeqpp/jVr36V8QP17t27SSQS3H777Ve0P81bjOYthm0PouIxaKhLDbt0nm9gW9cxtnUdY9Ds5FDBHez3ruRMTkVq+7gOR9v8HG3zYzVqrC51sbksi1UlTszG6x+SWW0G1m1zcWhvgKGBBAAnPwqh61C+WMIxMTsopejr60uFYT7f5JNmeL3eVBiWl5d3A49yTMhg528PdvBB8xD6uI5ZGrDR7ufJg/+HksH2sQcWVGD46tfR5pVd83Ne8/DIQhOewtkxPLJqmR2PJ5+enkscuBBCCJGBBGNiWtRXPJa6rdqa0F95Hj49Rsxo43zZgzQv2EHEml4HLK4UdSrIKT1ILBFgUbiBwnA7468xmkwm7rzzTlatWpWcBU4IIWaYw+Hgscce48c//vGEae8TiQS7d+/m448/xuv14vV6KSgowOPxYDZfuni+ZjJD1TK0qmXw+WdRQwPJ4v2nT5B95gQ7Og6zo+MwPdZsDhYsZ593JQ1Z81PbRxKK/S3D7G8ZxmE2sG6+m80Ls7ij0IHRcP16b1isBtZtc3J4b4DB/mQ49unxZM+xiiXyf1vMDKUUXV1dqTBsYGAg43qaplFcXMyiRYuoqKggKyvrBh9p0gdNg+zP2UxkdCKOpqG0x9cWWnjqs9cpO7l3bKHBgPbgE2gPfTH5/+MqXc3wSIMRPDI8UgghxE1KgjExZfrhvfzn/f+IMzpM3GBCfy9OxJJFc+UXaJp/H7opvQdeROmcUUFO60FiKsrdWhvuoQaUnkhb7/bbb6empgaXy3UjX44QQlyW0WicEIqN19PTQ09PD2fOnAGSH75zc3PTwrKCgoJLzmCnZeWg1WyFmq3JIv5tzajTJ/CcPs7n6g/xubZ9XLDns9+7gv3elbQ6x3rjBmM6exoH2dM4SLbVwIay5MyW1QV2DNfhw6zFYmDdVieHPwgw0Jf8X376RAilKyqrbdP+fGJ2Onz4cGooW01NzQ1/fl3XaW9vT9UMyzTTHiTrBi5YsICKigoqKipwOBw37BiVUgxGElwYjnJhOMaF4SifXAhwtjeMMk78W1lV7ORLhhYqX/shBMfVGCuej+Gr/xlt4eIrfu65ODxSCCGEuBEkGBNXRSkFQT/098JAH/rxg+zpzqd/499g18yEVRx74AI4S8CQ/usVUAlO6QHqVIgCl4EHrD1Ezn9KJBxm/DXKsrIyNmzYgMfjubEvTgghroLb7WZ4ePjyKzI2lKuvr4/a2trU8tzcXAoKCtLCMptt4odjzWCABRVoCyrggcdR4RDUfUrJ6eN88fQJvvjhe7Q4i1IhWZc9P7XtYETnV2cH+NXZATwW2FiRw+byHCpyrdPa48NsMVCz1cWR36HnaMAAACAASURBVPjp70mGY2c+CaMrWHybhGO3gqNHj6Zu36hgLB6P09raSkNDA42NjYTD4Yzrmc1mysrKqKysZOHChde1F7pSir5QnM7hGBf8YwHYaBgWil86kBqVYzXwF3X/CscPjS3UNLT7Hk3WE7Nc+jXcDMMjhRBCiBtBgjGRoqIRGOiDgV5Ufy8M9kF/Hwz2jd0f6INYFAUkjFbeX/IlQqVbcWjJxpNdM4N7Qdp+B1Sck3qATlOE9eVufssSoPnUkQnDGjweDxs3bmTBgvTthRBiNlq/fn3GKe43b95MdnY2Pp8Pn89Hd3c3/f39GffR399Pf38/Z8+eTS3LyspKBWWj3y/u0aLZ7LBiNdqK1QAoXycLT5+g7PQJvvzJ31Fv9rDPu5ID3hX0W8eGhvVE4fXaAV6vHaDEFGPTwmw2VRcyP3t6QgKzWaNms4sj+/z0+ZLhWO3JMEqHJUslHBPTIxqN0tzcTENDA83NzZP23rTZbJSXl1NZWcmCBQswmaav2asrRW8wntbza3wIFk1Mvc7VYDiRHop5CjH83n9CW7Js0m1keKQQQghx9SQYuwUoPQFDAyMhVy+qvy8VgCUG+okPB4kGosTjEDW7iJmdxMwuYiYHUbOLoGUeoexsIh4XMZMT3WRHM1rRRsKwyX6JulWUU3oAb4mZz1fkMd84zOFDB/j4ooLVLpeLdevWUV1dLQ0yIcScMTr75K5du1LLtm/fnlo+f/5Y/a9oNEpPTw/d3d2psKyvry/ZC/ciQ0NDDA0NUV9fn1rmcrnSwjKv14vT6Uw9rhUUoW19ALY+gIrHqWqsY8npE/zu6Zf4bFBnn3cFhwruwG8e26Yjbual+iAv1TexED+bi61sWFVBUY6TqTCZNdZudnF0X4De7mRoWPdpGKUUS5ba5P+8uCbBYJCmpiYaGho4f/48up6515XT6UwVz583bx4Gw7X3ekroiu5AjE5/eo+vC8NRuvwxYvq1hV92k4GSLDNFLgvFbgu7zvUzHJ34ejyRsQuI2pYdaF/4vWQoPo4MjxRCCCGmToKxOSw5rDGA6u9D7+8j2j9EdMBPbDhENBghFooTiypiCQNRk5O4yUnU7CRmLiZmXkzU7CRRcvV1Na6kiamUonClmS8unI8e9nPo0H4+PHcubR2LxcLdd9/NypUrp/Uq7ly0Zs0amWJciDmoqqoqLRgbDcUuZrFYKCkpoaSkJLUsHo9PCMt6e3szfuD3+/34/X4aGxtTyxwOx4SwzOVyoZlMsGQp2pKlWB77HZYPD3HHmRP8+9P7+bhhkP3OSo54lhE2jfUSa8ZF8wV4fmcrVTEfG3N1Ni5fSO7CBdcUZJlMGms2Oflwf4CermQ4dvZ0BF2H6jskHBNXZnh4OFU8v6OjI2OQDJCdnc2iRYuorKyksLDwqn6/YglFVyCaHPY4PvzyR+n2x7jWjl9ui4EidzL4KnabKXaN3c6yGtOOcUG2hR8eaieixlpY1kSUpxvfhpx8DL/7x2jLVgHXODyy0ExBkQyPFEIIISZza6cRs4xSilhMEYsqooEo0f5hYoN+osMhYoEosXCcWFQnGjcQ003ENAsxkyPZi8u4cGxHJiBr5Os6iiuFATIWcg5pCT5X5uTDowc5efJk2gc9g8HAHXfcwZo1a7Db7RO2vRXV1NTg8Xjo6emZ6UMRQtwgJpOJoqIiiorGiubH43H6+vrSwrKenh4SicSE7YPBIM3NzTQ3N6eW2Wy2tKCsoKCA7OxsDGu3YFm7hdVKsbq9mfCnH/NRg4/98TyO5VURM4zNaFdnLqDOD/98IMDSXe+yyRFgXVUh7mUr0JxXPhnK+HDM15kMx+o/i6AU3LZcwjGRWX9/P/X19TQ2NtLV1TXpeh6PJxWG5eXlXfL3KRLX6fJPHO54YThGTzDGNXb8IttmHAm8zCOhV/J2kcuC22qcdDsVCqI621AXWuFCGxtPn0CPZPOzih30WHPwRAZ4uvFtNrsjGP74/yOk7HTXR2R4pBBCCHGdSDB2HSQSyXArFlVEo6O39eTtiE7UHyEWjBILxZPL4hoxZSKGBdIaLxYgL33nlpGvaaKUIoIigk4URUTpRBj9St5XRrBYNOx2Ay67AbfDSK7ThNdlor05irnDgEkbuwIZ02Moex3PPfcp0Wg07fkWLVrE+vXrycnJmb4XIYQQNwmTyZQKtUYlEgn6+/tTQdloWJaprlI4HKa1tZXW1tbUMovFMiEsy7n/UTYZDGyMhAl+9ilHzjSyb9jCJ44FJAzJD/S6ZuCUu4xTwI/Pxll55NdsUF2sXZiDY9lKKF+MZpj8wz+A0aixeqOTYwcDdHUkw7GG2mTPsaUrJRwTyXaIz+ejoaGB+vr6SevxARQXF6eGSWZnZ6c9ForpdPqjF9X8Sn7vDcYn2ePl5dlNY8HXuBCsyG3GYb5E+KUUDPbDhVZUZ/vI9za40JosZ3GRysJ1PGksIGzKxxa34Mm+jU8dWfTu1Qn4Lz3JhwyPFEIIIaZGgjGgrSUy4f68BRbiMcYCrQlBV+bQKxZVJPTLNUg0wDryNW7RFCSUmhBojQZeabfH3VdGhctmJM9hSn7ZTRTZk9/zHWby7CZy7Sbs5kt0u18Av/zVcdobj6PrQTTNAgZQifRArLi4mI0bN1JcXDy1FyqEELcYo9GIx+PB4/Fw2223AaDrOgMDA2lhmc/nm3AxApL1zdrb22lvb08tM5vNqVkwvV4vy+/fwNa8PIY7uzj4cQP7fYrTRg9qJLiKG0x8lH8bH3EblsEod7/xCRsHX2JVkR3r0pVoS+9EyyuY5Pg17l7v5NihIJ3tyTCv6WwEpSuWrbJz9nSYVkcvwWCIqmXSi/hWoOs6Fy5cSA2TnGx2V4PBQGlpKZWVlVRUVKDMNjqHY5zsj3KhtYcLwzE6R4Y/9ocn9qq8EhpQ4DQlhz1e1PuryGXGarr00EOlJ6CnCy6M9ADrbENdaIMLbRAKXPb5Y0Y7zfPvpb7it1EjPTfD9nyayx5IrpChZpgMjxRCCCGm1y0fjLW1RPj4cIDxlbNOHA5y4nDoGvc4tYQrqi4OtzIHXKO9u8IjYdf45qBRg9yRgCvPYaLEbk4Lu0a/nJapda+Px+OcOnWKrqZD6HryCJSKMv5gcnJy2LBhAxUVFdIzQAghponBYCAvL4+8vLxUXTOlFIODgxPCsnA4PGH7WCxGR0cHHeMmQxkN4LxeL1+6qwCL082Z8wPs74hwTh8ryB81WjjoXcFB7woc8RBrj55m45v/kztsEcwjIRlLlqJZxi7+GIwad613cPxQkAttyXCsuT6KUtDSEAWSF6gkGLt5xeNx2traaGhooLGxkVAoczvLaDSRX1KK1VNK0FFIY1jjYEeUC3UdDEWuLfwyaOB1msfqfY0LwQpdZszGywdLKhqBro7U8MdUD7CuDohnnhUTQKERtuURsHsJ2b0E7QUEnYUE3fMIWj3EDJefrVWGRwohhBDX1y0fjNV+OIiaMDZxao0NXenElE4YRVjL3HsrykiopUaDrpFeXJfYr0aynkWe3cQ8u5k8h4n8ke/jA68smzFj3a8rOnZdJxwOEwwGCQQCad8vvh2JRCbdj6ZpbNmyhaVLl2I0XnqojRBCiKnTNI2cnBxycnJYvHgxkAzL/H5/WlDW3d2dcaKPRCJBV1dXWl0ng8HAXfn5bMnPpzdh4/SAiXqVja4l/68HTXbeL7qb94vuJivqZ13LSTZ++By3BTswLL492ZNs6SoomY/BoLFqnYMTh4N0tCaDhGQoNqatJUJpmRVxc4hGo7S0tNDQ0EBzc3PGHo0Aymgm4Cik3VhAhyEPPWiE8wCXHkI4nskAhS4Lxa70el/FbgsFTjMmw5W1i1RgOEPvr1bo7WayKvcxo20k9BoJvhwjtx2FhGz5KO1a20GKdVtdMjxSCCGEuM5u+WAsFDdPnoMloig9QkKPE1U6YU0R0DSGDUb8mjFt6OL4nl6xS8ZbmbksBrwjPbvyxvfscowObTSRYzNdccNuPKUU0Wg0Y7iV6f5ksz5d7XMuX758yvsRQojZbjbPKqtpGm63G7fbTWVlZWp5IBCYEJb5/f4J2+u6js/nw+fzATAfWKBpGBxZ9Cgn3WQxbHLjN7kZsrjYNW89u+atJy8ywIbuk2x6510qf/ETtJz8ZE+ypatYecdyNIOZ9paJvWxOfpjsRSTh2Nzz2r4ThDUrVhUhrpn4+3/+FxLBIfQME0cARDQLPosXn6WQfnMuSrt8ry2LUaPIlT7UcTQA8zjMGK80/FIK+nuSAdhI3a9UADY8OHF9NMLWvJHAq2AsABsJw2IW9xU9byYGQzJvy9T0sjsMeArNEx8QQgghxLS65YMxLTaMskycvtGv4ryo+pKh2YQLfTGuNPuyGjXyHReFXfaxsGu0jtflalhkEo/HCYVCk/boGn87Hr/2wrOTMRgMKKUyBmlu97U3EoUQYi6Zi7PKOp1OysvLKS8vTy0LBoOpkGz0+9DQ0IRtlVIkAoPkMkguyaGYCgganQwb3QyZshg2ZfGreet4c/5mioM9bOj+mI0nPmHBgd2gGVi+cDGdlX9K4qJmSCIBtSfDEozNMS/sPkLXmaPYRhpHZhUnNjyxiH7QYE+FYYOm7IsmHEqymbSMwVex20Ke3XRVPeJVPA6+zpHga1wPsM52iKQP5Ywbbcmwq2DRSOhVmAq/QnYPynDtTWarTcPhNOBwGXC6DDicxtRtq02j/XyUkx+GGJ8hGo1QvfzywyyFEEIIMXW3fDD2ofKzQrkwj59VUekc1SdeOR/PZNAm9OoaH3aNLrebrq4OhFKKUCh0Rb27MtWNmQ42mw2Hw4HD4cDpdKZuj94fXWaz2Th79ix79uxJC95MJhPr16+/LscmhBDi+nA4HJSVlVFWVpZaFg6HUz3GRgOzTLMGaoAzEcCZCFAU7UwtDxrsDJuy+LCwkvdK7iQ7EmSd71M2XviERIUxY4/tUHBisXExu3XUfox5kiuGw0ZXKgzzG12gaTjNBha50wvdjw6BzLYZr7p+lgqHoKs9Vf8rVQfMd4HRtCnZ6ys32esrf/W40Cv5PZrhIumVMhhIBV9jAZgxddtkuvTrGQ2Cjx7wYTI4iesB7qwpkIBYCCGEuEFu+WDsE6PGkD7EaoMLF0b8JPhQ99OoQqwpdSfDrovCrzy7Cbf1yhtuSilisdgle3SNvz0dQxkvZjKZJgRdmW7b7XZMpiv/tRgt+rxr167Usu3bt6eWCyGEmLtsNhvz589n/vz5qWXRaHRCWNbX15fxvcuhh3BEQxRGx+qWNec6OVWwgwr/CQxKJxBpJqEHMRqc5LrupMCQA+TeiJcnpolJz1xzVAGOlTuocVsoGheCua9h8h+lVHKY48XDHztboS/ZWzNutI4b5riM0KJtqeGOIbsH3XDtwxItVi3Z22sk/Brf88tm16ZcDL+0zMqr//Zq6v7ny/5kSvsTQgghxJW75YMxj8NMYzBMYyK991WBw8z/2FJ6yW0TicSkvbkuLlx/PYYyapp2yZBr/G2z2XzdZjCqqqpKC8YkFBNCiJuXxWJh3rx5zJs3L7UsFovR09OTFpb19vai6xN7f9n1MPZoGH+0O215Qg/QO3QIc8wOVFzvlyGmUcxow5LIMPup0cb/taHkqvaldD1Z6L5ztAD+uJ5gAT9haw4hu3dklscKgsVrCVYkw7CoNfuaX4PBAPZU4HVRry+nAZNZit8LIYQQN6tbPhh7ZmUBL+09TlngHDY9TNhgo81RzkPLFnP+/PlLhl7Xayij1Wq94t5dMl23EEKImWY2mykuLqa4uDi1LB6P09fXlwrKOru66OnpSQYfk1AkaLFknrlQzF6Vy1fTfOIARsZ+tgkMVC5fPek2KhaD7o60wveqs424z0fQmEMoVei+kKDrDoLLCwjZC6an11fasEcjzmnq9SWEEEKIuemWD8aKop3c5j+NUsnGnF0Ps9j/GWc/+Iyz0/g8RqMxFWhdKvC62qGMQgghxGxkMpnwer14vd7UskQiQX9/P+c7Otm3972Mk0KblQRjc81jm+7k1a52Gi+0YVZRYpqFiuJSHtt0JyoYGCt6f6EVvbONiG+QYEgjaPOMG/q4leDCAqJVOdd8HNpora9Jen5Jry8hhBBCZHLLJzAHDx5MhWJXS9M07Hb7hKL0mUIvi8UiVyKFEELc0oxGIx6PB4/Hw3v7Dk46/E7MLYnDe7n71Ge4yx8jbMvHFu6lrHYPHd/6HkFDVqrQfdCxhlDeg+gFlmt+LotVGwu+0np/GbHbNTSDtLWEEEIIcXVu+WBseHh40sfmzZt3yd5dNpsNg8Ew6fZCCCGEyOxaht+J2an5g7N8Vv1VlMEIQNjuoW7xk9e0L01Ln+FxLABLFro3S68vIYQQQkyzWRGMdXd38/zzz3P69GkA7rrrLp599lmysi49dfa1bjee2+3OGI653W4ef/zxq3gVQgghhLhSj226k9eAcx8fxaoiRDQri1eu4bFNd870oYmr1Fh4TyoUuxJmk56s7ZVlumjY463d62vNmjU4HA6CweBMH4oQQghxS5nxYGx4eJhvf/vbxONxHn30UXRd54033qClpYXvfe97k9bbutbtLrZ+/Xr27NmTNmukyWRi/fr10/L6bhXSmBNCCHG1Htt0J393Yh8ANhWRUGyOCtvyMz+gFGWVFhxu41j45TRittyawdfl1NTU4PF46OnpmelDEUIIIW4pMx6MvfXWW/T29vKDH/yA0tJSABYtWsR3v/td9u7dy7333jut212sqqoKgF27dqWWbd++PbVcXBlpzAkhhLgWcmFl7rObYoQSE+uG2U0xlq/OnYEjEkIIIYS4cjNeIOvgwYMsXbo0FW4BLF++nJKSEg4ePDjt22VycQgmoZgQQghxY9TU1HDPPfdQU1Mz04cirlH16myMWvpERkZNp3p19gwdkRBCCCHElZvRYMzv99PV1UV5efmEx8rLy2loaJjW7YQQQgghxPQqLbOyfK2TWMKPUopYws/ytU5Ky6wzfWhCCCGEEJc1o0Mp+/r6AMjLy5vwWG5uLqFQiGAwiMPhmJbtLkWGcgghhBBCXJvSMiuv/turqfufL/uTGTwaIYQQQogrN6PBWDgcBsBqnXhF0WKxpNa5OOC61u0uRWpkCSGEEEIIIYQQQtxaZjQY0/VkPQpNm3x2IoNh4mjPa91u9+7d7N69G4Dvf//7lJSUTFgn0zJx5eT8TY2cv6mR8zc1cv6mRs7f1Nyo83e5toD8HK/d97///Zk+hJuC/A5OjZy/qZHzNzVy/qZGzp+YKTNaY8xutwMQjUYnPDa6zGazTdt29957L9///vcnbbj92Z/92RUeuchEzt/UyPmbGjl/UyPnb2rk/E3NjTx/l2oLyM9x6uQcTo2cv6mR8zc1cv6mRs7f1Mj5EzNpRoMxj8cDQH9//4TH+vr6cDqdGQOua91OCCGEEEIIIYQQQohRMxqMOZ1OvF4vTU1NEx5rbm6moqJiWrcTQgghhBBCCCGEEGKU8Vvf+ta3ZvIAent7OXDgAOvWrSMrKwuAkydPsnPnTj7/+c9TXl4+rdtdjoRqUyPnb2rk/E2NnL+pkfM3NXL+pma2nL/ZchxzmZzDqZHzNzVy/qZGzt/UyPmbGjl/YqZoSik1kwcwNDTEf/2v/xWDwcAjjzxCNBrljTfeoKioiO985zuYzWa6urqoq6ujqqqKwsLCK95OCCGEEEIIIYQQQojJzHgwBtDR0cFzzz3HmTNnsFqt3HnnnTzzzDOpnmB79+7lRz/6EX/4h3/I1q1br3g7IYQQQgghhBBCCCEmMyuCsZnW3d3N888/z+nTpwG46667ePbZZyVgu0otLS382Z/9GY899hhPPPHETB/OnNHY2MjPfvYzzp49i8Fg4LbbbuPZZ5+V6Yov4x/+4R/o7Ozk4tHgH3/8Ma+++iqNjY1omsaSJUt48sknWbJkycwc6Cw12fkbGhri5z//OceOHSMajVJeXs6Xv/xlOX9c2e+WvJ9M7mr/Nm/ke4r83KaPtAWunrQDrp20BaZG2gJXR9oBUzOb2wFCzGjx/dlgeHiYb3/725w7d45HH32URx55hI8++ojvfOc7xOPxmT68OSORSPCjH/2IRCIx04cyp3R0dPCtb32L8+fP8/jjj/PYY49RX1/PN77xDfr6+mb68Gat9957j/fee2/C8jNnzvC9732PQCDAU089xRe/+MVUg6++vn4GjnR2muz8hUIhvvnNb3Lo0CHuu+8+nnzySfr6+vjLv/xLzp8/PwNHOntcye+WvJ9M7mr/Nm/ke4r83KaPtAWunrQDrp20BaZG2gJXR9oBUzOb2wFCAJhm+gBm2ltvvUVvby8/+MEPKC0tBWDRokV897vfZe/evdx7770zfIRzw2uvvUZra+tMH8acs3PnTsLhMN/+9rdTE0YsW7aMP//zP2fnzp0888wzM3yEs4uu67z66qv84he/yPj4T3/6U/Lz8/mrv/orrFYrAJs3b+brX/86L7zwAt/4xjdu5OHOOpc7f6+//jodHR1885vf5Pbbbwdg/fr1fO1rX+ONN97ga1/72o083FnlSn635P1kclf7t3kj31Pk5zZ9pC1w9aQdcPWkLTA10ha4NtIOmJrZ3A4QAqTHGAcPHmTp0qWpf14Ay5cvp6SkhIMHD87gkc0d58+f59VXX+Xxxx+f6UOZc7q7u3G73WmzqC5atAi3231LX5XLJBqN8qd/+qe8/PLLbNq0iby8vLTH/X4/LS0trFu3LvWGC5CTk8Ptt9/O2bNnb/QhzyqXO39KKT74/9u706CmrocN4E8ghE2ooIZFsYpLEeuO0qLQlFIXlKpTq9URxHE6dkbqaNFO69S6VKf+p4tV0Y4z6ozYKlgUTZG6F7AjdQGtIuOKoCASKMiSgASS9wOTvMSENWIS8vy+ec/NvYe75Dyec+9JejrGjBmjDcJA0/GLjIyEn5/fq66y2WjvtcX2xLCO3puvuk3heXs5mAU6hzmgY5gFjMMs0DnMAcYx9xxABFh5x1hNTQ1KSkp0wojGwIED8eDBAxPUyrJoHnMdMWIEgoODTV0di+Pp6YmamhpUVVVpl9XU1EAul8PNzc2ENTM/SqUSCoUCK1asQExMDGxtbXXKnZyc8PPPP2P69Ol6n62uroaNjVV/3bV5/EpLS1FeXo6RI0cCaArHdXV1AIApU6ZY9Shne64ttict68i9+arbFJ63l4NZoPOYAzqGWcA4zAKdwxxgHHPOAUQaVv0qpWbuhhdHSwDAzc0NtbW1UCgUcHJyetVVsxjHjx9HcXExVq9ezXfAO2HmzJnIysrCtm3bEBUVBYFAgAMHDkAoFGLatGmmrp5ZcXR0xPbt2/VCnIaNjQ28vLz0lhcUFODOnTsYNWpUV1fRrLV1/IqLiwEAr732Gg4cOICzZ8+itrYWHh4eWLRoEQICAl5ldc1Ke64ttict68i9+arbFJ63l4NZoPOYAzqGWcA4zAKdwxxgHHPOAUQaVj1sohkBaf5Ip4ZIJNJZh/Q9fvwYSUlJiIyMRK9evUxdHYvUu3dvzJ49G7m5uVi9ejVWrVqFmzdvYvny5QZHnKyZjY1Ni0GuJXV1dYiLiwMAzJo1qyuqZTHaOn5yuRwAkJiYiOzsbCxevBgxMTGwt7fH999/jxs3bryqqlqEF68tticdY+jeNEWbwvNmPGYB4zAHdAyzgHGYBV4e5gDjmEsOINKw6ifGVCoVAEAgELS4jrU/ct0SlUqFXbt2wc/Pz2ofq34ZEhIScPToUfj7+yMsLAwqlQqnT5/G1q1bERsba7Ujcy/D8+fP8b///Q8FBQWYNWuWzlwZpE/za0lyuRzbtm1Djx49ADT9zPhnn32GQ4cOaV+tsHaGrq3bt28DYHvSHoaOn6naFOYA4zALGI85oGsxC3QMs0D7MAcYx5xyAJGGVd+djo6OAJomonyRZpmDg8MrrZOlkEqlKCgowIIFC1BVVYWqqirtKFN9fT2qqqq0/+Egw+RyOf744w8MGjQI33zzDSZNmoSQkBCsX78e/fr1w+7du6FUKk1dTYskl8uxadMm3Lp1C++++y7mz59v6iqZPc0IZ2BgoDYIA4CzszMCAgKQl5fHkU60fG2xPWmflo6fqdoUnjfjMAsYhzmgazELdByzQNuYA4xjbjmASMOqnxjr3bs3AKCiokKvrLy8HM7OzvwCa8H169fR0NCANWvW6JVJpVJIpVLExcVBLBaboHaWobi4GEqlEhMnTtQZQRIKhQgODsavv/6KoqIiDBgwwHSVtECVlZXYvHkz8vPzERYWhk8++aTV0TtqopkTw9XVVa/M1dVVOwGvNX8ntnZtsT1pW2vHz1RtCs+bcZgFjMMc0HWYBTqHWaB1zAHGMcccQKRh1R1jzs7OEIvFePjwoV5Zfn4+fH19TVAryxAVFYWamhqdZZWVldixYwdCQkIQEhKCnj17mqh2lsHOzg4ADI5+aJap1epXWidLV1tbq21wp0+fjkWLFpm6ShbDx8cHdnZ2KCws1CuTyWSws7MzGJStRVvXFtuT1rV1/EzVpvC8GYdZwDjMAV2DWaDzmAVaxhxgHHPNAUQaVt0xBjQ9KpyamoqioiL07dsXAHDjxg08efIEH3zwgYlrZ74MfbnLZDIAgFgs5vwD7eDj4wM3NzekpaVh6tSp2ok56+vrkZ6eDhcXF/j4+Ji4lpZl7969yM/PR3h4OINwBzk4OGDcuHG4cuUKHj9+rL32ZDIZsrKyMH78eKueG6M91xbbk5a1dfxM2abwvHUes4BxmAO6BrNA5zELtIw5wDjmnAOIAHaMYebMmcjIyMDGjRsRERGB+vp6SKVS+Pr6Ijg42NTVo27MxsYGS5YswY8//og1a9YgNDQUKpUKf/31F548eYKYmBgIhVZ/i7ZbYWEhMjIy4OTkhAEDBiAjI0NvnZCQEBPUKf95bwAACvVJREFUzHIsXLgQubm52LBhA8LDwyEUCpGamgqRSGTVc7O099pie2KYud+bPG9kKswBL5+5f99YAmYBfcwBxuF9SZbA6ltbV1dXbNiwAfv370diYiLs7e0xfvx4REZGah9xJ+oqEyZMwNq1a5GUlIRDhw4BAAYOHIivvvoKo0ePNnHtLEtubi4AQKFQYNeuXQbXYaPbOrFYjM2bN+O3336DVCqFWq3GsGHDsHDhQnh4eJi6eibT3muL7Ylh5n5v8ryRKTEHvFzm/n1jCZgF9DEHGIf3JVkCgZqTFxARERERERERkRWyzpfEiYiIiIiIiIjI6rFjjIiIiIiIiIiIrBI7xoiIiIiIiIiIyCqxY4yIiIiIiIiIiKwSO8aIiIiIiIiIiMgqsWOMiIiIiIiIiIisEjvGiIiIiIiIiIjIKglNXQEiMn/r169Hbm5um+vNmTMHc+fObfd2b926hQ0bNiA8PBzR0dHa5f/++y+cnZ0xePDgzlS33WQyGWJiYhAQEIAvvviiS/dFRERkyZgFiIiou2LHGBG1SSKRwN/f32BZY2MjUlJSoFQq0bNnzw5tt0+fPpgzZw6GDh2qXXb69Gns2bMHq1atMqrORERE9PIwCxARUXfFjjEiapNEImmxbM+ePVAqlfD390dYWFiHtisWi/VGlZ89e9aZKhIREVEXYhYgIqLuinOMEVGnXbx4EadPn4aLiwuWL18OGxt+pRAREVkTZgEiIrJ0fGKMiDqlpKQEu3fvhkAgwLJly+Du7q4tU6vVOHPmDM6dO4fCwkKIRCL4+flh7ty5GDhwoHa9F+cVaT5/yQ8//AAAOHz4MICm1zSkUinS0tJQVlYGsViMiIgIVFRUIDExEXFxcRCLxdpt5+TkIDk5Gffv34dKpUL//v0RERGBt956y+Dfc+nSJRw+fBhPnz5F7969IZFIEBERAaFQ92vy6dOnOHz4MG7evAm5XA4PDw+EhITorbts2TL06dMH77zzDg4ePIi6ujq8//77iIqKMvLIExERmQdmAWYBIqLuwHb9+vXrTV0JIrIsDQ0N2LJlC2QyGWbMmIGpU6fqlO/cuRPJyclwcXHB22+/DS8vL1y7dg1nz57FG2+8oQ2tpaWlSE9Px5AhQzB69GgAgFwuR2lpKYKCghAYGIjhw4cDAH766SecOnUKvXr1QlBQEAQCAaRSKcrKyiCXyxEeHg5nZ2cAwLlz57B161bU1dUhMDAQQ4cOxcOHD3H27FnY2tpi2LBh2n2lpqZCLpcjLS0NQ4YMwciRI1FSUoLMzEwUFxfrhOe8vDysXbsWBQUFGDNmDEaPHo2qqiqkpaXhwYMHmDhxIgQCAQAgNTUV5eXluHLlCoKCgtC3b1+8+eab6NevX9eeHCIioleAWYBZgIiou+ATY0TUYQcPHsT9+/cxaNAgLFiwQKcsMzMTGRkZmDRpEpYtWwZbW1sAwOzZs/Hll18iLi4OcXFxeqOvQNP8JTKZDLm5uQgKCsKECRMAAP/88w8uX76M8ePHY+XKldrPnjx5Evv27dPZxn///Yd9+/bB29sbGzduhIuLCwBg/vz5+Pbbb5GYmIiAgAD0799f+5nKykpER0cjPDwcAFBfX48tW7bg4sWLCA0NxciRI6FWq7Fz504olUps2rQJvr6+2s/v378fJ06cwJkzZzBlyhTt8urqaixevBjTpk3r9LEmIiIyR8wCzAJERN0FJwEgog65fv06Tpw4AUdHR6xYsUIv1J4/fx4AEB0drQ3CQNPkupMnT0Z5eTlu3LjRoX2mp6cDACIjI3X2N3nyZHh7e+use+HCBSiVSsybN08bhAFAJBLho48+glqt1m5Pw8PDQ2ekWyQS4eOPP9ZuDwDu3buHx48fIzQ0VCcIA8C8efMgFAqRlpamV/eWXtcgIiKyVMwCzAJERN0JnxgjonarqKhAXFwc1Go1li5dCg8PD7118vLyYGdnh5MnT+qVFRUVAQDy8/MxduzYdu/3wYMHcHFxgaenp85yGxsbDB06FE+ePNHZPwDcvHkTjx490lm/rq5Ou//mhgwZojdZsK+vLwQCAQoKCnS2W1JSop3rpDlHR0fk5+dDrVZrX6EQCoVwc3Nr999JRERk7pgFmAWIiLobdowRUbuoVCrs2LEDVVVVCAsLQ1BQkMH1FAoFGhsbkZSU1OK2ampqOrTv6upqvdFgjRfDplwuBwCcOXOm3fvv2bOn3jpCoRAikUgboBUKBYCmUfLr16+3uO26ujo4OjoCaBptJiIi6i6YBZgFiIi6I3aMEVG7HDt2DDk5OfDx8UF0dHSL6zk4OMDBwQG//PLLS9u3o6OjNoy+qLa2Vm//ALBjxw6Do9iGaAJ0cwqFAs+fP0ePHj10tvvpp58iNDS03XUnIiLqLpgFmAWIiLojzjFGRG26c+cOfv/9d9jb22PlypWtjn72798f5eXlePbsmV5ZVlYWEhIS9F5faE7z6kFzvr6+KC8vR0VFhV7ZvXv3dP79+uuvA2h65eJFxcXFiI+Px9WrV3WWG1r37t272n0D0E7Qq3mNormGhgbEx8fjzz//NPg3ERERWTpmAWYBIqLuih1jRNSqmpoabNu2DY2NjViyZEmbPzEukUigVquxd+9eNDQ0aJdXVFRgz549SE5O1o64GqKZpLf5ZzXbPHDgAFQqlXZ5RkaGXpANDg6GjY0NEhISdAJ5Y2Mj9u3bh5SUFL3XJx49eoTMzEztvxUKBRISEiAQCCCRSAAA/v7+EIvFOH/+vDYoaxw7dgwpKSkGgzIREZGlYxaQAGAWICLqrvgqJRG1KikpCWVlZXBzc4NMJjM42ayGWCyGRCLB1atXcenSJcTGxmLUqFFQqVTIzMxEdXU1FixYoDdxbnPu7u4AgKNHjyI/Px9z5szBxIkTceHCBfz9998oLCzE8OHD8fTpU2RnZ8PFxQXV1dXaCXO9vLywcOFCxMfH4/PPP0dAQAB69OiBa9euoaioCGPHjkVwcLDOPj08PLB9+3ZcvnwZrq6uyMrKgkwmw6xZszB48GAATZP7xsTEYPPmzVi3bh0CAgLg4eGBvLw85OTkQCwW6/1cPRERUXfALMAsQETUnbFjjIhapZlzo6KiotVJdIGmkVSJRILY2FicPHkSaWlpOH/+PEQiEXx8fDB9+nRMmDCh1W0EBQXh2rVryM7OxqlTpyCRSODt7Y3Y2FgcOXIEFy5cwKlTp+Dp6YmYmBhcvXoVmZmZsLe3125jxowZ8Pb2RkpKCi5dugSVSgVPT09ERUVhypQpOj8dDwDjxo3DgAEDkJycjNLSUnh5eWHp0qV47733dNbz8/PDd999hyNHjiAnJwfZ2dlwd3fHtGnTMHv2bIMT9xIREVk6ZoH/xyxARNT9CNRqtdrUlSAiak1ZWRmcnJzg5OSkV7Zu3Trk5eUhPj7e4JwkREREZPmYBYiIqKtwjjEiMnvHjx9HdHQ0cnNzdZbfvXsXt2/fhr+/P4MwERFRN8YsQEREXYVPjBGR2cvLy8PXX38NW1tbBAYGwt3dHTKZDFeuXIGdnR02bdrU5kTAREREZLmYBYiIqKuwY4yILMLDhw+RnJyMe/fuobKyEq6urhgxYgQ+/PDDVifwJSIiou6BWYCIiLoCO8aIiIiIiIiIiMgqcY4xIiIiIiIiIiKySuwYIyIiIiIiIiIiq8SOMSIiIiIiIiIiskrsGCMiIiIiIiIiIqvEjjEiIiIiIiIiIrJK7BgjIiIiIiIiIiKr9H/vmK0/v0ANnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gene_24h(data2, 'PRR7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between treatments for PRR7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment_1</th>\n",
       "      <th>Treatment_2</th>\n",
       "      <th>Accession</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.3573</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.7008</td>\n",
       "      <td>0.0319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.6029</td>\n",
       "      <td>-1.1353</td>\n",
       "      <td>-0.0705</td>\n",
       "      <td>0.0104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Treatment_1   Treatment_2 Accession  ZT  meandiff   lower   upper   p-adj\n",
       "51       Arctic  Mid-latitude       IT4  12    0.3573  0.0138  0.7008  0.0319\n",
       "207      Arctic  Mid-latitude     NOR13  20   -0.6029 -1.1353 -0.0705  0.0104"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_treatment_24h(data2, 'PRR7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between accessions for PRR7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Accession_1</th>\n",
       "      <th>Accession_2</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.4925</td>\n",
       "      <td>-0.9517</td>\n",
       "      <td>-0.0333</td>\n",
       "      <td>0.0207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Treatment Accession_1 Accession_2  ZT  meandiff   lower   upper   p-adj\n",
       "287    Arctic       NOR13        NOR2  20   -0.4925 -0.9517 -0.0333  0.0207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_accs_24h(data2, 'PRR7')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PRR5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gene_24h(data2, 'PRR5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between treatments for PRR5\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment_1</th>\n",
       "      <th>Treatment_2</th>\n",
       "      <th>Accession</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>16</td>\n",
       "      <td>-1.1078</td>\n",
       "      <td>-2.1005</td>\n",
       "      <td>-0.1151</td>\n",
       "      <td>0.0130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
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       "      <td>0.4328</td>\n",
       "      <td>1.1653</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.9179</td>\n",
       "      <td>0.8182</td>\n",
       "      <td>3.0177</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>8</td>\n",
       "      <td>2.4221</td>\n",
       "      <td>0.3830</td>\n",
       "      <td>4.4611</td>\n",
       "      <td>0.0051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>-1.5250</td>\n",
       "      <td>-2.8966</td>\n",
       "      <td>-0.1534</td>\n",
       "      <td>0.0137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>1.4683</td>\n",
       "      <td>0.2711</td>\n",
       "      <td>2.6655</td>\n",
       "      <td>0.0032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.8003</td>\n",
       "      <td>0.3518</td>\n",
       "      <td>1.2488</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.9212</td>\n",
       "      <td>0.5496</td>\n",
       "      <td>3.2928</td>\n",
       "      <td>0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0008</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>5.4134</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment_1   Treatment_2 Accession  ZT  meandiff   lower   upper  \\\n",
       "137          Arctic  Mid-latitude       IT4  16   -1.1078 -2.1005 -0.1151   \n",
       "321          Arctic  Mid-latitude       IT1   8    0.7990  0.4328  1.1653   \n",
       "321          Arctic  Mid-latitude       IT4   8    1.9179  0.8182  3.0177   \n",
       "321          Arctic  Mid-latitude     NOR13   8    2.4221  0.3830  4.4611   \n",
       "49           Arctic      D-Arctic       IT4  12   -1.5250 -2.8966 -0.1534   \n",
       "94   D-Mid-latitude  Mid-latitude       IT4  12    1.4683  0.2711  2.6655   \n",
       "324  D-Mid-latitude  Mid-latitude       IT1   8    0.8003  0.3518  1.2488   \n",
       "324  D-Mid-latitude  Mid-latitude       IT4   8    1.9212  0.5496  3.2928   \n",
       "324  D-Mid-latitude  Mid-latitude     NOR13   8    3.0008  0.5882  5.4134   \n",
       "\n",
       "      p-adj  \n",
       "137  0.0130  \n",
       "321  0.0000  \n",
       "321  0.0000  \n",
       "321  0.0051  \n",
       "49   0.0137  \n",
       "94   0.0032  \n",
       "324  0.0000  \n",
       "324  0.0003  \n",
       "324  0.0024  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_treatment_24h(data2, 'PRR5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between accessions for PRR5\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>12</td>\n",
       "      <td>2.6621</td>\n",
       "      <td>1.2585</td>\n",
       "      <td>4.0658</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>12</td>\n",
       "      <td>-2.1495</td>\n",
       "      <td>-3.7804</td>\n",
       "      <td>-0.5186</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>12</td>\n",
       "      <td>-3.0584</td>\n",
       "      <td>-4.6277</td>\n",
       "      <td>-1.4891</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0301</td>\n",
       "      <td>0.5580</td>\n",
       "      <td>3.5023</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>16</td>\n",
       "      <td>-2.1767</td>\n",
       "      <td>-3.8076</td>\n",
       "      <td>-0.5459</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>2.8582</td>\n",
       "      <td>1.8974</td>\n",
       "      <td>3.8191</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>12</td>\n",
       "      <td>2.6569</td>\n",
       "      <td>1.7370</td>\n",
       "      <td>3.5769</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>12</td>\n",
       "      <td>-3.0358</td>\n",
       "      <td>-3.9966</td>\n",
       "      <td>-2.0749</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>12</td>\n",
       "      <td>-2.8345</td>\n",
       "      <td>-3.7544</td>\n",
       "      <td>-1.9145</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.9922</td>\n",
       "      <td>1.0723</td>\n",
       "      <td>2.9122</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>8</td>\n",
       "      <td>2.5522</td>\n",
       "      <td>1.5715</td>\n",
       "      <td>3.5329</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>-2.7109</td>\n",
       "      <td>-3.6309</td>\n",
       "      <td>-1.7910</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>8</td>\n",
       "      <td>-3.2708</td>\n",
       "      <td>-4.2515</td>\n",
       "      <td>-2.2902</td>\n",
       "      <td>-0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment Accession_1 Accession_2  ZT  meandiff   lower   upper  \\\n",
       "102        Arctic         IT1         IT4  12    1.7533  0.2812  3.2255   \n",
       "103        Arctic         IT1       NOR13  12    2.6621  1.2585  4.0658   \n",
       "126        Arctic         IT4        NOR2  12   -2.1495 -3.7804 -0.5186   \n",
       "147        Arctic       NOR13        NOR2  12   -3.0584 -4.6277 -1.4891   \n",
       "189        Arctic         IT1       NOR13  16    2.0301  0.5580  3.5023   \n",
       "225        Arctic       NOR13        NOR2  16   -2.1767 -3.8076 -0.5459   \n",
       "102  Mid-latitude         IT1         IT4  12    2.8582  1.8974  3.8191   \n",
       "103  Mid-latitude         IT1       NOR13  12    2.6569  1.7370  3.5769   \n",
       "126  Mid-latitude         IT4        NOR2  12   -3.0358 -3.9966 -2.0749   \n",
       "147  Mid-latitude       NOR13        NOR2  12   -2.8345 -3.7544 -1.9145   \n",
       "372  Mid-latitude         IT1         IT4   8    1.9922  1.0723  2.9122   \n",
       "373  Mid-latitude         IT1       NOR13   8    2.5522  1.5715  3.5329   \n",
       "376  Mid-latitude         IT4        NOR2   8   -2.7109 -3.6309 -1.7910   \n",
       "377  Mid-latitude       NOR13        NOR2   8   -3.2708 -4.2515 -2.2902   \n",
       "\n",
       "      p-adj  \n",
       "102  0.0042  \n",
       "103  0.0000  \n",
       "126  0.0006  \n",
       "147  0.0000  \n",
       "189  0.0002  \n",
       "225  0.0005  \n",
       "102 -0.0000  \n",
       "103 -0.0000  \n",
       "126 -0.0000  \n",
       "147 -0.0000  \n",
       "372  0.0000  \n",
       "373  0.0000  \n",
       "376 -0.0000  \n",
       "377 -0.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_accs_24h(data2, 'PRR5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TOC1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gene_24h(data2, 'TOC1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between treatments for TOC1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment_1</th>\n",
       "      <th>Treatment_2</th>\n",
       "      <th>Accession</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Treatment_1, Treatment_2, Accession, ZT, meandiff, lower, upper, p-adj]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_treatment_24h(data2, 'TOC1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between accessions for TOC1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Accession_1</th>\n",
       "      <th>Accession_2</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3162</td>\n",
       "      <td>0.0602</td>\n",
       "      <td>0.5722</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.3406</td>\n",
       "      <td>-0.5966</td>\n",
       "      <td>-0.0846</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>20</td>\n",
       "      <td>0.5013</td>\n",
       "      <td>0.2702</td>\n",
       "      <td>0.7324</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>20</td>\n",
       "      <td>0.3621</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.5825</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.4818</td>\n",
       "      <td>-0.7021</td>\n",
       "      <td>-0.2614</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Treatment Accession_1 Accession_2  ZT  meandiff   lower   upper   p-adj\n",
       "1      Arctic         IT1       NOR13   0    0.3162  0.0602  0.5722  0.0022\n",
       "53     Arctic       NOR13        NOR2   0   -0.3406 -0.5966 -0.0846  0.0005\n",
       "259    Arctic         IT1       NOR13  20    0.5013  0.2702  0.7324  0.0000\n",
       "273    Arctic         IT4       NOR13  20    0.3621  0.1418  0.5825  0.0000\n",
       "287    Arctic       NOR13        NOR2  20   -0.4818 -0.7021 -0.2614  0.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_accs_24h(data2, 'TOC1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LUX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gene_24h(data2, 'LUX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between treatments for LUX\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment_1</th>\n",
       "      <th>Treatment_2</th>\n",
       "      <th>Accession</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.1507</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>12</td>\n",
       "      <td>0.1288</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.2421</td>\n",
       "      <td>0.0098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.1424</td>\n",
       "      <td>-0.2611</td>\n",
       "      <td>-0.0236</td>\n",
       "      <td>0.0044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.1098</td>\n",
       "      <td>-0.1879</td>\n",
       "      <td>-0.0317</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.1036</td>\n",
       "      <td>-0.1881</td>\n",
       "      <td>-0.0192</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.1395</td>\n",
       "      <td>-0.2782</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>16</td>\n",
       "      <td>-0.0882</td>\n",
       "      <td>-0.1663</td>\n",
       "      <td>-0.0101</td>\n",
       "      <td>0.0108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>D-Arctic</td>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.1454</td>\n",
       "      <td>0.0533</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.1616</td>\n",
       "      <td>0.0480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>12</td>\n",
       "      <td>0.2238</td>\n",
       "      <td>0.0851</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.1261</td>\n",
       "      <td>-0.2330</td>\n",
       "      <td>-0.0192</td>\n",
       "      <td>0.0056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT4</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.1097</td>\n",
       "      <td>-0.2018</td>\n",
       "      <td>-0.0176</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>D-Mid-latitude</td>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0812</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.1618</td>\n",
       "      <td>0.0468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment_1     Treatment_2 Accession  ZT  meandiff   lower   upper  \\\n",
       "51           Arctic    Mid-latitude       IT4  12    0.0869  0.0231  0.1507   \n",
       "51           Arctic    Mid-latitude     NOR13  12    0.1288  0.0156  0.2421   \n",
       "207          Arctic    Mid-latitude     NOR13  20   -0.1424 -0.2611 -0.0236   \n",
       "49           Arctic        D-Arctic       IT1  12   -0.1098 -0.1879 -0.0317   \n",
       "49           Arctic        D-Arctic       IT4  12   -0.1036 -0.1881 -0.0192   \n",
       "49           Arctic        D-Arctic     NOR13  12   -0.1395 -0.2782 -0.0008   \n",
       "135          Arctic        D-Arctic       IT1  16   -0.0882 -0.1663 -0.0101   \n",
       "72         D-Arctic  D-Mid-latitude       IT4  12    0.1454  0.0533  0.2375   \n",
       "94   D-Mid-latitude    Mid-latitude       IT1  12    0.0810  0.0003  0.1616   \n",
       "94   D-Mid-latitude    Mid-latitude     NOR13  12    0.2238  0.0851  0.3625   \n",
       "234  D-Mid-latitude    Mid-latitude      NOR2  20   -0.1261 -0.2330 -0.0192   \n",
       "280  D-Mid-latitude    Mid-latitude       IT4  24   -0.1097 -0.2018 -0.0176   \n",
       "324  D-Mid-latitude    Mid-latitude       IT1   8    0.0812  0.0005  0.1618   \n",
       "\n",
       "      p-adj  \n",
       "51   0.0005  \n",
       "51   0.0098  \n",
       "207  0.0044  \n",
       "49   0.0002  \n",
       "49   0.0030  \n",
       "49   0.0469  \n",
       "135  0.0108  \n",
       "72   0.0000  \n",
       "94   0.0480  \n",
       "94   0.0000  \n",
       "234  0.0056  \n",
       "280  0.0050  \n",
       "324  0.0468  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_treatment_24h(data2, 'LUX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant results between accessions for LUX\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Accession_1</th>\n",
       "      <th>Accession_2</th>\n",
       "      <th>ZT</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>p-adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>20</td>\n",
       "      <td>0.1411</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.2525</td>\n",
       "      <td>0.0013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>IT4</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>20</td>\n",
       "      <td>0.1119</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.2181</td>\n",
       "      <td>0.0259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Arctic</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.1262</td>\n",
       "      <td>-0.2324</td>\n",
       "      <td>-0.0201</td>\n",
       "      <td>0.0043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>IT4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0845</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.1643</td>\n",
       "      <td>0.0249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>IT1</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>12</td>\n",
       "      <td>0.1321</td>\n",
       "      <td>0.0557</td>\n",
       "      <td>0.2086</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Mid-latitude</td>\n",
       "      <td>NOR13</td>\n",
       "      <td>NOR2</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.1195</td>\n",
       "      <td>-0.1959</td>\n",
       "      <td>-0.0431</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Treatment Accession_1 Accession_2  ZT  meandiff   lower   upper  \\\n",
       "259        Arctic         IT1       NOR13  20    0.1411  0.0298  0.2525   \n",
       "273        Arctic         IT4       NOR13  20    0.1119  0.0058  0.2181   \n",
       "287        Arctic       NOR13        NOR2  20   -0.1262 -0.2324 -0.0201   \n",
       "102  Mid-latitude         IT1         IT4  12    0.0845  0.0047  0.1643   \n",
       "103  Mid-latitude         IT1       NOR13  12    0.1321  0.0557  0.2086   \n",
       "147  Mid-latitude       NOR13        NOR2  12   -0.1195 -0.1959 -0.0431   \n",
       "\n",
       "      p-adj  \n",
       "259  0.0013  \n",
       "273  0.0259  \n",
       "287  0.0043  \n",
       "102  0.0249  \n",
       "103  0.0000  \n",
       "147  0.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_btw_accs_24h(data2, 'LUX')"
   ]
  }
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