RL2020-sigmoid-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb767e85-7eda-47fb-8667-70b771a260d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data_June20_raw=pd.read_csv(\"./Dataset_Potato_18_20/June2020.csv\").drop(columns=['Unnamed: 0'])\n",
    "data_Aug20_raw=pd.read_csv(\"./Dataset_Potato_18_20/August2020.csv\").drop(columns=['Unnamed: 0'])\n",
    "isotope_data_20=pd.read_csv('./Dataset_Potato_18_20/Isotope2020.csv').drop(columns=['Unnamed: 0'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4143ea57-347b-4974-8e3d-6207b5e7ac0f",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tube</th>\n",
       "      <th>ID</th>\n",
       "      <th>x</th>\n",
       "      <th>Ntret</th>\n",
       "      <th>camera</th>\n",
       "      <th>root_length_cm</th>\n",
       "      <th>depth</th>\n",
       "      <th>soil_depth</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>353</td>\n",
       "      <td>Aventra</td>\n",
       "      <td>3100</td>\n",
       "      <td>high</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>610</td>\n",
       "      <td>38.17</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>353</td>\n",
       "      <td>Aventra</td>\n",
       "      <td>3100</td>\n",
       "      <td>high</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.53</td>\n",
       "      <td>640</td>\n",
       "      <td>38.99</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>353</td>\n",
       "      <td>Aventra</td>\n",
       "      <td>3100</td>\n",
       "      <td>high</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>680</td>\n",
       "      <td>40.08</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>353</td>\n",
       "      <td>Aventra</td>\n",
       "      <td>3100</td>\n",
       "      <td>high</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>720</td>\n",
       "      <td>41.17</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>353</td>\n",
       "      <td>Aventra</td>\n",
       "      <td>3100</td>\n",
       "      <td>high</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>1.57</td>\n",
       "      <td>750</td>\n",
       "      <td>41.99</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5111</th>\n",
       "      <td>448</td>\n",
       "      <td>Argana</td>\n",
       "      <td>3195</td>\n",
       "      <td>low</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2350</td>\n",
       "      <td>85.55</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5112</th>\n",
       "      <td>448</td>\n",
       "      <td>Argana</td>\n",
       "      <td>3195</td>\n",
       "      <td>low</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2380</td>\n",
       "      <td>86.37</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5113</th>\n",
       "      <td>448</td>\n",
       "      <td>Argana</td>\n",
       "      <td>3195</td>\n",
       "      <td>low</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2420</td>\n",
       "      <td>87.46</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5114</th>\n",
       "      <td>448</td>\n",
       "      <td>Argana</td>\n",
       "      <td>3195</td>\n",
       "      <td>low</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2450</td>\n",
       "      <td>88.27</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5115</th>\n",
       "      <td>448</td>\n",
       "      <td>Argana</td>\n",
       "      <td>3195</td>\n",
       "      <td>low</td>\n",
       "      <td>Cam3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2490</td>\n",
       "      <td>89.36</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5116 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tube       ID     x Ntret camera  root_length_cm  depth  soil_depth  \\\n",
       "0      353  Aventra  3100  high   Cam3            0.00    610       38.17   \n",
       "1      353  Aventra  3100  high   Cam3            0.53    640       38.99   \n",
       "2      353  Aventra  3100  high   Cam3            0.00    680       40.08   \n",
       "3      353  Aventra  3100  high   Cam3            0.00    720       41.17   \n",
       "4      353  Aventra  3100  high   Cam3            1.57    750       41.99   \n",
       "...    ...      ...   ...   ...    ...             ...    ...         ...   \n",
       "5111   448   Argana  3195   low   Cam3            0.80   2350       85.55   \n",
       "5112   448   Argana  3195   low   Cam3            0.00   2380       86.37   \n",
       "5113   448   Argana  3195   low   Cam3            0.00   2420       87.46   \n",
       "5114   448   Argana  3195   low   Cam3            0.90   2450       88.27   \n",
       "5115   448   Argana  3195   low   Cam3            0.00   2490       89.36   \n",
       "\n",
       "     month  \n",
       "0     june  \n",
       "1     june  \n",
       "2     june  \n",
       "3     june  \n",
       "4     june  \n",
       "...    ...  \n",
       "5111  june  \n",
       "5112  june  \n",
       "5113  june  \n",
       "5114  june  \n",
       "5115  june  \n",
       "\n",
       "[5116 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_June20_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5256da21-e302-42e1-9567-0230f63d6a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing RadiMax modules for RL data preprocessing\n",
    "from  RadiMaxDataPreProcessing import RL_processing, fun_RL_computation, plot_RL,isotope_data_preprocess\n",
    "\n",
    "# Processing 2018 RL data for getting root lengths from pixels values  \n",
    "data_June20,data_June20,data_Aug20=RL_processing(data_June20_raw.copy(),data_June20_raw.copy(),data_Aug20_raw.copy(),Square_root=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2dd7bb04-8e8f-431b-a8ac-ecbbee1fba9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 38.,  46.,  55.,  63.,  72.,  80.,  89.,  97., 106., 114., 123.,\n",
       "       131., 140., 148., 157., 165., 174., 182., 191., 199., 208., 216.,\n",
       "       225., 233., 242., 250.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=25\n",
    "#Number of intervals for computation of RL 2018\n",
    "Soil_depth_range_20=np.floor(np.linspace(start =np.min([data_Aug20['soil_depth'].min(),data_June20['soil_depth'].min(),data_June20['soil_depth'].min()]) , stop = np.max([data_Aug20['soil_depth'].max(),data_June20['soil_depth'].max(),data_June20['soil_depth'].max()])+106, num = n+1))\n",
    "Soil_depth_range_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1b7604e-7536-4075-b9d5-5266385789f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Computation Root Lengths in Different Soil Layers in  2018\n",
    "RL_Aug20,_=fun_RL_computation(data_Aug20.copy(),'RL_Aug_SD_',n,Soil_depth_range_20.astype(int))\n",
    "RL_June20,_=fun_RL_computation(data_June20.copy(),'RL_June_SD_',n,Soil_depth_range_20.astype(int))\n",
    "RL_June20,Intervals=fun_RL_computation(data_June20.copy(),'RL_June_SD_',n,Soil_depth_range_20.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "174a5fae-5e43-4462-b5b4-3e983f22e48b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RL_June_SD_(38, 46]</th>\n",
       "      <th>RL_June_SD_(46, 55]</th>\n",
       "      <th>RL_June_SD_(55, 63]</th>\n",
       "      <th>RL_June_SD_(63, 72]</th>\n",
       "      <th>RL_June_SD_(72, 80]</th>\n",
       "      <th>RL_June_SD_(80, 89]</th>\n",
       "      <th>RL_June_SD_(89, 97]</th>\n",
       "      <th>RL_June_SD_(97, 106]</th>\n",
       "      <th>RL_June_SD_(106, 114]</th>\n",
       "      <th>RL_June_SD_(114, 123]</th>\n",
       "      <th>...</th>\n",
       "      <th>RL_June_SD_(174, 182]</th>\n",
       "      <th>RL_June_SD_(182, 191]</th>\n",
       "      <th>RL_June_SD_(191, 199]</th>\n",
       "      <th>RL_June_SD_(199, 208]</th>\n",
       "      <th>RL_June_SD_(208, 216]</th>\n",
       "      <th>RL_June_SD_(216, 225]</th>\n",
       "      <th>RL_June_SD_(225, 233]</th>\n",
       "      <th>RL_June_SD_(233, 242]</th>\n",
       "      <th>RL_June_SD_(242, 250]</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.270261</td>\n",
       "      <td>0.423461</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>0.159914</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.448200</td>\n",
       "      <td>0.689474</td>\n",
       "      <td>0.206851</td>\n",
       "      <td>0.198139</td>\n",
       "      <td>0.119352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.630583</td>\n",
       "      <td>0.471179</td>\n",
       "      <td>0.059835</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.425890</td>\n",
       "      <td>0.848606</td>\n",
       "      <td>0.746268</td>\n",
       "      <td>0.196819</td>\n",
       "      <td>0.708036</td>\n",
       "      <td>0.509111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.458450</td>\n",
       "      <td>0.613333</td>\n",
       "      <td>0.069597</td>\n",
       "      <td>0.345994</td>\n",
       "      <td>0.117804</td>\n",
       "      <td>0.176267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>357.0</td>\n",
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       "<p>5 rows × 26 columns</p>\n",
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      ],
      "text/plain": [
       "   RL_June_SD_(38, 46]  RL_June_SD_(46, 55]  RL_June_SD_(55, 63]  \\\n",
       "0             0.712196             0.557762             0.270261   \n",
       "1             0.448200             0.689474             0.206851   \n",
       "2             0.630583             0.471179             0.059835   \n",
       "3             1.425890             0.848606             0.746268   \n",
       "4             1.458450             0.613333             0.069597   \n",
       "\n",
       "   RL_June_SD_(63, 72]  RL_June_SD_(72, 80]  RL_June_SD_(80, 89]  \\\n",
       "0             0.423461             0.037500             0.159914   \n",
       "1             0.198139             0.119352             0.000000   \n",
       "2             0.000000             0.000000             0.000000   \n",
       "3             0.196819             0.708036             0.509111   \n",
       "4             0.345994             0.117804             0.176267   \n",
       "\n",
       "   RL_June_SD_(89, 97]  RL_June_SD_(97, 106]  RL_June_SD_(106, 114]  \\\n",
       "0                  0.0                   0.0                    0.0   \n",
       "1                  0.0                   0.0                    0.0   \n",
       "2                  0.0                   0.0                    0.0   \n",
       "3                  0.0                   0.0                    0.0   \n",
       "4                  0.0                   0.0                    0.0   \n",
       "\n",
       "   RL_June_SD_(114, 123]  ...  RL_June_SD_(174, 182]  RL_June_SD_(182, 191]  \\\n",
       "0                    0.0  ...                    0.0                    0.0   \n",
       "1                    0.0  ...                    0.0                    0.0   \n",
       "2                    0.0  ...                    0.0                    0.0   \n",
       "3                    0.0  ...                    0.0                    0.0   \n",
       "4                    0.0  ...                    0.0                    0.0   \n",
       "\n",
       "   RL_June_SD_(191, 199]  RL_June_SD_(199, 208]  RL_June_SD_(208, 216]  \\\n",
       "0                    0.0                    0.0                    0.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "\n",
       "   RL_June_SD_(216, 225]  RL_June_SD_(225, 233]  RL_June_SD_(233, 242]  \\\n",
       "0                    0.0                    0.0                    0.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "\n",
       "   RL_June_SD_(242, 250]    row  \n",
       "0                    0.0  353.0  \n",
       "1                    0.0  354.0  \n",
       "2                    0.0  355.0  \n",
       "3                    0.0  356.0  \n",
       "4                    0.0  357.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RL_June20.fillna(0,inplace=True)\n",
    "RL_June20.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "825ef1f8-fa2e-4ec8-8818-68a71875ae25",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>RL_Aug_SD_(38, 46]</th>\n",
       "      <th>RL_Aug_SD_(46, 55]</th>\n",
       "      <th>RL_Aug_SD_(55, 63]</th>\n",
       "      <th>RL_Aug_SD_(63, 72]</th>\n",
       "      <th>RL_Aug_SD_(72, 80]</th>\n",
       "      <th>RL_Aug_SD_(80, 89]</th>\n",
       "      <th>RL_Aug_SD_(89, 97]</th>\n",
       "      <th>RL_Aug_SD_(97, 106]</th>\n",
       "      <th>RL_Aug_SD_(106, 114]</th>\n",
       "      <th>RL_Aug_SD_(114, 123]</th>\n",
       "      <th>...</th>\n",
       "      <th>RL_Aug_SD_(174, 182]</th>\n",
       "      <th>RL_Aug_SD_(182, 191]</th>\n",
       "      <th>RL_Aug_SD_(191, 199]</th>\n",
       "      <th>RL_Aug_SD_(199, 208]</th>\n",
       "      <th>RL_Aug_SD_(208, 216]</th>\n",
       "      <th>RL_Aug_SD_(216, 225]</th>\n",
       "      <th>RL_Aug_SD_(225, 233]</th>\n",
       "      <th>RL_Aug_SD_(233, 242]</th>\n",
       "      <th>RL_Aug_SD_(242, 250]</th>\n",
       "      <th>row</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.386123</td>\n",
       "      <td>0.438742</td>\n",
       "      <td>0.206199</td>\n",
       "      <td>0.602130</td>\n",
       "      <td>0.185740</td>\n",
       "      <td>0.814621</td>\n",
       "      <td>0.654383</td>\n",
       "      <td>0.150633</td>\n",
       "      <td>0.376346</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.212533</td>\n",
       "      <td>0.468346</td>\n",
       "      <td>0.623055</td>\n",
       "      <td>0.774138</td>\n",
       "      <td>0.179849</td>\n",
       "      <td>0.178125</td>\n",
       "      <td>0.158768</td>\n",
       "      <td>0.188192</td>\n",
       "      <td>0.323755</td>\n",
       "      <td>0.138533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.087384</td>\n",
       "      <td>0.770993</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157648</td>\n",
       "      <td>0.371181</td>\n",
       "      <td>0.202412</td>\n",
       "      <td>0.066249</td>\n",
       "      <td>0.135995</td>\n",
       "      <td>0.356776</td>\n",
       "      <td>0.201073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.406217</td>\n",
       "      <td>0.524552</td>\n",
       "      <td>0.453849</td>\n",
       "      <td>0.471584</td>\n",
       "      <td>0.454547</td>\n",
       "      <td>0.385165</td>\n",
       "      <td>0.378171</td>\n",
       "      <td>0.331604</td>\n",
       "      <td>0.271308</td>\n",
       "      <td>0.240732</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.655920</td>\n",
       "      <td>0.846059</td>\n",
       "      <td>0.466582</td>\n",
       "      <td>1.118832</td>\n",
       "      <td>1.016576</td>\n",
       "      <td>0.310798</td>\n",
       "      <td>1.228650</td>\n",
       "      <td>0.563725</td>\n",
       "      <td>0.069597</td>\n",
       "      <td>0.086081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RL_Aug_SD_(38, 46]  RL_Aug_SD_(46, 55]  RL_Aug_SD_(55, 63]  \\\n",
       "0            0.386123            0.438742            0.206199   \n",
       "1            0.212533            0.468346            0.623055   \n",
       "2            0.087384            0.770993            0.000000   \n",
       "3            0.406217            0.524552            0.453849   \n",
       "4            0.655920            0.846059            0.466582   \n",
       "\n",
       "   RL_Aug_SD_(63, 72]  RL_Aug_SD_(72, 80]  RL_Aug_SD_(80, 89]  \\\n",
       "0            0.602130            0.185740            0.814621   \n",
       "1            0.774138            0.179849            0.178125   \n",
       "2            0.157648            0.371181            0.202412   \n",
       "3            0.471584            0.454547            0.385165   \n",
       "4            1.118832            1.016576            0.310798   \n",
       "\n",
       "   RL_Aug_SD_(89, 97]  RL_Aug_SD_(97, 106]  RL_Aug_SD_(106, 114]  \\\n",
       "0            0.654383             0.150633              0.376346   \n",
       "1            0.158768             0.188192              0.323755   \n",
       "2            0.066249             0.135995              0.356776   \n",
       "3            0.378171             0.331604              0.271308   \n",
       "4            1.228650             0.563725              0.069597   \n",
       "\n",
       "   RL_Aug_SD_(114, 123]  ...  RL_Aug_SD_(174, 182]  RL_Aug_SD_(182, 191]  \\\n",
       "0              0.011111  ...                   0.0                   0.0   \n",
       "1              0.138533  ...                   0.0                   0.0   \n",
       "2              0.201073  ...                   0.0                   0.0   \n",
       "3              0.240732  ...                   0.0                   0.0   \n",
       "4              0.086081  ...                   0.0                   0.0   \n",
       "\n",
       "   RL_Aug_SD_(191, 199]  RL_Aug_SD_(199, 208]  RL_Aug_SD_(208, 216]  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "\n",
       "   RL_Aug_SD_(216, 225]  RL_Aug_SD_(225, 233]  RL_Aug_SD_(233, 242]  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "\n",
       "   RL_Aug_SD_(242, 250]    row  \n",
       "0                   0.0  353.0  \n",
       "1                   0.0  354.0  \n",
       "2                   0.0  355.0  \n",
       "3                   0.0  356.0  \n",
       "4                   0.0  357.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RL_Aug20.fillna(0,inplace=True)\n",
    "RL_Aug20.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "18306d55-e295-4c70-bf14-aff6e6e240bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_RL(RL_June20,RL_June20,RL_Aug20,Intervals,n,2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71916fad-2d3f-4efa-836e-d8d083df35b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "RL_Interval_20=RL_June20.merge(RL_Aug20,on='row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1cb47a7-f644-4df1-9165-11b52c79bdfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1374/3583121184.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  F_RL_Interval_20=RL_Interval_20.append(RL_Interval_20[RL_Interval_20[\"row\"].eq(365)]).sort_values(\"row\").reset_index(drop=True)\n"
     ]
    }
   ],
   "source": [
    "F_RL_Interval_20=RL_Interval_20.append(RL_Interval_20[RL_Interval_20[\"row\"].eq(365)]).sort_values(\"row\").reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e879d151-7d37-4f81-9795-b48f6a9c337e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RL_June_SD_(38, 46]</th>\n",
       "      <th>RL_June_SD_(46, 55]</th>\n",
       "      <th>RL_June_SD_(55, 63]</th>\n",
       "      <th>RL_June_SD_(63, 72]</th>\n",
       "      <th>RL_June_SD_(72, 80]</th>\n",
       "      <th>RL_June_SD_(80, 89]</th>\n",
       "      <th>RL_June_SD_(89, 97]</th>\n",
       "      <th>RL_June_SD_(97, 106]</th>\n",
       "      <th>RL_June_SD_(106, 114]</th>\n",
       "      <th>RL_June_SD_(114, 123]</th>\n",
       "      <th>...</th>\n",
       "      <th>RL_Aug_SD_(165, 174]</th>\n",
       "      <th>RL_Aug_SD_(174, 182]</th>\n",
       "      <th>RL_Aug_SD_(182, 191]</th>\n",
       "      <th>RL_Aug_SD_(191, 199]</th>\n",
       "      <th>RL_Aug_SD_(199, 208]</th>\n",
       "      <th>RL_Aug_SD_(208, 216]</th>\n",
       "      <th>RL_Aug_SD_(216, 225]</th>\n",
       "      <th>RL_Aug_SD_(225, 233]</th>\n",
       "      <th>RL_Aug_SD_(233, 242]</th>\n",
       "      <th>RL_Aug_SD_(242, 250]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.712196</td>\n",
       "      <td>0.557762</td>\n",
       "      <td>0.270261</td>\n",
       "      <td>0.423461</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>0.159914</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.448200</td>\n",
       "      <td>0.689474</td>\n",
       "      <td>0.206851</td>\n",
       "      <td>0.198139</td>\n",
       "      <td>0.119352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.630583</td>\n",
       "      <td>0.471179</td>\n",
       "      <td>0.059835</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.425890</td>\n",
       "      <td>0.848606</td>\n",
       "      <td>0.746268</td>\n",
       "      <td>0.196819</td>\n",
       "      <td>0.708036</td>\n",
       "      <td>0.509111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.458450</td>\n",
       "      <td>0.613333</td>\n",
       "      <td>0.069597</td>\n",
       "      <td>0.345994</td>\n",
       "      <td>0.117804</td>\n",
       "      <td>0.176267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RL_June_SD_(38, 46]  RL_June_SD_(46, 55]  RL_June_SD_(55, 63]  \\\n",
       "0             0.712196             0.557762             0.270261   \n",
       "1             0.448200             0.689474             0.206851   \n",
       "2             0.630583             0.471179             0.059835   \n",
       "3             1.425890             0.848606             0.746268   \n",
       "4             1.458450             0.613333             0.069597   \n",
       "\n",
       "   RL_June_SD_(63, 72]  RL_June_SD_(72, 80]  RL_June_SD_(80, 89]  \\\n",
       "0             0.423461             0.037500             0.159914   \n",
       "1             0.198139             0.119352             0.000000   \n",
       "2             0.000000             0.000000             0.000000   \n",
       "3             0.196819             0.708036             0.509111   \n",
       "4             0.345994             0.117804             0.176267   \n",
       "\n",
       "   RL_June_SD_(89, 97]  RL_June_SD_(97, 106]  RL_June_SD_(106, 114]  \\\n",
       "0                  0.0                   0.0                    0.0   \n",
       "1                  0.0                   0.0                    0.0   \n",
       "2                  0.0                   0.0                    0.0   \n",
       "3                  0.0                   0.0                    0.0   \n",
       "4                  0.0                   0.0                    0.0   \n",
       "\n",
       "   RL_June_SD_(114, 123]  ...  RL_Aug_SD_(165, 174]  RL_Aug_SD_(174, 182]  \\\n",
       "0                    0.0  ...                   0.0                   0.0   \n",
       "1                    0.0  ...                   0.0                   0.0   \n",
       "2                    0.0  ...                   0.0                   0.0   \n",
       "3                    0.0  ...                   0.0                   0.0   \n",
       "4                    0.0  ...                   0.0                   0.0   \n",
       "\n",
       "   RL_Aug_SD_(182, 191]  RL_Aug_SD_(191, 199]  RL_Aug_SD_(199, 208]  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "\n",
       "   RL_Aug_SD_(208, 216]  RL_Aug_SD_(216, 225]  RL_Aug_SD_(225, 233]  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "\n",
       "   RL_Aug_SD_(233, 242]  RL_Aug_SD_(242, 250]  \n",
       "0                   0.0                   0.0  \n",
       "1                   0.0                   0.0  \n",
       "2                   0.0                   0.0  \n",
       "3                   0.0                   0.0  \n",
       "4                   0.0                   0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_RL_Interval_20.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f95abae8-f02e-4f58-be31-67bd50756f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "F_RL_Interval_20.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8087d639-9862-45b3-944e-f197b68d3872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.38612268, 0.43874246, 0.2061986 , 0.60212988, 0.18573954,\n",
       "       0.81462095, 0.65438283, 0.15063316, 0.37634649, 0.01111111,\n",
       "       0.05555556, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_RL_Interval_20.iloc[0,:].values[n+1:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f434b6d7-5ab3-4b8e-9e3c-9718a487c267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting PyPDF2\n",
      "  Downloading pypdf2-2.12.1-py3-none-any.whl (222 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.8/222.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: typing_extensions>=3.10.0.0 in /opt/conda/envs/python3/lib/python3.8/site-packages (from PyPDF2) (4.4.0)\n",
      "Installing collected packages: PyPDF2\n",
      "Successfully installed PyPDF2-2.12.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install PyPDF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7b6764a-b94f-4ed9-91f3-61e8e65e75c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tube No: 353.0\n",
      "9\n",
      "Tube No: 355.0\n",
      "10\n",
      "Tube No: 357.0\n",
      "8\n",
      "Tube No: 359.0\n",
      "11\n",
      "Tube No: 361.0\n",
      "9\n",
      "Tube No: 363.0\n",
      "7\n",
      "Tube No: 365.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python3/lib/python3.8/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tube No: 367.0\n",
      "11\n",
      "Tube No: 369.0\n",
      "10\n",
      "Tube No: 371.0\n",
      "11\n",
      "Tube No: 373.0\n",
      "Tube No: 375.0\n",
      "7\n",
      "Tube No: 377.0\n",
      "10\n",
      "Tube No: 379.0\n",
      "5\n",
      "Tube No: 381.0\n",
      "11\n",
      "Tube No: 383.0\n",
      "9\n",
      "Tube No: 385.0\n",
      "10\n",
      "Tube No: 387.0\n",
      "8\n",
      "Tube No: 389.0\n",
      "10\n",
      "Tube No: 391.0\n",
      "12\n",
      "Tube No: 393.0\n",
      "6\n",
      "Tube No: 395.0\n",
      "8\n",
      "Tube No: 397.0\n",
      "10\n",
      "Tube No: 399.0\n",
      "8\n",
      "Tube No: 401.0\n",
      "12\n",
      "Tube No: 403.0\n",
      "9\n",
      "Tube No: 405.0\n",
      "8\n",
      "Tube No: 407.0\n",
      "9\n",
      "Tube No: 409.0\n",
      "8\n",
      "Tube No: 411.0\n",
      "8\n",
      "Tube No: 413.0\n",
      "9\n",
      "Tube No: 415.0\n",
      "10\n",
      "Tube No: 417.0\n",
      "11\n",
      "Tube No: 419.0\n",
      "9\n",
      "Tube No: 421.0\n",
      "10\n",
      "Tube No: 423.0\n",
      "11\n",
      "Tube No: 425.0\n",
      "8\n",
      "Tube No: 427.0\n",
      "10\n",
      "Tube No: 429.0\n",
      "Tube No: 431.0\n",
      "10\n",
      "Tube No: 433.0\n",
      "9\n",
      "Tube No: 435.0\n",
      "Tube No: 437.0\n",
      "8\n",
      "Tube No: 439.0\n",
      "11\n",
      "Tube No: 441.0\n",
      "13\n",
      "Tube No: 443.0\n",
      "10\n",
      "Tube No: 445.0\n",
      "11\n",
      "Tube No: 447.0\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "from scipy import interpolate\n",
    "import numpy as np\n",
    "from numpy import arange\n",
    "from pandas import read_csv\n",
    "from scipy.optimize import curve_fit\n",
    "from matplotlib import pyplot\n",
    "import pandas as pd\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import os\n",
    "from PyPDF2 import PdfFileMerger\n",
    "merger = PdfFileMerger()\n",
    "\n",
    "   \n",
    "def sigmoid(x, x0,b,L,c):\n",
    "    return L*(scipy.special.expit((x-x0)*b,dtype = np.float128))+c  #expit(x) = 1/(1+exp(-b*(x-x0))).\n",
    "def func_exp_sq(t, a, tau):\n",
    "    return a*np.exp(-t*t*tau,dtype = np.float128)\n",
    "\n",
    "def sigmoid_initial_Infle_point(x,y):\n",
    "    xi=0\n",
    "    xp=0\n",
    "    x0_sig_inflec_p=0.1\n",
    "    ipoints=[.35,0.45,.65,.75,.9]\n",
    "    i=0\n",
    "    for x0 in ipoints:\n",
    "        print(x0)\n",
    "        args, cov =curve_fit(sigmoid, x, y,p0=[x0,5,2,.01],maxfev=10000000,ftol=1e-08, xtol=1e-08,gtol=.0001)       \n",
    "        x0_sig_inflec_c,b_sig,L,c=args\n",
    "        print(f'x0_sig_inflec_c{x0_sig_inflec_c}')\n",
    "        if x0_sig_inflec_c>1 or  x0_sig_inflec_c<0:\n",
    "          \n",
    "            if x0_sig_inflec_c<0:\n",
    "                continue\n",
    "            else:\n",
    "                \n",
    "                if (i==len(ipoints)-1):\n",
    "                    xi=xp\n",
    "                    break\n",
    "                else: \n",
    "                    continue\n",
    "                \n",
    "        if x0_sig_inflec_c > x0_sig_inflec_p:\n",
    "\n",
    "            xi=x0\n",
    "            x0_sig_inflec_p=x0_sig_inflec_c\n",
    "            \n",
    "        xp=x0\n",
    "        i=i+1\n",
    "    \n",
    "     \n",
    "    return xi   \n",
    "\n",
    "\n",
    "\n",
    "inflection=np.array([])\n",
    "tube=np.array([]) \n",
    "TRL_below_SI=np.array([])\n",
    "TRL_above_SI=np.array([])\n",
    "TRL=np.array([])\n",
    "\n",
    "\n",
    "\n",
    "SI=pd.DataFrame()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for i in range(0,F_RL_Interval_20.index[-1],2):# Alternating index\n",
    "#for i in range(0,12,2):\n",
    "    x=np.zeros((1,n)).flatten()\n",
    "    j=0\n",
    "    for k in Intervals:\n",
    "     x[j]=(Intervals[j].left+Intervals[j].right)/2\n",
    "     j=j+1\n",
    "   \n",
    "    y_aug_1=F_RL_Interval_20.iloc[i,:].values[n+1:] # y_Aug 1st tube\n",
    "    y_aug_2=F_RL_Interval_20.iloc[i+1,:].values[n+1:]# y_Aug 2nd tube\n",
    "\n",
    "    y_avg=(y_aug_1+y_aug_2)/2\n",
    "    \n",
    "    tube_no=F_RL_Interval_20.iloc[i,:]['row']\n",
    "    \n",
    "    \n",
    "    \n",
    "    data=pd.DataFrame({'soil_depth':x,'root_length_t1':y_aug_1,'root_length_t2':y_aug_2})\n",
    "    \n",
    "    x1=data['soil_depth']\n",
    "    y1=data['root_length_t1']\n",
    "    x2=data['soil_depth']\n",
    "    y2=data['root_length_t2']\n",
    "\n",
    "    x=pd.concat([x1, x2])\n",
    "    y=pd.concat([y1,y2])\n",
    "    #df=pd.DataFrame({'soil_depth':x,'root_length_cm':y})\n",
    "\n",
    "    df=pd.DataFrame({'soil_depth':x1,'root_length_cm':y_avg})\n",
    "    temp1=df.sort_values(by='soil_depth').reset_index(drop=True)\n",
    "    \n",
    "    x=temp1['soil_depth']\n",
    "    y=temp1['root_length_cm']\n",
    "    \n",
    "\n",
    "    x = (x- x.values[0])/(x.values[-1] - x.values[0])   # normalize    \n",
    "    print(f'Tube No: {tube_no}')\n",
    "\n",
    "    \n",
    "\n",
    "    xp_lav=round(x*(temp1['soil_depth'].values[-1] - temp1['soil_depth'].values[0])+ temp1['soil_depth'].values[0],2)   \n",
    "    \n",
    "    #xi=sigmoid_initial_Infle_point(x,y) \n",
    "    \n",
    "    args, cov =curve_fit(sigmoid, x, y,p0=[0.55,5,2,.01],maxfev=1000000,ftol=1e-08, xtol=1e-08,gtol=.00001) \n",
    "    x0_sig_inflec,b_sig,L,c=args  \n",
    "    \n",
    "    y_pred = sigmoid(x, *args)\n",
    "    SR2=r2_score(y, y_pred)\n",
    "    x0_sig_inflec_actual=round(x0_sig_inflec*(temp1['soil_depth'].values[-1] - temp1['soil_depth'].values[0])+ temp1['soil_depth'].values[0],2)\n",
    "    s=str(x0_sig_inflec_actual)+\" cm\"\n",
    "    if x0_sig_inflec_actual>0:\n",
    "        tube=np.append(tube,tube_no)\n",
    "        inflection=np.append(inflection,x0_sig_inflec_actual)\n",
    "        \n",
    "        ip=np.where(xp_lav>=x0_sig_inflec_actual)[-1][0]\n",
    "        print(ip)\n",
    "        RL=y.values[0:].sum()# all\n",
    "        RL1=y.values[ip:].sum()# below\n",
    "        RL2=y.values[0:ip].sum()#above\n",
    "        TRL=np.append(TRL,RL)\n",
    "        TRL_below_SI=np.append(TRL_below_SI,RL1)\n",
    "        TRL_above_SI=np.append(TRL_above_SI,RL2)\n",
    "    \n",
    "    '''fig, ax = plt.subplots(figsize=(6, 3))\n",
    "    ax.plot(xp_lav, y, 'o', xp_lav, sigmoid(x, *args),'-')\n",
    "    ax.set_prop_cycle(color=['green', 'blue'])\n",
    "\n",
    "    ax.annotate('Inflection:'+s, xy=(x0_sig_inflec_actual, sigmoid(x0_sig_inflec,x0_sig_inflec,b_sig,L,c)), xytext=(x0_sig_inflec_actual+4, int(y.max()+1)/2-.2),\n",
    "                 arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "    ax.vlines(x=x0_sig_inflec_actual, ymin=-0.1, ymax=int(y.max()+1),color='orange')\n",
    "    plt.title('August 2020 : row no. '+ str(tube_no.astype(int)))\n",
    "    ax.legend(['Root length data',r'Sigmoid: $R^{2}=$'+str(np.round(SR2,2))])\n",
    "\n",
    "    ax.set_xlabel('Soil depth (cm)')\n",
    "    ax.set_ylabel(r'Root length(cm)') '''\n",
    "    \n",
    "    '''if x0_sig_inflec>0:\n",
    "        y_pred = sigmoid(x, *args)\n",
    "        SR2=r2_score(y, y_pred)\n",
    "        x0_sig_inflec_actual=round(x0_sig_inflec*(temp1['soil_depth'].values[-1] - temp1['soil_depth'].values[0])+ temp1['soil_depth'].values[0],2)\n",
    "        s=str(x0_sig_inflec_actual)+\" cm\"\n",
    "        inflection=np.append(inflection,x0_sig_inflec_actual)\n",
    "        ax.plot(xp_lav, y, 'o', xp_lav, sigmoid(x, *args),'-')\n",
    "        ax.set_prop_cycle(color=['green', 'blue'])\n",
    "\n",
    "        ax.annotate('Inflection:'+s, xy=(x0_sig_inflec_actual, sigmoid(x0_sig_inflec,x0_sig_inflec,b_sig,L,c)), xytext=(x0_sig_inflec_actual+4, int(y.max()+1)/2-.2),\n",
    "                     arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "        ax.vlines(x=x0_sig_inflec_actual, ymin=-0.1, ymax=int(y.max()+1),color='orange')\n",
    "        plt.title('August 2020 : row no. '+ str(tube_no.astype(int)))\n",
    "        ax.legend(['Root length data',r'Sigmoid: $R^{2}=$'+str(np.round(SR2,2))])\n",
    "\n",
    "        ax.set_xlabel('Soil depth (cm)')\n",
    "        ax.set_ylabel(r'Root length(cm)')\n",
    "    else:\n",
    "        popt, pcov = curve_fit(func_exp_sq, x, y)\n",
    "        a, tau= popt\n",
    "        y_pred = func_exp_sq(x, a, tau)\n",
    "        SR2=r2_score(y, y_pred)\n",
    "      \n",
    "        #Inflection point of Folded Normal Curve\n",
    "        \n",
    "        xp=1.0/(np.sqrt(2*tau)) \n",
    "            \n",
    "        #Actual position of inflection point\n",
    "        xp_actual=round(xp*(temp1['soil_depth'].values[-1] - temp1['soil_depth'].values[0])+ temp1['soil_depth'].values[0],2)\n",
    "        inflection=np.append(inflection,xp_actual)  \n",
    "        s=str(xp_actual)+\" cm\"\n",
    "        ax.plot(xp_lav, y, 'o', xp_lav, sigmoid(x, *args),'-')\n",
    "        ax.set_prop_cycle(color=['green', 'blue'])\n",
    "        \n",
    "        ax.annotate('Inflection:'+s, xy=(xp_actual, func_exp_sq(xp,a,tau)), xytext=(xp_actual+4, int(y.max()+1)/2-.2),arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "        ax.vlines(x=xp_actual, ymin=-0.1, ymax=int(y.max()+1),color='orange')\n",
    "        plt.title('August 2020 : row no. '+ str(tube_no.astype(int)))\n",
    "        ax.legend(['Root length data',r'Folded normal distribution : $R^{2}=$'+str(np.round(SR2,2))])\n",
    "        ax.set_xlabel('Soil depth (cm)')\n",
    "        ax.set_ylabel(r'Root length(cm)')'''\n",
    "   \n",
    "    #plt.savefig(\"page_{:0}.pdf\".format(i))\n",
    "    #plt.clf()\n",
    "    #outp=open(\"page_{:0}.pdf\".format(i), 'rb')\n",
    "    #merger.append(outp)\n",
    "    #merger.append(open(\"page_{:0}.pdf\".format(i), 'rb'))\n",
    "    #outp.close()\n",
    "    #plt.close()\n",
    "  \n",
    "    #plt.show()\n",
    "     # Save each figure as a pdf file.\n",
    "\n",
    "\n",
    "         \n",
    "    \n",
    "\n",
    "SI=pd.concat([SI,pd.DataFrame({'row':tube}),pd.DataFrame({'SI':inflection}),pd.DataFrame({'RL_below_SI': TRL_below_SI}),pd.DataFrame({'RL_above_SI': TRL_above_SI}),pd.DataFrame({'TRL': TRL})],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d83dc884-25dc-4847-a460-4d4dc254fb84",
   "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>row</th>\n",
       "      <th>SI</th>\n",
       "      <th>RL_below_SI</th>\n",
       "      <th>RL_above_SI</th>\n",
       "      <th>TRL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>353.0</td>\n",
       "      <td>111.63</td>\n",
       "      <td>0.274101</td>\n",
       "      <td>3.460838</td>\n",
       "      <td>3.734940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>355.0</td>\n",
       "      <td>120.27</td>\n",
       "      <td>0.329639</td>\n",
       "      <td>3.133720</td>\n",
       "      <td>3.463358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>357.0</td>\n",
       "      <td>106.71</td>\n",
       "      <td>0.454298</td>\n",
       "      <td>4.569893</td>\n",
       "      <td>5.024191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>359.0</td>\n",
       "      <td>130.07</td>\n",
       "      <td>0.449340</td>\n",
       "      <td>4.042820</td>\n",
       "      <td>4.492160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>361.0</td>\n",
       "      <td>115.84</td>\n",
       "      <td>0.898502</td>\n",
       "      <td>6.388214</td>\n",
       "      <td>7.286716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>363.0</td>\n",
       "      <td>93.39</td>\n",
       "      <td>0.418746</td>\n",
       "      <td>1.693255</td>\n",
       "      <td>2.112001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>367.0</td>\n",
       "      <td>130.46</td>\n",
       "      <td>0.198934</td>\n",
       "      <td>4.171877</td>\n",
       "      <td>4.370811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>369.0</td>\n",
       "      <td>126.49</td>\n",
       "      <td>0.452862</td>\n",
       "      <td>4.550690</td>\n",
       "      <td>5.003552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>371.0</td>\n",
       "      <td>129.00</td>\n",
       "      <td>0.502594</td>\n",
       "      <td>6.573374</td>\n",
       "      <td>7.075968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>375.0</td>\n",
       "      <td>93.64</td>\n",
       "      <td>0.488894</td>\n",
       "      <td>2.016411</td>\n",
       "      <td>2.505305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>377.0</td>\n",
       "      <td>121.89</td>\n",
       "      <td>0.206101</td>\n",
       "      <td>3.664596</td>\n",
       "      <td>3.870697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>379.0</td>\n",
       "      <td>83.54</td>\n",
       "      <td>0.808927</td>\n",
       "      <td>1.063255</td>\n",
       "      <td>1.872182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>381.0</td>\n",
       "      <td>132.16</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>8.909604</td>\n",
       "      <td>9.354893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>383.0</td>\n",
       "      <td>113.01</td>\n",
       "      <td>0.441883</td>\n",
       "      <td>2.189372</td>\n",
       "      <td>2.631255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>385.0</td>\n",
       "      <td>125.25</td>\n",
       "      <td>0.641398</td>\n",
       "      <td>6.539264</td>\n",
       "      <td>7.180662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>387.0</td>\n",
       "      <td>103.34</td>\n",
       "      <td>0.342405</td>\n",
       "      <td>5.050162</td>\n",
       "      <td>5.392568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>389.0</td>\n",
       "      <td>125.12</td>\n",
       "      <td>0.455190</td>\n",
       "      <td>5.246024</td>\n",
       "      <td>5.701215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>391.0</td>\n",
       "      <td>140.40</td>\n",
       "      <td>0.178827</td>\n",
       "      <td>3.192122</td>\n",
       "      <td>3.370948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>393.0</td>\n",
       "      <td>86.79</td>\n",
       "      <td>1.536699</td>\n",
       "      <td>5.155400</td>\n",
       "      <td>6.692099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>395.0</td>\n",
       "      <td>102.82</td>\n",
       "      <td>1.536679</td>\n",
       "      <td>2.773367</td>\n",
       "      <td>4.310046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>397.0</td>\n",
       "      <td>119.20</td>\n",
       "      <td>0.252400</td>\n",
       "      <td>5.663517</td>\n",
       "      <td>5.915917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>399.0</td>\n",
       "      <td>107.06</td>\n",
       "      <td>0.563131</td>\n",
       "      <td>2.811981</td>\n",
       "      <td>3.375113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>401.0</td>\n",
       "      <td>136.41</td>\n",
       "      <td>0.116458</td>\n",
       "      <td>5.691911</td>\n",
       "      <td>5.808368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>403.0</td>\n",
       "      <td>110.52</td>\n",
       "      <td>0.566271</td>\n",
       "      <td>2.691425</td>\n",
       "      <td>3.257696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>405.0</td>\n",
       "      <td>102.72</td>\n",
       "      <td>0.396233</td>\n",
       "      <td>1.299231</td>\n",
       "      <td>1.695464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>407.0</td>\n",
       "      <td>118.13</td>\n",
       "      <td>0.851945</td>\n",
       "      <td>5.253329</td>\n",
       "      <td>6.105274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>409.0</td>\n",
       "      <td>101.74</td>\n",
       "      <td>0.410578</td>\n",
       "      <td>2.708656</td>\n",
       "      <td>3.119234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>411.0</td>\n",
       "      <td>105.12</td>\n",
       "      <td>0.804808</td>\n",
       "      <td>5.055949</td>\n",
       "      <td>5.860756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>413.0</td>\n",
       "      <td>113.84</td>\n",
       "      <td>0.633915</td>\n",
       "      <td>2.769182</td>\n",
       "      <td>3.403097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>415.0</td>\n",
       "      <td>120.43</td>\n",
       "      <td>0.375297</td>\n",
       "      <td>2.237757</td>\n",
       "      <td>2.613055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>417.0</td>\n",
       "      <td>130.28</td>\n",
       "      <td>0.408148</td>\n",
       "      <td>6.157500</td>\n",
       "      <td>6.565648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>419.0</td>\n",
       "      <td>115.02</td>\n",
       "      <td>1.116577</td>\n",
       "      <td>4.550547</td>\n",
       "      <td>5.667124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>421.0</td>\n",
       "      <td>122.71</td>\n",
       "      <td>0.400653</td>\n",
       "      <td>4.061960</td>\n",
       "      <td>4.462613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>423.0</td>\n",
       "      <td>133.95</td>\n",
       "      <td>0.257543</td>\n",
       "      <td>3.688376</td>\n",
       "      <td>3.945919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>425.0</td>\n",
       "      <td>109.95</td>\n",
       "      <td>1.116826</td>\n",
       "      <td>3.223058</td>\n",
       "      <td>4.339885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>427.0</td>\n",
       "      <td>120.03</td>\n",
       "      <td>0.463705</td>\n",
       "      <td>3.644753</td>\n",
       "      <td>4.108458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>431.0</td>\n",
       "      <td>126.69</td>\n",
       "      <td>0.435285</td>\n",
       "      <td>2.550713</td>\n",
       "      <td>2.985998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>433.0</td>\n",
       "      <td>115.48</td>\n",
       "      <td>0.237036</td>\n",
       "      <td>1.489844</td>\n",
       "      <td>1.726880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>437.0</td>\n",
       "      <td>103.82</td>\n",
       "      <td>1.160653</td>\n",
       "      <td>3.188544</td>\n",
       "      <td>4.349196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>439.0</td>\n",
       "      <td>131.91</td>\n",
       "      <td>0.168343</td>\n",
       "      <td>3.745101</td>\n",
       "      <td>3.913444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>441.0</td>\n",
       "      <td>149.92</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.243290</td>\n",
       "      <td>6.243290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>443.0</td>\n",
       "      <td>119.11</td>\n",
       "      <td>0.475012</td>\n",
       "      <td>3.917729</td>\n",
       "      <td>4.392741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>445.0</td>\n",
       "      <td>134.41</td>\n",
       "      <td>0.145467</td>\n",
       "      <td>2.340145</td>\n",
       "      <td>2.485612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>447.0</td>\n",
       "      <td>89.46</td>\n",
       "      <td>2.000285</td>\n",
       "      <td>4.733211</td>\n",
       "      <td>6.733496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      row      SI  RL_below_SI  RL_above_SI       TRL\n",
       "0   353.0  111.63     0.274101     3.460838  3.734940\n",
       "1   355.0  120.27     0.329639     3.133720  3.463358\n",
       "2   357.0  106.71     0.454298     4.569893  5.024191\n",
       "3   359.0  130.07     0.449340     4.042820  4.492160\n",
       "4   361.0  115.84     0.898502     6.388214  7.286716\n",
       "5   363.0   93.39     0.418746     1.693255  2.112001\n",
       "6   367.0  130.46     0.198934     4.171877  4.370811\n",
       "7   369.0  126.49     0.452862     4.550690  5.003552\n",
       "8   371.0  129.00     0.502594     6.573374  7.075968\n",
       "9   375.0   93.64     0.488894     2.016411  2.505305\n",
       "10  377.0  121.89     0.206101     3.664596  3.870697\n",
       "11  379.0   83.54     0.808927     1.063255  1.872182\n",
       "12  381.0  132.16     0.445289     8.909604  9.354893\n",
       "13  383.0  113.01     0.441883     2.189372  2.631255\n",
       "14  385.0  125.25     0.641398     6.539264  7.180662\n",
       "15  387.0  103.34     0.342405     5.050162  5.392568\n",
       "16  389.0  125.12     0.455190     5.246024  5.701215\n",
       "17  391.0  140.40     0.178827     3.192122  3.370948\n",
       "18  393.0   86.79     1.536699     5.155400  6.692099\n",
       "19  395.0  102.82     1.536679     2.773367  4.310046\n",
       "20  397.0  119.20     0.252400     5.663517  5.915917\n",
       "21  399.0  107.06     0.563131     2.811981  3.375113\n",
       "22  401.0  136.41     0.116458     5.691911  5.808368\n",
       "23  403.0  110.52     0.566271     2.691425  3.257696\n",
       "24  405.0  102.72     0.396233     1.299231  1.695464\n",
       "25  407.0  118.13     0.851945     5.253329  6.105274\n",
       "26  409.0  101.74     0.410578     2.708656  3.119234\n",
       "27  411.0  105.12     0.804808     5.055949  5.860756\n",
       "28  413.0  113.84     0.633915     2.769182  3.403097\n",
       "29  415.0  120.43     0.375297     2.237757  2.613055\n",
       "30  417.0  130.28     0.408148     6.157500  6.565648\n",
       "31  419.0  115.02     1.116577     4.550547  5.667124\n",
       "32  421.0  122.71     0.400653     4.061960  4.462613\n",
       "33  423.0  133.95     0.257543     3.688376  3.945919\n",
       "34  425.0  109.95     1.116826     3.223058  4.339885\n",
       "35  427.0  120.03     0.463705     3.644753  4.108458\n",
       "36  431.0  126.69     0.435285     2.550713  2.985998\n",
       "37  433.0  115.48     0.237036     1.489844  1.726880\n",
       "38  437.0  103.82     1.160653     3.188544  4.349196\n",
       "39  439.0  131.91     0.168343     3.745101  3.913444\n",
       "40  441.0  149.92     0.000000     6.243290  6.243290\n",
       "41  443.0  119.11     0.475012     3.917729  4.392741\n",
       "42  445.0  134.41     0.145467     2.340145  2.485612\n",
       "43  447.0   89.46     2.000285     4.733211  6.733496"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b4860091-2a32-4116-8709-44aeeba729e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "SI.to_csv('Aug2020_SI_RL_below_and_above_SI.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5e60a53-0909-48de-877c-1d2e131487d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"Aug20_SI_FN.pdf\", \"wb\") as fout:\n",
    "    merger.write(fout)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e439216c-eabe-4283-b1b7-f91835116bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,96,2):\n",
    "    os.remove(\"page_{:0}.pdf\".format(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc9001b3-319a-4731-b277-443581898ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "isotope_data_20=pd.read_csv('./Dataset_Potato_18_20/Isotope2020.csv').drop(columns=['Unnamed: 0'])\n",
    "isotope_data_average_bin2_bin3=isotope_data_preprocess(isotope_data_20, 2020,'Both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e01ff8c6-3c62-4f04-b627-9f7dd2b2ed71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.merge(isotope_data_average_bin2_bin3,SI,on='row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "623506bc-d9dc-43d6-9b8a-cf956e74fead",
   "metadata": {},
   "outputs": [
    {
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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>row</th>\n",
       "      <th>ID</th>\n",
       "      <th>bin</th>\n",
       "      <th>Tret</th>\n",
       "      <th>C13_E</th>\n",
       "      <th>N15_E</th>\n",
       "      <th>Nup</th>\n",
       "      <th>Nup15</th>\n",
       "      <th>Nup15_t</th>\n",
       "      <th>block</th>\n",
       "      <th>position</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>353</td>\n",
       "      <td>Aventra</td>\n",
       "      <td>2</td>\n",
       "      <td>high</td>\n",
       "      <td>-25.185963</td>\n",
       "      <td>67.443429</td>\n",
       "      <td>92.712762</td>\n",
       "      <td>0.362425</td>\n",
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       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3</td>\n",
       "      <td>353</td>\n",
       "      <td>Aventra</td>\n",
       "      <td>3</td>\n",
       "      <td>high</td>\n",
       "      <td>-25.572687</td>\n",
       "      <td>105.590892</td>\n",
       "      <td>149.932225</td>\n",
       "      <td>0.606963</td>\n",
       "      <td>0.057761</td>\n",
       "      <td>1</td>\n",
       "      <td>South</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>7</td>\n",
       "      <td>355</td>\n",
       "      <td>Mikado</td>\n",
       "      <td>3</td>\n",
       "      <td>high</td>\n",
       "      <td>-26.883754</td>\n",
       "      <td>0.051511</td>\n",
       "      <td>150.726505</td>\n",
       "      <td>0.552144</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>1</td>\n",
       "      <td>South</td>\n",
       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>355</td>\n",
       "      <td>Mikado</td>\n",
       "      <td>2</td>\n",
       "      <td>high</td>\n",
       "      <td>-26.893317</td>\n",
       "      <td>0.375992</td>\n",
       "      <td>173.379980</td>\n",
       "      <td>0.635334</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>11</td>\n",
       "      <td>357</td>\n",
       "      <td>Argana</td>\n",
       "      <td>3</td>\n",
       "      <td>high</td>\n",
       "      <td>-24.554915</td>\n",
       "      <td>5.476661</td>\n",
       "      <td>135.702227</td>\n",
       "      <td>0.499794</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>1</td>\n",
       "      <td>South</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>182</td>\n",
       "      <td>443</td>\n",
       "      <td>Mikado</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "      <td>-25.715035</td>\n",
       "      <td>1.682187</td>\n",
       "      <td>142.936343</td>\n",
       "      <td>0.524458</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>8</td>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>187</td>\n",
       "      <td>445</td>\n",
       "      <td>Frig</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>-25.770515</td>\n",
       "      <td>1.381610</td>\n",
       "      <td>122.735858</td>\n",
       "      <td>0.450204</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>8</td>\n",
       "      <td>South</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>186</td>\n",
       "      <td>445</td>\n",
       "      <td>Frig</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "      <td>-26.006666</td>\n",
       "      <td>2.906812</td>\n",
       "      <td>109.117587</td>\n",
       "      <td>0.400859</td>\n",
       "      <td>0.001161</td>\n",
       "      <td>8</td>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>190</td>\n",
       "      <td>447</td>\n",
       "      <td>Argana</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "      <td>-25.054739</td>\n",
       "      <td>5.847584</td>\n",
       "      <td>110.075429</td>\n",
       "      <td>0.405559</td>\n",
       "      <td>0.002353</td>\n",
       "      <td>8</td>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>191</td>\n",
       "      <td>447</td>\n",
       "      <td>Argana</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>-25.338755</td>\n",
       "      <td>19.682484</td>\n",
       "      <td>131.889158</td>\n",
       "      <td>0.492588</td>\n",
       "      <td>0.009478</td>\n",
       "      <td>8</td>\n",
       "      <td>South</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sample  row       ID  bin  Tret      C13_E       N15_E         Nup  \\\n",
       "0        2  353  Aventra    2  high -25.185963   67.443429   92.712762   \n",
       "48       3  353  Aventra    3  high -25.572687  105.590892  149.932225   \n",
       "49       7  355   Mikado    3  high -26.883754    0.051511  150.726505   \n",
       "1        6  355   Mikado    2  high -26.893317    0.375992  173.379980   \n",
       "50      11  357   Argana    3  high -24.554915    5.476661  135.702227   \n",
       "..     ...  ...      ...  ...   ...        ...         ...         ...   \n",
       "45     182  443   Mikado    2   low -25.715035    1.682187  142.936343   \n",
       "94     187  445     Frig    3   low -25.770515    1.381610  122.735858   \n",
       "46     186  445     Frig    2   low -26.006666    2.906812  109.117587   \n",
       "47     190  447   Argana    2   low -25.054739    5.847584  110.075429   \n",
       "95     191  447   Argana    3   low -25.338755   19.682484  131.889158   \n",
       "\n",
       "       Nup15   Nup15_t  block position  year  \n",
       "0   0.362425  0.022818      1    North  2020  \n",
       "48  0.606963  0.057761      1    South  2020  \n",
       "49  0.552144  0.000033      1    South  2020  \n",
       "1   0.635334  0.000244      1    North  2020  \n",
       "50  0.499794  0.002717      1    South  2020  \n",
       "..       ...       ...    ...      ...   ...  \n",
       "45  0.524458  0.000882      8    North  2020  \n",
       "94  0.450204  0.000623      8    South  2020  \n",
       "46  0.400859  0.001161      8    North  2020  \n",
       "47  0.405559  0.002353      8    North  2020  \n",
       "95  0.492588  0.009478      8    South  2020  \n",
       "\n",
       "[96 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isotope_data_20.sort_values(by='row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d52ca515-5c06-49dc-a861-4b29e6efa925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Delta_15N</th>\n",
       "      <th>Log_Delta_15N</th>\n",
       "      <th>Delta_13C</th>\n",
       "      <th>SI</th>\n",
       "      <th>RL_below_SI</th>\n",
       "      <th>RL_above_SI</th>\n",
       "      <th>TRL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86.517160</td>\n",
       "      <td>4.460343</td>\n",
       "      <td>-25.379325</td>\n",
       "      <td>111.63</td>\n",
       "      <td>0.274101</td>\n",
       "      <td>3.460838</td>\n",
       "      <td>3.734940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.213751</td>\n",
       "      <td>-1.542943</td>\n",
       "      <td>-26.888535</td>\n",
       "      <td>120.27</td>\n",
       "      <td>0.329639</td>\n",
       "      <td>3.133720</td>\n",
       "      <td>3.463358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.856917</td>\n",
       "      <td>1.349868</td>\n",
       "      <td>-24.374009</td>\n",
       "      <td>106.71</td>\n",
       "      <td>0.454298</td>\n",
       "      <td>4.569893</td>\n",
       "      <td>5.024191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63.242263</td>\n",
       "      <td>4.146973</td>\n",
       "      <td>-25.400361</td>\n",
       "      <td>130.07</td>\n",
       "      <td>0.449340</td>\n",
       "      <td>4.042820</td>\n",
       "      <td>4.492160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.646833</td>\n",
       "      <td>3.944429</td>\n",
       "      <td>-24.093670</td>\n",
       "      <td>115.84</td>\n",
       "      <td>0.898502</td>\n",
       "      <td>6.388214</td>\n",
       "      <td>7.286716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.073746</td>\n",
       "      <td>0.729357</td>\n",
       "      <td>-25.700138</td>\n",
       "      <td>93.39</td>\n",
       "      <td>0.418746</td>\n",
       "      <td>1.693255</td>\n",
       "      <td>2.112001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31.428663</td>\n",
       "      <td>3.447720</td>\n",
       "      <td>-24.441470</td>\n",
       "      <td>130.46</td>\n",
       "      <td>0.198934</td>\n",
       "      <td>4.171877</td>\n",
       "      <td>4.370811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.495708</td>\n",
       "      <td>2.740563</td>\n",
       "      <td>-25.391370</td>\n",
       "      <td>126.49</td>\n",
       "      <td>0.452862</td>\n",
       "      <td>4.550690</td>\n",
       "      <td>5.003552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32.787598</td>\n",
       "      <td>3.490050</td>\n",
       "      <td>-24.083751</td>\n",
       "      <td>129.00</td>\n",
       "      <td>0.502594</td>\n",
       "      <td>6.573374</td>\n",
       "      <td>7.075968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.377519</td>\n",
       "      <td>-0.974135</td>\n",
       "      <td>-26.762081</td>\n",
       "      <td>93.64</td>\n",
       "      <td>0.488894</td>\n",
       "      <td>2.016411</td>\n",
       "      <td>2.505305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.474636</td>\n",
       "      <td>1.700126</td>\n",
       "      <td>-25.002947</td>\n",
       "      <td>121.89</td>\n",
       "      <td>0.206101</td>\n",
       "      <td>3.664596</td>\n",
       "      <td>3.870697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.932986</td>\n",
       "      <td>-0.069365</td>\n",
       "      <td>-25.699341</td>\n",
       "      <td>83.54</td>\n",
       "      <td>0.808927</td>\n",
       "      <td>1.063255</td>\n",
       "      <td>1.872182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>48.727799</td>\n",
       "      <td>3.886250</td>\n",
       "      <td>-25.787075</td>\n",
       "      <td>132.16</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>8.909604</td>\n",
       "      <td>9.354893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.638684</td>\n",
       "      <td>-0.448345</td>\n",
       "      <td>-27.036016</td>\n",
       "      <td>113.01</td>\n",
       "      <td>0.441883</td>\n",
       "      <td>2.189372</td>\n",
       "      <td>2.631255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>38.864468</td>\n",
       "      <td>3.660080</td>\n",
       "      <td>-24.566844</td>\n",
       "      <td>125.25</td>\n",
       "      <td>0.641398</td>\n",
       "      <td>6.539264</td>\n",
       "      <td>7.180662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11.099847</td>\n",
       "      <td>2.406931</td>\n",
       "      <td>-24.484921</td>\n",
       "      <td>103.34</td>\n",
       "      <td>0.342405</td>\n",
       "      <td>5.050162</td>\n",
       "      <td>5.392568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>39.406859</td>\n",
       "      <td>3.673940</td>\n",
       "      <td>-25.942213</td>\n",
       "      <td>125.12</td>\n",
       "      <td>0.455190</td>\n",
       "      <td>5.246024</td>\n",
       "      <td>5.701215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.879264</td>\n",
       "      <td>-0.128670</td>\n",
       "      <td>-27.166735</td>\n",
       "      <td>140.40</td>\n",
       "      <td>0.178827</td>\n",
       "      <td>3.192122</td>\n",
       "      <td>3.370948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.691946</td>\n",
       "      <td>3.206477</td>\n",
       "      <td>-24.037102</td>\n",
       "      <td>86.79</td>\n",
       "      <td>1.536699</td>\n",
       "      <td>5.155400</td>\n",
       "      <td>6.692099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.644780</td>\n",
       "      <td>2.977812</td>\n",
       "      <td>-25.043110</td>\n",
       "      <td>102.82</td>\n",
       "      <td>1.536679</td>\n",
       "      <td>2.773367</td>\n",
       "      <td>4.310046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18.252122</td>\n",
       "      <td>2.904281</td>\n",
       "      <td>-24.847391</td>\n",
       "      <td>119.20</td>\n",
       "      <td>0.252400</td>\n",
       "      <td>5.663517</td>\n",
       "      <td>5.915917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.664908</td>\n",
       "      <td>0.509770</td>\n",
       "      <td>-26.078257</td>\n",
       "      <td>107.06</td>\n",
       "      <td>0.563131</td>\n",
       "      <td>2.811981</td>\n",
       "      <td>3.375113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>103.166795</td>\n",
       "      <td>4.636347</td>\n",
       "      <td>-25.718376</td>\n",
       "      <td>136.41</td>\n",
       "      <td>0.116458</td>\n",
       "      <td>5.691911</td>\n",
       "      <td>5.808368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.812255</td>\n",
       "      <td>0.594572</td>\n",
       "      <td>-25.897872</td>\n",
       "      <td>110.52</td>\n",
       "      <td>0.566271</td>\n",
       "      <td>2.691425</td>\n",
       "      <td>3.257696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.603871</td>\n",
       "      <td>-0.504394</td>\n",
       "      <td>-26.505301</td>\n",
       "      <td>102.72</td>\n",
       "      <td>0.396233</td>\n",
       "      <td>1.299231</td>\n",
       "      <td>1.695464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>38.487478</td>\n",
       "      <td>3.650333</td>\n",
       "      <td>-24.213075</td>\n",
       "      <td>118.13</td>\n",
       "      <td>0.851945</td>\n",
       "      <td>5.253329</td>\n",
       "      <td>6.105274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17.491215</td>\n",
       "      <td>2.861699</td>\n",
       "      <td>-24.561880</td>\n",
       "      <td>101.74</td>\n",
       "      <td>0.410578</td>\n",
       "      <td>2.708656</td>\n",
       "      <td>3.119234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.505186</td>\n",
       "      <td>2.526143</td>\n",
       "      <td>-24.291618</td>\n",
       "      <td>105.12</td>\n",
       "      <td>0.804808</td>\n",
       "      <td>5.055949</td>\n",
       "      <td>5.860756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.290107</td>\n",
       "      <td>1.665838</td>\n",
       "      <td>-25.453173</td>\n",
       "      <td>113.84</td>\n",
       "      <td>0.633915</td>\n",
       "      <td>2.769182</td>\n",
       "      <td>3.403097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.697829</td>\n",
       "      <td>0.529350</td>\n",
       "      <td>-26.749066</td>\n",
       "      <td>120.43</td>\n",
       "      <td>0.375297</td>\n",
       "      <td>2.237757</td>\n",
       "      <td>2.613055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>42.422245</td>\n",
       "      <td>3.747673</td>\n",
       "      <td>-25.542360</td>\n",
       "      <td>130.28</td>\n",
       "      <td>0.408148</td>\n",
       "      <td>6.157500</td>\n",
       "      <td>6.565648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>21.725502</td>\n",
       "      <td>3.078487</td>\n",
       "      <td>-24.009275</td>\n",
       "      <td>115.02</td>\n",
       "      <td>1.116577</td>\n",
       "      <td>4.550547</td>\n",
       "      <td>5.667124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.365744</td>\n",
       "      <td>1.680035</td>\n",
       "      <td>-25.079273</td>\n",
       "      <td>122.71</td>\n",
       "      <td>0.400653</td>\n",
       "      <td>4.061960</td>\n",
       "      <td>4.462613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13.148374</td>\n",
       "      <td>2.576298</td>\n",
       "      <td>-24.666534</td>\n",
       "      <td>133.95</td>\n",
       "      <td>0.257543</td>\n",
       "      <td>3.688376</td>\n",
       "      <td>3.945919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>38.803669</td>\n",
       "      <td>3.658515</td>\n",
       "      <td>-24.051550</td>\n",
       "      <td>109.95</td>\n",
       "      <td>1.116826</td>\n",
       "      <td>3.223058</td>\n",
       "      <td>4.339885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>26.742422</td>\n",
       "      <td>3.286251</td>\n",
       "      <td>-25.781350</td>\n",
       "      <td>120.03</td>\n",
       "      <td>0.463705</td>\n",
       "      <td>3.644753</td>\n",
       "      <td>4.108458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4.945168</td>\n",
       "      <td>1.598411</td>\n",
       "      <td>-24.689194</td>\n",
       "      <td>126.69</td>\n",
       "      <td>0.435285</td>\n",
       "      <td>2.550713</td>\n",
       "      <td>2.985998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.474616</td>\n",
       "      <td>-0.745250</td>\n",
       "      <td>-27.197491</td>\n",
       "      <td>115.48</td>\n",
       "      <td>0.237036</td>\n",
       "      <td>1.489844</td>\n",
       "      <td>1.726880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>17.464150</td>\n",
       "      <td>2.860150</td>\n",
       "      <td>-24.371860</td>\n",
       "      <td>103.82</td>\n",
       "      <td>1.160653</td>\n",
       "      <td>3.188544</td>\n",
       "      <td>4.349196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>15.546197</td>\n",
       "      <td>2.743816</td>\n",
       "      <td>-25.235203</td>\n",
       "      <td>131.91</td>\n",
       "      <td>0.168343</td>\n",
       "      <td>3.745101</td>\n",
       "      <td>3.913444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20.842405</td>\n",
       "      <td>3.036990</td>\n",
       "      <td>-25.387853</td>\n",
       "      <td>149.92</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.243290</td>\n",
       "      <td>6.243290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.276443</td>\n",
       "      <td>0.244078</td>\n",
       "      <td>-26.393852</td>\n",
       "      <td>119.11</td>\n",
       "      <td>0.475012</td>\n",
       "      <td>3.917729</td>\n",
       "      <td>4.392741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2.144211</td>\n",
       "      <td>0.762772</td>\n",
       "      <td>-25.888591</td>\n",
       "      <td>134.41</td>\n",
       "      <td>0.145467</td>\n",
       "      <td>2.340145</td>\n",
       "      <td>2.485612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>12.765034</td>\n",
       "      <td>2.546710</td>\n",
       "      <td>-25.196747</td>\n",
       "      <td>89.46</td>\n",
       "      <td>2.000285</td>\n",
       "      <td>4.733211</td>\n",
       "      <td>6.733496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Delta_15N  Log_Delta_15N  Delta_13C      SI  RL_below_SI  RL_above_SI  \\\n",
       "0    86.517160       4.460343 -25.379325  111.63     0.274101     3.460838   \n",
       "1     0.213751      -1.542943 -26.888535  120.27     0.329639     3.133720   \n",
       "2     3.856917       1.349868 -24.374009  106.71     0.454298     4.569893   \n",
       "3    63.242263       4.146973 -25.400361  130.07     0.449340     4.042820   \n",
       "4    51.646833       3.944429 -24.093670  115.84     0.898502     6.388214   \n",
       "5     2.073746       0.729357 -25.700138   93.39     0.418746     1.693255   \n",
       "6    31.428663       3.447720 -24.441470  130.46     0.198934     4.171877   \n",
       "7    15.495708       2.740563 -25.391370  126.49     0.452862     4.550690   \n",
       "8    32.787598       3.490050 -24.083751  129.00     0.502594     6.573374   \n",
       "9     0.377519      -0.974135 -26.762081   93.64     0.488894     2.016411   \n",
       "10    5.474636       1.700126 -25.002947  121.89     0.206101     3.664596   \n",
       "11    0.932986      -0.069365 -25.699341   83.54     0.808927     1.063255   \n",
       "12   48.727799       3.886250 -25.787075  132.16     0.445289     8.909604   \n",
       "13    0.638684      -0.448345 -27.036016  113.01     0.441883     2.189372   \n",
       "14   38.864468       3.660080 -24.566844  125.25     0.641398     6.539264   \n",
       "15   11.099847       2.406931 -24.484921  103.34     0.342405     5.050162   \n",
       "16   39.406859       3.673940 -25.942213  125.12     0.455190     5.246024   \n",
       "17    0.879264      -0.128670 -27.166735  140.40     0.178827     3.192122   \n",
       "18   24.691946       3.206477 -24.037102   86.79     1.536699     5.155400   \n",
       "19   19.644780       2.977812 -25.043110  102.82     1.536679     2.773367   \n",
       "20   18.252122       2.904281 -24.847391  119.20     0.252400     5.663517   \n",
       "21    1.664908       0.509770 -26.078257  107.06     0.563131     2.811981   \n",
       "22  103.166795       4.636347 -25.718376  136.41     0.116458     5.691911   \n",
       "23    1.812255       0.594572 -25.897872  110.52     0.566271     2.691425   \n",
       "24    0.603871      -0.504394 -26.505301  102.72     0.396233     1.299231   \n",
       "25   38.487478       3.650333 -24.213075  118.13     0.851945     5.253329   \n",
       "26   17.491215       2.861699 -24.561880  101.74     0.410578     2.708656   \n",
       "27   12.505186       2.526143 -24.291618  105.12     0.804808     5.055949   \n",
       "28    5.290107       1.665838 -25.453173  113.84     0.633915     2.769182   \n",
       "29    1.697829       0.529350 -26.749066  120.43     0.375297     2.237757   \n",
       "30   42.422245       3.747673 -25.542360  130.28     0.408148     6.157500   \n",
       "31   21.725502       3.078487 -24.009275  115.02     1.116577     4.550547   \n",
       "32    5.365744       1.680035 -25.079273  122.71     0.400653     4.061960   \n",
       "33   13.148374       2.576298 -24.666534  133.95     0.257543     3.688376   \n",
       "34   38.803669       3.658515 -24.051550  109.95     1.116826     3.223058   \n",
       "35   26.742422       3.286251 -25.781350  120.03     0.463705     3.644753   \n",
       "36    4.945168       1.598411 -24.689194  126.69     0.435285     2.550713   \n",
       "37    0.474616      -0.745250 -27.197491  115.48     0.237036     1.489844   \n",
       "38   17.464150       2.860150 -24.371860  103.82     1.160653     3.188544   \n",
       "39   15.546197       2.743816 -25.235203  131.91     0.168343     3.745101   \n",
       "40   20.842405       3.036990 -25.387853  149.92     0.000000     6.243290   \n",
       "41    1.276443       0.244078 -26.393852  119.11     0.475012     3.917729   \n",
       "42    2.144211       0.762772 -25.888591  134.41     0.145467     2.340145   \n",
       "43   12.765034       2.546710 -25.196747   89.46     2.000285     4.733211   \n",
       "\n",
       "         TRL  \n",
       "0   3.734940  \n",
       "1   3.463358  \n",
       "2   5.024191  \n",
       "3   4.492160  \n",
       "4   7.286716  \n",
       "5   2.112001  \n",
       "6   4.370811  \n",
       "7   5.003552  \n",
       "8   7.075968  \n",
       "9   2.505305  \n",
       "10  3.870697  \n",
       "11  1.872182  \n",
       "12  9.354893  \n",
       "13  2.631255  \n",
       "14  7.180662  \n",
       "15  5.392568  \n",
       "16  5.701215  \n",
       "17  3.370948  \n",
       "18  6.692099  \n",
       "19  4.310046  \n",
       "20  5.915917  \n",
       "21  3.375113  \n",
       "22  5.808368  \n",
       "23  3.257696  \n",
       "24  1.695464  \n",
       "25  6.105274  \n",
       "26  3.119234  \n",
       "27  5.860756  \n",
       "28  3.403097  \n",
       "29  2.613055  \n",
       "30  6.565648  \n",
       "31  5.667124  \n",
       "32  4.462613  \n",
       "33  3.945919  \n",
       "34  4.339885  \n",
       "35  4.108458  \n",
       "36  2.985998  \n",
       "37  1.726880  \n",
       "38  4.349196  \n",
       "39  3.913444  \n",
       "40  6.243290  \n",
       "41  4.392741  \n",
       "42  2.485612  \n",
       "43  6.733496  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,[2,4,3,5,6,7,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef891f84-749e-45b9-af43-cd070d7623fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "RL_table_main=df.iloc[:,[2,4,3,5,6,7,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ad12049-36de-4091-8360-9911fb664e8f",
   "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>Delta_15N</th>\n",
       "      <th>Log_Delta_15N</th>\n",
       "      <th>Delta_13C</th>\n",
       "      <th>SI</th>\n",
       "      <th>RL_below_SI</th>\n",
       "      <th>RL_above_SI</th>\n",
       "      <th>TRL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86.517160</td>\n",
       "      <td>4.460343</td>\n",
       "      <td>-25.379325</td>\n",
       "      <td>111.63</td>\n",
       "      <td>0.274101</td>\n",
       "      <td>3.460838</td>\n",
       "      <td>3.734940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.213751</td>\n",
       "      <td>-1.542943</td>\n",
       "      <td>-26.888535</td>\n",
       "      <td>120.27</td>\n",
       "      <td>0.329639</td>\n",
       "      <td>3.133720</td>\n",
       "      <td>3.463358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.856917</td>\n",
       "      <td>1.349868</td>\n",
       "      <td>-24.374009</td>\n",
       "      <td>106.71</td>\n",
       "      <td>0.454298</td>\n",
       "      <td>4.569893</td>\n",
       "      <td>5.024191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63.242263</td>\n",
       "      <td>4.146973</td>\n",
       "      <td>-25.400361</td>\n",
       "      <td>130.07</td>\n",
       "      <td>0.449340</td>\n",
       "      <td>4.042820</td>\n",
       "      <td>4.492160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.646833</td>\n",
       "      <td>3.944429</td>\n",
       "      <td>-24.093670</td>\n",
       "      <td>115.84</td>\n",
       "      <td>0.898502</td>\n",
       "      <td>6.388214</td>\n",
       "      <td>7.286716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.073746</td>\n",
       "      <td>0.729357</td>\n",
       "      <td>-25.700138</td>\n",
       "      <td>93.39</td>\n",
       "      <td>0.418746</td>\n",
       "      <td>1.693255</td>\n",
       "      <td>2.112001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31.428663</td>\n",
       "      <td>3.447720</td>\n",
       "      <td>-24.441470</td>\n",
       "      <td>130.46</td>\n",
       "      <td>0.198934</td>\n",
       "      <td>4.171877</td>\n",
       "      <td>4.370811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.495708</td>\n",
       "      <td>2.740563</td>\n",
       "      <td>-25.391370</td>\n",
       "      <td>126.49</td>\n",
       "      <td>0.452862</td>\n",
       "      <td>4.550690</td>\n",
       "      <td>5.003552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32.787598</td>\n",
       "      <td>3.490050</td>\n",
       "      <td>-24.083751</td>\n",
       "      <td>129.00</td>\n",
       "      <td>0.502594</td>\n",
       "      <td>6.573374</td>\n",
       "      <td>7.075968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.377519</td>\n",
       "      <td>-0.974135</td>\n",
       "      <td>-26.762081</td>\n",
       "      <td>93.64</td>\n",
       "      <td>0.488894</td>\n",
       "      <td>2.016411</td>\n",
       "      <td>2.505305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.474636</td>\n",
       "      <td>1.700126</td>\n",
       "      <td>-25.002947</td>\n",
       "      <td>121.89</td>\n",
       "      <td>0.206101</td>\n",
       "      <td>3.664596</td>\n",
       "      <td>3.870697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.932986</td>\n",
       "      <td>-0.069365</td>\n",
       "      <td>-25.699341</td>\n",
       "      <td>83.54</td>\n",
       "      <td>0.808927</td>\n",
       "      <td>1.063255</td>\n",
       "      <td>1.872182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>48.727799</td>\n",
       "      <td>3.886250</td>\n",
       "      <td>-25.787075</td>\n",
       "      <td>132.16</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>8.909604</td>\n",
       "      <td>9.354893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.638684</td>\n",
       "      <td>-0.448345</td>\n",
       "      <td>-27.036016</td>\n",
       "      <td>113.01</td>\n",
       "      <td>0.441883</td>\n",
       "      <td>2.189372</td>\n",
       "      <td>2.631255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>38.864468</td>\n",
       "      <td>3.660080</td>\n",
       "      <td>-24.566844</td>\n",
       "      <td>125.25</td>\n",
       "      <td>0.641398</td>\n",
       "      <td>6.539264</td>\n",
       "      <td>7.180662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11.099847</td>\n",
       "      <td>2.406931</td>\n",
       "      <td>-24.484921</td>\n",
       "      <td>103.34</td>\n",
       "      <td>0.342405</td>\n",
       "      <td>5.050162</td>\n",
       "      <td>5.392568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>39.406859</td>\n",
       "      <td>3.673940</td>\n",
       "      <td>-25.942213</td>\n",
       "      <td>125.12</td>\n",
       "      <td>0.455190</td>\n",
       "      <td>5.246024</td>\n",
       "      <td>5.701215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.879264</td>\n",
       "      <td>-0.128670</td>\n",
       "      <td>-27.166735</td>\n",
       "      <td>140.40</td>\n",
       "      <td>0.178827</td>\n",
       "      <td>3.192122</td>\n",
       "      <td>3.370948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.691946</td>\n",
       "      <td>3.206477</td>\n",
       "      <td>-24.037102</td>\n",
       "      <td>86.79</td>\n",
       "      <td>1.536699</td>\n",
       "      <td>5.155400</td>\n",
       "      <td>6.692099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.644780</td>\n",
       "      <td>2.977812</td>\n",
       "      <td>-25.043110</td>\n",
       "      <td>102.82</td>\n",
       "      <td>1.536679</td>\n",
       "      <td>2.773367</td>\n",
       "      <td>4.310046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18.252122</td>\n",
       "      <td>2.904281</td>\n",
       "      <td>-24.847391</td>\n",
       "      <td>119.20</td>\n",
       "      <td>0.252400</td>\n",
       "      <td>5.663517</td>\n",
       "      <td>5.915917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.664908</td>\n",
       "      <td>0.509770</td>\n",
       "      <td>-26.078257</td>\n",
       "      <td>107.06</td>\n",
       "      <td>0.563131</td>\n",
       "      <td>2.811981</td>\n",
       "      <td>3.375113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>103.166795</td>\n",
       "      <td>4.636347</td>\n",
       "      <td>-25.718376</td>\n",
       "      <td>136.41</td>\n",
       "      <td>0.116458</td>\n",
       "      <td>5.691911</td>\n",
       "      <td>5.808368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.812255</td>\n",
       "      <td>0.594572</td>\n",
       "      <td>-25.897872</td>\n",
       "      <td>110.52</td>\n",
       "      <td>0.566271</td>\n",
       "      <td>2.691425</td>\n",
       "      <td>3.257696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.603871</td>\n",
       "      <td>-0.504394</td>\n",
       "      <td>-26.505301</td>\n",
       "      <td>102.72</td>\n",
       "      <td>0.396233</td>\n",
       "      <td>1.299231</td>\n",
       "      <td>1.695464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>38.487478</td>\n",
       "      <td>3.650333</td>\n",
       "      <td>-24.213075</td>\n",
       "      <td>118.13</td>\n",
       "      <td>0.851945</td>\n",
       "      <td>5.253329</td>\n",
       "      <td>6.105274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17.491215</td>\n",
       "      <td>2.861699</td>\n",
       "      <td>-24.561880</td>\n",
       "      <td>101.74</td>\n",
       "      <td>0.410578</td>\n",
       "      <td>2.708656</td>\n",
       "      <td>3.119234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.505186</td>\n",
       "      <td>2.526143</td>\n",
       "      <td>-24.291618</td>\n",
       "      <td>105.12</td>\n",
       "      <td>0.804808</td>\n",
       "      <td>5.055949</td>\n",
       "      <td>5.860756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.290107</td>\n",
       "      <td>1.665838</td>\n",
       "      <td>-25.453173</td>\n",
       "      <td>113.84</td>\n",
       "      <td>0.633915</td>\n",
       "      <td>2.769182</td>\n",
       "      <td>3.403097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.697829</td>\n",
       "      <td>0.529350</td>\n",
       "      <td>-26.749066</td>\n",
       "      <td>120.43</td>\n",
       "      <td>0.375297</td>\n",
       "      <td>2.237757</td>\n",
       "      <td>2.613055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>42.422245</td>\n",
       "      <td>3.747673</td>\n",
       "      <td>-25.542360</td>\n",
       "      <td>130.28</td>\n",
       "      <td>0.408148</td>\n",
       "      <td>6.157500</td>\n",
       "      <td>6.565648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>21.725502</td>\n",
       "      <td>3.078487</td>\n",
       "      <td>-24.009275</td>\n",
       "      <td>115.02</td>\n",
       "      <td>1.116577</td>\n",
       "      <td>4.550547</td>\n",
       "      <td>5.667124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.365744</td>\n",
       "      <td>1.680035</td>\n",
       "      <td>-25.079273</td>\n",
       "      <td>122.71</td>\n",
       "      <td>0.400653</td>\n",
       "      <td>4.061960</td>\n",
       "      <td>4.462613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13.148374</td>\n",
       "      <td>2.576298</td>\n",
       "      <td>-24.666534</td>\n",
       "      <td>133.95</td>\n",
       "      <td>0.257543</td>\n",
       "      <td>3.688376</td>\n",
       "      <td>3.945919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>38.803669</td>\n",
       "      <td>3.658515</td>\n",
       "      <td>-24.051550</td>\n",
       "      <td>109.95</td>\n",
       "      <td>1.116826</td>\n",
       "      <td>3.223058</td>\n",
       "      <td>4.339885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>26.742422</td>\n",
       "      <td>3.286251</td>\n",
       "      <td>-25.781350</td>\n",
       "      <td>120.03</td>\n",
       "      <td>0.463705</td>\n",
       "      <td>3.644753</td>\n",
       "      <td>4.108458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4.945168</td>\n",
       "      <td>1.598411</td>\n",
       "      <td>-24.689194</td>\n",
       "      <td>126.69</td>\n",
       "      <td>0.435285</td>\n",
       "      <td>2.550713</td>\n",
       "      <td>2.985998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.474616</td>\n",
       "      <td>-0.745250</td>\n",
       "      <td>-27.197491</td>\n",
       "      <td>115.48</td>\n",
       "      <td>0.237036</td>\n",
       "      <td>1.489844</td>\n",
       "      <td>1.726880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>17.464150</td>\n",
       "      <td>2.860150</td>\n",
       "      <td>-24.371860</td>\n",
       "      <td>103.82</td>\n",
       "      <td>1.160653</td>\n",
       "      <td>3.188544</td>\n",
       "      <td>4.349196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>15.546197</td>\n",
       "      <td>2.743816</td>\n",
       "      <td>-25.235203</td>\n",
       "      <td>131.91</td>\n",
       "      <td>0.168343</td>\n",
       "      <td>3.745101</td>\n",
       "      <td>3.913444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20.842405</td>\n",
       "      <td>3.036990</td>\n",
       "      <td>-25.387853</td>\n",
       "      <td>149.92</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.243290</td>\n",
       "      <td>6.243290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.276443</td>\n",
       "      <td>0.244078</td>\n",
       "      <td>-26.393852</td>\n",
       "      <td>119.11</td>\n",
       "      <td>0.475012</td>\n",
       "      <td>3.917729</td>\n",
       "      <td>4.392741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2.144211</td>\n",
       "      <td>0.762772</td>\n",
       "      <td>-25.888591</td>\n",
       "      <td>134.41</td>\n",
       "      <td>0.145467</td>\n",
       "      <td>2.340145</td>\n",
       "      <td>2.485612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>12.765034</td>\n",
       "      <td>2.546710</td>\n",
       "      <td>-25.196747</td>\n",
       "      <td>89.46</td>\n",
       "      <td>2.000285</td>\n",
       "      <td>4.733211</td>\n",
       "      <td>6.733496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Delta_15N  Log_Delta_15N  Delta_13C      SI  RL_below_SI  RL_above_SI  \\\n",
       "0    86.517160       4.460343 -25.379325  111.63     0.274101     3.460838   \n",
       "1     0.213751      -1.542943 -26.888535  120.27     0.329639     3.133720   \n",
       "2     3.856917       1.349868 -24.374009  106.71     0.454298     4.569893   \n",
       "3    63.242263       4.146973 -25.400361  130.07     0.449340     4.042820   \n",
       "4    51.646833       3.944429 -24.093670  115.84     0.898502     6.388214   \n",
       "5     2.073746       0.729357 -25.700138   93.39     0.418746     1.693255   \n",
       "6    31.428663       3.447720 -24.441470  130.46     0.198934     4.171877   \n",
       "7    15.495708       2.740563 -25.391370  126.49     0.452862     4.550690   \n",
       "8    32.787598       3.490050 -24.083751  129.00     0.502594     6.573374   \n",
       "9     0.377519      -0.974135 -26.762081   93.64     0.488894     2.016411   \n",
       "10    5.474636       1.700126 -25.002947  121.89     0.206101     3.664596   \n",
       "11    0.932986      -0.069365 -25.699341   83.54     0.808927     1.063255   \n",
       "12   48.727799       3.886250 -25.787075  132.16     0.445289     8.909604   \n",
       "13    0.638684      -0.448345 -27.036016  113.01     0.441883     2.189372   \n",
       "14   38.864468       3.660080 -24.566844  125.25     0.641398     6.539264   \n",
       "15   11.099847       2.406931 -24.484921  103.34     0.342405     5.050162   \n",
       "16   39.406859       3.673940 -25.942213  125.12     0.455190     5.246024   \n",
       "17    0.879264      -0.128670 -27.166735  140.40     0.178827     3.192122   \n",
       "18   24.691946       3.206477 -24.037102   86.79     1.536699     5.155400   \n",
       "19   19.644780       2.977812 -25.043110  102.82     1.536679     2.773367   \n",
       "20   18.252122       2.904281 -24.847391  119.20     0.252400     5.663517   \n",
       "21    1.664908       0.509770 -26.078257  107.06     0.563131     2.811981   \n",
       "22  103.166795       4.636347 -25.718376  136.41     0.116458     5.691911   \n",
       "23    1.812255       0.594572 -25.897872  110.52     0.566271     2.691425   \n",
       "24    0.603871      -0.504394 -26.505301  102.72     0.396233     1.299231   \n",
       "25   38.487478       3.650333 -24.213075  118.13     0.851945     5.253329   \n",
       "26   17.491215       2.861699 -24.561880  101.74     0.410578     2.708656   \n",
       "27   12.505186       2.526143 -24.291618  105.12     0.804808     5.055949   \n",
       "28    5.290107       1.665838 -25.453173  113.84     0.633915     2.769182   \n",
       "29    1.697829       0.529350 -26.749066  120.43     0.375297     2.237757   \n",
       "30   42.422245       3.747673 -25.542360  130.28     0.408148     6.157500   \n",
       "31   21.725502       3.078487 -24.009275  115.02     1.116577     4.550547   \n",
       "32    5.365744       1.680035 -25.079273  122.71     0.400653     4.061960   \n",
       "33   13.148374       2.576298 -24.666534  133.95     0.257543     3.688376   \n",
       "34   38.803669       3.658515 -24.051550  109.95     1.116826     3.223058   \n",
       "35   26.742422       3.286251 -25.781350  120.03     0.463705     3.644753   \n",
       "36    4.945168       1.598411 -24.689194  126.69     0.435285     2.550713   \n",
       "37    0.474616      -0.745250 -27.197491  115.48     0.237036     1.489844   \n",
       "38   17.464150       2.860150 -24.371860  103.82     1.160653     3.188544   \n",
       "39   15.546197       2.743816 -25.235203  131.91     0.168343     3.745101   \n",
       "40   20.842405       3.036990 -25.387853  149.92     0.000000     6.243290   \n",
       "41    1.276443       0.244078 -26.393852  119.11     0.475012     3.917729   \n",
       "42    2.144211       0.762772 -25.888591  134.41     0.145467     2.340145   \n",
       "43   12.765034       2.546710 -25.196747   89.46     2.000285     4.733211   \n",
       "\n",
       "         TRL  \n",
       "0   3.734940  \n",
       "1   3.463358  \n",
       "2   5.024191  \n",
       "3   4.492160  \n",
       "4   7.286716  \n",
       "5   2.112001  \n",
       "6   4.370811  \n",
       "7   5.003552  \n",
       "8   7.075968  \n",
       "9   2.505305  \n",
       "10  3.870697  \n",
       "11  1.872182  \n",
       "12  9.354893  \n",
       "13  2.631255  \n",
       "14  7.180662  \n",
       "15  5.392568  \n",
       "16  5.701215  \n",
       "17  3.370948  \n",
       "18  6.692099  \n",
       "19  4.310046  \n",
       "20  5.915917  \n",
       "21  3.375113  \n",
       "22  5.808368  \n",
       "23  3.257696  \n",
       "24  1.695464  \n",
       "25  6.105274  \n",
       "26  3.119234  \n",
       "27  5.860756  \n",
       "28  3.403097  \n",
       "29  2.613055  \n",
       "30  6.565648  \n",
       "31  5.667124  \n",
       "32  4.462613  \n",
       "33  3.945919  \n",
       "34  4.339885  \n",
       "35  4.108458  \n",
       "36  2.985998  \n",
       "37  1.726880  \n",
       "38  4.349196  \n",
       "39  3.913444  \n",
       "40  6.243290  \n",
       "41  4.392741  \n",
       "42  2.485612  \n",
       "43  6.733496  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RL_table_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a28e2bd3-6dd7-4952-9f69-efdcc1992890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03022360886623137 -1.4075594640185218 0.2726470322070823 0.07336310148129337\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "#tips = sns.load_dataset(\"tips\") # get coeffs of linear fit\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(RL_table_main['SI'],RL_table_main['Log_Delta_15N'])\n",
    "print(slope, intercept,r_value, p_value)\n",
    "plt.figure(figsize=(7, 4))\n",
    "#use line_kws to set line label for legend\n",
    "ax = sns.regplot(x=\"SI\", y=\"Log_Delta_15N\", data=RL_table_main, color='b',line_kws={'label':\"y={0:.3f}x{1:.3f}\".format(np.round(slope,2),np.round(intercept,2))})\n",
    "ax.set(xlabel='SI in August 2020 (cm)', ylabel=\"Log_Delta_15N\")\n",
    "plt.text(87, 3.8, \"R=\"+str(round(r_value,3)), horizontalalignment='left', size='medium', color='black')\n",
    "ax.legend()\n",
    "plt.title('Log Delta 15N : Average_bin2_bin3')\n",
    "#plt.savefig('Delta13C_May_2018.png',dpi=500,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3a6953fc-6bbf-4c22-a9a7-8319b18a6b2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7608205122354867 1.6938344283854496 0.18962218958008842 0.21764188174500482\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "#tips = sns.load_dataset(\"tips\") # get coeffs of linear fit\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(RL_table_main['RL_below_SI'],RL_table_main['Log_Delta_15N'])\n",
    "print(slope, intercept,r_value, p_value)\n",
    "plt.figure(figsize=(7, 4))\n",
    "#use line_kws to set line label for legend\n",
    "ax = sns.regplot(x=\"RL_below_SI\", y=\"Log_Delta_15N\", data=RL_table_main, color='b',line_kws={'label':\"y={0:.3f}x+{1:.3f}\".format(np.round(slope,2),np.round(intercept,2))})\n",
    "ax.set(xlabel='RL_below_SI in August 2020 (cm)', ylabel=\"Log_Delta_15N\")\n",
    "plt.text(1.5, 1 ,\"R=\"+str(round(r_value,3)), horizontalalignment='left', size='medium', color='black')\n",
    "ax.legend()\n",
    "plt.title(' 2020: Log Delta 15N')\n",
    "#plt.savefig('Delta13C_May_2018.png',dpi=500,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0b33a667-90d2-41a7-acc3-1cdeb0b5ba0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5146/3845289956.py:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.triu(np.ones_like(RL_table_main.corr(), dtype=np.bool))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(16, 6))\n",
    "# define the mask to set the values in the upper triangle to True\n",
    "mask = np.triu(np.ones_like(RL_table_main.corr(), dtype=np.bool))\n",
    "heatmap = sns.heatmap(RL_table_main.corr(), mask=mask, vmin=-1, vmax=1, annot=True)\n",
    "heatmap.set_title('Correlation Isotopes: Average of bin 2 and 3 and Root lengths: Square Root in Aug 2020', fontdict={'fontsize':18}, pad=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f0c58d5-6600-4e2d-ba39-239a5b39240e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}