PTJPL Sensitivity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Callable\n",
    "from os import makedirs\n",
    "from os.path import join\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from verma_net_radiation_sensitivity.verma_net_radiation_sensitivity import process_verma_net_radiation_table\n",
    "from PTJPL_sensitivity import process_PTJPL_table\n",
    "from sensitivity import perturbed_run, sensitivity_analysis, divide_absolute_by_unperturbed\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import mstats\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import FuncFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalization_function = divide_absolute_by_unperturbed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>tower</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>orbit</th>\n",
       "      <th>scene</th>\n",
       "      <th>tile</th>\n",
       "      <th>time_UTC</th>\n",
       "      <th>date_UTC</th>\n",
       "      <th>doy</th>\n",
       "      <th>...</th>\n",
       "      <th>VISdiff</th>\n",
       "      <th>NIRdiff</th>\n",
       "      <th>VISdir</th>\n",
       "      <th>NIRdir</th>\n",
       "      <th>SWout</th>\n",
       "      <th>LWin</th>\n",
       "      <th>LWout</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>Topt</th>\n",
       "      <th>fAPARmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>10150</td>\n",
       "      <td>9</td>\n",
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       "      <td>2020-04-20 00:00:00</td>\n",
       "      <td>2020-04-20</td>\n",
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       "      <td>US-Ha2</td>\n",
       "      <td>42.5393</td>\n",
       "      <td>-72.1779</td>\n",
       "      <td>10352</td>\n",
       "      <td>9</td>\n",
       "      <td>18TYN</td>\n",
       "      <td>2020-05-03 00:00:00</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>293.358154</td>\n",
       "      <td>368.124775</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5545</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>US-Ha2</td>\n",
       "      <td>42.5393</td>\n",
       "      <td>-72.1779</td>\n",
       "      <td>10684</td>\n",
       "      <td>7</td>\n",
       "      <td>18TYN</td>\n",
       "      <td>2020-05-24 00:00:00</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>145</td>\n",
       "      <td>...</td>\n",
       "      <td>3.627162</td>\n",
       "      <td>3.519072</td>\n",
       "      <td>0.337294</td>\n",
       "      <td>0.020315</td>\n",
       "      <td>0.662989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385.565499</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>US-Ha2</td>\n",
       "      <td>42.5393</td>\n",
       "      <td>-72.1779</td>\n",
       "      <td>11069</td>\n",
       "      <td>8</td>\n",
       "      <td>18TYN</td>\n",
       "      <td>2020-06-18 00:00:00</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>170</td>\n",
       "      <td>...</td>\n",
       "      <td>7.418505</td>\n",
       "      <td>8.696527</td>\n",
       "      <td>1.935220</td>\n",
       "      <td>0.123967</td>\n",
       "      <td>2.254700</td>\n",
       "      <td>380.029433</td>\n",
       "      <td>454.438012</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
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       "      <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",
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       "      <th>247</th>\n",
       "      <td>247</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
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       "      <td>6</td>\n",
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       "      <td>2020-08-11 00:00:00</td>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>224</td>\n",
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       "      <td>14.372975</td>\n",
       "      <td>12.682295</td>\n",
       "      <td>3.003822</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.351180</td>\n",
       "      <td>414.848071</td>\n",
       "      <td>439.377199</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>248</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
       "      <td>-95.1921</td>\n",
       "      <td>16668</td>\n",
       "      <td>8</td>\n",
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       "      <td>2021-06-14 00:00:00</td>\n",
       "      <td>2021-06-14</td>\n",
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       "      <td>...</td>\n",
       "      <td>32.456092</td>\n",
       "      <td>15.308895</td>\n",
       "      <td>93.412739</td>\n",
       "      <td>127.317082</td>\n",
       "      <td>27.991697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405.251251</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
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       "      <td>16810</td>\n",
       "      <td>8</td>\n",
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       "      <td>2021-06-23 00:00:00</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>174</td>\n",
       "      <td>...</td>\n",
       "      <td>38.645010</td>\n",
       "      <td>27.577193</td>\n",
       "      <td>80.881654</td>\n",
       "      <td>98.257540</td>\n",
       "      <td>28.021802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>388.004691</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>250</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
       "      <td>-95.1921</td>\n",
       "      <td>16810</td>\n",
       "      <td>8</td>\n",
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       "      <td>2021-06-23 00:00:00</td>\n",
       "      <td>2021-06-23</td>\n",
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       "      <td>98.257540</td>\n",
       "      <td>28.021802</td>\n",
       "      <td>400.532814</td>\n",
       "      <td>463.095778</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>US-xUN</td>\n",
       "      <td>46.2339</td>\n",
       "      <td>-89.5373</td>\n",
       "      <td>12274</td>\n",
       "      <td>4</td>\n",
       "      <td>15TYM</td>\n",
       "      <td>2020-09-04 00:00:00</td>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>248</td>\n",
       "      <td>...</td>\n",
       "      <td>2.846623</td>\n",
       "      <td>2.261136</td>\n",
       "      <td>0.267030</td>\n",
       "      <td>0.001731</td>\n",
       "      <td>0.797800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>339.018157</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4592</td>\n",
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      ],
      "text/plain": [
       "     Unnamed: 0.1   tower      lat      lon  orbit  scene   tile  \\\n",
       "0               0  US-Ha2  42.5393 -72.1779   9254      9  18TYN   \n",
       "1               1  US-Ha2  42.5393 -72.1779  10150      9  18TYN   \n",
       "2               2  US-Ha2  42.5393 -72.1779  10352      9  18TYN   \n",
       "3               3  US-Ha2  42.5393 -72.1779  10684      7  18TYN   \n",
       "4               4  US-Ha2  42.5393 -72.1779  11069      8  18TYN   \n",
       "..            ...     ...      ...      ...    ...    ...    ...   \n",
       "247           247  US-xUK  39.0404 -95.1921  11909      6  15SUD   \n",
       "248           248  US-xUK  39.0404 -95.1921  16668      8  15SUD   \n",
       "249           249  US-xUK  39.0404 -95.1921  16810      8  15SUD   \n",
       "250           250  US-xUK  39.0404 -95.1921  16810      8  15SUD   \n",
       "251           251  US-xUN  46.2339 -89.5373  12274      4  15TYM   \n",
       "\n",
       "                time_UTC    date_UTC  doy  ...    VISdiff    NIRdiff  \\\n",
       "0    2020-02-22 00:00:00  2020-02-22   53  ...   0.000000   0.000000   \n",
       "1    2020-04-20 00:00:00  2020-04-20  111  ...   0.000000   0.000000   \n",
       "2    2020-05-03 00:00:00  2020-05-03  124  ...   0.000000   0.000000   \n",
       "3    2020-05-24 00:00:00  2020-05-24  145  ...   3.627162   3.519072   \n",
       "4    2020-06-18 00:00:00  2020-06-18  170  ...   7.418505   8.696527   \n",
       "..                   ...         ...  ...  ...        ...        ...   \n",
       "247  2020-08-11 00:00:00  2020-08-11  224  ...  14.372975  12.682295   \n",
       "248  2021-06-14 00:00:00  2021-06-14  165  ...  32.456092  15.308895   \n",
       "249  2021-06-23 00:00:00  2021-06-23  174  ...  38.645010  27.577193   \n",
       "250  2021-06-23 00:00:00  2021-06-23  174  ...  38.645010  27.577193   \n",
       "251  2020-09-04 00:00:00  2020-09-04  248  ...   2.846623   2.261136   \n",
       "\n",
       "        VISdir      NIRdir      SWout        LWin       LWout  hour_of_day  \\\n",
       "0     0.000000    0.000000   0.000000  223.482510  310.837408           19   \n",
       "1     0.000000    0.000000   0.000000  249.470817  483.042043           19   \n",
       "2     0.000000    0.000000   0.000000  293.358154  368.124775           19   \n",
       "3     0.337294    0.020315   0.662989         NaN  385.565499           19   \n",
       "4     1.935220    0.123967   2.254700  380.029433  454.438012           19   \n",
       "..         ...         ...        ...         ...         ...          ...   \n",
       "247   3.003822    0.000000   5.351180  414.848071  439.377199           17   \n",
       "248  93.412739  127.317082  27.991697         NaN  405.251251           17   \n",
       "249  80.881654   98.257540  28.021802         NaN  388.004691           17   \n",
       "250  80.881654   98.257540  28.021802  400.532814  463.095778           17   \n",
       "251   0.267030    0.001731   0.797800         NaN  339.018157           18   \n",
       "\n",
       "     Topt  fAPARmax  \n",
       "0     0.0    0.5545  \n",
       "1     0.0    0.5545  \n",
       "2     0.0    0.5545  \n",
       "3     0.0    0.5545  \n",
       "4     0.0    0.5545  \n",
       "..    ...       ...  \n",
       "247   5.8    0.4664  \n",
       "248   5.8    0.4664  \n",
       "249   5.8    0.4664  \n",
       "250   5.8    0.4664  \n",
       "251   0.0    0.4592  \n",
       "\n",
       "[252 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df = pd.read_csv(\"calval_final_50_PTJPL_inputs.csv\")\n",
    "input_df = input_df[input_df.fAPARmax != 0]\n",
    "input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3301"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmin(input_df.fAPARmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_verma_PTJPL_table(input_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    return process_PTJPL_table(process_verma_net_radiation_table(input_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>tower</th>\n",
       "      <th>lat</th>\n",
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       "      <th>fAPARmax</th>\n",
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       "      <th>LE_soil</th>\n",
       "      <th>Rn_canopy</th>\n",
       "      <th>LE_canopy</th>\n",
       "      <th>LE_interception</th>\n",
       "      <th>LE</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>US-Ha2</td>\n",
       "      <td>42.5393</td>\n",
       "      <td>-72.1779</td>\n",
       "      <td>10150</td>\n",
       "      <td>9</td>\n",
       "      <td>18TYN</td>\n",
       "      <td>2020-04-20 00:00:00</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>111</td>\n",
       "      <td>...</td>\n",
       "      <td>483.042043</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>US-Ha2</td>\n",
       "      <td>42.5393</td>\n",
       "      <td>-72.1779</td>\n",
       "      <td>10352</td>\n",
       "      <td>9</td>\n",
       "      <td>18TYN</td>\n",
       "      <td>2020-05-03 00:00:00</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>124</td>\n",
       "      <td>...</td>\n",
       "      <td>368.124775</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>US-Ha2</td>\n",
       "      <td>42.5393</td>\n",
       "      <td>-72.1779</td>\n",
       "      <td>10684</td>\n",
       "      <td>7</td>\n",
       "      <td>18TYN</td>\n",
       "      <td>2020-05-24 00:00:00</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>145</td>\n",
       "      <td>...</td>\n",
       "      <td>385.565499</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>US-Ha2</td>\n",
       "      <td>42.5393</td>\n",
       "      <td>-72.1779</td>\n",
       "      <td>11069</td>\n",
       "      <td>8</td>\n",
       "      <td>18TYN</td>\n",
       "      <td>2020-06-18 00:00:00</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>170</td>\n",
       "      <td>...</td>\n",
       "      <td>454.438012</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5545</td>\n",
       "      <td>1.775181</td>\n",
       "      <td>0.431629</td>\n",
       "      <td>15.560667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.415658</td>\n",
       "      <td>1.847287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>247</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
       "      <td>-95.1921</td>\n",
       "      <td>11909</td>\n",
       "      <td>6</td>\n",
       "      <td>15SUD</td>\n",
       "      <td>2020-08-11 00:00:00</td>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>224</td>\n",
       "      <td>...</td>\n",
       "      <td>439.377199</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
       "      <td>6.179087</td>\n",
       "      <td>2.670700</td>\n",
       "      <td>21.327608</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4.932300</td>\n",
       "      <td>7.603002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>248</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
       "      <td>-95.1921</td>\n",
       "      <td>16668</td>\n",
       "      <td>8</td>\n",
       "      <td>15SUD</td>\n",
       "      <td>2021-06-14 00:00:00</td>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>165</td>\n",
       "      <td>...</td>\n",
       "      <td>405.251251</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
       "      <td>-95.1921</td>\n",
       "      <td>16810</td>\n",
       "      <td>8</td>\n",
       "      <td>15SUD</td>\n",
       "      <td>2021-06-23 00:00:00</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>174</td>\n",
       "      <td>...</td>\n",
       "      <td>388.004691</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>250</td>\n",
       "      <td>US-xUK</td>\n",
       "      <td>39.0404</td>\n",
       "      <td>-95.1921</td>\n",
       "      <td>16810</td>\n",
       "      <td>8</td>\n",
       "      <td>15SUD</td>\n",
       "      <td>2021-06-23 00:00:00</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>174</td>\n",
       "      <td>...</td>\n",
       "      <td>463.095778</td>\n",
       "      <td>17</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4664</td>\n",
       "      <td>29.718771</td>\n",
       "      <td>7.325757</td>\n",
       "      <td>202.369340</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>23.188107</td>\n",
       "      <td>30.513896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>US-xUN</td>\n",
       "      <td>46.2339</td>\n",
       "      <td>-89.5373</td>\n",
       "      <td>12274</td>\n",
       "      <td>4</td>\n",
       "      <td>15TYM</td>\n",
       "      <td>2020-09-04 00:00:00</td>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>248</td>\n",
       "      <td>...</td>\n",
       "      <td>339.018157</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0.1   tower      lat      lon  orbit  scene   tile  \\\n",
       "0               0  US-Ha2  42.5393 -72.1779   9254      9  18TYN   \n",
       "1               1  US-Ha2  42.5393 -72.1779  10150      9  18TYN   \n",
       "2               2  US-Ha2  42.5393 -72.1779  10352      9  18TYN   \n",
       "3               3  US-Ha2  42.5393 -72.1779  10684      7  18TYN   \n",
       "4               4  US-Ha2  42.5393 -72.1779  11069      8  18TYN   \n",
       "..            ...     ...      ...      ...    ...    ...    ...   \n",
       "247           247  US-xUK  39.0404 -95.1921  11909      6  15SUD   \n",
       "248           248  US-xUK  39.0404 -95.1921  16668      8  15SUD   \n",
       "249           249  US-xUK  39.0404 -95.1921  16810      8  15SUD   \n",
       "250           250  US-xUK  39.0404 -95.1921  16810      8  15SUD   \n",
       "251           251  US-xUN  46.2339 -89.5373  12274      4  15TYM   \n",
       "\n",
       "                time_UTC    date_UTC  doy  ...       LWout  hour_of_day  Topt  \\\n",
       "0    2020-02-22 00:00:00  2020-02-22   53  ...  310.837408           19   0.0   \n",
       "1    2020-04-20 00:00:00  2020-04-20  111  ...  483.042043           19   0.0   \n",
       "2    2020-05-03 00:00:00  2020-05-03  124  ...  368.124775           19   0.0   \n",
       "3    2020-05-24 00:00:00  2020-05-24  145  ...  385.565499           19   0.0   \n",
       "4    2020-06-18 00:00:00  2020-06-18  170  ...  454.438012           19   0.0   \n",
       "..                   ...         ...  ...  ...         ...          ...   ...   \n",
       "247  2020-08-11 00:00:00  2020-08-11  224  ...  439.377199           17   5.8   \n",
       "248  2021-06-14 00:00:00  2021-06-14  165  ...  405.251251           17   5.8   \n",
       "249  2021-06-23 00:00:00  2021-06-23  174  ...  388.004691           17   5.8   \n",
       "250  2021-06-23 00:00:00  2021-06-23  174  ...  463.095778           17   5.8   \n",
       "251  2020-09-04 00:00:00  2020-09-04  248  ...  339.018157           18   0.0   \n",
       "\n",
       "     fAPARmax    Rn_soil   LE_soil   Rn_canopy  LE_canopy  LE_interception  \\\n",
       "0      0.5545   0.000000  0.000000    0.000000   0.000000         0.000000   \n",
       "1      0.5545   0.000000  0.000000    0.000000   0.000000         0.000000   \n",
       "2      0.5545   0.000000  0.000000    0.000000   0.000000         0.000000   \n",
       "3      0.5545        NaN       NaN         NaN        NaN              NaN   \n",
       "4      0.5545   1.775181  0.431629   15.560667   0.000000         1.415658   \n",
       "..        ...        ...       ...         ...        ...              ...   \n",
       "247    0.4664   6.179087  2.670700   21.327608   0.000001         4.932300   \n",
       "248    0.4664        NaN       NaN         NaN        NaN              NaN   \n",
       "249    0.4664        NaN       NaN         NaN        NaN              NaN   \n",
       "250    0.4664  29.718771  7.325757  202.369340   0.000032        23.188107   \n",
       "251    0.4592        NaN       NaN         NaN        NaN              NaN   \n",
       "\n",
       "            LE  \n",
       "0     0.000000  \n",
       "1     0.000000  \n",
       "2     0.000000  \n",
       "3          NaN  \n",
       "4     1.847287  \n",
       "..         ...  \n",
       "247   7.603002  \n",
       "248        NaN  \n",
       "249        NaN  \n",
       "250  30.513896  \n",
       "251        NaN  \n",
       "\n",
       "[252 rows x 86 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed = process_verma_PTJPL_table(input_df)\n",
    "processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(range(int(min(processed.ST_C)), int(max(processed.ST_C)) + 1, 5))\n",
    "plt.scatter(x=processed.ST_C, y=processed.LE, color='black', s=10, zorder=5)\n",
    "plt.grid(True, zorder=0)\n",
    "plt.xlabel(\"ECOSTRESS Surface Temperature (°C)\")\n",
    "plt.ylabel(\"PT-JPL Latent Heat Flux (W/m$^2$)\")\n",
    "plt.title(\"Unperturbed Comparison\\nof ECOSTRESS Surface Temperature\\nto PT-JPL Latent Heat Flux\")\n",
    "\n",
    "plt.savefig(\"Unperturbed Comparison of ECOSTRESS Surface Temperature to PT-JPL Latent Heat Flux.jpeg\", format='jpeg', bbox_inches='tight')\n",
    "plt.savefig(\"Unperturbed Comparison of ECOSTRESS Surface Temperature to PT-JPL Latent Heat Flux.svg\", format='svg', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.89, 49.33)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df = pd.read_csv(\"calval_final_50_PTJPL_inputs.csv\")\n",
    "\n",
    "if \"Ta\" in input_df and \"Ta_C\" not in input_df:\n",
    "    # input_df.rename({\"Ta\": \"Ta_C\"}, inplace=True)\n",
    "    input_df[\"Ta_C\"] = input_df[\"Ta\"]\n",
    "\n",
    "input_df = input_df[input_df.fAPARmax.apply(lambda fAPARmax: fAPARmax > 0.001)]\n",
    "input_df = input_df[input_df.NDVI.apply(lambda NDVI: NDVI > 0.05)]\n",
    "\n",
    "np.nanmin(input_df.ST_C), np.nanmax(input_df.ST_C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input_variable</th>\n",
       "      <th>output_variable</th>\n",
       "      <th>input_unperturbed</th>\n",
       "      <th>input_perturbation</th>\n",
       "      <th>input_perturbation_std</th>\n",
       "      <th>input_perturbed</th>\n",
       "      <th>output_unperturbed</th>\n",
       "      <th>output_perturbation</th>\n",
       "      <th>output_perturbation_std</th>\n",
       "      <th>output_perturbed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>3.521134</td>\n",
       "      <td>0.125620</td>\n",
       "      <td>31.551134</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.045002</td>\n",
       "      <td>0.024361</td>\n",
       "      <td>1.802285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>4.976553</td>\n",
       "      <td>0.177544</td>\n",
       "      <td>33.006553</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.063603</td>\n",
       "      <td>0.03443</td>\n",
       "      <td>1.783684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>1.221271</td>\n",
       "      <td>0.043570</td>\n",
       "      <td>29.251271</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>0.008449</td>\n",
       "      <td>1.831679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>-2.666465</td>\n",
       "      <td>0.095129</td>\n",
       "      <td>25.363535</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>0.034079</td>\n",
       "      <td>0.018448</td>\n",
       "      <td>1.881366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>5.244739</td>\n",
       "      <td>0.187112</td>\n",
       "      <td>33.274739</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.06703</td>\n",
       "      <td>0.036286</td>\n",
       "      <td>1.780257</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25095</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>-9.272730</td>\n",
       "      <td>0.301945</td>\n",
       "      <td>21.43727</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>2.079502</td>\n",
       "      <td>0.068149</td>\n",
       "      <td>32.593398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25096</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>-0.570969</td>\n",
       "      <td>0.018592</td>\n",
       "      <td>30.139031</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>0.128045</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>30.641941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25097</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>0.423533</td>\n",
       "      <td>0.013791</td>\n",
       "      <td>31.133533</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>-0.094982</td>\n",
       "      <td>0.003113</td>\n",
       "      <td>30.418915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25098</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>-7.249570</td>\n",
       "      <td>0.236065</td>\n",
       "      <td>23.46043</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>1.625788</td>\n",
       "      <td>0.05328</td>\n",
       "      <td>32.139684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25099</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>2.134131</td>\n",
       "      <td>0.069493</td>\n",
       "      <td>32.844131</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>-0.4786</td>\n",
       "      <td>0.015685</td>\n",
       "      <td>30.035296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13300 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      input_variable output_variable input_unperturbed  input_perturbation  \\\n",
       "400             ST_C              LE             28.03            3.521134   \n",
       "401             ST_C              LE             28.03            4.976553   \n",
       "402             ST_C              LE             28.03            1.221271   \n",
       "403             ST_C              LE             28.03           -2.666465   \n",
       "404             ST_C              LE             28.03            5.244739   \n",
       "...              ...             ...               ...                 ...   \n",
       "25095           ST_C              LE             30.71           -9.272730   \n",
       "25096           ST_C              LE             30.71           -0.570969   \n",
       "25097           ST_C              LE             30.71            0.423533   \n",
       "25098           ST_C              LE             30.71           -7.249570   \n",
       "25099           ST_C              LE             30.71            2.134131   \n",
       "\n",
       "       input_perturbation_std input_perturbed output_unperturbed  \\\n",
       "400                  0.125620       31.551134           1.847287   \n",
       "401                  0.177544       33.006553           1.847287   \n",
       "402                  0.043570       29.251271           1.847287   \n",
       "403                  0.095129       25.363535           1.847287   \n",
       "404                  0.187112       33.274739           1.847287   \n",
       "...                       ...             ...                ...   \n",
       "25095                0.301945        21.43727          30.513896   \n",
       "25096                0.018592       30.139031          30.513896   \n",
       "25097                0.013791       31.133533          30.513896   \n",
       "25098                0.236065        23.46043          30.513896   \n",
       "25099                0.069493       32.844131          30.513896   \n",
       "\n",
       "      output_perturbation output_perturbation_std  output_perturbed  \n",
       "400             -0.045002                0.024361          1.802285  \n",
       "401             -0.063603                 0.03443          1.783684  \n",
       "402             -0.015608                0.008449          1.831679  \n",
       "403              0.034079                0.018448          1.881366  \n",
       "404              -0.06703                0.036286          1.780257  \n",
       "...                   ...                     ...               ...  \n",
       "25095            2.079502                0.068149         32.593398  \n",
       "25096            0.128045                0.004196         30.641941  \n",
       "25097           -0.094982                0.003113         30.418915  \n",
       "25098            1.625788                 0.05328         32.139684  \n",
       "25099             -0.4786                0.015685         30.035296  \n",
       "\n",
       "[13300 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_variable = \"ST_C\"\n",
    "output_variable = \"LE\"\n",
    "\n",
    "results = perturbed_run(\n",
    "    input_df=input_df, \n",
    "    input_variable=input_variable, \n",
    "    output_variable=output_variable, \n",
    "    forward_process=process_verma_PTJPL_table,\n",
    "    normalization_function=normalization_function\n",
    ")\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input_variable</th>\n",
       "      <th>output_variable</th>\n",
       "      <th>input_unperturbed</th>\n",
       "      <th>input_perturbation</th>\n",
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       "      <th>output_perturbation_std</th>\n",
       "      <th>output_perturbed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>3.521134</td>\n",
       "      <td>0.125620</td>\n",
       "      <td>31.551134</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.045002</td>\n",
       "      <td>0.024361</td>\n",
       "      <td>1.802285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>4.976553</td>\n",
       "      <td>0.177544</td>\n",
       "      <td>33.006553</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.063603</td>\n",
       "      <td>0.03443</td>\n",
       "      <td>1.783684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>1.221271</td>\n",
       "      <td>0.043570</td>\n",
       "      <td>29.251271</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>0.008449</td>\n",
       "      <td>1.831679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>-2.666465</td>\n",
       "      <td>0.095129</td>\n",
       "      <td>25.363535</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>0.034079</td>\n",
       "      <td>0.018448</td>\n",
       "      <td>1.881366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>28.03</td>\n",
       "      <td>5.244739</td>\n",
       "      <td>0.187112</td>\n",
       "      <td>33.274739</td>\n",
       "      <td>1.847287</td>\n",
       "      <td>-0.06703</td>\n",
       "      <td>0.036286</td>\n",
       "      <td>1.780257</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25095</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>-9.272730</td>\n",
       "      <td>0.301945</td>\n",
       "      <td>21.43727</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>2.079502</td>\n",
       "      <td>0.068149</td>\n",
       "      <td>32.593398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25096</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>-0.570969</td>\n",
       "      <td>0.018592</td>\n",
       "      <td>30.139031</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>0.128045</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>30.641941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25097</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>0.423533</td>\n",
       "      <td>0.013791</td>\n",
       "      <td>31.133533</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>-0.094982</td>\n",
       "      <td>0.003113</td>\n",
       "      <td>30.418915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25098</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>-7.249570</td>\n",
       "      <td>0.236065</td>\n",
       "      <td>23.46043</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>1.625788</td>\n",
       "      <td>0.05328</td>\n",
       "      <td>32.139684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25099</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>30.71</td>\n",
       "      <td>2.134131</td>\n",
       "      <td>0.069493</td>\n",
       "      <td>32.844131</td>\n",
       "      <td>30.513896</td>\n",
       "      <td>-0.4786</td>\n",
       "      <td>0.015685</td>\n",
       "      <td>30.035296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13300 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      input_variable output_variable input_unperturbed  input_perturbation  \\\n",
       "400             ST_C              LE             28.03            3.521134   \n",
       "401             ST_C              LE             28.03            4.976553   \n",
       "402             ST_C              LE             28.03            1.221271   \n",
       "403             ST_C              LE             28.03           -2.666465   \n",
       "404             ST_C              LE             28.03            5.244739   \n",
       "...              ...             ...               ...                 ...   \n",
       "25095           ST_C              LE             30.71           -9.272730   \n",
       "25096           ST_C              LE             30.71           -0.570969   \n",
       "25097           ST_C              LE             30.71            0.423533   \n",
       "25098           ST_C              LE             30.71           -7.249570   \n",
       "25099           ST_C              LE             30.71            2.134131   \n",
       "\n",
       "       input_perturbation_std input_perturbed output_unperturbed  \\\n",
       "400                  0.125620       31.551134           1.847287   \n",
       "401                  0.177544       33.006553           1.847287   \n",
       "402                  0.043570       29.251271           1.847287   \n",
       "403                  0.095129       25.363535           1.847287   \n",
       "404                  0.187112       33.274739           1.847287   \n",
       "...                       ...             ...                ...   \n",
       "25095                0.301945        21.43727          30.513896   \n",
       "25096                0.018592       30.139031          30.513896   \n",
       "25097                0.013791       31.133533          30.513896   \n",
       "25098                0.236065        23.46043          30.513896   \n",
       "25099                0.069493       32.844131          30.513896   \n",
       "\n",
       "      output_perturbation output_perturbation_std  output_perturbed  \n",
       "400             -0.045002                0.024361          1.802285  \n",
       "401             -0.063603                 0.03443          1.783684  \n",
       "402             -0.015608                0.008449          1.831679  \n",
       "403              0.034079                0.018448          1.881366  \n",
       "404              -0.06703                0.036286          1.780257  \n",
       "...                   ...                     ...               ...  \n",
       "25095            2.079502                0.068149         32.593398  \n",
       "25096            0.128045                0.004196         30.641941  \n",
       "25097           -0.094982                0.003113         30.418915  \n",
       "25098            1.625788                 0.05328         32.139684  \n",
       "25099             -0.4786                0.015685         30.035296  \n",
       "\n",
       "[13300 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_results = results.dropna()\n",
    "filtered_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(filtered_results.input_perturbation, filtered_results.output_perturbation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.xticks(range(int(min(processed.ST_C)), int(max(processed.ST_C)) + 1, 5))\n",
    "# plt.ylim(-350, 150)\n",
    "plt.scatter(x=results.input_perturbation, y=results.output_perturbation, color='black', s=10, zorder=5)  # Adjust the 's' parameter to make dots thinner\n",
    "plt.grid(True, zorder=0)\n",
    "plt.xlabel(\"Change in Surface Temperature (°C)\")\n",
    "plt.ylabel(\"Change in PT-JPL Latent Heat Flux (W/m$^2$)\")\n",
    "plt.title(\"Comparison of Change\\nin Surface Temperature\\nto PT-JPL Latent Heat Flux\")\n",
    "\n",
    "plt.savefig(\"Comparison of Change in Surface Temperature to PT-JPL Latent Heat Flux.jpeg\", format='jpeg', bbox_inches='tight')\n",
    "plt.savefig(\"Comparison of Change in Surface Temperature to PT-JPL Latent Heat Flux.svg\", format='svg', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7877163568149159"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation = mstats.pearsonr(\n",
    "    np.array(filtered_results.input_perturbation_std).astype(np.float64), \n",
    "    np.array(filtered_results.output_perturbation_std).astype(np.float64)\n",
    ")[0]\n",
    "\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregoryhalverson/Projects/ET-Sensitivity/sensitivity/sensitvity.py:161: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  perturbation_df = pd.concat([perturbation_df, run_results])\n",
      "/Users/gregoryhalverson/Projects/ET-Sensitivity/sensitivity/sensitvity.py:179: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  sensitivity_metrics_df = pd.concat([sensitivity_metrics_df, pd.DataFrame([[\n",
      "/Users/gregoryhalverson/Projects/ET-Sensitivity/vegetation_conversion/vegetation_conversion.py:45: RuntimeWarning: divide by zero encountered in log\n",
      "  LAI = rt.clip(-np.log(1 - fIPAR) * (1 / KPAR), min_LAI, max_LAI)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input_variable</th>\n",
       "      <th>output_variable</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.785873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.617597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>mean_normalized_change</td>\n",
       "      <td>0.037333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>LE</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.632092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>LE</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.399541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>LE</td>\n",
       "      <td>mean_normalized_change</td>\n",
       "      <td>0.201091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albedo</td>\n",
       "      <td>LE</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.641663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albedo</td>\n",
       "      <td>LE</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.411731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albedo</td>\n",
       "      <td>LE</td>\n",
       "      <td>mean_normalized_change</td>\n",
       "      <td>0.056511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ta_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.194445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ta_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.037809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ta_C</td>\n",
       "      <td>LE</td>\n",
       "      <td>mean_normalized_change</td>\n",
       "      <td>0.524844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>LE</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.638837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>LE</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.408113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>LE</td>\n",
       "      <td>mean_normalized_change</td>\n",
       "      <td>0.512848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  input_variable output_variable                  metric     value\n",
       "0           ST_C              LE             correlation  0.785873\n",
       "0           ST_C              LE                      r2  0.617597\n",
       "0           ST_C              LE  mean_normalized_change  0.037333\n",
       "0           NDVI              LE             correlation  0.632092\n",
       "0           NDVI              LE                      r2  0.399541\n",
       "0           NDVI              LE  mean_normalized_change  0.201091\n",
       "0         albedo              LE             correlation  0.641663\n",
       "0         albedo              LE                      r2  0.411731\n",
       "0         albedo              LE  mean_normalized_change  0.056511\n",
       "0           Ta_C              LE             correlation  0.194445\n",
       "0           Ta_C              LE                      r2  0.037809\n",
       "0           Ta_C              LE  mean_normalized_change  0.524844\n",
       "0             RH              LE             correlation  0.638837\n",
       "0             RH              LE                      r2  0.408113\n",
       "0             RH              LE  mean_normalized_change  0.512848"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_variables = [\"ST_C\", \"NDVI\", \"albedo\", \"Ta_C\", \"RH\"]\n",
    "output_variables = [\"LE\"]\n",
    "\n",
    "perturbation_df, sensitivity_metrics_df = sensitivity_analysis(\n",
    "    input_df=input_df,\n",
    "    input_variables=input_variables,\n",
    "    output_variables=output_variables,\n",
    "    forward_process=process_verma_PTJPL_table,\n",
    "    normalization_function=normalization_function\n",
    ")\n",
    "\n",
    "sensitivity_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bv/410kt7f93x78j9dww00h1pz80000gn/T/ipykernel_9676/1678908124.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([\"Surface\\nTemperature\", \"NDVI\", \"Albedo\", \"Air\\nTemperature\", \"Relative\\nHumidity\"])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sensitivity_metrics_df\n",
    "df = df[(df.output_variable == \"LE\") & (df.metric == \"mean_normalized_change\")]\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value * 100, color='black')\n",
    "ax.set_xticklabels([\"Surface\\nTemperature\", \"NDVI\", \"Albedo\", \"Air\\nTemperature\", \"Relative\\nHumidity\"])\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Average Percent Change in Output Perturbation\")\n",
    "plt.title(\"PT-JPL Latent Heat Flux Sensitivity Magnitude\")\n",
    "# plt.ylim(0, 160)  # Set y-axis range from 0 to 160\n",
    "plt.grid(axis='y', color='lightgray', linestyle='-', linewidth=0.5)  # Add light gray horizontal gridlines only\n",
    "\n",
    "# Add percent sign to y-axis tick labels\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f'{int(y)}%'))\n",
    "\n",
    "plt.savefig(\"PT-JPL Latent Heat Flux Sensitivity Magnitude.jpeg\", format='jpeg', bbox_inches='tight')\n",
    "plt.savefig(\"PT-JPL Latent Heat Flux Sensitivity Magnitude.svg\", format='svg', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bv/410kt7f93x78j9dww00h1pz80000gn/T/ipykernel_9676/2265298213.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([\"Surface\\nTemperature\", \"NDVI\", \"Albedo\", \"Air\\nTemperature\", \"Relative\\nHumidity\"])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sensitivity_metrics_df\n",
    "df = df[(df.output_variable == \"LE\") & (df.metric == \"correlation\")]\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value, color='black')\n",
    "ax.set_xticklabels([\"Surface\\nTemperature\", \"NDVI\", \"Albedo\", \"Air\\nTemperature\", \"Relative\\nHumidity\"])\n",
    "# plt.ylim(0, 0.8)\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Correlation of Input Perturbation to Output Perturbation\")\n",
    "plt.title(\"PT-JPL Latent Heat Flux Sensitivity Correlation\")\n",
    "plt.grid(axis='y')  # Add horizontal gridlines\n",
    "\n",
    "plt.savefig(\"PT-JPL Latent Heat Flux Sensitivity Correlation.jpeg\", format='jpeg', bbox_inches='tight')\n",
    "plt.savefig(\"PT-JPL Latent Heat Flux Sensitivity Correlation.svg\", format='svg', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sensitivity",
   "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": "3.11.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
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