1D_DIA_Maize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>23446.847903</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550403</td>\n",
       "      <td>38936.400172</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>0.149521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>23446.847903</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        District  Maize_Production      TotalNPK  LivestockCensus_2012_Cattle  \\\n",
       "24       BALLARI            104045   8404.154608                 23446.847903   \n",
       "25         BIDAR              2791    249.381182                  2371.831678   \n",
       "26    KALABURAGI             14621    710.101614                  6221.617910   \n",
       "27       YADGIRI              2673    368.617469                  1253.317144   \n",
       "28       RAICHUR               723     61.547519                   188.537479   \n",
       "29        KOPPAL            190252  10997.550403                 38936.400172   \n",
       "30  VIJAYANAGARA            104045   8404.154608                 23446.847903   \n",
       "\n",
       "    AreaUnderCereals_Maize  Maize_Ratio  \n",
       "24                 49869.5     0.136288  \n",
       "25                  1382.0     0.010114  \n",
       "26                  2634.0     0.013150  \n",
       "27                   961.0     0.004033  \n",
       "28                  2600.0     0.000525  \n",
       "29                 48210.0     0.149521  \n",
       "30                 49869.5     0.136288  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pattern(col_name):\n",
    "    return re.sub(r'_[0-9]{3}$', '', col_name)\n",
    "\n",
    "Zones = pd.read_excel('Zones.xlsx')\n",
    "Dataset = pd.read_csv('KAG 2016-17/Agriculture/Agriculture_KAG_2016_17.csv')\n",
    "Dataset.columns = map(remove_pattern, Dataset.columns)\n",
    "\n",
    "param = pd.read_csv('Params/3_Param_Maize_Multicollinearity.csv')\n",
    "\n",
    "Working_Directory = pd.read_csv('Working_Directory.csv')\n",
    "Working_Directory = Working_Directory[['District','Maize_Production','TotalNPK',\n",
    "'LivestockCensus_2012_Cattle', 'AreaUnderCereals_Maize','Maize_Ratio']].copy()\n",
    "\n",
    "for x in param.columns:\n",
    "    if(x != 'AreaUnderCereals_Maize'):\n",
    "        Working_Directory[x] = Working_Directory[x]*Working_Directory['Maize_Ratio']\n",
    "\n",
    "Working_Directory[24:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "0                  2567.461578                  1129.0    438.180210\n",
       "1                 25037.139270                 14867.0   3036.080250\n",
       "2                  7729.614277                  1987.0    222.225258\n",
       "3                 83821.593090                 92194.0  10902.406570\n",
       "4                 85657.632090                173400.0  25702.835480"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Case_file = pd.read_csv('Params/3_Param_Maize_Multicollinearity.csv')\n",
    "for x in Case_file.columns:\n",
    "    Working_Directory[x] = Case_file[x]\n",
    "y = Working_Directory['Maize_Production']\n",
    "Case_file[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>249.381182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>61.547519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>10997.550400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "24                 23446.847900                 49869.5   8404.154608\n",
       "25                  2371.831678                  1382.0    249.381182\n",
       "26                  6221.617910                  2634.0    710.101614\n",
       "27                  1253.317144                   961.0    368.617469\n",
       "28                   188.537479                  2600.0     61.547519\n",
       "29                 38936.400170                 48210.0  10997.550400\n",
       "30                 23446.847900                 49869.5   8404.154608"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if(len(Case_file) < 31):\n",
    "    row_24 = Case_file.iloc[24]\n",
    "    halved_row_24 = row_24 / 2\n",
    "    Case_file.iloc[24] = halved_row_24\n",
    "    Case_file.loc[len(Case_file)] = halved_row_24\n",
    "\n",
    "Case_file[24:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    }
   ],
   "source": [
    "# Ensure that the index of y and Case_file are aligned\n",
    "y = y.loc[Case_file.index]\n",
    "\n",
    "# Identify rows in Case_file that have any zeros\n",
    "rows_to_remove = (Case_file == 0).any(axis=1)\n",
    "\n",
    "rows_to_remove |= Working_Directory['Maize_Production']  < 1000\n",
    "# rows_to_remove |= Working_Directory['Maize_Production'] > 500000\n",
    "\n",
    "# Update Case_file and y\n",
    "Case_file = Case_file[~rows_to_remove].reset_index(drop=True)\n",
    "y = Working_Directory['Maize_Production'][~rows_to_remove].reset_index(drop=True)\n",
    "\n",
    "# Verify the length of Case_file\n",
    "case_file_length = len(Case_file)\n",
    "print(case_file_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            District      KDG_District\n",
      "0          BENGALURU   Bengaluru Urban\n",
      "1       BENGALURU(R)   Bengaluru Rural\n",
      "2         RAMANAGARA        Ramanagara\n",
      "3        CHITRADURGA       Chitradurga\n",
      "4         DAVANAGERE        Davanagere\n",
      "5              KOLAR             Kolar\n",
      "6    CHIKKABALLAPURA   Chikkaballapura\n",
      "7         SHIVAMOGGA        Shivamogga\n",
      "8           TUMAKURU          Tumakuru\n",
      "9     CHIKKAMAGALURU    Chikkamagaluru\n",
      "10  DAKSHINA KANNADA  Dakshina Kannada\n",
      "11             UDUPI             Udupi\n",
      "12            HASSAN            Hassan\n",
      "13            KODAGU            Kodagu\n",
      "14            MANDYA            Mandya\n",
      "15            MYSURU            Mysuru\n",
      "16    CHAMARAJANAGAR   Chamarajanagara\n",
      "17          BELAGAVI          Belagavi\n",
      "18        VIJAYAPURA        Vijayapura\n",
      "19          BAGALKOT         Bagalkote\n",
      "20          DHARAWAD           Dharwad\n",
      "21             GADAG             Gadag\n",
      "22            HAVERI            Haveri\n",
      "23    UTTARA KANNADA    Uttara Kannada\n",
      "24           BALLARI           Ballari\n",
      "25             BIDAR             Bidar\n",
      "26        KALABURAGI        Kalaburagi\n",
      "27           YADGIRI            Yadgir\n",
      "28           RAICHUR           Raichur\n",
      "29            KOPPAL            Koppal\n",
      "30      VIJAYANAGARA      Vijayanagara\n"
     ]
    }
   ],
   "source": [
    "abbreviation_mapping = {\n",
    "    'BENGALURU': 'BLR',\n",
    "    'BENGALURU(R)': 'BLR(R)',\n",
    "    'RAMANAGARA': 'RGA',\n",
    "    'CHITRADURGA': 'CDA',\n",
    "    'DAVANAGERE': 'DVG',\n",
    "    'KOLAR': 'KLR',\n",
    "    'CHIKKABALLAPURA': 'CKA',\n",
    "    'SHIVAMOGGA': 'SMG',\n",
    "    'TUMAKURU': 'TKR',\n",
    "    'CHIKKAMAGALURU': 'CMG',\n",
    "    'DAKSHINA KANNADA': 'DKA',\n",
    "    'UDUPI': 'UPI',\n",
    "    'HASSAN': 'HSN',\n",
    "    'KODAGU': 'KDG',\n",
    "    'MANDYA': 'MDY',\n",
    "    'MYSURU': 'MYS',\n",
    "    'CHAMARAJANAGAR': 'CNR',\n",
    "    'BELAGAVI': 'BLG',\n",
    "    'VIJAYAPURA': 'VJP',\n",
    "    'BAGALKOT': 'BKT',\n",
    "    'DHARAWAD': 'DWD',\n",
    "    'GADAG': 'GDG',\n",
    "    'HAVERI': 'HVR',\n",
    "    'UTTARA KANNADA': 'UTK',\n",
    "    'BALLARI': 'BLL',\n",
    "    'BIDAR': 'BDR',\n",
    "    'KALABURAGI': 'KLB',\n",
    "    'YADGIRI': 'YDR',\n",
    "    'RAICHUR': 'RCR',\n",
    "    'KOPPAL': 'KPL',\n",
    "    'VIJAYANAGARA': 'VNG',\n",
    "}\n",
    "\n",
    "Working_Directory['Abbreviation'] = Working_Directory['District'].map(abbreviation_mapping)\n",
    "Working_Directory[:5]\n",
    "\n",
    "# Create a dictionary to map district names\n",
    "district_mapping = {\n",
    "    'BENGALURU': 'Bengaluru Urban',\n",
    "    'BENGALURU(R)': 'Bengaluru Rural',\n",
    "    'RAMANAGARA': 'Ramanagara',\n",
    "    'CHITRADURGA': 'Chitradurga',\n",
    "    'DAVANAGERE': 'Davanagere',\n",
    "    'KOLAR': 'Kolar',\n",
    "    'CHIKKABALLAPURA': 'Chikkaballapura',\n",
    "    'SHIVAMOGGA': 'Shivamogga',\n",
    "    'TUMAKURU': 'Tumakuru',\n",
    "    'CHIKKAMAGALURU': 'Chikkamagaluru',\n",
    "    'DAKSHINA KANNADA': 'Dakshina Kannada',\n",
    "    'UDUPI': 'Udupi',\n",
    "    'HASSAN': 'Hassan',\n",
    "    'KODAGU': 'Kodagu',\n",
    "    'MANDYA': 'Mandya',\n",
    "    'MYSURU': 'Mysuru',\n",
    "    'CHAMARAJANAGAR': 'Chamarajanagara',\n",
    "    'BELAGAVI': 'Belagavi',\n",
    "    'VIJAYAPURA': 'Vijayapura',\n",
    "    'BAGALKOT': 'Bagalkote',\n",
    "    'DHARAWAD': 'Dharwad',\n",
    "    'GADAG': 'Gadag',\n",
    "    'HAVERI': 'Haveri',\n",
    "    'UTTARA KANNADA': 'Uttara Kannada',\n",
    "    'BALLARI': 'Ballari',\n",
    "    'BIDAR': 'Bidar',\n",
    "    'KALABURAGI': 'Kalaburagi',\n",
    "    'YADGIRI': 'Yadgir',\n",
    "    'RAICHUR': 'Raichur',\n",
    "    'KOPPAL': 'Koppal',\n",
    "    'VIJAYANAGARA': 'Vijayanagara'\n",
    "}\n",
    "\n",
    "Working_Directory['KDG_District'] = Working_Directory['District'].map(district_mapping)\n",
    "\n",
    "print(Working_Directory[['District', 'KDG_District']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_st = pd.DataFrame()\n",
    "y_st['Maize_Production'] = Working_Directory[~rows_to_remove]['Maize_Production'].reset_index(drop=True)\n",
    "\n",
    "\n",
    "columns_to_standardize = ['LivestockCensus_2012_Cattle','AreaUnderCereals_Maize','TotalNPK']\n",
    "\n",
    "Case_file =sm.add_constant(Case_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Maize_Production   R-squared:                       0.953\n",
      "Model:                            OLS   Adj. R-squared:                  0.947\n",
      "Method:                 Least Squares   F-statistic:                     160.9\n",
      "Date:                Mon, 07 Oct 2024   Prob (F-statistic):           5.01e-16\n",
      "Time:                        12:03:06   Log-Likelihood:                -332.85\n",
      "No. Observations:                  28   AIC:                             673.7\n",
      "Df Residuals:                      24   BIC:                             679.0\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "const                       -7629.7025   1.08e+04     -0.704      0.488      -3e+04    1.47e+04\n",
      "LivestockCensus_2012_Cattle     0.5784      0.304      1.901      0.069      -0.050       1.206\n",
      "AreaUnderCereals_Maize          1.7786      0.272      6.546      0.000       1.218       2.339\n",
      "TotalNPK                        5.8348      1.444      4.040      0.000       2.854       8.815\n",
      "==============================================================================\n",
      "Omnibus:                        3.140   Durbin-Watson:                   2.453\n",
      "Prob(Omnibus):                  0.208   Jarque-Bera (JB):                1.842\n",
      "Skew:                           0.206   Prob(JB):                        0.398\n",
      "Kurtosis:                       4.187   Cond. No.                     1.34e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.34e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.57835467, 1.77855209, 5.83482942])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=sm.OLS(y_st['Maize_Production'],Case_file).fit()\n",
    "print(model.summary())\n",
    "model.params.values\n",
    "coef=model.params.values[1:]\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>KDG_District</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>0.018798</td>\n",
       "      <td>BLR</td>\n",
       "      <td>Bengaluru Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>42583</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>0.157645</td>\n",
       "      <td>BLR(R)</td>\n",
       "      <td>Bengaluru Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>0.029741</td>\n",
       "      <td>RGA</td>\n",
       "      <td>Ramanagara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>0.303824</td>\n",
       "      <td>CDA</td>\n",
       "      <td>Chitradurga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>0.257947</td>\n",
       "      <td>DVG</td>\n",
       "      <td>Davanagere</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOLAR</td>\n",
       "      <td>1759</td>\n",
       "      <td>220.782906</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>KLR</td>\n",
       "      <td>Kolar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>105491</td>\n",
       "      <td>8196.896749</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>0.289061</td>\n",
       "      <td>CKA</td>\n",
       "      <td>Chikkaballapura</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>226532</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>0.184215</td>\n",
       "      <td>SMG</td>\n",
       "      <td>Shivamogga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>66868</td>\n",
       "      <td>2746.313635</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>0.071991</td>\n",
       "      <td>TKR</td>\n",
       "      <td>Tumakuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>61353</td>\n",
       "      <td>8156.290370</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>0.120188</td>\n",
       "      <td>CMG</td>\n",
       "      <td>Chikkamagaluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAKSHINA KANNADA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>DKA</td>\n",
       "      <td>Dakshina Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UDUPI</td>\n",
       "      <td>109</td>\n",
       "      <td>2.304863</td>\n",
       "      <td>94.499008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>UPI</td>\n",
       "      <td>Udupi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HASSAN</td>\n",
       "      <td>333937</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>0.248999</td>\n",
       "      <td>HSN</td>\n",
       "      <td>Hassan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KODAGU</td>\n",
       "      <td>17921</td>\n",
       "      <td>3519.022414</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>0.085941</td>\n",
       "      <td>KDG</td>\n",
       "      <td>Kodagu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MANDYA</td>\n",
       "      <td>19387</td>\n",
       "      <td>1448.830355</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>0.023925</td>\n",
       "      <td>MDY</td>\n",
       "      <td>Mandya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MYSURU</td>\n",
       "      <td>137154</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>0.111996</td>\n",
       "      <td>MYS</td>\n",
       "      <td>Mysuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>147886</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>0.306251</td>\n",
       "      <td>CNR</td>\n",
       "      <td>Chamarajanagara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>576758</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>0.268283</td>\n",
       "      <td>BLG</td>\n",
       "      <td>Belagavi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>226327</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>0.192153</td>\n",
       "      <td>VJP</td>\n",
       "      <td>Vijayapura</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>251699</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>0.270487</td>\n",
       "      <td>BKT</td>\n",
       "      <td>Bagalkote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>90634</td>\n",
       "      <td>6447.725408</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>0.172487</td>\n",
       "      <td>DWD</td>\n",
       "      <td>Dharwad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GADAG</td>\n",
       "      <td>138476</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>0.240174</td>\n",
       "      <td>GDG</td>\n",
       "      <td>Gadag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HAVERI</td>\n",
       "      <td>413487</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>0.334321</td>\n",
       "      <td>HVR</td>\n",
       "      <td>Haveri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>24088</td>\n",
       "      <td>1025.616827</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>0.066964</td>\n",
       "      <td>UTK</td>\n",
       "      <td>Uttara Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>0.136288</td>\n",
       "      <td>BLL</td>\n",
       "      <td>Ballari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>0.010114</td>\n",
       "      <td>BDR</td>\n",
       "      <td>Bidar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>0.013150</td>\n",
       "      <td>KLB</td>\n",
       "      <td>Kalaburagi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.004033</td>\n",
       "      <td>YDR</td>\n",
       "      <td>Yadgir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>RCR</td>\n",
       "      <td>Raichur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>0.149521</td>\n",
       "      <td>KPL</td>\n",
       "      <td>Koppal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>0.136288</td>\n",
       "      <td>VNG</td>\n",
       "      <td>Vijayanagara</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            District  Maize_Production      TotalNPK  \\\n",
       "0          BENGALURU              2879    438.180210   \n",
       "1       BENGALURU(R)             42583   3036.080250   \n",
       "2         RAMANAGARA             10450    222.225258   \n",
       "3        CHITRADURGA            271975  10902.406570   \n",
       "4         DAVANAGERE            593544  25702.835480   \n",
       "5              KOLAR              1759    220.782906   \n",
       "6    CHIKKABALLAPURA            105491   8196.896749   \n",
       "7         SHIVAMOGGA            226532  10428.246260   \n",
       "8           TUMAKURU             66868   2746.313635   \n",
       "9     CHIKKAMAGALURU             61353   8156.290370   \n",
       "10  DAKSHINA KANNADA                 0      0.000000   \n",
       "11             UDUPI               109      2.304863   \n",
       "12            HASSAN            333937  17039.273890   \n",
       "13            KODAGU             17921   3519.022414   \n",
       "14            MANDYA             19387   1448.830355   \n",
       "15            MYSURU            137154   6945.331415   \n",
       "16    CHAMARAJANAGAR            147886   7289.684770   \n",
       "17          BELAGAVI            576758  48288.815130   \n",
       "18        VIJAYAPURA            226327  10420.071650   \n",
       "19          BAGALKOT            251699  22940.521250   \n",
       "20          DHARAWAD             90634   6447.725408   \n",
       "21             GADAG            138476   5591.244159   \n",
       "22            HAVERI            413487  21731.170720   \n",
       "23    UTTARA KANNADA             24088   1025.616827   \n",
       "24           BALLARI            104045   8404.154608   \n",
       "25             BIDAR              2791    249.381182   \n",
       "26        KALABURAGI             14621    710.101614   \n",
       "27           YADGIRI              2673    368.617469   \n",
       "28           RAICHUR               723     61.547519   \n",
       "29            KOPPAL            190252  10997.550400   \n",
       "30      VIJAYANAGARA            104045   8404.154608   \n",
       "\n",
       "    LivestockCensus_2012_Cattle  AreaUnderCereals_Maize  Maize_Ratio  \\\n",
       "0                   2567.461578                  1129.0     0.018798   \n",
       "1                  25037.139270                 14867.0     0.157645   \n",
       "2                   7729.614277                  1987.0     0.029741   \n",
       "3                  83821.593090                 92194.0     0.303824   \n",
       "4                  85657.632090                173400.0     0.257947   \n",
       "5                   2278.829821                   640.0     0.009950   \n",
       "6                  71206.945310                 59377.0     0.289061   \n",
       "7                 104722.000400                 43827.0     0.184215   \n",
       "8                  37944.093760                 24207.0     0.071991   \n",
       "9                  40572.445630                 25831.0     0.120188   \n",
       "10                     0.000000                     0.0     0.000000   \n",
       "11                    94.499008                     8.0     0.000375   \n",
       "12                151008.140300                 79058.0     0.248999   \n",
       "13                  7599.582730                  3448.0     0.085941   \n",
       "14                  8774.134623                  4892.0     0.023925   \n",
       "15                 61547.727440                 37283.0     0.111996   \n",
       "16                 80396.926680                 38569.0     0.306251   \n",
       "17                159047.086200                131163.0     0.268283   \n",
       "18                 48619.507080                 40207.0     0.192153   \n",
       "19                 75578.052000                 44300.0     0.270487   \n",
       "20                 33424.645600                 30900.0     0.172487   \n",
       "21                 34261.981770                 34163.0     0.240174   \n",
       "22                 95150.640270                170696.0     0.334321   \n",
       "23                 22563.771080                  4576.0     0.066964   \n",
       "24                 23446.847900                 49869.5     0.136288   \n",
       "25                  2371.831678                  1382.0     0.010114   \n",
       "26                  6221.617910                  2634.0     0.013150   \n",
       "27                  1253.317144                   961.0     0.004033   \n",
       "28                   188.537479                  2600.0     0.000525   \n",
       "29                 38936.400170                 48210.0     0.149521   \n",
       "30                 23446.847900                 49869.5     0.136288   \n",
       "\n",
       "   Abbreviation      KDG_District  \n",
       "0           BLR   Bengaluru Urban  \n",
       "1        BLR(R)   Bengaluru Rural  \n",
       "2           RGA        Ramanagara  \n",
       "3           CDA       Chitradurga  \n",
       "4           DVG        Davanagere  \n",
       "5           KLR             Kolar  \n",
       "6           CKA   Chikkaballapura  \n",
       "7           SMG        Shivamogga  \n",
       "8           TKR          Tumakuru  \n",
       "9           CMG    Chikkamagaluru  \n",
       "10          DKA  Dakshina Kannada  \n",
       "11          UPI             Udupi  \n",
       "12          HSN            Hassan  \n",
       "13          KDG            Kodagu  \n",
       "14          MDY            Mandya  \n",
       "15          MYS            Mysuru  \n",
       "16          CNR   Chamarajanagara  \n",
       "17          BLG          Belagavi  \n",
       "18          VJP        Vijayapura  \n",
       "19          BKT         Bagalkote  \n",
       "20          DWD           Dharwad  \n",
       "21          GDG             Gadag  \n",
       "22          HVR            Haveri  \n",
       "23          UTK    Uttara Kannada  \n",
       "24          BLL           Ballari  \n",
       "25          BDR             Bidar  \n",
       "26          KLB        Kalaburagi  \n",
       "27          YDR            Yadgir  \n",
       "28          RCR           Raichur  \n",
       "29          KPL            Koppal  \n",
       "30          VNG      Vijayanagara  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.834829424182065\n"
     ]
    }
   ],
   "source": [
    "labels = [\"Cattle\", \"MArea\", \"NPK\"]\n",
    "coef_dict = dict(zip(labels, coef))\n",
    "def get_slope(label):\n",
    "    return coef_dict.get(label, \"Label not found\")\n",
    "print(get_slope(\"NPK\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def calculate_and_visualize(perturbation, crop, Working_Directory, what):\n",
    "    # Mapping of factor names based on 'what'\n",
    "    factor_mapping = {\n",
    "        'NPK': 'TotalNPK',\n",
    "        'Cattle': 'LivestockCensus_2012_Cattle',\n",
    "        'MArea': 'AreaUnderCereals_Maize',\n",
    "    }\n",
    "    \n",
    "    # Dynamically select the correct factor and slope column based on 'what'\n",
    "    factor_name = factor_mapping[what]\n",
    "    production_col = f'{crop}_Production'\n",
    "    slope = get_slope(what)\n",
    "\n",
    "    # Calculate the factor\n",
    "    factor = Working_Directory[factor_name]\n",
    "    \n",
    "    # Initialize an empty list for new values\n",
    "    New_values = []\n",
    "    \n",
    "    # Calculate new values based on the provided formula\n",
    "    for i in range(len(Working_Directory['District'])):\n",
    "        y_new = Working_Directory[production_col][i] + slope * (perturbation * factor[i] / 100)\n",
    "        New_values.append(int(y_new))\n",
    "    \n",
    "    # Add the new values to the DataFrame with a specific column name\n",
    "    Working_Directory[f'{crop[0].upper()} - {what} {perturbation}'] = New_values\n",
    "    Working_Directory['Change'] =  ((New_values - Working_Directory[production_col])/Working_Directory[production_col])*100\n",
    "    \n",
    "    # Ensure all values are finite\n",
    "    Working_Directory = Working_Directory.replace([np.inf, -np.inf], np.nan).dropna(subset=['Change'])\n",
    "    \n",
    "    # Visualization with color-coded zones\n",
    "    plt.figure(figsize=(14, 9))\n",
    "    for zone in Working_Directory['District'].unique():\n",
    "        bars = plt.bar(Working_Directory['District'], \n",
    "                       Working_Directory['Change'], \n",
    "                       label=zone)\n",
    "        \n",
    "        # Add value labels to the bars\n",
    "        for bar, value in zip(bars, Working_Directory['Change']):\n",
    "            plt.text(bar.get_x() + bar.get_width() / 2, \n",
    "                     bar.get_height(), \n",
    "                     f'{value:.1f}', \n",
    "                     ha='center', \n",
    "                     va='bottom', \n",
    "                     fontsize=10)\n",
    "    \n",
    "    plt.xlabel('District')\n",
    "    plt.ylabel(f'Percentage Change in {crop} Production')\n",
    "    plt.title(f'Percentage Change in {crop} Production Values when {what} increases by {perturbation}%')\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "perturbation = 20\n",
    "what = 'NPK'  # Factor to be used\n",
    "crop = 'Maize'  # Or 'Maize'\n",
    "\n",
    "# Assuming Working_Directory is a DataFrame that has all necessary columns defined\n",
    "calculate_and_visualize(perturbation, crop, Working_Directory, what)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def calculate_and_visualize(perturbation, crop, Working_Directory, what,plot = True):\n",
    "    # Mapping of factor names based on 'what'\n",
    "    factor_mapping = {\n",
    "        'NPK': 'TotalNPK',\n",
    "        'Cattle': 'LivestockCensus_2012_Cattle',\n",
    "        'MArea': 'AreaUnderCereals_Maize',\n",
    "    }\n",
    "    \n",
    "    # Dynamically select the correct factor and slope column based on 'what'\n",
    "    factor_name = factor_mapping[what]\n",
    "    production_col = f'{crop}_Production'\n",
    "    slope = get_slope(what)\n",
    "\n",
    "    # Calculate the factor\n",
    "    factor = Working_Directory[factor_name]\n",
    "    \n",
    "    # Initialize an empty list for new values\n",
    "    New_values = []\n",
    "    \n",
    "    # Calculate new values based on the provided formula\n",
    "    for i in range(len(Working_Directory['District'])):\n",
    "        y_new = Working_Directory[production_col][i] + slope * (perturbation * factor[i] / 100)\n",
    "        New_values.append(int(y_new))\n",
    "    \n",
    "    # Add the new values to the DataFrame with a specific column name\n",
    "    Working_Directory[f'{crop[0].upper()} - {what} {perturbation}'] = New_values\n",
    "    Working_Directory['Change'] =  (New_values - Working_Directory[production_col])\n",
    "    Working_Directory['Percentage Change'] =  ((New_values - Working_Directory[production_col])/Working_Directory[production_col])*100\n",
    "    Working_Directory = Working_Directory.replace([np.inf, -np.inf], np.nan).dropna(subset=['Percentage Change'])\n",
    "    \n",
    "    # Visualization\n",
    "    if(plot == True):\n",
    "        plt.figure(figsize=(14, 9))\n",
    "        bars = plt.bar(Working_Directory['District'], \n",
    "                    Working_Directory['Percentage Change'])\n",
    "        \n",
    "        # Add value labels to the bars\n",
    "        for bar, value in zip(bars, Working_Directory['Percentage Change']):\n",
    "            plt.text(bar.get_x() + bar.get_width() / 2, \n",
    "                    bar.get_height(), \n",
    "                    f'{value:.1f}', \n",
    "                    ha='center', \n",
    "                    va='bottom', \n",
    "                    fontsize=10)\n",
    "        \n",
    "        plt.xlabel('District')\n",
    "        plt.ylabel(f'Percentage Change in {crop} Production')\n",
    "        plt.title(f'Percentage Change in {crop} Production Values when {what} increases by {perturbation}%')\n",
    "        plt.xticks(rotation=90)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "# Example usage:\n",
    "perturbation = 20\n",
    "what = 'NPK'  # Factor to be used\n",
    "crop = 'Maize'  # Or 'Maize'\n",
    "\n",
    "# Assuming Working_Directory is a DataFrame that has all necessary columns defined\n",
    "calculate_and_visualize(perturbation, crop, Working_Directory, what,plot = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1   2   3   4   5   6   7   8   9   10  ...  22  23  24  25  26  27  28  \\\n",
      "1    0   1   1   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "2    1   0   1   0   0   1   1   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "3    1   1   0   0   0   0   0   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "4    0   0   0   0   1   0   0   0   1   1  ...   0   0   0   1   0   0   0   \n",
      "5    0   0   0   1   0   0   0   1   0   1  ...   0   1   0   1   0   0   0   \n",
      "6    0   1   0   0   0   0   1   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "7    0   1   0   0   0   1   0   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "8    0   0   0   0   1   0   0   0   0   1  ...   0   1   1   0   0   0   0   \n",
      "9    0   1   1   1   0   0   1   0   0   1  ...   0   0   0   0   0   0   0   \n",
      "10   0   0   0   1   1   0   0   1   1   0  ...   0   0   0   0   0   0   0   \n",
      "11   0   0   0   0   0   0   0   0   0   1  ...   0   0   0   0   0   0   0   \n",
      "12   0   0   0   0   0   0   0   1   0   1  ...   0   0   1   0   0   0   0   \n",
      "13   0   0   0   0   0   0   0   0   1   1  ...   0   0   0   0   0   0   0   \n",
      "14   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "15   0   0   1   0   0   0   0   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "16   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "17   0   0   1   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "18   0   0   0   0   0   0   0   0   0   0  ...   1   0   1   0   0   0   0   \n",
      "19   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   1   1   \n",
      "20   0   0   0   0   0   0   0   0   0   0  ...   1   0   0   0   0   0   0   \n",
      "21   0   0   0   0   0   0   0   0   0   0  ...   1   1   1   0   0   0   0   \n",
      "22   0   0   0   0   0   0   0   0   0   0  ...   0   1   0   1   0   0   0   \n",
      "23   0   0   0   0   1   0   0   1   0   0  ...   1   0   1   1   0   0   0   \n",
      "24   0   0   0   0   0   0   0   1   0   0  ...   0   1   0   0   0   0   0   \n",
      "25   0   0   0   1   1   0   0   0   0   0  ...   1   1   0   0   0   0   0   \n",
      "26   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   1   0   \n",
      "27   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   1   0   1   \n",
      "28   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   1   0   \n",
      "29   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   1   0   0   1   \n",
      "30   0   0   0   0   0   0   0   0   0   0  ...   1   0   0   1   0   0   0   \n",
      "31   0   0   0   1   1   0   0   0   0   0  ...   1   1   0   1   0   0   0   \n",
      "\n",
      "    29  30  31  \n",
      "1    0   0   0  \n",
      "2    0   0   0  \n",
      "3    0   0   0  \n",
      "4    0   0   1  \n",
      "5    0   0   1  \n",
      "6    0   0   0  \n",
      "7    0   0   0  \n",
      "8    0   0   0  \n",
      "9    0   0   0  \n",
      "10   0   0   0  \n",
      "11   0   0   0  \n",
      "12   0   0   0  \n",
      "13   0   0   0  \n",
      "14   0   0   0  \n",
      "15   0   0   0  \n",
      "16   0   0   0  \n",
      "17   0   0   0  \n",
      "18   0   0   0  \n",
      "19   1   0   0  \n",
      "20   1   1   0  \n",
      "21   0   0   0  \n",
      "22   0   1   1  \n",
      "23   0   0   1  \n",
      "24   0   0   0  \n",
      "25   1   1   1  \n",
      "26   0   0   0  \n",
      "27   0   0   0  \n",
      "28   1   0   0  \n",
      "29   0   1   0  \n",
      "30   1   0   1  \n",
      "31   0   1   0  \n",
      "\n",
      "[31 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "adjacency_excel = pd.read_excel('Karnataka_District_Adjacency_File.xlsx')\n",
    "\n",
    "district_codes = sorted(set(adjacency_excel['District_Code']))\n",
    "\n",
    "n = len(district_codes)\n",
    "adj_matrix = np.zeros((n, n), dtype=int)\n",
    "\n",
    "code_to_index = {code: idx for idx, code in enumerate(district_codes)}\n",
    "\n",
    "for i in range(len(adjacency_excel)):\n",
    "    district_code = adjacency_excel['District_Code'][i]\n",
    "    neighbors = [int(num) for num in str(adjacency_excel['District_Neighbors'][i]).split(',') if num.strip().isdigit()]\n",
    "    \n",
    "    for neighbor in neighbors:\n",
    "        adj_matrix[code_to_index[district_code]][code_to_index[neighbor]] = 1\n",
    "        adj_matrix[code_to_index[neighbor]][code_to_index[district_code]] = 1  # Assuming adjacency is bidirectional\n",
    "\n",
    "adj_matrix_df = pd.DataFrame(adj_matrix, index=district_codes, columns=district_codes)\n",
    "\n",
    "print(adj_matrix_df)\n",
    "\n",
    "adj_matrix_np = adj_matrix_df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_list(lst):\n",
    "    min_val = min(lst)\n",
    "    max_val = max(lst)\n",
    "    normalized_lst = [(x - min_val) / (max_val - min_val) for x in lst]\n",
    "    return normalized_lst\n",
    "\n",
    "Name = 'M - NPK 20'\n",
    "Capability_list = normalize_list(Working_Directory[f'{Name}'])\n",
    "# Capability_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stress values: [0.03953111 0.00928339 0.07692577 0.13624826 0.6529705  0.12410975\n",
      " 0.1194918  0.01101074 0.09293209 0.2425317  0.17644276 0.13205151\n",
      " 0.47217277 0.22797444 0.19929175 0.01094401 0.15380816 0.75189201\n",
      " 0.0826101  0.05429428 0.33549002 0.19503999 0.38664906 0.40514509\n",
      " 0.22854291 0.01953376 0.10441804 0.12921419 0.26315374 0.11481688\n",
      " 0.29639604]\n",
      "Stability values: [0.96046889 0.99071661 0.92307423 0.86375174 0.3470295  0.87589025\n",
      " 0.8805082  0.98898926 0.90706791 0.7574683  0.82355724 0.86794849\n",
      " 0.52782723 0.77202556 0.80070825 0.98905599 0.84619184 0.24810799\n",
      " 0.9173899  0.94570572 0.66450998 0.80496001 0.61335094 0.59485491\n",
      " 0.77145709 0.98046624 0.89558196 0.87078581 0.73684626 0.88518312\n",
      " 0.70360396]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_avg_capability(capability, neighbors):\n",
    "    num_neighbors = len(neighbors)\n",
    "    if num_neighbors == 0:\n",
    "        return 0\n",
    "    sum_capabilities = sum(capability[n] for n in neighbors)\n",
    "    return sum_capabilities / num_neighbors\n",
    "\n",
    "def calculate_stress_stability(capability, adjacency_matrix):\n",
    "    n = len(capability)\n",
    "    stress = np.zeros(n)\n",
    "    stability = np.zeros(n)\n",
    "    \n",
    "    for i in range(n):\n",
    "        neighbors = [j for j in range(n) if adjacency_matrix[i][j] == 1 and i != j]\n",
    "        if len(neighbors) > 0:\n",
    "            avg_capability = calculate_avg_capability(capability, neighbors)\n",
    "            stress[i] = np.linalg.norm(avg_capability - capability[i])\n",
    "            stability[i] = 1 - stress[i]\n",
    "        else:\n",
    "            stress[i] = 0  # If no neighbors, stress is zero\n",
    "            stability[i] = 1\n",
    "        \n",
    "    return stress, stability\n",
    "\n",
    "Name = 'M - NPK 20'\n",
    "\n",
    "Capability_list = normalize_list(Working_Directory[f'{Name}'])\n",
    "stress, stability = calculate_stress_stability(Capability_list, adj_matrix_np)\n",
    "\n",
    "print(\"Stress values:\", stress)\n",
    "print(\"Stability values:\", stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combined calculation function\n",
    "def calculate_metrics(Working_Directory, crop, perturbation, what):\n",
    "    # Normalize the capability list\n",
    "    capability_name = f'{crop[0].upper()} - {what} {perturbation}'\n",
    "    Capability_list = normalize_list(Working_Directory[capability_name])\n",
    "    \n",
    "    def calculate_avg_capability(capability, neighbors):\n",
    "        num_neighbors = len(neighbors)\n",
    "        if num_neighbors == 0:\n",
    "            return 0\n",
    "        return sum(capability[n] for n in neighbors) / num_neighbors\n",
    "\n",
    "    # Calculate stress and stability\n",
    "    stress, stability = calculate_stress_stability(Capability_list, adj_matrix_np)\n",
    "    \n",
    "    # Calculate the impact as percentage change\n",
    "    original_production = Working_Directory[f'{crop}_Production']\n",
    "    new_production = Working_Directory[capability_name]\n",
    "    impact = ((new_production - original_production) / original_production) * 100\n",
    "\n",
    "    # Column names\n",
    "    stability_column_name = f'Stability_{crop[0].upper()}_{what}_{perturbation}'\n",
    "    stress_column_name = f'Stress_{crop[0].upper()}_{what}_{perturbation}'\n",
    "    impact_column_name = f'Impact_{crop[0].upper()}_{what}_{perturbation}'\n",
    "\n",
    "    return stability, stress, impact, stability_column_name, stress_column_name, impact_column_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impact calculation function\n",
    "def calculate_impact(Working_Directory, crop, perturbation, what):\n",
    "    # Normalize the capability list\n",
    "    capability_name = f'{crop[0].upper()} - {what} {perturbation}'\n",
    "\n",
    "    # Calculate the impact as percentage change\n",
    "    original_production = Working_Directory[f'{crop}_Production']\n",
    "    new_production = Working_Directory[capability_name]\n",
    "    impact = ((new_production - original_production) / original_production) * 100\n",
    "\n",
    "    # Column names\n",
    "    impact_column_name = f'Impact_{crop[0].upper()}_{what}_{perturbation}'\n",
    "\n",
    "    return impact, impact_column_name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combined calculation function\n",
    "def calculate_Change_Impact(Working_Directory, crop, perturbation, what):\n",
    "    # Normalize the capability list\n",
    "    calculate_and_visualize(perturbation, crop, Working_Directory, what,False)\n",
    "    capability_name = f'{crop[0].upper()} - {what} {perturbation}'\n",
    "    \n",
    "    # Calculate the impact as percentage change\n",
    "    original_production = Working_Directory[f'{crop}_Production']\n",
    "    new_production = Working_Directory[capability_name]\n",
    "    change = (new_production - original_production) \n",
    "    impact = ((new_production - original_production) / original_production) * 100\n",
    "\n",
    "    return change,impact,new_production\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>District</th>\n",
       "      <th>KDG_District</th>\n",
       "      <th>Original Maize Production Value</th>\n",
       "      <th>Perturbation</th>\n",
       "      <th>Factor</th>\n",
       "      <th>New Maize Production Values</th>\n",
       "      <th>Percentage Change</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>Bengaluru Urban</td>\n",
       "      <td>2879</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>3134</td>\n",
       "      <td>8.857242</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>Bengaluru Rural</td>\n",
       "      <td>42583</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>44354</td>\n",
       "      <td>4.158937</td>\n",
       "      <td>1771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>Ramanagara</td>\n",
       "      <td>10450</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>10579</td>\n",
       "      <td>1.234450</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>Chitradurga</td>\n",
       "      <td>271975</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>278336</td>\n",
       "      <td>2.338818</td>\n",
       "      <td>6361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>Davanagere</td>\n",
       "      <td>593544</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>608541</td>\n",
       "      <td>2.526687</td>\n",
       "      <td>14997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>Kalaburagi</td>\n",
       "      <td>14621</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>13684</td>\n",
       "      <td>-6.408590</td>\n",
       "      <td>-937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>Yadgir</td>\n",
       "      <td>2673</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>2331</td>\n",
       "      <td>-12.794613</td>\n",
       "      <td>-342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>Raichur</td>\n",
       "      <td>723</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>-201</td>\n",
       "      <td>-127.800830</td>\n",
       "      <td>-924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>Koppal</td>\n",
       "      <td>190252</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>173103</td>\n",
       "      <td>-9.013834</td>\n",
       "      <td>-17149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>Vijayanagara</td>\n",
       "      <td>104045</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>86305</td>\n",
       "      <td>-17.050315</td>\n",
       "      <td>-17740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>372 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         District     KDG_District  Original Maize Production Value  \\\n",
       "0       BENGALURU  Bengaluru Urban                             2879   \n",
       "1    BENGALURU(R)  Bengaluru Rural                            42583   \n",
       "2      RAMANAGARA       Ramanagara                            10450   \n",
       "3     CHITRADURGA      Chitradurga                           271975   \n",
       "4      DAVANAGERE       Davanagere                           593544   \n",
       "..            ...              ...                              ...   \n",
       "367    KALABURAGI       Kalaburagi                            14621   \n",
       "368       YADGIRI           Yadgir                             2673   \n",
       "369       RAICHUR          Raichur                              723   \n",
       "370        KOPPAL           Koppal                           190252   \n",
       "371  VIJAYANAGARA     Vijayanagara                           104045   \n",
       "\n",
       "     Perturbation                  Factor  New Maize Production Values  \\\n",
       "0              10                TotalNPK                         3134   \n",
       "1              10                TotalNPK                        44354   \n",
       "2              10                TotalNPK                        10579   \n",
       "3              10                TotalNPK                       278336   \n",
       "4              10                TotalNPK                       608541   \n",
       "..            ...                     ...                          ...   \n",
       "367           -20  AreaUnderCereals_Maize                        13684   \n",
       "368           -20  AreaUnderCereals_Maize                         2331   \n",
       "369           -20  AreaUnderCereals_Maize                         -201   \n",
       "370           -20  AreaUnderCereals_Maize                       173103   \n",
       "371           -20  AreaUnderCereals_Maize                        86305   \n",
       "\n",
       "     Percentage Change  Change  \n",
       "0             8.857242     255  \n",
       "1             4.158937    1771  \n",
       "2             1.234450     129  \n",
       "3             2.338818    6361  \n",
       "4             2.526687   14997  \n",
       "..                 ...     ...  \n",
       "367          -6.408590    -937  \n",
       "368         -12.794613    -342  \n",
       "369        -127.800830    -924  \n",
       "370          -9.013834  -17149  \n",
       "371         -17.050315  -17740  \n",
       "\n",
       "[372 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perturbations and factor mapping\n",
    "crop = 'Maize'\n",
    "perturbations = [10, -10, 20, -20]\n",
    "factor_mapping = {\n",
    "    'NPK': 'TotalNPK',\n",
    "    'Cattle': 'LivestockCensus_2012_Cattle',\n",
    "    'MArea': 'AreaUnderCereals_Maize',\n",
    "}\n",
    "\n",
    "# Initialize an empty DataFrame to store the results\n",
    "Maize_Diff_Impact = pd.DataFrame()\n",
    "\n",
    "# Loop over each perturbation and factor\n",
    "for perturbation in perturbations:\n",
    "    for factor, mapped_factor in factor_mapping.items():\n",
    "        # Call the function\n",
    "        Change, impact, New_Production = calculate_Change_Impact(Working_Directory, crop, perturbation, factor)\n",
    "        District = Working_Directory['District']\n",
    "        KDG_District = Working_Directory['KDG_District']\n",
    "        Orig = Working_Directory[f'{crop}_Production']\n",
    "        \n",
    "        # Create a DataFrame with the results\n",
    "        temp_df = pd.DataFrame({\n",
    "            'District' : District,\n",
    "            'KDG_District' : KDG_District,\n",
    "            'Original Maize Production Value' : Orig,\n",
    "            'Perturbation': perturbation,\n",
    "            'Factor': mapped_factor,\n",
    "            'New Maize Production Values': New_Production,\n",
    "            'Percentage Change': impact,\n",
    "            'Change': Change\n",
    "        })\n",
    "        \n",
    "        # Append the results to the final DataFrame\n",
    "        Maize_Diff_Impact = pd.concat([Maize_Diff_Impact, temp_df], ignore_index=True)\n",
    "\n",
    "# Display or save the final DataFrame\n",
    "Maize_Diff_Impact.to_csv('DIA\\Maize_Diff_Impact.csv',index=False)\n",
    "Maize_Diff_Impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>District</th>\n",
       "      <th>KDG_District</th>\n",
       "      <th>Original Maize Production Value</th>\n",
       "      <th>Perturbation</th>\n",
       "      <th>Factor</th>\n",
       "      <th>New Maize Production Values</th>\n",
       "      <th>Percentage Change</th>\n",
       "      <th>Change</th>\n",
       "      <th>Initial Stress</th>\n",
       "      <th>Initial Stability</th>\n",
       "      <th>Stress</th>\n",
       "      <th>Stability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>Bengaluru Urban</td>\n",
       "      <td>2879</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>3134</td>\n",
       "      <td>8.857242</td>\n",
       "      <td>255</td>\n",
       "      <td>0.039824</td>\n",
       "      <td>0.960176</td>\n",
       "      <td>0.039985</td>\n",
       "      <td>0.960015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>Bengaluru Rural</td>\n",
       "      <td>42583</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>44354</td>\n",
       "      <td>4.158937</td>\n",
       "      <td>1771</td>\n",
       "      <td>0.008582</td>\n",
       "      <td>0.991418</td>\n",
       "      <td>0.009014</td>\n",
       "      <td>0.990986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>Ramanagara</td>\n",
       "      <td>10450</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>10579</td>\n",
       "      <td>1.234450</td>\n",
       "      <td>129</td>\n",
       "      <td>0.076609</td>\n",
       "      <td>0.923391</td>\n",
       "      <td>0.077377</td>\n",
       "      <td>0.922623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>Chitradurga</td>\n",
       "      <td>271975</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>278336</td>\n",
       "      <td>2.338818</td>\n",
       "      <td>6361</td>\n",
       "      <td>0.144899</td>\n",
       "      <td>0.855101</td>\n",
       "      <td>0.141539</td>\n",
       "      <td>0.858461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>Davanagere</td>\n",
       "      <td>593544</td>\n",
       "      <td>10</td>\n",
       "      <td>TotalNPK</td>\n",
       "      <td>608541</td>\n",
       "      <td>2.526687</td>\n",
       "      <td>14997</td>\n",
       "      <td>0.668253</td>\n",
       "      <td>0.331747</td>\n",
       "      <td>0.665559</td>\n",
       "      <td>0.334441</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>Kalaburagi</td>\n",
       "      <td>14621</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>13684</td>\n",
       "      <td>-6.408590</td>\n",
       "      <td>-937</td>\n",
       "      <td>0.105540</td>\n",
       "      <td>0.894460</td>\n",
       "      <td>0.110013</td>\n",
       "      <td>0.889987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>Yadgir</td>\n",
       "      <td>2673</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>2331</td>\n",
       "      <td>-12.794613</td>\n",
       "      <td>-342</td>\n",
       "      <td>0.131219</td>\n",
       "      <td>0.868781</td>\n",
       "      <td>0.136897</td>\n",
       "      <td>0.863103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>Raichur</td>\n",
       "      <td>723</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>-201</td>\n",
       "      <td>-127.800830</td>\n",
       "      <td>-924</td>\n",
       "      <td>0.259924</td>\n",
       "      <td>0.740076</td>\n",
       "      <td>0.267152</td>\n",
       "      <td>0.732848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>Koppal</td>\n",
       "      <td>190252</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>173103</td>\n",
       "      <td>-9.013834</td>\n",
       "      <td>-17149</td>\n",
       "      <td>0.118701</td>\n",
       "      <td>0.881299</td>\n",
       "      <td>0.124362</td>\n",
       "      <td>0.875638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>Vijayanagara</td>\n",
       "      <td>104045</td>\n",
       "      <td>-20</td>\n",
       "      <td>AreaUnderCereals_Maize</td>\n",
       "      <td>86305</td>\n",
       "      <td>-17.050315</td>\n",
       "      <td>-17740</td>\n",
       "      <td>0.305372</td>\n",
       "      <td>0.694628</td>\n",
       "      <td>0.310649</td>\n",
       "      <td>0.689351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>372 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "         District     KDG_District  Original Maize Production Value  \\\n",
       "0       BENGALURU  Bengaluru Urban                             2879   \n",
       "1    BENGALURU(R)  Bengaluru Rural                            42583   \n",
       "2      RAMANAGARA       Ramanagara                            10450   \n",
       "3     CHITRADURGA      Chitradurga                           271975   \n",
       "4      DAVANAGERE       Davanagere                           593544   \n",
       "..            ...              ...                              ...   \n",
       "367    KALABURAGI       Kalaburagi                            14621   \n",
       "368       YADGIRI           Yadgir                             2673   \n",
       "369       RAICHUR          Raichur                              723   \n",
       "370        KOPPAL           Koppal                           190252   \n",
       "371  VIJAYANAGARA     Vijayanagara                           104045   \n",
       "\n",
       "     Perturbation                  Factor  New Maize Production Values  \\\n",
       "0              10                TotalNPK                         3134   \n",
       "1              10                TotalNPK                        44354   \n",
       "2              10                TotalNPK                        10579   \n",
       "3              10                TotalNPK                       278336   \n",
       "4              10                TotalNPK                       608541   \n",
       "..            ...                     ...                          ...   \n",
       "367           -20  AreaUnderCereals_Maize                        13684   \n",
       "368           -20  AreaUnderCereals_Maize                         2331   \n",
       "369           -20  AreaUnderCereals_Maize                         -201   \n",
       "370           -20  AreaUnderCereals_Maize                       173103   \n",
       "371           -20  AreaUnderCereals_Maize                        86305   \n",
       "\n",
       "     Percentage Change  Change  Initial Stress  Initial Stability    Stress  \\\n",
       "0             8.857242     255        0.039824           0.960176  0.039985   \n",
       "1             4.158937    1771        0.008582           0.991418  0.009014   \n",
       "2             1.234450     129        0.076609           0.923391  0.077377   \n",
       "3             2.338818    6361        0.144899           0.855101  0.141539   \n",
       "4             2.526687   14997        0.668253           0.331747  0.665559   \n",
       "..                 ...     ...             ...                ...       ...   \n",
       "367          -6.408590    -937        0.105540           0.894460  0.110013   \n",
       "368         -12.794613    -342        0.131219           0.868781  0.136897   \n",
       "369        -127.800830    -924        0.259924           0.740076  0.267152   \n",
       "370          -9.013834  -17149        0.118701           0.881299  0.124362   \n",
       "371         -17.050315  -17740        0.305372           0.694628  0.310649   \n",
       "\n",
       "     Stability  \n",
       "0     0.960015  \n",
       "1     0.990986  \n",
       "2     0.922623  \n",
       "3     0.858461  \n",
       "4     0.334441  \n",
       "..         ...  \n",
       "367   0.889987  \n",
       "368   0.863103  \n",
       "369   0.732848  \n",
       "370   0.875638  \n",
       "371   0.689351  \n",
       "\n",
       "[372 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perturbations and factor mapping\n",
    "crop = 'Maize'\n",
    "perturbations = [10, -10, 20, -20]\n",
    "factor_mapping = {\n",
    "    'NPK': 'TotalNPK',\n",
    "    'Cattle': 'LivestockCensus_2012_Cattle',\n",
    "    'MArea': 'AreaUnderCereals_Maize',\n",
    "}\n",
    "\n",
    "# Initialize an empty DataFrame to store the results\n",
    "Maize_1D_Diff_Impact = pd.DataFrame()\n",
    "\n",
    "Name = 'Maize_Production'\n",
    "\n",
    "Capability_list = normalize_list(Working_Directory[f'{Name}'])\n",
    "init_stress, init_stability = calculate_stress_stability(Capability_list, adj_matrix_np)\n",
    "\n",
    "# Loop over each perturbation and factor\n",
    "for perturbation in perturbations:\n",
    "    for factor, mapped_factor in factor_mapping.items():\n",
    "        # Call the function\n",
    "        Change, impact, New_Production = calculate_Change_Impact(Working_Directory, crop, perturbation, factor)\n",
    "        stability, stress, impact, stability_column_name, stress_column_name, impact_column_name = calculate_metrics(Working_Directory, crop, perturbation, factor)\n",
    "        \n",
    "        District = Working_Directory['District']\n",
    "        KDG_District = Working_Directory['KDG_District']\n",
    "        Orig = Working_Directory[f'{crop}_Production']\n",
    "        \n",
    "        temp_df = pd.DataFrame({\n",
    "            'District' : District,\n",
    "            'KDG_District' : KDG_District,\n",
    "            'Original Maize Production Value' : Orig,\n",
    "            'Perturbation': perturbation,\n",
    "            'Factor': mapped_factor,\n",
    "            'New Maize Production Values': New_Production,\n",
    "            'Percentage Change': impact,\n",
    "            'Change': Change,\n",
    "            'Initial Stress': init_stress,\n",
    "            'Initial Stability': init_stability,\n",
    "            'Stress': stress,\n",
    "            'Stability': stability\n",
    "        })\n",
    "        \n",
    "        # Append the results to the final DataFrame\n",
    "        Maize_1D_Diff_Impact = pd.concat([Maize_1D_Diff_Impact, temp_df], ignore_index=True)\n",
    "\n",
    "# Display or save the final DataFrame\n",
    "Maize_1D_Diff_Impact.to_csv('1D\\Maize_1D_Diff_Impact.csv',index=False)\n",
    "Maize_1D_Diff_Impact"
   ]
  },
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   "source": [
    "import plotly.express as px\n",
    "\n",
    "# Visualization function\n",
    "def Calculate_and_visualize_1D(Working_Directory, crop, perturbation, what):\n",
    "    calculate_and_visualize(perturbation, crop, Working_Directory, what, False)\n",
    "    stability, stress, impact, stability_column_name, stress_column_name, impact_column_name = calculate_metrics(Working_Directory, crop, perturbation, what)\n",
    "    \n",
    "    # Add metrics to Working_Directory DataFrame\n",
    "    Working_Directory[stability_column_name] = stability\n",
    "    Working_Directory[stress_column_name] = stress\n",
    "    Working_Directory[impact_column_name] = impact\n",
    "    \n",
    "    # Prepare data for Plotly\n",
    "    for_plots = Working_Directory[[stability_column_name, impact_column_name, 'Abbreviation']].copy()\n",
    "    for_plots.rename(columns={stability_column_name: 'stability', impact_column_name: 'Impact'}, inplace=True)\n",
    "\n",
    "    # Create scatter plot with Plotly\n",
    "    fig_increase = px.scatter(\n",
    "        for_plots,\n",
    "        x='stability',\n",
    "        y='Impact',\n",
    "        title=f'Impact vs Stability ({crop} +{perturbation}% {what})',\n",
    "        labels={'stability': 'Stability', 'Impact': 'Impact'},\n",
    "        text='Abbreviation',\n",
    "        size_max=8,\n",
    "        width=800,\n",
    "        height=500\n",
    "    )\n",
    "\n",
    "    # Update hover labels\n",
    "    fig_increase.update_traces(\n",
    "        hoverlabel=dict(bgcolor='grey', font=dict(color='white')),\n",
    "        textposition='top center'\n",
    "    )\n",
    "\n",
    "    # Calculate and add average lines\n",
    "    avg_x_increase = for_plots['stability'].mean()\n",
    "    avg_y_increase = for_plots['Impact'].mean()\n",
    "\n",
    "    fig_increase.add_shape(\n",
    "        type='line',\n",
    "        x0=min(for_plots['stability']),\n",
    "        x1=max(for_plots['stability']),\n",
    "        y0=avg_y_increase,\n",
    "        y1=avg_y_increase,\n",
    "        line=dict(color='blue', width=2, dash='dash')\n",
    "    )\n",
    "    fig_increase.add_shape(\n",
    "        type='line',\n",
    "        x0=avg_x_increase,\n",
    "        x1=avg_x_increase,\n",
    "        y0=min(for_plots['Impact']),\n",
    "        y1=max(for_plots['Impact']),\n",
    "        line=dict(color='red', width=2, dash='dash')\n",
    "    )\n",
    "\n",
    "    # Add annotations for average lines\n",
    "    fig_increase.add_annotation(\n",
    "        x=avg_x_increase,\n",
    "        y=max(for_plots['Impact']),\n",
    "        text='Avg Stability',\n",
    "        showarrow=False,\n",
    "        font=dict(color='red')\n",
    "    )\n",
    "    fig_increase.add_annotation(\n",
    "        x=max(for_plots['stability']),\n",
    "        y=avg_y_increase,\n",
    "        text='Avg Impact',\n",
    "        showarrow=False,\n",
    "        font=dict(color='blue')\n",
    "    )\n",
    "\n",
    "    # Add tooltips\n",
    "    fig_increase.update_traces(\n",
    "        hoverinfo='text+name',\n",
    "        hovertext=[\"Impact: \" + str(y) +\n",
    "                   f\"<br>Stability: {x}\"\n",
    "                   for x, y in zip(for_plots['stability'], for_plots['Impact'])]\n",
    "    )\n",
    "\n",
    "    # Show the plot\n",
    "    fig_increase.show()\n",
    "\n",
    "# Example usage\n",
    "crop = 'Maize'\n",
    "perturbation = 20\n",
    "what = 'NPK'\n",
    "Calculate_and_visualize_1D(Working_Directory, crop, perturbation, what)\n"
   ]
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