07_plotting_full_scinet_rf_output_zones.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "517c45bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import ast\n",
    "import json\n",
    "import joblib\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8db8836-36eb-440c-b498-21289c28a083",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "210aa799",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dir = \"HPC_code/rf_model_output_zones2/results\"  # Directory where the results files are stored\n",
    "results_files = os.listdir(results_dir)  # List all the files in the directory\n",
    "\n",
    "# Initialize an empty list to store the results\n",
    "results_list = []\n",
    "\n",
    "for file in results_files:\n",
    "    # Construct the full path to the file\n",
    "    file_path = os.path.join(results_dir, file)\n",
    "\n",
    "    # Open the file and load the results into a dictionary\n",
    "    # with open(file_path, 'r') as f:\n",
    "    #     result_dict = json.load(f)\n",
    "\n",
    "    # Read file contents \n",
    "    with open(file_path, 'r') as f:\n",
    "        file_contents = f.read()\n",
    "\n",
    "    if not file_contents.strip():\n",
    "        print(f\"Skipping empty file: {file_path}\")\n",
    "        continue\n",
    "    # Convert string to dictionary\n",
    "    try:\n",
    "        result_dict = ast.literal_eval(file_contents)\n",
    "    except SyntaxError as e:\n",
    "        print(f\"Error parsing file: {file_path}\")\n",
    "        print(e)\n",
    "\n",
    "    # Split the filename at the underscore and extract the relevant parts\n",
    "    file_parts = file.split(\"_\")\n",
    "    result_dict['field'] = file_parts[0]\n",
    "    result_dict['year'] = file_parts[1]\n",
    "    result_dict['soil_test'] = file_parts[2]\n",
    "    result_dict['phosphorus_treatment'] = file_parts[3].split('.')[0]\n",
    "\n",
    "    # Add the dictionary to the list\n",
    "    results_list.append(result_dict)\n",
    "\n",
    "# Load the results into a pandas DataFrame\n",
    "df = pd.DataFrame(results_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e4c9775",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 9)\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>model_name</th>\n",
       "      <th>num_input_params</th>\n",
       "      <th>r2</th>\n",
       "      <th>mse</th>\n",
       "      <th>features_importances</th>\n",
       "      <th>field</th>\n",
       "      <th>year</th>\n",
       "      <th>soil_test</th>\n",
       "      <th>phosphorus_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>../rf_model_output_zones2/models/16A_2018_H3A_...</td>\n",
       "      <td>22</td>\n",
       "      <td>0.458842</td>\n",
       "      <td>13.206981</td>\n",
       "      <td>{'H3A_Al_0_2': 0.05056962096199954, 'H3A_Ca_0_...</td>\n",
       "      <td>16A</td>\n",
       "      <td>2018</td>\n",
       "      <td>H3A</td>\n",
       "      <td>np</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>../rf_model_output_zones2/models/16A_2018_H3A_...</td>\n",
       "      <td>23</td>\n",
       "      <td>0.623655</td>\n",
       "      <td>9.184711</td>\n",
       "      <td>{'H3A_Al_0_2': 0.046355671913939564, 'H3A_Ca_0...</td>\n",
       "      <td>16A</td>\n",
       "      <td>2018</td>\n",
       "      <td>H3A</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>../rf_model_output_zones2/models/16A_2018_M3_n...</td>\n",
       "      <td>22</td>\n",
       "      <td>0.457693</td>\n",
       "      <td>13.235003</td>\n",
       "      <td>{'M3_Al_0_2': 0.0533344201553255, 'M3_Ca_0_2':...</td>\n",
       "      <td>16A</td>\n",
       "      <td>2018</td>\n",
       "      <td>M3</td>\n",
       "      <td>np</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>../rf_model_output_zones2/models/16A_2018_M3_p...</td>\n",
       "      <td>23</td>\n",
       "      <td>0.630215</td>\n",
       "      <td>9.024602</td>\n",
       "      <td>{'M3_Al_0_2': 0.0481940154254916, 'M3_Ca_0_2':...</td>\n",
       "      <td>16A</td>\n",
       "      <td>2018</td>\n",
       "      <td>M3</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>../rf_model_output_zones2/models/16A_2018_Ols_...</td>\n",
       "      <td>22</td>\n",
       "      <td>0.463568</td>\n",
       "      <td>13.091637</td>\n",
       "      <td>{'Ols_Al_0_2': 0.044449594485097534, 'Ols_Ca_0...</td>\n",
       "      <td>16A</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ols</td>\n",
       "      <td>np</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          model_name  num_input_params  \\\n",
       "0  ../rf_model_output_zones2/models/16A_2018_H3A_...                22   \n",
       "1  ../rf_model_output_zones2/models/16A_2018_H3A_...                23   \n",
       "2  ../rf_model_output_zones2/models/16A_2018_M3_n...                22   \n",
       "3  ../rf_model_output_zones2/models/16A_2018_M3_p...                23   \n",
       "4  ../rf_model_output_zones2/models/16A_2018_Ols_...                22   \n",
       "\n",
       "         r2        mse                               features_importances  \\\n",
       "0  0.458842  13.206981  {'H3A_Al_0_2': 0.05056962096199954, 'H3A_Ca_0_...   \n",
       "1  0.623655   9.184711  {'H3A_Al_0_2': 0.046355671913939564, 'H3A_Ca_0...   \n",
       "2  0.457693  13.235003  {'M3_Al_0_2': 0.0533344201553255, 'M3_Ca_0_2':...   \n",
       "3  0.630215   9.024602  {'M3_Al_0_2': 0.0481940154254916, 'M3_Ca_0_2':...   \n",
       "4  0.463568  13.091637  {'Ols_Al_0_2': 0.044449594485097534, 'Ols_Ca_0...   \n",
       "\n",
       "  field  year soil_test phosphorus_treatment  \n",
       "0   16A  2018       H3A                   np  \n",
       "1   16A  2018       H3A                    p  \n",
       "2   16A  2018        M3                   np  \n",
       "3   16A  2018        M3                    p  \n",
       "4   16A  2018       Ols                   np  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3b98fb0-b116-44e5-93eb-ab4953550fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H3A_Al_0_2': 0.05056962096199954,\n",
       " 'H3A_Ca_0_2': 0.04351326798600642,\n",
       " 'H3A_Fe_0_2': 0.0560877031101293,\n",
       " 'H3A_K_0_2': 0.045622446331703835,\n",
       " 'H3A_Mg_0_2': 0.03308798241373009,\n",
       " 'H3A_P_0_2': 0.0385561072260487,\n",
       " 'H3A_Al_2_6': 0.09167621274499978,\n",
       " 'H3A_Ca_2_6': 0.04912107954252646,\n",
       " 'H3A_Fe_2_6': 0.10949849036175768,\n",
       " 'H3A_K_2_6': 0.03457363779262982,\n",
       " 'H3A_Mg_2_6': 0.04069028729339548,\n",
       " 'H3A_P_2_6': 0.037951764787007396,\n",
       " 'Zone_1': 0.027645157322206126,\n",
       " 'Zone_2': 0.0,\n",
       " 'Zone_3': 8.832492730233146e-05,\n",
       " 'Zone_4': 0.02060378578459155}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copy dataframe \n",
    "df2 = df.copy()\n",
    "\n",
    "# Keys to remove\n",
    "keys_to_remove = ['elevation', 'slope', 'aspect', 'TOC_0_2', 'TC_0_2', 'IC_0_2', \n",
    "                  'TN_0_2', 'TOC_2_6', 'TC_2_6', 'IC_2_6', 'TN_2_6','PhosphorusTreatment']\n",
    "\n",
    "# Remove keys  \n",
    "for i, row in df2.iterrows():\n",
    "    for key in keys_to_remove:\n",
    "        if key in row['features_importances']:\n",
    "            del row['features_importances'][key]\n",
    "\n",
    "# Sum importances into new list\n",
    "summed_importances = []\n",
    "for i, row in df2.iterrows():\n",
    "    summed_importances.append(sum(row['features_importances'].values()))\n",
    "\n",
    "# Create new column    \n",
    "df2['soil_test_importances'] = summed_importances\n",
    "\n",
    "# Confirm keys were removed\n",
    "df2['features_importances'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "075d477c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape your data\n",
    "pivot_df = df.pivot_table(index=['field', 'year', 'soil_test'], columns='phosphorus_treatment', values='r2')\n",
    "\n",
    "# Subtract the 'np' values from the 'p' values\n",
    "pivot_df['r2_diff'] = pivot_df['p'] - pivot_df['np']\n",
    "\n",
    "# Reset the index to return the dataframe to a similar format as before\n",
    "pivot_df = pivot_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b6a527c-4fc7-4d21-bb8c-a07d9a8ca3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(15, 12), sharex=True)\n",
    "\n",
    "# Plot 1 \n",
    "sns.barplot(ax=ax1, data=df, x='field', y='r2', hue='soil_test', palette='rainbow')\n",
    "ax1.set_title('R2 values by Field and Soil Test')\n",
    "ax1.set_ylabel('R2')\n",
    "ax1.legend(title='Soil Test',bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "ax1.set_xlabel('')\n",
    "ax1.text(0, 1.03, 'A', transform=ax1.transAxes, \n",
    "       fontsize=14, fontweight='bold')\n",
    "\n",
    "# Plot 2\n",
    "sns.barplot(ax=ax2, data=df2, x='field', y='soil_test_importances', hue='soil_test', palette='rainbow')  \n",
    "ax2.set_title('Sum of importances for soil test features by Field and Soil Test')\n",
    "ax2.set_ylabel('Sum of importances')\n",
    "ax2.set_xticks(range(5))\n",
    "# ax2.set_xticklabels(range(1,6))\n",
    "ax2.legend(title='Soil Test',bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "ax2.set_xlabel('Field')\n",
    "ax2.text(0, 1.03, 'B', transform=ax2.transAxes, \n",
    "       fontsize=14, fontweight='bold')\n",
    "\n",
    "# Adjust layout \n",
    "fig.tight_layout()\n",
    "# plt.savefig('Out/Figures/Full_RF_R2.jpg', bbox_inches = 'tight', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c20c09b-a919-4bc6-bcc4-9ab539681235",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dir = \"HPC_code/rf_model_output_zones2/results\"\n",
    "models_dir = \"HPC_code/rf_model_output_zones2/models\"\n",
    "X_dir = \"HPC_code/rf_model_output_zones2/X\"\n",
    "\n",
    "# Load all results dicts\n",
    "results = []\n",
    "for file_path in os.listdir(results_dir):\n",
    "    # print(file_path)\n",
    "    with open(os.path.join(results_dir, file_path)) as f:\n",
    "        results.append(json.load(f))\n",
    "\n",
    "for result in results:\n",
    "\n",
    "  # Extract model path\n",
    "  model_path = result['model_name'].split('/')[-1]\n",
    "  \n",
    "  # Load model\n",
    "  model = joblib.load(os.path.join(models_dir, model_path))\n",
    "  result['model'] = model\n",
    "\n",
    "  # Construct X_train path from model path\n",
    "  x_train_path = model_path.replace('models', 'X').replace('joblib', 'csv')\n",
    "\n",
    "  # Load X_train\n",
    "  x_train = pd.read_csv(os.path.join(X_dir, x_train_path))\n",
    "  result['X_train'] = x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0cdfe95-bf46-4bcb-879a-558f8063a4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6993c6da-d204-43e8-b330-7939f9d9e5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "43f24a3d-c943-49e1-aa07-3c5a7a8d423c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract feature names\n",
    "feature_names = set() \n",
    "for result in results:\n",
    "    # print(result['model_name'])\n",
    "    feature_names.update(result['features_importances'].keys())\n",
    "\n",
    "# Initialize dicts\n",
    "avg_importances = {f: 0 for f in feature_names}\n",
    "feature_counts = {f: 0 for f in feature_names}\n",
    "\n",
    "for result in results:\n",
    "\n",
    "  for f, imp in result['features_importances'].items():\n",
    "    \n",
    "    # Increment count for this feature\n",
    "    feature_counts[f] += 1\n",
    "    \n",
    "    # Add importance contribution\n",
    "    avg_importances[f] += imp\n",
    "\n",
    "# Calculate average based on counts \n",
    "for f in avg_importances:\n",
    "  avg_importances[f] /= feature_counts[f] \n",
    "\n",
    "# Plot\n",
    "# Sort the importances\n",
    "sorted_importances = dict(sorted(avg_importances.items(), key=lambda x: x[1]))\n",
    "\n",
    "# Get sorted keys and values \n",
    "feat_names = list(sorted_importances.keys())\n",
    "importances = list(sorted_importances.values())\n",
    "\n",
    "# Dictionary of colors \n",
    "color_map = {'H3A': 'C0', 'DI': 'C1', 'Ols': 'C2', 'M3': 'C3', 'Other': 'C4'} \n",
    "\n",
    "# Get colors for each bar\n",
    "colors = []\n",
    "for feat in feat_names:\n",
    "    substring = feat.split('_')[0]\n",
    "    if substring in color_map:\n",
    "        colors.append(color_map[substring])\n",
    "    else:\n",
    "        colors.append(color_map['Other'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 6))\n",
    "\n",
    "# Plot bars\n",
    "plt.bar(range(len(importances)), importances, color=colors)\n",
    "\n",
    "# Set xtick labels  \n",
    "plt.xticks(range(len(feat_names)), feat_names, rotation=90)\n",
    "\n",
    "# Legend\n",
    "legend_elements = [mpatches.Patch(facecolor=color_map[x], label=x) for x in color_map]\n",
    "plt.legend(handles=legend_elements)\n",
    "plt.savefig('Output/Figures/All_feature_importances.jpg', bbox_inches = 'tight', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb21f94b-662b-40b2-8428-890f4a5a8c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "from sklearn.inspection import partial_dependence\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33818113-13d3-45d6-8733-a2c2a188f9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.0'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e518e1b-521e-4fb7-9b58-4afef175525c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_data():\n",
    "    plotting_df = pd.DataFrame()\n",
    "    temp_dfs = []\n",
    "\n",
    "    # Filter df \n",
    "    df_odd = df[df.index % 2 != 0]\n",
    "\n",
    "    # Iterate through fields\n",
    "    for i, field in enumerate(df_odd['field'].unique()):\n",
    "        # Filter to rows for this field\n",
    "        field_df = df_odd[(df_odd['field'] == field)]\n",
    "      \n",
    "        # Plot on field's axis\n",
    "        for j in field_df.index:\n",
    "            temp_df = pd.DataFrame()\n",
    "\n",
    "            # Generate partial dependence data\n",
    "            pdp_result = partial_dependence(results[j]['model'], \n",
    "                                            results[j]['X_train'], \n",
    "                                            ['PhosphorusTreatment'],\n",
    "                                            kind=\"average\")\n",
    "            \n",
    "            # Unpacking the results\n",
    "            pdp_values = pdp_result['average'][0]  # Average PD values\n",
    "            pdp_axes = pdp_result['values'][0]     # Axis values for the features\n",
    "    \n",
    "            # Extract data\n",
    "            x = pdp_axes\n",
    "            y = pdp_values\n",
    "            year = df_odd.loc[j, 'year']\n",
    "            temp_df['x'] = x.tolist()\n",
    "            temp_df['y'] = y.tolist()\n",
    "            temp_df['y'] = [y * 25.4016 for y in temp_df['y']]  # Converting from bu/acre to kg/ha\n",
    "            temp_df['year'] = year\n",
    "            temp_df['field'] = field\n",
    "            temp_dfs.append(temp_df)\n",
    "    \n",
    "    plotting_df = pd.concat(temp_dfs)\n",
    "\n",
    "    return plotting_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "657ad89d-6336-4613-b972-fcecde7e5f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "plotting_df = extract_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "31580ae4-94cd-451c-879a-2e1abe86340b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "179dc420-8cd1-4011-af72-57beb9165537",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51721012-1cf1-4bf8-8d72-b53f0b898813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Reshape your data\n",
    "pivot_df = df.pivot_table(index=['field', 'year', 'soil_test'], columns='phosphorus_treatment', values='r2')\n",
    "\n",
    "# Subtract the 'np' values from the 'p' values\n",
    "pivot_df['r2_diff'] = pivot_df['p'] - pivot_df['np']\n",
    "\n",
    "# Reset the index to return the dataframe to a similar format as before\n",
    "pivot_df = pivot_df.reset_index()\n",
    "\n",
    "# Create figure \n",
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "# Define subplot grid\n",
    "gs = gridspec.GridSpec(nrows=2, ncols=5) \n",
    "\n",
    "# Top subplot \n",
    "ax0 = fig.add_subplot(gs[0, :])\n",
    "\n",
    "# Bottom subplots\n",
    "ax1 = fig.add_subplot(gs[1,0]) \n",
    "ax2 = fig.add_subplot(gs[1,1])\n",
    "ax3 = fig.add_subplot(gs[1,2])\n",
    "ax4 = fig.add_subplot(gs[1,3])\n",
    "ax5 = fig.add_subplot(gs[1,4])\n",
    "\n",
    "sns.barplot(data=pivot_df, x='field', y='r2_diff', hue='year', palette='rainbow', ax = ax0)\n",
    "ax0.set_title(f'Difference in r2 values by Field and Year for all soil tests')\n",
    "ax0.set_ylabel('R2 difference (P - No P)')\n",
    "ax0.set_xlabel('Field')\n",
    "ax0.legend(title='Year')\n",
    "# ax0.set_xticks(range(5), range(1, 6)) \n",
    "\n",
    "ax0.text(0, 1.03, 'A', transform=ax0.transAxes, \n",
    "      fontsize=14, fontweight='bold')\n",
    "\n",
    "fields = plotting_df['field'].unique()\n",
    "\n",
    "# Plot each field separately \n",
    "for i, ax in enumerate([ax1, ax2, ax3, ax4, ax5]):\n",
    "\n",
    "  # Filter data \n",
    "  field_df = plotting_df[plotting_df['field'] == fields[i]].copy()\n",
    "  # Converting to metric\n",
    "  field_df['x_metric'] = field_df['x']*(0.45359/0.0404686) * 0.436 # Converting to P from P2O5\n",
    "\n",
    "  # Plot field's data\n",
    "  sns.lineplot(data=field_df, x='x_metric', y='y', hue='year', palette='rainbow', ax=ax)\n",
    "  ax.set_xlabel('P Treatment (kg/ha P)')\n",
    "  ax.set_title(fields[i])\n",
    "  ax.set_ylim(0, 4500)\n",
    "  ax.set_xlim(0, 200)\n",
    "  ax.set_ylabel('')\n",
    "  if i == 3:\n",
    "    ax.legend(title='Year')\n",
    "  else: \n",
    "    ax.get_legend().remove()\n",
    "# Set title\n",
    "ax1.set_ylabel('Yield (kg/ha)')\n",
    "ax1.text(0, 1.03, 'B', transform=ax1.transAxes, \n",
    "       fontsize=14, fontweight='bold')\n",
    "\n",
    "# Finalize plot  \n",
    "plt.tight_layout()\n",
    "plt.savefig('Output/Figures/Combined_Full_RF_R2Diff_P_PDP.jpg', bbox_inches = 'tight', dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ae7bc9-b8c0-4c50-a5ce-9b9555fbef1b",
   "metadata": {},
   "source": [
    "# Calculating recommended P application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7432fe52-7f41-4747-b8ca-f24a4a7df584",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48f43ded-0576-41dd-9325-e8e7ddedc5c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Field  Year  Recommended_P\n",
      "0    16A  2018      48.959627\n",
      "1    16A  2019      94.294648\n",
      "2    16A  2020      47.586682\n",
      "3    16A  2022       0.000000\n",
      "4   6-12  2018       0.000000\n",
      "5   6-12  2019       0.000000\n",
      "6   6-12  2020      57.992149\n",
      "7   6-12  2022      53.477631\n",
      "8   SW16  2018       0.000000\n",
      "9   SW16  2019      39.834805\n",
      "10  SW16  2020      78.244612\n",
      "11  SW16  2022       0.000000\n",
      "12   Y10  2018       0.000000\n",
      "13   Y10  2019       0.000000\n",
      "14   Y10  2020      33.869520\n",
      "15   Y10  2022       0.000000\n",
      "16    Y8  2018       0.000000\n",
      "17    Y8  2019       0.000000\n",
      "18    Y8  2020       0.000000\n",
      "19    Y8  2022       0.000000\n"
     ]
    }
   ],
   "source": [
    "# Define function to calculate recommended P application\n",
    "def calculate_recommended_P(a, b, Y_max, X_max):\n",
    "    if b > 0:\n",
    "        P_recommended = (0.95 * Y_max - a) / b\n",
    "        P_recommended = max(P_recommended, 0)  # Set to 0 if negative\n",
    "        P_recommended = min(P_recommended, X_max)  # Set to X_max if above\n",
    "    else:\n",
    "        P_recommended = 0  # Set to 0 if slope is negative\n",
    "    return P_recommended\n",
    "\n",
    "# Initialize a list to store recommended P application rates\n",
    "recommended_P_list = []\n",
    "\n",
    "# Loop over each field and each year\n",
    "for field in fields:\n",
    "    for year in plotting_df['year'].unique():\n",
    "        # Filter data for the specific field and year\n",
    "        field_year_df = plotting_df[(plotting_df['field'] == field) & (plotting_df['year'] == year)].copy()\n",
    "        \n",
    "        # Convert units to metric\n",
    "        field_year_df.loc[:, 'x_metric'] = field_year_df['x']*0.45359/0.0404686 * 0.436\n",
    "\n",
    "        # Extract x (P rate) and y (Yield)\n",
    "        X = field_year_df['x_metric'].values.reshape(-1, 1)\n",
    "        Y = field_year_df['y'].values\n",
    "\n",
    "        # Perform linear regression\n",
    "        model = LinearRegression()\n",
    "        model.fit(X, Y)\n",
    "        \n",
    "        # Extract intercept and slope\n",
    "        a = model.intercept_\n",
    "        b = model.coef_[0]\n",
    "        \n",
    "        # Calculate the recommended P application rate\n",
    "        P_recommended = calculate_recommended_P(a, b, np.max(Y), np.max(X))\n",
    "        \n",
    "        # Append result to list\n",
    "        recommended_P_list.append({'Field': field, 'Year': year, 'Recommended_P': P_recommended})\n",
    "\n",
    "# Convert the list of dictionaries to a DataFrame for easier manipulation and presentation\n",
    "recommended_P_df = pd.DataFrame(recommended_P_list)\n",
    "\n",
    "# Save or print the DataFrame as needed\n",
    "print(recommended_P_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "868b9849-72d9-4b30-978a-579c71e2c56e",
   "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>Recommended_P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Field</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16A</th>\n",
       "      <td>47.710239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6-12</th>\n",
       "      <td>27.867445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW16</th>\n",
       "      <td>29.519854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y10</th>\n",
       "      <td>8.467380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y8</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recommended_P\n",
       "Field               \n",
       "16A        47.710239\n",
       "6-12       27.867445\n",
       "SW16       29.519854\n",
       "Y10         8.467380\n",
       "Y8          0.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_P_df[['Field','Recommended_P']].groupby(['Field']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2f40ec80-98d4-4754-a226-b637eeac423d",
   "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>Field</th>\n",
       "      <th>Year</th>\n",
       "      <th>Recommended_P</th>\n",
       "      <th>Soil_Test_Recommended_P</th>\n",
       "      <th>Percent_Fertilizer_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16A</td>\n",
       "      <td>2018</td>\n",
       "      <td>48.96</td>\n",
       "      <td>49</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16A</td>\n",
       "      <td>2019</td>\n",
       "      <td>94.29</td>\n",
       "      <td>49</td>\n",
       "      <td>192.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16A</td>\n",
       "      <td>2020</td>\n",
       "      <td>47.59</td>\n",
       "      <td>49</td>\n",
       "      <td>97.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16A</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6-12</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6-12</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>57.99</td>\n",
       "      <td>49</td>\n",
       "      <td>118.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6-12</td>\n",
       "      <td>2022</td>\n",
       "      <td>53.48</td>\n",
       "      <td>49</td>\n",
       "      <td>109.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SW16</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SW16</td>\n",
       "      <td>2019</td>\n",
       "      <td>39.83</td>\n",
       "      <td>39</td>\n",
       "      <td>102.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SW16</td>\n",
       "      <td>2020</td>\n",
       "      <td>78.24</td>\n",
       "      <td>39</td>\n",
       "      <td>200.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SW16</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Y10</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Y10</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Y10</td>\n",
       "      <td>2020</td>\n",
       "      <td>33.87</td>\n",
       "      <td>25</td>\n",
       "      <td>135.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Y10</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Y8</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Y8</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Y8</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Y8</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Field  Year  Recommended_P  Soil_Test_Recommended_P  \\\n",
       "0    16A  2018          48.96                       49   \n",
       "1    16A  2019          94.29                       49   \n",
       "2    16A  2020          47.59                       49   \n",
       "3    16A  2022           0.00                       49   \n",
       "4   6-12  2018           0.00                       49   \n",
       "5   6-12  2019           0.00                       49   \n",
       "6   6-12  2020          57.99                       49   \n",
       "7   6-12  2022          53.48                       49   \n",
       "8   SW16  2018           0.00                       39   \n",
       "9   SW16  2019          39.83                       39   \n",
       "10  SW16  2020          78.24                       39   \n",
       "11  SW16  2022           0.00                       39   \n",
       "12   Y10  2018           0.00                       25   \n",
       "13   Y10  2019           0.00                       25   \n",
       "14   Y10  2020          33.87                       25   \n",
       "15   Y10  2022           0.00                       25   \n",
       "16    Y8  2018           0.00                       15   \n",
       "17    Y8  2019           0.00                       15   \n",
       "18    Y8  2020           0.00                       15   \n",
       "19    Y8  2022           0.00                       15   \n",
       "\n",
       "    Percent_Fertilizer_Rate  \n",
       "0                     99.92  \n",
       "1                    192.44  \n",
       "2                     97.12  \n",
       "3                      0.00  \n",
       "4                      0.00  \n",
       "5                      0.00  \n",
       "6                    118.35  \n",
       "7                    109.14  \n",
       "8                      0.00  \n",
       "9                    102.14  \n",
       "10                   200.63  \n",
       "11                     0.00  \n",
       "12                     0.00  \n",
       "13                     0.00  \n",
       "14                   135.48  \n",
       "15                     0.00  \n",
       "16                     0.00  \n",
       "17                     0.00  \n",
       "18                     0.00  \n",
       "19                     0.00  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treatments dictionary with medium treatment value for each field\n",
    "# treatments = {\n",
    "#     \"16A\": {\"Medium\": {\"value\": 22.5}},\n",
    "#     \"6-12\": {\"Medium\": {\"value\": 22.5}},\n",
    "#     \"Y10\": {\"Medium\": {\"value\": 11.3}},\n",
    "#     \"Y8\": {\"Medium\": {\"value\": 6.8}},\n",
    "#     \"SW16\": {\"Medium\": {\"value\": 18}}\n",
    "# }\n",
    "treatments = {\n",
    "    \"16A\": {\"Medium\": {\"value\": 49}},\n",
    "    \"6-12\": {\"Medium\": {\"value\": 49}},\n",
    "    \"Y10\": {\"Medium\": {\"value\": 25}},\n",
    "    \"Y8\": {\"Medium\": {\"value\": 15}},\n",
    "    \"SW16\": {\"Medium\": {\"value\": 39}}\n",
    "}\n",
    "\n",
    "# # Function to convert values to metric units\n",
    "# def convert_to_metric(value):\n",
    "#     return value * (0.45359 / 0.0404686) \n",
    "\n",
    "# # Convert medium treatment values to metric units\n",
    "# for field in treatments:\n",
    "#     treatments[field]['Medium']['value'] = convert_to_metric(treatments[field]['Medium']['value'])\n",
    "\n",
    "# Add new columns to DataFrame\n",
    "recommended_P_df['Soil_Test_Recommended_P'] = recommended_P_df['Field'].map(lambda x: treatments.get(x, {}).get('Medium', {}).get('value', None))\n",
    "recommended_P_df['Percent_Fertilizer_Rate'] = (recommended_P_df['Recommended_P'] / recommended_P_df['Soil_Test_Recommended_P']) * 100\n",
    "recommended_P_df.loc[:, 'Recommended_P'] = recommended_P_df.loc[:, 'Recommended_P']\n",
    "\n",
    "recommended_P_df.to_csv('Output/Tables/Table S1.csv')\n",
    "(round(recommended_P_df, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "83348640-59da-4461-85cd-1ce43bfa178d",
   "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>Percent_Fertilizer_Rate</th>\n",
       "      <th>Recommended_P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Field</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16A</th>\n",
       "      <td>97.367835</td>\n",
       "      <td>47.710239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6-12</th>\n",
       "      <td>56.872336</td>\n",
       "      <td>27.867445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW16</th>\n",
       "      <td>75.691934</td>\n",
       "      <td>29.519854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y10</th>\n",
       "      <td>33.869520</td>\n",
       "      <td>8.467380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Percent_Fertilizer_Rate  Recommended_P\n",
       "Field                                        \n",
       "16A                  97.367835      47.710239\n",
       "6-12                 56.872336      27.867445\n",
       "SW16                 75.691934      29.519854\n",
       "Y10                  33.869520       8.467380\n",
       "Y8                    0.000000       0.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_df = recommended_P_df[['Field','Percent_Fertilizer_Rate']].groupby('Field').mean()\n",
    "output_df ['Recommended_P'] = recommended_P_df[['Field','Recommended_P']].groupby('Field').mean()['Recommended_P']\n",
    "output_df.to_csv('Output/Tables/Table S2.csv')\n",
    "output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "617a2430-b7bd-4764-979b-3c7bde2e816b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient (b): -0.02286271969091123\n",
      "Intercept (a): 1827.1331086335977\n",
      "Mean squared error: 10.115330203337097\n",
      "Coefficient of determination (R2): 0.2051701409842288\n",
      "Recommended P: 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Filter data for field \"Y10\" and year 2022\n",
    "field_year_df = plotting_df[(plotting_df['field'] == 'Y10') & (plotting_df['year'] == '2022')].copy()\n",
    "\n",
    "# Convert units to metric\n",
    "field_year_df['x_metric'] = field_year_df['x']*0.45359/0.0404686\n",
    "\n",
    "# Extract x (P rate in metric units) and y (Yield)\n",
    "X = field_year_df['x_metric'].values.reshape(-1, 1)\n",
    "Y = field_year_df['y'].values\n",
    "\n",
    "# Perform linear regression\n",
    "model = LinearRegression()\n",
    "model.fit(X, Y)\n",
    "\n",
    "# Predict Y based on X\n",
    "Y_pred = model.predict(X)\n",
    "\n",
    "# Calculate and print model diagnostics\n",
    "print(\"Coefficient (b):\", model.coef_[0])\n",
    "print(\"Intercept (a):\", model.intercept_)\n",
    "print(\"Mean squared error:\", mean_squared_error(Y, Y_pred))\n",
    "print(\"Coefficient of determination (R2):\", r2_score(Y, Y_pred))\n",
    "\n",
    "# Calculate recommended P using your formula for this specific case\n",
    "Y_max = np.max(Y)\n",
    "X_max = np.max(X)\n",
    "P_recommended = calculate_recommended_P(model.intercept_, model.coef_[0], Y_max, X_max)\n",
    "print(\"Recommended P:\", P_recommended)\n",
    "\n",
    "# Plot the original data and the linear fit\n",
    "plt.scatter(X, Y, color='black')\n",
    "plt.plot(X, Y_pred, color='blue')\n",
    "plt.xlabel('P rate (metric units)')\n",
    "plt.ylabel('Yield')\n",
    "plt.title('Field Y10 for Year 2022')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36aa8da7-83bd-496e-a042-434624800056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Field  Year  Coefficient\n",
      "0    16A  2018     0.243887\n",
      "1    16A  2019     1.332121\n",
      "2    16A  2020     0.607351\n",
      "3    16A  2022     0.103452\n",
      "4   6-12  2018     0.003296\n",
      "5   6-12  2019     0.151537\n",
      "6   6-12  2020     1.613799\n",
      "7   6-12  2022     0.571641\n",
      "8   SW16  2018     0.045683\n",
      "9   SW16  2019     0.455780\n",
      "10  SW16  2020     1.242091\n",
      "11  SW16  2022     0.313615\n",
      "12   Y10  2018     0.028429\n",
      "13   Y10  2019    -0.000997\n",
      "14   Y10  2020     0.573459\n",
      "15   Y10  2022    -0.022863\n",
      "16    Y8  2018     0.032689\n",
      "17    Y8  2019     0.042466\n",
      "18    Y8  2020     0.823634\n",
      "19    Y8  2022     0.200994\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty list to store the coefficients and associated field and year\n",
    "coeff_list = []\n",
    "\n",
    "# Loop over each unique field and year in the DataFrame\n",
    "for field in plotting_df['field'].unique():\n",
    "    for year in plotting_df['year'].unique():\n",
    "        # Filter data for the specific field and year\n",
    "        field_year_df = plotting_df[(plotting_df['field'] == field) & (plotting_df['year'] == year)].copy()\n",
    "        \n",
    "        # Check if there are enough data points to fit a model\n",
    "        if len(field_year_df) < 2:\n",
    "            continue\n",
    "        \n",
    "        # Convert units to metric\n",
    "        field_year_df['x_metric'] = field_year_df['x']*0.45359/0.0404686  # You may want to issue a copy warning\n",
    "        \n",
    "        # Extract x (P rate in metric units) and y (Yield)\n",
    "        X = field_year_df['x_metric'].values.reshape(-1, 1)\n",
    "        Y = field_year_df['y'].values\n",
    "\n",
    "        # Perform linear regression\n",
    "        model = LinearRegression()\n",
    "        model.fit(X, Y)\n",
    "        \n",
    "        # Extract and store the coefficient (slope)\n",
    "        coeff = model.coef_[0]\n",
    "        coeff_list.append({'Field': field, 'Year': year, 'Coefficient': coeff})\n",
    "\n",
    "# Convert the list of dictionaries to a DataFrame for easier analysis\n",
    "coeff_df = pd.DataFrame(coeff_list)\n",
    "\n",
    "# Save or print the DataFrame as needed\n",
    "print(coeff_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "660324e5-f871-46be-a2ad-ca1b62cf9819",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ca7697cb-0881-4a23-96ef-12cc7c07f812",
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = plotting_df['field'].unique()\n",
    "# Create a list to store the results\n",
    "results = []\n",
    "\n",
    "# Plot each field separately \n",
    "for i, ax in enumerate([ax1, ax2, ax3, ax4, ax5]):\n",
    "\n",
    "  # Filter data \n",
    "  field_df = plotting_df[plotting_df['field'] == fields[i]]\n",
    "\n",
    "  # Iterate through the unique years within the field\n",
    "  for year in field_df['year'].unique():\n",
    "    subset = field_df[field_df['year'] == year]\n",
    "    x = subset['x']\n",
    "    y = subset['y']\n",
    "\n",
    "    # Perform linear regression\n",
    "    slope, intercept, r_value, p_value, std_err = linregress(x, y)\n",
    "\n",
    "    # Append results to list\n",
    "    results.append({\n",
    "        'Field': 'Field ' + str(i + 1),\n",
    "        'Year': year,\n",
    "        'Slope': slope,\n",
    "        'P-value': p_value\n",
    "    })\n",
    "results_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "37776218-8a9f-46fd-b20c-49c22816830b",
   "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>Field</th>\n",
       "      <th>Year</th>\n",
       "      <th>Slope</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Field 1</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.733592</td>\n",
       "      <td>1.345550e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Field 1</td>\n",
       "      <td>2019</td>\n",
       "      <td>14.930998</td>\n",
       "      <td>6.857369e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Field 1</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.807456</td>\n",
       "      <td>1.790309e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Field 1</td>\n",
       "      <td>2022</td>\n",
       "      <td>1.159534</td>\n",
       "      <td>2.560465e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Field 2</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.036945</td>\n",
       "      <td>1.169705e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Field 2</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.698489</td>\n",
       "      <td>7.409077e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Field 2</td>\n",
       "      <td>2020</td>\n",
       "      <td>18.088170</td>\n",
       "      <td>7.525710e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Field 2</td>\n",
       "      <td>2022</td>\n",
       "      <td>6.407203</td>\n",
       "      <td>1.293063e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Field 3</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.512037</td>\n",
       "      <td>1.916493e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Field 3</td>\n",
       "      <td>2019</td>\n",
       "      <td>5.108582</td>\n",
       "      <td>1.484979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Field 3</td>\n",
       "      <td>2020</td>\n",
       "      <td>13.921902</td>\n",
       "      <td>6.016395e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Field 3</td>\n",
       "      <td>2022</td>\n",
       "      <td>3.515135</td>\n",
       "      <td>2.001943e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Field 4</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.318642</td>\n",
       "      <td>5.905898e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Field 4</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.011179</td>\n",
       "      <td>7.275232e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Field 4</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.427577</td>\n",
       "      <td>1.329760e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Field 4</td>\n",
       "      <td>2022</td>\n",
       "      <td>-0.256255</td>\n",
       "      <td>1.392123e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Field 5</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.366396</td>\n",
       "      <td>9.978916e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Field 5</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.475980</td>\n",
       "      <td>8.617671e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Field 5</td>\n",
       "      <td>2020</td>\n",
       "      <td>9.231659</td>\n",
       "      <td>2.336666e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Field 5</td>\n",
       "      <td>2022</td>\n",
       "      <td>2.252829</td>\n",
       "      <td>5.198707e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Field  Year      Slope       P-value\n",
       "0   Field 1  2018   2.733592  1.345550e-03\n",
       "1   Field 1  2019  14.930998  6.857369e-04\n",
       "2   Field 1  2020   6.807456  1.790309e-05\n",
       "3   Field 1  2022   1.159534  2.560465e-08\n",
       "4   Field 2  2018   0.036945  1.169705e-02\n",
       "5   Field 2  2019   1.698489  7.409077e-05\n",
       "6   Field 2  2020  18.088170  7.525710e-06\n",
       "7   Field 2  2022   6.407203  1.293063e-07\n",
       "8   Field 3  2018   0.512037  1.916493e-04\n",
       "9   Field 3  2019   5.108582  1.484979e-08\n",
       "10  Field 3  2020  13.921902  6.016395e-07\n",
       "11  Field 3  2022   3.515135  2.001943e-04\n",
       "12  Field 4  2018   0.318642  5.905898e-07\n",
       "13  Field 4  2019  -0.011179  7.275232e-01\n",
       "14  Field 4  2020   6.427577  1.329760e-06\n",
       "15  Field 4  2022  -0.256255  1.392123e-01\n",
       "16  Field 5  2018   0.366396  9.978916e-03\n",
       "17  Field 5  2019   0.475980  8.617671e-02\n",
       "18  Field 5  2020   9.231659  2.336666e-05\n",
       "19  Field 5  2022   2.252829  5.198707e-03"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "12adf7bc-0e81-4f4c-97ee-313ec329e506",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dcbfcd26-e3b6-4f0f-85ac-0d74899d4499",
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = plotting_df['field'].unique()\n",
    "# Create a DataFrame to store the results\n",
    "comparison_results_df = pd.DataFrame(columns=['Field', 'Year', 'Comparison', 'T-Statistic', 'P-value'])\n",
    "index = 0\n",
    "\n",
    "# Plot each field separately\n",
    "for i, ax in enumerate([ax1, ax2, ax3, ax4, ax5]):\n",
    "\n",
    "    # Filter data for specific field\n",
    "    field_df = plotting_df[plotting_df['field'] == fields[i]]\n",
    "\n",
    "    # Iterate through the unique years within the field\n",
    "    for year in field_df['year'].unique():\n",
    "        subset = field_df[field_df['year'] == year]\n",
    "        subset = subset[subset['x'] != 0]\n",
    "        unique_x_values = subset['x'].unique()\n",
    "\n",
    "        # Check if there are exactly three X values\n",
    "        if len(unique_x_values) != 3:\n",
    "            print(f\"Field {fields[i]}, Year {year} does not have exactly three X values. Skipping.\")\n",
    "            continue\n",
    "\n",
    "        # Extract the groups based on the X values\n",
    "        group_low = subset[subset['x'] == unique_x_values[0]]['y']\n",
    "        group_middle = subset[subset['x'] == unique_x_values[1]]['y']\n",
    "        group_high = subset[subset['x'] == unique_x_values[2]]['y']\n",
    "\n",
    "        # Perform t-tests for pairwise comparisons\n",
    "        for comparison, (group1, group2) in zip(['Low vs Medium', 'Low vs High', 'Medium vs High'], [(group_low, group_middle), (group_low, group_high), (group_middle, group_high)]):\n",
    "            t_statistic, p_value = ttest_ind(group1, group2)\n",
    "\n",
    "            # Append results to DataFrame\n",
    "            comparison_results_df.loc[index] = {\n",
    "                'Field': 'Field ' + str(i + 1),\n",
    "                'Year': year,\n",
    "                'Comparison': comparison,\n",
    "                'T-Statistic': np.abs(t_statistic),\n",
    "                'P-value': p_value\n",
    "            }\n",
    "            index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bce45726-2304-446b-9404-9e415b40f9ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of valid subsets processed: 20\n",
      "No valid recommendations found based on the provided data.\n"
     ]
    }
   ],
   "source": [
    "# Constants\n",
    "COST_OF_FERTILIZER = 1153/1000 # $/ton / kg/ton = $/kg\n",
    "\n",
    "# Calculate the cost for each phosphorus application\n",
    "plotting_df['cost_dollars_ha'] = plotting_df['x'] * 1.12085 * COST_OF_FERTILIZER # lbs/ac * (kg/ha)/(lbs/ac) * $/kg = $/ha\n",
    "\n",
    "# Create a function to compute yield increase per dollar\n",
    "def compute_yield_per_dollar(row):\n",
    "    if row['cost_dollars_ha'] == 0:\n",
    "        return 0\n",
    "    return row['y'] / row['cost_dollars_ha'] # kg/ha / $/ha = kg/$\n",
    "\n",
    "# Apply the function to the dataframe\n",
    "plotting_df['yield_dollars_per_bu'] = plotting_df.apply(compute_yield_per_dollar, axis=1)\n",
    "\n",
    "# Initialize results dictionary\n",
    "results = {}\n",
    "\n",
    "fields = plotting_df['field'].unique()\n",
    "\n",
    "# Diagnostic print to check subsets with valid x values\n",
    "valid_subsets = 0\n",
    "\n",
    "# Iterate through the unique fields\n",
    "for field in fields:\n",
    "    field_df = plotting_df[plotting_df['field'] == field]\n",
    "    for year in field_df['year'].unique():\n",
    "        subset = field_df[(field_df['year'] == year) & (field_df['x'] != 0.0)] # Exclude the 0.0 value\n",
    "        unique_x_values = sorted(subset['x'].unique())\n",
    "        \n",
    "        # If there are exactly three unique x values, then proceed with analysis\n",
    "        if len(unique_x_values) == 3:\n",
    "            valid_subsets += 1\n",
    "\n",
    "# Group by treatment level and compute the mean yield per dollar\n",
    "avg_cost_per_ha = plotting_df.groupby('x')['yield_dollars_per_bu'].mean()\n",
    "\n",
    "# Print the number of valid subsets\n",
    "print(f\"\\nNumber of valid subsets processed: {valid_subsets}\")\n",
    "\n",
    "# Find the best recommendation across all fields and years\n",
    "best_recommendation = max(results, key=results.get, default=None)\n",
    "\n",
    "if best_recommendation:\n",
    "    print(f\"The best fertilizer recommendation overall is: {best_recommendation[2]} for Field {best_recommendation[0]} in Year {best_recommendation[1]}\")\n",
    "    print(\"\\nAverage yield per dollar for each recommendation level across fields and years:\")\n",
    "    for (field, year, rec), avg_yield in results.items():\n",
    "        print(f\"Field {field}, Year {year}, Recommendation {rec}: {avg_yield:.2f}\")\n",
    "else:\n",
    "    print(\"No valid recommendations found based on the provided data.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5cae680f-aa9e-44b2-be7e-f882fc4698c1",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>year</th>\n",
       "      <th>field</th>\n",
       "      <th>cost_dollars_ha</th>\n",
       "      <th>yield_dollars_per_bu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>861.928344</td>\n",
       "      <td>2018</td>\n",
       "      <td>16A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.3</td>\n",
       "      <td>952.586246</td>\n",
       "      <td>2018</td>\n",
       "      <td>16A</td>\n",
       "      <td>14.603443</td>\n",
       "      <td>65.230253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.5</td>\n",
       "      <td>958.623986</td>\n",
       "      <td>2018</td>\n",
       "      <td>16A</td>\n",
       "      <td>29.077651</td>\n",
       "      <td>32.967724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.8</td>\n",
       "      <td>959.700646</td>\n",
       "      <td>2018</td>\n",
       "      <td>16A</td>\n",
       "      <td>43.681094</td>\n",
       "      <td>21.970619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>860.584660</td>\n",
       "      <td>2018</td>\n",
       "      <td>16A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>1357.447146</td>\n",
       "      <td>2022</td>\n",
       "      <td>Y8</td>\n",
       "      <td>13.052635</td>\n",
       "      <td>103.997944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1331.968741</td>\n",
       "      <td>2022</td>\n",
       "      <td>Y8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1349.710735</td>\n",
       "      <td>2022</td>\n",
       "      <td>Y8</td>\n",
       "      <td>4.393956</td>\n",
       "      <td>307.174374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.8</td>\n",
       "      <td>1350.260216</td>\n",
       "      <td>2022</td>\n",
       "      <td>Y8</td>\n",
       "      <td>8.787912</td>\n",
       "      <td>153.649714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>1350.455823</td>\n",
       "      <td>2022</td>\n",
       "      <td>Y8</td>\n",
       "      <td>13.052635</td>\n",
       "      <td>103.462318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       x            y  year field  cost_dollars_ha  yield_dollars_per_bu\n",
       "0    0.0   861.928344  2018   16A         0.000000              0.000000\n",
       "1   11.3   952.586246  2018   16A        14.603443             65.230253\n",
       "2   22.5   958.623986  2018   16A        29.077651             32.967724\n",
       "3   33.8   959.700646  2018   16A        43.681094             21.970619\n",
       "0    0.0   860.584660  2018   16A         0.000000              0.000000\n",
       "..   ...          ...   ...   ...              ...                   ...\n",
       "3   10.1  1357.447146  2022    Y8        13.052635            103.997944\n",
       "0    0.0  1331.968741  2022    Y8         0.000000              0.000000\n",
       "1    3.4  1349.710735  2022    Y8         4.393956            307.174374\n",
       "2    6.8  1350.260216  2022    Y8         8.787912            153.649714\n",
       "3   10.1  1350.455823  2022    Y8        13.052635            103.462318\n",
       "\n",
       "[240 rows x 6 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4ceb0070-6ea2-429a-a552-2b49cbcb57ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A snapshot of the average yield per dollar values:\n",
      "Key: 0.0, Average Yield per Dollar: 0.0\n",
      "Key: 3.4, Average Yield per Dollar: 478.5551196574695\n",
      "Key: 5.6, Average Yield per Dollar: 205.2485786871935\n",
      "Key: 6.8, Average Yield per Dollar: 240.4369128615219\n",
      "Key: 9.0, Average Yield per Dollar: 160.8475460784105\n",
      "Key: 10.1, Average Yield per Dollar: 161.9922034261987\n",
      "Key: 11.3, Average Yield per Dollar: 114.67490002080592\n",
      "Key: 16.9, Average Yield per Dollar: 93.84233722868383\n",
      "Key: 18.0, Average Yield per Dollar: 82.29644959944079\n",
      "Key: 22.5, Average Yield per Dollar: 77.59114277554151\n",
      "Key: 27.0, Average Yield per Dollar: 55.7638680422248\n",
      "Key: 28.2, Average Yield per Dollar: 71.78275116222493\n",
      "Key: 33.8, Average Yield per Dollar: 43.662219461949896\n"
     ]
    }
   ],
   "source": [
    "print(\"A snapshot of the average yield per dollar values:\")\n",
    "for key, avg_yield in avg_cost_per_ha.items():\n",
    "    print(f\"Key: {key}, Average Yield per Dollar: {avg_yield}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5d07a829-6811-4931-a541-fb339fc47b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "treatments = {\n",
    "    \"16A\": {\n",
    "        \"Low\": {\"plots\": ['3', '10', '18'], \"value\": 11.3},\n",
    "        \"Medium\": {\"plots\": ['7', '14', '15'], \"value\": 22.5},\n",
    "        \"High\": {\"plots\": ['6', '11', '19'], \"value\": 33.8}\n",
    "    },\n",
    "    \"6-12\": {\n",
    "        \"Low\": {\"plots\": ['6', '10', '20'], \"value\": 16.9},\n",
    "        \"Medium\": {\"plots\": ['4', '14', '18'], \"value\": 22.5},\n",
    "        \"High\": {\"plots\": ['8', '12', '16'], \"value\": 28.2}\n",
    "    },\n",
    "    \"Y10\": {\n",
    "        \"Low\": {\"plots\": ['5', '13', '21'], \"value\": 5.6},\n",
    "        \"Medium\": {\"plots\": ['3', '9', '15'], \"value\": 11.3},\n",
    "        \"High\": {\"plots\": ['7', '15', '19'], \"value\": 16.9}\n",
    "    },\n",
    "    \"Y8\": {\n",
    "        \"Low\": {\"plots\": ['5', '7', '21', '23'], \"value\": 3.4},\n",
    "        \"Medium\": {\"plots\": ['10', '12', '18', '20'], \"value\": 6.8},\n",
    "        \"High\": {\"plots\": ['13', '15', '26', '28'], \"value\": 10.1}\n",
    "    },\n",
    "    \"SW16\": {\n",
    "        \"Low\": {\"plots\": ['3', '11', '13'], \"value\": 9},\n",
    "        \"Medium\": {\"plots\": ['1', '9', '17'], \"value\": 18},\n",
    "        \"High\": {\"plots\": ['5', '7', '17'], \"value\": 27}\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "438ed244-349e-4a7a-bd1b-e218ae10a1f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to map treatment based on field and x values\n",
    "def map_treatment(row):\n",
    "    field = row['field']\n",
    "    x_value = row['x']\n",
    "\n",
    "    if field in treatments:\n",
    "        for treatment, data in treatments[field].items():\n",
    "            if x_value == data['value']:\n",
    "                return treatment\n",
    "    return 'Control'\n",
    "    \n",
    "# Apply the function to the DataFrame to get the Treatment column\n",
    "plotting_df['Treatment'] = plotting_df.apply(map_treatment, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ee78e28b-6554-4454-8424-a7e35ea8a00e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of valid subsets processed: 20\n"
     ]
    }
   ],
   "source": [
    "plotting_df['dollars_per_ha'] = plotting_df['y']*0.20 # kg/ha * $/kg = $/ha\n",
    "\n",
    "# Create a function to compute yield increase per dollar\n",
    "def compute_dollar_per_acre(row):\n",
    "    if row['y'] == 0:\n",
    "        return row['dollars_per_ha']\n",
    "    return row['dollars_per_ha'] - row['cost_dollars_ha'] # $/ha - $/ha\n",
    "\n",
    "# Apply the function to the dataframe\n",
    "plotting_df['dollars_per_ha_minus_cost'] = plotting_df.apply(compute_dollar_per_acre, axis=1)\n",
    "\n",
    "# Initialize results dictionary\n",
    "results = {}\n",
    "\n",
    "fields = plotting_df['field'].unique()\n",
    "\n",
    "# Diagnostic print to check subsets with valid x values\n",
    "valid_subsets = 0\n",
    "\n",
    "# Iterate through the unique fields\n",
    "for field in fields:\n",
    "    field_df = plotting_df[plotting_df['field'] == field]\n",
    "    for year in field_df['year'].unique():\n",
    "        subset = field_df[(field_df['year'] == year)]\n",
    "        unique_x_values = sorted(subset['x'].unique())\n",
    "        \n",
    "        # If there are exactly three unique x values, then proceed with analysis\n",
    "        if len(unique_x_values) == 4:\n",
    "            valid_subsets += 1\n",
    "\n",
    "# Print the number of valid subsets\n",
    "print(f\"\\nNumber of valid subsets processed: {valid_subsets}\")\n",
    "\n",
    "# Group by treatment level and compute the mean yield per dollar\n",
    "avg_yield_per_dollar_with_controls = plotting_df.groupby('x')['dollars_per_ha_minus_cost'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c3b76e8a-bedb-474b-be7f-c0f0fb4a31c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d021547d-3715-4820-af63-c7ea2a8e67aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting to kg/ha\n",
    "plotting_df['x_metric'] = plotting_df['x']*0.45359/0.0404686\n",
    "\n",
    "# Set the style of Seaborn for better visualization\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Create a figure and axes for the subplots in a 2x3 grid\n",
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10), sharey=True, sharex = True)\n",
    "\n",
    "# Flatten the axes array for easier iteration\n",
    "axes_flat = axes.flatten()\n",
    "\n",
    "# Iterate over each unique field\n",
    "fields = plotting_df['field'].unique()\n",
    "\n",
    "for index, field in enumerate(fields):\n",
    "    # Filter the dataframe for the current field\n",
    "    field_df = plotting_df[plotting_df['field'] == field].copy()\n",
    "    field_df.loc[:,'x_metric'] = field_df.loc[:,'x_metric'] * 0.436 # Converting to P from P2O5\n",
    "    \n",
    "    # Plotting the line plot for the current field\n",
    "    sns.lineplot(x='x_metric', y='dollars_per_ha_minus_cost', data=field_df, ax=axes_flat[index], marker='o')\n",
    "    \n",
    "    # Compute regression statistics\n",
    "    slope, intercept, r_value, p_value, std_err = linregress(field_df['x_metric'], field_df['dollars_per_ha_minus_cost'])\n",
    "    axes_flat[index].plot(field_df['x_metric'], intercept + slope*field_df['x_metric'], 'r', label=f'Slope={slope:.2f}, R^2={r_value**2:.2f}')\n",
    "    \n",
    "    # Set title for each subplot\n",
    "    axes_flat[index].set_title(f'Field {field}', fontsize=14)\n",
    "    \n",
    "    # Display the legend for the current field's subplot\n",
    "    axes_flat[index].legend(loc='upper left')\n",
    "    \n",
    "    # Set x and y labels for the first subplot only\n",
    "    if index == 0 or index == 3:\n",
    "        axes_flat[index].set_ylabel('Average harvest value ($/ha)', fontsize=12)\n",
    "    else:\n",
    "        axes_flat[index].set_ylabel('')\n",
    "    \n",
    "    axes_flat[index].set_xlabel('Phosphorus Application (kg/ha)', fontsize=12)\n",
    "    axes_flat[index].set_ylim(0, 1000)\n",
    "\n",
    "# Remove the plot from the last subplot (making it empty)\n",
    "axes_flat[-1].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.suptitle('Average Yield Per Dollar vs. Phosphorus Application Levels', fontsize=16, y=1.05)\n",
    "plt.savefig('Output/Figures/Economics_rf_total_revenue.jpg', bbox_inches = 'tight', dpi = 300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}