{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a476e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d100776c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the precipitation data from the CSV file\n",
    "data = pd.read_csv('Precipitation_Dataset.csv', parse_dates=['Date'], index_col='Date')\n",
    "\n",
    "dataset1 = data[\"ERA6\"]\n",
    "dataset2 = data[\"CRUTS_adj\"]\n",
    "dataset3 = data[\"MERRA3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c346941",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.0 years : Starting 1980-01-01 to 2021-12-01\n"
     ]
    }
   ],
   "source": [
    "# Ensure datasets have the same length\n",
    "assert len(dataset1) == len(dataset2) == len(dataset3)\n",
    "n = len(dataset1)\n",
    "print(n/12, 'years', ': Starting 1980-01-01 to 2021-12-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd99e567",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bhavarth/.local/lib/python3.9/site-packages/deprecat/classic.py:215: FutureWarning: In v4.0, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n",
      "  return wrapped_(*args_, **kwargs_)\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sd_dataset3, sd_dataset2, sd_dataset1, true_precip, sd_x, mu_x]\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [12000/12000 00:12&lt;00:00 Sampling 4 chains, 0 divergences]\n",
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       "    "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 13 seconds.\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "# Create a PyMC3 model (Advanced Bayesian Model with 2000 samples and 1000 tuning steps)\n",
    "\n",
    "with pm.Model() as model:\n",
    "    # Priors for true precipitation values\n",
    "    mu_x = pm.Normal('mu_x', mu=0, sd=10)\n",
    "    sd_x = pm.HalfNormal('sd_x', sd=10)\n",
    "    true_precip = pm.Normal('true_precip', mu=mu_x, sd=sd_x, shape=n)\n",
    "    \n",
    "    # Priors for measurement error variances\n",
    "    sd_dataset1 = pm.HalfNormal('sd_dataset1', sd=10)\n",
    "    sd_dataset2 = pm.HalfNormal('sd_dataset2', sd=10)\n",
    "    sd_dataset3 = pm.HalfNormal('sd_dataset3', sd=10)\n",
    "    \n",
    "    # Likelihoods for observed data\n",
    "    obs_dataset1 = pm.Normal('obs_dataset1', mu=true_precip, sd=sd_dataset1, observed=dataset1)\n",
    "    obs_dataset2 = pm.Normal('obs_dataset2', mu=true_precip, sd=sd_dataset2, observed=dataset2)\n",
    "    obs_dataset3 = pm.Normal('obs_dataset3', mu=true_precip, sd=sd_dataset3, observed=dataset3)\n",
    "    \n",
    "    # Run MCMC to sample from the posterior distribution\n",
    "    trace = pm.sample(2000, tune=1000, target_accept=0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27b823df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Posterior point estimates (mean) for true precipitation values\n",
    "true_precip_mean = np.mean(trace['true_precip'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0fc15f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Posterior 95% credible intervals for true precipitation values\n",
    "true_precip_ci_lower = np.percentile(trace['true_precip'], 2.5, axis=0)\n",
    "true_precip_ci_upper = np.percentile(trace['true_precip'], 97.5, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ec051b3",
   "metadata": {},
   "outputs": [
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       "        0.12526746,  3.93923607,  0.47020871,  7.55319118])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# True Precipitation output from Bayesian Data Fusion Model\n",
    "\n",
    "true_precip_mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d64f003",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame with dates, mean, and confidence intervals\n",
    "\n",
    "date_range = pd.date_range(start='1980-01-01', end='2021-12-01', freq='MS')\n",
    "bayesian_output = pd.DataFrame({\n",
    "    'Date': date_range,\n",
    "    'True_Precipitation_Mean (inches)': true_precip_mean,\n",
    "    'CI_Lower (inches)': true_precip_ci_lower,\n",
    "    'CI_Upper (inches)': true_precip_ci_upper\n",
    "})\n",
    "\n",
    "# Save to Excel file\n",
    "bayesian_output.to_excel('Bayesian_Precipitation_Output_Advanced_Bayesian.xlsx', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "837b268b",
   "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>Date</th>\n",
       "      <th>True_Precipitation_Mean (inches)</th>\n",
       "      <th>CI_Lower (inches)</th>\n",
       "      <th>CI_Upper (inches)</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-01</td>\n",
       "      <td>5.547958</td>\n",
       "      <td>4.966222</td>\n",
       "      <td>6.148757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-01</td>\n",
       "      <td>5.915069</td>\n",
       "      <td>5.339251</td>\n",
       "      <td>6.504874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-01</td>\n",
       "      <td>1.989294</td>\n",
       "      <td>1.413247</td>\n",
       "      <td>2.561482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-01</td>\n",
       "      <td>1.444300</td>\n",
       "      <td>0.885698</td>\n",
       "      <td>2.013650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-01</td>\n",
       "      <td>1.560322</td>\n",
       "      <td>0.991171</td>\n",
       "      <td>2.124003</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>0.125445</td>\n",
       "      <td>-0.439774</td>\n",
       "      <td>0.682461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>0.125267</td>\n",
       "      <td>-0.445047</td>\n",
       "      <td>0.702191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>3.939236</td>\n",
       "      <td>3.366365</td>\n",
       "      <td>4.502416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>0.470209</td>\n",
       "      <td>-0.098012</td>\n",
       "      <td>1.050404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>7.553191</td>\n",
       "      <td>6.952467</td>\n",
       "      <td>8.148941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>504 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  True_Precipitation_Mean (inches)  CI_Lower (inches)  \\\n",
       "0   1980-01-01                          5.547958           4.966222   \n",
       "1   1980-02-01                          5.915069           5.339251   \n",
       "2   1980-03-01                          1.989294           1.413247   \n",
       "3   1980-04-01                          1.444300           0.885698   \n",
       "4   1980-05-01                          1.560322           0.991171   \n",
       "..         ...                               ...                ...   \n",
       "499 2021-08-01                          0.125445          -0.439774   \n",
       "500 2021-09-01                          0.125267          -0.445047   \n",
       "501 2021-10-01                          3.939236           3.366365   \n",
       "502 2021-11-01                          0.470209          -0.098012   \n",
       "503 2021-12-01                          7.553191           6.952467   \n",
       "\n",
       "     CI_Upper (inches)  \n",
       "0             6.148757  \n",
       "1             6.504874  \n",
       "2             2.561482  \n",
       "3             2.013650  \n",
       "4             2.124003  \n",
       "..                 ...  \n",
       "499           0.682461  \n",
       "500           0.702191  \n",
       "501           4.502416  \n",
       "502           1.050404  \n",
       "503           8.148941  \n",
       "\n",
       "[504 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayesian_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9d89296",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_input_advanced = pd.DataFrame({\n",
    "    'Date': date_range,\n",
    "    'Precipitation (inches)': true_precip_mean\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ff32daa1",
   "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>Date</th>\n",
       "      <th>Precipitation (inches)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-01</td>\n",
       "      <td>5.547958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-01</td>\n",
       "      <td>5.915069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-01</td>\n",
       "      <td>1.989294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-01</td>\n",
       "      <td>1.444300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-01</td>\n",
       "      <td>1.560322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>0.125445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>0.125267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>3.939236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>0.470209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>7.553191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>504 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Precipitation (inches)\n",
       "0   1980-01-01                5.547958\n",
       "1   1980-02-01                5.915069\n",
       "2   1980-03-01                1.989294\n",
       "3   1980-04-01                1.444300\n",
       "4   1980-05-01                1.560322\n",
       "..         ...                     ...\n",
       "499 2021-08-01                0.125445\n",
       "500 2021-09-01                0.125267\n",
       "501 2021-10-01                3.939236\n",
       "502 2021-11-01                0.470209\n",
       "503 2021-12-01                7.553191\n",
       "\n",
       "[504 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_input_advanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4f998405",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the LSTM input DataFrame to a CSV file\n",
    "lstm_input_advanced.to_csv('Advanced_Bayesian_LSTM_Input_Precipitation.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "600026c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading and preprocessing the data\n",
    "df = pd.read_csv('Advanced_Bayesian_LSTM_Input_Precipitation.csv', parse_dates=['Date'])\n",
    "df.set_index('Date', inplace=True)\n",
    "target = df['Precipitation (inches)'].values.reshape(-1, 1)\n",
    "\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_target = scaler.fit_transform(target)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ef2cbda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-07 16:47:51.258364: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /sw/pkgs/arc/cudnn/11.2-v8.1.1/lib64:/sw/pkgs/arc/cuda/11.2.2/lib64\n",
      "2024-01-07 16:47:51.258399: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2024-01-07 16:47:51.258431: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (gl0006.arc-ts.umich.edu): /proc/driver/nvidia/version does not exist\n",
      "2024-01-07 16:47:51.258800: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "388/388 [==============================] - 2s 3ms/step - loss: 0.0178\n",
      "Epoch 2/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0153\n",
      "Epoch 3/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0147\n",
      "Epoch 4/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0146\n",
      "Epoch 5/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0147\n",
      "Epoch 6/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0143\n",
      "Epoch 7/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0145\n",
      "Epoch 8/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0141\n",
      "Epoch 9/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0145\n",
      "Epoch 10/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0140\n",
      "Epoch 11/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0146\n",
      "Epoch 12/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0144\n",
      "Epoch 13/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0142\n",
      "Epoch 14/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0141\n",
      "Epoch 15/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0142\n",
      "Epoch 16/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0143\n",
      "Epoch 17/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0136\n",
      "Epoch 18/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0139\n",
      "Epoch 19/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0139\n",
      "Epoch 20/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0140\n",
      "Epoch 21/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0139\n",
      "Epoch 22/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0138\n",
      "Epoch 23/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0139\n",
      "Epoch 24/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0137\n",
      "Epoch 25/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0138\n",
      "Epoch 26/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0137\n",
      "Epoch 27/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0139\n",
      "Epoch 28/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0136\n",
      "Epoch 29/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0137\n",
      "Epoch 30/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0138\n",
      "Epoch 31/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0136\n",
      "Epoch 32/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0136\n",
      "Epoch 33/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0137\n",
      "Epoch 34/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0135\n",
      "Epoch 35/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0137\n",
      "Epoch 36/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0135\n",
      "Epoch 37/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0137\n",
      "Epoch 38/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0136\n",
      "Epoch 39/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0134\n",
      "Epoch 40/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0135\n",
      "Epoch 41/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0136\n",
      "Epoch 42/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0135\n",
      "Epoch 43/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0134\n",
      "Epoch 44/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0135\n",
      "Epoch 45/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0135\n",
      "Epoch 46/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0133\n",
      "Epoch 47/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0131\n",
      "Epoch 48/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0136\n",
      "Epoch 49/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0135\n",
      "Epoch 50/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0133\n",
      "Epoch 51/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0133\n",
      "Epoch 52/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0134\n",
      "Epoch 53/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0133\n",
      "Epoch 54/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0134\n",
      "Epoch 55/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0133\n",
      "Epoch 56/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0132\n",
      "Epoch 57/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0133\n",
      "Epoch 58/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0131\n",
      "Epoch 59/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0133\n",
      "Epoch 60/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0130\n",
      "Epoch 61/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0132\n",
      "Epoch 62/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0133\n",
      "Epoch 63/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0131\n",
      "Epoch 64/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0130\n",
      "Epoch 65/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0131\n",
      "Epoch 66/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0130\n",
      "Epoch 67/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0132\n",
      "Epoch 68/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0131\n",
      "Epoch 69/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0129\n",
      "Epoch 70/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0132\n",
      "Epoch 71/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0130\n",
      "Epoch 72/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0129\n",
      "Epoch 73/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0129\n",
      "Epoch 74/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0127\n",
      "Epoch 75/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0126\n",
      "Epoch 76/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0130\n",
      "Epoch 77/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0128\n",
      "Epoch 78/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0128\n",
      "Epoch 79/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0127\n",
      "Epoch 80/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0128\n",
      "Epoch 81/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0124\n",
      "Epoch 82/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0129\n",
      "Epoch 83/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0125\n",
      "Epoch 84/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0126\n",
      "Epoch 85/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0126\n",
      "Epoch 86/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0121\n",
      "Epoch 87/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0125\n",
      "Epoch 88/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0126\n",
      "Epoch 89/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0123\n",
      "Epoch 90/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0125\n",
      "Epoch 91/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0124\n",
      "Epoch 92/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0122\n",
      "Epoch 93/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0121\n",
      "Epoch 94/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0123\n",
      "Epoch 95/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0121\n",
      "Epoch 96/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0124\n",
      "Epoch 97/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0118\n",
      "Epoch 98/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0121\n",
      "Epoch 99/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0121\n",
      "Epoch 100/100\n",
      "388/388 [==============================] - 1s 2ms/step - loss: 0.0120\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14c7a98771f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memory_range = 15\n",
    "\n",
    "# Splitting the data\n",
    "training_set = int(len(scaled_target) * 0.8)\n",
    "train_data = scaled_target[:training_set]\n",
    "test_data = scaled_target[training_set - memory_range:]\n",
    "\n",
    "# Preparing training and validation sets\n",
    "\n",
    "X_train, y_train = [], []\n",
    "for i in range(memory_range, len(train_data)):\n",
    "    X_train.append(train_data[i - memory_range:i, 0])\n",
    "    y_train.append(train_data[i, 0])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "# Building the LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, input_shape=(memory_range, 1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "# Training the model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a44b46f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing validation data\n",
    "X_test = []\n",
    "y_test = target[training_set:, 0]\n",
    "for i in range(memory_range, len(test_data)):\n",
    "    X_test.append(test_data[i - memory_range:i, 0])\n",
    "X_test = np.array(X_test)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f4479eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making LSTM predictions\n",
    "predicted_Precipitation = model.predict(X_test)\n",
    "predicted_Precipitation = scaler.inverse_transform(predicted_Precipitation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54ab257f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.72665495],\n",
       "       [0.9834133 ],\n",
       "       [1.2403903 ],\n",
       "       [2.088826  ],\n",
       "       [3.3114097 ],\n",
       "       [2.08162   ],\n",
       "       [1.4630088 ],\n",
       "       [1.4759743 ],\n",
       "       [1.0599924 ],\n",
       "       [0.7947309 ],\n",
       "       [0.48761082],\n",
       "       [0.26309657],\n",
       "       [0.50309664],\n",
       "       [0.71794426],\n",
       "       [0.84449506],\n",
       "       [0.8161129 ],\n",
       "       [1.3330413 ],\n",
       "       [3.3241754 ],\n",
       "       [3.0917566 ],\n",
       "       [2.1396642 ],\n",
       "       [1.1288342 ],\n",
       "       [0.7544532 ],\n",
       "       [1.1125761 ],\n",
       "       [0.7674741 ],\n",
       "       [0.7074069 ],\n",
       "       [0.40997496],\n",
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       "       [2.6376097 ],\n",
       "       [2.4239347 ],\n",
       "       [1.8945041 ],\n",
       "       [1.7753617 ],\n",
       "       [1.3977308 ],\n",
       "       [0.8818249 ],\n",
       "       [0.45142123],\n",
       "       [0.25974378],\n",
       "       [0.42617905],\n",
       "       [0.7258325 ],\n",
       "       [2.6655512 ],\n",
       "       [4.2855906 ],\n",
       "       [3.5241196 ],\n",
       "       [2.459148  ],\n",
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       "       [2.052386  ],\n",
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       "       [0.32643846],\n",
       "       [0.59660697],\n",
       "       [0.88865536],\n",
       "       [0.92814255],\n",
       "       [2.3060472 ],\n",
       "       [1.561041  ],\n",
       "       [1.4465376 ],\n",
       "       [1.1593459 ],\n",
       "       [3.3687012 ],\n",
       "       [1.6991112 ],\n",
       "       [1.0572323 ],\n",
       "       [0.29486597],\n",
       "       [0.49881995],\n",
       "       [0.44331378],\n",
       "       [0.4642984 ],\n",
       "       [0.8239087 ],\n",
       "       [2.349303  ],\n",
       "       [3.3737538 ],\n",
       "       [4.33654   ],\n",
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       "       [0.9857374 ],\n",
       "       [0.4156044 ],\n",
       "       [0.37274644],\n",
       "       [0.38818154],\n",
       "       [0.65552294],\n",
       "       [0.7755995 ],\n",
       "       [1.8133367 ],\n",
       "       [3.9707196 ],\n",
       "       [2.8196955 ],\n",
       "       [1.5572878 ],\n",
       "       [1.3014637 ],\n",
       "       [1.1356714 ],\n",
       "       [0.61922   ],\n",
       "       [0.3732566 ],\n",
       "       [0.25940526],\n",
       "       [0.50025415],\n",
       "       [0.7088572 ],\n",
       "       [1.0534807 ],\n",
       "       [2.833575  ],\n",
       "       [4.5506253 ],\n",
       "       [2.7764704 ],\n",
       "       [2.1937923 ],\n",
       "       [2.1575453 ],\n",
       "       [1.0830392 ],\n",
       "       [0.51835793],\n",
       "       [0.2999685 ],\n",
       "       [0.5865828 ],\n",
       "       [0.49310032],\n",
       "       [0.5757416 ],\n",
       "       [2.6094322 ],\n",
       "       [2.8444395 ]], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_Precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d49e372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predicted_Precipitation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69e347a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE is: 1.1097723611571233\n",
      "RMSE is: 1.8126438962140035\n"
     ]
    }
   ],
   "source": [
    "# Calculating MAE and RMSE\n",
    "m = mean_absolute_error(y_test, predicted_Precipitation)\n",
    "print(\"MAE is:\", m)\n",
    "a = np.sqrt(mean_squared_error(y_test, predicted_Precipitation))\n",
    "print(\"RMSE is:\", a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f9945906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correcting the padding length\n",
    "len_training = training_set\n",
    "valid_predictions_padded = np.concatenate([np.full(len_training, np.nan), predicted_Precipitation.ravel()])\n",
    "\n",
    "# Check if the lengths match\n",
    "if len(valid_predictions_padded) != len(df):\n",
    "    raise ValueError(\"Length of padded validation predictions does not match the length of the DataFrame.\")\n",
    "\n",
    "# Convert data to NumPy arrays for plotting\n",
    "historical_data = df['Precipitation (inches)'].to_numpy()\n",
    "dates = df.index.to_numpy()\n",
    "\n",
    "# Plotting the historical data and the predictions\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(dates, historical_data, color='blue', label='Historical Data')\n",
    "plt.plot(dates, valid_predictions_padded, color='red', label='Validation Data')\n",
    "plt.title('Precipitation (Inches) (1980-2021)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Precipitation (Inches)')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "470b8b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = y_test - predicted_Precipitation.flatten()\n",
    "\n",
    "# Create a plot for residuals\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(dates[training_set:], residuals, color='green', label='Residuals')\n",
    "plt.axhline(y=0, color='black', linestyle='--') \n",
    "plt.title('Residual Plot for Precipitation (Inches)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Residuals')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "261f3551",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure look_back is consistent\n",
    "look_back = memory_range\n",
    "\n",
    "# Prepare the last sequence of known data as input for forecasting\n",
    "last_sequence = scaled_target[-look_back:]\n",
    "\n",
    "# Forecast function\n",
    "def forecast(model, last_sequence, look_back, future_steps):\n",
    "    forecast_values = []\n",
    "    input_data = last_sequence.reshape(1, look_back, 1)\n",
    "    \n",
    "    for _ in range(future_steps):\n",
    "        predicted_value = model.predict(input_data)\n",
    "        forecast_values.append(predicted_value[0, 0])\n",
    "        input_data = np.roll(input_data, -1)\n",
    "        input_data[0, -1, 0] = predicted_value\n",
    "\n",
    "    return np.array(forecast_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a086b38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasted Precipitation (Inches): [[3.567576  ]\n",
      " [2.270288  ]\n",
      " [1.9907008 ]\n",
      " [1.2299744 ]\n",
      " [0.60075843]\n",
      " [0.35416532]\n",
      " [0.33423758]\n",
      " [0.49704373]\n",
      " [0.8160735 ]\n",
      " [1.3279597 ]\n",
      " [2.1578047 ]\n",
      " [3.3186567 ]\n",
      " [2.1819718 ]\n",
      " [1.8095843 ]\n",
      " [1.5217533 ]\n",
      " [1.0707757 ]\n",
      " [0.63469106]\n",
      " [0.36327428]\n",
      " [0.34380525]\n",
      " [0.5229432 ]\n",
      " [0.85224473]\n",
      " [1.4318154 ]\n",
      " [2.9153764 ]\n",
      " [4.267493  ]\n",
      " [2.40106   ]\n",
      " [2.305581  ]\n",
      " [1.7740961 ]\n",
      " [1.1209722 ]\n",
      " [0.62744826]\n",
      " [0.35507318]\n",
      " [0.33694804]\n",
      " [0.51911896]\n",
      " [0.85484207]\n",
      " [1.4358387 ]\n",
      " [2.8511298 ]\n",
      " [4.232132  ]\n",
      " [2.291719  ]\n",
      " [2.2141438 ]\n",
      " [1.7564858 ]\n",
      " [1.1629583 ]\n",
      " [0.6665868 ]\n",
      " [0.37311286]\n",
      " [0.34285653]\n",
      " [0.51517135]\n",
      " [0.8425771 ]\n",
      " [1.4178685 ]\n",
      " [2.8505666 ]\n",
      " [4.2732635 ]\n",
      " [2.3136277 ]\n",
      " [2.2540312 ]\n",
      " [1.8009537 ]\n",
      " [1.1921818 ]\n",
      " [0.6855236 ]\n",
      " [0.38156205]\n",
      " [0.33958456]\n",
      " [0.5047399 ]\n",
      " [0.82688344]\n",
      " [1.3907353 ]\n",
      " [2.7770605 ]\n",
      " [4.3514137 ]]\n"
     ]
    }
   ],
   "source": [
    "# Forecast future steps\n",
    "future_steps = 60\n",
    "forecast_values_normalized = forecast(model, last_sequence, look_back, future_steps)\n",
    "forecast_values = scaler.inverse_transform(forecast_values_normalized.reshape(-1, 1))\n",
    "\n",
    "# Print forecasted values\n",
    "print(\"Forecasted Precipitation (Inches):\", forecast_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5cbd8072",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate future dates for plotting\n",
    "last_date = df.index[-1]\n",
    "future_dates = pd.date_range(start=last_date, periods=future_steps + 1, freq='MS')[1:]\n",
    "\n",
    "# Convert future_dates to a NumPy array of datetime objects\n",
    "future_dates_np = future_dates.to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e324ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now plot the data\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(dates, historical_data, color='blue', label='Historical Data')\n",
    "plt.plot(dates, valid_predictions_padded, color='red', label='Validation Data')\n",
    "plt.plot(future_dates_np, forecast_values, color='green', label='Forecasting Data')\n",
    "plt.gca().xaxis.set_major_locator(mdates.YearLocator(4))\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "plt.title('Precipitation (Inches) (1980-2027)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Precipitation (Inches)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ae0747a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}