{
 "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": 3,
   "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": 4,
   "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",
      "Only 100 samples in chain.\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='600' class='' max='600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [600/600 00:02&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 50 tune and 100 draw iterations (200 + 400 draws total) took 3 seconds.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n"
     ]
    }
   ],
   "source": [
    "# Create a PyMC3 model (Simple Bayesian Model with 100 samples and 50 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(100, tune=50, target_accept=0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "id": "5ec051b3",
   "metadata": {},
   "outputs": [
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       "        0.14621235,  3.9733376 ,  0.46984419,  7.56977727])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# True Precipitation output from Bayesian Data Fusion Model\n",
    "\n",
    "true_precip_mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_Simple_Bayesian.xlsx', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "837b268b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>True_Precipitation_Mean (inches)</th>\n",
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       "      <th>CI_Upper (inches)</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-01</td>\n",
       "      <td>5.572669</td>\n",
       "      <td>4.911256</td>\n",
       "      <td>6.188027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-01</td>\n",
       "      <td>5.907690</td>\n",
       "      <td>5.293045</td>\n",
       "      <td>6.406962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-01</td>\n",
       "      <td>1.974106</td>\n",
       "      <td>1.354021</td>\n",
       "      <td>2.553516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-01</td>\n",
       "      <td>1.434900</td>\n",
       "      <td>0.884733</td>\n",
       "      <td>1.962680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-01</td>\n",
       "      <td>1.564738</td>\n",
       "      <td>1.002790</td>\n",
       "      <td>2.117332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>0.117338</td>\n",
       "      <td>-0.388468</td>\n",
       "      <td>0.679280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>0.146212</td>\n",
       "      <td>-0.436570</td>\n",
       "      <td>0.688872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>3.973338</td>\n",
       "      <td>3.495223</td>\n",
       "      <td>4.608217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>0.469844</td>\n",
       "      <td>-0.124644</td>\n",
       "      <td>1.063867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>7.569777</td>\n",
       "      <td>7.015262</td>\n",
       "      <td>8.101519</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                          5.572669           4.911256   \n",
       "1   1980-02-01                          5.907690           5.293045   \n",
       "2   1980-03-01                          1.974106           1.354021   \n",
       "3   1980-04-01                          1.434900           0.884733   \n",
       "4   1980-05-01                          1.564738           1.002790   \n",
       "..         ...                               ...                ...   \n",
       "499 2021-08-01                          0.117338          -0.388468   \n",
       "500 2021-09-01                          0.146212          -0.436570   \n",
       "501 2021-10-01                          3.973338           3.495223   \n",
       "502 2021-11-01                          0.469844          -0.124644   \n",
       "503 2021-12-01                          7.569777           7.015262   \n",
       "\n",
       "     CI_Upper (inches)  \n",
       "0             6.188027  \n",
       "1             6.406962  \n",
       "2             2.553516  \n",
       "3             1.962680  \n",
       "4             2.117332  \n",
       "..                 ...  \n",
       "499           0.679280  \n",
       "500           0.688872  \n",
       "501           4.608217  \n",
       "502           1.063867  \n",
       "503           8.101519  \n",
       "\n",
       "[504 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayesian_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9d89296",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_input_simple = pd.DataFrame({\n",
    "    'Date': date_range,\n",
    "    'Precipitation (inches)': true_precip_mean\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>5.572669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-01</td>\n",
       "      <td>5.907690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-01</td>\n",
       "      <td>1.974106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-01</td>\n",
       "      <td>1.434900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-01</td>\n",
       "      <td>1.564738</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>0.117338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>0.146212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>3.973338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>0.469844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>7.569777</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                5.572669\n",
       "1   1980-02-01                5.907690\n",
       "2   1980-03-01                1.974106\n",
       "3   1980-04-01                1.434900\n",
       "4   1980-05-01                1.564738\n",
       "..         ...                     ...\n",
       "499 2021-08-01                0.117338\n",
       "500 2021-09-01                0.146212\n",
       "501 2021-10-01                3.973338\n",
       "502 2021-11-01                0.469844\n",
       "503 2021-12-01                7.569777\n",
       "\n",
       "[504 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_input_simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f998405",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the LSTM input DataFrame to a CSV file\n",
    "lstm_input_simple.to_csv('Simple_Bayesian_LSTM_Input_Precipitation.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "600026c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading and preprocessing the data\n",
    "df = pd.read_csv('Simple_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": 14,
   "id": "4ef2cbda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-07 16:36:34.144905: 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 16:36:34.144944: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2024-01-07 16:36:34.144964: 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 16:36:34.145402: 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: 0.0181\n",
      "Epoch 2/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0148\n",
      "Epoch 3/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0151\n",
      "Epoch 4/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0146\n",
      "Epoch 5/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0146\n",
      "Epoch 6/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0146\n",
      "Epoch 7/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0146\n",
      "Epoch 8/100\n",
      "388/388 [==============================] - 2s 5ms/step - loss: 0.0143\n",
      "Epoch 9/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0147\n",
      "Epoch 10/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0143\n",
      "Epoch 11/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0145\n",
      "Epoch 12/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0144\n",
      "Epoch 13/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0141\n",
      "Epoch 14/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0140\n",
      "Epoch 15/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0143\n",
      "Epoch 16/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0138\n",
      "Epoch 17/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0140\n",
      "Epoch 18/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0144\n",
      "Epoch 19/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0139\n",
      "Epoch 20/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0137\n",
      "Epoch 21/100\n",
      "388/388 [==============================] - 2s 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: 0.0137\n",
      "Epoch 24/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0138\n",
      "Epoch 25/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0139\n",
      "Epoch 26/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0137\n",
      "Epoch 27/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0137\n",
      "Epoch 28/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0136\n",
      "Epoch 29/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0136\n",
      "Epoch 30/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0136\n",
      "Epoch 31/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0137\n",
      "Epoch 32/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0134\n",
      "Epoch 33/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0137\n",
      "Epoch 34/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0136\n",
      "Epoch 35/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0133\n",
      "Epoch 36/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0135\n",
      "Epoch 37/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0136\n",
      "Epoch 38/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0136\n",
      "Epoch 39/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0136\n",
      "Epoch 40/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0133\n",
      "Epoch 41/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0135\n",
      "Epoch 42/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0133\n",
      "Epoch 43/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0135\n",
      "Epoch 44/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0133\n",
      "Epoch 45/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0130\n",
      "Epoch 46/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0133\n",
      "Epoch 47/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0133\n",
      "Epoch 48/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0133\n",
      "Epoch 49/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0132\n",
      "Epoch 50/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0132\n",
      "Epoch 51/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0133\n",
      "Epoch 52/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0136\n",
      "Epoch 53/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0131\n",
      "Epoch 54/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0131\n",
      "Epoch 55/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0127\n",
      "Epoch 56/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0132\n",
      "Epoch 57/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0126\n",
      "Epoch 58/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0132\n",
      "Epoch 59/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0128\n",
      "Epoch 60/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0131\n",
      "Epoch 61/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0130\n",
      "Epoch 62/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0130\n",
      "Epoch 63/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0129\n",
      "Epoch 64/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0126\n",
      "Epoch 65/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0131\n",
      "Epoch 66/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0126\n",
      "Epoch 67/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0128\n",
      "Epoch 68/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0128\n",
      "Epoch 69/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0130\n",
      "Epoch 70/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0132\n",
      "Epoch 71/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0129\n",
      "Epoch 72/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0129\n",
      "Epoch 73/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0125\n",
      "Epoch 74/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0129\n",
      "Epoch 75/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0126\n",
      "Epoch 76/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0124\n",
      "Epoch 77/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0126\n",
      "Epoch 78/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0125\n",
      "Epoch 79/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0124\n",
      "Epoch 80/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0123\n",
      "Epoch 81/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0124\n",
      "Epoch 82/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0125\n",
      "Epoch 83/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0123\n",
      "Epoch 84/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0122\n",
      "Epoch 85/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0121\n",
      "Epoch 86/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0120\n",
      "Epoch 87/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0120\n",
      "Epoch 88/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0123\n",
      "Epoch 89/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0119\n",
      "Epoch 90/100\n",
      "388/388 [==============================] - 1s 3ms/step - loss: 0.0120\n",
      "Epoch 91/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0119\n",
      "Epoch 92/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0121\n",
      "Epoch 93/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0118\n",
      "Epoch 94/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0114\n",
      "Epoch 95/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0114\n",
      "Epoch 96/100\n",
      "388/388 [==============================] - 1s 4ms/step - loss: 0.0117\n",
      "Epoch 97/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0115\n",
      "Epoch 98/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0114\n",
      "Epoch 99/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0115\n",
      "Epoch 100/100\n",
      "388/388 [==============================] - 2s 4ms/step - loss: 0.0116\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14b9eaeeb040>"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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": 16,
   "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": 17,
   "id": "54ab257f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0892701 ],\n",
       "       [1.3918755 ],\n",
       "       [1.715603  ],\n",
       "       [2.7216356 ],\n",
       "       [3.2225132 ],\n",
       "       [2.2302012 ],\n",
       "       [1.8769441 ],\n",
       "       [1.7699193 ],\n",
       "       [1.4006417 ],\n",
       "       [1.2446771 ],\n",
       "       [0.9005198 ],\n",
       "       [0.55022436],\n",
       "       [0.6828396 ],\n",
       "       [0.981304  ],\n",
       "       [1.2713273 ],\n",
       "       [1.3200966 ],\n",
       "       [1.700795  ],\n",
       "       [3.42663   ],\n",
       "       [2.8787503 ],\n",
       "       [2.5335767 ],\n",
       "       [1.5231059 ],\n",
       "       [1.0116318 ],\n",
       "       [1.2317535 ],\n",
       "       [1.1782801 ],\n",
       "       [1.232917  ],\n",
       "       [0.8974656 ],\n",
       "       [0.66175294],\n",
       "       [0.96011055],\n",
       "       [1.7010306 ],\n",
       "       [2.6397896 ],\n",
       "       [2.7670434 ],\n",
       "       [2.5480638 ],\n",
       "       [2.5201652 ],\n",
       "       [1.7638571 ],\n",
       "       [1.0376563 ],\n",
       "       [0.70182365],\n",
       "       [0.5844445 ],\n",
       "       [0.7354208 ],\n",
       "       [1.0433906 ],\n",
       "       [2.8710113 ],\n",
       "       [4.3124375 ],\n",
       "       [4.6096187 ],\n",
       "       [2.269683  ],\n",
       "       [2.1799033 ],\n",
       "       [3.053569  ],\n",
       "       [2.0404825 ],\n",
       "       [1.6695693 ],\n",
       "       [1.0320919 ],\n",
       "       [0.7543529 ],\n",
       "       [0.90700465],\n",
       "       [1.0464269 ],\n",
       "       [1.0931561 ],\n",
       "       [2.6785185 ],\n",
       "       [2.6295474 ],\n",
       "       [2.509493  ],\n",
       "       [1.9088757 ],\n",
       "       [2.981168  ],\n",
       "       [1.7935075 ],\n",
       "       [1.534154  ],\n",
       "       [0.83555984],\n",
       "       [0.7345245 ],\n",
       "       [0.711303  ],\n",
       "       [0.7187222 ],\n",
       "       [1.0213808 ],\n",
       "       [2.671523  ],\n",
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       "       [4.5993867 ],\n",
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       "       [2.2590375 ],\n",
       "       [1.3196797 ],\n",
       "       [0.8753968 ],\n",
       "       [0.731686  ],\n",
       "       [0.6878908 ],\n",
       "       [0.837044  ],\n",
       "       [0.97451156],\n",
       "       [2.022732  ],\n",
       "       [4.717049  ],\n",
       "       [4.061665  ],\n",
       "       [2.752721  ],\n",
       "       [1.7820079 ],\n",
       "       [1.2154955 ],\n",
       "       [0.9051502 ],\n",
       "       [0.7197126 ],\n",
       "       [0.67131734],\n",
       "       [0.8666452 ],\n",
       "       [1.0006124 ],\n",
       "       [1.1322683 ],\n",
       "       [2.284353  ],\n",
       "       [3.9719114 ],\n",
       "       [2.6229284 ],\n",
       "       [2.496484  ],\n",
       "       [2.6296272 ],\n",
       "       [1.3443837 ],\n",
       "       [0.7750923 ],\n",
       "       [0.66079885],\n",
       "       [0.9660971 ],\n",
       "       [1.0017252 ],\n",
       "       [1.0712353 ],\n",
       "       [3.0892541 ],\n",
       "       [3.5857663 ]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_Precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d49e372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predicted_Precipitation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69e347a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE is: 1.2001635008721825\n",
      "RMSE is: 1.7794387844804644\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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "id": "a086b38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasted Precipitation (Inches): [[3.6492689 ]\n",
      " [2.7684886 ]\n",
      " [2.580196  ]\n",
      " [1.8752749 ]\n",
      " [1.2205776 ]\n",
      " [0.9249058 ]\n",
      " [0.81982297]\n",
      " [0.87821716]\n",
      " [1.0813714 ]\n",
      " [1.4845158 ]\n",
      " [2.167253  ]\n",
      " [3.3847976 ]\n",
      " [3.3204072 ]\n",
      " [2.7847528 ]\n",
      " [2.361987  ]\n",
      " [2.1090956 ]\n",
      " [1.5420604 ]\n",
      " [1.0514301 ]\n",
      " [0.7949108 ]\n",
      " [0.80397505]\n",
      " [1.0047457 ]\n",
      " [1.4016845 ]\n",
      " [2.2022786 ]\n",
      " [3.710744  ]\n",
      " [3.9165125 ]\n",
      " [2.732107  ]\n",
      " [2.6046977 ]\n",
      " [2.3822339 ]\n",
      " [1.7131846 ]\n",
      " [1.1753953 ]\n",
      " [0.8510009 ]\n",
      " [0.7845462 ]\n",
      " [0.9441744 ]\n",
      " [1.3012484 ]\n",
      " [1.9943057 ]\n",
      " [3.399961  ]\n",
      " [4.1251287 ]\n",
      " [3.0232682 ]\n",
      " [2.5704892 ]\n",
      " [2.4651167 ]\n",
      " [1.9011238 ]\n",
      " [1.29763   ]\n",
      " [0.9153842 ]\n",
      " [0.78128135]\n",
      " [0.89088774]\n",
      " [1.196307  ]\n",
      " [1.778547  ]\n",
      " [3.0186005 ]\n",
      " [4.154358  ]\n",
      " [3.3263848 ]\n",
      " [2.5907135 ]\n",
      " [2.5280728 ]\n",
      " [2.0819614 ]\n",
      " [1.4247416 ]\n",
      " [0.9895506 ]\n",
      " [0.78768504]\n",
      " [0.8442172 ]\n",
      " [1.1012892 ]\n",
      " [1.594767  ]\n",
      " [2.6432242 ]]\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": 24,
   "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": 25,
   "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
}