{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from datetime import datetime \n",
    "from datetime import date\n",
    "import matplotlib.pyplot as plt\n",
    "# import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "dataPATH = r\"C:\\Users\\levim\\OneDrive\\Documents\\MastersAI_ES\\TeamProject-5ARIP10\\smart-buildings\\Data\"\n",
    "\n",
    "### Load ALL data ###\n",
    "all_data = pd.read_csv(dataPATH + r\"\\long_merge.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load selection of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\levim\\AppData\\Local\\Temp\\ipykernel_27084\\3547628995.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  extended_energy_data['date'] = pd.to_datetime(extended_energy_data['date'])\n"
     ]
    }
   ],
   "source": [
    "# Prepar energy data set with extended features\n",
    "feature_list = ['date', 'hvac_N', 'hvac_S', 'air_temp_set_1', 'solar_radiation_set_1']\n",
    "extended_energy_data = all_data[feature_list]\n",
    "\n",
    "extended_energy_data['date'] = pd.to_datetime(extended_energy_data['date'])\n",
    "extended_energy_data.set_index('date', inplace=True)\n",
    "\n",
    "# eed = extended energy data\n",
    "# Resampling back to 15 minutes and 1 hour\n",
    "eed_15m = extended_energy_data.resample('15T').mean()\n",
    "eed_1h = extended_energy_data.resample('60T').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [],
   "source": [
    "# Assuming you want to apply a moving average window of size 3 on the 'column_name' column\n",
    "window_size = 12*4 # Half a day\n",
    "eed_15m_avg = eed_15m.copy()\n",
    "eed_15m_avg['hvac_N'] = eed_15m['hvac_N'].rolling(window=window_size).mean()\n",
    "eed_15m_avg['hvac_S'] = eed_15m['hvac_S'].rolling(window=window_size).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17591., 17652., 17713., 17775., 17836., 17897.]),\n",
       " [Text(17591.0, 0, '2018-03'),\n",
       "  Text(17652.0, 0, '2018-05'),\n",
       "  Text(17713.0, 0, '2018-07'),\n",
       "  Text(17775.0, 0, '2018-09'),\n",
       "  Text(17836.0, 0, '2018-11'),\n",
       "  Text(17897.0, 0, '2019-01')])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "\n",
    "start_date  = '2018-02-02'\n",
    "end_date    = '2019-02-03'\n",
    "\n",
    "plt.plot(eed_15m['hvac_N'].loc[start_date:end_date])\n",
    "plt.plot(eed_15m['moving_average'].loc[start_date:end_date])\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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