alt_calendar_viz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative Calendar Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was used for testing alternative visualization methods, such as different locations (New York) as well as aggregating multiple year's worth of data to create the visualiztion for a system that gets progressively smaller."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copying over some of the code used for calendar visualization to visualize some other locations/normalization methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "# Cleaning off first row and units\n",
    "def clean_units(df):\n",
    "    units_list = df.iloc[0]\n",
    "    df = df.iloc[1:]\n",
    "\n",
    "    # Cleaning units\n",
    "    df = df.apply(pd.to_numeric)\n",
    "\n",
    "    return df, units_list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a stopgap method to get decreasing system sizes: here, combining three year's worth of data even though it is really just one year repeated with different sizings. This is creating a minor visual bug where the Weekdays at the beginning and end of the years do not match up exactly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha.DESKTOP-U2HVTMF\\AppData\\Local\\Temp\\ipykernel_11364\\4239136166.py:2: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  atx_45t25b_df = pd.read_csv('BEopt Viz\\\\atx_45t25b.csv')\n",
      "C:\\Users\\micha.DESKTOP-U2HVTMF\\AppData\\Local\\Temp\\ipykernel_11364\\4239136166.py:3: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  atx_3t15b_df = pd.read_csv('BEopt Viz\\\\atx_3t15b.csv')\n",
      "C:\\Users\\micha.DESKTOP-U2HVTMF\\AppData\\Local\\Temp\\ipykernel_11364\\4239136166.py:4: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  atx_15t10b_df = pd.read_csv('BEopt Viz\\\\atx_15t10b.csv')\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>living_space_temp</th>\n",
       "      <th>heating_setpoint</th>\n",
       "      <th>cooling_setpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:00:00</th>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 01:00:00</th>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 02:00:00</th>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 03:00:00</th>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 04:00:00</th>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-12-31 19:00:00</th>\n",
       "      <td>74.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 20:00:00</th>\n",
       "      <td>74.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 21:00:00</th>\n",
       "      <td>74.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 22:00:00</th>\n",
       "      <td>74.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:00:00</th>\n",
       "      <td>74.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26280 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     living_space_temp  heating_setpoint  cooling_setpoint\n",
       "2021-01-01 00:00:00               72.0                72                74\n",
       "2021-01-01 01:00:00               72.0                72                74\n",
       "2021-01-01 02:00:00               72.0                72                74\n",
       "2021-01-01 03:00:00               72.0                72                74\n",
       "2021-01-01 04:00:00               72.0                72                74\n",
       "...                                ...               ...               ...\n",
       "2023-12-31 19:00:00               74.0                72                74\n",
       "2023-12-31 20:00:00               74.0                72                74\n",
       "2023-12-31 21:00:00               74.0                72                74\n",
       "2023-12-31 22:00:00               74.0                72                74\n",
       "2023-12-31 23:00:00               74.0                72                74\n",
       "\n",
       "[26280 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing and combining all datasets to get three yearly subplots\n",
    "atx_45t25b_df = pd.read_csv('BEopt Viz\\\\atx_45t25b.csv')\n",
    "atx_3t15b_df = pd.read_csv('BEopt Viz\\\\atx_3t15b.csv')\n",
    "atx_15t10b_df = pd.read_csv('BEopt Viz\\\\atx_15t10b.csv')\n",
    "atx_45t25b_df, units = clean_units(atx_45t25b_df)\n",
    "atx_3t15b_df, units = clean_units(atx_3t15b_df)\n",
    "atx_15t10b_df, units = clean_units(atx_15t10b_df)\n",
    "\n",
    "atx_df = pd.concat([atx_45t25b_df, atx_3t15b_df, atx_15t10b_df])\n",
    "\n",
    "# Slicing out only wanted columns and labels and changing to datetime\n",
    "full_labels_list = atx_df.columns.tolist()\n",
    "\n",
    "living_space_columns = ['My Design - Temperature| Living Space', 'My Design - Temperature| Heating Setpoint', 'My Design - Temperature| Cooling Setpoint']\n",
    "short_labels = ['living_space_temp', 'heating_setpoint', 'cooling_setpoint'] #temps in F\n",
    "# Creating date range\n",
    "date_range = pd.date_range(start=\"2021-01-01\", end=\"2024-01-01\", freq=\"1H\", inclusive=\"left\")\n",
    "\n",
    "living_df = (atx_df[living_space_columns].set_axis(short_labels, axis=1) # slicing and renaming columns\n",
    "                                         .set_index(date_range))         # changing to hourly index\n",
    "            \n",
    "living_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding columns: deviation over/under the heating/cooling setpoint\n",
    "\n",
    "setpoint_diff = living_df['cooling_setpoint'] - living_df['heating_setpoint']\n",
    "living_df['heating_deviation'] = living_df['living_space_temp'] - living_df['heating_setpoint']\n",
    "living_df['cooling_deviation'] = living_df['living_space_temp'] - living_df['cooling_setpoint']\n",
    "\n",
    "# True if the temperature is below the heating setpoint or above the cooling setpoint.\n",
    "living_df['exceeds_setpoints'] = np.where((living_df['living_space_temp'] > living_df['cooling_setpoint']) | (living_df['living_space_temp']<living_df['heating_setpoint']), True, False)\n",
    "\n",
    "# Slicing a dataframe with timepoints that exceed the temperature setpoints\n",
    "#excess_df = living_df[living_df['exceeds_setpoints'] == True]\n",
    "\n",
    "# Make new column for too hot, to cold, or in range\n",
    "range_conditions = [living_df['living_space_temp'] < living_df['heating_setpoint'], living_df['living_space_temp'] > living_df['cooling_setpoint'], (living_df['living_space_temp'].between(living_df['heating_setpoint'], living_df['cooling_setpoint']))]\n",
    "range_choices = ['Too Cold', 'Too Hot', 'In Range']\n",
    "living_df['in_range'] = np.select(range_conditions, range_choices)\n",
    "\n",
    "# Make new column for amount of excess degrees, 0 if within comfortable range.\n",
    "excess_conditions = [living_df['in_range'] == 'Too Hot', living_df['in_range'] == 'Too Cold', living_df['in_range'] == 'In Range']\n",
    "excess_choices = [living_df['cooling_deviation'], living_df['heating_deviation'], 0]\n",
    "living_df['excess_temp'] = np.select(excess_conditions, excess_choices)\n",
    "\n",
    "too_hot_df = living_df[living_df['in_range'] == 'Too Hot'] # If exceeds, cooling_deviation is magnitude\n",
    "too_cold_df = living_df[living_df['in_range'] == 'Too Cold'] # If exceeds, heating_deviation is magnitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cooling_setpoint</th>\n",
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       "      <th>cooling_deviation</th>\n",
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       "    <tr>\n",
       "      <th>2023-12-31 20:00:00</th>\n",
       "      <td>74.0</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>In Range</td>\n",
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       "    <tr>\n",
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       "      <td>74.0</td>\n",
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       "      <td>74.0</td>\n",
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      ],
      "text/plain": [
       "                     living_space_temp  heating_setpoint  cooling_setpoint  \\\n",
       "2021-01-01 00:00:00               72.0                72                74   \n",
       "2021-01-01 01:00:00               72.0                72                74   \n",
       "2021-01-01 02:00:00               72.0                72                74   \n",
       "2021-01-01 03:00:00               72.0                72                74   \n",
       "2021-01-01 04:00:00               72.0                72                74   \n",
       "...                                ...               ...               ...   \n",
       "2023-12-31 19:00:00               74.0                72                74   \n",
       "2023-12-31 20:00:00               74.0                72                74   \n",
       "2023-12-31 21:00:00               74.0                72                74   \n",
       "2023-12-31 22:00:00               74.0                72                74   \n",
       "2023-12-31 23:00:00               74.0                72                74   \n",
       "\n",
       "                     heating_deviation  cooling_deviation  exceeds_setpoints  \\\n",
       "2021-01-01 00:00:00                0.0               -2.0              False   \n",
       "2021-01-01 01:00:00                0.0               -2.0              False   \n",
       "2021-01-01 02:00:00                0.0               -2.0              False   \n",
       "2021-01-01 03:00:00                0.0               -2.0              False   \n",
       "2021-01-01 04:00:00                0.0               -2.0              False   \n",
       "...                                ...                ...                ...   \n",
       "2023-12-31 19:00:00                2.0                0.0              False   \n",
       "2023-12-31 20:00:00                2.0                0.0              False   \n",
       "2023-12-31 21:00:00                2.0                0.0              False   \n",
       "2023-12-31 22:00:00                2.0                0.0              False   \n",
       "2023-12-31 23:00:00                2.0                0.0              False   \n",
       "\n",
       "                     in_range  excess_temp  \n",
       "2021-01-01 00:00:00  In Range          0.0  \n",
       "2021-01-01 01:00:00  In Range          0.0  \n",
       "2021-01-01 02:00:00  In Range          0.0  \n",
       "2021-01-01 03:00:00  In Range          0.0  \n",
       "2021-01-01 04:00:00  In Range          0.0  \n",
       "...                       ...          ...  \n",
       "2023-12-31 19:00:00  In Range          0.0  \n",
       "2023-12-31 20:00:00  In Range          0.0  \n",
       "2023-12-31 21:00:00  In Range          0.0  \n",
       "2023-12-31 22:00:00  In Range          0.0  \n",
       "2023-12-31 23:00:00  In Range          0.0  \n",
       "\n",
       "[26280 rows x 8 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "living_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha.DESKTOP-U2HVTMF\\AppData\\Local\\Temp\\ipykernel_11364\\1742728942.py:10: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  living_dod_df = living_dod_df.groupby([living_dod_df['datetime'].dt.date]).mean()\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "\n",
    "# Condensing to day by day. Aggregating by excess minimum and maximum temperature.\n",
    "living_dod_df = living_df.reset_index().rename(columns={'index': 'datetime'})\n",
    "high_low_df = living_dod_df.groupby([living_dod_df['datetime'].dt.date]).agg({'excess_temp': [np.min, np.max]})\n",
    "\n",
    "high_low_df['excess_high_low'] = high_low_df['excess_temp']['amin'] + high_low_df['excess_temp']['amax'] #TODO: doesn't account for edge case day exceeds both heating and cooling\n",
    "\n",
    "# Aggregating the rest of the dataframe by mean temperature for that day and adding column.\n",
    "living_dod_df = living_dod_df.groupby([living_dod_df['datetime'].dt.date]).mean()\n",
    "living_dod_df['excess_high_low'] = high_low_df['excess_high_low']\n",
    "living_dod_df\n",
    "\n",
    "excess_df = living_dod_df[living_dod_df['exceeds_setpoints'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>living_space_temp</th>\n",
       "      <th>heating_setpoint</th>\n",
       "      <th>cooling_setpoint</th>\n",
       "      <th>heating_deviation</th>\n",
       "      <th>cooling_deviation</th>\n",
       "      <th>exceeds_setpoints</th>\n",
       "      <th>excess_temp</th>\n",
       "      <th>excess_high_low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-12</th>\n",
       "      <td>72.002083</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>-1.997917</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-14</th>\n",
       "      <td>72.699167</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.699167</td>\n",
       "      <td>-1.300833</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>-0.000833</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>72.366667</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>-1.633333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>73.495417</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.495417</td>\n",
       "      <td>-0.504583</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>72.914167</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.914167</td>\n",
       "      <td>-1.085833</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>-0.001667</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-19</th>\n",
       "      <td>68.686667</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>-3.313333</td>\n",
       "      <td>-5.313333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.313333</td>\n",
       "      <td>-6.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-20</th>\n",
       "      <td>72.040417</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.040417</td>\n",
       "      <td>-1.959583</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>-0.595417</td>\n",
       "      <td>-1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-21</th>\n",
       "      <td>72.848333</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.848333</td>\n",
       "      <td>-1.151667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.002917</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-27</th>\n",
       "      <td>72.376667</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.376667</td>\n",
       "      <td>-1.623333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-0.049167</td>\n",
       "      <td>-0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>72.707917</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.707917</td>\n",
       "      <td>-1.292083</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.011667</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>304 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            living_space_temp  heating_setpoint  cooling_setpoint  \\\n",
       "datetime                                                            \n",
       "2021-01-12          72.002083              72.0              74.0   \n",
       "2021-01-14          72.699167              72.0              74.0   \n",
       "2021-01-16          72.366667              72.0              74.0   \n",
       "2021-01-25          73.495417              72.0              74.0   \n",
       "2021-01-31          72.914167              72.0              74.0   \n",
       "...                       ...               ...               ...   \n",
       "2023-12-19          68.686667              72.0              74.0   \n",
       "2023-12-20          72.040417              72.0              74.0   \n",
       "2023-12-21          72.848333              72.0              74.0   \n",
       "2023-12-27          72.376667              72.0              74.0   \n",
       "2023-12-28          72.707917              72.0              74.0   \n",
       "\n",
       "            heating_deviation  cooling_deviation  exceeds_setpoints  \\\n",
       "datetime                                                              \n",
       "2021-01-12           0.002083          -1.997917           0.041667   \n",
       "2021-01-14           0.699167          -1.300833           0.041667   \n",
       "2021-01-16           0.366667          -1.633333           0.041667   \n",
       "2021-01-25           1.495417          -0.504583           0.041667   \n",
       "2021-01-31           0.914167          -1.085833           0.041667   \n",
       "...                       ...                ...                ...   \n",
       "2023-12-19          -3.313333          -5.313333           1.000000   \n",
       "2023-12-20           0.040417          -1.959583           0.458333   \n",
       "2023-12-21           0.848333          -1.151667           0.083333   \n",
       "2023-12-27           0.376667          -1.623333           0.250000   \n",
       "2023-12-28           0.707917          -1.292083           0.166667   \n",
       "\n",
       "            excess_temp  excess_high_low  \n",
       "datetime                                  \n",
       "2021-01-12    -0.001250            -0.03  \n",
       "2021-01-14    -0.000833            -0.02  \n",
       "2021-01-16     0.000833             0.02  \n",
       "2021-01-25     0.000833             0.02  \n",
       "2021-01-31    -0.001667            -0.04  \n",
       "...                 ...              ...  \n",
       "2023-12-19    -3.313333            -6.41  \n",
       "2023-12-20    -0.595417            -1.95  \n",
       "2023-12-21    -0.002917            -0.06  \n",
       "2023-12-27    -0.049167            -0.57  \n",
       "2023-12-28    -0.011667            -0.19  \n",
       "\n",
       "[304 rows x 8 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excess_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating progressively decreasing sized calendar visualizations - all on 2023, years are just for concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1250x510 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import calplot\n",
    "from  matplotlib.colors import LinearSegmentedColormap, TwoSlopeNorm, CenteredNorm\n",
    "\n",
    "# Creating custom color map to place white at 0 value.\n",
    "cmap_min = excess_df['excess_high_low'].min()\n",
    "cmap_max = excess_df['excess_high_low'].max()\n",
    "ts_norm = TwoSlopeNorm(vmin = cmap_min, vcenter = 0, vmax = cmap_max)\n",
    "\n",
    "excess_df.index = pd.to_datetime(excess_df.index)\n",
    "\n",
    "# Plotting excess days on calendar heatmap, centered norm\n",
    "calplot.calplot(excess_df['excess_high_low'],suptitle = 'Austin - Calendar | Progressively Decreasing System Sizings', norm=ts_norm, cmap = 'bwr')\n",
    "plt.savefig('Viz_Outputs\\\\aus_cal_decreasing_size.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}