notebooks/Plotting/HeatmapYearMonth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a8de845-ab6b-442a-b70a-6d2ebee549e4",
   "metadata": {},
   "source": [
    "![lop](../../images/logo_diive1_128px.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12573e8-3455-4d42-aa3e-0fb6e05752df",
   "metadata": {},
   "source": [
    "<span style='font-size:40px; display:block;'>\n",
    "<b>\n",
    "    Heatmap YearMonth\n",
    "</b>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da92a450-6d6a-43b7-9104-6ded5ec6afde",
   "metadata": {},
   "source": [
    "---\n",
    "**Notebook version**: `1` (25 Mar 2024)  \n",
    "**Author**: Lukas Hörtnagl (holukas@ethz.ch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a4631a-a44c-45b2-93c6-b915dd872584",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cf5648-c62e-41e1-b4db-ecaeee73fbe3",
   "metadata": {},
   "source": [
    "# **Description**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd923d65-b370-4e7a-bf03-b19ae26cd957",
   "metadata": {},
   "source": [
    "- Plots heatmap showing values (z) of time series as year (y) vs month (x).\n",
    "- Time series must have monthly time resolution.\n",
    "- This example uses 10 years of air temperature measurements, recorded at half-hourly time resolution that is resampled to monthly time resolution before plotting.\n",
    "- `HeatmapDateTime` uses `HeatmapBase` (`diive.core.plotting.heatmap_base.HeatmapBase`) as base class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42350015-8c37-4acb-9784-60e348d84675",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521bb3ed-bc90-40c1-9e59-615ab97f80c3",
   "metadata": {},
   "source": [
    "# **Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa24ee26-81bb-4d95-a9f9-d7bfe6e55439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diive version: v0.74.0\n"
     ]
    }
   ],
   "source": [
    "import importlib.metadata\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", UserWarning)\n",
    "from diive.configs.exampledata import load_exampledata_parquet\n",
    "from diive.core.plotting.heatmap_datetime import HeatmapYearMonth\n",
    "version_diive = importlib.metadata.version(\"diive\")\n",
    "print(f\"diive version: v{version_diive}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d394b82d-fad4-4f52-9a36-7086c176846d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(HeatmapYearMonth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91a427c-5e9c-49dd-9bf8-33b1e5cc520f",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebfdf10-f24d-49b1-9855-3870c0bb10c1",
   "metadata": {},
   "source": [
    "# **Load example data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8df9b16-1f15-4a57-b475-83f417815538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded .parquet file F:\\Sync\\luhk_work\\20 - CODING\\21 - DIIVE\\diive\\diive\\configs\\exampledata\\exampledata_PARQUET_CH-DAV_FP2022.5_2013-2022_ID20230206154316_30MIN.parquet (0.053 seconds). Detected time resolution of <30 * Minutes> / 30min \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TIMESTAMP_MIDDLE\n",
       "2013-01-01 00:15:00   -1.618\n",
       "2013-01-01 00:45:00   -1.630\n",
       "2013-01-01 01:15:00   -1.644\n",
       "2013-01-01 01:45:00   -1.657\n",
       "2013-01-01 02:15:00   -0.567\n",
       "                       ...  \n",
       "2022-12-31 21:45:00    3.463\n",
       "2022-12-31 22:15:00    5.283\n",
       "2022-12-31 22:45:00    5.537\n",
       "2022-12-31 23:15:00    4.963\n",
       "2022-12-31 23:45:00    6.490\n",
       "Freq: 30min, Name: Tair_f, Length: 175296, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = load_exampledata_parquet()\n",
    "series_30min = df['Tair_f']\n",
    "series_30min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97c961f8-37f6-4fcc-a042-2acdf66583be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP_MIDDLE\n",
       "2013-01-01    -4.127289\n",
       "2013-02-01    -7.613169\n",
       "2013-03-01    -1.802983\n",
       "2013-04-01     3.542244\n",
       "2013-05-01     4.567939\n",
       "                ...    \n",
       "2022-08-01    13.990134\n",
       "2022-09-01     7.777842\n",
       "2022-10-01     9.117476\n",
       "2022-11-01     1.759813\n",
       "2022-12-01    -1.577778\n",
       "Freq: MS, Name: Tair_f, Length: 120, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_monthly_means = series_30min.resample('1MS', label='left').mean()\n",
    "series_monthly_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f97b0670-5a15-46a6-961f-ab439252bf5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_monthly_means.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09387737-0732-496d-bf79-da8f339bb5ec",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df38fda9-066a-4fef-986a-cfe921fb88c0",
   "metadata": {},
   "source": [
    "# (1) **Plot monthly heatmap**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d0d90b2-1c8d-4443-ac7e-1c87c99fc5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm = HeatmapYearMonth(\n",
    "    series_monthly=series_monthly_means,\n",
    "    title=\"Mean air temperature\",\n",
    "    zlabel=\"°C\",\n",
    "    cb_digits_after_comma=0,\n",
    "    figsize=(5, 8)\n",
    ")\n",
    "hm.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8abacf-0826-48a9-a688-7bd02bba2dcc",
   "metadata": {},
   "source": [
    "## Get ax in which the plot was generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf7985bc-d334-49ca-bae3-4a3d75fe1aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Mean air temperature'}, xlabel='Month', ylabel='Year'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_ax = hm.get_ax()\n",
    "this_ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05b5151-561f-489b-9cfa-3c98c3d2167b",
   "metadata": {},
   "source": [
    "## Get data that was used to create the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ef8a3d5-a230-4f94-bd46-0b9720bd0970",
   "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>x_vals</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_vals</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-4.127289</td>\n",
       "      <td>-7.613169</td>\n",
       "      <td>-1.802983</td>\n",
       "      <td>3.542244</td>\n",
       "      <td>4.567939</td>\n",
       "      <td>9.312250</td>\n",
       "      <td>13.911503</td>\n",
       "      <td>12.700735</td>\n",
       "      <td>8.835740</td>\n",
       "      <td>6.455432</td>\n",
       "      <td>-1.454567</td>\n",
       "      <td>-0.418102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>-1.758389</td>\n",
       "      <td>-1.760185</td>\n",
       "      <td>1.261626</td>\n",
       "      <td>4.103477</td>\n",
       "      <td>5.872437</td>\n",
       "      <td>10.825834</td>\n",
       "      <td>11.193386</td>\n",
       "      <td>9.861532</td>\n",
       "      <td>8.899755</td>\n",
       "      <td>6.744112</td>\n",
       "      <td>4.176977</td>\n",
       "      <td>-1.852366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>-3.424022</td>\n",
       "      <td>-4.642639</td>\n",
       "      <td>0.013280</td>\n",
       "      <td>2.999358</td>\n",
       "      <td>7.183376</td>\n",
       "      <td>11.388701</td>\n",
       "      <td>15.575397</td>\n",
       "      <td>14.015612</td>\n",
       "      <td>7.071502</td>\n",
       "      <td>4.600675</td>\n",
       "      <td>3.170463</td>\n",
       "      <td>2.282356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>-2.897780</td>\n",
       "      <td>-1.724999</td>\n",
       "      <td>-1.704113</td>\n",
       "      <td>3.230956</td>\n",
       "      <td>5.949297</td>\n",
       "      <td>9.814674</td>\n",
       "      <td>12.694337</td>\n",
       "      <td>12.403714</td>\n",
       "      <td>10.281148</td>\n",
       "      <td>3.805754</td>\n",
       "      <td>0.864235</td>\n",
       "      <td>0.304792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-6.134009</td>\n",
       "      <td>-0.702418</td>\n",
       "      <td>2.126041</td>\n",
       "      <td>2.126046</td>\n",
       "      <td>8.013755</td>\n",
       "      <td>12.940166</td>\n",
       "      <td>12.722626</td>\n",
       "      <td>13.716017</td>\n",
       "      <td>6.102577</td>\n",
       "      <td>5.936708</td>\n",
       "      <td>-1.048244</td>\n",
       "      <td>-4.428223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-1.429528</td>\n",
       "      <td>-7.421057</td>\n",
       "      <td>-1.648860</td>\n",
       "      <td>6.191065</td>\n",
       "      <td>8.871070</td>\n",
       "      <td>11.634234</td>\n",
       "      <td>13.981358</td>\n",
       "      <td>13.754745</td>\n",
       "      <td>10.794253</td>\n",
       "      <td>7.008341</td>\n",
       "      <td>2.641491</td>\n",
       "      <td>-2.511433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-6.910549</td>\n",
       "      <td>-0.084676</td>\n",
       "      <td>0.102886</td>\n",
       "      <td>2.826584</td>\n",
       "      <td>3.579712</td>\n",
       "      <td>14.384816</td>\n",
       "      <td>13.852238</td>\n",
       "      <td>13.148321</td>\n",
       "      <td>9.259018</td>\n",
       "      <td>7.433496</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>-0.786954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>-0.855530</td>\n",
       "      <td>-0.612688</td>\n",
       "      <td>-0.341668</td>\n",
       "      <td>5.991480</td>\n",
       "      <td>7.420426</td>\n",
       "      <td>9.644551</td>\n",
       "      <td>13.119326</td>\n",
       "      <td>12.977969</td>\n",
       "      <td>9.930872</td>\n",
       "      <td>3.772030</td>\n",
       "      <td>3.356790</td>\n",
       "      <td>-2.482149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>-5.839219</td>\n",
       "      <td>-0.372517</td>\n",
       "      <td>-1.237921</td>\n",
       "      <td>0.637986</td>\n",
       "      <td>4.483079</td>\n",
       "      <td>12.972103</td>\n",
       "      <td>11.986405</td>\n",
       "      <td>11.010687</td>\n",
       "      <td>10.380368</td>\n",
       "      <td>5.082464</td>\n",
       "      <td>-0.202026</td>\n",
       "      <td>-1.779989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-2.639465</td>\n",
       "      <td>-2.415209</td>\n",
       "      <td>0.668415</td>\n",
       "      <td>2.887385</td>\n",
       "      <td>9.277779</td>\n",
       "      <td>13.289816</td>\n",
       "      <td>14.504341</td>\n",
       "      <td>13.990134</td>\n",
       "      <td>7.777842</td>\n",
       "      <td>9.117476</td>\n",
       "      <td>1.759813</td>\n",
       "      <td>-1.577778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "x_vals        1         2         3         4         5          6          7          8          9         10        11        12\n",
       "y_vals                                                                                                                            \n",
       "2013   -4.127289 -7.613169 -1.802983  3.542244  4.567939   9.312250  13.911503  12.700735   8.835740  6.455432 -1.454567 -0.418102\n",
       "2014   -1.758389 -1.760185  1.261626  4.103477  5.872437  10.825834  11.193386   9.861532   8.899755  6.744112  4.176977 -1.852366\n",
       "2015   -3.424022 -4.642639  0.013280  2.999358  7.183376  11.388701  15.575397  14.015612   7.071502  4.600675  3.170463  2.282356\n",
       "2016   -2.897780 -1.724999 -1.704113  3.230956  5.949297   9.814674  12.694337  12.403714  10.281148  3.805754  0.864235  0.304792\n",
       "2017   -6.134009 -0.702418  2.126041  2.126046  8.013755  12.940166  12.722626  13.716017   6.102577  5.936708 -1.048244 -4.428223\n",
       "2018   -1.429528 -7.421057 -1.648860  6.191065  8.871070  11.634234  13.981358  13.754745  10.794253  7.008341  2.641491 -2.511433\n",
       "2019   -6.910549 -0.084676  0.102886  2.826584  3.579712  14.384816  13.852238  13.148321   9.259018  7.433496  0.375751 -0.786954\n",
       "2020   -0.855530 -0.612688 -0.341668  5.991480  7.420426   9.644551  13.119326  12.977969   9.930872  3.772030  3.356790 -2.482149\n",
       "2021   -5.839219 -0.372517 -1.237921  0.637986  4.483079  12.972103  11.986405  11.010687  10.380368  5.082464 -0.202026 -1.779989\n",
       "2022   -2.639465 -2.415209  0.668415  2.887385  9.277779  13.289816  14.504341  13.990134   7.777842  9.117476  1.759813 -1.577778"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = hm.get_plot_data()\n",
    "plot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac2237d-27e7-43ef-b041-04165ea10995",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812a4b56-e499-4dc1-ad03-f3277743f9f7",
   "metadata": {},
   "source": [
    "# (2) **Plot multiple heatmaps in one figure**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1fcd20-9e78-4922-8003-4c0649e936b9",
   "metadata": {},
   "source": [
    "## Resample and aggregate data using different methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3696b182-df49-4a76-8f1d-8122f1e59e41",
   "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>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>ptp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP_MIDDLE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-4.127289</td>\n",
       "      <td>7.800</td>\n",
       "      <td>25.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>-7.613169</td>\n",
       "      <td>5.167</td>\n",
       "      <td>23.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01</th>\n",
       "      <td>-1.802983</td>\n",
       "      <td>9.100</td>\n",
       "      <td>25.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-01</th>\n",
       "      <td>3.542244</td>\n",
       "      <td>15.267</td>\n",
       "      <td>26.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>4.567939</td>\n",
       "      <td>15.367</td>\n",
       "      <td>19.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>13.990134</td>\n",
       "      <td>25.270</td>\n",
       "      <td>18.953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>7.777842</td>\n",
       "      <td>22.087</td>\n",
       "      <td>23.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>9.117476</td>\n",
       "      <td>19.460</td>\n",
       "      <td>18.263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>1.759813</td>\n",
       "      <td>13.493</td>\n",
       "      <td>19.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>-1.577778</td>\n",
       "      <td>10.667</td>\n",
       "      <td>25.740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean     max     ptp\n",
       "TIMESTAMP_MIDDLE                           \n",
       "2013-01-01        -4.127289   7.800  25.300\n",
       "2013-02-01        -7.613169   5.167  23.400\n",
       "2013-03-01        -1.802983   9.100  25.367\n",
       "2013-04-01         3.542244  15.267  26.900\n",
       "2013-05-01         4.567939  15.367  19.067\n",
       "...                     ...     ...     ...\n",
       "2022-08-01        13.990134  25.270  18.953\n",
       "2022-09-01         7.777842  22.087  23.340\n",
       "2022-10-01         9.117476  19.460  18.263\n",
       "2022-11-01         1.759813  13.493  19.870\n",
       "2022-12-01        -1.577778  10.667  25.740\n",
       "\n",
       "[120 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggs = df['Tair_f'].resample('1MS', label='left').agg({'mean', 'max', np.ptp})  # numpy's ptp gives the data range\n",
    "aggs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58d122d9-fff9-4be7-ba73-2acb98365b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP_MIDDLE\n",
       "2013-01-01    -4.127289\n",
       "2013-02-01    -7.613169\n",
       "2013-03-01    -1.802983\n",
       "2013-04-01     3.542244\n",
       "2013-05-01     4.567939\n",
       "                ...    \n",
       "2022-08-01    13.990134\n",
       "2022-09-01     7.777842\n",
       "2022-10-01     9.117476\n",
       "2022-11-01     1.759813\n",
       "2022-12-01    -1.577778\n",
       "Freq: MS, Name: mean, Length: 120, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggs['mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a2c925f-5253-4750-9bb9-625949c34b1a",
   "metadata": {},
   "source": [
    "## Plot all aggregates next to each other in same figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da3b2b29-a5ad-4014-af7e-f419b5f716ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure\n",
    "fig = plt.figure(facecolor='white', figsize=(16, 9))\n",
    "\n",
    "# Gridspec for layout\n",
    "gs = gridspec.GridSpec(1, 3)  # rows, cols\n",
    "gs.update(wspace=0.5, hspace=0.3, left=0.03, right=0.97, top=0.97, bottom=0.03)\n",
    "ax_mean = fig.add_subplot(gs[0, 0])\n",
    "ax_max = fig.add_subplot(gs[0, 1])\n",
    "ax_range = fig.add_subplot(gs[0, 2])\n",
    "\n",
    "HeatmapYearMonth(ax=ax_mean, series_monthly=aggs['mean'], zlabel=\"\", cb_digits_after_comma=0).plot()\n",
    "HeatmapYearMonth(ax=ax_max, series_monthly=aggs['max'], zlabel=\"\", cb_digits_after_comma=0).plot()\n",
    "HeatmapYearMonth(ax=ax_range, series_monthly=aggs['ptp'], zlabel=\"\", cb_digits_after_comma=0).plot()\n",
    "\n",
    "ax_mean.set_title(\"Air temperature mean\", color='black')\n",
    "ax_max.set_title(\"Air temperature max\", color='black')\n",
    "ax_range.set_title(\"Air temperature range\", color='black')\n",
    "\n",
    "ax_mean.tick_params(left=True, right=False, top=False, bottom=True,\n",
    "                    labelleft=True, labelright=False, labeltop=False, labelbottom=True)\n",
    "ax_max.tick_params(left=True, right=False, top=False, bottom=True,\n",
    "                   labelleft=True, labelright=False, labeltop=False, labelbottom=True)\n",
    "ax_range.tick_params(left=True, right=False, top=False, bottom=True,\n",
    "                     labelleft=True, labelright=False, labeltop=False, labelbottom=True)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3d4a03-e864-4e0a-8845-0c9956fc5c74",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05555fc-afd7-41be-8d9c-3d743374975e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **End of notebook**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "858aa379-5643-4afe-b53f-eb370dee2835",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 2024-04-20 16:11:15\n"
     ]
    }
   ],
   "source": [
    "dt_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(f\"Finished {dt_string}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23e2f42a-cea0-4c9e-82c9-d3b47b3665c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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