notebooks/Analyses/CalculateZaggregatesInQuantileClassesOfXY.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",
    "    Calculate z-aggregates in quantiles (classes) of x and y\n",
    "</b>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da92a450-6d6a-43b7-9104-6ded5ec6afde",
   "metadata": {},
   "source": [
    "---\n",
    "**Notebook version**: `1` (23 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": "2de7b49a-bd26-4a75-aa69-6f659578bb94",
   "metadata": {},
   "source": [
    "- By default, x and y are binned into 10 classes and the aggregate of z (mean, median, min, max, counts) is shown in each of the resulting 100 classes (10*10)."
   ]
  },
  {
   "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",
   "id": "fa24ee26-81bb-4d95-a9f9-d7bfe6e55439",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.055005Z",
     "start_time": "2024-08-22T14:05:32.495667Z"
    }
   },
   "source": [
    "import importlib.metadata\n",
    "import warnings\n",
    "from datetime import datetime\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from diive.configs.exampledata import load_exampledata_parquet\n",
    "from diive.pkgs.analyses.quantilexyaggz import QuantileXYAggZ\n",
    "from diive.core.plotting.heatmap_xyz import HeatmapPivotXYZ\n",
    "\n",
    "version_diive = importlib.metadata.version(\"diive\")\n",
    "print(f\"diive version: v{version_diive}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diive version: v0.79.0\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "d394b82d-fad4-4f52-9a36-7086c176846d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.149677Z",
     "start_time": "2024-08-22T14:05:35.140701Z"
    }
   },
   "source": [
    "help(QuantileXYAggZ)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class QuantileXYAggZ in module diive.pkgs.analyses.quantilexyaggz:\n",
      "\n",
      "class QuantileXYAggZ(builtins.object)\n",
      " |  QuantileXYAggZ(x: pandas.core.series.Series, y: pandas.core.series.Series, z: pandas.core.series.Series, n_quantiles: int = 10, min_n_vals_per_bin: int = 1, binagg_z: Literal['mean', 'min', 'max', 'median', 'count'] = 'mean')\n",
      " |  \n",
      " |  Calculate z-aggregates in quantiles (classes) of x and y\n",
      " |  \n",
      " |  By default, x and y are binned into 10 classes (n_quantiles: int = 10) and\n",
      " |  the mean of z is shown in each of the resulting 100 classes (10*10).\n",
      " |  \n",
      " |  The result is a pivoted dataframe and its longform.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, x: pandas.core.series.Series, y: pandas.core.series.Series, z: pandas.core.series.Series, n_quantiles: int = 10, min_n_vals_per_bin: int = 1, binagg_z: Literal['mean', 'min', 'max', 'median', 'count'] = 'mean')\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  run(self)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties defined here:\n",
      " |  \n",
      " |  longformdf\n",
      " |  \n",
      " |  pivotdf\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "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",
   "id": "b8df9b16-1f15-4a57-b475-83f417815538",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.447793Z",
     "start_time": "2024-08-22T14:05:35.263708Z"
    }
   },
   "source": [
    "df = load_exampledata_parquet()\n",
    "df.head()"
   ],
   "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 (0.063 seconds). Detected time resolution of <30 * Minutes> / 30min \n"
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       "TIMESTAMP_MIDDLE                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               \n",
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       "2013-01-01 01:45:00  0.290         1.354        -0.088          1.771            0.0            0.0             0.0  13.066      NaN  192.083         0.538            0.538         1.537              NaN          0.538             0.538  831.903 -0.018                  0.0     NaN      0.0        1.892        1.450         2.308           1.219           1.926            1.622               0.518   0.0      0.0  37.767          34.975  -1.657     -1.657                0.288  1.332     1.332\n",
       "2013-01-01 02:15:00  0.377         0.545        -0.080          0.797            0.0            0.0             0.0  16.995      NaN  192.677         1.544              NaN         1.709              NaN          1.765               NaN  831.680  0.007                  0.0     NaN      1.0        2.090        1.630         2.562           1.347           2.105            1.790               0.578   0.0      0.0  39.533          34.967  -0.567     -0.567                0.288  3.545     3.545"
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       "      <td>0.0</td>\n",
       "      <td>39.533</td>\n",
       "      <td>34.967</td>\n",
       "      <td>-0.567</td>\n",
       "      <td>-0.567</td>\n",
       "      <td>0.288</td>\n",
       "      <td>3.545</td>\n",
       "      <td>3.545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "af463064-e976-4f0a-9ff5-4417ae32ae61",
   "metadata": {},
   "source": [
    "## Make subset"
   ]
  },
  {
   "cell_type": "code",
   "id": "f97b0670-5a15-46a6-961f-ab439252bf5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.543027Z",
     "start_time": "2024-08-22T14:05:35.528067Z"
    }
   },
   "source": [
    "# Make subset of three required columns\n",
    "vpd_col = 'VPD_f'\n",
    "ta_col = 'Tair_f'\n",
    "swin_col = 'Rg_f'\n",
    "df = df[[vpd_col, ta_col, swin_col]].copy()"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "8ca57200-2fc0-4abc-9ddb-2db9d73a6c0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.638034Z",
     "start_time": "2024-08-22T14:05:35.608118Z"
    }
   },
   "source": [
    "# Use data May and Sep\n",
    "df = df.loc[(df.index.month >= 5) & (df.index.month <= 9)].copy()"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "16643573-d9ff-479c-9cae-b5cf015ed278",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.731718Z",
     "start_time": "2024-08-22T14:05:35.713643Z"
    }
   },
   "source": [
    "# Use daytime data \n",
    "daytime_locs = (df[swin_col] > 0)\n",
    "df = df[daytime_locs].copy()\n",
    "df = df.dropna()"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "d2141828-2323-40fc-8f85-f9d207981d51",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.777596Z",
     "start_time": "2024-08-22T14:05:35.767623Z"
    }
   },
   "source": [
    "df"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     VPD_f  Tair_f     Rg_f\n",
       "TIMESTAMP_MIDDLE                           \n",
       "2013-05-01 05:15:00  2.843   7.667    2.912\n",
       "2013-05-01 05:45:00  1.425   5.967   21.093\n",
       "2013-05-01 06:15:00  1.460   6.167   50.277\n",
       "2013-05-01 06:45:00  1.587   6.700   96.650\n",
       "2013-05-01 07:15:00  1.586   6.900  162.190\n",
       "...                    ...     ...      ...\n",
       "2022-09-30 16:15:00  0.156   3.217  111.235\n",
       "2022-09-30 16:45:00  0.146   3.170   77.094\n",
       "2022-09-30 17:15:00  0.121   3.203   56.054\n",
       "2022-09-30 17:45:00  0.128   3.367   27.603\n",
       "2022-09-30 18:15:00  0.076   3.233    0.613\n",
       "\n",
       "[45447 rows x 3 columns]"
      ],
      "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",
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       "    }\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>VPD_f</th>\n",
       "      <th>Tair_f</th>\n",
       "      <th>Rg_f</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>2013-05-01 05:15:00</th>\n",
       "      <td>2.843</td>\n",
       "      <td>7.667</td>\n",
       "      <td>2.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01 05:45:00</th>\n",
       "      <td>1.425</td>\n",
       "      <td>5.967</td>\n",
       "      <td>21.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01 06:15:00</th>\n",
       "      <td>1.460</td>\n",
       "      <td>6.167</td>\n",
       "      <td>50.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01 06:45:00</th>\n",
       "      <td>1.587</td>\n",
       "      <td>6.700</td>\n",
       "      <td>96.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01 07:15:00</th>\n",
       "      <td>1.586</td>\n",
       "      <td>6.900</td>\n",
       "      <td>162.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30 16:15:00</th>\n",
       "      <td>0.156</td>\n",
       "      <td>3.217</td>\n",
       "      <td>111.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30 16:45:00</th>\n",
       "      <td>0.146</td>\n",
       "      <td>3.170</td>\n",
       "      <td>77.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30 17:15:00</th>\n",
       "      <td>0.121</td>\n",
       "      <td>3.203</td>\n",
       "      <td>56.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30 17:45:00</th>\n",
       "      <td>0.128</td>\n",
       "      <td>3.367</td>\n",
       "      <td>27.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30 18:15:00</th>\n",
       "      <td>0.076</td>\n",
       "      <td>3.233</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45447 rows × 3 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "id": "09387737-0732-496d-bf79-da8f339bb5ec",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83913f5e-b9ee-4ce0-b7fe-98f420d1e16c",
   "metadata": {},
   "source": [
    "# (1) **Calculate mean of z in 10 quantile classes of x and y**"
   ]
  },
  {
   "cell_type": "code",
   "id": "fb8fa4b1-e577-4d8c-91ab-bf27fa33ee54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:35.918243Z",
     "start_time": "2024-08-22T14:05:35.840987Z"
    }
   },
   "source": [
    "q = QuantileXYAggZ(\n",
    "    x=df[swin_col],\n",
    "    y=df[ta_col],\n",
    "    z=df[vpd_col],\n",
    "    n_quantiles=10,\n",
    "    min_n_vals_per_bin=3,\n",
    "    binagg_z='mean'\n",
    ")\n",
    "q.run()\n",
    "pivotdf = q.pivotdf.copy()\n",
    "pivotdf"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BIN_Rg_f           0          10         20         30         40         50         60         70         80         90\n",
       "BIN_Tair_f                                                                                                              \n",
       "0            0.738029   0.882306   0.907608   0.783157   0.882975   1.210846   1.484725   1.906702   2.124990   2.569857\n",
       "10           1.109053   1.247962   1.250752   1.205217   1.362100   1.741093   2.281106   2.827544   3.221641   3.404728\n",
       "20           1.359257   1.609306   1.517805   1.466337   1.760117   2.230852   2.803953   3.519970   3.936581   4.287607\n",
       "30           1.803614   1.966310   1.899482   1.815355   2.243760   3.295720   3.494331   4.207591   4.818127   5.080783\n",
       "40           2.191474   2.617581   2.407728   2.455835   2.951037   3.803165   4.380593   4.944201   5.332908   6.178695\n",
       "50           3.147071   3.443443   3.602171   3.537326   3.662930   4.668226   5.442874   6.001373   6.345775   7.025564\n",
       "60           4.698872   5.107422   5.021604   5.149920   5.501511   6.077052   6.522451   7.160304   7.789453   8.057696\n",
       "70           6.514295   6.915953   7.306498   7.352755   7.474100   8.062731   8.563870   8.860974   9.411137   9.825721\n",
       "80           9.090340   9.430653   9.720280   9.983868  10.115974  10.705982  11.236660  11.251576  12.075453  12.163915\n",
       "90          13.102229  13.863063  14.488767  14.659287  14.923097  15.827548  16.207302  16.459568  16.957323  17.613251"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>BIN_Rg_f</th>\n",
       "      <th>0</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
       "      <th>50</th>\n",
       "      <th>60</th>\n",
       "      <th>70</th>\n",
       "      <th>80</th>\n",
       "      <th>90</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIN_Tair_f</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>0</th>\n",
       "      <td>0.738029</td>\n",
       "      <td>0.882306</td>\n",
       "      <td>0.907608</td>\n",
       "      <td>0.783157</td>\n",
       "      <td>0.882975</td>\n",
       "      <td>1.210846</td>\n",
       "      <td>1.484725</td>\n",
       "      <td>1.906702</td>\n",
       "      <td>2.124990</td>\n",
       "      <td>2.569857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.109053</td>\n",
       "      <td>1.247962</td>\n",
       "      <td>1.250752</td>\n",
       "      <td>1.205217</td>\n",
       "      <td>1.362100</td>\n",
       "      <td>1.741093</td>\n",
       "      <td>2.281106</td>\n",
       "      <td>2.827544</td>\n",
       "      <td>3.221641</td>\n",
       "      <td>3.404728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.359257</td>\n",
       "      <td>1.609306</td>\n",
       "      <td>1.517805</td>\n",
       "      <td>1.466337</td>\n",
       "      <td>1.760117</td>\n",
       "      <td>2.230852</td>\n",
       "      <td>2.803953</td>\n",
       "      <td>3.519970</td>\n",
       "      <td>3.936581</td>\n",
       "      <td>4.287607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.803614</td>\n",
       "      <td>1.966310</td>\n",
       "      <td>1.899482</td>\n",
       "      <td>1.815355</td>\n",
       "      <td>2.243760</td>\n",
       "      <td>3.295720</td>\n",
       "      <td>3.494331</td>\n",
       "      <td>4.207591</td>\n",
       "      <td>4.818127</td>\n",
       "      <td>5.080783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.191474</td>\n",
       "      <td>2.617581</td>\n",
       "      <td>2.407728</td>\n",
       "      <td>2.455835</td>\n",
       "      <td>2.951037</td>\n",
       "      <td>3.803165</td>\n",
       "      <td>4.380593</td>\n",
       "      <td>4.944201</td>\n",
       "      <td>5.332908</td>\n",
       "      <td>6.178695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>3.147071</td>\n",
       "      <td>3.443443</td>\n",
       "      <td>3.602171</td>\n",
       "      <td>3.537326</td>\n",
       "      <td>3.662930</td>\n",
       "      <td>4.668226</td>\n",
       "      <td>5.442874</td>\n",
       "      <td>6.001373</td>\n",
       "      <td>6.345775</td>\n",
       "      <td>7.025564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>4.698872</td>\n",
       "      <td>5.107422</td>\n",
       "      <td>5.021604</td>\n",
       "      <td>5.149920</td>\n",
       "      <td>5.501511</td>\n",
       "      <td>6.077052</td>\n",
       "      <td>6.522451</td>\n",
       "      <td>7.160304</td>\n",
       "      <td>7.789453</td>\n",
       "      <td>8.057696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>6.514295</td>\n",
       "      <td>6.915953</td>\n",
       "      <td>7.306498</td>\n",
       "      <td>7.352755</td>\n",
       "      <td>7.474100</td>\n",
       "      <td>8.062731</td>\n",
       "      <td>8.563870</td>\n",
       "      <td>8.860974</td>\n",
       "      <td>9.411137</td>\n",
       "      <td>9.825721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>9.090340</td>\n",
       "      <td>9.430653</td>\n",
       "      <td>9.720280</td>\n",
       "      <td>9.983868</td>\n",
       "      <td>10.115974</td>\n",
       "      <td>10.705982</td>\n",
       "      <td>11.236660</td>\n",
       "      <td>11.251576</td>\n",
       "      <td>12.075453</td>\n",
       "      <td>12.163915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>13.102229</td>\n",
       "      <td>13.863063</td>\n",
       "      <td>14.488767</td>\n",
       "      <td>14.659287</td>\n",
       "      <td>14.923097</td>\n",
       "      <td>15.827548</td>\n",
       "      <td>16.207302</td>\n",
       "      <td>16.459568</td>\n",
       "      <td>16.957323</td>\n",
       "      <td>17.613251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "id": "d6037f6a-e647-4959-8b02-325dde55e484",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a17d7a6-b55c-431a-8637-e6075d383c00",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "id": "e2e19bbf-0603-48a2-8cf7-c9c7f2f1e7bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:36.547905Z",
     "start_time": "2024-08-22T14:05:35.969088Z"
    }
   },
   "source": [
    "hm = HeatmapPivotXYZ(pivotdf=pivotdf)\n",
    "hm.plot(\n",
    "    cb_digits_after_comma=0,\n",
    "    xlabel=r'Short-wave incoming radiation ($\\mathrm{percentile}$)',\n",
    "    ylabel=r'Air temperature ($\\mathrm{percentile}$)',\n",
    "    zlabel=r'Vapor pressure deficit (mean)'\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x900 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "id": "60533041-5c72-45b7-ac8d-0d1f8092cd89",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9990d22-8498-4cf6-abf3-5877cda181b8",
   "metadata": {},
   "source": [
    "# (2) **Calculate counts of z in 100 quantile classes of x and y**"
   ]
  },
  {
   "cell_type": "code",
   "id": "04813fa5-5ff7-423c-8671-87559d6a5dc9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:37.156649Z",
     "start_time": "2024-08-22T14:05:36.624196Z"
    }
   },
   "source": [
    "q = QuantileXYAggZ(\n",
    "    x=df[swin_col],\n",
    "    y=df[ta_col],\n",
    "    z=df[vpd_col],\n",
    "    n_quantiles=100,\n",
    "    min_n_vals_per_bin=3,\n",
    "    binagg_z='count'\n",
    ")\n",
    "q.run()\n",
    "pivotdf = q.pivotdf.copy()\n",
    "pivotdf"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BIN_Rg_f      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  ...   75    76    77    78   79   80    81    82    83    84    85    86    87    88    89    90    91    92    93    94    95    96    97    98    99\n",
       "BIN_Tair_f                                                                                                                                             ...                                                                                                                                                   \n",
       "0            8.0  9.0  8.0  7.0  11.0   6.0   8.0  11.0  8.0   6.0  8.0  8.0   8.0  18.0  5.0  11.0   7.0  10.0  10.0  11.0  5.0  6.0  8.0   8.0  1.0  ...  NaN   NaN   NaN   NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN\n",
       "1           10.0  3.0  4.0  8.0  11.0  11.0  12.0   8.0  9.0   7.0  6.0  4.0   7.0   8.0  7.0   2.0   NaN  11.0   5.0   7.0  3.0  4.0  8.0  13.0  5.0  ...  NaN   NaN   NaN   NaN  3.0  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN\n",
       "2            8.0  5.0  8.0  3.0   6.0   6.0   7.0   6.0  7.0  11.0  8.0  7.0  11.0  14.0  5.0   7.0  11.0  11.0   3.0  11.0  9.0  4.0  8.0   7.0  4.0  ...  3.0   NaN   4.0   NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN\n",
       "3            6.0  9.0  7.0  9.0   8.0   8.0   8.0  12.0  4.0   5.0  7.0  6.0   7.0   7.0  7.0   3.0   6.0   7.0   6.0   3.0  7.0  8.0  7.0   3.0  9.0  ...  NaN   NaN   NaN   NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN   NaN   NaN   3.0   NaN   NaN   NaN   NaN   3.0   NaN\n",
       "4            8.0  7.0  NaN  6.0   6.0   5.0   8.0   7.0  7.0   5.0  4.0  7.0   5.0   9.0  8.0   3.0   6.0   5.0   4.0   6.0  7.0  7.0  6.0   7.0  3.0  ...  NaN   4.0   3.0   4.0  NaN  NaN   NaN   5.0   NaN   NaN   NaN   NaN   4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   7.0\n",
       "...          ...  ...  ...  ...   ...   ...   ...   ...  ...   ...  ...  ...   ...   ...  ...   ...   ...   ...   ...   ...  ...  ...  ...   ...  ...  ...  ...   ...   ...   ...  ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...\n",
       "95           NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN  NaN   NaN  NaN  NaN   4.0   NaN  NaN   NaN   NaN   NaN   3.0   NaN  NaN  NaN  NaN   NaN  NaN  ...  7.0   7.0   7.0  17.0  5.0  7.0   4.0  10.0   7.0  12.0  10.0  12.0  12.0  19.0   9.0  15.0  12.0   9.0  12.0  13.0  20.0  17.0  14.0  11.0  10.0\n",
       "96           NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN  NaN   NaN  NaN  NaN   NaN   3.0  NaN   NaN   NaN   NaN   NaN   NaN  NaN  NaN  NaN   NaN  NaN  ...  7.0   8.0  11.0   6.0  4.0  6.0   9.0   7.0   8.0  12.0  11.0  12.0  13.0   9.0  16.0  22.0  18.0   8.0  16.0   5.0  21.0  13.0  24.0   8.0   5.0\n",
       "97           NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN  NaN   NaN  NaN  NaN   NaN   NaN  NaN   NaN   NaN   NaN   3.0   NaN  NaN  NaN  NaN   NaN  NaN  ...  3.0   4.0   7.0   6.0  9.0  8.0   3.0   7.0  14.0  17.0   5.0  17.0  10.0  13.0  15.0  14.0  15.0  10.0  16.0  21.0  19.0  12.0  10.0  11.0   6.0\n",
       "98           NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN  NaN   NaN  NaN  NaN   NaN   NaN  NaN   NaN   NaN   NaN   NaN   NaN  NaN  NaN  NaN   NaN  NaN  ...  8.0  13.0   6.0   6.0  4.0  8.0   8.0   8.0   7.0   9.0  11.0  10.0  20.0  13.0   8.0  13.0  20.0  18.0  17.0  22.0  20.0  18.0  12.0   8.0   5.0\n",
       "99           NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN  NaN   NaN  NaN  NaN   NaN   NaN  NaN   NaN   NaN   NaN   NaN   NaN  NaN  NaN  NaN   NaN  NaN  ...  7.0  10.0   6.0   8.0  8.0  7.0  10.0  12.0   7.0   7.0   7.0  10.0  15.0   7.0  18.0  10.0  17.0  17.0  18.0  20.0  26.0  25.0  18.0  18.0   NaN\n",
       "\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>95</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>17.0</td>\n",
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       "      <td>10.0</td>\n",
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       "      <th>96</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 100 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "id": "f20df38c-6535-418e-9ff0-47cb41b7bfa3",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ffe0124-66d7-45f1-abc0-efb22f386d05",
   "metadata": {},
   "source": [
    "## Plot\n",
    "- In this case there are many classes (100*100), therefore it is recommended to manually define `tickpos` and `ticklabels`."
   ]
  },
  {
   "cell_type": "code",
   "id": "49dafc5f-8874-4a2d-a4f9-6f377b6388e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:37.426649Z",
     "start_time": "2024-08-22T14:05:37.205549Z"
    }
   },
   "source": [
    "hm = HeatmapPivotXYZ(pivotdf=pivotdf)\n",
    "hm.plot(\n",
    "    cb_digits_after_comma=0,\n",
    "    xlabel=r'Short-wave incoming radiation ($\\mathrm{percentile}$)',\n",
    "    ylabel=r'Air temperature ($\\mathrm{percentile}$)',\n",
    "    zlabel=r'Vapor pressure deficit (counts)',\n",
    "    tickpos=[25, 50, 75],\n",
    "    ticklabels=['25', '50', '75']\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x900 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "219abd81-7b51-4cf1-8965-73efd9085755",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05555fc-afd7-41be-8d9c-3d743374975e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **End of notebook**"
   ]
  },
  {
   "cell_type": "code",
   "id": "858aa379-5643-4afe-b53f-eb370dee2835",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:37.567760Z",
     "start_time": "2024-08-22T14:05:37.552802Z"
    }
   },
   "source": [
    "dt_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(f\"Finished {dt_string}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 2024-08-22 16:05:37\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "23e2f42a-cea0-4c9e-82c9-d3b47b3665c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:05:37.756289Z",
     "start_time": "2024-08-22T14:05:37.741330Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  }
 ],
 "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"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}