{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "858a1a52",
   "metadata": {},
   "source": [
    "# Groundwater Production Dossier (GPD)\n",
    "A Groundwater Production Dossier contains the production figures of a groundwater use system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebf77d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import brodata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e256c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd = brodata.gpd.GroundwaterProductionDossier.from_bro_id(\"GPD000000012107\")\n",
    "gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cca9bb6",
   "metadata": {},
   "source": [
    "There are multiple reports in this Groundwater Production Dossier, where each report contains multiple volume-registrations. The metadata of the reports are contained in the attribute `report`, and the volumes are contained in the attribute `volumeSeries`. These two pandas DataFrames are linked to eachother by the column `reportId`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a66bd0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd.report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c98f75",
   "metadata": {},
   "source": [
    "Show the volume-series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b075ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd.volumeSeries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a48aaa",
   "metadata": {},
   "source": [
    "Plot the volume series and separate volumes by the `waterInOut` attribute; for injections, further separate by `temperatureIn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88bf61e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.axhline(0, color=\"black\", lw=0.5)\n",
    "for waterInOut in gpd.volumeSeries[\"waterInOut\"].unique():\n",
    "    maskInOut = gpd.volumeSeries[\"waterInOut\"] == waterInOut\n",
    "    for temperatureIn in gpd.volumeSeries.loc[maskInOut, \"temperatureIn\"].unique():\n",
    "        if pd.isna(temperatureIn):\n",
    "            maskTemperatureIn = gpd.volumeSeries[\"temperatureIn\"].isna()\n",
    "            label = f\"{waterInOut}\"\n",
    "        else:\n",
    "            maskTemperatureIn = gpd.volumeSeries[\"temperatureIn\"] == temperatureIn\n",
    "            label = f\"{waterInOut} - {temperatureIn}\"\n",
    "        mask = maskInOut & maskTemperatureIn\n",
    "        df = gpd.volumeSeries.loc[mask]\n",
    "\n",
    "        # sort by the column `beginDate`\n",
    "        df = df.sort_values(\"beginDate\")\n",
    "\n",
    "        # make a matplotlib plot of the volume series, with a constant value between beginDate and endDate\n",
    "        x = df.loc[:, [\"beginDate\", \"endDate\"]].values.ravel()\n",
    "\n",
    "        y = np.vstack((df[\"volume\"].values, df[\"volume\"].values)).transpose().ravel()\n",
    "        if waterInOut == \"onttrokken\":\n",
    "            y = -y\n",
    "\n",
    "        # make sure to draw a line between the endDate of one row and the beginDate of the next row if they are not consecutive\n",
    "        mask = (\n",
    "            df[\"endDate\"].values[:-1] + pd.Timedelta(1, \"D\")\n",
    "            != df[\"beginDate\"].values[1:]\n",
    "        )\n",
    "        ind = ((np.where(mask)[0] + 1) * 2).tolist()\n",
    "        ind = [0] + ind + [len(x)]\n",
    "\n",
    "        # get next color from the color cycle\n",
    "        color = ax._get_lines.get_next_color()\n",
    "        for i in range(len(ind) - 1):\n",
    "            ax.plot(\n",
    "                x[ind[i] : ind[i + 1]],\n",
    "                y[ind[i] : ind[i + 1]],\n",
    "                label=label if i == 0 else None,\n",
    "                color=color,\n",
    "            )\n",
    "\n",
    "ax.set_xlim(gpd.volumeSeries[\"beginDate\"].min(), gpd.volumeSeries[\"endDate\"].max())\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b2a4ae",
   "metadata": {},
   "source": [
    "Show the rest of the contents of the Groundwater Production Dossier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c549f6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd_data = gpd.to_dict()\n",
    "gpd_data.pop(\"report\")\n",
    "gpd_data.pop(\"volumeSeries\")\n",
    "gpd_data"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}