{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f478ddab",
   "metadata": {},
   "source": [
    "# Groundwater Analysis Report (GAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4dbf32c-76d0-4e90-a8ce-9315beb98b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import brodata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160ca9be",
   "metadata": {},
   "source": [
    "Download a GroundwaterAnalysisReport using `GroundwaterAnalysisReport.from_bro_id(bro_id)`. The object contains the data from the BRO XML file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c31ba251",
   "metadata": {},
   "outputs": [],
   "source": [
    "gar = brodata.gar.GroundwaterAnalysisReport.from_bro_id(\"GAR000000019636\")\n",
    "gar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a2f472",
   "metadata": {},
   "source": [
    "Measurements are stored in the `laboratoryAnalysis` attribute (a pandas DataFrame)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d68cb85",
   "metadata": {},
   "outputs": [],
   "source": [
    "gar.laboratoryAnalysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f723311",
   "metadata": {},
   "source": [
    "Use `to_dict()` to view the remaining contents of the GroundwaterAnalysisReport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fcb9c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "gar_data = gar.to_dict()\n",
    "gar_data.pop(\"laboratoryAnalysis\")\n",
    "gar_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4736603f-f644-4bec-a223-5d1b5a3ce5f6",
   "metadata": {},
   "source": [
    "## Multiple objects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5708ba-b730-4047-8649-06c87ad4896f",
   "metadata": {},
   "source": [
    "### All measurements of one tube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1877a680-b797-4330-ae99-2a7de998a914",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmw_id = \"GMW000000017707\"\n",
    "tube_number = 1\n",
    "df = brodata.gmw.get_tube_observations(gmw_id, tube_number, kind=\"gar\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00093c5d-40a7-405e-819c-d08e5c7dc494",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_list = brodata.gar.get_parameter_list()\n",
    "parameter_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7634825-2877-4729-8144-395bb1e847c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the parameter description\n",
    "df[\"parameter_description\"] = parameter_list.loc[df[\"parameter\"], \"description\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3fb4a55-9257-4800-a229-2c833c4a143d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# show all the unique parameters and the number of measurements\n",
    "parameter_description_counts = df[\"parameter_description\"].value_counts()\n",
    "parameter_description_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "233742f0-1b44-442f-9e29-980643b3be6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_description = \"sulfaat\"\n",
    "parameter_code = brodata.gar.get_parameter_code(parameter_description, parameter_list=parameter_list)\n",
    "mask = df[\"parameter\"] == parameter_code\n",
    "ax = df.loc[mask, \"analysisMeasurementValue\"].plot(marker=\".\")\n",
    "uom = df.loc[mask, \"uom\"].unique()\n",
    "assert len(uom) == 1\n",
    "ax.set_ylabel(uom[0])\n",
    "ax.set_title(f\"{parameter_description} {gmw_id}_{tube_number}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29168b6-62ad-4d93-8f19-f493cc48660a",
   "metadata": {},
   "source": [
    "### All measurements within an extent\n",
    "As with Groundwater Level Dossiers, you can download GAR data within an extent via `brodata.gm.get_data_in_extent` or `brodata.gmw.get_data_in_extent`. Both return a GeoDataFrame where `laboratoryAnalysis` contains measurement data and `groundwaterAnalysisReport` lists GAR BRO IDs for each tube.\n",
    "\n",
    "For simplicity we use `brodata.gm.get_data_in_extent` (see the GLD example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c6c5cdf-465e-4105-8839-6f05bea84cf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "extent = [115000, 120000, 438000, 441000]\n",
    "gdf = brodata.gm.get_data_in_extent(extent=extent, kind=\"gar\", combine=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a503d6cd-2818-4410-810f-44f21e023163",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9ca7c1",
   "metadata": {},
   "source": [
    "Plot the GeoDataFrame to inspect locations; some monitoring wells may overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289d1c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots()\n",
    "ax.axis(\"scaled\")\n",
    "ax.axis(extent)\n",
    "gdf.plot(ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d835b70",
   "metadata": {},
   "source": [
    "Plot measurement series for the tubes in a single figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f92ab53",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots()\n",
    "parameter_description = \"sulfaat\"\n",
    "parameter_code = brodata.gar.get_parameter_code(parameter_description, parameter_list=parameter_list)\n",
    "uom = \"mg/l\"\n",
    "for index in gdf.index:\n",
    "    df = gdf.at[index, \"laboratoryAnalysis\"]\n",
    "    mask = df[\"parameter\"] == parameter_code\n",
    "    if not mask.any():\n",
    "        continue\n",
    "    assert (df.loc[mask, \"uom\"] == uom).all()\n",
    "    ax = df.loc[mask, \"analysisMeasurementValue\"].plot(marker=\".\", ax=ax, label=index)\n",
    "ax.set_ylabel(uom)\n",
    "ax.set_title(parameter_description)\n",
    "ax.legend();"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}