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  {
   "cell_type": "markdown",
   "id": "39a8c83c-4d90-498a-b1e0-078df578db42",
   "metadata": {},
   "source": [
    "# Geosphere Austria tutorial\n",
    "This tutorial walks through the use of a point data class for accessing station data in Austria\n",
    "A video tutorial of this notebook can be found [here](https://www.loom.com/share/fcced7c00c47400e9a2b8b7b017949e5?sid=6a4cc255-48d0-48f0-8e14-a8aed0ef4bb3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da7e02c8-6932-4310-8108-7ad7f6bfbade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import metloom class https://data.hub.geosphere.at/dataset/\n",
    "from metloom.pointdata import GeoSphereHistPointData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee565b4d-e25d-4f43-8d85-f3b9fef5b31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16111f97-d305-4358-9088-eccb5b18cf99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install mapclassify and folium for gdf.explore()\n",
    "!pip install mapclassify\n",
    "!pip install folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31656640-c479-4a2a-aa11-2ada3b5d6194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read and explore shape\n",
    "gdf = gpd.read_file(\"./data/geosphere_test.kml\")\n",
    "gdf.explore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fe8a969-ff71-4beb-8259-556c66309f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define variable of interest https://github.com/M3Works/metloom/blob/main/metloom/variables.py\n",
    "variable = GeoSphereHistPointData.ALLOWED_VARIABLES.TEMP\n",
    "# Get the points within our region of interest\n",
    "points = GeoSphereHistPointData.points_from_geometry(gdf, [variable])\n",
    "print(len(points))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "028e0a56-aa66-43a7-b554-16dcfe2c35bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explore the points we located\n",
    "points_df = points.to_dataframe()\n",
    "points_df = points_df.set_crs(\"EPSG:4326\")\n",
    "\n",
    "points_df.explore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb075ff5-50a5-4de2-8362-511c669e61b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define start and end date of interest\n",
    "start = pd.to_datetime(\"2023-01-01\")\n",
    "end = pd.to_datetime(\"2023-01-11\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18544661-2f36-4097-b26f-2d6508acdc52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe for storing combined data\n",
    "final_df = pd.DataFrame()\n",
    "\n",
    "# loop through first 15 points and store the data\n",
    "for p in points.points[:15]:\n",
    "    result = p.get_daily_data(start, end, [variable])\n",
    "    if result is None:\n",
    "        print(f\"{p.name} did not return data\")\n",
    "    else:\n",
    "        # reset the index to just be datetime\n",
    "        result = result.reset_index().set_index(\"datetime\")\n",
    "        # store off the data in the final dataframe\n",
    "        final_df[p.name + p.id] = result[variable.name]\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55be4633-430d-46bb-8c8d-9792bca0c365",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the timeseries\n",
    "final_df.plot(ylabel=variable.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73cde7e4-2e99-4a3d-a19a-5a2b909737b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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