{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2ddf5cd",
   "metadata": {},
   "source": [
    "(polygons-based-data-selection)=\n",
    "# Polygons-based data selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967ed6b7",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook allows for the extraction of data within specific polygons defined in a [**ZIP shapefile**](https://en.wikipedia.org/wiki/Shapefile) or a [**GeoJSON**](https://geojson.org/). \\\n",
    "It processes geospatial data by using polygon geometry to subset and mask the dataset accordingly.\n",
    "\n",
    "This notebook demonstrates how to download *eastward sea water velocity (uo)* and *northward sea water velocity (vo)* from the Mediterranean coastline with the product `GLOBAL_ANALYSISFORECAST_PHY_001_024` and the dataset ``cmems_mod_glo_phy_anfc_0.083deg_P1D-m``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. Import the required libraries\n",
    "The process begins by importing all necessary libraries to read shapefiles, handle NetCDF data, perform rasterization, and visualize the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd44ff64",
   "metadata": {},
   "source": [
    "> **⚠️ Warning:**  \n",
    "> Before running this notebook, it is essential to install the **Copernicus Marine Toolbox** by following the [installation page](installation-page).\n",
    "> \n",
    "> Once the toolbox and the corresponding environment are set up:  \n",
    "> - The ``gdal`` package should be installed first.\n",
    "> - Followed by the installation of ``fiona``.\n",
    "> - Finally, ``geopandas`` should be installed.\n",
    ">  \n",
    "> Not following this order may lead to installation errors. \\\n",
    "> Afterward, make sure to install any other missing packages required to run the notebook.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import tempfile\n",
    "import zipfile\n",
    "import copernicusmarine\n",
    "import geopandas as gpd\n",
    "import glob\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import rasterio as rio\n",
    "from rasterio.features import rasterize\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "931d5d1f",
   "metadata": {},
   "source": [
    "## Step 2. Define the necessary functions\n",
    "In this section, four functions are defined to extract data from the desired polygons. Each function is explained in detail but will only be utilized later in the ``mask_dataset_by_polygons`` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Function to create a GeoDataFrame from a GeoJSON or a ZIP archive containing Shapefiles\n",
    "\n",
    "This function loads polygon data from a **GeoJSON file** or a **ZIP archive containing Shapefiles**, merges multiple shapefiles if needed, and computes the bounding box of the extracted geometries. 🚀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_gdf(file_path):\n",
    "\n",
    "    # Check if the file exists\n",
    "    if not os.path.isfile(file_path):\n",
    "        raise FileNotFoundError(f\"File not found: {file_path}\")\n",
    "\n",
    "    # Case 1: If it's a GeoJSON file, load it directly\n",
    "    if file_path.endswith(\".geojson\"):\n",
    "        gdf = gpd.read_file(file_path)\n",
    "        print(\"GeoJSON successfully loaded!\")\n",
    "    \n",
    "    # Case 2: If it's a ZIP file, look for shapefiles\n",
    "    elif file_path.endswith(\".zip\"):\n",
    "        parent_dir = os.path.dirname(file_path)\n",
    "        with tempfile.TemporaryDirectory(dir=parent_dir) as temp_dir:\n",
    "            # Verify that the file is a valid ZIP archive\n",
    "            if zipfile.is_zipfile(file_path):\n",
    "                with zipfile.ZipFile(file_path, 'r') as zip_ref:\n",
    "                    # Extract all contents of the ZIP file into the temporary directory\n",
    "                    zip_ref.extractall(temp_dir)\n",
    "\n",
    "                # Search for all extracted .shp (shapefile) files\n",
    "                shp_files = glob.glob(f\"{temp_dir}/**/*.shp\", recursive=True)\n",
    "                if not shp_files:\n",
    "                    raise FileNotFoundError(\"No .shp files found in the ZIP archive.\")\n",
    "                \n",
    "                print(\"Shapefile successfully loaded!\")\n",
    "                # Read and merge all found shapefiles into a single GeoDataFrame\n",
    "                gdfs = [gpd.read_file(shp) for shp in shp_files]\n",
    "                gdf = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True))\n",
    "            else:\n",
    "                raise ValueError(\"The provided ZIP file is not valid.\")\n",
    "    else:\n",
    "        raise ValueError(\"Unsupported file format. Use a .geojson file or a .zip archive containing shapefiles.\")\n",
    "\n",
    "    # Compute the bounding box of the GeoDataFrame (min/max latitude & longitude)\n",
    "    lon_min, lat_min, lon_max, lat_max = gdf.total_bounds\n",
    "    bbox_upload = {\n",
    "        \"lat_min\": lat_min, \n",
    "        \"lat_max\": lat_max, \n",
    "        \"lon_min\": lon_min, \n",
    "        \"lon_max\": lon_max\n",
    "    }\n",
    "    \n",
    "    return gdf, bbox_upload "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd89e339",
   "metadata": {},
   "source": [
    "### 2. Function to create a spatial mask from the polygon\n",
    "This function generates a mask using the shapefile’s geometry to crop the dataset accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_mask(gdf, dataset):\n",
    "    # Extract the bounding box from the polygon\n",
    "    x_min, y_min, x_max, y_max = gdf.total_bounds\n",
    "    ds_cropped = dataset.sel(longitude=slice(x_min, x_max), latitude=slice(y_min, y_max))\n",
    "\n",
    "    # Extract dataset resolution parameters\n",
    "    lon_min, lon_max = ds_cropped.longitude[0], ds_cropped.longitude[-1]\n",
    "    lat_min, lat_max = ds_cropped.latitude[0], ds_cropped.latitude[-1]\n",
    "    n_lon, n_lat = len(ds_cropped.longitude), len(ds_cropped.latitude)\n",
    "\n",
    "    # Generate a transformation matrix for rasterization\n",
    "    tr = rio.transform.from_bounds(\n",
    "        west=lon_min, south=lat_min, \n",
    "        east=lon_max, north=lat_max, \n",
    "        width=n_lon, height=n_lat\n",
    "    )\n",
    "\n",
    "    # Simplify the polygon to improve rasterization efficiency\n",
    "    gdf_simplified = gdf.copy()\n",
    "    gdf_simplified.geometry = gdf_simplified.geometry.simplify(0.01)\n",
    "\n",
    "    # Convert polygon into a raster mask\n",
    "    mask = rasterize(\n",
    "        gdf_simplified.geometry,\n",
    "        out_shape=(n_lat, n_lon),\n",
    "        transform=tr\n",
    "    )\n",
    "\n",
    "    # Convert mask to an xarray DataArray with proper coordinates\n",
    "    mask_da = xr.DataArray(\n",
    "        mask[::-1, :], \n",
    "        coords=[ds_cropped.latitude, ds_cropped.longitude], \n",
    "        dims=['latitude', 'longitude']\n",
    "    )\n",
    "\n",
    "    return mask_da"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5c1380",
   "metadata": {},
   "source": [
    "### 3. Function to apply the polygon mask to the dataset\n",
    "This function applies the generated mask to the dataset, filtering out data outside the polygon region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def masked(dataset, mask_da):\n",
    "    # Create an empty dataset to store masked variables\n",
    "    dataset_masked = xr.Dataset({})\n",
    "\n",
    "    # Retrieve the list of available variables in the dataset\n",
    "    variable_list = list(dataset.data_vars)\n",
    "    \n",
    "    # Apply the mask to each variable\n",
    "    var_masked_dict = {}\n",
    "    for var in variable_list:\n",
    "        var_masked_dict[var] = dataset[var].where(mask_da, drop=True)\n",
    "\n",
    "    # Assign the masked variables back to the new dataset\n",
    "    dataset_masked = dataset_masked.assign(**var_masked_dict)\n",
    "    \n",
    "    return dataset_masked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Function to Optimize a NetCDF Dataset When Saving\n",
    "This function optimizes a NetCDF dataset by applying efficient encoding settings to reduce file size and improve read/write performance. The optimization is only applied when saving the dataset as a NetCDF file.\n",
    "\n",
    "If no output file is specified, the dataset remains unchanged and is simply returned as an in-memory object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encoding(dataset_masked, output_file=None):\n",
    "    # If no output file is specified, return the dataset without saving\n",
    "    if output_file is None:\n",
    "        return\n",
    "    \n",
    "    prepare_encoding = {}\n",
    " \n",
    "    # List of encoding attributes supported by netCDF4\n",
    "    netcdf4_expected_encoding_value = [\n",
    "        'scale_factor', 'add_offset', 'dtype', 'complevel', '_FillValue',\n",
    "        'fletcher32', 'zlib', 'chunksizes', 'contiguous', 'shuffle',\n",
    "        'compression', 'least_significant_digit'\n",
    "    ]\n",
    " \n",
    "    # Iterate over each variable in the dataset and filter only the supported encoding attributes\n",
    "    for variable in dataset_masked.data_vars:\n",
    "        encoding = dataset_masked[variable].encoding\n",
    " \n",
    "        # Filter encoding options to keep only those expected by netCDF4\n",
    "        filtered_encoding = {key: value for key, value in encoding.items() if key in netcdf4_expected_encoding_value}\n",
    " \n",
    "        # Ensure key encoding options are set if not already defined\n",
    "        if 'zlib' not in filtered_encoding:  # Enables compression\n",
    "            filtered_encoding['zlib'] = True\n",
    "        if 'complevel' not in filtered_encoding:  # Compression level (1 = fast, 9 = max)\n",
    "            filtered_encoding['complevel'] = 1\n",
    "        if 'contiguous' not in filtered_encoding:  # Ensures chunk-based storage (faster for large files)\n",
    "            filtered_encoding['contiguous'] = False\n",
    "        if 'shuffle' not in filtered_encoding:  # Enables shuffle filter for better compression\n",
    "            filtered_encoding['shuffle'] = True\n",
    " \n",
    "        # Store optimized encoding settings for each variable\n",
    "        prepare_encoding[variable] = filtered_encoding\n",
    "    \n",
    "    ## ================ Save the dataset as a compressed NetCDF file ================\n",
    "    dataset_masked.to_netcdf(\n",
    "        path=output_file,\n",
    "        mode='w',  # Overwrites if a new filename is chosen\n",
    "        engine='netcdf4',\n",
    "        encoding=prepare_encoding  # Apply optimized encoding settings\n",
    "    )\n",
    "    print(f\"Dataset successfully saved at: {output_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Function to Mask and Subset a Copernicus Marine Dataset Using Polygons\n",
    "\n",
    "The `mask_dataset_by_polygons` uses `create_gdf`, `create_mask`, `masked` and `encoding` to subset a **Copernicus Marine dataset** using a **polygon file** (GeoJSON or Shapefile ZIP), apply a **spatial mask** and return the masked dataset **either in-memory or as a saved file**. \n",
    "\n",
    "### 🔹 Steps:\n",
    "\n",
    "1. Loads a polygon file and extracts its **minimum bounding box**.\n",
    "2. Downloads a **subset of data** from the Copernicus Marine dataset within the bounding box using the Toolbox.\n",
    "3. **Creates a mask** from the provided polygons and applies it to the dataset.\n",
    "4. **Encodes** the masked dataset and saves it to a file.\n",
    "\n",
    "### 📌 Parameters:\n",
    "\n",
    "1. **`polygon_file`** (*str*): Path to the file containing the polygons.  \n",
    "2. **`dataset_id`** (*str*): Copernicus Marine dataset ID to download.  \n",
    "3. **`variables`** (*list*): List of variables to extract.  \n",
    "4. **`start_date`** (*str*): Start date (format `YYYY-MM-DD`).  \n",
    "5. **`end_date`** (*str*): End date (format `YYYY-MM-DD`).  \n",
    "6. **`min_depth`** (*float, optional*): Minimum depth (in meters), used if provided.  \n",
    "7. **`max_depth`** (*float, optional*): Maximum depth (in meters), used if provided.  \n",
    "8. **`output_file`** (*str, optional*): Output file name to save the masked dataset, used if provided.\n",
    "\n",
    "### 🔹 Returns:\n",
    "- **`dataset_masked`** (*xarray.Dataset*):  \n",
    "  - Returned in-memory unless `output_file` is provided, in which case it is also saved as a NetCDF file.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_dataset_by_polygons(polygon_file, dataset_id, variables, start_date, end_date , min_depth=None, max_depth=None, output_file=None):\n",
    "    # 1. Create a GeoDataFrame from the provided polygon file (GeoJSON or Shapefile ZIP)\n",
    "    #    and extract its bounding box (minimum area covering the polygons).  \n",
    "    gdf, bbox_upload = create_gdf(polygon_file)\n",
    "\n",
    "    # 2. Download a subset of the Copernicus Marine dataset based on the bounding box coordinates\n",
    "    depth_filter = {\"minimum_depth\": min_depth, \"maximum_depth\": max_depth} if min_depth is not None and max_depth is not None else {}\n",
    "\n",
    "\n",
    "    DS_subset = copernicusmarine.open_dataset(\n",
    "    dataset_id=dataset_id,\n",
    "    minimum_longitude=bbox_upload[\"lon_min\"],\n",
    "    maximum_longitude=bbox_upload[\"lon_max\"],\n",
    "    minimum_latitude=bbox_upload[\"lat_min\"],\n",
    "    maximum_latitude=bbox_upload[\"lat_max\"],\n",
    "    start_datetime=start_date,\n",
    "    end_datetime=end_date,\n",
    "    variables=variables,\n",
    "    **depth_filter\n",
    "    )\n",
    "\n",
    "    # 3. Create a mask from the provided polygons and apply it to the dataset\n",
    "    mask = create_mask(gdf,DS_subset)\n",
    "    dataset_masked = masked(DS_subset, mask)\n",
    "\n",
    "    # 4. Encode the masked dataset and save it to a file\n",
    "    encoding(dataset_masked, output_file) if output_file else dataset_masked\n",
    "\n",
    "    return dataset_masked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. Masking the Dataset Using a Coastal Mediterranean Shapefile  \n",
    "\n",
    "The `mask_dataset_by_polygons` function is used to retrieve the **masked dataset** based on the **coastal Mediterranean shapefile** (`med_coastline.zip`) and the **Copernicus Marine product**:  \n",
    "[GLOBAL_ANALYSISFORECAST_PHY_001_024](https://data.marine.copernicus.eu/product/GLOBAL_ANALYSISFORECAST_PHY_001_024/description).  \n",
    "\n",
    "In this example, the function extracts **ocean current variables** (`uo`, `vo`) for the selected **date range** and **depth range**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset =  mask_dataset_by_polygons(polygon_file = \"med_coastline.zip\", dataset_id = 'cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m', variables = ['uo','vo'], \n",
    "                         start_date = '2025-02-06', end_date = '2025-02-14', \n",
    "                         min_depth = 0, max_depth = 100, output_file = 'dataset_masked2.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the masked dataset (optional)\n",
    "\n",
    "This step is **optional** and provides a **preview the masked dataset**.  \n",
    "The first available time and depth index (if applicable) are selected, and the data is displayed using a color map.  \n",
    "This visualization serves as a verification step to ensure that the masking process has been correctly applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select data\n",
    "ds = dataset.isel(time=0, depth=0 if \"depth\" in dataset.dims else slice(None))\n",
    "\n",
    "# Extract coordinates and values\n",
    "lon, lat, uo = ds.longitude.values, ds.latitude.values, ds.uo.values\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.pcolormesh(lon, lat, uo)\n",
    "plt.colorbar(label=\"uo\")\n",
    "plt.xlabel(\"Longitude\"), plt.ylabel(\"Latitude\")\n",
    "plt.title(f\"uo | Time: {ds.time.values} | Depth: {ds.get('depth', 'Surface').values}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7be4b0",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This notebook provides essential functions for handling geospatial data using polygon-based masking. \n",
    "It ensures that only relevant data within the selected region is processed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "copernicusmarine",
   "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": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}