{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised Wishart classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to apply the supervised Wishart classification to a PolSARpro NetCDF dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages and set directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "from pathlib import Path\n",
    "import xarray as xr\n",
    "from polsarpro.io import open_netcdf_beam\n",
    "from polsarpro.classification import wishart_supervised\n",
    "\n",
    "# optional import for progress bar\n",
    "from dask.diagnostics import ProgressBar\n",
    "\n",
    "# change to your data paths\n",
    "# original dataset\n",
    "input_alos_data = Path(\"/data/psp/test_files/SAN_FRANCISCO_ALOS1_slc.nc\")\n",
    "output_dir = Path(\"/data/psp/res\")\n",
    "\n",
    "# Hand drawn regions\n",
    "label_path = Path(\"../data/sample_labels_sf_alos1.nc\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "We load the SNAP NetCDF-BEAM dataset using the `open_netcdf_beam` function. \n",
    "To obtain such a dataset, please refer to the \"Getting Started\" tutorial or the `quickstart-tutorial.ipynb` notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to test on S matrix made with SNAP\n",
    "S = open_netcdf_beam(input_alos_data)\n",
    "\n",
    "# initial regions labeled as {1...N}, 0 for unlabelled\n",
    "labels = xr.open_dataarray(label_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply the classification\n",
    "\n",
    "Let's apply the algorithm and write the result to a NetCDF file.\n",
    "Optionally we can use a progress bar to monitor the progress of the computation.\n",
    "\n",
    "**Note:** Unlike the C implementation, this version does not require precomputed H/A/Alpha parameters. However, for backward compatibility, you may provide existing parameters via the h_a_alpha_result argument. In that case, the H/A/Alpha parameters may have been computed using a different boxcar window size than the one used in this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change to the name of your liking\n",
    "file_out = output_dir / \"psp_wishart_super.nc\"\n",
    "\n",
    "# netcdf writer cannot overwrite\n",
    "if os.path.isfile(file_out):\n",
    "    os.remove(file_out)\n",
    "\n",
    "with ProgressBar():\n",
    "    wishart_supervised(S, training_labels=labels, boxcar_size=[7, 7]).to_netcdf(file_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We open the previously saved dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = xr.open_dataset(file_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we may plot the individual outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Blue: sea, green: vegetation, orange: built-up\n",
    "colors = [\"#387EB9\", \"#4EAF49\", \"#FF7F00\"]\n",
    "# Create the discrete colormap\n",
    "my_cmap = ListedColormap(colors)\n",
    "\n",
    "res.wishart_supervised_class.plot.imshow(cmap=my_cmap)"
   ]
  }
 ],
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