{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5293f5c5",
   "metadata": {},
   "source": [
    "# Load Data from the eodc STAC catalogue into Xarray\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0282890e",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from odc import stac as odc_stac\n",
    "except ImportError:\n",
    "    %pip install odc-stac\n",
    "    from odc import stac as odc_stac\n",
    "\n",
    "try:\n",
    "    from netCDF4 import Dataset\n",
    "except ImportError:\n",
    "    %pip install netCDF4\n",
    "    from netCDF4 import Dataset\n",
    "\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pystac_client import Client\n",
    "\n",
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7db18f",
   "metadata": {},
   "source": [
    "## Access the catalog and desired collection\n",
    "\n",
    "First we load the data, we want to load as a Xarray. This is described in the [Data Discorvery Notebook](https://github.com/eodcgmbh/eodc-examples/blob/main/demos/python-stac_DataDiscovery.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9903a2f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "eodc_catalog = Client.open(\n",
    "    \"https://stac.eodc.eu/api/v1\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8699d67",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "time_range = \"2024-03-01/2024-05-01\"\n",
    "\n",
    "# Bounding box of Austria\n",
    "bbox_aut = [9.25, 46.31, 17.46, 49.18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0989187",
   "metadata": {},
   "outputs": [],
   "source": [
    "search = eodc_catalog.search(\n",
    "    collections=[\"SENTINEL2_L1C\"],\n",
    "    bbox = bbox_aut,\n",
    "    datetime=time_range\n",
    ")\n",
    "\n",
    "print(\"We found \", search.matched(), \" items, that match our filter criteria.\")\n",
    "\n",
    "items = search.items()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e3b7fb",
   "metadata": {},
   "source": [
    "    We found  14  items, that match our filter criteria."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf987bf",
   "metadata": {},
   "source": [
    "### Specify Dataset parameters and load into Xarray\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9c30f02",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "crs = \"EPSG:4312\"\n",
    "res = 0.00018 # 20 meters in degree \n",
    "bands = [\"red\", \"nir\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22a6b730",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This step can take time\n",
    "print (\"loading data started  : \", datetime.now())\n",
    "ds = odc_stac.load(items,\n",
    "                    crs=crs,\n",
    "                    resolution=res,\n",
    "                    bbox=bbox_aut,\n",
    "                    bands = bands,\n",
    "                       )\n",
    "print (\"loading data completed: \", datetime.now())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9d85c8",
   "metadata": {},
   "source": [
    "    loading data started  :  2024-07-04 13:19:36.876273\n",
    "    loading data completed:  2024-07-04 13:49:39.271929"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89b765f",
   "metadata": {},
   "source": [
    "### Save results as netCDF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b9171f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.to_netcdf(\"./data/stac_xarray.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617a40f0",
   "metadata": {},
   "source": [
    "## Load netCDF\n",
    "\n",
    "Open the saved NetCDF file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cacc87bf",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "nc_file = xr.open_dataset(\"./data/stac_xarray.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f6d20ca",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "red = nc_file['red']\n",
    "nir = nc_file['nir']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36ce8f71",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def normalized_difference(x, y):\n",
    "    nd = (x - y) / (x + y)\n",
    "    return nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e78d8791",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndvi = normalized_difference(nir, red)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d362ccb3",
   "metadata": {},
   "source": [
    "### Plot the NetCDF Data\n",
    "\n",
    "Select data for a specific time step and create a plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab2e0b1e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "time_data = ndvi.isel(time = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f91fa1b4",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "p = plt.imshow(time_data, vmin = -1,  vmax = 1, cmap = \"inferno\")\n",
    "plt.colorbar(p)\n",
    "plt.xlabel(\"Longitude\")\n",
    "plt.ylabel(\"Latitude\")\n",
    "plt.title(\"NetCDF Data NDVI\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d085ef7",
   "metadata": {},
   "source": [
    "\n",
    "To create different plots, you need to run the notebook yourself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ea92f63",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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