Add mangroves visualiser and update the testing notebook

README.md

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+# Digital Earth Pacific Mangroves
+
+## Running the test notebook
+
+First install dependencies:
+
+```bash
+pip install \
+    git+https://github.com/digitalearthpacific/dep-tools/ \
+    git+https://github.com/jessjaco/azure-logger
+```
+
+Now clone the repo:
+
+```bash
+git clone https://github.com/digitalearthpacific/dep-mangroves
+```
+
+Now run the notebook "Test_Mangroves.ipynb". Data is written to the `data`
+folder in the same directory.

Visualise_Mangroves.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "from odc.stac import load\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "\n",
+    "import odc.geo # noqa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "catalog = \"https://stac.staging.digitalearthpacific.org\"\n",
+    "collection = \"dep_s2_mangroves\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Coordinates grabbed from Google Maps\n",
+    "# Southeast PNG\n",
+    "ll = (-10.590125, 149.844629)\n",
+    "ur = (-10.360110, 150.195631)\n",
+    "\n",
+    "# # Ba river mouth, Fiji\n",
+    "# ll = (-17.500881, 177.608558)\n",
+    "# ur = (-17.420771, 177.702546)\n",
+    "\n",
+    "bbox = (ll[1], ll[0], ur[1], ur[0])\n",
+    "\n",
+    "# Find STAC items\n",
+    "client = Client.open(catalog)\n",
+    "items = client.search(collections=[collection], bbox=bbox).items()\n",
+    "items = [i for i in items]\n",
+    "\n",
+    "print(f\"Found {len(items)} items\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {\n",
+    "    collection: {\n",
+    "        \"assets\": {\n",
+    "            \"mangroves\": {\"data_type\": \"int16\"}\n",
+    "        }\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "data = load(items, bbox=bbox, bands=[\"mangroves\"], stac_cfg=config)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.mangroves.plot.imshow(\n",
+    "    col=\"time\",\n",
+    "    col_wrap=4,\n",
+    "    levels=[0, 1, 2, 3],\n",
+    "    colors=[\"white\", \"yellow\", \"green\", \"darkgreen\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the values you want to count (0, 1, and 2)\n",
+    "values_to_count = [0, 1, 2]\n",
+    "\n",
+    "# Initialize an empty DataArray to store the counts\n",
+    "count_array = xr.DataArray(\n",
+    "    np.zeros((len(data[\"time\"]), len(values_to_count))),\n",
+    "    coords={\"time\": data[\"time\"], \"values\": values_to_count},\n",
+    "    dims=[\"time\", \"values\"],\n",
+    ")\n",
+    "\n",
+    "# Loop through each value and count occurrences in each year\n",
+    "for time in data.time:\n",
+    "    year = time.values.astype(\"datetime64[Y]\")\n",
+    "    one_year_data = data.sel(time=time)\n",
+    "    count = one_year_data.mangroves.groupby(one_year_data.mangroves).count()\n",
+    "    for i, v in enumerate(values_to_count):\n",
+    "        if v not in count.mangroves:\n",
+    "            # Add the missing count to the array\n",
+    "            count_array.loc[{\"time\": time, \"values\": v}] = 0\n",
+    "        else:\n",
+    "            # Add the count to the array\n",
+    "            val = count.sel(mangroves=v)\n",
+    "            count_array.loc[{\"time\": time, \"values\": v}] = val * 10 / 10000\n",
+    "\n",
+    "# Rename the count variable\n",
+    "count_array = count_array.rename(\"count\")\n",
+    "count_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_array.plot.line(x=\"time\", hue=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for time in data.time:\n",
+    "#     year = time.values.astype(\"datetime64[Y]\")\n",
+    "#     one_year_data = data.sel(time=time)\n",
+    "#     one_year_data.mangroves.odc.write_cog(f\"mangroves_{year}.tif\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}