add fire

Makefile

@@ -2,6 +2,7 @@ PROJECT := c3s_eqc_toolbox_template
 CONDA := conda
 CONDAFLAGS :=
 COV_REPORT := html
+WP :=
 
 default: qa type-check
 
@@ -17,6 +18,7 @@ type-check:
 conda-env-update:
 	$(CONDA) env update $(CONDAFLAGS) -f ci/environment-ci.yml
 	$(CONDA) env update $(CONDAFLAGS) -f environment.yml
+	$(CONDA) env update $(CONDAFLAGS) -f environments/environment_wp$(WP).yml
 
 docker-build:
 	docker build -t $(PROJECT) .

README.md

@@ -20,7 +20,7 @@ conda activate DEVELOP
 
 Before pushing to GitHub, run the following commands:
 
-1. Update conda environment: `make conda-env-update`
+1. Update conda environment choosing your wp (e.g., 5): `make conda-env-update WP=5`
 1. Install this package: `pip install -e .`
 1. Sync with the latest [template](https://github.com/ecmwf-projects/cookiecutter-conda-package) (optional): `make template-update`
 1. Run quality assurance checks: `make qa`

notebooks/wp5/LUCL_land_cover_classification_reliability.ipynb

@@ -42,7 +42,7 @@
    "id": "092084cc",
    "metadata": {},
    "source": [
-    "## Define Parameters"
+    "## Define parameters"
    ]
   },
   {

notebooks/wp5/fire_burned_area.ipynb

@@ -0,0 +1,342 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fire Burned Area (FIRE) events"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import geopandas as gpd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import regionmask\n",
+    "import shapely\n",
+    "import xarray as xr\n",
+    "from c3s_eqc_automatic_quality_control import download, plot, utils\n",
+    "\n",
+    "plt.style.use(\"seaborn-v0_8-notebook\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Time period\n",
+    "year_start = 2001\n",
+    "year_stop = 2019\n",
+    "\n",
+    "# Region of interest\n",
+    "lon_slice = slice(-10, 4)\n",
+    "lat_slice = slice(45, 35)\n",
+    "\n",
+    "# Shapefile with regions\n",
+    "shapefile_url = \"https://gisco-services.ec.europa.eu/distribution/v2/nuts/shp/NUTS_RG_20M_2021_4326.shp.zip\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "request = (\n",
+    "    \"satellite-fire-burned-area\",\n",
+    "    {\n",
+    "        \"format\": \"zip\",\n",
+    "        \"origin\": \"esa_cci\",\n",
+    "        \"sensor\": \"modis\",\n",
+    "        \"version\": \"5_1_1cds\",\n",
+    "        \"year\": [str(year) for year in range(year_start, year_stop + 1)],\n",
+    "        \"variable\": \"grid_variables\",\n",
+    "        \"month\": [\"06\", \"07\", \"08\"],\n",
+    "        \"nominal_day\": \"01\",\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download and regionalise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download and regionalize\n",
+    "ds = download.download_and_transform(\n",
+    "    *request,\n",
+    "    transform_func=utils.regionalise,\n",
+    "    transform_func_kwargs={\"lon_slice\": lon_slice, \"lat_slice\": lat_slice},\n",
+    "    chunks={\"year\": 1},\n",
+    ")\n",
+    "\n",
+    "# Reindex using year/month (shift months + 1)\n",
+    "ds = ds.assign_coords(\n",
+    "    year=(\"time\", ds[\"time\"].dt.year.astype(int).values),\n",
+    "    month=(\"time\", ds[\"time\"].dt.month.astype(int).values + 1),\n",
+    ")\n",
+    "ds = ds.set_index(time=(\"year\", \"month\")).unstack(\"time\")\n",
+    "\n",
+    "# Convert units\n",
+    "da = ds[\"burned_area\"]\n",
+    "with xr.set_options(keep_attrs=True):\n",
+    "    da *= 1.0e-6\n",
+    "da.attrs[\"units\"] = \"km2\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find and plot highest and lowest maxima"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_max = da.max(set(da.dims) - {\"year\"}, keep_attrs=True)\n",
+    "max_year = int(da_max.idxmax(\"year\").values)\n",
+    "min_year = int(da_max.idxmin(\"year\").values)\n",
+    "da_max.plot(xticks=da[\"year\"])\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.grid()\n",
+    "plt.title(f\"Yearly maxima: {max_year=}, {min_year=}\")\n",
+    "\n",
+    "facet = plot.projected_map(\n",
+    "    da.sel(year=[max_year, min_year]),\n",
+    "    plot_func=\"imshow\",\n",
+    "    col=\"year\",\n",
+    "    row=\"month\",\n",
+    "    cmap=\"Reds\",\n",
+    "    projection=ccrs.PlateCarree(),\n",
+    ")\n",
+    "for ax in facet.axs.flatten():\n",
+    "    ax.add_feature(cfeature.BORDERS)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mask regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lon_bounds = [-10.18, 1]\n",
+    "lat_bounds = [36.5, 43]\n",
+    "crs = \"epsg:4326\"\n",
+    "\n",
+    "lon_bounds += sorted(lon_bounds, reverse=True)\n",
+    "lat_bounds = [lat for lat in lat_bounds for _ in range(2)]\n",
+    "bbox = shapely.Polygon(zip(lon_bounds, lat_bounds))\n",
+    "\n",
+    "gdf = gpd.read_file(shapefile_url)\n",
+    "gdf = gdf[gdf[\"LEVL_CODE\"] == 2]\n",
+    "gdf = gdf[gdf.intersects(bbox)]\n",
+    "gdf = gdf[gdf[\"NUTS_ID\"].str.startswith((\"ES\", \"PT\"))]\n",
+    "gdf = gdf.to_crs(crs)\n",
+    "\n",
+    "ds.rio.write_crs(crs, inplace=True)\n",
+    "\n",
+    "regions = regionmask.from_geopandas(gdf, names=\"NUTS_NAME\")\n",
+    "mask = regions.mask(ds[\"longitude\"], ds[\"latitude\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bar charts and maps grouped by year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_grouped = da.groupby(mask.rename(\"region\")).sum((\"latitude\", \"longitude\"))\n",
+    "da_grouped = da_grouped.assign_coords(region=regions.names)\n",
+    "ylim = (0, da_grouped.sum(\"month\").max().values)\n",
+    "ylabel = f\"Sum of {da.attrs['long_name']} [{da.attrs['units']}]\"\n",
+    "for year, da_grouped_year in da_grouped.groupby(\"year\"):\n",
+    "    df = da_grouped_year.to_pandas()\n",
+    "    ax = gdf.plot(\n",
+    "        df.sum().values,\n",
+    "        cmap=\"YlOrRd\",\n",
+    "        vmin=ylim[0],\n",
+    "        vmax=ylim[-1],\n",
+    "        edgecolor=\"k\",\n",
+    "        legend=True,\n",
+    "        legend_kwds={\"label\": ylabel},\n",
+    "    )\n",
+    "    ax.set_title(year)\n",
+    "    plt.show()\n",
+    "\n",
+    "    ax = df.T.plot.bar(\n",
+    "        title=year,\n",
+    "        ylabel=ylabel,\n",
+    "        stacked=True,\n",
+    "        ylim=ylim,\n",
+    "    )\n",
+    "    ax.grid()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bar charts grouped by region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for region, da_grouped_region in da_grouped.groupby(\"region\"):\n",
+    "    df = da_grouped_region.squeeze().to_pandas()\n",
+    "    ax = df.plot.bar(\n",
+    "        title=region,\n",
+    "        ylabel=ylabel,\n",
+    "        stacked=True,\n",
+    "        ylim=ylim,\n",
+    "    )\n",
+    "    ax.grid()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_grouped_yearly = da_grouped.sum(\"month\", keep_attrs=True)\n",
+    "lines = da_grouped_yearly.plot(\n",
+    "    hue=\"region\", marker=\"o\", xticks=da[\"year\"], add_legend=False\n",
+    ")\n",
+    "_ = plt.legend(lines, da_grouped[\"region\"].values, bbox_to_anchor=(1, 1))\n",
+    "_ = plt.xticks(rotation=90)\n",
+    "_ = plt.title(\"Annual sum\")\n",
+    "_ = plt.grid()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Climatology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_climatology = da_grouped_yearly.mean(\"year\")\n",
+    "ylabel = f\"{da.attrs['long_name']} [{da.attrs['units']}]\"\n",
+    "title = f\"Climatology ({year_start}-{year_stop})\"\n",
+    "ax = gdf.plot(\n",
+    "    da_climatology.values,\n",
+    "    cmap=\"YlOrRd\",\n",
+    "    edgecolor=\"k\",\n",
+    "    legend=True,\n",
+    "    legend_kwds={\"label\": ylabel},\n",
+    ")\n",
+    "ax.set_title(title)\n",
+    "plt.show()\n",
+    "\n",
+    "ax = da_climatology.to_pandas().plot.bar(ylabel=ylabel)\n",
+    "ax.set_title(title)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eqc",
+   "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}