add reflectance

notebooks/wp5/WIP_ocean_color_reflectance.ipynb

@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d2078cb1",
+   "metadata": {},
+   "source": [
+    "# Ocean color reflectance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25f9797c",
+   "metadata": {},
+   "source": [
+    "## Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b0ba8de-66d3-4280-b486-ea3bafa4ae28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tempfile\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from c3s_eqc_automatic_quality_control import diagnostics, download, plot, utils\n",
+    "\n",
+    "plt.style.use(\"seaborn-v0_8-notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20d3c6af",
+   "metadata": {},
+   "source": [
+    "## Define Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0061522-8772-42d4-8f1a-f6bbdb03ad25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Time period\n",
+    "year_start = 1998\n",
+    "year_stop = 1999\n",
+    "\n",
+    "# Variable to analyse\n",
+    "wavelength = 443\n",
+    "assert wavelength in (412, 443, 490, 510, 560, 665)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42726c21",
+   "metadata": {},
+   "source": [
+    "## Define request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "382c1767-9a60-4db1-8791-fc5f65887d10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collection_id = \"satellite-ocean-colour\"\n",
+    "\n",
+    "request = {\n",
+    "    \"variable\": \"remote_sensing_reflectance\",\n",
+    "    \"projection\": \"regular_latitude_longitude_grid\",\n",
+    "    \"version\": \"6_0\",\n",
+    "    \"format\": \"zip\",\n",
+    "}\n",
+    "\n",
+    "# Parameters to speed up I/O\n",
+    "open_mfdataset_kwargs = {\n",
+    "    \"concat_dim\": \"time\",\n",
+    "    \"combine\": \"nested\",\n",
+    "    \"data_vars\": \"minimal\",\n",
+    "    \"coords\": \"minimal\",\n",
+    "    \"compat\": \"override\",\n",
+    "    \"parallel\": True,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dce537a-7786-4472-9e99-11c25236f5b3",
+   "metadata": {},
+   "source": [
+    "## Functions to cache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e9c43b1-48ef-4a00-90cf-d062cb59c9af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rechunk(obj):\n",
+    "    chunks = {\"time\": -1, \"year\": 1, \"longitude\": 270, \"latitude\": 270}\n",
+    "    return obj.chunk(**{k: v for k, v in chunks.items() if k in obj.dims})\n",
+    "\n",
+    "\n",
+    "def rrs_annual_weighted_log_mean(ds, wavelength):\n",
+    "    name = f\"Rrs_{wavelength}\"\n",
+    "    da = rechunk(ds[name])\n",
+    "    with tempfile.TemporaryDirectory() as tmpdir:\n",
+    "        da.to_zarr(tmpdir)\n",
+    "        da = xr.open_dataarray(tmpdir, engine=\"zarr\", chunks=dict(da.chunksizes))\n",
+    "        weights = np.abs(np.cos(np.deg2rad(da[\"latitude\"])))\n",
+    "        with xr.set_options(keep_attrs=True):\n",
+    "            da = 10 ** diagnostics.annual_weighted_mean(\n",
+    "                np.log10(da * weights), weights=False\n",
+    "            )\n",
+    "        da = rechunk(da.compute())\n",
+    "        da.encoding[\"chunksizes\"] = tuple(map(max, da.chunks))\n",
+    "        return da.to_dataset(name=name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d8a7b28",
+   "metadata": {},
+   "source": [
+    "## Download and transform data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cb75234-f402-4ce7-9585-4192ea1b2fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = []\n",
+    "for year in range(year_start, year_stop + 1):\n",
+    "    print(f\"{year=}\")\n",
+    "    requests = download.update_request_date(\n",
+    "        request, start=f\"{year}-01\", stop=f\"{year}-12\", stringify_dates=True\n",
+    "    )\n",
+    "    ds = download.download_and_transform(\n",
+    "        collection_id,\n",
+    "        requests,\n",
+    "        transform_chunks=False,\n",
+    "        transform_func=rrs_annual_weighted_log_mean,\n",
+    "        transform_func_kwargs={\"wavelength\": wavelength},\n",
+    "        chunks={\"year\": 1, \"month\": 1},\n",
+    "        **open_mfdataset_kwargs,\n",
+    "    )\n",
+    "    datasets.append(rechunk(ds))\n",
+    "ds = xr.concat(datasets, \"year\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94b4fe5c-2eae-4847-bb17-66fbd531b522",
+   "metadata": {},
+   "source": [
+    "## Plot global maps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afd7484e-f23a-4558-83ad-779504e68c8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da = ds[f\"Rrs_{wavelength}\"]\n",
+    "plot_kwargs = {\"col\": \"year\", \"col_wrap\": 2, \"robust\": True}\n",
+    "\n",
+    "da = da.coarsen(latitude=5, longitude=5).mean()\n",
+    "facet = plot.projected_map(da, projection=ccrs.Robinson(), **plot_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a4a6b69-cca8-4b7f-84ba-a724909db848",
+   "metadata": {},
+   "source": [
+    "## Plot regional maps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a030fe6-6cf4-4f65-914a-fdb70ee1d943",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_region = utils.regionalise(da, lon_slice=slice(-55, -40), lat_slice=slice(30, 15))\n",
+    "facet = plot.projected_map(da_region, projection=ccrs.PlateCarree(), **plot_kwargs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}