Add first draft of geomad notebook

.gitignore

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+.ipynb_checkpoints/
+

S1_GeoMAD_Mosaic_Test.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sentinel-1 GeoMAD\n",
+    "\n",
+    "This notebook runs a process to load Sentinel-1 data and produce\n",
+    "a geometric median and median absolute deviations. Two test regions\n",
+    "are currently available, one in Tasmania and one in Moretons Bay.\n",
+    "\n",
+    "Key requirements include Datacube and a specific branch of\n",
+    "[odc-algo](https://github.com/opendatacube/odc-algo/tree/add-rust-geomedian-impl)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import odc.geo.xr  # noqa: F401\n",
+    "from datacube import Datacube\n",
+    "from distributed import Client\n",
+    "from dask import config\n",
+    "from numpy import log10\n",
+    "\n",
+    "from odc.algo import geomedian_with_mads\n",
+    "from odc.stac import configure_rio\n",
+    "\n",
+    "from collections import namedtuple\n",
+    "\n",
+    "# from easi_tools.notebook_utils import localcluster_dashboard\n",
+    "# from easi_tools import EasiDefaults"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Connect to the datacube and set up some config\n",
+    "dc = Datacube()\n",
+    "\n",
+    "# Rasterio defaults\n",
+    "configure_rio(cloud_defaults=True)\n",
+    "\n",
+    "# Easi defaults\n",
+    "# easi = EasiDefaults()\n",
+    "\n",
+    "# A simple data structure for our study sites\n",
+    "study_site = namedtuple(\"study_site\", [\"name\", \"bbox\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Configure Dask using defaults\n",
+    "client = Client()\n",
+    "\n",
+    "# print(localcluster_dashboard(client=client, server=easi.hub))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Change process parameters here\n",
+    "\n",
+    "# Study site in Tasmania\n",
+    "site = study_site(\"southern_tasmania\", [146.2357, -43.6796, 147.147, -42.9305])\n",
+    "\n",
+    "# Study site in Moreton Bay\n",
+    "# site = study_site(\"moreton_bay\", [152.00, -27.50, 153.20, -26.50])\n",
+    "\n",
+    "year = \"2023\"\n",
+    "\n",
+    "data = dc.load(\n",
+    "    product=\"sentinel1_grd_gamma0_beta\",\n",
+    "    lon=(site.bbox[0], site.bbox[2]),\n",
+    "    lat=(site.bbox[1], site.bbox[3]),\n",
+    "    time=year,\n",
+    "    resolution=(-20, 20),\n",
+    "    output_crs=\"EPSG:3577\",\n",
+    "    dask_chunks=dict(x=2048, y=2048),\n",
+    "    group_by=\"solar_day\",\n",
+    "    measurements=[\"vv\", \"vh\"],\n",
+    ")\n",
+    "\n",
+    "# Mask out nodata values\n",
+    "data = data.where(data.vv > 0)\n",
+    "\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Get the geomedian and mad bands\n",
+    "geomad = geomedian_with_mads(data, work_chunks=(2048, 2048), num_threads=32)\n",
+    "\n",
+    "# Calculate means\n",
+    "for band in [\"vv\", \"vh\"]:\n",
+    "    geomad[f\"{band}_mean\"] = data[band].mean(\"time\")\n",
+    "\n",
+    "geomad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# This step run the computation on Dask. Open the dashboard\n",
+    "# link above to watch it process.\n",
+    "computed = geomad.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "computed[\"vv\"].plot.imshow(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "computed[\"vv_mean\"].plot.imshow(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "(computed[\"vv\"] - computed[\"vv_mean\"]).plot.imshow(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "computed[\"vv_log\"] = 10 * log10(computed.vv)\n",
+    "computed[\"vh_log\"] = 10 * log10(computed.vh)\n",
+    "computed[\"vh_vv_log\"] = 0.5 * (computed.vh_log / computed.vv_log)\n",
+    "\n",
+    "computed.odc.explore(bands=[\"vv_log\", \"vh_log\", \"vh_vv_log\"], vmin=-30, vmax=0)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Alex Test Environment",
+   "language": "python",
+   "name": "test"
+  },
+  "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": 4
+}