:broom:

README.md

@@ -24,4 +24,4 @@ This application accesses boundary polygons from the National Parks Service and
 
 ### Code
 
-The required code for analysis is in `fire.py`, with solara visualization in `pages/01_leafmap.py`.  (For interactive )
+The required code for analysis is in `fire.py`, with solara visualization in `pages/01_leafmap.py`.  (For interactive use, see `solar-app.pynb`)

pages/{01_leafmap.py → 00_leafmap.py}

@@ -1,32 +1,23 @@
 import leafmap
 import solara
-import pystac_client
-import planetary_computer
-import odc.stac
-import geopandas as gpd
-import dask.distributed
-import matplotlib.pyplot as plt
-import rioxarray
-from osgeo import gdal
-import matplotlib.pyplot as plt
+
+# external script defines polygons, etc
+from fire import *
 
 zoom = solara.reactive(14)
 center = solara.reactive((34, -116))
 
-nps = gpd.read_file("/vsicurl/https://huggingface.co/datasets/cboettig/biodiversity/resolve/main/data/NPS.gdb")
-calfire = gpd.read_file("/vsicurl/https://huggingface.co/datasets/cboettig/biodiversity/resolve/main/data/fire22_1.gdb",  layer = "firep22_1")
-jtree = nps[nps.PARKNAME == "Joshua Tree"].to_crs(calfire.crs)
-jtree_fires = jtree.overlay(calfire, how="intersection")
-
-recent = jtree_fires[jtree_fires.YEAR_ > "2015"]
-big = recent[recent.Shape_Area == recent.Shape_Area.max()].to_crs("EPSG:4326")
-datetime = big.ALARM_DATE.item() + "/" + big.CONT_DATE.item()
-box = big.buffer(0.01).bounds.to_numpy()[0]  # Fire bbox + buffer  #box = jtree.to_crs("EPSG:4326").bounds.to_numpy()[0] # Park bbox
-
+# location of cached COGs
 before_url = "https://huggingface.co/datasets/cboettig/solara-data/resolve/main/before.tif"
 after_url = "https://huggingface.co/datasets/cboettig/solara-data/resolve/main/after.tif"
 
-
+# custom polygon appearance
+style = {
+    "stroke": False,
+    "fill": True,
+    "fillColor": "#ff6666",
+    "fillOpacity": 0.5,
+}
 
 class Map(leafmap.Map):
     def __init__(self, **kwargs):

pages/{00_home.py → 01_docs.py}

@@ -12,9 +12,18 @@ def Page():
         **A collection of [Solara](https://github.com/widgetti/solara) web apps for geospatial applications.**
 
         - Web App: <https://cboettig-solara-test.hf.space>
-        - GitHub: <https://github.com/boettiger-lab/solara-test>
-        - Hugging Face: <https://huggingface.co/spaces/cboettig/solara-test>
+        - GitHub Source code: <https://github.com/boettiger-lab/solara-test>
+        - Docker container: `ghcr.io/boettiger-lab/solara-geospatial:latest`
 
+        This application accesses boundary polygons from the National Parks Service
+        and fire polygon data from CalFire to determine the location of all recorded
+        fires in Joshua Tree national park.  We select the largest area fire since
+        2015 in the database (currently turns out to be Elk Trail Fire) and access
+        all Sentinel-2 imagery from the two weeks before and after the fire alarm date.
+        From this imagery, we compute the Normalized Burn Severity metric (NBS)
+        around the fire polygon before and after the fire (using cloud-native approach
+        of `pystac`, `odc.stac`, and dask), and plot this on a leaflet map overlay with
+        splitmap and fire polygons. 
         """
 
         solara.Markdown(markdown)

solara-app.ipynb

@@ -1,8 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solara Demo Notebook\n",
+    "\n",
+    "This notebook is identical to `fire.py` + `pages/01_leafmap.py`.  Running this notebook interactively (e.g. in Jupyter or VSCode) should render the solara page produced by `pages/01_leafmap.py`, and can be used for testing/developing the solara interface."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,16 +22,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# generate COGs for the selected polygon.\n",
+    "# This task is run by GitHub Actions and cached in HuggingFace Datasets, so can be skipped\n",
     "run()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,6 +43,7 @@
     "before_url = \"https://huggingface.co/datasets/cboettig/solara-data/resolve/main/before.tif\"\n",
     "after_url = \"https://huggingface.co/datasets/cboettig/solara-data/resolve/main/after.tif\"\n",
     "\n",
+    "# customize the style!\n",
     "style = {\n",
     "    \"stroke\": False,\n",
     "    \"fill\": True,\n",
@@ -42,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,13 +103,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "40a3f4affe8749f6882c35fe09663017",
+       "model_id": "2c176da4d4594b4d879b29410747b282",
        "version_major": 2,
        "version_minor": 0
       },

solara-test.ipynb

@@ -1,249 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import leafmap\n",
-    "import solara\n",
-    "import pystac_client\n",
-    "import planetary_computer\n",
-    "import odc.stac\n",
-    "import geopandas as gpd\n",
-    "import dask.distributed\n",
-    "import matplotlib.pyplot as plt\n",
-    "import rioxarray\n",
-    "from osgeo import gdal\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Stashed public copies of NPS polygons and CalFire polygons\n",
-    "\n",
-    "\n",
-    "zoom = solara.reactive(14)\n",
-    "center = solara.reactive((34, -116))\n",
-    "nps = gpd.read_file(\"/vsicurl/https://huggingface.co/datasets/cboettig/biodiversity/resolve/main/data/NPS.gdb\")\n",
-    "calfire = gpd.read_file(\"/vsicurl/https://huggingface.co/datasets/cboettig/biodiversity/resolve/main/data/fire22_1.gdb\",  layer = \"firep22_1\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "# fire = gpd.read_file(\"/vsizip/vsicurl/https://edcintl.cr.usgs.gov/downloads/sciweb1/shared/MTBS_Fire/data/composite_data/burned_area_extent_shapefile/mtbs_perimeter_data.zip\"\n",
-    "\n",
-    "# extract and reproject the Joshua Tree NP Polygon\n",
-    "jtree = nps[nps.PARKNAME == \"Joshua Tree\"].to_crs(calfire.crs)\n",
-    "# All Fires in the DB that intersect the Park\n",
-    "jtree_fires = jtree.overlay(calfire, how=\"intersection\")\n",
-    "\n",
-    "# Extract a polygon if interest.   > 2015 for Sentinel, otherwise we can use LandSat\n",
-    "recent = jtree_fires[jtree_fires.YEAR_ > \"2015\"]\n",
-    "big = recent[recent.Shape_Area == recent.Shape_Area.max()].to_crs(\"EPSG:4326\")\n",
-    "box = big.buffer(0.01).bounds.to_numpy()[0]  # Fire bbox + buffer\n",
-    "#box = jtree.to_crs(\"EPSG:4326\").bounds.to_numpy()[0] # Park bbox\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "alarm_date = datetime.strptime(big.ALARM_DATE.item(), \"%Y-%m-%dT%H:%M:%S+00:00\")  \n",
-    "before_date = alarm_date - timedelta(days=14)\n",
-    "after_date = alarm_date + timedelta(days=14)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search_dates = big.ALARM_DATE.item() + \"/\" + big.CONT_DATE.item()\n",
-    "search_dates = before_date.strftime(\"%Y-%m-%d\") + \"/\" + after_date.strftime(\"%Y-%m-%d\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def stac_search(box, datetime):    \n",
-    "    # STAC Search for this imagery in space/time window\n",
-    "    items = (\n",
-    "        pystac_client.Client.\n",
-    "        open(\"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
-    "            modifier=planetary_computer.sign_inplace).\n",
-    "        search(collections=[\"sentinel-2-l2a\"],\n",
-    "                bbox=box,\n",
-    "                datetime=datetime,\n",
-    "                query={\"eo:cloud_cover\": {\"lt\": 10}}).\n",
-    "        item_collection())\n",
-    "    return items\n",
-    "\n",
-    "def compute_nbs(items, box):\n",
-    "    # Time to compute:\n",
-    "    client = dask.distributed.Client()\n",
-    "    # landsat_bands = [\"nir08\",  \"swir16\"]\n",
-    "    sentinel_bands = [\"B08\", \"B12\", \"SCL\"] # NIR, SWIR, and Cloud Mask\n",
-    "\n",
-    "    # The magic of gdalwarper. Can also resample, reproject, and aggregate on the fly\n",
-    "    data = odc.stac.load(items,\n",
-    "                        bands=sentinel_bands,\n",
-    "                        bbox=box\n",
-    "    )\n",
-    "    # Compute the Normalized Burn Ratio, must be float\n",
-    "    swir = data[\"B12\"].astype(\"float\")\n",
-    "    nir = data[\"B08\"].astype(\"float\")\n",
-    "    # can resample and aggregate in xarray. compute with dask\n",
-    "    nbs = (((nir - swir) / (nir + swir)).\n",
-    "        #  resample(time=\"MS\").\n",
-    "        #  median(\"time\", keep_attrs=True).\n",
-    "            compute()\n",
-    "    )\n",
-    "    return nbs\n",
-    "\n",
-    "items = stac_search(box, search_dates)\n",
-    "nbs = compute_nbs(items, box)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "nbs.isel(time=0).rio.to_raster(raster_path=\"before.tif\", driver=\"COG\")\n",
-    "nbs.isel(time=(nbs.time.size-1)).rio.to_raster(raster_path=\"after.tif\", driver=\"COG\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "before_url = \"https://huggingface.co/datasets/cboettig/solara-data/resolve/main/before.tif\"\n",
-    "after_url = \"https://huggingface.co/datasets/cboettig/solara-data/resolve/main/after.tif\"\n",
-    "\n",
-    "style = {\n",
-    "    \"stroke\": False,\n",
-    "    \"fill\": True,\n",
-    "    \"fillColor\": \"#ff6666\",\n",
-    "    \"fillOpacity\": 0.5,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Map(leafmap.Map):\n",
-    "    def __init__(self, **kwargs):\n",
-    "        super().__init__(**kwargs)\n",
-    "        # Add what you want below\n",
-    "        # self.add_gdf(jtree, layer_name = \"Joshua Tree NP\")\n",
-    "        self.add_gdf(jtree_fires, layer_name = \"All Fires\", style=style)\n",
-    "        self.add_gdf(big, layer_name = big.FIRE_NAME.item())\n",
-    "        #self.add_raster(\"before.tif\", layer_name = \"before\", colormap=\"viridis\")\n",
-    "        #self.add_raster(\"after.tif\", layer_name = \"after\", colormap=\"viridis\")\n",
-    "        self.split_map(before_url, after_url, \n",
-    "                       left_label= \"before fire\", \n",
-    "                       right_label = \"after fire\")\n",
-    "        #self.add_stac_gui()\n",
-    "\n",
-    "\n",
-    "@solara.component\n",
-    "def Page():\n",
-    "    with solara.Column(style={\"min-width\": \"500px\"}):\n",
-    "        # solara components support reactive variables\n",
-    "        # solara.SliderInt(label=\"Zoom level\", value=zoom, min=1, max=20)\n",
-    "        # using 3rd party widget library require wiring up the events manually\n",
-    "        # using zoom.value and zoom.set\n",
-    "        Map.element(  # type: ignore\n",
-    "            zoom=zoom.value,\n",
-    "            on_zoom=zoom.set,\n",
-    "            center=center.value,\n",
-    "            on_center=center.set,\n",
-    "            scroll_wheel_zoom=True,\n",
-    "            toolbar_ctrl=False,\n",
-    "            data_ctrl=False,\n",
-    "            height=\"780px\",\n",
-    "        )\n",
-    "        solara.Text(f\"Zoom: {zoom.value}\")\n",
-    "        solara.Text(f\"Center: {center.value}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "822080e3202b494888a05630fb5bf798",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "Cannot show widget. You probably want to rerun the code cell above (<i>Click in the code cell, and press Shift+Enter <kbd>⇧</kbd>+<kbd>↩</kbd></i>)."
-      ],
-      "text/plain": [
-       "Cannot show ipywidgets in text"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Page()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "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": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}