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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 → pages/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/01_docs.py

@@ -0,0 +1,29 @@
+import solara
+
+
+@solara.component
+def Page():
+    with solara.Column(align="center"):
+        markdown = """
+        ## A Solara Template for Geospatial Applications
+        
+        ### Introduction
+
+        **A collection of [Solara](https://github.com/widgetti/solara) web apps for geospatial applications.**
+
+        - Web App: <https://cboettig-solara-test.hf.space>
+        - 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
       },