From 4f78a5033b35ccee9630295a0d5d6edfc760253f Mon Sep 17 00:00:00 2001 From: Harshini Date: Thu, 12 Feb 2026 16:42:07 -0600 Subject: [PATCH 1/4] nisar_dem --- docs/source/science/NISAR/NISAR_DEM.ipynb | 15652 ++++++++++++++++++++ 1 file changed, 15652 insertions(+) create mode 100644 docs/source/science/NISAR/NISAR_DEM.ipynb diff --git a/docs/source/science/NISAR/NISAR_DEM.ipynb b/docs/source/science/NISAR/NISAR_DEM.ipynb new file mode 100644 index 00000000..afd28279 --- /dev/null +++ b/docs/source/science/NISAR/NISAR_DEM.ipynb @@ -0,0 +1,15652 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ba432c50-550a-4506-9eac-dbce5d02d569", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eab19d3c-e0a9-4345-931a-834e07a49954", + "metadata": {}, + "outputs": [], + "source": [ + "Authors: Harshini Girish(UAH), Alex Mandel(Development Seed)\n", + "\n", + "Date:Febuary 12, 2026\n", + "\n", + "Description:This notebook shows how to avoid full downloads by querying CMR for NISAR DEM granules (collection C3803703055-ASF), extracting each granule’s bounding-box footprint + data link, then using Earthdata login + temporary S3 credentials to open the DEM directly from S3 via /vsis3/ and stream only a small window of pixels with Rasterio/GDAL, finally visualizing the footprints and the streamed preview on an interactive Folium map." + ] + }, + { + "cell_type": "markdown", + "id": "bad7654e-2ec5-4a68-8d6e-0280c2d68236", + "metadata": {}, + "source": [ + "## Run This Notebook\n", + "To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n", + "\n", + "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors." + ] + }, + { + "cell_type": "markdown", + "id": "6e621e6d-3d09-40a1-80b9-8476706b85fd", + "metadata": {}, + "source": [ + "## Additional Resources\n", + "- [OPERA Surface Displacement](https://docs.maap-project.org/en/latest/science/OPERA/OPERA_Surface_Displacement.html)\n", + "- [NISAR Access](https://docs.maap-project.org/en/latest/science/NISAR/NISAR_access.html)\n", + "- [Searching NISAR–BIOMASS Overlapping Data](https://docs.maap-project.org/en/develop/technical_tutorials/search/searching_NISAR_BIOMASS_overlapping_data.html)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3bade9ac-0e9c-4aab-95ae-d6ba66f5ec7a", + "metadata": {}, + "source": [ + "## About the Dataset\n", + "\n", + "The CMR collection C3803703055-ASF is the ASF-hosted NISAR DEM distribution, which provides the Modified Copernicus Digital Elevation Model products used by the NISAR mission. It contains global elevation data packaged for cloud delivery (including DEM tiles and supporting index/metadata files) and is distributed through the NISAR EarthdataCloud endpoints. The collection supports both standard HTTP access and direct in-region S3 access (via an S3 bucket/prefix and a temporary credentials endpoint), which makes it well-suited for “streaming” workflows where you read only the needed byte ranges/windows from remote GeoTIFF/COG tiles instead of downloading full rasters." + ] + }, + { + "cell_type": "markdown", + "id": "8a471361-f522-4c9f-a888-fe905f46b85d", + "metadata": {}, + "source": [ + "## Import and Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "508578d2-862a-4e3c-9ca7-c0269981484a", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import io\n", + "import base64\n", + "from urllib.parse import urlparse\n", + "\n", + "import requests\n", + "import numpy as np\n", + "\n", + "import earthaccess\n", + "import rasterio as rio\n", + "from rasterio.windows import from_bounds\n", + "\n", + "import geopandas as gpd\n", + "from shapely.geometry import box, mapping\n", + "\n", + "import folium\n", + "from PIL import Image" + ] + }, + { + "cell_type": "markdown", + "id": "d10be82c-0793-4672-9048-a4ab3059f1ff", + "metadata": {}, + "source": [ + "## Input\n", + "Define an Area of Interest (AOI)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6ee943c8-4f8d-455e-bfe2-28e996376b57", + "metadata": {}, + "outputs": [], + "source": [ + "# AOI inside the chosen tile (S90_W180) \n", + "target_aoi_bounds = (-179.8, -89.8, -179.2, -89.2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7404665-9ea2-414e-a0cc-c14084efe6cb", + "metadata": {}, + "source": [ + "## Searching the Data " + ] + }, + { + "cell_type": "markdown", + "id": "651db75d-8ebd-4386-98bb-3a58478a2211", + "metadata": {}, + "source": [ + "This cell queries NASA’s Common Metadata Repository using the `granules.umm_json` endpoint to retrieve granule metadata for a specific collection, `fetch_granules()` sends an HTTP request (waiting up to `timeout=60` seconds) and returns the list of granules under `[\"items\"]`. For each granule’s UMM metadata, `bounding_boxes()` extracts any `BoundingRectangles` and converts them into simple tuples that can later be turned into footprint polygons for mapping/intersection. `data_link()` then looks through `RelatedUrls` and picks the best file access link—preferring `s3://` direct access** for streaming via `/vsis3`, and otherwise falling back to the first HTTP link that looks like a raster/index file. Finally, the last lines run the query for the NISAR DEM collection and print how many granules were returned on that page." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2a973cfd-1dad-4d08-a70d-cc02f2ea9705", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules fetched: 200\n" + ] + } + ], + "source": [ + "CMR_URL = \"https://cmr.earthdata.nasa.gov/search/granules.umm_json\"\n", + "\n", + "def fetch_granules(concept_id, n=200, page=1):\n", + " r = requests.get(\n", + " CMR_URL,\n", + " params={\"collection_concept_id\": concept_id, \"page_size\": n, \"page_num\": page},\n", + " timeout=60,\n", + " )\n", + " r.raise_for_status()\n", + " return r.json()[\"items\"]\n", + "\n", + "def bounding_boxes(umm):\n", + " rects = (\n", + " umm.get(\"SpatialExtent\", {})\n", + " .get(\"HorizontalSpatialDomain\", {})\n", + " .get(\"Geometry\", {})\n", + " .get(\"BoundingRectangles\", [])\n", + " )\n", + " return [(r[\"WestBoundingCoordinate\"], r[\"SouthBoundingCoordinate\"],\n", + " r[\"EastBoundingCoordinate\"], r[\"NorthBoundingCoordinate\"]) for r in rects]\n", + "\n", + "def data_link(umm):\n", + " # Prefer direct s3:// access; otherwise first http(s) .tif/.tiff/.vrt link\n", + " urls = [ru.get(\"URL\", \"\") for ru in umm.get(\"RelatedUrls\", [])]\n", + " for u in urls:\n", + " if u.startswith(\"s3://\"):\n", + " return u\n", + " for u in urls:\n", + " if u.lower().startswith(\"http\") and u.lower().endswith((\".tif\", \".tiff\", \".vrt\")):\n", + " return u\n", + " return None\n", + "\n", + "concept_id = \"C3803703055-ASF\"\n", + "items = fetch_granules(concept_id, n=200, page=1)\n", + "print(\"Granules fetched:\", len(items))\n" + ] + }, + { + "cell_type": "markdown", + "id": "a1fecb19-cecc-4da9-b0a3-2debd2e4bf35", + "metadata": {}, + "source": [ + "### Build Footprints GeoDataFrame" + ] + }, + { + "cell_type": "markdown", + "id": "00181775-44e5-4e5d-85ab-1b22375c13cd", + "metadata": {}, + "source": [ + "This cell builds a GeoDataFrame of granule footprints for the CMR collection `C3803703055-ASF`. It loops through each returned granule (`items`), pulls out the UMM metadata and the granule concept-id, then uses `bounding_boxes(umm)` to extract one or more bounding rectangles for that granule. For every bounding box, it appends a row containing the granule id, a readable title (`GranuleUR`), a preferred access link, and a Shapely polygon created from the bounds (`box(*b)`). Finally, it converts the list of rows into a GeoPandas `GeoDataFrame` with the geometry column set to `\"geometry\"` and CRS set to `EPSG:4326`, so you can map the footprints, filter by AOI overlap, and later use `data_url` to stream the selected raster without downloading the full file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "60b3685f-de77-46bb-9c47-c108c9f78656", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Footprints: 200\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
concept_idtitledata_urlgeometry
0G3964549387-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W180_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-179 -90, -179 -89, -180 -89, -180 -...
1G3964549482-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W179_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-178 -90, -178 -89, -179 -89, -179 -...
2G3964549304-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W178_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-177 -90, -177 -89, -178 -89, -178 -...
3G3964549526-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W177_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-176 -90, -176 -89, -177 -89, -177 -...
4G3964549467-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W176_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-175 -90, -175 -89, -176 -89, -176 -...
\n", + "
" + ], + "text/plain": [ + " concept_id title \\\n", + "0 G3964549387-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W180_00_... \n", + "1 G3964549482-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W179_00_... \n", + "2 G3964549304-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W178_00_... \n", + "3 G3964549526-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W177_00_... \n", + "4 G3964549467-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W176_00_... \n", + "\n", + " data_url \\\n", + "0 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", + "1 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", + "2 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", + "3 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", + "4 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", + "\n", + " geometry \n", + "0 POLYGON ((-179 -90, -179 -89, -180 -89, -180 -... \n", + "1 POLYGON ((-178 -90, -178 -89, -179 -89, -179 -... \n", + "2 POLYGON ((-177 -90, -177 -89, -178 -89, -178 -... \n", + "3 POLYGON ((-176 -90, -176 -89, -177 -89, -177 -... \n", + "4 POLYGON ((-175 -90, -175 -89, -176 -89, -176 -... " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for it in items:\n", + " umm = it[\"umm\"]\n", + " gran_id = it[\"meta\"][\"concept-id\"]\n", + " title = umm.get(\"GranuleUR\", gran_id)\n", + " url = data_link(umm)\n", + "\n", + " for b in bounding_boxes(umm):\n", + " rows.append({\"concept_id\": gran_id, \"title\": title, \"data_url\": url, \"geometry\": box(*b)})\n", + "\n", + "gdf = gpd.GeoDataFrame(rows, geometry=\"geometry\", crs=\"EPSG:4326\")\n", + "print(\"Footprints:\", len(gdf))\n", + "gdf.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "01e0ec81-ef8b-4c79-92c6-a5fb19ff28f5", + "metadata": {}, + "source": [ + "### Interactive map \n", + "This cell builds an interactive Folium map centered on your AOI (`target_aoi_bounds`). It first computes the AOI center and initializes a basemap. Then it loops through each row of `gdf` (your granule footprints GeoDataFrame) and adds the footprint geometry as a `GeoJson` layer styled in green, with a tooltip showing the granule `title`. After that, it adds the AOI itself as a red outline by converting the AOI bounds into a Shapely `box()` polygon and passing it through `mapping()` so Folium can render it. Finally, `LayerControl()` is added so you can toggle layers, and the last line outputs `m` to display the map.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "526f3b98-5b92-42ec-a090-8469e289f5e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "minx, miny, maxx, maxy = target_aoi_bounds\n", + "m = folium.Map(location=[(miny + maxy) / 2, (minx + maxx) / 2], zoom_start=6, tiles=\"OpenStreetMap\")\n", + "\n", + "for _, r in gdf.iterrows():\n", + " folium.GeoJson(\n", + " r.geometry,\n", + " style_function=lambda _: {\"color\": \"green\", \"weight\": 2, \"fillOpacity\": 0.05},\n", + " tooltip=r[\"title\"],\n", + " ).add_to(m)\n", + "\n", + "folium.GeoJson(\n", + " mapping(box(*target_aoi_bounds)),\n", + " style_function=lambda _: {\"color\": \"red\", \"weight\": 3, \"fillOpacity\": 0.0},\n", + " name=\"AOI\",\n", + ").add_to(m)\n", + "\n", + "folium.LayerControl().add_to(m)\n", + "m\n" + ] + }, + { + "cell_type": "markdown", + "id": "f31156c3-de9d-4306-b5a4-8f5eb4c3bdcc", + "metadata": {}, + "source": [ + "## Cloud Optimized Remote Access " + ] + }, + { + "cell_type": "markdown", + "id": "4629eaa5-7db1-472b-85c8-18f1e2373240", + "metadata": {}, + "source": [ + "### Set up Access\n", + "This cell logs you into NASA Earthdata (via `earthaccess`) and then calls the NISAR EarthdataCloud S3 credentials endpoint to obtain temporary AWS credentials (access key, secret key, session token). Those credentials are placed into environment variables so GDAL/Rasterio can authenticate when reading files directly from the NISAR S3 bucket using `/vsis3/...`. This is what makes “remote/streamed” reads work without downloading the full GeoTIFF locally. (`timeout=60` just means the request will wait up to 60 seconds before failing.)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "62902d97-caa3-4360-804a-376aa84527f6", + "metadata": {}, + "outputs": [], + "source": [ + "earthaccess.login()\n", + "sess = earthaccess.get_requests_https_session()\n", + "\n", + "resp = sess.get(\"https://nisar.asf.earthdatacloud.nasa.gov/s3credentials\", timeout=60)\n", + "resp.raise_for_status()\n", + "creds = resp.json()\n", + "\n", + "\n", + "os.environ[\"AWS_ACCESS_KEY_ID\"] = creds[\"accessKeyId\"]\n", + "os.environ[\"AWS_SECRET_ACCESS_KEY\"] = creds[\"secretAccessKey\"]\n", + "os.environ[\"AWS_SESSION_TOKEN\"] = creds[\"sessionToken\"]\n", + "os.environ[\"AWS_DEFAULT_REGION\"] = \"us-west-2\"\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "716d3293-a103-44b2-b8e4-249ae9a9541b", + "metadata": {}, + "source": [ + "### Streaming Preview \n", + "\n", + "This cell defines the smallest set of helpers needed to (a) convert an `s3://...` URL into a GDAL `/vsis3/...` path, (b) set a couple of GDAL options to reduce slow directory listing behavior, and (c) read only a small window from the middle of the raster (e.g., 15% of the width/height). It then turns that window into a transparent PNG (NoData masked out) and computes the exact geographic bounds of the streamed window using the window’s transform. The output is a PNG data URL plus `[south, west]` and `[north, east]` bounds that Folium can use to align an overlay correctly on a web map.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "091bd276-f020-4571-af49-bea50a1f38e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chosen: v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W180_00_C01-tif\n", + "Data URL: s3://sds-n-cumulus-prod-nisar-products/DEM/v1.2/EPSG4326/S90/S90_W180/DEM_S90_00_W180_00_C01.tif\n" + ] + } + ], + "source": [ + "#Minimal converter (only S3 direct access)\n", + "def s3_to_vsis3(s3_url: str) -> str:\n", + " return \"/vsis3/\" + s3_url[len(\"s3://\"):]\n", + "\n", + "\n", + "gdal_opts = {\n", + " \"GDAL_DISABLE_READDIR_ON_OPEN\": \"EMPTY_DIR\",\n", + " \"AWS_REGION\": \"us-west-2\",\n", + "}\n", + "\n", + "# Helper: pick a small center window + turn it into a PNG overlay\n", + "def stream_center_preview_as_png(src, frac=0.15):\n", + " left, bottom, right, top = src.bounds\n", + " cx, cy = (left + right) / 2, (bottom + top) / 2\n", + " w, h = (right - left) * frac, (top - bottom) * frac\n", + " preview_bounds = (cx - w/2, cy - h/2, cx + w/2, cy + h/2)\n", + "\n", + " win = rio.windows.from_bounds(*preview_bounds, transform=src.transform)\n", + " arr = src.read(1, window=win, masked=True)\n", + "\n", + " vals = arr.compressed()\n", + " if vals.size == 0:\n", + " return None, None # empty window\n", + "\n", + " vmin, vmax = np.percentile(vals, [2, 98])\n", + " img = np.clip((arr - vmin) / (vmax - vmin + 1e-9), 0, 1)\n", + " img = (img * 255).astype(\"uint8\")\n", + "\n", + " alpha = np.where(np.ma.getmaskarray(arr), 0, 220).astype(\"uint8\")\n", + " rgba = np.dstack([img, img, img, alpha])\n", + "\n", + " buf = io.BytesIO()\n", + " Image.fromarray(rgba, mode=\"RGBA\").save(buf, format=\"PNG\")\n", + " data_url = \"data:image/png;base64,\" + base64.b64encode(buf.getvalue()).decode(\"utf-8\")\n", + "\n", + "# compute exact lat/lon bounds of the streamed window for proper alignment\n", + " t = src.window_transform(win)\n", + " h_px, w_px = arr.shape\n", + " win_left, win_top = t.c, t.f\n", + " win_right = win_left + t.a * w_px\n", + " win_bottom = win_top + t.e * h_px \n", + "\n", + " return data_url, [[win_bottom, win_left], [win_top, win_right]]\n", + "\n", + "\n", + "#run: choose first granule and stream it \n", + "\n", + "chosen = gdf.iloc[0] # first tile\n", + "print(\"Chosen:\", chosen[\"title\"])\n", + "print(\"Data URL:\", chosen[\"data_url\"])\n", + "\n", + "vsis3_path = s3_to_vsis3(chosen[\"data_url\"])\n", + "\n", + "with rio.Env(**gdal_opts):\n", + " with rio.open(vsis3_path) as src:\n", + " overlay_png, overlay_bounds = stream_center_preview_as_png(src, frac=0.15)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2c8868f4-4a7c-44fe-b2e9-1b26d4159088", + "metadata": {}, + "source": [ + "### Interactive map \n", + "\n", + "This cell builds a Folium map and centers it on the streamed window if it exists (otherwise it falls back to the chosen tile’s footprint center). It then draws: (1) all available granule footprints in green for context, (2) the selected granule footprint in blue so you can see which tile you streamed from, and (3) the streamed PNG preview as an `ImageOverlay` positioned using the exact `[south, west]` / `[north, east]` bounds computed earlier. Finally, it adds a layer control so you can toggle the overlay and footprint layers on/off and visually confirm the streamed raster window is aligned on the basemap.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "27e35c4d-60a9-4b26-badb-8a65fcd282f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# --- map ---\n", + "# center map on streamed window (or on chosen footprint center if window empty)\n", + "if overlay_bounds:\n", + " (south, west), (north, east) = overlay_bounds\n", + " center = [(south + north) / 2, (west + east) / 2]\n", + "else:\n", + " # fallback: center on chosen footprint\n", + " minx, miny, maxx, maxy = chosen.geometry.bounds\n", + " center = [(miny + maxy) / 2, (minx + maxx) / 2]\n", + "\n", + "m = folium.Map(location=center, zoom_start=6, tiles=\"OpenStreetMap\")\n", + "\n", + "# footprints \n", + "for _, r in gdf.iterrows():\n", + " folium.GeoJson(\n", + " r.geometry,\n", + " style_function=lambda _: {\"color\": \"green\", \"weight\": 2, \"fillOpacity\": 0.05},\n", + " tooltip=r[\"title\"],\n", + " ).add_to(m)\n", + "\n", + "# chosen footprint\n", + "folium.GeoJson(\n", + " chosen.geometry,\n", + " style_function=lambda _: {\"color\": \"blue\", \"weight\": 3, \"fillOpacity\": 0},\n", + " tooltip=f\"CHOSEN: {chosen['title']}\",\n", + " name=\"Chosen tile\",\n", + ").add_to(m)\n", + "\n", + "# streamed preview overlay\n", + "if overlay_png is None:\n", + " print(\"Preview window is empty (all NoData). Pick a different granule.\")\n", + "else:\n", + " folium.raster_layers.ImageOverlay(\n", + " image=overlay_png,\n", + " bounds=overlay_bounds,\n", + " opacity=0.75,\n", + " name=\"Streamed DEM preview\",\n", + " ).add_to(m)\n", + "\n", + "folium.LayerControl().add_to(m)\n", + "m\n" + ] + } + ], + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d5fd74392096d6fa1fc8b3e7788392e7d002291f Mon Sep 17 00:00:00 2001 From: Harshini Date: Thu, 12 Feb 2026 16:44:53 -0600 Subject: [PATCH 2/4] Add NISAR_dem notebook --- docs/source/science_examples.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/science_examples.rst b/docs/source/science_examples.rst index dcb11ac9..a2d3f28c 100644 --- a/docs/source/science_examples.rst +++ b/docs/source/science_examples.rst @@ -20,6 +20,7 @@ You can also find links to Open Source Science guidelines for the MAAP platform. science/ATL03/ATL03.ipynb science/ATL08/ATL08.ipynb science/NISAR/NISAR_access.ipynb + science/NISAR/NISAR_dem.ipynb science/ESA_BIOMASS/ESA_BIOMASS_Data_Access.ipynb science/ESA_BIOMASS/ESA_BIOMASS_Simulated_Data_Access.ipynb science/OPERA/OPERA_Surface_Displacement.ipynb From dc6f55e10d7267d7730bf3e04366f0875c342160 Mon Sep 17 00:00:00 2001 From: Harshini Date: Thu, 12 Feb 2026 16:49:36 -0600 Subject: [PATCH 3/4] removed blank cell. --- docs/source/science/NISAR/NISAR_DEM.ipynb | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/docs/source/science/NISAR/NISAR_DEM.ipynb b/docs/source/science/NISAR/NISAR_DEM.ipynb index afd28279..10918fea 100644 --- a/docs/source/science/NISAR/NISAR_DEM.ipynb +++ b/docs/source/science/NISAR/NISAR_DEM.ipynb @@ -1,19 +1,17 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, - "id": "ba432c50-550a-4506-9eac-dbce5d02d569", + "cell_type": "markdown", + "id": "eb30718a-017a-445f-9518-be1e9e99b1d6", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "# NISAR DEM: Cloud-Optimized Access and Visualization" + ] }, { - "cell_type": "code", - "execution_count": null, - "id": "eab19d3c-e0a9-4345-931a-834e07a49954", + "cell_type": "markdown", + "id": "b93405e4-684b-4bf4-bf25-fc99a8d398df", "metadata": {}, - "outputs": [], "source": [ "Authors: Harshini Girish(UAH), Alex Mandel(Development Seed)\n", "\n", From 97208378f476b2913ec311cf39477b0365eed53e Mon Sep 17 00:00:00 2001 From: Harshini Date: Wed, 18 Feb 2026 14:15:04 -0600 Subject: [PATCH 4/4] Update NISAR_DEM.ipynb --- docs/source/science/NISAR/NISAR_DEM.ipynb | 15695 +------------------- 1 file changed, 190 insertions(+), 15505 deletions(-) diff --git a/docs/source/science/NISAR/NISAR_DEM.ipynb b/docs/source/science/NISAR/NISAR_DEM.ipynb index 10918fea..4fc80d4a 100644 --- a/docs/source/science/NISAR/NISAR_DEM.ipynb +++ b/docs/source/science/NISAR/NISAR_DEM.ipynb @@ -2,295 +2,125 @@ "cells": [ { "cell_type": "markdown", - "id": "eb30718a-017a-445f-9518-be1e9e99b1d6", + "id": "8467047e-e36e-4037-b815-3485c29a5c4d", "metadata": {}, "source": [ - "# NISAR DEM: Cloud-Optimized Access and Visualization" - ] - }, - { - "cell_type": "markdown", - "id": "b93405e4-684b-4bf4-bf25-fc99a8d398df", - "metadata": {}, - "source": [ - "Authors: Harshini Girish(UAH), Alex Mandel(Development Seed)\n", - "\n", - "Date:Febuary 12, 2026\n", - "\n", - "Description:This notebook shows how to avoid full downloads by querying CMR for NISAR DEM granules (collection C3803703055-ASF), extracting each granule’s bounding-box footprint + data link, then using Earthdata login + temporary S3 credentials to open the DEM directly from S3 via /vsis3/ and stream only a small window of pixels with Rasterio/GDAL, finally visualizing the footprints and the streamed preview on an interactive Folium map." - ] - }, - { - "cell_type": "markdown", - "id": "bad7654e-2ec5-4a68-8d6e-0280c2d68236", - "metadata": {}, - "source": [ - "## Run This Notebook\n", - "To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n", - "\n", - "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors." - ] - }, - { - "cell_type": "markdown", - "id": "6e621e6d-3d09-40a1-80b9-8476706b85fd", - "metadata": {}, - "source": [ - "## Additional Resources\n", - "- [OPERA Surface Displacement](https://docs.maap-project.org/en/latest/science/OPERA/OPERA_Surface_Displacement.html)\n", - "- [NISAR Access](https://docs.maap-project.org/en/latest/science/NISAR/NISAR_access.html)\n", - "- [Searching NISAR–BIOMASS Overlapping Data](https://docs.maap-project.org/en/develop/technical_tutorials/search/searching_NISAR_BIOMASS_overlapping_data.html)\n" - ] - }, - { - "cell_type": "markdown", - "id": "3bade9ac-0e9c-4aab-95ae-d6ba66f5ec7a", - "metadata": {}, - "source": [ - "## About the Dataset\n", - "\n", - "The CMR collection C3803703055-ASF is the ASF-hosted NISAR DEM distribution, which provides the Modified Copernicus Digital Elevation Model products used by the NISAR mission. It contains global elevation data packaged for cloud delivery (including DEM tiles and supporting index/metadata files) and is distributed through the NISAR EarthdataCloud endpoints. The collection supports both standard HTTP access and direct in-region S3 access (via an S3 bucket/prefix and a temporary credentials endpoint), which makes it well-suited for “streaming” workflows where you read only the needed byte ranges/windows from remote GeoTIFF/COG tiles instead of downloading full rasters." - ] - }, - { - "cell_type": "markdown", - "id": "8a471361-f522-4c9f-a888-fe905f46b85d", - "metadata": {}, - "source": [ - "## Import and Install Packages" + "# NISAR DEM" ] }, { "cell_type": "code", "execution_count": 1, - "id": "508578d2-862a-4e3c-9ca7-c0269981484a", + "id": "bb09ce2f-225b-4217-ac6f-c751b324a3c3", "metadata": {}, "outputs": [], "source": [ "import os\n", - "import io\n", - "import base64\n", - "from urllib.parse import urlparse\n", + "import time\n", + "from pathlib import Path\n", "\n", "import requests\n", "import numpy as np\n", - "\n", - "import earthaccess\n", - "import rasterio as rio\n", + "import rasterio\n", "from rasterio.windows import from_bounds\n", - "\n", - "import geopandas as gpd\n", - "from shapely.geometry import box, mapping\n", - "\n", - "import folium\n", - "from PIL import Image" - ] - }, - { - "cell_type": "markdown", - "id": "d10be82c-0793-4672-9048-a4ab3059f1ff", - "metadata": {}, - "source": [ - "## Input\n", - "Define an Area of Interest (AOI)" + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "6ee943c8-4f8d-455e-bfe2-28e996376b57", - "metadata": {}, - "outputs": [], - "source": [ - "# AOI inside the chosen tile (S90_W180) \n", - "target_aoi_bounds = (-179.8, -89.8, -179.2, -89.2)\n" - ] - }, - { - "cell_type": "markdown", - "id": "a7404665-9ea2-414e-a0cc-c14084efe6cb", - "metadata": {}, - "source": [ - "## Searching the Data " - ] - }, - { - "cell_type": "markdown", - "id": "651db75d-8ebd-4386-98bb-3a58478a2211", - "metadata": {}, - "source": [ - "This cell queries NASA’s Common Metadata Repository using the `granules.umm_json` endpoint to retrieve granule metadata for a specific collection, `fetch_granules()` sends an HTTP request (waiting up to `timeout=60` seconds) and returns the list of granules under `[\"items\"]`. For each granule’s UMM metadata, `bounding_boxes()` extracts any `BoundingRectangles` and converts them into simple tuples that can later be turned into footprint polygons for mapping/intersection. `data_link()` then looks through `RelatedUrls` and picks the best file access link—preferring `s3://` direct access** for streaming via `/vsis3`, and otherwise falling back to the first HTTP link that looks like a raster/index file. Finally, the last lines run the query for the NISAR DEM collection and print how many granules were returned on that page." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2a973cfd-1dad-4d08-a70d-cc02f2ea9705", + "execution_count": 2, + "id": "7fd0dcea-0a39-4563-a8d5-cca321b35f50", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Granules fetched: 200\n" - ] + "data": { + "text/plain": [ + "('https://nisar.asf.earthdatacloud.nasa.gov/NISAR/DEM/v1.2/EPSG4326/EPSG4326.vrt',\n", + " 's3://sds-n-cumulus-prod-nisar-products/DEM/v1.2/EPSG4326/EPSG4326.vrt')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "CMR_URL = \"https://cmr.earthdata.nasa.gov/search/granules.umm_json\"\n", - "\n", - "def fetch_granules(concept_id, n=200, page=1):\n", - " r = requests.get(\n", - " CMR_URL,\n", - " params={\"collection_concept_id\": concept_id, \"page_size\": n, \"page_num\": page},\n", - " timeout=60,\n", - " )\n", - " r.raise_for_status()\n", - " return r.json()[\"items\"]\n", - "\n", - "def bounding_boxes(umm):\n", - " rects = (\n", - " umm.get(\"SpatialExtent\", {})\n", - " .get(\"HorizontalSpatialDomain\", {})\n", - " .get(\"Geometry\", {})\n", - " .get(\"BoundingRectangles\", [])\n", - " )\n", - " return [(r[\"WestBoundingCoordinate\"], r[\"SouthBoundingCoordinate\"],\n", - " r[\"EastBoundingCoordinate\"], r[\"NorthBoundingCoordinate\"]) for r in rects]\n", - "\n", - "def data_link(umm):\n", - " # Prefer direct s3:// access; otherwise first http(s) .tif/.tiff/.vrt link\n", - " urls = [ru.get(\"URL\", \"\") for ru in umm.get(\"RelatedUrls\", [])]\n", - " for u in urls:\n", - " if u.startswith(\"s3://\"):\n", - " return u\n", - " for u in urls:\n", - " if u.lower().startswith(\"http\") and u.lower().endswith((\".tif\", \".tiff\", \".vrt\")):\n", - " return u\n", - " return None\n", - "\n", - "concept_id = \"C3803703055-ASF\"\n", - "items = fetch_granules(concept_id, n=200, page=1)\n", - "print(\"Granules fetched:\", len(items))\n" + "# --- DEM version + projection directory ---\n", + "DEM_VERSION = \"v1.2\"\n", + "EPSG_DIR = \"EPSG4326\" # or \"EPSG3413\" (Arctic), \"EPSG3031\" (Antarctic)\n", + "\n", + "# --- AOI bbox ---\n", + "# For EPSG4326: lon/lat bbox (min_lon, min_lat, max_lon, max_lat)\n", + "minx, miny, maxx, maxy = -116.7, 35.9, -116.6, 36.0\n", + "BBOX = (minx, miny, maxx, maxy)\n", + "\n", + "# --- ASF HTTPS VRT ---\n", + "ASF_HTTP_BASE = \"https://nisar.asf.earthdatacloud.nasa.gov/NISAR/DEM\"\n", + "HTTP_VRT_URL = f\"{ASF_HTTP_BASE}/{DEM_VERSION}/{EPSG_DIR}/{EPSG_DIR}.vrt\"\n", + "HTTP_VRT_VSI = f\"/vsicurl/{HTTP_VRT_URL}\"\n", + "\n", + "# --- ASF S3 VRT ---\n", + "ASF_S3_BUCKET = \"sds-n-cumulus-prod-nisar-products\"\n", + "S3_KEY = f\"DEM/{DEM_VERSION}/{EPSG_DIR}/{EPSG_DIR}.vrt\"\n", + "S3_VRT_S3URI = f\"s3://{ASF_S3_BUCKET}/{S3_KEY}\"\n", + "S3_VRT_VSI = f\"/vsis3/{ASF_S3_BUCKET}/{S3_KEY}\"\n", + "\n", + "HTTP_VRT_URL, S3_VRT_S3URI" ] }, { "cell_type": "markdown", - "id": "a1fecb19-cecc-4da9-b0a3-2debd2e4bf35", + "id": "a0c3e23e-922b-454d-8b8c-37c4cb505bc7", "metadata": {}, "source": [ - "### Build Footprints GeoDataFrame" + "# HTTP workflow" ] }, { - "cell_type": "markdown", - "id": "00181775-44e5-4e5d-85ab-1b22375c13cd", + "cell_type": "code", + "execution_count": 3, + "id": "b78057f9-185b-4faa-a6ea-9442f173e244", "metadata": {}, + "outputs": [], "source": [ - "This cell builds a GeoDataFrame of granule footprints for the CMR collection `C3803703055-ASF`. It loops through each returned granule (`items`), pulls out the UMM metadata and the granule concept-id, then uses `bounding_boxes(umm)` to extract one or more bounding rectangles for that granule. For every bounding box, it appends a row containing the granule id, a readable title (`GranuleUR`), a preferred access link, and a Shapely polygon created from the bounds (`box(*b)`). Finally, it converts the list of rows into a GeoPandas `GeoDataFrame` with the geometry column set to `\"geometry\"` and CRS set to `EPSG:4326`, so you can map the footprints, filter by AOI overlap, and later use `data_url` to stream the selected raster without downloading the full file.\n" + "def subset_to_geotiff(dataset_path: str, bbox, out_tif: str):\n", + " minx, miny, maxx, maxy = bbox\n", + "\n", + " with rasterio.open(dataset_path) as src:\n", + " win = from_bounds(minx, miny, maxx, maxy, transform=src.transform)\n", + " data = src.read(1, window=win)\n", + " new_transform = src.window_transform(win)\n", + "\n", + " profile = src.profile.copy()\n", + " profile.update(\n", + " driver=\"GTiff\",\n", + " height=data.shape[0],\n", + " width=data.shape[1],\n", + " transform=new_transform,\n", + " tiled=True,\n", + " compress=\"deflate\",\n", + " BIGTIFF=\"IF_SAFER\",\n", + " )\n", + "\n", + " with rasterio.open(out_tif, \"w\", **profile) as dst:\n", + " dst.write(data, 1)\n", + "\n", + " return out_tif" ] }, { "cell_type": "code", "execution_count": 4, - "id": "60b3685f-de77-46bb-9c47-c108c9f78656", + "id": "fff0723b-34c0-4559-83b5-fface5109a84", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Footprints: 200\n" - ] - }, { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
concept_idtitledata_urlgeometry
0G3964549387-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W180_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-179 -90, -179 -89, -180 -89, -180 -...
1G3964549482-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W179_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-178 -90, -178 -89, -179 -89, -179 -...
2G3964549304-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W178_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-177 -90, -177 -89, -178 -89, -178 -...
3G3964549526-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W177_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-176 -90, -176 -89, -177 -89, -177 -...
4G3964549467-ASFv1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W176_00_...s3://sds-n-cumulus-prod-nisar-products/DEM/v1....POLYGON ((-175 -90, -175 -89, -176 -89, -176 -...
\n", - "
" - ], "text/plain": [ - " concept_id title \\\n", - "0 G3964549387-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W180_00_... \n", - "1 G3964549482-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W179_00_... \n", - "2 G3964549304-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W178_00_... \n", - "3 G3964549526-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W177_00_... \n", - "4 G3964549467-ASF v1-2-EPSG4326-S90-S90_W180-DEM_S90_00_W176_00_... \n", - "\n", - " data_url \\\n", - "0 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", - "1 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", - "2 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", - "3 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", - "4 s3://sds-n-cumulus-prod-nisar-products/DEM/v1.... \n", - "\n", - " geometry \n", - "0 POLYGON ((-179 -90, -179 -89, -180 -89, -180 -... \n", - "1 POLYGON ((-178 -90, -178 -89, -179 -89, -179 -... \n", - "2 POLYGON ((-177 -90, -177 -89, -178 -89, -178 -... \n", - "3 POLYGON ((-176 -90, -176 -89, -177 -89, -177 -... \n", - "4 POLYGON ((-175 -90, -175 -89, -176 -89, -176 -... " + "{'status': 302,\n", + " 'location': 'https://urs.earthdata.nasa.gov/oauth/authorize?client_id=qpa3q_b7OJ32gGjwA9DxHg&response_type=code&redirect_uri=https://nisar.asf.earthdatacloud.nasa.gov/login&state=%2FNISAR%2FDEM%2Fv1.2%2FEPSG4326%2FEPSG4326.vrt&app_type=401',\n", + " 'content_type': 'application/json',\n", + " 'head': ''}" ] }, "execution_count": 4, @@ -299,15331 +129,186 @@ } ], "source": [ - "rows = []\n", - "for it in items:\n", - " umm = it[\"umm\"]\n", - " gran_id = it[\"meta\"][\"concept-id\"]\n", - " title = umm.get(\"GranuleUR\", gran_id)\n", - " url = data_link(umm)\n", - "\n", - " for b in bounding_boxes(umm):\n", - " rows.append({\"concept_id\": gran_id, \"title\": title, \"data_url\": url, \"geometry\": box(*b)})\n", - "\n", - "gdf = gpd.GeoDataFrame(rows, geometry=\"geometry\", crs=\"EPSG:4326\")\n", - "print(\"Footprints:\", len(gdf))\n", - "gdf.head()\n" - ] - }, - { - "cell_type": "markdown", - "id": "01e0ec81-ef8b-4c79-92c6-a5fb19ff28f5", - "metadata": {}, - "source": [ - "### Interactive map \n", - "This cell builds an interactive Folium map centered on your AOI (`target_aoi_bounds`). It first computes the AOI center and initializes a basemap. Then it loops through each row of `gdf` (your granule footprints GeoDataFrame) and adds the footprint geometry as a `GeoJson` layer styled in green, with a tooltip showing the granule `title`. After that, it adds the AOI itself as a red outline by converting the AOI bounds into a Shapely `box()` polygon and passing it through `mapping()` so Folium can render it. Finally, `LayerControl()` is added so you can toggle layers, and the last line outputs `m` to display the map.\n" + "def probe_http_no_redirect(url: str, timeout=20):\n", + " r = requests.get(url, allow_redirects=False, timeout=timeout)\n", + " return {\n", + " \"status\": r.status_code,\n", + " \"location\": r.headers.get(\"Location\"),\n", + " \"content_type\": r.headers.get(\"content-type\"),\n", + " \"head\": r.text[:120],\n", + " }\n", + "\n", + "probe = probe_http_no_redirect(HTTP_VRT_URL)\n", + "probe" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "526f3b98-5b92-42ec-a090-8469e289f5e8", + "execution_count": 10, + "id": "6d2067a0-e63b-4d27-b64d-6874d434c9e5", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP workflow not usable\n" + ] } ], "source": [ - "minx, miny, maxx, maxy = target_aoi_bounds\n", - "m = folium.Map(location=[(miny + maxy) / 2, (minx + maxx) / 2], zoom_start=6, tiles=\"OpenStreetMap\")\n", - "\n", - "for _, r in gdf.iterrows():\n", - " folium.GeoJson(\n", - " r.geometry,\n", - " style_function=lambda _: {\"color\": \"green\", \"weight\": 2, \"fillOpacity\": 0.05},\n", - " tooltip=r[\"title\"],\n", - " ).add_to(m)\n", - "\n", - "folium.GeoJson(\n", - " mapping(box(*target_aoi_bounds)),\n", - " style_function=lambda _: {\"color\": \"red\", \"weight\": 3, \"fillOpacity\": 0.0},\n", - " name=\"AOI\",\n", - ").add_to(m)\n", - "\n", - "folium.LayerControl().add_to(m)\n", - "m\n" - ] - }, - { - "cell_type": "markdown", - "id": "f31156c3-de9d-4306-b5a4-8f5eb4c3bdcc", - "metadata": {}, - "source": [ - "## Cloud Optimized Remote Access " + "out_http = \"dem_subset_http.tif\"\n", + "\n", + "if probe.get(\"status\") == 200 and str(probe.get(\"head\",\"\")).lstrip().startswith(\" str:\n", - " return \"/vsis3/\" + s3_url[len(\"s3://\"):]\n", - "\n", - "\n", - "gdal_opts = {\n", - " \"GDAL_DISABLE_READDIR_ON_OPEN\": \"EMPTY_DIR\",\n", - " \"AWS_REGION\": \"us-west-2\",\n", - "}\n", - "\n", - "# Helper: pick a small center window + turn it into a PNG overlay\n", - "def stream_center_preview_as_png(src, frac=0.15):\n", - " left, bottom, right, top = src.bounds\n", - " cx, cy = (left + right) / 2, (bottom + top) / 2\n", - " w, h = (right - left) * frac, (top - bottom) * frac\n", - " preview_bounds = (cx - w/2, cy - h/2, cx + w/2, cy + h/2)\n", - "\n", - " win = rio.windows.from_bounds(*preview_bounds, transform=src.transform)\n", - " arr = src.read(1, window=win, masked=True)\n", - "\n", - " vals = arr.compressed()\n", - " if vals.size == 0:\n", - " return None, None # empty window\n", - "\n", - " vmin, vmax = np.percentile(vals, [2, 98])\n", - " img = np.clip((arr - vmin) / (vmax - vmin + 1e-9), 0, 1)\n", - " img = (img * 255).astype(\"uint8\")\n", - "\n", - " alpha = np.where(np.ma.getmaskarray(arr), 0, 220).astype(\"uint8\")\n", - " rgba = np.dstack([img, img, img, alpha])\n", - "\n", - " buf = io.BytesIO()\n", - " Image.fromarray(rgba, mode=\"RGBA\").save(buf, format=\"PNG\")\n", - " data_url = \"data:image/png;base64,\" + base64.b64encode(buf.getvalue()).decode(\"utf-8\")\n", - "\n", - "# compute exact lat/lon bounds of the streamed window for proper alignment\n", - " t = src.window_transform(win)\n", - " h_px, w_px = arr.shape\n", - " win_left, win_top = t.c, t.f\n", - " win_right = win_left + t.a * w_px\n", - " win_bottom = win_top + t.e * h_px \n", - "\n", - " return data_url, [[win_bottom, win_left], [win_top, win_right]]\n", + "os.environ[\"AWS_ACCESS_KEY_ID\"] = creds[\"accessKeyId\"]\n", + "os.environ[\"AWS_SECRET_ACCESS_KEY\"] = creds[\"secretAccessKey\"]\n", + "os.environ[\"AWS_SESSION_TOKEN\"] = creds[\"sessionToken\"]\n", "\n", "\n", - "#run: choose first granule and stream it \n", + "os.environ[\"AWS_REGION\"] = \"us-west-2\"\n", + "os.environ[\"AWS_DEFAULT_REGION\"] = \"us-west-2\"\n", "\n", - "chosen = gdf.iloc[0] # first tile\n", - "print(\"Chosen:\", chosen[\"title\"])\n", - "print(\"Data URL:\", chosen[\"data_url\"])\n", "\n", - "vsis3_path = s3_to_vsis3(chosen[\"data_url\"])\n", + "os.environ[\"AWS_REQUEST_PAYER\"] = \"requester\"\n", + "os.environ[\"CPL_AWS_REQUEST_PAYER\"] = \"requester\"\n", "\n", - "with rio.Env(**gdal_opts):\n", - " with rio.open(vsis3_path) as src:\n", - " overlay_png, overlay_bounds = stream_center_preview_as_png(src, frac=0.15)\n", - "\n" + "print(\"AWS_* env vars set.\")\n", + "print(\"S3 VRT:\", S3_VRT_S3URI)" ] }, { - "cell_type": "markdown", - "id": "2c8868f4-4a7c-44fe-b2e9-1b26d4159088", + "cell_type": "code", + "execution_count": 8, + "id": "4cce502f-c22c-4ded-aab7-d3b7def52789", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S3 subset wrote: dem_subset_s3.tif\n" + ] + } + ], "source": [ - "### Interactive map \n", - "\n", - "This cell builds a Folium map and centers it on the streamed window if it exists (otherwise it falls back to the chosen tile’s footprint center). It then draws: (1) all available granule footprints in green for context, (2) the selected granule footprint in blue so you can see which tile you streamed from, and (3) the streamed PNG preview as an `ImageOverlay` positioned using the exact `[south, west]` / `[north, east]` bounds computed earlier. Finally, it adds a layer control so you can toggle the overlay and footprint layers on/off and visually confirm the streamed raster window is aligned on the basemap.\n" + "out_s3 = \"dem_subset_s3.tif\"\n", + "\n", + "try:\n", + " subset_to_geotiff(S3_VRT_VSI, BBOX, out_s3)\n", + " print(\"S3 subset wrote:\", out_s3)\n", + "except Exception as e:\n", + " print(\"S3 subset failed:\", type(e).__name__, e)\n", + " raise" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "27e35c4d-60a9-4b26-badb-8a65fcd282f6", + "execution_count": 9, + "id": "36b3fe36-c825-4690-b208-ea80ed3791b2", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], + "image/png": "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", "text/plain": [ - "" + "
" ] }, - "execution_count": 18, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# --- map ---\n", - "# center map on streamed window (or on chosen footprint center if window empty)\n", - "if overlay_bounds:\n", - " (south, west), (north, east) = overlay_bounds\n", - " center = [(south + north) / 2, (west + east) / 2]\n", - "else:\n", - " # fallback: center on chosen footprint\n", - " minx, miny, maxx, maxy = chosen.geometry.bounds\n", - " center = [(miny + maxy) / 2, (minx + maxx) / 2]\n", - "\n", - "m = folium.Map(location=center, zoom_start=6, tiles=\"OpenStreetMap\")\n", - "\n", - "# footprints \n", - "for _, r in gdf.iterrows():\n", - " folium.GeoJson(\n", - " r.geometry,\n", - " style_function=lambda _: {\"color\": \"green\", \"weight\": 2, \"fillOpacity\": 0.05},\n", - " tooltip=r[\"title\"],\n", - " ).add_to(m)\n", - "\n", - "# chosen footprint\n", - "folium.GeoJson(\n", - " chosen.geometry,\n", - " style_function=lambda _: {\"color\": \"blue\", \"weight\": 3, \"fillOpacity\": 0},\n", - " tooltip=f\"CHOSEN: {chosen['title']}\",\n", - " name=\"Chosen tile\",\n", - ").add_to(m)\n", - "\n", - "# streamed preview overlay\n", - "if overlay_png is None:\n", - " print(\"Preview window is empty (all NoData). Pick a different granule.\")\n", - "else:\n", - " folium.raster_layers.ImageOverlay(\n", - " image=overlay_png,\n", - " bounds=overlay_bounds,\n", - " opacity=0.75,\n", - " name=\"Streamed DEM preview\",\n", - " ).add_to(m)\n", - "\n", - "folium.LayerControl().add_to(m)\n", - "m\n" + "if Path(out_s3).exists():\n", + " with rasterio.open(out_s3) as src:\n", + " arr = src.read(1)\n", + " nodata = src.nodata\n", + "\n", + " if nodata is not None:\n", + " arr = np.where(arr == nodata, np.nan, arr)\n", + "\n", + " plt.figure(figsize=(7, 5))\n", + " plt.imshow(arr)\n", + " plt.title(out_s3)\n", + " plt.colorbar()\n", + " plt.tight_layout()\n", + " plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e121b4d-f091-474d-a449-a00ae780e62f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {