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* correct syntax datetime.utc * Changes to incorporate geoserver in collection Also put stac_io to default from pystac as in CoCliCo Note, geoserver link is still a dummy.. Cannot fix it yet (will be soon) * rm storage account * add cog media type * forgiveness when testing if collection exists * example for reading deltadtm data * add storage account name * deltadtm v1.1 * fix --------- Co-authored-by: floriscalkoen <[email protected]> Co-authored-by: EtienneKras <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1 @@ | ||
| AZURE_STORAGE_SAS_TOKEN="paste-your-sas-token-here" | ||
| CLIENT_AZURE_STORAGE_CONNECTION_STRING="paste-your-connection-string-here" | ||
| AZURE_STORAGE_SAS_TOKEN="paste-your-sas-token-here" |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,187 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "0", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# DeltaDTM\n", | ||
| "\n", | ||
| "A global coastal digital terrain model, based on CopernicusDEM, ESA WorldCover, ICESat-2 and GEDI data. For more information, see [Pronk et al. (2024)](https://www.nature.com/articles/s41597-024-03091-9) DeltaDTM: A global coastal digital terrain model. " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import geopandas as gpd\n", | ||
| "import hvplot.xarray\n", | ||
| "import pystac\n", | ||
| "import rioxarray\n", | ||
| "import shapely\n", | ||
| "import xarray as xr\n", | ||
| "from ipyleaflet import Map, basemaps\n", | ||
| "\n", | ||
| "storage_options = {\"account_name\": \"coclico\"}" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "2", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Read a snapshot with the spatial extents of all tiles\n", | ||
| "\n", | ||
| "Connect to the CoCliCo STAC and read the spatial extents using stac-geoparquet." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "3", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from coastpy.io.utils import read_items_extent\n", | ||
| "\n", | ||
| "coclico_catalog = pystac.Catalog.from_file(\n", | ||
| " \"https://coclico.blob.core.windows.net/stac/v1/catalog.json\"\n", | ||
| ")\n", | ||
| "\n", | ||
| "ddtm_collection = coclico_catalog.get_child(\"deltares-delta-dtm\")\n", | ||
| "ddtm_extents = read_items_extent(\n", | ||
| " ddtm_collection, columns=[\"geometry\", \"assets\", \"href\"]\n", | ||
| ")\n", | ||
| "\n", | ||
| "ddtm_extents.head()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "4", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Zoom to your area of interest" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "5", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "m = Map(basemap=basemaps.Esri.WorldImagery, scroll_wheel_zoom=True)\n", | ||
| "m.center = 15.827, -95.96\n", | ||
| "m.zoom = 15\n", | ||
| "m.layout.height = \"800px\"\n", | ||
| "m" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "6", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "west, south, east, north = m.west, m.south, m.east, m.north\n", | ||
| "# Note: small little hack to ensure the notebook also works when running all cells at once\n", | ||
| "if not west:\n", | ||
| " west, south, east, north = (\n", | ||
| " 30.28415679931641,\n", | ||
| " 31.276790311057272,\n", | ||
| " 30.630912780761722,\n", | ||
| " 31.51123970051334,\n", | ||
| " )\n", | ||
| "roi = gpd.GeoDataFrame(\n", | ||
| " geometry=[shapely.geometry.box(west, south, east, north)], crs=4326\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "7", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Find the tiles for your region of interest" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from coastpy.io.cloud import to_https_url\n", | ||
| "\n", | ||
| "hrefs = gpd.sjoin(ddtm_extents, roi).href.to_list()\n", | ||
| "href = hrefs[0]\n", | ||
| "href = to_https_url(href, storage_options={\"account_name\": \"coclico\"})" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "9", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Read data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "10", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "da = rioxarray.open_rasterio(href, chunks={}, lock=False).squeeze().drop_vars(\"band\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "11", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "da.where(lambda xx: xx != xx.attrs[\"_FillValue\"]).hvplot(\n", | ||
| " x=\"x\", y=\"y\", geo=True, tiles=\"ESRI\"\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "12", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python [conda env:coastal-full] *", | ||
| "language": "python", | ||
| "name": "conda-env-coastal-full-py" | ||
| }, | ||
| "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 | ||
| } |
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